Renewable Fuel Standard Program 

(RFS2) Regulatory Impact Analysis 
EPA-420-R-10-006

February 2010

Assessment and Standards Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

Renewable Fuel Standard Program 

(RFS2) Regulatory Impact Analysis 
2

Table of Contents 

Statement of Need.........................................................................................................3 

Overview.......................................................................................................................4 

List of Acronyms and Abbreviations............................................................................9 

Chapter 1: Renewable Fuel Production and Consumption........................................14 

Chapter 2: Lifecycle GHG Analysis.........................................................................299 

Chapter 3: Impacts of the Program on Non-GHG Pollutants...................................505 

Chapter 4: Impacts on Cost of Renewable Fuels, Gasoline, and Diesel ...................699 

Chapter 5: Economic Impacts and Benefits...............................................................865 

Chapter 6: Impacts on Water.....................................................................................956 

Chapter 7: Initial Regulatory Flexibility Analysis.....................................................988 

Appendix A: Biodiesel Effects on Heavy-Duty Highway Engines and Vehicles....1002 

Endnotes....................................................................................................................1030 
3

Statement of Need 

 

 The original Renewable Fuel Standard (RFS) program was adopted by EPA to implement 

the provisions of the Energy Policy Act of 2005 (EPAct), which added section 211(o) to the 

Clean Air Act (CAA). With the passage of the Energy Independence and Security Act of 2007 

(EISA), Congress made several important revisions to the renewable fuel requirements. This 

rule revises the RFS program regulations to implement these EISA provisions. 
4

Overview

The displacement of gasoline and diesel with renewable fuels has a wide range of

environmental and economic impacts. This Regulatory Impact Analysis (RIA) utilizes case

study approaches to assess the impacts of an increase in production, distribution, and use of the

renewable fuels sufficient to meet the RFS2 volumes established by Congress in the Energy

Independence and Security Act of 2007 (EISA). This reflects our updated assessment compared

to the draft RIA conducted in support of the Notice of Proposed Rulemaking (NPRM). Because

the standards were mandated by Congress in EISA, the impacts we are assessing are not being

used to justify or support the decisions for the RFS2 volume standards, but rather to provide an

assessment of the projected impacts of these standards when fully implemented. This

information can then be used in future public policy decisions. As explained below, the

estimates contained in this RIA should not be interpreted as the impact of the RFS2 standards

themselves because market forces may lead to increased production of renewable fuels even in

the absence of the RFS2 standards. Rather, the impacts estimated in this RIA must be

understood to refer to the consequences of an expansion of renewable fuel use, whether caused

by the RFS2 program or by market forces.

The analytical approach taken by EPA in this RIA is to predict what the world would be

like, in terms of a range of economic and environmental factors, if renewable fuel use increases

to the level required by the RFS2 standards. We then compare this to two reference cases

without the RFS2 progam. The primary reference case is a projection made prior to EISA by the

US Energy Information Administration (EIA) in their 2007 Annual Energy Outlook (AEO2007)

of renewable fuel volumes that would have been expected in 2022 (13.56 billion gallons). We

then combined this with AEO2009 energy consumption and cost estimates. While AEO2007 is

not as up-to-date as AEO2009, we could not use later projections by EIA for renewable fuel use

because they already include the impact of the RFS2 standards as required by EISA as well as

fuel economy improvements under CAFE as required in EISA. Upon completion of our fuel cost

analyses as described in Chapter 4, however, it became apparent that by 2022, we are projecting

that renewable fuel production costs will decline and crude oil prices rise to the point that

renewable fuels are less expensive than gasoline and diesel fuel, even in the absence of any tax

subsidies. One of the primary drivers for this is the fact that AEO2009 projects $116 per barrel

of crude oil in 2022 (instead of the $53 per barrel projection in AEO2007). This implies that

market forces will lead to a greater increase in renewable fuel volumes than was projected in

AEO2007, even in the absence of the RFS2 standards.

However, it is difficult to estimate the extent to which these market forces, in the absence

of the RFS2 standards, would indeed spur investments to increase renewable fuel production and

usage. Given the magnitude of the capital investment needed for the RFS2 renewable fuel

volumes, the risk associated with these investments due to the fact that for the bulk of the volume

we are relying on new cellulosic biofuel technology, and the uncertainty in future crude oil

prices, market forces alone may result in a level of investment insufficient to achieve the

renewable fuel volumes mandated by RFS2. EPA believes that cellulosic renewable fuels are

least likely to achieve the RFS2 mandates due to market forces alone. While current DOE and

USDA programs are helping to stimulate the market for cellulosic renewable fuels, investment in
5

this segment of the fuels market is still very limited. The limitations of market forces are

reflected in the projections of AEO2009, which despite projecting large increases in oil prices

still projects that renewable fuel volumes will be less than those required by RFS2.

Given the difficulty in projecting renewable fuel volumes in the absence of the RFS2

standards, EPA chose to rely on the projections in AEO2007 as its primary reference case. EPA

believes that the actual renewable fuel volumes achieved in the absence of the RFS2 standards

would fall somewhere between its reference case projections and the volumes mandated by

RFS2. The impacts estimated in this RIA therefore cannot be interpreted as the impact of the

RFS2 program itself. Rather, they are an estimate of the impact of an increase in use of

renewable fuels, whether caused by RFS2 or by market forces.

Another important limitation of this analysis is that it does not consider certain offsetting

effects. In particular, for our emissions (GHG and non-GHG) and air quality analyses we have

assumed that the production of renewable fuels to satisfy the RFS2 results in an energy

equivalent decrease in production of petroleum-derived fuels. This is despite the fact that our

other analyses predict that increased renewable fuel use will reduce worldwide crude oil prices,

which in turn could lead to an increase in the quantity of crude oil demanded. Thus, there may

be offsetting effects that are not completely captured by our analysis. For example, an increase

in world demand for crude oil resulting from depressed prices caused by the increased use of

renewable fuels in the U.S could partially offset some of the decrease in GHG emissions we have

projected. At the same time, there may be other indirect impacts as well that might go in the

opposite direction, since crude oil is used for more than just the gasoline and diesel fuel being

displaced by renewable fuels.

The table below provides the results of many of the analyses contained throughout this

RIA. Only shown are the results for the RFS2 volume control case relative to the AEO2007

reference case, and only the results for 2022 when the program is fully phased in.
6

Impact Summary of the Renewable Fuel Volumes Required by RFS2 in 2022 Relative to

the AEO2007 Reference Case (2007 Dollars)

Category Impact in 2022 Chapter

Discussed

Emissions and Air Quality

GHG Emissions -138 million metric tons 2.7

Non-GHG Emissions

(criteria and toxic

pollutants)

-1 to +10% depending on the pollutant 3.2

Nationwide Ozone +0.12 ppb population-weighted seasonal max

8hr average

5.4

Nationwide PM2.5 +0.002 μg/m3population-weighted annual

average PM2.5

5.4

Nationwide Ethanol +0.409 μg/m3population-weighted annual

average

3.4

Other Nationwide

Air Toxics

-0.0001 to -0.023 μg/m3population-weighted

annual average depending on the pollutant

3.4

PM2.5-related Premature

Mortality

33 to 85 additional cases of adult mortality

(estimates vary by study)

5.4

Ozone-related Premature

Mortality

36 to 160 additional cases of adult mortality

(estimates vary by study)

5.4

Other Environmental Impacts

Loadings to the

Mississippi River from the

Upper Mississippi River

Basin

Nitrogen: +1.43 billion lbs. (1.2%)

Phosphorus: +132 million lbs. (0.7%)

6.4

Fuel Costs

Gasoline Costs -2.4¢/gal 4.4

Diesel Costs -12.1 ¢/gal 4.4

Overall Fuel Cost -$11.8 Billion 4.4

Gasoline and Diesel

Consumption

- 13.6 Bgal 4.4

Capital Costs

Total Capital Costs Thru

2022

$90.5 Billion 4.4

Food Costs

Corn +8.2% 5.1

Soybeans +10.3% 5.1

Food +$10 per capita 5.1

Economic Impacts
7

Energy Security +$2.6 Billion 5.2

Monetized Health Impacts -$0.63 to -$2.2 Billion 5.4

Monetized GHG Impacts

(SCC)a

+$0.6 to $12.2 Billion (estimates vary by SCC

assumption)

5.3

Oil Imports -$41.5 Billion 5.2

Farm Gate Food +$3.6 Billion 5.1

Farm Income +$13 Billion (+36%) 5.1

Corn Exports -$57 Million (-8%) 5.1

Soybean Exports -$453 Million (-14%) 5.1

Total Benefits in 2022b

+$13 to $26 Billion (estimates vary by SCC

assumption)

5.5

a The models used to estimate SCC values have not been exercised in a systematic manner that would allow

researchers to assess the probability of different values. Therefore, the interim SCC values should not be considered

to form a range or distribution of possible or likely values. See Section 5.3 for a complete summary of the interim

SCC values.

b

Sum of Overall Fuel Costs, Energy Security, Monetized Health Impacts, and GHG Impacts (SCC) in 2022. This

measure does not include the costs of the investments needed to increase renewable fuel production. Those capital

costs through 2022 total to $90.5 billion.

The document is organized as follows:

Chapter 1: Renewable Fuel Production and Consumption

This chapter describes the various feedstocks and renewable fuel types that could potentially be

used to meet the renewable fuel volumes required by EISA. The availability and challenges of

harvesting, storing, and transporting these feedstocks are discussed, as well as the different

renewable fuel production technologies, industry plans, and potential growth projections for

future facilities. A discussion of renewable fuel distribution and consumption is included.

Chapter 1.2 defines the reference and RFS2 control cases that were used throughout the rest of

this Regulatory Impact Analysis to assess the impacts of the increased renewable fuel volumes

needed to reach the RFS2 mandated volumes.

Chapter 2: Lifecycle GHG Analysis

This chapter describes the methodology used to determine the lifecycle greenhouse gas (GHG)

emissions of the renewable fuels required by EISA, and to determine which fuels qualify for the

four GHG reduction thresholds established in EISA. Future inclusion of other feedstocks and

fuel is discussed, as well as the overall GHG benefits of the RFS program. It also contains our

assessment of the GHG emission reductions projected to result from the increased use of

renewable fuels.

Chapter 3: Impacts on Non-GHG Pollutants

This chapter discusses the expected impacts of increased renewable fuel volumes on emissions

of hydrocarbons (HC), oxides of nitrogen (NOx), carbon monoxide (CO), particulate matter

(PM10 and PM2.5), sulfur oxides (SOx), ammonia (NH3), ethanol, and air toxic emissions of

benzene, 1,3-butadiene, acetaldehyde, formaldehyde, acrolein, and naphthalene. Emissions from
8

vehicles and off-road equipment, as well as emissions from the entire fuel production and

distribution chain are considered. . This chapter also presents the projected impacts of increased

renewable fuel volumes on ambient concentrations of PM2.5, ozone and air toxic pollutants, and

describes the health and environmental effects associated with these pollutants.

Chapter 4: Impacts on Cost of Renewable Fuels, Gasoline, and Diesel

The impact of increasing the use of renewable fuels on the production and distribution costs of

transportation fuels are discussed. Renewable fuel production and distribution costs are

presented along with their impact on gasoline and diesel fuel costs. Per-gallon and nationwide

costs are presented.

Chapter 5: Economic Impacts

This chapter summarizes the impacts of increased renewable fuel use on the U.S. and

international agricultural sector, U.S. petroleum imports, and the consequences of reduced oil

imports on U.S. energy security. It also examines the greenhouse gas benefits and the copollutant health and environmental impacts from the wider use of renewable fuels in the U.S.

needed to meet the RFS2 mandated volumes.

Chapter 6: Impacts on Water

This chapter discusses the impacts of increased renewable fuel volumes on water quality and

quantity. Changes in the Upper Mississippi River Basin watershed were modeled.

Chapter 7: Final Regulatory Flexibility Analysis

The Final Regulatory Flexibility Analysis (FRFA) evaluates the impacts of the RFS2 standards

on potential small entities. In developing the FRFA, we conducted outreach and held meetings

with representatives from the various small entities that could be affected by the rulemaking.

Small business recommendations and final rule provisions are discussed.

Appendix

EPA conducted a comprehensive analysis of the NOx, PM, HC, and CO emission impacts of

biodiesel blends based on heavy-duty, in-use diesel chassis and engine exhaust emissions data.
9

List of Acronyms and Abbreviations

ACE American Coalition for Ethanol

ACS American Cancer Society

ADM Archer Daniels Midland

AEO Annual Energy Outlook (an EIA publication)

AHC Aromatic hydrocarbons

ARMS Agricultural Resource Management Survey

ASTM American Society of Testing and Materials

B0, B5, B20, etc Percent of biodiesel, e.g., B5= 5% biodiesel, 95% diesel

bbl Barrel

BEA Bureau of Economic Analysis

Bgal, bgal, bilgal, bg Billions of gallons

BGY Billions of gallons per year

BPCD Barrels Per calendar day

BPSD Barrels per stream day

bpd, bbls/day Barrels Per Day

Brix A measurement of the sugar content of a solution at a given temperature

BTL Biomass-to-liquid

BTU British Thermal Unit

BU Bushel

Bu/acre Bushels per acre

BZ Benzene

C Carbon

C&D Construction and Demolition

CA California

CAA Clean Air Act

CAIR Clean Air Interstate Rule

CARB California Air Resources Board

CaRFG3 California Phase 3 RFG

CBG Cleaner Burning Gasoline

CBI Caribbean Basin Initiative

CB05 Carbon Bond 05

CD Census Division

CFEIS EPA’s Certification and Fuel Economy Information System

CFR Code of Federal Regulations

c/gal Cents per gallon

CG Conventional Gasoline

CH3CHO Acetaldehyde

CH3C(O)OO∙ Acetyl peroxy radical 

CH3C(O)OONO2 Peroxyacetyl nitrate

CHF Congestive heart failure

CHP Combined Heat and Power Technology

CIMT Carotid intima-media thickness

CMAQ Community Multi-scale Air Quality model

CO Carbon Monoxide

CO2 Carbon Dioxide

COHb Carboxyhemoglobin

Co-op Cooperative

CRC Coordinating Research Council

CRGNSA Columbia River Gorge National Scenic Area
10

CRP Conservation Reserve Program

CTL Coal-to-liquid

DDGS Distillers’ Dried Grains with Solubles

DGS Distillers’ Grains with Solubles

DHHS Department of Health and Human Services

DOE Department of Energy

DRIA Draft Regulatory Impact Analysis

dt Dry ton

E&C Engineering and Construction

E0 Gasoline Blend which Does Not Contain Ethanol

E10 Gasoline Blend containing a nominal 10 percent ethanol by volume

E85 Gasoline Blend containing 85 percent ethanol by volume

E200 Percent of Fuel Evaporated at 200 Degrees F (ASTM D 86)

E300 Percent of Fuel Evaporated at 300 Degrees F (ASTM D 86)

EIA Energy Information Administration (part of the U.S. Department of Energy)

EISA Energy Independence and Security Act

Energy Act Energy Policy Act of 2005 (also the Act)

EPA Environmental Protection Agency

EPAct Energy Policy Act of 2005 (also ‘the Energy Act’ or ‘the Act’)

ETBE Ethyl Tertiary Butyl Ether

ETOH Ethanol

EU European Union

ex CA Excluding California

F, °F Fahrenheit

F-T Fischer-Tropsch

FAME Fatty acid methyl ester

FAPRI Farm and Agricultural Policy Research Institute

FASOM Forestry and Agriculture Sector Optimization Model

FBP Feed Boiling Point (also Final Boiling Point)

FCC Fluidized Catalytic Cracker

FCCU Fluidized Catalytic Cracking Unit

FHWA Federal Highway Administration

FOEB Fuel Oil Equivalent Barrel

FR Federal Register

FRM Final Rulemaking

FRTP Fixed Reduction Trigger Point

FFV Flexible Fuel Vehicle

FTP Federal test procedure

g/Btu Grams per Btu

g/day Grams per day

Gal, gal Gallon

GDP Gross Domestic Product

GEOS Goddard Earth Observing System

GHG Greenhouse Gases

GPA Geographic Phase-in Area

GREET Greenhouse Gas, Regulated Emissions, and Energy Use in Transportation model

GWP Global warming potentials

ha Hectare

H2O Water

HC Hydrocarbon(s)

HCO Heavy Cycle Oil (a refinery stream)

HCHO Formaldehyde

HDN Naphtha Hydrotreater (also Hydro-Denitrogenation Unit)
11

HEI Health Effects Institute

HNO3 Nitric acid

HSR Heavy Straight Run (a refinery stream)

HVGO Heavy Vacuum Gas Oil (a refinery stream)

IARC International Agency for Research on Carcinogens

IBP Initial Boiling Point

IRFA Initial Regulatory Flexibility Analysis

k Thousand

kbbl Thousand barrels

kg kilogram

kwh Kilowatt Hour

L, l Liter

Lb, lb Pound

LCC Land Capability Classification

LCO Light Cycle Oil (a refinery stream)

LEV Low emission vehicle

LLE Liquid-Liquid Extraction

LNS Light Naphtha Splitter

LP Linear Programming (a type of refinery model)

LSR Light Straight Run (a refinery stream)

m

2

Square meter

MCIP Meteorology-Chemistry Interface Processor

mg/m3 Milligrams per cubic meter

MGY, MMgy Million Gallons per Year

mm Millimeter

MM Million

MMBTU Million British Thermal Units

MMbbls/cd Millions of barrels per calendar day

MMGal/yr Millions of gallons per year

MOBILE (5, 6, 6.2) EPA’s Motor Vehicle Emission Inventory Model (versions)

MON Motor Octane Number

MOVES Motor Vehicle Emissions Simulator

MOVES2006 EPA’s Next Generation Highway Vehicle Emission Model

MSAT Mobile Source Air Toxics

MSAT1 2001 Mobile Source Air Toxics Rule

MSAT2 2006 Proposed Mobile Source Air Toxics Rule

MSW Municipal Solid Waste

Mt Metric ton

MTBE Methyl Tertiary-Butyl Ether

N Nitrogen

NAAQS National Ambient Air Quality Standards

NAICS North American Industrial Classification System

NASS National Agricultural Statistics Service

NATA National Air Toxic Assessment

NBB National Biodiesel Board

NCGA National Corn Growers Association

NCI National Cancer Institute

NCLAN National Crop Loss Assessment Network

NCSU North Carolina State University

NGL Natural gas plant liquids

NH3 Ammonia

NIOSH National Institute of Occupational Safety and Health

NMHC Non-Methane Hydrocarbons
12

NMIM National Mobile Inventory Model (EPA software tool)

NMOG Non-methane organic gases

NONROAD EPA’s Non-road Engine Emission Model

NONROAD2005 EPA’s Non-road Engine Emission Model Released in 2005

NO Nitric oxide

NO2 Nitrogen dioxide

NOx Oxides of nitrogen

NPRM Notice of Proposed Rulemaking

NRC National Research Council

NRCS Natural Resource Conservation Service

NREL National Renewable Energy Laboratory

O3 Ozone

OA Organic aerosol

OC Organic carbon

∙OH Hydroxyl radical 

OM Organic mass

OMB Office of Management and Budget

OMHCE Organic Material Hydrocarbon Equivalent

ORD Office of Research and Development

ORNL Oak Ridge National Laboratory

OTAQ Office of Transportation and Air Quality

Oxy-fuel, oxyfuel Winter oxygenated fuel program

PADD Petroleum Administration for Defense District

PAHs Polycyclic aromatic hydrocarbons

PAN Peroxyacetyl nitrate

PM Particulate Matter

PM10 Coarse Particle

PM2.5 Fine Particle

PM AQCD Particulate Matter Air Quality Criteria Document

PMA Petroleum Marketing Annual (an EIA publication)

POM Polycyclic Organic Matter

PONA Paraffin, Olefin, Naphthene, Aromatic

ppb Parts per billion

ppm Parts Per million

PPN Peroxypropionyl nitrate

PRTP Percentage Reduction Trigger Point

PSI Pounds per Square Inch

QBtu Quadrillion btu

Quadrillion 1015

(R+M)/2 Octane calculation (RON+MON)/2

R&D Research and Development

RBOB Reformulated Blendstock for Oxygenate Blending

rd Renewable diesel

RFA Regulatory Flexibility Act

RFG Reformulated Gasoline

RFS Renewable Fuels Standard

RFS1 Renewable Fuels Standard Program promulgated in 2007.

RFS2 Renewable Fuels Standard Changes

RIA Regulatory Impact Analysis

RIMS Regional Input-Output Modeling System

RIN Renewable Identification Number

RON Research octane number

RPMG Renewable Products Marketing Group
13

RSM Response Surface Model

RVP Reid Vapor Pressure

S Sulfur

SBA Small Business Administration

SBAR Panel, or ‘the Panel’ Small Business Advocacy Review Panel

SBREFA Small Business Regulatory Enforcement Fairness Act (of 1996)

scf Standard cubic feet

SER Small Entity Representative

SI Spark Ignition

SOA Secondary Organic Aerosol

SOC Secondary organic carbon

SOC Soil organic carbon

SOx Oxides of Sulfur

SULEV Super ultra low emission vehicle

SVOC Semi-volatile organic compound

T50 Temperature at which 50% (by volume) of fuel evaporates (ASTM D 86)

T90 Temperature at which 90% (by volume) of fuel evaporates (ASTM D 86)

TAME Tertiary Amyl Methyl Ether

Ton 2000 lbs

Tonne Metric tonne (equivalent to 1.1 tons); also metric ton

TRQ Tariff rate quotas

ULEV Ultra low emission vehicle

U.S.C. United States Code

USDA U.S. Department of Agriculture

VGO Vacuum Gas Oil (a refinery stream)

VMT Vehicle Miles Traveled

VOC Volatile Organic Compound

vol% Percent by volume, volume percent

WDGS Wet Distillers Grain w/ Solubles

wt% Percent by weight, weight percent

yr, y Year
14

Chapter 1: Renewable Fuel Production and Consumption

1.1 Biofuel Feedstock Availability

Currently, the main feedstocks used for renewable fuel production in the U.S. are corn for

ethanol and soy for biodiesel. As technologies improve, we expect more emphasis on using

cellulosic feedstocks such as agricultural residues, forestry residues, etc. However, limitations

may occur due to concerns over sustainable removal rates for initial cellulosic feedstocks. Thus,

dedicated energy crops which are touted as requiring low fertilizer and energy inputs as well as

having the ability of being grown on marginal lands may also enter the market. The following

sections discuss the current and potential availability of biofuel feedstocks and the potential

challenges that must be overcome in order for enough feedstock to be collected and converted to

biofuel to meet the EISA requirement of 36 billion gallons of renewable fuel by 2022.

1.1.1 Starch/Sugar Feedstocks

The following sections describe starch and sugar feedstocks that can be used to produce

ethanol. Currently, the majority of ethanol that is produced in the U.S. is from corn. Recently,

there have been plans to convert sugarcane grown in the U.S. into ethanol as well as the

introduction of relatively new crop varieties for biofuel conversion. We also describe feedstocks

used in the production of ethanol outside the U.S.

1.1.1.1 Domestic Corn and Other Grain Ethanol

Today’s ethanol is primarily corn-based ethanol, which accounts for the majority of the

over 10 billion gallons of domestic fuel ethanol estimated to be produced by the end of 2009.

According to multiple sources, as much as 18 billion gallons of corn ethanol could be produced

by the 2016-18 timeframe, see Table 1.1-1.1For the final rule, we modeled 15 billion gallons of

corn ethanol to meet the EISA standards. We used the Forestry and Agriculture Sector

Optimization Model (FASOM) and the Farm and Agricultural Policy Research Institute (FAPRICARD) model to assess the impact of increased renewable fuel volume from business-as-usual

on crop acreage, crop allocation to fuel vs. other uses, costs, etc. See Section 1.2 for more

discussion on the renewable fuel volumes assumed for our analyses and Chapter 5 of the RIA for

more details on the agricultural modeling. Important modeling parameters considered include

crop yields and ethanol yield per bushel of feedstock as these factors impact the amount of

feedstock necessary per gallon of biofuel produced. Table 1.1-1 also shows a summary of the

parameters used and the results from our analyses.
15

Table 1.1-1. Corn Ethanol Production Forecast Parameters and Corresponding Years

Source

(cited in text above)

Fuel Volumes/Year

(billion gallons)

Acres Planted

(millions)

Yield

(bu/acre)

Corn Allocation

to Ethanol

Ethanol Conversion

(gal/bu)b

USDA Baseline 14/2018 90 175 35% 2.76

USDA Study 15/2016 92 170 37% 2.8

NCGA Analysisc12.8-17.8/2016 76-78a178-193 33-40% 2.9-3.0

EPA FRM Analysis

(Base Yield Case)

15/2022 92/81a185 41% 2.85

EPA FRM Analysis

(Higher Yield Case)

15/2022 77/71a233 36% 2.85

aAcres harvested

bWe assume all figures above include denaturant, but most references do not specify; Differences also occur

depending on whether dry or wet mills are assumed, wet mills have slightly lower yields

cNational Corn Growers Association

Corn is mainly grown in 12 states within the United States: Illinois, Indiana, Iowa,

Kansas, Kentucky, Michigan, Minnesota, Missouri, Nebraska, Ohio, South Dakota, and

Wisconsin.2See Table 1.1-2.

Table 1.1-2.

U.S. Corn for Grain Area Harvested by State in 2008 and Forecasted November 1, 2009

State

Total Harvested

2008 (1000 Acres)

2009 Forecast

(1000 Acres)

Illinois 11,900 11,800

Indiana 5,460 5,440

Iowa 12,800 13,350

Kansas 3,630 3,870

Kentucky 1,120 1,130

Michigan 2,140 1,990

Minnesota 7,200 7,100

Missouri 2,650 2,900

Nebraska 8,550 8,900

Ohio 3,120 3,120

South Dakota 4,400 4,600

Wisconsin 2,880 2,900

Other States 12,790 12,194

Total 78,640 79,294

Corn yield per acre has been increasing over the past three decades.1,3 See Figure 1.1-1.

In our economic modeling assessment under the base yield case, the national average corn yield

is approximately 185 bu/acre in 2022, with specific yields calculated at the regional level. The

national average depends on crop production in each region in a given year (see Chapter 5 of this

RIA). These yield increases over time are consistent with the United States Department of

Agriculture (USDA) projections.4 As further described in Chapter 5, we also investigated a

1 Calculated from 1977-2007.
16

higher corn yield scenario of 233 bu/acre in 2022, developed in consultation with our colleagues

at USDA as well as industry groups e.g. Monsanto and Pioneer.

Figure 1.1-1. U.S. Corn Yields (1978-2009)

The percent of U.S. corn produced allocated to ethanol has increased in recent years. In

2007, the percent of U.S. corn used for ethanol was around 23 percent and in 2008 the percent

had increased to 30 percent. As of December 2009, the majority of corn is still being used as

animal feed (42 percent), with smaller portions going to ethanol (33 percent), exports (16

percent), and human food and seed (9 percent).5For the final rule, the FASOM projects that

approximately 41 percent of corn would need to be allocated to the ethanol industry by 2022

under the base corn yield assumption and 36 percent of corn under the higher corn yield

assumptions to produce 15 billion gallons of corn ethanol.

The amount of corn allocated to fuel vs. other uses has caused much controversy over the

production and use of corn-based ethanol in the past few years. There is concern that the use of

corn for fuel could potentially divert corn needed to feed people. On the other hand, it is entirely

possible that other countries (e.g. Argentina or Brazil) could increase their production of corn to

match the increase in demand for food and fuel, thus meeting both needs. In addition, higher

crop yields in all countries could decrease the amount of land necessary for a fixed amount of

renewable fuel produced. We rely on our modeling results to help inform us of the potential

impacts of an increased growth in renewable fuels (see Chapter 5 for more detail).

Over the last 15 years, ethanol industry optimization of cooking, mashing, and

fermentation conditions has increased the amount of ethanol produced from a bushel of corn.

According to USDA reports, by about 2010 we can expect all plants on-line to yield an average

of 2.76 gallons per bushel.6,7 In addition, based on discussions with USDA, we believe it is

reasonable to expect an increase in corn kernel starch content of 2-4 percent over the next decade
17

through advances in plant breeding and new corn varieties. Combining these figures, we project

industry average denatured ethanol yields to reach 2.85 gallons per bushel by 2022 for dry mills

and 2.63 gallons per bushel for wet mills. See Section 1.4 of this RIA for more information on

corn ethanol biofuel production technologies, e.g. dry mill vs. wet mill.

Other grains that can be processed into ethanol include grain sorghum (milo), wheat, and

barley. The production of ethanol from these grains generally involves the same processes as the

production from corn, and can be used together in the same plant.

1.1.1.2 Imported Sugar Ethanol

After corn, sugar crops (i.e. sugar beets and sugarcane) are the world’s next largest

feedstock sources for ethanol.8,9 Sugar beets are mainly grown in France, Germany, and in the

U.S., with the majority of the feedstock typically used to produce sugar for food and feed.

Compared to sugar beets, sugarcane is produced in much higher volume and has been able to

support a growing sugar and ethanol market. Due to a higher availability of sugarcane feedstock

for ethanol production, we expect that imported ethanol to the U.S. will likely come from

sugarcane.

World production of sugarcane is approximately 1.4 billion metric tons (MT) and is

concentrated mainly in tropical regions, particularly in Latin America, Africa, and South and

Southeast Asia. Roughly 100 countries produce sugarcane today.10 Brazil is currently the

world’s largest producer of sugarcane (569 million MT in the 2008/9 harvest season) and offers

the greatest potential for growth, due primarily to the availability of suitable lands for expanding

sugarcane cultivation.11 In Brazil, just 20% of the arable land is cultivated, totaling 156 million

acres. The following Table 1.1-3., describes the land available/used in 2007. As there are 494

million acres of pastureland and a considerable area of unused arable land (190 million acres), it

is believed that there could be a large expansion in sugarcane.12

Table 1.1-3. Brazil Land Areas in 2007.13

Brazil (Total Area) 2100

Total Preserved Areas and Other Uses* 1260

Total Arable Area 840

Cultivated Land (All Crops) 156

Soybeans 51

Corn 35

Oranges 2

Sugar Cane 19

Sugar Area 11

Ethanol Area 8

Pastureland 494

Available land (ag, livestock) 190

Million Acres

*Areas include Amazon Rain Forest, protected areas, conservation

and reforestation areas, cities and towns, roads, lakes, and rivers.

The statistics above, however, do not indicate whether the land available requires any

additional usage of water or has the proper soil and climate conditions for sugarcane. According
18

to one study, there is at least 148 million acres of additional land available with proper soil and

climate conditions for sugarcane without utilizing environmentally protected land (i.e. Amazon

and native reserves) and without the use of irrigation.14 This translates to approximately 90

billion gallons of ethanol potential (using a yield of approximately 600 gal/acre which is a

conservative estimate based on existing technology). Although it is not probable that all this

land will be converted to sugarcane ethanol, the estimate puts into prospective the large potential

for sugarcane ethanol to be produced in Brazil.

Another study commissioned by the Brazilian Government produced an analysis in which

Brazil’s arable land was evaluated for its suitability for cane. The benefit of this study is that it

provides more detail on the land quality and yield assumptions used in its estimates than the

study and statistics shown above.15 The study eliminated areas protected by environmental

regulations and those with a slope greater than 12% (those not suitable for mechanized farming).

The following Table 1.1-4 shows an estimate of the available land that could be used for

sugarcane expansion. The potential fuel volume from these acres is dependent on whether or not

irrigation takes place. Overall, with greater irrigation, more acres are available that fall in the

higher potential yield categories than without irrigation. As can be seen, there are potentially

large areas of land available for sugarcane expansion in Brazil.

Table 1.1-4.

Potential Volumes Utilizing Available Land for Sugarcane Expansion2,16,17

w/o irrigation w/ irrigation w/o irrigation w/ irrigation

High 659 20 94 13 62

Good 592 281 242 166 143

Average 524 369 414 193 217

Inadequate 0 224 143 0 0

Total 894 894 373 422

Potential

Ethanol Yield

(gal/acre)

Potential Area

(million acres)

Potential Ethanol Volume

(billion gallons)

The actual potential for ethanol from sugarcane will, however, be further limited by the

amount of sugarcane diverted towards food and other uses. Taking into account demands for

food and feed, the Oak Ridge National Laboratory (ORNL) Biofuel Feedstock Assessment for

Selected Countries report suggests that perhaps more than 30 billion gallons of ethanolequivalent fuel could be produced from available sugarcane supply by 2017. Brazil is estimated

to produce approximately 2/3 of the potential supply. The majority of this supply would likely

be consumed within the country, with the leftover potentially available for export to the U.S. and

other countries. Recent government and industry estimates indicate that approximately 3.8-4.2

bgal of ethanol could be available for export from Brazil by 2022 (with close to 17 billion

gallons being produced and 13 billion gallons consumed domestically). See Section 1.5.2.1 of

2

Adapted from CGEE, ABDI, Unicamp, and NIPE, Scaling Up the Ethanol Program in Brazil. Assumed a

conversion factor of 20 gallons of ethanol per tonne of sugarcane feedstock to compute gal/acre. A “high” potential

refers to ethanol yields that are higher than current industry averages, while “good” refers to good quality land and

productivity that is about equal to the current average. Explanations for “Average” and “Inadequate” were not

provided.
19

this RIA for further details on Brazilian ethanol production and consumption. Thus, there

appears to be a large enough potential for Brazil to increase production of sugarcane to meet its

internal demands as well as export to the United States and other countries.

Countries other than Brazil generally lack the land resources, appropriate soils, and

climate for large expansion of sugarcane production.18 India and China are the second and third

largest producers, however, most of the cultivatable land area is already in use and government

policies discourage reallocation of arable land for biofuel production. Although Argentina and

Columbia have significant underutilized lands available, these resources generally do not have

suitable soil and climate characteristics for sugarcane production. Due to these factors, Brazil is

the most likely country able to produce substantial volumes of sugarcane for biofuel production

in the future.

1.1.1.3 Domestic Sugar Ethanol

Currently, there are no U.S. plants producing ethanol from sugar feedstocks.19 Brazil and

several other countries are producing ethanol from sugarcane, sugarbeets, and molasses, showing

that it is economically feasible to convert these feedstocks into ethanol (see Section 1.1.1.2).

However, the economics of producing ethanol from sugar feedstocks in these countries is not

directly comparable to the economics of producing ethanol from sugar feedstocks in the U.S.

Over the longer term, the profitability of producing ethanol from sugarcane, sugarbeets, and

molasses depends on the prices of these crops, the costs of conversion, and the price of gasoline.

Sugarcane in the U.S. is grown mainly in Florida and Louisiana, with smaller amounts

from Hawaii and Texas. See Table 1.1-5. Sugarbeets, on the other hand, are grown in more

northern states, with the majority of production in Minnesota, Michigan, and Idaho as shown in

Table 1.1-6. As noted, these feedstocks are not currently used for commercial production of

ethanol, however, this may change in the near future.

Table 1.1-5.

Sugarcane Area Harvested (for sugar only, not seed) by State in 2008 and 2009

State

Total Harvested

2008 (1000 Acres)

Total Harvested

2009 (1000 Acres)

Florida 384 372

Hawaii 20 20

Louisiana 380 375

Texas 37 39

Total 821 806
20

Table 1.1-6.

Sugarbeet Area Harvested by State in 2008 and 2009

State

Total Harvested

2008 (1000 Acres)

Total Harvested

2009 (1000 Acres)

Idaho 116 163

Michigan 136 136

Minnesota 399 455

Other States 354 397

Total 1005 1151

Recent news indicates that there are plans in the U.S. to produce ethanol from sugar

feedstocks. For instance, sugarcane has been grown in California’s Imperial Valley specifically

for the purpose of making ethanol and using the cane’s biomass to generate electricity to power

the ethanol distillery as well as export excess electricity to the electric grid.20 There are at least

two projects being developed at this time that could result in several hundred million gallons of

ethanol produced. One company is California Ethanol and Power which is currently in the

development stage and plans to build a facility that produces 60 million gallon per year of

sugarcane ethanol and 50 megawatts of electricity.21 The company plans to break ground by

early 2010 and be operational by 2011. The sugarcane is being grown on marginal and existing

cropland that is unsuitable for food crops and will replace forage crops like alfalfa, Bermuda

grass, Klein grass, etc. Harvesting is expected to be fully mechanized. Another company is

Pacific West Energy LLC which plans to produce 12-15 million gallons per year of ethanol on

the island of Kauai in Hawaii, perhaps as early as 2010. Hawaii is well suited for sugarcane

ethanol production due to several factors, including lower costs for feedstock compared to those

in the continental U.S., high prices for electricity and liquid fuels, and state production

incentives.22 Thus, there is potential for these projects and perhaps others to help contribute to

the EISA biofuels mandate.

There is also potential for the use of new crops with certain traits similar to traditional

sugar and corn feedstocks. For example, a new crop referred to as Sugarcorn is a hybrid cross

between sugarcane and corn.23 The plant contains genes from Midwestern corn, tropical maize

and sugarcane, resulting in a variety that doesn’t flower to produce grain but instead produces

sugar in its stalks. Researchers are currently working to increase sugar yields, increase the

plant’s hardiness and develop ways to prevent the plant from being pollinated by nearby crops of

traditional corn. Potential benefits include reduced water and fertilizer consumption during the

growth of the plant.

Another crop receiving greater attention is sweet sorghum. Sweet sorghum refers to

varieties of sorghum with high concentration of soluble sugars in the sap.24 They are used for the

production of syrup, alcoholic beverages, crystal sugar, etc. The interest in bioenergy production

from sweet sorghum comes from the easy accessibility of readily fermentable sugars combined

with very high yields for biomass. Yield varies with location and variety and ranges from 8-49

tons/acre. After extraction of the juice, the bagasse can also be used as cellulosic feedstock or

other purposes. Groups interested in building facilities in the U.S. that can process sorghum juice
21

include the Tampa Bay Area Ethanol Consortium in Florida and the Texas BioEnergy Marketing

Associates in Texas.

1.1.2 Cellulosic Biofuel Feedstocks

Various cellulosic feedstocks can potentially be used to produce cellulosic biofuel. These

include agricultural residues, forest residues, urban waste, and dedicated energy crops. We

describe each type in the following sections.

1.1.2.1 Agricultural Residues

The harvesting of agricultural residues could provide a large source of readily available

feedstock for cellulosic biofuels. We estimated the amount of crop residue could potentially be

produced, and of that, how much could be removed or harvested to determine the total amount

that could be available to produce biofuel in 2022. The amount of residue that can be harvested

is limited by how much residue must be left on the field to maintain soil health and by the

mechanical efficiency of the harvesting operation. We discuss harvesting limitations due to

maintaining soil health below, while mechanical efficiencies, storage, and transport issues are

discussed in Section 1.3 of this RIA. Feedstock costs are discussed in Section 4.1.1.2 of the

RIA.

Sustainable Removal

In terms of soil health, residues perform many positive functions for agricultural soils.

Recent studies and reviews have attempted to address these issues. Existing research can be used

to some extent to guide practices or make estimates, especially for corn stover harvest in the

Corn Belt, which has been studied more extensively than other residues except, perhaps, wheat.

In a review by five USDA Agricultural Research Service (ARS) scientists, Wilhelm et al.

acknowledged the complexity of interactions between soil type, climate, and management when

considering crop residue effects on soil. They recommended that removal rates be based on

regional yield, climatic conditions and cultural practices, with no specific rates given.25 Using

the Revised Universal Soil Loss Equation (RUSLE) technology and the Wind Erosion Equation

(WEQ), Nelson predicted safe residue removal rates for minimizing soil loss in the Eastern and

Midwestern U.S. These predictions varied widely over time and location as a result of the

complex interactions discussed by Wilhelm et al. 26,27 In another recent review, sponsored by the

U.S. Department of Energy (DOE), Mann et al. concluded that before specific recommendations

could be made, more information was needed on the long term effects of residue harvest,

including: 1) water quality; 2) soil biota; 3) transformations of different forms of soil organic

carbon (SOC); and 4) subsoil SOC dynamics.28 Current USDA Natural Resource Conservation

Service (NRCS) practice standards for residue management do not recommend specific residue

quantities and point to the use of the RUSLE2 model for guidance.29 Despite broad recognition

of the need for specific guidelines for residue removal, none yet exist.

With the upsurge in biofuels and the obvious prospects of removing significant quantities

of residue, many questions remain regarding the long-term effects on soils from residue removal.
22

Residues have not yet been removed at the contemplated rates over a period sufficiently long for

the effects to be clearly determined. Another difficulty is that while the effects of removing a

residue may appear to one observer to have affected the soil in a certain manner, it may not be

completely clear that the observed effects were fully related to the residue removal or, were in

fact related to a change or to combinations of changes in other variables that were simply missed.

A second observer may view the same results in an honest, but different manner. There are

many variables and many different interactions among them that assigning effects is very

difficult at best. There simply are no real-world data available for determining long-term effects.

Nevertheless, we can describe some of the interactions that take place and how they can

potentially affect soil health.

Soil erosion is an extremely important national issue. Most, if not all, agricultural

cropland in the United States experiences some degree of soil erosion each year due to rainfall

(water) and/or wind forces. Rainfall erosion (sheet and rill) occurs when rain directly strikes the

soil, dislodging particles in the top layer.3 When soil becomes saturated, particles are transported

down the slope of the field. Soil erosion due to wind occurs in much the same manner as rainfall

with wind forces dislodging soil particles and carrying them along and above the field surface

(creep and saltation) or suspending them above the field.4 While eroded soil does not disappear,

the erosion process moves soil particles to other locations in the field (either downslope or

downwind) where they can be transferred into waterways or onto non-croplands.

The amount of soil erosion that agricultural cropland experiences is a function of many

factors: field operations (field preparation, tillage, etc.) in preparation for the next crop, timing of

field operations, present throughout the year, soil type, field characteristics such as field slope,

and the amount of residue (cover) left on the field from harvest until the next crop planting. Crop

rotation cover provided by agricultural crop residues, both fallen and standing, helps to minimize

rainfall and wind energy as it strikes or blows across the ground as well as helping to keep soil

particles from being transported after they have been dislodged. Climatic conditions such as

rainfall, wind, temperature, etc. must be accounted for. Studies predict that up to 30% of surface

residue can be removed from some no-till systems without increased erosion or runoff.

The NRCS has established tolerable soil loss limits (T values) for all soil types in all

counties throughout the United States. The tolerable soil loss values denote the maximum rate of

soil erosion that can occur for a particular soil type that does not lead to prolonged soil

deterioration and/or loss of productivity. Tolerable soil loss limits take into account the rate of

topsoil formation, role of topsoil formation, loss of nutrients, erosion rate at which gully erosion

would commence, and potential erosion-control factors that farmers would be able to implement.

However, T values are not a function of the type of crop grown.

Another important aspect associated with soil conservation involves soil tilth. Soil tilth is

defined as the physical condition of the soil as related to its ease of tillage, fitness as a seed bed,

impedance to seedling emergence and root penetration, and all other physical conditions that

3

rill: A small intermittent watercourse with steep sides, usually only a few inches deep;

www.hancockcoingov.org/surveyor/drainage_glossary_of_terms.asp.

4

saltation: the movement of sand-sized particles by a skipping and bouncing action in the direction the wind is

blowing
23

influence crop development. Tilth depends upon soil granulation and its stability (soil

workability) as well as organic matter content, moisture content, porosity, water retention, degree

of aeration, rate of water infiltration, drainage, and capillary-water capacity, all of which are

affected by crop residue removal. Preliminary values of required tilth have been estimated by

the NRCS.

Various tillage operations are associated with management of agricultural crop residues

and planting preparation throughout the year. Type and number of tillage operations employed

for any particular crop from the time of harvest until the next planting have a tremendous effect

on the amount of soil lost to erosion during the year, and hence, the amount of residue that can

possibly be removed for energy purposes. It must be noted that even though crop residues may

be used for energy purposes, the farmer is, first and foremost, in the business of producing grain.

Therefore, he will be concerned with using those tillage operations that will provide him with the

highest possible yield at the next harvest, and not necessarily those that tend to maximize erosion

control on his lands.30

All agricultural cropland upon which nearly any crop is grown within a particular county

can exhibit a wide variation in soil erodibility, field slope and length, climate conditions, and

management practices. Within any one particular county there can be many different soil types

(50 or more) used to grow agricultural crops. In addition, and possibly more importantly, not all

soil types within a county may be suitable for agricultural crop production. Some soils possess

characteristics that make them highly susceptible to erosion that may not be able to sustain

certain cropping practices. Production of conventional agricultural crops on these lands may

severely and/or permanently reduce the soil’s ability to provide sustained, economical

production. For this reason, the NRCS implemented a land capability classification (LCC) that

ranges from I (one) to VIII (eight) that is applied to all soils within a county.

With added nitrogen fertilizers, residues can increase soil organic matter (SOM).

However, roots appear to be the largest contributor to new SOM, making residues less important

for carbon accrual. Residue removal leading to higher erosion and runoff rates would greatly

decrease SOM and nutrients. Residue harvest may also require increased fertilizer inputs to

make up for nutrients removed in the plant material. When returned to the land, crop residue

also replenishes soil organic carbon (SOC) that typically has already been reduced 30 to 50% of

precultivation levels through crop production activities. Soil organic carbon retains and recycles

nutrients, improves soil structure, enhances water exchange characteristics and aeration, and

sustains microbial life within the soil. It’s been reported that crop yield and the value of

environmental services (C and N sequestration) were greater for soils with greater SOC. Limited

research has shown that removing stover reduces grain and stover yield of subsequent crops and

further lowers soil organic matter levels.31

Residue removal can result in detrimental changes in many biological soil quality

indicators including soil carbon, microbial activity, fungal biomass and earthworm populations,

indicating reduced soil function. Some disease-producing organisms are enhanced by residue

removal, others by residue retention, depending on crop and region. Residue cover can also

reduce evaporation from the soil surface, thereby conserving moisture and increasing the number

of days a crop can survive in drought conditions. Improved soil physical properties related to
24

crop residues, such as reduced bulk density, e.g., the soil is looser and lighter, and greater

aggregate stability, also lead to better water infiltration and retention.

In colder climates, residues are linked to reduced yields due to lower soil temperatures

resulting in poor germination. Stubble mulching, as opposed to residue chopping, can help

overcome this problem. Even though residue-associated yield reductions have been found on

poorly drained, fine-textured soils, these soils often have low erosion risk and residues might

safely be removed.

Despite the many important benefits of crop residues, research shows their effects can

vary. For instance, some reports showed lower yields in systems with high crop residues due to

increased disease or poor germination; others reported higher yields when soil moisture is

limiting. Other studies suggest that residues do not contribute significantly to soil carbon. Many

studies found that additional N fertilizer is needed when residues are left on soils to avoid N

uptake (immobilization) from soil or allow for soil carbon accrual. For appropriate residue

removal recommendations, the conditions leading to these varied effects of residues must be

elucidated.

Soil health as related to residue removal is an extremely complex issue for which, as yet,

there are no specific guidelines for residue removal. Wrong decisions, carried out over extended

periods could have far reaching deleterious effects. Sustainable residue removal rates for biofuel

production vary by system, according to such factors as management and cropping practice, crop

yield, climate, topography, soil type and existing soil quality. Keeping in mind that gravimetric

rates are not the same as percent soil cover (% mass is not the same as % coverage), appropriate

conversion is necessary and varies by crop and region. While areas with low slopes and high

yields may support residue harvest, in many areas the residue amounts required to maintain soil

quality could be even higher than current practices. What is meant by ‘high’ and ‘low’ slopes

has yet to be absolutely determined, which determination also depends on soil type and other

cropping practices. Removal rates will need to be reduced as climates become warmer or more

humid, for lower C:N residue or lower yielding crops, as soil disturbance (e.g. tillage) increases,

or as soils become coarser textured, compared to the conditions in which most studies occurred

(in the U.S. Midwest Corn Belt for no-till corn).32 The most important aspect of this is that any

or all of the interacting variables that determine how much residue can be removed, can, and

usually do, change from year-to-year, across both wide regions of the country as well as across

single counties and farms. A change in one variable nearly always changes how all the variables

interact.

Given all the issues we’ve discussed regarding residue removal and soil health, rather

than try to predict, county-by-county how much residue will be available, we assumed in our

FASOM modeling that the available amount will be somewhere between 0% and 50%, at least

until the issues we have discussed are settled. We based the amount removable based on the

tillage practice: 0% removed for conventional tillage, 35% removed for conservational tillage,

and 50% removed for no-till for corn stover.33 Removal rates for wheat straw were based on the

Billion Ton study.34 We believe that given the uncertainties in removal rates, our assumptions

are reasonable.
25

Agricultural Residue Summary

Corn and wheat are currently receiving the most attention across the industry due to their

concentrated production areas and because they generate the majority of total residue produced.

This also means they will more likely be able to support commercial scale production. In

aggregate, the other residues provide fairly significant quantities of material, but because they

are spread out, e.g., less densely planted both in the field and in a county or state, they are less

likely able to support commercial operations.

We analyzed various reports on the availability of agricultural residues. These are

summarized in Table 1.1-7. The agricultural residue estimates in Table 1.1-7 are based on

historical/recent data, and thus, could be considered conservative in comparison to the future

(2022) which would typically have higher crop yields or increases in acres harvested.

Table 1.1-7. Estimated Agricultural Residue Feedstock Availability (per year)35,36,37,38,39

Source Total Available Total Removable Sustainably Crops Analyzed

USDA >500 million tons not specified

Eight leading U.S. Crops, e.g. corn, wheat,

soy, oats, barley, rice (did not specify other

two)

NREL 495 million tons 173 million tons

Corn, wheat, soybeans, cotton, sorghum,

barley, oats, rice, rye, canola, beans, peas,

peanuts, potatoes, safflower, sunflower,

sugarcane, and flaxseed

Gallagher not specified 156 million tons Corn, wheat, sorghum, barley, oats, rice

Walsh not specified

144 million tons at $40/dry ton, ~150

million tons at >$40/dry ton for corn; 7

million tons at $40/dry ton, ~10-11 million

tons at >$40/dry ton for wheat Corn and wheat

Graham 216 million tons

65 million tons at 30% removal rate and

current conditions; 112 million tons at

50% removal rate using no-till conditions Corn

Based on our FASOM modeling for the final rule, corn stover was the most economical

agricultural residue projected to be used to produce ethanol in order to meet the 16 Bgal EISA

cellulosic biofuel requirement. We estimate that by 2022 about 400 million wet tons of corn

stover could be produced, see Table 1.1-8. Approximately 53 million dry tons of corn stover

would be needed to produce the 4.9 billion gallons of cellulosic biofuel estimated to be used by

our agricultural modeling in 2022.5Smaller amounts will be required from sugarcane bagasse,

wheat residue, as well as sweet sorghum pulp (bagasse) to produce another 0.8 billion gallons of

cellulosic biofuel.6Thus, the residue collected to meet EISA would be a small fraction of the

total residue produced nationwide – though potentially higher fractions in some local areas. See

Section 1.8.1.3 for more details on the use of agricultural residues for our cellulosic plant siting

analysis developed for the air quality modeling.

5 Assuming conversion yield of 92.3 gal/dry ton as updated by NREL yields. Adjusted for moisture content, see

FASOM documentation (Beach, 2010) for more details.

6 Bagasse is technically a by-product of the sugarcane process and not an agricultural residue, we include it here for

simplification. Sweet sorghum pulp is also a by-product of sweet sorghum processing.
26

Table 1.1-8.

FASOM Estimated Total Agricultural Residue Feedstock Possible in 2022

(million wet tons)7

State/Region Barley Corn Oats Rice Sorghum Wheat Total

Alabama 0.0 1.2 0.0 0.0 0.1 0.6 1.9

Arizona 0.1 0.1 0.0 0.0 0.0 0.4 0.6

Arkansas 0.0 1.3 0.0 8.1 0.4 0.9 10.8

California 0.5 0.9 0.0 2.0 0.1 2.9 6.6

Colorado 0.5 6.3 0.1 0.0 0.7 9.9 17.4

Connecticut 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Delaware 0.1 0.6 0.0 0.0 0.0 0.2 0.9

Florida 0.0 0.2 0.0 0.0 0.0 0.1 0.3

Georgia 0.0 5.5 0.1 0.0 0.2 1.5 7.2

Idaho 2.9 0.4 0.0 0.0 0.0 4.4 7.7

Illinois 0.0 65.3 0.1 0.0 0.3 3.6 69.2

Indiana 0.0 33.0 0.0 0.0 0.0 1.7 34.8

Iowa 0.0 79.1 0.3 0.0 0.2 0.1 79.7

Kansas 0.0 12.4 0.2 0.0 9.9 29.3 51.8

Kentucky 0.1 7.8 0.0 0.0 0.1 2.8 10.9

Louisiana 0.0 0.3 0.0 2.2 0.1 0.5 3.1

Maine 0.0 0.0 0.0 0.0 0.0 0.0 0.1

Maryland 0.2 1.7 0.0 0.0 0.0 1.0 2.9

Massachusetts 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Michigan 0.0 9.2 0.1 0.0 0.0 3.7 13.1

Minnesota 0.3 39.7 0.3 0.0 0.0 4.2 44.6

Mississippi 0.0 1.2 0.0 1.1 0.3 1.1 3.8

Missouri 0.0 14.7 0.0 0.4 0.7 3.7 19.5

Montana 3.0 0.0 0.8 0.0 0.0 6.6 10.4

Nebraska 0.0 53.2 0.0 0.0 1.1 6.4 60.7

Nevada 0.1 0.0 0.0 0.0 0.0 0.2 0.3

New Hampshire 0.0 0.0 0.0 0.0 0.0 0.0 0.0

New Jersey 0.0 0.0 0.0 0.0 0.0 0.1 0.1

New Mexico 0.0 0.6 0.0 0.0 0.7 1.6 2.9

New York 0.0 0.3 0.1 0.0 0.0 0.3 0.7

North Carolina 0.2 9.0 0.2 0.0 0.6 1.3 11.3

North Dakota 6.5 3.0 1.1 0.0 0.0 16.1 26.7

Ohio 0.0 15.1 1.1 0.0 0.0 3.9 20.0

Oklahoma 0.2 0.7 0.1 0.0 1.4 17.1 19.5

Oregon 1.3 0.1 0.4 0.0 0.0 0.3 2.1

Pennsylvania 0.0 0.1 0.0 0.0 0.0 0.9 1.1

Rhode Island 0.0 0.0 0.0 0.0 0.0 0.0 0.0

South Carolina 0.0 1.2 0.0 0.0 0.0 0.4 1.7

South Dakota 0.8 15.2 1.3 0.0 0.9 9.9 28.1

Tennessee 0.0 2.7 0.0 0.0 0.1 1.4 4.2

Texas 0.1 7.5 0.8 1.2 9.6 13.6 32.7

Utah 0.4 0.1 0.0 0.0 0.0 1.7 2.2

Vermont 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Virginia 0.3 2.5 0.0 0.0 0.0 2.0 4.8

Washington 1.5 0.6 0.1 0.0 0.0 7.5 9.8

West Virginia 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Wisconsin 0.1 12.9 0.4 0.0 0.0 1.2 14.7

Wyoming 0.5 0.2 0.1 0.0 0.0 0.6 1.4

Total 20 406 8 15 27 166 642

7 Assumes straw to grain ratio for barley and wheat (1.5:1) and for corn, oats, rice, and sorghum (1:1); Also assumes

0.024 ton/bu for barley and oats, 0.028 ton/bu for corn, 0.05 ton/cwt for rice and sorghum, and 0.03 ton/bu for

wheat. For more details on assumptions please refer to the following: Beach, Robert; McCarl, Bruce, U.S.

Agricultural and Forestry Impacts of the Energy Independence and Security Act: FASOM Results and Model

Description, RTI International, January, 2010.
27

1.1.2.2 Dedicated Energy Crops

Crops developed and grown specifically as a renewable source of cellulosic material for

biofuel production are not yet commercial, but have significant potential. Currently, crops such

as corn that are grown and harvested for energy uses in the United States are also used for

agricultural purposes and serve many important uses other than biofuel production. This

competition could be reduced by the use of non-agricultural feedstocks for cellulosic biofuel

production. Urban wastes and forest and agriculture residues could likely be the first feedstocks

used in cellulosic biofuel production due to lower feedstock costs and availability; However,

there are many uncertainties over land availability and sustainable removal rates for residues.

Many of the energy crops investigated are perennial species grown from roots or

rhizomes that remain in the ground after harvesting the above-ground biomass. While most

agricultural crops are annual species, perennials are considered beneficial in many ways.

Dedicated perennial energy crops have the potential to grow on marginal lands, produce high

yields, and may have low input needs. Once a perennial crop is established costs are reduced, as

the need for tillage is lowered. The root system that remains in the soil can also facilitate the

acquisition of nutrients thus decreasing the need for large fertilizer inputs. In southern climates,

perennials have the potential for higher yield per acre of land than annual crops. This is due to

the fact that perennial plants develop more quickly in the spring and the canopy of foliage can

sustain for longer in the fall. This makes it possible for the plants to be more photosynthetically

active and have a more efficient energy conversion system. Perennial energy crops also increase

soil productivity, sequester carbon, and provide refuge for wildlife.

The following sections describe several of the most commonly discussed dedicated

energy crops (switchgrass, miscanthus, and hybrid poplars) as well as some less familiarly

known crops and the potential marginal lands on which they can be grown. While not all of

these energy crops were specifically modeled in our agricultural models, switchgrass (which is

often used as the main “model” energy crop), was projected to be a likely and significant

feedstock for the production of renewable fuel to meet EISA.8,40 For the final rule, FASOM

projected that 7.9 ethanol-equivalent billion gallons out of the 16 billion gallon cellulosic biofuel

required would come from switchgrass. See Chapter 5 for more details on the agricultural

modeling.

Switchgrass

The energy crop that has received the most attention is switchgrass. Switchgrass is a

perennial warm season grass that is native to the United States. It typically reaches heights of 3-

5 feet, but can grow to more than 10 feet in some southern regions. It has a deep root system that

extends many feet below the earth. It may be the ideal energy crop mainly because it can

tolerate many soil types and climates from drought conditions to floods. It is also resistant to

many pests and diseases. The photosynthetic pathway of switchgrass (and other perennials)

allows it to produce high biomass yields with low amounts of chemical input. In the spring,

switchgrass develops a photosynthetic canopy of biomass more quickly, and it also persists

8 Assuming 16 Bgal cellulosic biofuel total, 2.3 Bgal from Urban Waste; 13.7 Bgal of cellulosic biofuel for ag

residues, forestry biomass, and/or energy crops would be needed.
28

longer in the fall than annual plants, allowing for a high net conversion of solar energy per

year.

41

Highly variable yields have been estimated at 1-12 dry tons/acre per year depending on

soil, location, and variety. A yield of 4-5.5 dry tons/acre is a reasonable average today.42 In a

long term study sponsored by the DOE, average yield after 10 years of growth was 4.8-7.6

tons/acre for switchgrass when harvested annually.9,43 Biannual harvests were also done

experimentally to try and achieve the maximum yields possible but the harvests showed little

difference in total yield. Biannual harvests resulted in approximately 70% of the yield for the

first cut and 30% for the second.44

Water and nitrogen availability are the main resources that limit production of warmweather grasses such as switchgrass. Nitrogen accessibility for these plants depends on many

factors. Harvesting frequency, soil content, and removal rates all affect the nitrogen available to

the plant. In a study by S.B. McLaughlin, initial nitrogen fertilization rates were 40-120 kg/ha

(36-107 lbs/acre); however they discovered that a reduction to only 20 kg/ha (17.8 lbs/acre) of

nitrogen was sufficient to produce similar yields in single cut systems in the mid-Atlantic

region.45 Reduced nitrogen amounts were similar in other regions of the country.

Miscanthus

Miscanthus is a tall perennial grass that has been evaluated as a potential energy crop

most extensively in Europe where it is already being grown for biofuel purposes. The genus is

primarily tropic or sub-tropic in origin but there is a wide climactic range at the species level.46

This characteristic makes it more suitable for establishment over the ranging climates of North

America. Giant miscanthus (Miscanthus x gigantus) is a hybrid variety that can grow 12-14 feet

tall. It is a cold-tolerant warm season grass and has similar characteristics to switchgrass with

high yields and low amounts of input.47 In the Midwest, the growing season of Miscanthus is

April to October. The plant grows large green foliage that maximizes in approximately late

August. As the temperature falls the foliage fades and drops off leaving the stem. The stem is

the commercially important part of the plant and resembles bamboo. Stems can reach nine feet

in length, 1⁄2 to 3⁄4 in diameter, and are harvested in the winter after drying occurs.48

Establishment of a crop takes approximately 2 years, with maximum yields reached in the

third year depending on soil fertility. In established crops 5-10 shoots per square foot can be

developed. Yields in various studies from the University of Illinois were 9-16 tons/acre in

various regions in Illinois. The southern regions of the state with poor soil quality also saw high

yields illustrating that miscanthus is suitable for growth and high achievable yields on marginal

land.49 Yields in Europe ranged widely, with irrigated crops reaching 12 tons/acre and unirrigated yields of 4-10 tons/acre in the fall. According to trials conducted in Europe, the quality

of miscanthus biomass for conversion to biofuel improves by delaying harvesting until after the

winter months and the plant has time to dry sufficiently. However, this reduced yields by 30

percent. 50 In comparison to switchgrass, research out of Illinois also concluded that miscanthus

can yield more biomass for conversion to biofuel because of its even higher photosynthetic

efficiency and longer growing season.51 In terms of input, miscanthus uses nitrogen extremely

9

Switchgrass variety used in this study was Alamo. Other varieties could result in different yields.
29

efficiently and therefore does not need to be fertilized for high yields to be achieved. There is

also no need for pesticides; however, herbicides have been used to control weed populations.52

Challenges in growing and producing miscanthus crop include high establishment costs,

problems in winter survival during the first year, and potentially high water needs. European

cost estimates are similar to other perennial plants at approximately $64 per dry ton; however

they estimate that a growing cycle of 10-12 years is required to recover the start-up costs of $267

per planted acre.53 The bulk of the high initial cost comes from planting and harvesting

machinery. Establishment of a stronger market for growing these energy crops, as well as

increased knowledge of propagation of the species, will inevitably lower overhead costs.54

Hybrid Poplar

The poplar tree is another option being investigated for use as a dedicated energy crop.

Woody perennial plants have some of the same characteristics of the perennial grasses that make

them suitable for possible use as an energy crop. They retain significant amounts of root

biomass below ground, require little tillage, grow fast large canopies, and require less

fertilization than their agricultural counterparts.

Technological advances in harvesting and genetics may help produce species that will be

more suitable for use as an energy crop. Genetic information has helped to understand the

characteristics the poplar tree. The complex genetic information obtained from the genome of

this plant will make possible the engineering of faster growing trees with more biomass available

for harvest.55

Other Potential Feedstocks

Several other perennial plants have the possibility to be used as dedicated energy crops.

As previously described, the characteristics of perennial species make some optimal for use in

this capacity. Because these plants have not been grown in agricultural sectors, they have not

been extensively researched and fully optimized. Corn is a crop that has been scientifically

studied for decades because of its continued importance in the market. Dedicated energy crops

must see this type of investment to bring about further knowledge of basic biology which will

lead to advances in breeding and eventual domestication of the species that have promise. The

DOE along with university researchers have identified several other plants as potential energy

crops. These include additional types of grasses such as reed canary grass, high biomass forage

sorghum, and energy cane. Yields for forage sorghum are high and vary from 10-20 dry tons per

acre depending on the genotype used.56 High tonnage energy cane perhaps offers the greatest

potential for much of East Texas and the U.S. Gulf Coast, as commercially grown varieties can

produce up to 40 dry tons per acre under optimal conditions.57 Hybrid willow, silver maple,

black locust, sweetgum, and eucalyptus are other perennial woody plants that are possibilities.58

Significantly accelerated testing and selection for populations will be necessary in

establishing these plants. Breeding for desired traits and adaptability across a wide array of

environments in multiple physiologic and geographic regions will be necessary. No single

species of dedicated energy plant will be optimal for all areas of the country, especially
30

considering the amount of biofuels needed. Temperature, rainfall, and soil composition are

highly variable across the continental United States; therefore, using a diverse group of plant

species optimal for each growing region is a likely strategy. With current information and

characteristics of each plant, the DOE has estimated where the possible growing areas could

occur (see Figure 1.1-2). 59

Figure 1.1-2.

Possible Geographic Distribution of Dedicated Energy Crops

Marginal Land Assessment

One of the benefits of perennial species is their suitability for growth on marginal lands.

A study by Elliot Campbell of Stanford University assessed abandoned land availability and the

potential for this land to be used for energy crops. 60 Because of the increased demand for

biomass energy, using abandoned crop or pasture lands to grow some of these crops could be a

better alternative than converting forested areas or using agriculture lands. This study estimated

the amount of global abandoned land available, the amount of biomass that could be grown on

these lands, and the corresponding use of that biomass for energy purposes.

Historical land use data, satellite imagery, and a global ecosystem model were used for

the estimates. The study considered “abandoned land” as land that was previously used for

pasture or crops but has since been abandoned and not converted to urban or forested areas.

Historical land use data was obtained from the History Database of the Global Environment 3.0

(HYDE) which consisted of gridded maps which show the fraction of crop and pasture land

within each grid cell for decades between 1700 and 2000. The Center for Sustainability and the

Global Environment (SAGE) land use database was used to check and supplement the HYDE

database. They used a MODIS satellite map to exclude areas that have transitioned into forest or

urban areas. Two different mathematical approaches were then used to estimate a conservative

and a high estimate of total land available. Biomass production was estimated using the

Carnegie-Ames-Stanford Approach ecosystem model which takes into account climate data, soil
31

texture, land cover and the normalized difference vegetation index (NDVI), but does not take

into account fertilizer use or irrigation, which could increase yields.

The low and high estimates for global abandoned land, excluding forested and urban

areas are 951 and 1166 million acres. The authors found that these lands could produce between

1.6 and 2.1 billion tons of biomass respectively. In the United States an average of approximately

146 million acres of abandoned land was estimated. Assuming natural growth on these lands,

approximately 321 million tons/year of biomass could be produced. At just 80 gallons of ethanol

per ton of biomass, there could be the potential to produce approximately 26 billion gallons from

a grass crop such as switchgrass. It is pointed out that there will be significant differences

between crop types and management styles which will effect growth and yields. Although

perennial grasses can be grown on these lands, yields may be lower than they would be on more

suitable agricultural lands.

On a state-by-state basis, the areas with the highest amount of available abandoned lands

are in the West. Texas has the largest amount of abandoned land estimated at 10.37 million

acres. Wyoming, Utah, Oregon, New Mexico, Nevada, Colorado and California each contribute

over 5 million abandoned acres to the total. Midwestern states including Iowa, Wisconsin,

Illinois, and Ohio have approximately 3-4 million acres of abandoned land each (see Table 1.1-

9). These lands may be more conducive to crop production than the more arid parts of the West.

However, the condition and quality of these lands is unknown at this time. It would be difficult

to estimate the specific types of energy crops that could be grown on these lands. Also, in the

DOE assessment previously referenced, most of the Western states are not implicated as areas of

possible biomass growth (above Figure 1.1-2).
32

Table 1.1-9. Abandoned Agriculture Land and Potential Production by State61

Area Area Production Ethanol Production Rate

(Million ha) (Million acres) (MM tons biomass/yr) (gallons/tons)

Alabama 1.4 3.46 13.2 3.82

Alaska 0.3 0.74 0.4 0.54

Arizona 1.9 4.69 2.4 0.51

Arkansas 1.1 2.72 11.1 4.09

California 3.6 8.89 13.2 1.48

Colorado 2.7 6.67 8.1 1.21

Connecticut 0.1 0.25 0.6 2.43

Delaware 0.1 0.25 0.5 2.02

District of Columbia 0 0 0 0

Florida 0.5 1.24 2.7 2.19

Georgia 1.6 3.95 15.2 3.85

Idaho 1.4 3.46 4.7 1.36

Illinois 1.6 3.95 11.4 2.88

Indiana 1.2 2.96 8.5 2.87

Iowa 1.6 3.95 12.7 3.21

Kansas 0.3 0.74 1.8 2.43

Kentucky 0.8 1.98 6.7 3.39

Louisiana 0.9 2.22 7.8 3.51

Maine 0.1 0.25 0.8 3.24

Maryland 0.4 0.99 2.7 2.73

Massachusetts 0.2 0.49 1.1 2.23

Michigan 1.5 3.71 9 2.43

Minnesota 1.6 3.95 10.7 2.71

Mississippi 1 2.47 9.1 3.68

Missouri 1.5 3.71 14.1 3.81

Montana 1.7 4.2 6.8 1.62

Nebraska 0.4 0.99 2.2 2.23

Nevada 2.1 5.19 3 0.58

New Hampshire 0 0 0.3 0

New Jersey 0.2 0.49 1.9 3.85

New Mexico 3 7.41 5.4 0.73

New York 1.7 4.2 10.2 2.43

North Carolina 0.7 1.73 6.2 3.59

North Dakota 1 2.47 4.4 1.78

Ohio 1.4 3.46 8.9 2.57

Oklahoma 1.1 2.72 8.8 3.24

Oregon 2.2 5.43 8.2 1.51

Pennsylvania 1 2.47 8.2 3.32

Rhode Island 0 0 0.2 0

South Carolina 0.8 1.98 7.3 3.69

South Dakota 0.3 0.74 2 2.7

Tennessee 1.1 2.72 10.3 3.79

Texas 4.2 10.37 25.3 2.44

Utah 2.6 6.42 4.7 0.73

Vermont 0.1 0.25 1 4.05

Virginia 0.7 1.73 6.7 3.88

Washington 0.9 2.22 4 1.8

West Virginia 0.1 0.25 0.5 2.02

Wisconsin 1.4 3.46 9.9 2.86

Wyoming 2.8 6.92 6.1 0.88

Totals 58.9 145.5 321

25.68 Bgal Ethanol/yr

State

Total Ethanol Volumea

a. Assuming a conservative 80 gal/ton conversion rate
33

The estimates of abandoned agricultural land do not include land enrolled in the

Conservation Reserve Program (CRP), which could be an additional source of land available for

energy crops. Land in this program is farmland that is converted to trees, grass, and areas for

wildlife cover, but is considered crop land by the models in the abandoned land study.

Environmental benefits of this land include the creation of wildlife habitat, increasing soil

productivity, reducing soil erosion and improving ground and surface water quality.62 As of

November 2009, there were 31.2 million acres under the CRP contract which is down 2.6 million

acres from the prior year.63 Approximately 28 million CRP acres are growing with native or

introduced grasses, suggesting that there is a significant amount of switchgrass already in the

environment. Figure 1.1-3 shows the land allocation in the United States in 2008.64 Recently, the

2008 Farm Bill capped the number of acres in the CRP at 32 million acres for 2010-2012.

Following historical trends, it is possible that some of these acres will go into crop production.

While some of this land may go for biofuel production, the benefits of producing energy crops

will have to be weighed against the benefits of having the land in the CRP.

Figure 1.1-3. 2008 CRP Enrollment

1.1.2.3 Wood Residues

There is a substantial amount of forestland here in the U.S. It is estimated that 749

million acres, or one-third, of the U.S. land area is forested. Of this forested land, two-thirds (504

million acres) is considered timberland which contains more than 20 ft3of woody material per

acre – the other one-third of the forest land contains less than 20 ft3of woody material per acre.

Most of this forested land, 58 percent, is privately owned, another 29 percent of the forest land is

publicly owned, and 13 percent is owned by the forest industry. A higher percentage of the land

is privately owned in the East, and a higher percentage of the land is publicly owned in the West.
34

Of the 749 million acres of forestland, 77 are reserved as parks or wilderness and would

likely be considered off limits for harvesting for biomass. Also, 168 million acres of timberland

is considered not suitable for harvesting for biomass because of poor soil, lack of moisture, high

elevation, or rockiness.65

The U.S. forestry industry harvests a portion of this forest land to produce its products,

and in the process of doing so, it generates woody residues that can be recovered for the purpose

of producing cellulosic biofuels. Major sources of solid waste wood generated in the U.S.

include forestry residues, primary and secondary mill residues, and urban wood residues. All

this material is being produced through the everyday practices of the forestry industry providing

its primary wood products to the various industries it supplies. In addition, forests which are not

currently harvested for wood could be thinned. This thinning of the forests would not just be to

provide biomass, but as part of a strategy which may be beneficial for the forests, or to avoid

external costs such as forest fires. Each of these categories is further described below:

Forestry residues

In-forest operations generally include four major sources of materials: logging residues,

other removals, fuelwood, and fuel treatment wood.66 In the process of removing, or logging, the

larger woody portion of the trees (5 inch diameter and greater), the logging industry creates

logging residues. Logging residues typically include tops of harvested trees and unwanted trees

cut or knocked down and left on site, including dead and cull trees. Other removals are growing

stock and other sources cut and burned or otherwise destroyed in the process of converting forest

land to non-forest uses, such as for making way for new housing or industrial developments.

They also include growing stock removed in forestry cultural operations. Forest residues are

also available from fuelwood, which is harvested wood used in the residential and industrial

sectors for energy. Thus, forest residues are already being created or harvested today.

Primary and secondary mill residues

Harvested wood from forests is converted into consumer products at wood processing

mills. Primary mills convert roundwood products (i.e., tree trunks and logs) into other wood

products, including sawmills that produce lumber, pulp mills, veneer mills, etc. Secondary mills

use the products from primary mills to produce other products such as millwork, containers and

pallets, buildings and mobile homes, furniture, flooring and paper and paper products. While

primary and secondary mills are typically separate facilities, both primary wood processing and

secondary conversion to finished consumer products can occur in the same facility.67 Both

primary and secondary mills produce residue and woody waste material. For example, the

residue generated by primary mills includes bark, slabs and edgings, sawdust and peeler cares.

This waste material could be used as feedstock to produce biofuels.

Urban wood residues

The two principal sources of urban wood residues are municipal solid waste (MSW) and

construction and demolition (C&D) debris. Municipal solid waste contains solid wood from

both wastewood and yard trimmings. Yard trimmings include herbaceous material and woody
35

trimmings. Construction waste is made of contemporary building materials with little

contamination. Sources include new residential construction, new nonresidential building

construction and repair and remodeling of existing buildings. Demolition waste, on the other

hand, is a heterogeneous mixture of material from demolishing buildings and structures and is

difficult to remove uncontaminated portions. The potential contribution of urban wood residues

to the production of biofuels is discussed in the Section 1.1.2.4 of this RIA.

The Thinning of Forests

While the above categories are associated with existing forest harvesting or other removal

activities, the thinning of forests would largely be a new activity. Many U.S. forests have

become overgrown and very dense with forest material, and a portion of this overgrown forest

will die, dry out and decay. This decaying forest material can provide a source of fuel for forest

fires that are expensive to fight or contain. Over the previous 10 years forest fires have

consumed 49 million acres and cost the U.S. taxpayer $8.2 billion. 68 This cost does not include

the additional cost due to the loss of human life, the loss of personal property and the impact on

the environment. Thinning forests involves the removal of excess forest material from the

forests that could help to prevent some of these forest fires, or at least help to reduce their

impact. Also, thinning these forests to prevent them from becoming overly dense could

potentially help them to remain healthier. There are many thinning operations today, but the

material is burned or left to decompose instead. The removed excess woody material from

overgrown forests could provide a source of biomass for producing biofuels.

Accessibility of Wood Residues

Despite the availability of woody residues for producing cellulosic biofuels, there are

several obstacles for woody residues that are not present when utilizing feedstocks such as

agricultural residues. For instance, forestlands will likely be managed less intensively than

agricultural lands because forests provide multiple-use benefits (e.g., wildlife habitat, recreation,

and ecological and environmental services).69 This in effect makes it more difficult to take steps

to increase the productivity of forest areas. Also, there are factors or site conditions that can

affect tree growth, including poor soils, lack of moisture, high elevation, and rockiness. The

limits caused by some of these factors would likely not be overcome, resulting in lower

productivity than what could be theoretically possible. Also, a couple of these factors, the high

elevation and rockiness, results in areas of forestland which is inaccessible by forestry

equipment. Forestry residues are also demanded for other purposes other than for production of

a transportation fuel (e.g. for process fuel). These reasons would make it more challenging to

collect and use woody residues in large quantities compared to agricultural residues.

On the other hand, there may be some benefits to the use of woody residues. One

example is the removal of excess forestry biomass to reduce the risk of fires and/or to improve

forest health. In addition, resources such as primary and secondary mill residues and urban

wood residues are already collected at the processing facility and it seems probable that some

cellulosic facilities could be co-located to mills and/or landfills to increase the likelihood of

having close and steady feedstocks readily available. Some states may also be endowed with

larger wood resources than agricultural residues.
36

In making estimates of potential forest residue availability, certain assumptions about

accessibility and recoverability are typically made. For example, some studies assume that

residue collection is completed at the same time as harvesting, meaning that all residues are

regarded as one hundred percent accessible.70 This might become possible due to integrated

harvesting systems which could harvest forest biomass in a single pass operation such that

residual forest residue for producing biofuels could be produced along with conventional forest

products.71 Other estimates for accessibility have been lower, with about sixty percent of North

American temperate forest considered accessible (not reserved or high-elevation and within 15

miles of major transportation infrastructure).72 In terms of recoverability, some studies have

assumed sixty-five percent of logging residues and fifty percent of other removal residues as

being recoverable while others report an average potential recovery of sixty percent and as much

as sixty-five percent when utilizing newer technology.73 Refer to Section 1.3 for more discussion

on the harvesting and transport of wood residues.

Sustainable Removal

While there has been some discussion of sustainable removal practices for crop residues,

there has been less review on the topic for woody residues. As forest residues have been

traditionally left in the forest to decompose, there remains much to be learned about the

harvesting of forest residues in a sustainable way that still leaves sufficient nutrients to maintain

the forest and to replenish the soil. This is reiterated in reports on woody residue removal which

emphasize the need for more detailed studies on the range of ecological effects, from wildlife to

soils.

Currently, practices for how much forest residue should be maintained in the forest to

maintain forest health vary substantially. For example, a district for one study on the removal of

forestry residues required about 5 tons per acre be left whereas other districts had no such

requirements.74 In a different source, a summary of national forest land management plans from

1995 indicated about 60 percent of western national forest timberland base to be suitable for

timber production operations.75 This issue is not only applicable in the United States, but also in

Europe, where the use of forest biomass for energy is also being considered. A Swedish study

showed that the main incentive for forest owners not to sell forestry residues was concerns for

soil fertility.76 Therefore, although there have been suggested limitations to the amount of

residue suitable for removal there has yet to be consensus over the optimal amount.

Some recent long-term soil productivity studies are beginning to provide some useful

data post-harvesting. One study, which assessed the soil condition 5 years after harvest of the

woody biomass, showed that for most of the sites there was not a significant impact on soil

carbon and nitrogen and compaction, while at one site there was a significant reduction of soil

carbon and nitrogen. Another study which tracked the soil quality 10 years after harvesting the

forest biomass came to some interesting conclusions.77 Complete removal of the surface organic

matter did lead to declines in the concentration of soil carbon, however, this effect was attributed

to the loss of the forest floor. Soil compaction did reduce productivity in clay soils, but

increased the productivity in sandy soils, and was not a factor if an understory was present.78

Thus, these two studies suggest that forestry operations, if they are designed for the soil type and
37

the area that the operations are taking place, may be designable to protect the sustainability of

forests. However, additional studies and data review is likely necessary to fully understand these

impacts.

Yet another issue regarding sustainable removal is the affect of forest residue extraction

on biodiversity. The removal of forest residue may affect biodiversity because lower amounts of

wood in the forest imply fewer habitats for species using wood for breeding. Species may also

be threatened because certain insects colonize in wood that may be burned for energy purposes.

Several forestry management methods, such as lower planting densities, aggressive thinnings,

prescribed burning, and longer rotations have been suggested as ways to maintain biodiversity in

actively managed forests.79 Quantitative predictions about how much habitat loss various

species can tolerate are almost impossible to make. Instead, one study recommended making

qualitative predictions on which types of habitats or wood types are most threatened. For

instance, this study examined Sweden’s forest fuel extractions and concluded that coniferous

wood can be harvested to a rather large extent, whereas deciduous tree species should be retained

to a larger degree.80 Another study in the southern Appalachians suggests that selective

harvesting to maintain a forest with regions of many different ages and structural classes is key

to maintaining biodiversity.81 As different regions will certainly have species specific to their

own regions, more research is necessary to determine appropriate recommendations on

maintaining biodiversity.

Another issue that has been considered is the occurrence of soil disturbance due to the

use of forest residue collection equipment. Studies have shown that the growth of woody plants

and yields of harvestable plant products are decreased by soil compaction from residue collection

equipment, because of the combined effects of high soil strength, decreased infiltration of water

and poor soil aeration.82 In another study, the use of a residue bundling machine caused some

measurable amounts of soil disturbance and an increase in “soil exposed” area at some

locations.83 Thus, it is important to limit the severity of soil disturbances with minimal passes

and relatively low ground pressure.

Energy Content of Forest Residue and Biofuel

Woody material obtained by the harvesting or thinning of forest is somewhat more

energy dense compared to other forms of biomass. On its Biomass Program webpage, the

Department of Energy lists the higher heating values (lower heating values were not available)

for many different types of biomass for dry samples.84 These values for woody biomass are

summarized in Table 1.1-10.
38

Table 1.1-10. Energy Content of Forest Material

Tree name Higher Heating Value

(BTU/lb dry wood)

Hybrid Popular 8,384 - 8,491

Black Locust 8,409 - 8,582

Eucalyptus 8,384 - 8,432

American Sycamore 8,354 - 8,481

Eastern Cottonwood 8,431

Monterey Pine 8,422

Because woody material is energy dense, it can produce a large amount of renewable fuel

per ton of feedstock. Based on recommendations from our cellulosic modeling efforts with the

National Renewable Laboratory (NREL), we assumed 101.5 gallons of ethanol could be

produced per ton for hardwood feedstocks in 2022. This is 10 percent more than the yield of

92.3 gallons of ethanol per ton used for agricultural residues and switchgrass. The reasoning for

the higher yields for hardwoods is their potential for higher carbohydrate compositions and thus

more sugars available for conversion to ethanol. These yields were used in our forest and

agricultural modeling as described in Chapter 5. NREL also completed a more recent feedstock

analysis indicating that yield differences may be smaller, i.e. closer to 95 gal/dry ton for

hardwoods. This work will be beneficial as we continue to make improvements to our analyses

in the future. For more information on feedstock considerations and their impacts on biorefining

refer to the NREL report in the docket.85

Availability of Forest Residue

The quantity of forest residue available to produce biofuels was estimated by two

different studies. We summarize those two studies, and then summarize data which we received

directly from the U.S. Forest Service. In addition, we were able to incorporate the forestry sector

component in the FASOM model, as further described in Chapter 5. As these feedstocks are

now allowed to compete with the various agricultural feedstocks and energy crops in the market,

we believe it is a more robust analysis than our prior proposal method of analyzing the

agriculture and forestry sectors separately. Therefore, our final rule is based on results taken

from our forest and agricultural modeling in FASOM.

Billion Ton Study

A landmark assessment of the potential biomass available from existing forest land in the

U.S. was recently conducted by the USDA and the Department of Energy (DOE).86 This

landmark assessment was titled “Biomass as Feedstock for a Bioenergy and Bioproducts

Industry: The Technical Feasibility of a Billion Ton Supply,” which is also known as the Billion

Ton Study. We reviewed this study and are summarizing much of the information contained in

that report here because it is very useful background about U.S. forest land and its potential

contribution to biofuels production.
39

The total forest inventory is estimated to be about 20.2 billion dry tons. The report

authors estimated that about 2.2 percent of the total forest inventory is harvested each year,

which corresponds to 444 million dry tons. This removal rate is estimated to be less than the

annual average forest growth, which suggests, at least on an aggregate basis, that this removal

rate is sustainable. It is estimated that 78 percent of this removal was for roundwood products

(sawlogs, pulpwood, veneer logs and fuel wood), 16 percent was logging residue and about 6

percent was classified as other removals. Thus, the Billion Ton study authors estimate that 67

million dry tons of logging residue could potentially be available for biofuel production, which is

comprised of 49 million dry tons of primary logging residue, and 18 million dry tons of other

removals. The Billion Ton study estimates that 65 percent of the total logging and other residue

would be recovered for use. The two reasons cited for not collecting the other 35 percent is that

some of the logging residue is comprised of small pieces, such as small branches and leaves,

which would not be economically recoverable, and that it would be necessary to leave behind a

portion of the logging residue to protect the sustainability of the forest as well as the wildlife

which thrives in the forest. For these reasons, the Billion Ton Study authors estimated that 41

million dry tons of forest residue could be sustainably removed from the U.S. forests as

byproduct from existing logging operations. Virtually all this removal is from privately owned

land where the logging operations occur today.

Additional forest residue is available downstream of the logging operations at mills. In

the process of making their products, primary wood processing mills create some wood residue.

However, almost all of this waste wood is recovered or burned for process heat. For example,

the bark from the logged wood is burned as fuel or converted into mulch. The Billion Ton

authors estimated that just under 2 million dry tons per year of residue would be available from

the primary wood processing mills as feedstock for producing biofuels.

The Billion Ton study estimated that additional wood waste could also be available from

secondary wood processing mills, which refine crude wood into more refined products. The

report authors could not find any data on how much residue is produced by these secondary

wood processing mills, however, a study of these facilities did provide an estimate.

Approximately 15.6 million dry tons per year were estimated to be available from the smaller of

these secondary wood processing mills, however, the report estimated that only 40 percent, or 6

million dry tons per year, would be available for biofuels production.

Another industry which processes harvested wood is the pulp and paper mill industry.

These companies process wood into fiber to make paper and cardboard. Most of the pulp and

paper mills use the Kraft process or sulfate pulping process which converts half of the woody

material into fiber, while the other half is a byproduct termed black liquor. The black liquor

contains a substantial amount of biomass. The pulp and paper industry is already using all of this

black liquor, plus purchasing and using some fossil fuels, to generate the electricity and heat that

it needs for its plants. Therefore, the authors of the Billion Ton Study estimated that there would

not be any residue available from the pulp and paper industry to produce biofuels.

The Billion Ton study estimated that another potential source of biomass from forests

would be the selective thinning of forests to help reduce the risk of fire, or to facilitate the

fighting of fires in the case that fires break out. Using a forest evaluation tool called the Fuel
40

Treatment Evaluator, the Forest Service estimated tree densities for forests all across the U.S.

and identified forests which contain excess woody material. The forests which contain excess

woody material are candidates for providing additional biomass for producing biofuels. The

Forest Service estimated the total amount of excess woody material to be 8.4 billion dry tons.

The Forest Service next estimated the portion of this excess woody material that could be

harvested for biofuels production. Despite the fact that this inventory exists today, the Billion

Ton Study authors assumed that this excess woody inventory would be used over a 30 year

period to reflect a sustainable removal rate. This assumption reduces the total yearly available

amount of excess woody biomass to 280 million dry tons per year. Another limiting factor is

that much of our nations forest is remote, thus, only 60 percent of this excess woody material

was estimated to be removable for use. The next assumption made is that the best of this woody

material, which is the woody material more than 5 inches in diameter and which comprises 70

percent of this material, would be used for feedstock for the logging industry. Thus, the

remaining 30 percent would be residue that would serve as feedstock for the biofuels industry.

Finally, the last assumption made is that of the excess woody material harvested, 15 percent

would be lost between harvesting and use, thus the total amount of woody biomass was adjusted

to be 15 percent lower. These assumptions result in 18 million dry tons of additional woody

biomass that could be used to supply the biofuels industry annually, and 42 million dry tons that

would supply the logging industry.

As shown below in Table 1.1-11, the Billion Ton Study estimates that a total of 67

million dry tons per year would be available from non-urban forests. It is important to note that

not all of the forest biomass in the Billion Ton Study, specifically wood from national forests and

perhaps much of the fuel wood, would be eligible to be used as a qualifying biofuel feedstock

under the RFS2 program. Despite this limitation, the Billion Ton Study is an important source of

information, especially when considering the maximum amount of sustainably removable forest

biomass.

Table 1.1-11.

Quantity of Forest Biomass Available for Producing Biofuels

Quantity

(million dry tons)

Logging Residue 41

Primary Mill Residue 2

Secondary Mill Residue 6

Forest Thinnings 18

Total 67

The Billion Ton Study authors projected that forest harvesting and mill activity will

increase in the future, thus increasing the amount of forest residues that would be available for

producing biofuels. The authors estimated the future forest residue supply in the year 2050 and

concluded that the logging residue is expected to increase from 41 million dry tons to 64 million

dry tons. Also in 2050, the primary and secondary mill residue quantity is projected to increase

from a total of 8 million dry tons per year to a total of 24 million dry tons per year. No estimate

was provided for any increase, or decrease, in the amount of forest woody material that would be
41

available from thinning forests. If the projected 39 million dry ton increases in forest residue

comes to fruition, then the total amount of forest residue that would be available for producing

biofuels in 2050 would be 106 million dry tons per year. We are primarily interested in

compliance with the RFS2 biofuels standard in 2022, which is just over 1/3rd of the way between

today and 2050. Thus, by interpolating the projected future forest residue in 2022 relative to

current levels and those in 2050, the report supports the conclusion that 79 million dry tons of

forest residue would be available in 2022.

U.S. Cellulosic Biomass Study

Another estimate for the amount of forest residue that could be used to produce biofuels

was made by Marie Walsh in a report titled “US Cellulosic Biomass Supplies and

Distribution”.87 This report also uses the Forest Service data base for its estimates, so its

conclusions resemble those of the Billion Ton study. However, an important difference between

this Cellulosic Biomass Study and the Billion Ton Study is that Marie Walsh estimated a cost

curve for the amount of biomass available for her Cellulosic Biomass study for multiple future

years.

In this report, Marie Walsh estimates that 63 million dry tons of logging residue is

created in the lower 48 states. Of this total amount of logging residue, 65 percent is estimated to

be accessible by roads, and not all the accessible logging residue is considered recoverable

because some of it is too small to recover. This study also estimates the cost for recovering this

available logging residue for future years for five year intervals through 2030. The amount of

logging residue available at different price points and for different years is summarized in Table

1.1-12.

Table 1.1-12.

Quantity of Logging Residue Available at Varying Prices

(million dry tons)

$20/dt $25/dt $30/dt $35/dt $40/dt $45/dt $50/dt $75/dt $100/dt

2007 0.06 1.84 6.22 10.89 24.02 31.29 31.29 36.19 38.50

2010 0.065 1.81 6.41 13.23 29.37 38.70 38.70 45.02 47.89

2015 0.065 1.95 6.80 13.62 29.99 39.35 39.35 45.71 48.60

2020 0.067 2.10 7.22 14.41 31.51 41.20 41.20 47.79 50.77

2025 0.067 2.17 7.46 14.81 32.32 42.19 42.19 48.90 51.95

2030 0.068 2.25 7.70 15.22 33.12 43.17 43.17 50.01 53.13

To qualify under RFS2, the biofuel producer would need to show that the forest residue is

from a qualifying planted forest as specified under RFS2. This could limit the quantity of

biomass available under RFS2 to lower levels that those shown in the table.

Marie Walsh also identified the quantity of woody material that would be available at

specific prices from other removal supplies – trees removed to make way for the construction of

buildings. Marie Walsh estimates that a total of approximately 24 million dry tons of forest

residue falls within this category. She estimated that perhaps 50 percent of this material would
42

be available for biofuel production. Marie Walsh added the other removal supplies to the

logging residue and estimated their availability at different price points, increasing the available

biomass by 25 percent. The combined total is summarized in Table 1.1-13.

Table 1.1-13.

Quantity of Forest Residue and Other Removals Available at Varying Prices

(million dry tons)

$20/dt $25/dt $30/dt $35/dt $40/dt $45/dt $50/dt $75/dt $100/dt

2007 0.09 2.63 10.49 15.16 32.16 41.62 41.62 47.71 50.49

2010 0.09 2.63 10.76 17.59 38.08 49.17 49.17 56.68 60.03

2015 0.09 2.79 11.26 18.08 38.87 50.00 50.00 57.56 60.93

2020 0.09 2.96 11.80 19.00 40.58 52.04 52.04 59.84 63.31

2025 0.10 3.07 12.15 19.50 41.56 53.21 53.21 61.15 64.68

2030 0.10 3.17 12.51 30.02 42.55 54.39 54.39 62.47 66.07

To qualify under RFS2, the biofuel producer would need to show that the “other removal

supplies” that it is interested in purchasing would qualify under RFS2. Some of this category

could quality as MSW while another portion of it may qualify if the trees are being removed to

prevent a wildfire from damaging the nearby buildings. However, the RFS2 definitions could

limit the quantity of this category of biomass that could qualify under RFS2.

This report also estimates the amount of primary and secondary mill residues available

for biofuels production. Like the Billion Ton study, Marie Walsh also concludes that only a very

small amount of primary mill residue is estimated to be currently unused and available for

producing biofuels. She concludes that out of the 88.7 million dry tons of primary mill residue

which are generated, that only 1.3 million dry tons is not used for fuel, fiber or other sources as

discussed above. However, she provides an additional assessment that, at the right price, the

primary mill residue could be drawn away from these other users of the primary mill residue.

The assumption is that for fiber uses, the primary mill residue could be drawn away from the

current users at 35% of the product price. For other uses, including for fuel, it is assumed that at

65% of the market price of the raw wood value, the primary mill residue could be purchased

away from the current users. Table 1.1-14 below estimates the price that specific estimated

primary mill residue volumes could be available for producing biofuels.
43

Table 1.1-14.

Quantity of Primary Mill Residue is Available at Varying Prices

(million dry tons)

$20/dt $25/dt $30/dt $35/dt $40/dt $45/dt $50/dt $75/dt $100/dt

2007 0.43 4.93 6.03 19.34 20.14 41.46 42.38 50.31 51.04

2010 0.55 5.70 7.29 21.91 22.80 46.03 47.37 56.29 57.33

2015 0.56 5.93 7.51 22.88 23.77 48.00 49.34 58.55 59.61

2020 0.58 6.16 7.74 23.85 24.73 49.97 51.31 60.82 61.88

2025 0.59 6.34 7.93 24.58 25.47 51.46 52.82 62.55 63.61

2030 0.60 6.52 8.12 25.31 26.20 52.96 54.31 64.28 65.35

The author also attempted to estimate the amount of secondary mill residue that could be

available for producing biofuels. She observed that data is scant on the amount of secondary

mill residue. She referenced a study (Rooney, 1998) that estimated that only a very small

volume of secondary mill residue would be available for producing biofuels. Of 12.5 million dry

tons of secondary mill residue which is generated, only 1.2 million dry tons is available for

producing biofuels. Unlike the analysis conducted for primary mill residue, the author did not

attempt to estimate the extent that biofuels producers could bid the secondary mill residue away

from the current users.

Marie Walsh also assumes that three very difficult-to-quantify sources of forest material

could be available as biomass for producing biofuels. One of these potential sources is the forest

material that could be available through the thinning of overgrown forests to help reduce the fire

risk within these forests. Marie Walsh referenced one study which estimated that 100 to 200

million acres of overgrown forest could be harvested. No estimate, however, was provided for

the amount of this forest material that could be available from forest thinning.

Another potential source of forest material for biofuel production that the study discussed

is a portion of the estimated 35.4 million tons of fuel wood used to heat homes and to provide

heat for industries. The author cited a report which estimated that fuel wood use decreased from

1986 to 2000, but began to increase again and is expected to increase through 2050. This

presumably means that if the demand for fuel wood is lower than previously, that some of that

fuel wood could be available for producing biofuels. However, in this report, Marie Walsh did

not make any firm estimate for this.

The Marie Walsh report also discussed that forest pulpwood supply is exceeding demand

in the Southeast. The demand of forest pulpwood decreased from 131 to 121 million tons per

year from 1993 to 2003, and this demand is expected to further decrease through 2020, and some

have projected that this decrease in demand will continue beyond 2020. During the period

between 1993 and 2003, pulpwood acreage and management intensity have increased, which

suggests that the Southeast is and will continue to be over supplied. This oversupply of forest

pulpwood could potentially provide additional biomass to the biofuels industry, although she did

not provide any firm estimate for this nor an estimate of how much might qualify under RFS2.
44

It is important to note that not all of the forest biomass in the US Cellulosic Biomass

Study would be eligible to be used as a biofuel feedstock under the RFS2 program. Despite this

limitation, like the Billion Ton Study, this study is an important source of information, especially

when considering the maximum amount of sustainably removable forest biomass.

While both of these studies provide quality assessments for the total amount of forest

residue available for producing biomass, they both have an important limitation as well. The

limitation is that these reports did not assess whether the forest residue in any particular area,

along with other potential biomass, is of sufficient density to adequately supply a potential

cellulosic biofuel plant. This feedstock density assessment must also consider the feedstock

availability requirements made by cellulosic plant investors or banks, which may choose to

require that a certain excess amount of feedstock be available to justify the use of that biomass in

a cellulosic ethanol plant. Without considering these limitations, these studies may overestimate

the quantity of biomass that would be truly usable and also the ultimate amount of biofuel that

could be produced. Some of these issues were addressed in our cellulosic plant siting analysis in

Section 1.8. Also, a study by the Western Governor’s Association, which was designed to

account for local biomass density, assessed the quantity of forest and other biomass that could be

used for producing biofuels.88 Because this study was only conducted for the Western United

States instead of the entire country, we did not summarize it here. However, the study is being

expanded nationwide and once completed it will provide nationwide results based on this very

robust, bottom-up approach.

U.S. Forest Service Data

To assess forest residue supply within the feedstock density and supply constraints, we

obtained county-by-county forest residue data from the U.S. Forest Service.89 The information

was provided by the subcategories of logging residue, primary mill residue, timberland

thinnings, and other removals. The information also included urban forest residue, however,

because that material is included with the other MSW, we did not consider it here (discussed

later in Section 1.1.2.4). Like the studies discussed above, the national forest lands are omitted

from consideration, and the urban forest residue is not considered here, but in the section

discussing MSW. Most, if not all, of this material, therefore, would be eligible to be used as a

feedstock for the production of biofuels under the RFS2 program, with the possible exception of

some of the unused mill residues. The information was also provided at different price points.

The quantities of forest residues are summarized by source type in Tables 1.1-15, 1.1-16 and 1.1-

17. To avoid presenting a large amount of data, we aggregated the county data by state, and we

are presenting the data at specific price points: $30/dry ton, $45/dry ton and $70/dry ton.
45

Table 1.1-15.

Volume of Forest Residue Available for Producing Biofuel

Biomass Available at $30/ton

Logging

Residue

Other

Removals

Timerland

Thinnings

Unused Mill

Residue

Total

Quantity

Alabama 1,202,541 253,620 433,519 7,117 1,896,798

Arizona 8,849 22,436 33,085 1,351 65,721

Arkansas 851,772 385,492 369,083 12,889 1,619,236

California 334,870 0 871,351 65,088 1,271,309

Colorado 9,203 7 0 2,302 11,511

Connecticut 4,195 15,339 10,465 3,949 33,949

Delaware 15,051 12,109 4,918 0 32,077

Florida 535,215 257,704 240,947 2,202 1,036,067

Georgia 1,556,954 496,631 553,627 45,138 2,652,350

Idaho 126,573 0 41,548 6,006 174,126

Illinois 139,101 117,589 115,431 18,523 390,644

Indiana 281,242 52,087 198,112 10,627 542,068

Iowa 56,049 27,580 48,991 159 132,780

Kansas 7,329 44,202 9,676 8,720 69,928

Kentucky 513,989 332,179 344,948 55,196 1,246,311

Louisiana 1,317,139 440,293 300,924 30,075 2,088,431

Maine 1,206,438 470 80,314 42,483 1,329,705

Maryland 90,722 415 40,994 17,067 149,197

Massachusetts 35,461 31,043 13,801 0 80,305

Michigan 379,463 122,476 327,640 13,763 843,343

Minnesota 348,807 331,492 132,712 26,878 839,889

Mississippi 1,548,534 355,071 425,344 95,138 2,424,088

Missouri 387,434 265,146 342,077 79,787 1,074,443

Montana 131,335 0 66,592 9,136 207,063

Nebraska 10,572 9,386 11,707 4,971 36,637

Nevada 15 53 0 0 67

New Hampshire 157,321 174 47,802 7,019 212,316

New Jersey 2,959 39 2,288 1,437 6,723

New Mexico 11,929 1,279 25,898 4,902 44,008

New York 367,003 54,671 163,336 27,390 612,400

North Carolina 1,013,165 629,632 560,814 12,811 2,216,422

North Dakota 1,453 7,601 3,822 265 13,141

Ohio 185,398 9,053 83,676 22,600 300,726

Oklahoma 173,869 98,794 53,043 495 326,200

Oregon 760,276 31 527,702 16,316 1,304,326

Pennsylvania 543,663 699 224,978 170,972 940,312

Rhode Island 884 22,860 2,800 389 26,934

South Carolina 714,551 348,289 301,850 1,051 1,365,741

South Dakota 6,972 14,436 2,993 2,294 26,695

Tennessee 316,706 244,920 423,906 187,583 1,173,115

Texas 616,777 218,464 185,718 3,021 1,023,979

Utah 2,973 7 9,909 4,437 17,325

Vermont 104,876 18,652 48,395 0 171,923

Virginia 741,673 406,800 436,870 39,366 1,624,709

Washington 641,144 22 925,479 21,446 1,588,091

West Virginia 488,356 24,714 161,653 118,779 793,502

Wisconsin 568,800 491,132 260,293 60,410 1,380,636

Wyoming 11,343 0 14,050 34,014 59,407

Total 18,530,943 6,165,088 9,485,083 1,295,560 35,476,674
46

Table 1.1-16.

Tons of Forest Residue Available for Producing Biofuel

Biomass Available at $45/ton

Logging

Residue

Other

Removals

Timerland

Thinnings

Unused Mill

Residue

Total

Quantity

Alabama 1,202,541 253,620 506,045 7,117 1,969,324

Arizona 13,566 21,210 34,967 1,351 71,094

Arkansas 851,772 385,492 429,414 12,889 1,679,567

California 583,478 0 949,468 65,088 1,598,034

Colorado 10,056 11 30,619 2,302 42,988

Connecticut 4,301 16,095 10,465 3,949 34,810

Delaware 17,932 14,145 6,700 0 38,777

Florida 535,215 257,704 266,597 2,202 1,061,718

Georgia 1,556,954 496,631 644,295 45,138 2,743,018

Idaho 216,303 0 52,594 6,006 274,902

Illinois 139,153 117,589 115,431 18,523 390,696

Indiana 281,464 52,087 221,845 10,627 566,023

Iowa 56,050 27,607 49,551 159 133,367

Kansas 7,329 44,202 9,676 8,720 69,928

Kentucky 513,989 332,179 407,371 55,196 1,308,735

Louisiana 1,317,139 440,293 330,512 30,075 2,118,019

Maine 1,280,511 495 102,442 42,483 1,425,931

Maryland 94,579 421 40,994 17,067 153,060

Massachusetts 39,127 33,191 13,801 0 86,119

Michigan 391,732 128,600 410,302 13,763 944,398

Minnesota 358,518 341,894 159,990 26,878 887,280

Mississippi 1,548,534 355,071 467,935 95,138 2,466,679

Missouri 387,434 265,146 466,082 79,787 1,198,448

Montana 215,597 0 70,775 9,136 295,507

Nebraska 10,710 9,434 11,707 4,971 36,822

Nevada 22 71 0 0 93

New Hampshire 165,519 197 57,566 7,019 230,301

New Jersey 3,184 40 2,423 1,437 7,084

New Mexico 17,239 1,287 26,862 4,902 50,291

New York 384,457 56,552 189,696 27,390 658,094

North Carolina 1,013,165 629,632 668,420 12,811 2,324,028

North Dakota 1,454 7,601 3,822 265 13,142

Ohio 186,022 9,069 88,572 22,600 306,263

Oklahoma 173,869 98,794 62,700 495 335,858

Oregon 1,341,835 34 574,948 16,316 1,933,133

Pennsylvania 1,341,835 34 574,948 170,972 2,087,789

Rhode Island 957 25,039 2,800 389 29,185

South Carolina 714,551 348,289 352,018 1,051 1,415,909

South Dakota 11,872 15,581 3,253 2,294 32,999

Tennessee 316,706 244,920 507,698 187,583 1,256,906

Texas 616,777 218,464 219,187 3,021 1,057,448

Utah 3,758 0 10,786 4,437 18,980

Vermont 108,542 19,182 53,836 0 181,560

Virginia 741,673 406,800 524,372 39,366 1,712,212

Washington 1,067,587 23 981,839 21,446 2,070,895

West Virginia 488,356 24,714 241,184 118,779 873,033

Wisconsin 576,938 499,302 327,027 60,410 1,463,677

Wyoming 18,163 0 18,202 34,014 70,380

Total 20,928,463 6,198,742 11,301,737 1,295,560 39,724,502
47

Table 1.1-17.

Tons of Forest Residue Available for Producing Biofuels

Biomass available at $70/ton

Logging

Residue

Other

Removals

Timerland

Thinnings

Unused Mill

Residue

Total

Quantity

Alabama 1,202,541 253,620 581,654 7,117 2,044,933

Arizona 13,566 24,510 38,678 1,351 78,105

Arkansas 851,772 385,492 492,094 12,889 1,742,247

California 583,478 0 1,000,615 65,088 1,649,181

Colorado 10,056 11 30,619 2,302 42,988

Connecticut 4,301 16,095 10,465 3,949 34,810

Delaware 17,932 14,145 6,700 0 38,777

Florida 535,215 257,704 332,353 2,202 1,127,474

Georgia 1,556,954 496,631 776,911 45,138 2,875,634

Idaho 216,303 0 61,926 6,006 284,235

Illinois 139,153 117,589 115,431 18,523 390,696

Indiana 281,464 52,087 221,845 10,627 566,023

Iowa 56,050 27,607 49,551 159 133,367

Kansas 7,329 44,202 9,676 8,720 69,928

Kentucky 513,989 332,179 463,904 55,196 1,365,268

Louisiana 1,317,139 440,293 375,052 30,075 2,162,559

Maine 1,280,511 495 166,117 42,483 1,489,605

Maryland 94,579 421 40,994 17,067 153,060

Massachusetts 39,127 33,191 13,801 0 86,119

Michigan 391,732 128,600 533,107 13,763 1,067,203

Minnesota 358,518 341,894 200,599 26,878 927,889

Mississippi 1,548,534 355,071 516,598 95,138 2,515,342

Missouri 387,434 265,146 643,929 79,787 1,376,295

Montana 215,597 0 83,023 9,136 307,755

Nebraska 10,710 9,434 11,707 4,971 36,822

Nevada 22 71 0 0 93

New Hampshire 165,519 197 58,098 7,019 230,833

New Jersey 3,184 40 2,423 1,437 7,084

New Mexico 17,239 1,287 32,187 4,902 55,616

New York 384,457 56,552 192,851 27,390 661,249

North Carolina 1,013,165 629,632 800,455 12,811 2,456,063

North Dakota 1,454 7,601 3,822 265 13,142

Ohio 186,022 9,069 88,572 22,600 306,263

Oklahoma 173,869 98,794 81,634 495 354,792

Oregon 1,251,094 34 566,594 16,316 1,834,037

Pennsylvania 546,418 707 340,497 170,972 1,058,594

Rhode Island 957 25,039 2,800 389 29,185

South Carolina 714,551 348,289 395,555 1,051 1,459,446

South Dakota 11,872 15,581 4,129 2,294 33,875

Tennessee 316,706 244,920 516,550 187,583 1,265,759

Texas 616,777 218,464 253,670 3,021 1,091,931

Utah 3,758 7 14,717 4,437 22,918

Vermont 108,542 19,182 71,105 0 198,829

Virginia 741,673 406,800 630,366 39,366 1,818,206

Washington 1,067,587 23 1,029,985 21,446 2,119,041

West Virginia 488,356 24,714 287,639 118,779 919,489

Wisconsin 576,938 499,302 420,775 60,410 1,557,425

Wyoming 18,163 0 21,598 34,014 73,775

Total 20,042,304 6,202,722 12,593,373 1,295,560 40,133,959
48

The U.S. Forest Service data reveals that there are large amounts of forest material in the

Southeast, the far Northeast and the Northwest portions of the U.S. The data also shows that the

price curve for this forest material is fairly flat over the range summarized here. This suggests

that the forests which are already accessible by roads provide access to low cost forest material

from the thinning of timberland. However, to access more and more of the timberland, the costs

ramp up quickly. These numbers are also significantly different than those presented in the

proposed rule. This is due to a misunderstanding in how the number should be interpreted.

According to our contacts at the U.S. Forest Service whether logging residue or timberland

thinnings would be available would depend on the type of logging operation being used. We

cannot, therefore, assume that 100% of the logging residue and timberland thinnings would be

available, as this would be double counting the potential for wood residues. Instead, we must

assume that a certain percentage of logging operations would produce logging residue and that

the rest would produce timberland thinnings. Based on suggestions from the U.S. Forest Service

we have assumed that 50% of logging operations would produce logging residue and 50% would

produce forestry thinnings. Additionally, the U.S. Forest Service data includes unused mill

residue, which may not be qualifying biofuel feedstock under RFS2 depending on the source of

the wood. While these changes result in a significant decrease in the amount of wood residue

available from current forestry operations they have no impact on our analyses. This is due to

the fact that the amount of wood residues used in cellulosic biofuel production, as projected by

the FASOM model, is still far less than the total available wood residue.

It is also important to note that this data is based solely on current forestry operations. It

represents the amount of wood residue that would be available today if these residues were

recovered. The United States contains much forest land that is not currently in active production

due to insufficient demand and low prices for forestry products. If demand for cellulosic

feedstock sufficiently increased the demand for forestry products it is very possible that logging

operations would expand to meet this need. In this sense, the data from the U.S. Forest service is

not an evaluation of the maximum amount of forestry residue that could be sustainable removed,

but rather a measure of how much residue could be recovered based on current logging

operations. Logging operations are financed based on their higher value products (i.e., lumber),

not based on demand for lower value products (i.e., residues), so it is unlikely investments would

be made to harvest forest residues absent demand for lumber operations. Nevertheless, this data

is valuable, as the value for cellulosic biomass would likely have to be significantly higher than

we are projecting in order to drive logging operation expansion.

Forestry Sector Modeling in FASOM

In addition to the agriculture sector, the FASOM model also contains a forestry

component, which details forest acres across the U.S. as well as production of forestry products.

Running the forestry and agriculture components of the model simultaneously shows the

interaction between these two sectors as they compete for land, as well as the effect on products

and prices in each respective sector. In total, FASOM includes a representation of seven major

land use categories, including cropland, cropland pasture, forestland, forest pasture, rangeland,

developed land, and acres enrolled in the Conservation Reserve Program (CRP). More

information on these land categories can be found in Chapter 5.1.2.
49

Various products from the forestry sector in the FASOM model can be used to produce

cellulosic renewable fuel. These products include hardwood and softwood milling, and logging

residues. The FASOM model projected that 110 million gallons from forestry logging would be

used to meet the cellulosic biofuel standard under EISA.

Wood Summary

We compared the quantity of potential biomass supplies projected to be available in 2022

by the two studies and the data that the Forest Service provided us in Table 1.1-18.

Table 1.1-18.

Forest Biomass Availability in 2022 at Different Prices (million dry tons)

Price ($/ton)

30 45 70

Billion Ton Study 79

U.S. Cellulosic

Biomass Study 20 103 118

Forest Service Data 35 40 40

For the rule we were able to incorporate the forestry sector model in FASOM which

projected 110 million gallons of forestry biomass would be used to meet the cellulosic biofuel

standard. This would require close to 1 million dry tons per year of forestry biomass. As noted

by the studies and data from the U.S. Forest Service, this amount is a small fraction of the large

amount of forestry biomass potentially available. Although there is additional forestry biomass

available for cellulosic renewable fuel production, other sources of cellulosic renewable fuel

(switchgrass and corn residue, in particular) are relatively more profitable for producers of

cellulosic renewable fuel feedstocks. For details on the economic impacts of the RFS2 program,

including prices of cellulosic feedstocks as modeled in FASOM, see Chapter 5.

1.1.2.4 Urban wastes

Cellulosic feedstocks available at the lowest cost to the ethanol producer will likely be

chosen first. This suggests that urban waste which is already being gathered today and typically

incurs a fee for its disposal may be among the first to be used. Urban wastes are used today in a

variety of ways. Most commonly, wastes are ground into mulch, dumped into land-fills, or

incinerated. Estimating the amount of urban waste available for biofuel production involves

understanding the types of materials that can be found in urban waste, potential competing uses

of urban waste, and the challenges with separating a mixed feedstock.

Municipal Solid Waste

MSW consists of paper, glass, metals, plastics, wood, yard trimmings, food scraps,

rubber, leather, textiles, etc. See Figure 1.1-4 for the percent composition of MSW generated
50

(before recycling) in 2008.90 Construction and demolition debris is not included in the estimate

and is discussed separately below.

Figure 1.1-4.

Total MSW Generation (by Material), 2008

250 Million Tons (Before Recycling).

The portion of MSW that can qualify as renewable biomass under the program is already

discussed in the preamble, Section II.B.4.d. The bulk of the biogenic portion of MSW that can

be converted into biofuel is cellulosic material such as wood, yard trimmings, paper, and much

of food wastes. Paper made up the majority of the total MSW generated in 2008, approximately

31 percent.

Although recycling/recovery rates are increasing over time, there appears to still be a

large fraction of biogenic material that ends up unused and in land-fills. In order to project the

portion of material that can potentially be used for biofuel purposes, we must understand how the

composition of landfilled material changes over time. To do this, we first analyzed the trends

from 2000-2007 for the percent composition of total MSW generated from paper/paperboard,

wood, and yard trimmings over time as shown in Table 1.1-19 in order to project the percent

composition of total MSW generated for the year 2022 for those categories (i.e. calculated to be

24.5% paper, 5.6% wood, and 12.8% yard trimmings and 15.1% food scraps).91 In general, there

appears to be a decrease in the percentage of total MSW generated from paper, slight increase for

food scraps, and a relatively stable percent composition of wood and yard trimmings.
51

Table 1.1-19. Percent Composition of Total MSW Generated

(including recyclable material): Paper, Wood, and Yard Trimmings

2000 2004 2005 2006 2007 ... 2022

Material

Paper/paperboard 36.7 34.6 33.9 33.6 32.7 ... 24.5

Wood 5.5 5.6 5.6 5.5 5.6 ... 5.6

Yard Trimmings 12.8 12.7 12.8 12.7 12.8 ... 12.8

Food Scraps 11.2 11.8 12.1 12.2 12.5 ... 15.1

We also analyzed the trends from 2000-2007 for the percent composition of total MSW

discarded (i.e. after recycling has occurred) to project the percent compositions for the year 2022

(i.e. calculated to be 8% paper, 8% wood, 3% yard trimmings, and 21.5% food scraps), see Table

1.1-20 and Table 1.1-21. Comparing Table 1.1-19 and Table 1.1-20, we note that historically

there is a lower percent of paper and yard trimmings that is discarded than generated for MSW.

This makes sense because a large percentage of these materials are recycled. Other than

recycling, some MSW material is also combusted for energy use. This material we assume

would be unavailable for biofuel use, and therefore report in Table 1.1-21 the percent

composition of total MSW discarded after accounting for both recycling and combustion for

energy use.

Table 1.1-20. Percent Composition of Total MSW Discarded

(not including recycled material): Paper, Wood, and Yard Trimmings

2000 2004 2005 2006 2007

Material

Paper/paperboard 29.6 26.6 25.1 24.1 22.3

Wood 7.0 7.3 7.5 7.4 7.6

Yard Trimmings 8.7 7.0 7.1 7.2 6.9

Food Scraps 15.4 16.7 17.3 17.6 18.2

Table 1.1-21. Percent Composition of Total MSW Discarded

(not including recycled or combusted material): Paper, Wood, and Yard Trimmings

2000 2004 2005 2006 2007 ... 2022

Material

Paper/paperboard 25.4 23.2 21.9 21.1 19.5 ... 8.0

Wood 6.0 6.4 6.6 6.5 6.6 ... 8.0

Yard Trimmings 7.5 6.1 6.2 6.3 6.0 ... 3.0

Food Scraps 13.2 14.6 15.1 15.4 15.9 21.5

The total amount of MSW generated (prior to recycling) is assumed to increase over time

due to population growth. Biocycle magazine (2008) reports MSW estimates for each state in

the U.S. based off of 2006 population data.92 We used U.S. Census Bureau population

projections by state to scale up or down the MSW estimates depending on whether the state

populations increase or decrease by 2022. The total amount of MSW generated (prior to

recycling) was estimated to be 415 million tons in 2022. As we are interested in the volume of

MSW available for biofuel use, we focused only on waste estimated to be landfilled, which is a

portion of the total MSW generated. We used estimates on the percentage of MSW landfilled by
52

state from Biocycle in order to estimate the amount of MSW potentially available to biofuels

(after recycling).

Knowing the total amount of MSW landfilled is only part of the picture. We also need to

understand the types of cellulosic material likely to make up the MSW landfilled. For this, we

were able to gather state composition data (i.e. percent wood vs. paper vs. other materials) of

landfills for MSW generated, however, we were in fact interested in acquiring state composition

data for the MSW landfilled.

93,94,95,96,97,98,99,100,101,102,103 Using the state composition data, we

estimated the percent composition of MSW landfilled by state using a ratio of percent

composition of national material generated (estimated in Table 1.1-19) and landfilled (estimated

in Table 1.1-21) and state percent composition data for MSW generated (gathered from the

multiple state reports). We then multiplied the volume of MSW (in tons) generated for each

state in the year 2022 by the percent of MSW estimated to be landfilled (provided in Biocycle)

and by the percent composition of MSW landfilled by state. Some states did not provide

composition data, therefore, we estimated average percentages based on the states within a

similar location in the U.S. where data was provided (e.g. if Utah data was unavailable, we

assumed compositions would be similar to other rocky mountain states).

Furthermore, the amount of MSW potentially available is limited by assumptions on

percent moisture and percent contamination. We assumed that paper, wood, yard trimmings, and

food scraps have a 10%, 20%, 40%, and 70% moisture content, respectively.104,105 We also

assumed that wood is approximately 50% contaminated, due to objects such as nails, paint,

chemicals, etc. typically associated with such feedstocks.106 Paper and food wastes are assumed

to be mostly uncontaminated, assuming 95% uncontaminated.107 Yard trimmings are also

assumed to be largely uncontaminated, assuming 75% uncontaminated. We account for

contamination because it is likely to affect the quality of the wood waste and could potentially

cause problems in the processing steps of cellulosic material to biofuel depending on the process

utilized. Thus, for this analysis we conservatively assumed that the estimated contaminated

portions would not be used for biofuel production. In addition, not all yard trimming can be

assumed to be wood, 90% is assumed to be from wood. 108 We also estimated the amount of food

waste that is cellulosic material to be 45%.109 We estimate that 23.8 million dry tons could be

available after accounting for these factors from paper, 0.9 million dry tons from yard trimmings,

5.3 million dry tons from wood, and 6.5 million dry tons from food waste.

Construction and Demolition (C&D) Debris

C&D debris mostly comes from building demolition and renovation, and the rest comes

from new construction. Roughly equal percentages of building-related waste are estimated to

come from the residential and commercial building sectors. The composition of C&D materials

varies significantly, depending on the type of project from which it is being generated. For

example, materials from older buildings is likely to contain plaster and lead piping, while new

construction materials may contain significant amounts of drywall, laminates, and plastics. For

building materials, EPA estimates the overall percentage of debris in C&D materials falls within

the following ranges:
53

Table 1.1-22.110

Percentage Composition of C&D Debris

(by volume)

Concrete and mixed rubble 40-50%

Wood 20-30%

Drywall 5-15%

Asphalt roofing 1-10%

Metals 1-5%

Bricks 1-5%

Plastics 1-5%

In 1996, total C&D debris generated was estimated to be approximately 124 million

metric tons.111 As seen in Table 1.1-22 above, only a portion of this, however, would be made of

woody material. We based our estimate of C&D wood in 2022 on the equation adopted from

Wiltsee’s analysis.112 The equation estimated C&D wood based on population size. We

estimated approximately 31 million tons could be available from this resource by 2022; however,

we assumed that 50% of that could potentially be contaminated and a portion of the feedstock

would likely already be recovered. Thus, we estimate that only 8 million dry tons would be

available for biofuels.

Urban waste summary

After estimating the total amount of urban waste available as described in the sections

above, we further estimated the potential locations that could utilize this material. This is

described in more detail in Section 1.8.1.3, the cellulosic ethanol plant siting analysis. From this

analysis we determined that of the 44.5 million dry tons of MSW and C&D wood waste

available, approximately 26 million dry tons would be used to produce 2.3 ethanol-equivalent

billion gallons of fuel.10 We estimated urban wastes outside our agricultural modeling as the

models do not focus on such feedstocks. The other portion of the 16 billion gallons of cellulosic

biofuel standard (13.7 billion gallons) was split among the other feedstock types, namely

agricultural residues, forestry biomass, and energy crops, depending on the economic

competitiveness. Refer to Chapter 5 more details on the FASOM and FAPRI-CARD modeling.

1.1.2.5 Imported Cellulosic Feedstocks or Biofuels

Cellulosic biofuel could also be produced internationally. One example of internationally

produced cellulosic biofuel is ethanol produced from bagasse from sugarcane processing in

Brazil. Currently, Brazil burns bagasse to produce steam and generate bioelectricity. However,

improving efficiencies over the coming decade as well as mechanization of sugarcane harvesting

(no burning of biomass in fields) may allow an increasing portion of bagasse to be allocated to

other uses, including cellulosic biofuel, as additional straw could potentially be collected and

used to produce bioelectricity.

10 Assuming 90 gal/dry ton ethanol conversion yield for urban waste in 2022
54

In fact, a recent study was performed under the Memorandum of Understanding to

advance collaboration in biofuels, signed by Brazil and the United States on March 2007.113 The

key objective of the work was to compare the techno-economic performance for thermochemical

and biochemical conversion of sugarcane residues to ethanol. Studies such as this one help

identify the anticipated costs and challenges with utilizing cellulosic feedstocks for biofuels.

Another study assessed the biomass feedstock potential for selected countries outside the

United States and projected supply available for export or for biofuel production.11,114 For the

study’s baseline projection in 2017, it was estimated that approximately 21 billion ethanolequivalent gallons could be produced from cellulosic feedstocks at $36/dry tonne or less. The

majority (~80%) projected is from bagasse, with the rest from forest products. Brazil was

projected to have the most potential for cellulosic feedstock production from both bagasse and

forest products. Other countries including India, China, and those belonging to the Caribbean

Basin Initiative (CBI) also have some potential although much smaller feedstock supplies are

projected as compared to Brazil.

1.1.2.6 Cellulosic Feedstock Summary

Table 1.1-23 summarizes our internal estimate of the types of cellulosic feedstocks

projected to be used and their corresponding volume contribution to 16 billion gallons cellulosic

biofuel by 2022 for the purposes of our impacts assessment. Refer to previous sections for more

details on how the values in this summary table were derived. The majority of feedstock is

projected to come from dedicated energy crops, with smaller volumes from agricultural residues,

forestry biomass, and urban waste.

Table 1.1-23.

Cellulosic Feedstocks Assumed To Meet EISA In 202212

Feedstock Volume

(Ethanol-equivalent Bgal)

Agricultural Residues 5.7

Corn Stover 4.9

Sugarcane Bagasse 0.6

Wheat Residue 0.1

Sweet Sorghum Pulp 0.1

Forestry Biomass 0.1

Urban Waste 2.3

Dedicated Energy Crops

(Switchgrass)

7.9

Total 16.0

11 Countries evaluated include Argentina, Brazil, Canada, China, Colombia, India, Mexico, and CBI

12 Volumes are represented here as ethanol-equivalent volumes, a mix of diesel and ethanol volumes.
55

1.1.3 Biodiesel & Renewable Diesel Feedstocks

In general, plant and animal oils are valuable commodities with many uses other than

transportation fuel. Therefore we expect the primary limiting factor in the supply of both

biodiesel and renewable diesel to be feedstock availability and price. Primary drivers for this are

increasing worldwide demand for use as food as incomes rise in developing countries, as well as

increased recognition that these materials have value based on their energy or hydrocarbon

content as substitutes for petroleum. Expansion of biodiesel market volumes beyond the

mandates is dependent on it being able to compete on a price basis with the petroleum diesel

being displaced.

The primary feedstock for domestic biodiesel production in the U.S. has historically been

soybean oil, with other plant and animal fats and recycled greases making up a varying portion

of the biodiesel pool as commodity prices rise and fall. For example, following a rise in soy oil

prices and then a decline in diesel prices, the share of biodiesel being produced from rendered or

reclaimed fats or other cheap feedstocks increased steeply in 2008 and 2009. 115 Another

feedstock we project to become a significant and economical alternative over the next decade is

corn oil produced during ethanol production (see 1.1.3.2 below).

1.1.3.1 Virgin Plant Oils

Agricultural commodity modeling we have done for this proposal (see Chapter 5 of this

document) suggests that soybean oil production will stay relatively flat in the future, meaning

supplies will be tight and prices supported at a high level as biofuel and food-related demand

increases. Modeling scenarios conducted for the year 2022 with the EISA mandates indicates

that domestic soy oil production would support about 660 million gallons of biodiesel

production. This material is most likely to be processed by biodiesel plants (as opposed to

renewable diesel hydrotreating processes) due to the large available capacity of these facilities

and their proximity to soybean production. Compared to other feedstocks, virgin plant oils are

most easily processed into biofuel via simple transesterification due to their homogeneity of

composition and lack of contaminates.

1.1.3.2 Corn Oil Extracted During Ethanol Production

A source of feedstock which could provide significant volume is oil extracted from corn

or its fermentation co-products in the dry mill ethanol production process. Often called corn

fractionation, dry separation, or corn oil extraction, these are a collection of processes used to get

additional product streams of value from the corn. This idea is not new, as existing wet mill

plants create several streams of product from their corn input, including oil. In a dry mill setting,

the kernel can be separated into the bran, starch, and germ components ahead of fermentation, or

alternatively, oil can be extracted from the distillers’ grains after fermentation. Both have

advantages and disadvantages related to plant capital cost and energy consumption, as well as

yield of ethanol and the other coproducts. For more information on these technologies, see

Section 1.4.1.3.
56

Extraction of oil from the thin stillage or distillers’ grains with solubles (DGS) streams is

a proven technology that can be retrofitted into existing plants relatively cheaply. Front-end

separation (fractionation) requires more intensive capital investment than is required to extract

oil from the DGS, and therefore is best designed into the plant at the time of construction.

However, it yields a larger array of co-products, and generally also results in ethanol process

energy savings since less unfermentable material is going through the process train. The corn oil

produced from the fractionation process is food grade corn oil and therefore has a significantly

higher market value than the inedible corn oil produced by the oil extraction process. For our

analyses for the final rule we have chosen to focus only on the oil produced by extraction, as we

believe the higher value of the food grade corn oil makes it highly unlikely it will be used in

biodiesel production.

Information on the expected oil extraction rates, capital costs, and energy use of corn oil

extraction systems is based on conversations with several technology providers. Depending on

the configuration, this system can extract 25-75 percent of the oil from the fermentation coproducts, producing an oil stream which can be used as feedstock by biodiesel facilities. Since it

offers another stream of revenue from the corn flowing into ethanol plants, we assumed

approximately 70 percent of projected total ethanol production will implement some type of corn

oil extraction system by 2022, generating approximately 680 million gallons per year of corn oil

biofuel feedstock.116,13 We expect this material to be processed in biodiesel plants with

pretreatment capabilities for handling feedstocks with significant free fatty acid (FFA) content.

At this time it is uncertain whether there will be third party aggregators of this extracted oil, or

whether individual ethanol plants will contract directly with nearby biodiesel facilities, which

may ultimately impact where and how this feedstock is processed.

1.1.3.3 Yellow Grease and Other Rendered Fats

Rendered animal fats and reclaimed cooking oils and greases are another potentially

significant source of biodiesel feedstock. The National Renderer’s Association gives a quantity

of approximately 11 billion lbs of fats and greases available annually for all uses, and suggests

this will grow by 1% per year.117 This figure is broken down into several categories, and

includes “yellow grease” and “other grease” collected and processed by rendering companies

each year. The NRA defines yellow grease as material primarily derived from restaurant grease

or cooking oil (they do not define “other grease” but we can assume this is trap grease or other

reclaimed material). Adding together the NRA’s “yellow grease” and “other grease” categories,

we arrive at 2.7 billion lbs per year (all figures there are for 2005).

Similarly, a 2004 report prepared for New York State Energy Research and Development

Authority by LECG, LLC describes yellow grease as material produced by restaurants and food

service.118 (This report describes grease recovered from sewer traps as brown grease, and

suggests it is too low in quality to be used for biodiesel production.) Based on USDA and US

Census data, LECG shows production of yellow grease by restaurants to be on the order of 9 lbs

per capita per year, equivalent to about 2.7 billion lbs/yr. Unfortunately, it's not clear whether

this quantity would include or be in addition to the NRA figures, but given the similarity of

13 The projected fraction of plants doing corn oil extraction was based on a conversation with several technology

providers and various people working in the ethanol industry.
57

numbers, it seems reasonable to suspect that the NRA total includes the same sources of grease

as assessed by LECG.

Thus, the figures we use here assume that the NRA figures already include collection of a

large portion of restaurant and trap grease by rendering companies; we have not included

additional waste greases that other studies have suggested might be available based on per-capita

use of cooking oils, wastewater treatment disposal, etc. Perhaps there is some additional waste

grease not being collected or counted by the NRA that is, or could be, aggregated and direct to

biofuel production, but there is unfortunately no good way for us to determine this.

Our projections would use approximately 22% of this material for biofuel use in 2022 (=

380 million gallons x 7.5 lb/gal / 13 million lbs). In a written statement by David Meeker of the

NRA, he asserts that it could be feasible for as much as 30% of the 11 billion lbs to be directed to

biofuel production on a long-term basis. 119 The feasibility of consumption of this volume of

rendered material was also supported by comments from a large rendering company (Darling

International).

Much of biodiesel production seems to rely on niches of feedstock availability and

market outlets. We project that approximately 230 MMgal/yr of rendered or reclaimed fats will

be processed by biodiesel plants possessing acid pretreatment capabilities to handle these highfree fatty acid feedstocks. We project another 150 MMgal/yr of this material will be used by

renewable diesel facilities. It is possible that renewable diesel manufacturers will arrange direct

contract or joint venture with animal processing or rendering operations, taking advantage of

volumes or prices of feedstock that may not typically be available on the open market to smaller,

unaffiliated biodiesel plants.

Some comments submitted to the docket by Endicott Biofuels, LLC, suggest there are

additional sources of waste greases and oils sufficient to produce an additional 2 or more billion

gallons’ worth of biofuel (beyond what we account for above) if they could be collected and

processed. We have chosen to ignore these volumes in this analysis, as their use will likely

require further pre-treatment and additional processing steps beyond the capabilities most of the

installed biodiesel production capacity. However, it is conceivable that these materials may

begin to be used in significant quantities as dictated by regulatory or economic conditions.

1.1.3.4 Algae

Algae are single-celled algae species that grow quickly and can be cultivated to produce

biomass for the downstream production of fuel based on the oil and residuals found in the

biomass. Many of these algae species are targeted for their high lipid content, and thus are a

promising feedstock for biofuel production. While some algae companies are focusing on the

use of algae for biodiesel production, it is important to note that algae can alternatively be used

for producing ethanol or crude oil for gasoline or diesel which could also help contribute to the

advanced biofuel mandate.14 Some of the potential benefits of using algae as a biofuel feedstock

are that algae can be grown on marginal land, can require low water inputs, can recycle waste

streams from other processes, does not compete with food production, and has high oil yield.

14Algenol and Sapphire Energy, see http://www.algenolbiofuels.com/ and http://www.sapphireenergy.com/
58

Mass cultivation of microalgae has been ongoing since the 1950s for medical and

pharmaceutical purposes. Since the 1980s, algae-to-biofuel research has been heavily funded by

governments such as Japan, France, Germany and the United States. The research program in

the US was especially large. The Aquatic Species Program, backed by the National Renewable

Energy Laboratory, ran from 1978-1996 to look at the use of aquatic plants, specifically algae, as

sources of energy. From about 1982 through the termination of the program, research

concentrated on algae for biofuel production, specifically in open ponds.120 Two branches to

research large scale algaculture systems were funded: the “High Rate Pond” and the “Algae

Raceway Production System” from 1980 to 1987. By 1988 several large (1,000 m2) systems

were designed and built at the “Outdoor Test Facility.”121 However, overall productivity of the

ponds was lower than expected at around 10 grams algae / m2/ day, due to cold temperatures and

native species of algae taking over the ponds. After the program ended the total amount of algae

research was relatively small because of lack of funding and growing interest in cellulosic

ethanol.122 In the 1990s Japan’s NEDO-RITE Optical Fiber Bioreactor project obtained support

from several private companies, laboratories, and academic institutions. However, the program

was unsuccessful due to high costs for producing algae. Most recently, universities and start-up

companies have been conducting pilot studies on the cultivation and processing of algae. With

the high price of oil in 2008 and increased interest from airline providers to cut costs, fuel

companies and start-ups have begun collaboration efforts to develop alternative biofuels from

algae.

For analyses purposes, we assumed that 100 million gallons of algae-based biodiesel

would be available by 2022 to help meet the biomass-based diesel standard. We believe this is

reasonable given several announcements from the algae industry about their production plans

which is further described in Section 1.5.4.3.15

A recent report released in October 2009 entitled “Cultivating Clean Energy: The

Promise of Algae Biofuels” is a good resource for understanding the basic pathways for algaebased biofuels and summarizes some of the areas that can be improved to further

commercialization of algae-based biofuels.123 We discuss some of the information contained in

the report, below.

In addition, we have consulted with the National Renewable Energy Laboratory (NREL)

on developing several reasonable pathway scenarios for algae producing oils for biodiesel.124

While there are many different technologies and fuel combinations being considered for algaebased biofuels, we believe the analyses completed by NREL for the FRM are representative of

what is possible for the algae industry by 2022. As time permits, we hope to evaluate different

configurations and their impact on production parameters. To provide further understanding, the

modeling completed by NREL also included sensitivity analyses which evaluated various

parameters and their affect on the costs of production (e.g. nutrients required, CO2 delivered,

15 Sapphire Energy plans for 135 MMgal by 2018 and 1 Bgal by 2025; Petrosun plans for 30 MMgal/yr facility in

Arizona; Solazyme plans for 100 MMgal by 2012/13; US Biofuels plans for 4 MMgal by 2010, 50 MMgal by full

scale. Only several companies have thus far revealed production plans, and more are announced each day. It is

important to realize that future projections are highly uncertain, and we have taken into account the best information

we could acquire at the time.
59

etc.). The following sections also summarize some of the assumptions and results from the

NREL modeling for algae; see the technical document for more details. Also, refer to Chapter 2

for a discussion of how we used the modeling of algae pathways from NREL for our lifecycle

analyses. For more information on the costs of production for algae from biodiesel, refer to

Chapter 4.

Cultivation

Algae require several inputs, including water, land, nutrients, and in most cases, light to

sustain growth. The configuration of the algal system impacts the amount of these inputs

needed. Microalgae, which can have a high mass percentage of triacylglycerols, or natural oils,

can be cultivated typically using either of two methods.

One method that is currently in use, and was studied widely by the Aquatic Species

Program, involves using large, open ponds to grow algae; generally considered the most efficient

and low-cost option is the so- called “raceway” ponds, as their shape is similar to an oval

racetrack. A paddle wheel is used to keep the water in motion around the pond. Other open

pond systems include unstirred and circular ponds; however, these may have more limited use

for large scale fuel production.

The other method of algae cultivation utilizes closed “photobioreactors” which can fall in

two groups, flat plate and tubular. Flat plate PBRs are made up of a clear plastic containment

system and tubular PBR’s are clear tubes that carry a circulation of culture between degassing

and harvesting. Tube PBRs are generally considered more feasible for large scale use since they

are modular and can accommodate higher flows.125 PBRs can also be placed indoor or outdoor.

Indoor closed PBRs usually require artificial illumination. Outdoor closed PBRs use natural

daylight and in some cases also artificial light. There are also variations on cultivation systems

such as hybrid (combined open and closed) cultivation, heterotrophic cultivation (without light),

and integrated biofixation systems.16

Due to higher cell densities, the use of photobioreactors typically has lower land use in

comparison to open pond systems producing the same volume of fuel. While other oil crops may

need large amounts of agricultural land in order to meet a sizable portion of US liquid fuel

demand, algae may limit the amount of land needed due to its high productivity and do not

require the displacement of agricultural crops. 126,127,128

When cultivated in enclosed photobioreactors, evaporation of water is limited, and water

extracted during the drying process can be mostly reclaimed.129 Even in open raceway-style

ponds where evaporation is not negligible, water requirements are still considerably lower than

with conventional agricultural crops. It is estimated that, in order to produce enough algal

biomass for 60 billion gallon biodiesel/year, 20-120 trillion gallons of water/year are needed.

This is several orders of magnitude lower than the 4,000 trillion gallon/year used to irrigate the

entire US corn crop.130

16 For more information on these variations, refer to the recent report “Cultivating Clean Energy: The Promise of

Algae Biofuels”.
60

Algae can also thrive in brackish water, with salt concentrations up to twice that of

seawater, which is often available in saline groundwater aquifers in the southwest.131, 132

However, the salt, other minerals, and contaminants may pose a problem to the dewatering and

extraction process, depending on the method used.

Aside from sunlight, land, and water, algae require two main physical inputs for growth:

CO2 and nutrients.133 Nutrients can be obtained from conventional fertilizers, or from domestic

or industrial waste sources, such as farm refuse and manure.134 Co-locating algae farms with

animal husbandry, in order to directly use the manure as a nutrient, would reduce transportation

costs.135 In addition, both of these inputs can be obtained from waste streams from other energy

processes. They can be coupled with coal-burning power plants or even ethanol plants, and can

effectively recycle between 50% and 90% of flue gasses, depending on the size of the algae

farm.136,137,138 The highly controlled environment of algae photobioreactors make them

especially suitable to process and recycle CO2 in flue gasses, as the gas can be bubbled or

channeled into the water.139

The US southwest is perhaps one of the most promising locations for economic algae-forbiofuel cultivation, due to its high solar insolation (see Figure 1.1-5), availability of saltwater

aquifers, and relatively low current land use.140,141 Ideally, algae farms could be co-located with

coal-burning power plants in order to recycle the carbon emissions. One study states that 1,700

power plants throughout the United States have enough surrounding land to support a

commercial-scale algae system, however, only a limited number of these are in the southwest,

due to lower population densities.142

Figure 1.1-5.

PV Solar Radiation in the United States

In terms of yields, certain species of algae can produce 80 percent of their body weight as

oils, however, oil levels of 20-50 percent are more common.143,144 Raceway systems are

typically lower cost but have lower productivity compared to photobioreactors. The following

Table 1.1-24 is based on the modeling of algae production from NREL and gives an idea of the

yields that are reasonable under a base case (assumptions reasonable but still challenging in near
61

future), aggressive case (assumes identification of a strain with near optimal growth rates and

lipid content) and a max case (represents near theoretical maximum based on photosynthetic

efficiencies).

Table 1.1-24. Potential Algae Yield17 (gal/acre per year)

Base Case Aggressive Case Max Case

op PBR op PBR op PBR

2108 5271 6748 16863 12151 30395

Harvesting

Harvesting is necessary to recover biomass from the cultivation system. Commonly used

techniques include flocculation, dissolved air flotation (DAF), centrifugation, microfiltration,

and decantation. Additional techniques include discrete sedimentation, membrane filtration,

phototactic autoconcentration, tilapia-enhanced sedimentation, tube settling, and ultrasonic

separation. Wet biomass may also be dewatered or dried. Dewatering decreases the moisture

content by draining or mechanical means. Additional drying can follow using e.g. drum dryer,

freeze dryer, spray dryer, rotary dryer, or by solar drying.

Oil Extraction and Recovery

Oil from algae can be extracted through chemical, mechanical, or electrical processes to

separate the algal oil from the cell membrane. The TAGs (Triacylglycerides) are typically the

main product which goes to biodiesel production. The remainder consists of carbohydrates,

proteins, nutrients, and ash), usually referred to as algal residue.

The extraction step is commonly regarded as the most speculative in terms of large-scale

feasibility.145 Thus extraction is a critical area of research going forward to achieve practical

algal lipid production. Some of the methods discussed are solvent extraction, supercritical fluid

extraction, mechanical extraction, osmotic shock, and sonication.

The spent biomass (i.e. algal residue) can be used in anaerobic digestion and power

generation via gas turbine which provides power to run the plant. The other method commonly

discussed is its use as animal feed.

Oil Conversion to Biofuel

Algal oil can undergo transesterification to produce biodiesel or be hydroprocessed to

renewable products (e.g. renewable diesel) depending on the slate of products desired. In some

cases, the entire algae biomass is converted using thermochemical and biochemical methods

such as pyrolysis, gasification, liquefaction, and fermentation to produce biofuels. See Figure

1.1-6 for the various pathways for the processing of algal biomass.

17 Only land required for pond/PBR, not including land necessary for processing equipment
62

Figure 1.1-6. Pathways for Processing Algal Biomass146

1.1.3.5 Other Potential Feedstocks

The following sections describe several oil crops that have also been discussed as

potential biofuel feedstocks. While we have not projected the use of these crops in our current

impact analyses, these may still be used to help meet the biomass-based diesel and total

renewable fuel standards under EISA.

Jatropha

Jatropha is a genus of plants, consisting of both shrubs and trees, some of which hold

promise as a feedstock for the production of biofuels. One species in particular, Jatropha

curcas, yields seeds that contain between 25-45 percent lipids, which can be processed to

produce biodiesel. The production potential of J. curcas has led to the popularity of the crop as a

biofuel feedstock. In particular, some potential advantages include growth on marginal lands,

tolerance to drought, low nutrient and labor inputs, and high oil yield.

J. curcas has been traditionally cultivated for living fences, to conserve soil moisture,

reclaim soil, control erosion, and used locally in soap production, insecticide, and medicinal

application.147,148 Most recently, J. curcas has been investigated as an energy crop. J. curcas

originated in Central America, mainly growing in arid and semi-arid conditions; now it is also

found in the tropical regions of Africa, Asia, and North and South America.149 Because of J.

curcas’ deep root system, it can grow in lands that have been previously heavily cultivated or

otherwise have low levels of essential minerals and nutrients in the top levels of soil; this results

in the recycling of nutrients from deeper soil levels.150,151 In addition, because the plant is a

perennial (living up to 50 years) the root system stays in place, which can significantly reduce

erosion and even reverse desertification.152,153

As a wild plant which has not yet been domesticated, J. curcas has a large potential for

improving many qualities, such as minimizing inputs, maximizing yields, and developing
63

tolerance for various climates. For example, there is still little known on water use efficiency of

J. curcas as a crop.154 Even though J. curcas can survive moderate droughts by dropping its

leaves, the effect of water starvation on seed yield and oil content in the seeds is mostly

unknown. Water use efficiency of sister species Jatropha pandurifolia and Jatropha gossypifolia

are reported as 3.68 and 2.52 mol CO2/mol H2O, respectively.155 This is similar to that of other

oil seed species like soybean, with a water use efficiency of 3.90 mol CO2 /mol H2O.156 Thus, it

is conceivable that water requirements of J. curcas will be similar to that of other oil seed

species; although the plant may survive droughts, it may not produce efficiently or economically

when it is water-deprived.157 Obviously, further studies relating water use to crop production

must be performed.

Because J. curcas has been observed growing on low quality soils with low nutrient

amounts, it is often assumed that the plant would be able to grow as a commercial crop in these

conditions. However, research indicates that J. curcas growth and production of seed is severely

limited by soil fertility.158,159 The long-term impact of monocultures of jatropha on soil health

has also not been studied thoroughly. Some studies indicate that J. curcas may not be

sustainable, unless specific steps are taken to ensure the plantations’ long term health.160,161

The seed of J. curcas, as previously mentioned, has a lipid content of roughly 25-45

percent by weight.162,163 Thus, assuming a seed oil content of 35 percent and an extraction

efficiency of 75 percent, this would yield 404-2040 kg oil/ha or 439-2217 liter oil/ha.164 This is

somewhat higher than other oil producing crops like soybean, sesame, sunflower, rapeseed, and

castor with a range of 375-1200 liter oil/ha.

Currently, J. curcas is present in 2 states (Florida and Hawaii) in the U.S.165 Because of

J. curcas’ intolerance to frost, only small portions of the United States are typically considered

for cultivation with current crop varieties. Areas with the most suitable climate conditions for

cultivation can extend as far as 30° N in latitude; this would include the southern parts of Texas

and Louisiana, and most of Florida.166 Assuming no irrigation (as J. curcas is assumed to be a

low-input crop), an absolute minimum of 500 mm of rainfall is required for substantial

production, this occurs in the easternmost portions of Texas, and all of Louisiana and Florida.167

The University of Florida has done some breeding and genetic manipulation of J. curcas, with

the goal of increasing hardiness and tolerance to colder climates.168 As research progresses and

new varieties are identified, future locations for growth of J. curcas may be possible than

currently suggested.

Canola

Canola is a type of rapeseed that has been bred to produce edible oil with low levels of

erucic acid and meal with low levels of toxins, allowing it to be used for livestock feed. It was

developed in Canada, but is now grown in many places around the world including the United

States. Currently about 1.2 million acres of canola is grown in the United States. Canola can be

grown as either a spring or winter crop, with yields for winter canola being significantly higher

than those for spring canola (1,500 pounds per acre vs. 3,500 pounds per acre).169 The oil

content of the canola seeds is approximately 40%. The oil produced from the canola seeds is low

in saturated fat and high in omega-3 fatty acids, making it desirable for use as edible oil. These
64

traits, combined with the higher price of canola oil result in very little canola oil being used in

biodiesel production today. The high oil yields and potential for canola to be grown as a winter

crop may result in an increase in canola oil production in the United States and a greater

availability for its use as a biodiesel feedstock.

Camelina

Camelina is an oilseed crop native to Northern Europe and Central Asia that has been

grown in Europe for thousands of years for use as an edible oil as well as industrial purposes. It

is primarily considered a weed in North America and is most common in the northern Midwest.

Recently there has been interest in the potential use of the oil extracted from camelina seeds as a

biodiesel feedstock. Camelina’s suitability to northern climates, low moisture, and marginal

soils allow it to be grown in areas that are unsuitable for other major oilseed crops such as soy

beans, sunflower, and canola. Camelina also requires the use of few, if any, herbicides, as well

as little to no tillage.170 Camelina can also be used to break the continuous planting cycle of

small grains, effectively reducing the disease, insect, and weed pressure in fields planted with

wheat the following year.171 Camelina seeds contain approximately 40% oil and have averaged

yields of over 1100 kg/ha (~1000 pounds per acre) in long term trials in Rosemount, Minnesota,

though future yields improvements through selective breeding are likely.172 Camelina is

currently being grown on approximately 50,000 acres of land the U.S., primarily in Montana,

eastern Washington, and the Dakotas.173

Pennycress

Pennycress is a winter annual weed currently established in every state except Hawaii,

but especially prevalent in the Midwest and northwest. It has several qualities that make it a

potential feedstock source for biodiesel production. Pennycress germinates in the fall and grows

as a winter cover crop. Pennycress flowers and produces seeds in late April and May. These

seeds can be harvested in early June, allowing the fields to be planted with soy beans.

Pennycress seed yields have been as high as 2000 pounds per acre in wild stands and yield 36%

oil when crushed.174 In addition to the oil, the seeds of pennycress also contain the chemical

sinigrin, which has potential uses as a bio-fumigant. The crushed pennycress seeds could be

used as a weed killer for high value crops that would also provide value as a fertilizer. There are

concerns, however, about the invasive nature of pennycress. Even though it does not compete

directly with summer annual crops due to its growing season, there is some concern that, once

established, pennycress would be very difficult to remove. It has also not been grown as a

commercial crop, and little is yet known about planting and cultivation requirements.

Pennycress is currently being grown on several five to ten acre plots by BioFuels

Manufacturers of Illinois. They plan to construct a 45 million gallon per year biodiesel plant in

Peoria County that would use soy oil, animal fats, and vegetable oils as feedstock when it begins

operation and plans to use pennycress oil as a significant feedstock source in the future.175 While

the initial attempts to grow pennycress were unsuccessful, more recent on farm trials have

resulted in the successful establishment of pennycress.176 Pennycress has a large potential as a

secondary crop if its cultivation proves to be profitable.
65

1.1.3.6 Imported Biodiesel

The European Union is expected to continue as the largest consumer of biodiesel, with

use projected to be almost 3 billion gallons per year by 2018.177,178,179 Given the E.U.’s limited

land suitable for oilseed crops, it is likely that a significant portion of this fuel will be either

imported or made from imported feedstock.180 During this timeframe, other significant

producers of biodiesel or its feedstocks, such as Argentina, Brazil, Indonesia, and Malaysia, are

expected to increase domestic use of biofuels due to mandates or simple economic advantage.181

Our primary control scenario projects U.S. use of biodiesel to rise to approximately 1.5

billion gallons by 2022. Given competition for imports by the E.U. and Asian markets where

retail fuel prices are among the highest in the world, as well as the feasibility to produce this

biofuel from domestically-sourced feedstocks (see discussion above), we do not expect imports

to contribute to U.S. biodiesel for the foreseeable future. Thus, we are not assuming any imports

of biodiesel for our analyses.

1.1.3.7 Biodiesel Feedstock Summary

Table 1.1-25 shows the volumes and uses of biodiesel and renewable diesel feedstocks as

projected for the analyses in this rulemaking. Total volume here (1.67 billion gallons) fulfills the

Biomass Based Diesel category requirements (1.0 billion gallons) and contributes to the Other

Advanced Biofuel (0.67 billion gallons), as projected for our primary control case (see Section

1.2.2).

Table 1.1-25.

Estimated 2022 Biodiesel & Renewable Diesel Volumes

Based on Feedstock Availability (million gallons of fuel)

Feedstock type Base catalyzed

biodiesel

Acid pretreatment

biodiesel

Renewable

diesel

Virgin vegetable oil 660 - -

Corn oil from ethanol production - 680 -

Rendered animal fats and greases - 230 150

Algae oil or other advanced source 100 - -

1.2 RFS2 Biofuel Volumes

Our assessment of the renewable fuel volumes required to meet the Energy Independence

and Security Act (EISA) necessitates establishing a primary set of fuel types and volumes on

which to base our assessment of the impacts of the new standards. EISA contains four broad

categories: cellulosic biofuel, biomass-based diesel, total advanced biofuel, and total renewable

fuel. As these categories could be met with a wide variety of fuel choices, in order to assess the

impacts of the rule, we projected a set of reasonable renewable fuel volumes based on our best

estimate of likely fuels that could come to market.
66

The following subsections detail our rationale for projecting the amount and type of fuels

needed to meet EISA. To assess the impacts of the increase in renewable fuel volume from

business-as-usual (what is likely to have occurred without EISA), we have established reference

and control cases. The reference cases are projections of renewable fuel volumes without the

enactment of EISA and are described in further detail in Section 1.2.1. It is difficult to ascertain

how much of the impact from the displacement of gasoline and diesel with renewable fuels

might be due to the natural growth in renewable fuel use due to market forces as crude oil prices

rise versus what might be forced by the RFS2 standards. Regardless, these assessments provide

important information on the wider public policy considerations related to renewable fuel

production and use, climate change, and national energy security. The control cases are

projections of the volumes and types of renewable fuel that might be used to comply with the

EISA volume mandates. For the NPRM we had focused on one primary control case whereas

for the final rule we have expanded the analysis to include two additional sensitivity cases. We

assume in each of the cases the same ethanol-equivalence basis as was used in the RFS1

rulemaking to meet the standard. Volumes listed in the tables for this section are in straightgallons and allow for the reader to calculate ethanol-equivalent gallons if necessary (i.e. times

1.5 for biodiesel or 1.7 for cellulosic diesel and renewable diesel). Sections 1.2.2 and 1.2.3 aim

to describe the control cases in greater detail as well as provide fuel volumes and types for years

prior to 2022.

The main difference between the volumes used for the NPRM and the volumes used for

the FRM is the inclusion of cellulosic diesel for the FRM. The NPRM made the simplifying

assumption that the cellulosic biofuel standard would be met entirely with cellulosic ethanol.

However, due to growing interest and recent developments in hydrocarbon-based or so-called

“drop-in” renewable fuels as well as butanol, and marketplace challenges for consuming high

volumes of ethanol, we have included projections of more non-ethanol renewables in our

primary control case for the final rule.18 In the future, this could include various forms of “green

hydrocarbons” (i.e., cellulosic gasoline, diesel and jet) and higher alcohols, but for simplicity our

analyses have modeled it all as cellulosic diesel fuel. We have also included some algae-derived

biofuels in our FRM analyses given the large interest and potential for such fuels. We have

continued to assume zero volume for renewable fuels or blendstocks such as biogas, jatropha,

palm, imported cellulosic biofuel, and other alcohols or ethers in our control cases. Although we

have not included these renewable fuels and blendstocks in our impact analyses, it is important to

note that they can still be counted under our program if they meet the lifecycle thresholds and

definitions for renewable biomass, and recent information suggests that some of them may be

likely.

18 Comments received from Advanced Biofuels Association, Testimony on June 9, 2009 suggesting a number of

advanced biofuel technologies will be able to produce renewable diesel, jet fuels, gasoline, and gasoline component

fuels (e.g. butanol, iso-octane). Similar comments were received from the New York State Department of

Environmental Conservation (Docket EPA-HQ-OAR-2005-0161-2143), OPEI and AllSAFE (Docket EPA-HQOAR-2005-0161-2241), and the Low Carbon Synthetic Fuels Association (Docket EPA-HQ-OAR-2005-0161-

2310).
67

1.2.1 Reference Cases

Our primary reference case renewable fuel volumes are based on the Energy Information

Administration’s (EIA) Annual Energy Outlook (AEO) 2007 reference case projections.19 While

AEO 2007 is not as up-to-date as AEO 2008 or AEO 2009, we chose to use AEO 2007 because

later versions of AEO already include the impact of increased renewable fuel volumes under

EISA as well as fuel economy improvements under CAFE as required in EISA, whereas AEO

2007 did not.

For the final rule we also assessed a number of the impacts relative to the reference case

assuming the mandated renewable fuel volumes under RFS1 from the Energy Policy Act of 2005

(EPAct). This allows for a more complete assessment of the impacts of the EISA volume

mandates, especially when combined with the impacts assessment conducted for the RFS1

rulemaking (though many factors have changed since then). Table 1.2-1 and Table 1.2-2

summarize the renewable fuel volumes for years 2022 and prior, for the AEO 2007 and the RFS1

reference cases, respectively.

19 AEO 2007 was only used to derive renewable fuel volume projections for the primary reference case. AEO 2009

was used for future crude oil cost estimates and for estimating total transportation fuel energy use.
68

Table 1.2-1. AEO 2007 Reference Case Renewable Fuel Volumes (billion gallons)

Advanced Biofuel

Cellulosic

Biofuel

BiomassBased Diesela

Other

Advanced

Biofuel

NonAdvanced

Biofuel

Year Cellulosic

Ethanolc

FAME

Biodieselb

Imported

Ethanol Corn Ethanol

Total

Renewable

Fuel

2010 0.12 0.32 0.29 10.49 11.22

2011 0.19 0.33 0.16 10.69 11.37

2012 0.25 0.33 0.18 10.81 11.57

2013 0.25 0.33 0.19 10.93 11.70

2014 0.25 0.23 0.20 11.01 11.69

2015 0.25 0.25 0.39 11.10 11.99

2016 0.25 0.35 0.51 11.16 12.27

2017 0.25 0.36 0.53 11.30 12.44

2018 0.25 0.36 0.54 11.49 12.64

2019 0.25 0.37 0.58 11.69 12.89

2020 0.25 0.37 0.60 11.83 13.05

2021 0.25 0.38 0.63 12.07 13.33

2022 0.25 0.38 0.64 12.29 13.56

a

Biomass-Based Diesel could include FAME biodiesel, cellulosic diesel, and non-co-processed renewable diesel.

b

Only fatty acid methyl ester (FAME) biodiesel volumes were considered

c AEO 2007 reference case assumes actual production of cellulosic biofuel (i.e. not corn ethanol plants utilizing 90% biomass for

energy) and therefore was assumed to be 0.25 billion gallons.
69

Table 1.2-2. RFS1 Reference Case Renewable Fuel Volumes (billion gallons)

Advanced Biofuel

Cellulosic

Biofuel

BiomassBased Diesela

Other

Advanced

Biofuel

NonAdvanced

Biofuel

Year Cellulosic

Ethanolc

FAME

Biodieselb

Imported

Ethanol Corn Ethanol

Total

Renewable

Fuel

2010 n/a n/a n/a n/a n/a

2011 n/a n/a n/a n/a n/a

2012 0.00 0.303 0.00 7.046 7.35

2013 0.00 0.303 0.00 7.046 7.35

2014 0.00 0.303 0.00 7.046 7.35

2015 0.00 0.303 0.00 7.046 7.35

2016 0.00 0.303 0.00 7.046 7.35

2017 0.00 0.303 0.00 7.046 7.35

2018 0.00 0.303 0.00 7.046 7.35

2019 0.00 0.303 0.00 7.046 7.35

2020 0.00 0.303 0.00 7.046 7.35

2021 0.00 0.303 0.00 7.046 7.35

2022 0.00 0.303 0.00 7.046 7.35

a

Biomass-Based Diesel could include FAME biodiesel, cellulosic diesel, and non-co-processed renewable diesel.

b

Only fatty acid methyl ester (FAME) biodiesel volumes were considered

c Under the RFS 1 reference case, we assumed the 250-million gallon cellulosic standard set by EPAct would be met primarily by corn

ethanol plants utilizing 90% biomass for energy, thus actual production of cellulosic biofuel is zero.
70

1.2.2 Primary Control Case

Table 1.2-3 summarizes the fuel types and volumes for the primary control case for the

years 2010-2022. Although actual volumes and feedstocks will likely be different, we believe

the projections made here are within the range of expected outcomes when the standards are met

and allow for an assessment of the potential impacts of the RFS2 rule. More details on

contributions of different feedstock types within the renewable fuel categories here can be found

in Section 1.1.
71

Table 1.2-3.

Primary Control Case Projected Renewable Fuel Volumes (billion gallons)

Advanced Biofuel

Cellulosic Biofuel Biomass-Based Diesela Other Advanced Biofuel

Non-Advanced

Biofuel

Year

Cellulosic

Ethanol

Cellulosic

Dieselb

FAMEc

Biodiesel NCRDd Other

Biodiesele

Imported

Ethanol Corn Ethanol

Total

Renewable

Fuelf

2010 0.03 0.04 0.61 0.04 0.22 0.29 11.24 12.48

2011 0.08 0.10 0.72 0.08 0.17 0.16 12.07 13.38

2012 0.15 0.20 0.92 0.08 0.12 0.18 12.83 14.48

2013 0.31 0.41 0.92 0.08 0.28 0.19 13.42 15.61

2014 0.54 0.71 0.85 0.15 0.39 0.20 14.09 16.93

2015 0.92 1.22 0.85 0.15 0.53 0.39 14.79 18.85

2016 1.31 1.73 0.85 0.15 0.56 0.63 15.00 20.23

2017 1.69 2.24 0.85 0.15 0.60 1.07 15.00 21.60

2018 2.15 2.85 0.85 0.15 0.64 1.51 15.00 23.15

2019 2.61 3.46 0.85 0.15 0.68 1.96 15.00 24.71

2020 3.23 4.28 0.85 0.15 0.72 1.88 15.00 26.11

2021 4.15 5.50 0.85 0.15 0.77 1.81 15.00 28.23

2022 4.92 6.52 0.85 0.15 0.82 2.24 15.00 30.50

a Biomass-Based Diesel could include FAME biodiesel, cellulosic diesel, and non-co-processed renewable diesel.

bCellulosic Diesel includes 1.96 billion gallons from Fischer-Tropsch Biomass-to-Liquids (BTL) processes and 4.56 billion gallons from this or other types of

cellulosic diesel processes in year 2022. In order to calculate the split of cellulosic ethanol vs. cellulosic diesel in years prior to 2022, we assumed the same

percentage of the total cellulosic biofuel standard as in year 2022, i.e. 31% cellulosic ethanol and 69% cellulosic diesel.

c

Fatty acid methyl ester (FAME) biodiesel

d Non-Co-processed Renewable Diesel (NCRD)

e Other Biodiesel is biodiesel that could be produced in addition to the amount needed to meet the biomass-based diesel standard.

f May not total due to rounding.
72

1.2.2.1 Cellulosic Biofuel

As defined in EISA, cellulosic biofuel means renewable fuel produced from any

cellulose, hemicellulose, or lignin that is derived from renewable biomass and that has lifecycle

greenhouse gas emissions, as determined by the Administrator, that are at least 60% less than the

baseline lifecycle greenhouse gas emissions.

When many people think of cellulosic biofuel, they immediately think of cellulosic

ethanol. However, cellulosic biofuel could be comprised of other alcohols, synthetic gasoline,

synthetic diesel fuel or heating oil, and synthetic jet fuel, propane, and biogas. Whether

cellulosic biofuel is ethanol will depend on a number of factors, including production costs, the

form of tax subsidies, credit programs, and issues associated with blending the biofuel into the

fuel pool. For instance, under the Farm Bill of 2008, both cellulosic ethanol and cellulosic diesel

receive the same tax subsidies ($1.01 per gallon each). The tax subsidy, however, gives ethanol

producers a considerable advantage over those producing cellulosic diesel due to the feedstock

quantity needed per gallon produced (i.e. typically the higher the energy content of the product,

the more feedstock that is required). On an energy basis, cellulosic ethanol would receive

approximately $13/mmBtu while cellulosic diesel would receive approximately $8/mmBtu.

It will also depend on the relative demand for gasoline and diesel fuel. For example, European

refineries have been undersupplying the European market with diesel fuel supply and

oversupplying it with gasoline, and based on the recent diesel fuel price margins over gasoline, it

seems that the U.S. is falling in line with Europe. Therefore, if the U.S. trend is toward being

relatively oversupplied with gasoline, there could be a price advantage towards producing

renewable fuels that displace diesel fuel rather than a gasoline fuel replacement like ethanol.

One large advantage that cellulosic diesel has over ethanol is the ability for the fuel to be

blended easily into the current distribution infrastructure at sizeable volumes. There are

currently factors tending to limit the amount of ethanol that can be blended into the fuel pool (see

Section 1.7. of the RIA for more discussion). Thus, the production of cellulosic diesel instead of

cellulosic ethanol could help increase consumption of renewable fuels.

Cellulosic biofuel could also be produced internationally. One example of internationally

produced cellulosic biofuel is ethanol produced from bagasse from sugarcane processing in

Brazil. Currently, Brazil burns bagasse to produce steam and generate bioelectricity. However,

improving efficiencies over the coming decade as well as mechanization of sugarcane harvesting

(no burning of biomass in fields) may allow an increasing portion of bagasse to be allocated to

other uses, including cellulosic biofuel, as additional straw could potentially be collected and

used to produce bioelectricity. Although international production of cellulosic biofuel is

possible, it is uncertain whether this supply would be available primarily to the U.S. or whether

other nations would consume the fuel domestically. Therefore, our analyses for cellulosic

biofuel primarily focus on North America, and for our impact analyses just on domestic supplies.

As discussed, there is uncertainty as to which mix of cellulosic biofuels will be produced

to fulfill the 16 Bgal mandate by 2022. For assessing the impacts of the RFS2 standards, we

used AEO 2009 (April release) cellulosic ethanol volumes (4.92 billion gallons), as well as the

cellulosic biomass-to-liquids (BTL) diesel volumes (1.96 billion gallons) using Fischer-Tropsch
73

(FT) processes. We consider BTL diesel from FT processes as a subset of cellulosic diesel. In

order to reach a total of 16 billion ethanol-equivalent gallons, we assumed that an additional 4.56

billion gallons of cellulosic diesel could be produced from BTL or other cellulosic diesel

processes.

1.2.2.2 Biomass-Based Diesel

Biomass-based diesel as defined in EISA means renewable fuel that is biodiesel as

defined in section 312(f) of the Energy Policy Act of 1992 with lifecycle greenhouse gas

emissions, as determined by the Administrator, that are at least 50% less than the baseline

lifecycle greenhouse gas emissions. Biomass-based diesel can include fatty acid methyl ester

(FAME) biodiesel, renewable diesel (RD) that has not been co-processed with a petroleum

feedstock, as well as cellulosic diesel. Although cellulosic diesel produced through the FischerTropsch (F-T) process or other processes could potentially contribute to the biomass-based diesel

category, we have assumed for our analyses that the fuel and its corresponding feedstocks

(cellulosic biomass) are already accounted for in the cellulosic biofuel category as discussed in

the previous Section 1.2.2.1.

FAME and RD processes can make acceptable quality fuel from vegetable oils, fats, and

greases, and thus will generally compete for the same feedstock pool. For our analyses, we have

assumed that the volume contribution from FAME biodiesel and RD will be a function of the

available feedstock types. For our analysis we assumed that virgin plant oils would be

preferentially processed by biodiesel plants. Other feedstocks assumed to be used by biodiesel

plants are fuel-grade corn oil from corn oil extraction, fats, waste oils, and waste greases. For

the FRM we have also included a small volume of oil feedstock from algae for biodiesel

production. We note that there are a wide range of new feedstocks being researched and

developed for the production of biodiesel, e.g. camelina and pennycress. While these new

feedstocks may prove to be commercially available in the future, we have not assumed that they

are used for analyses purposes.

For RD, we assumed that the feedstocks used are from fats, waste oils, and waste greases.

This is because the RD process involves hydrotreating (or thermal depolymerization), which is

more severe and uses multiple chemical mechanisms to reform the fat molecules into diesel

range material. The FAME process, by contrast, relies on more specific chemical mechanisms

and requires pre-treatment if the feedstocks contain more than trace amounts of free fatty acids

or other contaminates which are typical of recycled fats and greases. In terms of volume

availability of feedstocks, supplies of fats, waste oils, and waste greases are more limited than

virgin vegetable oils. As a result, our control case assumes the majority of biomass-based diesel

volume is met using biodiesel facilities processing vegetable oils, with RD making up a smaller

portion and using solely fats, waste oils, and waste greases.

The RD production volume must be further classified as co-processed or non-coprocessed, depending on whether the renewable material was mixed with petroleum during the

hydrotreating operation. EISA specifically forbids co-processed RD from being counted as

biomass-based diesel, but it can still count toward the total advanced biofuel requirement. What

fraction of RD will ultimately be co-processed is uncertain at this time, since little or no
74

commercial production of RD is currently underway, and little public information is available

about the comparative economics and feasibility of the two methods. Current industry plans

indicate, however, that co-processing renewable diesel may not be as favorable as non-coprocessed RD, and therefore, we have chosen to assume zero volumes of co-processed RD.20

Non-co-processed RD volumes are based on production plans from Syntroleum.

1.2.2.3 Other Advanced Biofuel

As defined in EISA, advanced biofuel means renewable fuel, other than ethanol derived

from corn starch, that has lifecycle greenhouse gas emissions, as determined by the

Administrator, that are at least 50% less than baseline lifecycle greenhouse gas emissions. As

defined in EISA, advanced biofuel includes the cellulosic biofuel, biomass-based diesel, and coprocessed renewable diesel categories that were mentioned in Section 1.2.2.1 and Section 1.2.2.2

above. However, EISA requires greater volumes of advanced biofuel than just the volumes

required of these fuels. It is entirely possible that greater volumes of cellulosic biofuel, biomassbased diesel, and co-processed renewable diesel than required by the Act could be produced in

the future. Our control case assumes that the cellulosic biofuel volumes will not exceed those

required under EISA. We do assume, however, that additional biodiesel than that needed to meet

the biomass-based diesel volume will be used to meet the total advanced biofuel volume.

Despite additional volumes assumed from biodiesel, to fully meet the total advanced biofuel

volume required under EISA, other types of advanced biofuel are necessary through 2022.

We have assumed for the analyses conducted that for our control case the most likely

source of advanced fuel other than cellulosic biofuel and biomass-based diesel would be from

imported sugarcane ethanol and perhaps limited amounts of co-processed renewable diesel. Our

assessment of international fuel ethanol production and demand indicate that anywhere from 3.8-

4.2 Bgal of sugarcane ethanol from Brazil could be available for export by 2020/2022. If this

volume were to be made available to the U.S., then there would be sufficient volume to meet the

advanced biofuel standard. To calculate the amount of imported ethanol needed to meet the

EISA advanced biofuel standards, we assumed it would make up the difference not met by

cellulosic biofuel, biomass-based diesel and additional biodiesel categories. The amount of

imported ethanol required by 2022 is approximately 2.2 Bgal.21 Refer to Section 1.5.2 for a

more detailed discussion on imported ethanol.

Recent news indicates that there are also plans for sugarcane ethanol to be produced in

the U.S in places where the sugar subsidy does not apply. For instance, sugarcane has been

grown in California’s Imperial Valley specifically for the purpose of making ethanol and using

the cane’s biomass to generate electricity to power the ethanol distillery as well as export excess

electricity to the electric grid.22 There are at least two projects being developed at this time that

20 On May 13, 2009 ConocoPhillips and Tyson suspended plans for building RD co-processing facilities. The tax

credit for RD co-processing that helped fund the project was cut from $1 a gallon to 50 cents a gallon as part of the

credit bill approved by Congress and signed by President Bush in late 2008. The non-co-processing tax credit

remains at $1 a gallon.

21 The exceptions were for the years from 2010-2015 in the control case, where we assumed AEO 2007 imported

ethanol volumes; otherwise, imported ethanol volumes would be zero and lower than the reference case volumes.

22 Personal communication with Nathalie Hoffman, Managing Member of California Renewable Energies, LLC,

August 27, 2008
75

could result in several hundred million gallons of ethanol produced. The sugarcane is being

grown on marginal and existing cropland that is unsuitable for food crops and will replace forage

crops like alfalfa, Bermuda grass, Klein grass, etc. Harvesting is expected to be fully

mechanized. Thus, there is potential for these projects and perhaps others to help contribute to

the EISA biofuels mandate. This could lower the volume needed to be imported from Brazil.

Butanol is another potential motor vehicle fuel which could be produced from biomass

and used in lieu of ethanol to comply with the RFS2 standard. Production of butanol is being

pursued by a number of companies including a partnership between BP and Dupont. Other

companies which have expressed the intent to produce biobutanol are Baer Biofuels and Gevo.

The near term technology being pursued for producing butanol involves fermentation of starch

compounds, although it can also be produced from cellulose. Butanol has several inherent

advantages compared to ethanol. First, it has higher energy density than ethanol which would

improve fuel economy (mpg). Second, butanol is much less water soluble which may allow the

butanol to be blended in at the refinery and the resulting butanol-gasoline blend then more easily

shipped through pipelines. This would reduce distribution costs associated with ethanol’s need

to be shipped separately from its gasoline blendstock and also save on the blending costs

incurred at the terminal. Third, butanol contains less oxygen, allowing it to be blended in higher

concentrations than 10% which would likely allow butanol to be blended with gasoline at high

enough concentrations to avoid the need for most or all of high concentration ethanol-gasoline

blends, such as E85, that require the use of fuel flexible vehicles. Thus, butanol would enable

achieving most of the RFS2 standard by blending a lower concentration of renewable fuel than

having to resort to a sizable volume of E85 as in the case of ethanol. The need to blend ethanol

as E85 provides some difficult challenges. The use of butanol may be one means of avoiding

these blending difficulties.

At the same time, butanol has a couple of less desirable aspects relative to ethanol. First,

butanol is lower in octane compared to ethanol – ethanol has a very high blending octane of

around 115, while butanol’s octane ranges from 87 octane numbers for normal butanol and 94

octane numbers for isobutanol. Potential butanol producers are likely to pursue producing

isobutanol over normal butanol because of isobutanol’s higher octane content. Higher octane is

a valuable attribute of any gasoline blendstock because it helps to reduce refining costs. A

second negative property of butanol is that it has a much higher viscosity compared to either

gasoline or ethanol. High viscosity makes a fuel harder to pump, and more difficult to atomize

in the combustion chamber in an internal combustion engine. The third downside to butanol is

that it is more expensive to produce than ethanol, although the higher production cost is partially

offset by its higher energy density.

Another potential source of renewable transportation fuel is biomethane refined from

biogas. Biogas is a term meaning a combustible mixture of methane and other light gases

derived from biogenic sources. It can be combusted directly in some applications, but for use in

highway vehicles it is typically purified to closely resemble fossil natural gas for which the

vehicles are typically designed. The definition of biogas as given in EISA is sufficiently broad

to cover combustible gases produced by biological decomposition of organic matter, as in a

landfill or wastewater treatment facility, as well as those produced via thermochemical

processing of biomass.
76

Currently, the largest source of biogas is landfill gas collection, where the majority of

fuel is combusted to generate electricity, with a small portion being upgraded to methane suitable

for use in heavy duty vehicle fleets. Current literature suggests approximately 24 billion ethanolequivalent gallons of biogas (referring to energy content) could potentially be produced in the

long term, with about two thirds coming from biomass gasification and about one third coming

from waste streams such as landfills and human and animal sewage digestion.182, 183 Because the

majority of the biogas volume estimates assume biomass as a feedstock, we have chosen not to

include this fuel in our analyses since we are projecting most available biomass will be used for

cellulosic liquid biofuel production in the long term. The remaining biogas potentially available

from waste-related sources would come from a large number of small streams requiring

purification and connection to storage and/or distribution facilities, which would involve

significant economic hurdles. An additional and important source of uncertainty is whether there

would be a sufficient number of vehicles configured to consume these volumes of biogas. Thus,

we expect future biogas fuel streams to continue to find mostly non-transportation uses such as

electrical power generation or facility heating.

1.2.2.4 Other Renewable Fuel

The remaining portion of total renewable fuel not met with advanced biofuel is assumed

to come from corn-based ethanol (including small amounts from other starch grains and waste

sugars). EISA effectively sets a limit for participation in the RFS program of 15 Bgal of corn

ethanol, and we are assuming for our analysis that sufficient corn ethanol will be produced to

meet the 15-Bgal limit that either meets the 20% GHG threshold or is grandfathered. It should

be noted, however, that there is no specific “corn-ethanol” mandated volume, and that any

advanced biofuel produced above and beyond what is required for the advanced biofuel

requirements could reduce the amount of corn ethanol needed to meet the total renewable fuel

standard. This occurs in our projections during the earlier years (2010-2015) in which we project

that some fuels could compete favorably with corn ethanol (e.g. biodiesel and imported ethanol).

Beginning around 2016, fuels qualifying as advanced biofuels likely will be devoted to meeting

the increasingly stringent volume mandates for advanced biofuel. It is also important to note that

more than 15 Bgal of corn ethanol could be produced and RINs generated for that volume under

the RFS2 regulations. However, obligated parties would not be required to purchase more than

15 Bgal worth of non-advanced biofuel RINs, e.g. corn ethanol RINs.

We are assuming for our analysis that sufficient corn ethanol will be produced to meet

the 15 Bgal limit. This assumes that corn ethanol plants are constructed or modified to meet the

20% GHG threshold, or that sufficient corn ethanol production exists that is grandfathered and

not required to meet the 20% threshold. Our current projection is that up to 15 Bgal could be

grandfathered, but actual volumes will be determined at the time of facility registration. Refer to

Section 1.5.1.4 for more information.

1.2.3 Additional Control Cases Considered

Since there is significant uncertainty for what fuels will be produced to meet the 16

billion gallon cellulosic biofuel standard, we have decided to investigate two other sensitivity

cases for our cost and emission impact analyses conducted for the rule. The first case, we refer
77

to as the “low-ethanol” control case and assume only 250 million gallons of cellulosic ethanol

(from AEO 2007 reference case). The rest of the 16 billion gallon cellulosic biofuel standard is

made up of cellulosic diesel (9.26 billion gallons), as shown in Table 1.2-4. The second case, we

refer to as the “high-ethanol” control case and assume the entire 16 billion gallon cellulosic

biofuel standard is met with cellulosic ethanol, see Table 1.2-5.
78

Table 1.2-4.

Low-Ethanol Control Case Projected Renewable Fuel Volumes (billion gallons)

Advanced Biofuel

Cellulosic Biofuel Biomass-Based Diesela Other Advanced Biofuel

Non-Advanced

Biofuel

Year

Cellulosic

Ethanol

Cellulosic

Dieselb

FAMEc

Biodiesel NCRDd Other

Biodiesele

Imported

Ethanol Corn Ethanol

Total

Renewable

Fuelf

2010 0.00 0.06 0.61 0.04 0.22 0.29 11.24 12.47

2011 0.00 0.14 0.72 0.08 0.17 0.16 12.07 13.35

2012 0.01 0.29 0.92 0.08 0.12 0.18 12.83 14.42

2013 0.02 0.58 0.92 0.08 0.28 0.19 13.42 15.49

2014 0.03 1.01 0.85 0.15 0.39 0.20 14.09 16.72

2015 0.05 1.74 0.85 0.15 0.53 0.39 14.79 18.49

2016 0.07 2.46 0.85 0.15 0.56 0.63 15.00 19.72

2017 0.09 3.18 0.85 0.15 0.60 1.07 15.00 20.94

2018 0.11 4.05 0.85 0.15 0.64 1.51 15.00 22.31

2019 0.13 4.92 0.85 0.15 0.68 1.96 15.00 23.69

2020 0.16 6.08 0.85 0.15 0.72 1.88 15.00 24.85

2021 0.21 7.82 0.85 0.15 0.77 1.81 15.00 26.61

2022 0.25 9.26 0.85 0.15 0.82 2.24 15.00 28.57

a Biomass-Based Diesel could include FAME biodiesel, cellulosic diesel, and non-co-processed renewable diesel.

bCellulosic Diesel includes 1.96 billion gallons from Fischer-Tropsch Biomass-to-Liquids (BTL) processes and 4.56 billion gallons from this and other types of

cellulosic diesel processes in year 2022. In order to calculate the split of cellulosic ethanol vs. cellulosic diesel in years prior to 2022, we assumed the same

percentage of the total cellulosic biofuel standard as in year 2022, i.e. 2% cellulosic ethanol and 98% cellulosic diesel.

c

Fatty acid methyl ester (FAME) biodiesel

d Non-Co-processed Renewable Diesel (NCRD)

e Other Biodiesel is biodiesel that could be produced in addition to the amount needed to meet the biomass-based diesel standard.

f May not total due to rounding.
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Table 1.2-5.

High-Ethanol Control Case Projected Renewable Fuel Volumes (billion gallons)

Advanced Biofuel

Cellulosic Biofuel Biomass-Based Diesela Other Advanced Biofuel

Non-Advanced

Biofuel

Year

Cellulosic

Ethanol

Cellulosic

Dieselb

FAMEc

Biodiesel NCRDd Other

Biodiesele

Imported

Ethanol Corn Ethanol

Total

Renewable

Fuelf

2010 0.10 0.00 0.61 0.04 0.22 0.29 11.24 12.51

2011 0.25 0.00 0.72 0.08 0.17 0.16 12.07 13.45

2012 0.50 0.00 0.92 0.08 0.12 0.18 12.83 14.62

2013 1.00 0.00 0.92 0.08 0.28 0.19 13.42 15.89

2014 1.75 0.00 0.85 0.15 0.39 0.20 14.09 17.43

2015 3.00 0.00 0.85 0.15 0.53 0.39 14.79 19.70

2016 4.25 0.00 0.85 0.15 0.56 0.63 15.00 21.44

2017 5.50 0.00 0.85 0.15 0.60 1.07 15.00 23.17

2018 7.00 0.00 0.85 0.15 0.64 1.51 15.00 25.15

2019 8.50 0.00 0.85 0.15 0.68 1.96 15.00 27.13

2020 10.50 0.00 0.85 0.15 0.72 1.88 15.00 29.11

2021 13.50 0.00 0.85 0.15 0.77 1.81 15.00 32.08

2022 16.00 0.00 0.85 0.15 0.82 2.24 15.00 35.06

a Biomass-Based Diesel could include FAME biodiesel, cellulosic diesel, and non-co-processed renewable diesel.

bCellulosic Diesel is assumed to be zero, while cellulosic ethanol is assumed to be 100% of the cellulosic biofuel standard.

c

Fatty acid methyl ester (FAME) biodiesel

d Non-Co-processed Renewable Diesel (NCRD)

e Other Biodiesel is biodiesel that could be produced in addition to the amount needed to meet the biomass-based diesel standard.

f May not total due to rounding.
80

1.3 Feedstock Harvesting, Transportation, & Storage

A reliable and affordable source of cellulosic feedstocks will be vital for the development

of a large scale cellulosic biofuel industry. While Section 1.1 of the RIA examined the

availability of cellulosic feedstocks for conversion to biofuels, this section focuses instead on the

process of harvesting, storing, and transporting these feedstocks to the biofuel production

facilities. For biofuels that use traditional crops such as corn, soy bean oil, or sugar cane, these

feedstock storage and delivery systems are already well established. For other feedstocks,

however, such as herbaceous energy crops or wood residue, new feedstock supply systems will

have to be put into place. Each of these potential feedstocks presents unique challenges that

must be overcome in order for them to be used for large scale biofuel production. For more

information on the costs associated with the harvest, storage, and transportation processes see

Chapter 4 of the RIA.

1.3.1 Feedstock Harvesting

Feedstock harvesting refers to all the steps necessary to make the feedstock available at

the roadside for transportation and storage. For MSW, this is a relatively simple process. MSW

is already collected on a large scale and in order to enable it to be used as a feedstock all that is

required is that it be sorted to remove the portion that is undesirable for biofuel production.

Agricultural residues and herbaceous energy crops, on the other hand, are not currently being

harvested on a large scale and therefore new processes must be developed to make them

available to be used in the production of biofuels.

1.3.1.1 Municipal Solid Waste (MSW) Collection

As discussed above, MSW is one of the potential sources of renewable fuel feedstock that

already has a well developed collection system already in place. In many cases cities and

municipalities are already recovering recyclable materials, such as metals, plastics, and paper,

from the collected waste streams. After these valuable materials have been removed from the

waste stream the remainder of the waste material can, in many cases, be used for the production

of renewable with little or no additional separation required. Alternatively, a waste stream of

similar quality may be able to be obtained without the potentially expensive separation process if

the waste material is separated by the waste producer at the curbside. One potential producer of

biofuels from MSW indicated in a confidential conversation that this was the method they

planned to use to obtain their feedstock.

In parts of the country where these recyclable materials are not currently recovered it will

be necessary for the biofuel producer who wishes to use this material to first remove the metals,

plastics, and other contaminated materials before this material may be used. This sorting can be

done either by hand or with an automated process. Cleaner streams are produced when the waste

stream is sorted by hand, however this is a slower and more expensive process. Potential biofuel

producers indicated to us that the automated separation systems that currently exist produce

waste streams of acceptable quality and are thus more likely to be used due to their lower costs.

If the biofuel producer was responsible for waste separation it is likely that the separation facility
81

and the biofuel production facility would be located at the same site, and thus no transportation

would be necessary between these two facilities.

1.3.1.2 Wood Residue Collection and Harvest

Another potential source of feedstock that may be converted to cellulosic biofuels are

wood residues. This category of feedstock refers to a large range of currently unused wood

wastes from forestry and wood processing industries. Significant sources of wood residue are

either currently available or expected to be available in the near future in the form of mill

residues, forest residue, and forestry thinnings.

Mill Residue

One source of currently available wood residue is mill residue. Mill residue is a waste

product of both primary mills, mills that convert roundwood into other wood products, and

secondary mills, those that produce finished consumer products. Because this residue is

currently being produced at the primary and secondary mills all that would be required for its use

as a cellulosic biofuel feedstock is it’s collection and transportation to the biofuel production

facility or for the co-located construction of a biofuel facility.

Forest Residue

The largest portion of wood residue available as cellulosic feedstock is forest residues.

However, unlike residues such as primary or secondary mill residues that could be available onsite at a processing facility, forestry residues would need to be collected and transported

similarly to conventional forest products. The amount of residues potentially available is a

function of harvest amount, logging method, and type and location of timberlands.184 In

addition, residue availability is limited by economic factors. According to one study, “the actual

operations of harvesting, collecting, processing and transporting loose forest residues are costly

and present an economic barrier to recovery and utilization of wood for energy”.185 Thus, there

are still challenges that need to be addressed before large-scale use of forestry residues is

possible.

Currently, the most cost-effective method of recovering forest residue for biomass is inwoods chipping.186 This method is suitable for operations where there is whole-tree skidding to

roadside, good road access to chip vans and chippers, and sufficient biomass volume per acre.

However, in-woods chipping systems are not as effective when ground-based skidding is

restricted or when there are no merchantable products other than biomass. In addition, the chip

vans designed to haul wood chips were built for highway use and often do not have sufficient

suspension systems for remote forest roads. There are also high costs for wood grinders with

low production rates.187 Fortunately, there have been developments in alternative methods to

reduce the costs of biomass collection systems.

There has been much focus recently on developing methods of densifying residues in

order to increase productivity of handling operations (i.e. hauling, skidding, and loading). New

approaches to removing forestry residues are currently being evaluated (e.g. slash bundling
82

machines, horizontal grinders, and roll on/off container transport). One of the advantages of

using slash bundling machines is the ability to store biomass longer than in chip form. Storing

biomass at roadside in the form of biomass bundles could provide a more secure and stable

biomass supply than with chips which are smaller and have greater surface area for potential

weathering. Utilizing roll on/off containers allows for recovery of residue from difficult-toaccess locations and in such situations could be competitive with regular highway chip vans.

While these are just some of the ways to improve recovery operations for forestry

residues, these methods still have challenges. For example, there are some difficulties with

bundling of brittle residues or short, large diameter pieces. In addition, some residues may

include rocks or trash that can result in additional saw maintenance and reduced utilization.

With millions of acres of forest, there is no single residue treatment option that will meet the

needs of all situations. Forest land managers will need to weigh the different options for dealing

with forest residues to determine the most cost-effective means for residue removal in their

specific locations.

Forest Thinnings

A third source of wood residue is forestry thinnings. Forest thinnings refer to woody

material removed from forests that have become overgrown, either to reduce the risk of forest

fires or to increase productivity of the forest. The material removed is too small or damaged in

some other way and is unsuitable to be sold as roundwood. Because of its low value, much of

the wood residue removed from forests today as forest thinnings is either burned or left to

decompose. Currently the cost to fell the thinnings is paid for by the land owner. Therefore, in

order to use this material as a cellulosic feedstock the forest thinnings would only have to be

collected from the forest and moved to the roadside. Once at the roadside they would likely be

either chipped or bundled using the process previously discussed to increase the density of the

thinnings, and thus reduce the transportation costs.

1.3.1.3 Agricultural Residue Harvest

Agricultural residue is a very large and potentially readily available cellulosic feedstock

source for biofuels producers. While the residues of some crops have been harvested for many

years, much crop residue is left on the fields in order in increase soil quality and protect against

erosion from wind and rain as discussed in Section 1.1. Despite the many benefits of leaving

agricultural residue on the fields we believe that it is possible to remove some portion of the

agricultural residues without significant negative impacts to the soil quality in many parts of the

country. We also believe that agricultural residues will make up a large portion of the cellulosic

feedstocks used for biofuel production by 2022. The following section discusses the likely

process for agricultural residue harvest and the associated challenges we anticipate. We have

chosen to focus our discussion on corn stover as it is expected that it will be used more

extensively than any other agricultural residue, and because there is more uncertainty

surrounding its harvest than other small grains, such as wheat, oats, barley, and rice, that are

regularly harvested currently.
83

Corn stover harvest, at present, requires multiple machines: combines, shredders, rakes,

balers, bale wagons, and stackers just to get the stover bales to the side of the field; dry matter is

lost during each operation. Currently, there are no harvesting machines designed specifically for

residue harvest, other than perhaps, for small grain straws that use common hay equipment. One

proposal for corn stover harvest is to shut the spreader off on the grain combine in order to form

a windrow, of sorts, following which the windrow is baled.188 However, modern combines leave

most of the stalk standing. In order to harvest as much of the stover as possible, it is necessary to

shred the standing stalks and then rake all of it together prior to baling.189 The baler pickup must

be set high enough to avoid picking up dirt and dirt clods, the dirt-particles from which are very

hard on harvesting equipment and that would demand a cleanup stage in downstream processing,

which of itself would translate into overall dry matter losses. As such, it is likely that the baler

will leave some amount of stover. After baling, the bales, whether round or square, would be

picked up from the field and moved to the roadside, where they would await transportation to a

storage facility.

We anticipate that by 2022, the corn stover harvest will be reduced to a single-pass

operation during which the amount of residue left on the field will be less a function of harvest

efficiency and more a function of the farmer/grower and the harvesting company being able to

determine how much residue must be left to maintain soil health. A combine designed

specifically for the job must still be constructed, but we expect that it will cut the whole stalk a

few inches above the soil, leaving some stalk anchored to the ground. A single-pass harvester

could cut the entire plant a few inches above the ground and pull all of it, e.g., stalks, leaves,

cobs, and grain into the combine, where they become a single, mixed grain and stover stream.

The harvester blows the entire stream into tractor-pulled grain-carts that run along-side the

harvester. When a cart is filled, it is replaced by an empty cart, and the full cart is hauled to the

field side, where it’s unloaded into bulk ‘walking-floor’ semi trailers, and hauled to a co-op or

depot type elevator/facility for further processing and storage. At the elevator, the stover/grain

mix is unloaded into equipment for further processing before it’s sent to storage. Although a

facility (equipment, buildings, etc.) at an elevator for separating the corn grain from the stover

has not been constructed, we anticipate that it could operate very much like a modern grain

harvester/combine, except it will obviously be stationary. The entire stream could be fed, by

chain or belt, where it drops between a cylinder covered with rough steel bars and a piece of

equipment called a concave. As the cobs are rubbed between the steel bars and concave, the

corn grain rubs off and drops onto a perforated belt; most of the stover remains are larger than

corn grain pieces, and is moved rearward toward the spreader. The corn grain and small stover

particle fall through and are carried to a chaffer.

Small grain straws, such as those from wheat, oats, barley, and rice have been harvested

for many years. A significant difference between the harvesting equipment used for corn stover

and these grains, is that the small grain plant is cut off near the ground and passes through the

combine at the time of harvest. It falls to the ground from the harvester into somewhat of a

windrow; in some cases, the windrow many need to be raked together before baling to gain

maximum removal efficiency. Since the whole grain plant had dried prior to harvest, it’s not

necessary to wait for the straw to dry before it’s baled. Small grain straws can be baled, hauled,

and stacked in standard small bales or in larger 3’ x 4’ x 8’ square bales with current hay

equipment.
84

Sugarcane bagasse is not harvested, in the sense we’ve discussed ‘a harvest.’ It is a

byproduct of sugar production from sugarcane, delivered by truck and trailer from the sugar

processing facility to the ethanol plant. If sugarcane bagasse were to be used as a cellulosic

feedstock the only additional step that would be required would be to transport the bagasse from

the sugar fermentation facility to the cellulosic biofuel production facility if they are not colocated.

1.3.1.4 Energy Crop Harvest

Energy crops are another very large potential, yet currently unutilized, source of

cellulosic feedstock. As with corn stover, no harvesting process for energy crops currently

exists. Additionally, the harvesting process used for energy crops will vary greatly depending on

whether the energy crop is herbaceous, such as switchgrass or miscanthus, or woody, such as

hybrid poplar. Nevertheless, we believe that the harvesting practices for energy crops will

resemble those currently used for small grains and tree plantations respectively.

Herbaceous Energy Crops

The harvesting process for herbaceous energy crops, such as switchgrass and miscanthus,

is expected to closely resemble that described for corn stover in the preceding section. When the

herbaceous energy crops are sufficiently dry they will be cut with a mower or swather, similar to

those used to harvest hay, and left on the field in windrows. The energy crops will then be baled

and moved to the roadside where they will await transportation to a storage location.

While it is possible to harvest herbaceous energy crops using currently available

equipment designed for hay and other agricultural residues, the high yields of these crops present

several challenges. The higher production rates per acre of energy crops, when compared to hay

or corn stover, will require unique equipment designs. There is also likely to be a small harvest

window where the crop is ready to be harvested, but before the onset of winter weather,

especially in northern parts of the country. As more energy crops are grown and harvested as

feedstocks for biofuels and energy sources in other sectors, it is likely that harvesting equipment

will be developed that is optimized for energy crops.

There may also be significant regional variation in the harvesting process for energy

crops. Energy crops grown in the south will have a longer harvest window, as winter weather

arrives at a later date, and in many cases is not severe enough to halt harvesting operations.

Longer growing seasons in the south may also enable multiple harvests in the same year to

further increase yields. Finally, in parts of the country where year round harvest is possible

energy crops may be able to be harvested on an as needed basis, negating the need for secondary

storage and significantly reducing the delivered cost of the energy crops to the biofuel producers.

For more information on cellulosic feedstock storage and it’s impact on feedstock price see

sections 1.3.2 and 4.1.1.2.
85

Woody Energy Crops

As with herbaceous energy crops, it is possible to harvest woody energy crops with

equipment currently in use by logging operations and the pulp and paper industry. Trees can be

cut and gathered using a feller buncher and then transported to the roadside. Once at the

roadside they can either be chipped and blown into chip vans for transportation to the biofuel

production facility, or bundled using the process described above in the forest residue section. It

is more likely, however, that woody energy crops will be harvested using equipment specifically

designed for that purpose and able to take advantage of the regular spacing of the trees found on

tree plantations. In Europe self propelled harvesters that cut and chip the woody energy crops

are being used. The wood chips are then stored in large stacks until they are transported to the

facility where they will be used190. Work is also being done in Canada to design a harvester

capable of cutting, shredding, and baling woody energy crops. These bales would then be

transported to a storage area and allowed to dry before being chipped and used for biofuel

production. We anticipate that woody energy crops will be harvested using a process optimized

to fit the individual woody energy crop plantation, likely resembling one of the processes just

described.

1.3.2 Feedstock Transportation and Storage

Once cellulosic feedstocks have been made available at the roadside, either through

collection or harvesting, they must then be transported to the biofuel production facility. For

some feedstocks, such as sorted MSW, this may be as simple as delivering the feedstock to a

biofuel production facility rather than a landfill. For other feedstocks, such as agricultural

residue or energy crops, it will require a much more complicated process involving multiple

relocations, loadings, and unloadings, as well as storage in a secondary storage facility. The

complexity of the transportation of the feedstock from the location where it is produced to the

biofuel production facility is most dependent on whether the feedstock is available year round

and harvested on an as needed basis or collected or harvested on an annual or semi-annual basis.

1.3.2.1 Secondary Storage

One potential challenge for cellulosic biofuel producers is where the cellulosic material

will be stored before it is converted into fuel. Some feedstocks, such as MSW or wood residues,

can be collected or harvested year round. It will therefore only be necessary for the biofuel

production facility to store a small amount of feedstock on site, we estimate 3-4 days worth, and

additional feedstock can be received regularly directly from the producers. Agricultural residues

and herbaceous energy crops, however, are harvested annually or semi-annually, and therefore

the biofuel producer must be able to store a years worth of feedstock. Because of the low energy

density of cellulosic feedstocks it would not be feasible to store a years worth of feedstock at the

biofuel production site, as this would require an area of several hundred acres for feedstock

storage alone at larger facilities.

One method that has been suggested is storing baled feedstock at the roadside on the

farms where it is produced. It would then be loaded onto trucks and transported to the biofuel

production facility as needed. This method of cellulosic storage at the farms where it is
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produced would be problematic. Storing significant quantities of feedstock at the farm sites

could force land that would otherwise be used for feedstock production to be instead used for

feedstock storage. Heavy traffic by the bale loaders and trucks used for transportation could

cause significant damage to the farmers’ fields. Finally, because access to these feedstocks

would often be over unimproved private roads on the farmers land there is a real risk that

feedstock supply could be interrupted by extended periods of inclement weather. We believe

that the combination of these factors makes feedstock storage at the site where it is produced

unlikely.

Another storage option would be to use secondary storage sites. In this method of

feedstock storage baled cellulosic feedstock would be moved from the site of production to a

secondary storage facility at the time of harvest. It would then be transported from the secondary

storage site to the biofuel production facility as needed. Feedstock from many farms would be

collected at a single secondary storage site. The number of secondary storage sites would

depend on the size of the biofuel production plant and the density of the feedstock production.

Storing cellulosic feedstock in secondary storage sites increases the delivered cost of the

feedstock, but could be necessary due to the limitations of on farm storage mentioned above.

In addition to where the cellulosic feedstock is stored, there is also the question of how

the feedstock will be stored. Many different ways of storing the feedstock have been suggested,

ranging from stacked bales exposed to the weather, to bales wrapped in plastic, to storage in

covered buildings or pole barns. The issue of whether the feedstock should be baled as round or

square bales also effects how the feedstock should be stored. Round bales store better in the

open than square bales since rain, and particularly snow, collect on flat surfaces more readily

than on round. When stacked, however, round bales usually cannot be stacked more than three

bales high without the risk of deformation, instability of the stack, and dry matter loss. Square

bales, however, can be stacked as many as five high without the risk of instability.

In making the decision whether to store the bales in an indoor facility the cost of the

storage facility must be weighed against the dry matter loss that will result from storing the bales

in the open. Dry stover bales stored indoors or outdoors had average dry matter losses of 5% and

15%, respectively. Wrapping dry bales in net or plastic wrap and storing on a well drained

surface significantly reduced dry matter loss compared to storing twine wrapped dry bales on the

ground.191 Wrapping bales in net of plastic, however, is usually done at the time of bailing at the

farm site, and it is not clear whether it is feasible to transport and stack wrapped bales at a

secondary storage site. Indoor storage is, in most cases, a concrete slab with a roof, supported by

poles, with open sides (pole-barn). Depending on the number of bales to be stored, the slab must

be sized to include aprons around all four edges with aisles between stacks to accommodate

stacking and hauling equipment and for fire safety. Considering these many factors, we believe

that indoor storage is the storage method that will be most widely utilized. This is the storage

method which was used in our cost analysis, which can been seen in more detail in Section 4.1.1.

1.3.2.2 Municipal Solid Waste (MSW) Transportation

Transportation and storage of MSW as a feedstock is relatively simple. If the biofuel

producer is using MSW that has already been separated, all that would be required would be to
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transport the feedstock from the facility where it is separated, most likely a recycling center, to

the biofuel production facility. This would be done in large over-the-road trucks. The biofuel

producer would have to store several days worth of feedstock at the fuel production site to ensure

that fuel production is not interrupted, but because MSW is produced and collected year round

no secondary storage would be necessary.

If the biofuel producer is receiving unsorted MSW it is probable that the sorting facility

would be co-located with the biofuel production facility. If the biofuel production facility is near

the source of MSW it may be possible for the local refuse collection trucks to deliver the MSW

to the plant directly. If the biofuel production facility is located some distance from the MSW

source it will be more cost effective to transfer the MSW from local refuse collection trucks to

large over-the-road trucks for transportation to the biofuel production facility. Once again, no

secondary storage would be required due to the consistent availability of MSW. In this case,

however, the biofuel producer would have to arrange for the transportation of recovered

recyclable materials, as well as contaminated waste that cannot be used to produce biofuels.

1.3.2.3 Wood Residue Transportation

Wood residues are expected to be collected from the places they are produced, the

primary or secondary mill for mill residues and the roadside of the forestry operation for forest

residue and forest thinnings, and transported directly to the biofuel production facility in large

over-the-road trucks. For each of the three types of wood residues we expect that the wood will

be chipped or processed in some other way to increase the density of the residue before

transportation. This will reduce transportation costs by allowing a greater mass of wood residue

to be transported by each truck. As with MSW, secondary storage is unlikely to be necessary for

wood residues as they are available to be harvested throughout the year.

1.3.2.4 Agricultural Residue and Energy Crop Transportation and Storage

Unlike MSW and wood residues, which are available to be harvested and collected

throughout the course of the year, agricultural residues and herbaceous energy crops are

harvested on an annual or semi-annual basis. As a result, a large amount of feedstock, enough to

supply the biofuel production facility for a whole year, must be stored and delivered throughout

the year. We expect secondary storage sites, as described above, will be the best option.

Following the baling operation, the bales of agricultural residue or energy crops will be picked

up from the field in 10-bale loads, by vehicles designed for that purpose. Such vehicles are

currently used to gather hay bales today. The bales are subsequently unloaded or dropped at the

field-edge. Later, the bales are loaded onto wagons pulled by high-speed tractors that haul as

many as 20-bales per load to satellite storage (the pole-barns described in Section 1.3.2.1). The

bales are unloaded and stacked for storage until they are needed at the ethanol plant. Transport

to the plant is by over-the-road trucks and trailers that can haul net-loads of up to about 45- to

50-tons. However, because the bale density is low (on average, about half the weight of a

similarly sized hay bale), the maximum number of bales a truck can haul usually weighs much

less than the maximum allowable weight. Grinding the baled feedstock before transportation to

the biofuel production facility would increase the density of the feedstock, and therefore increase

the mass that each truck could transport and lower the overall transportation costs.
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As discussed in Section 1.3.1.2, we anticipate that the corn stover harvest could become a

single-pass operation by 2022. In this case corn stover would be transported from the farm to a

specialized cellulosic feedstock depot to be processed. Equipment at the cellulosic feedstock

depot would chop and dry the corn stover. This distributed preprocessing facility can provide

significant cost benefits by producing a higher value cellulosic feedstock with improved

handling, transporting, and merchandising potential. In addition, data supporting the preferential

deconstruction of feedstock materials due to their bio-composite structure identifies the potential

for significant improvements in equipment efficiencies and compositional quality upgrades.192

The stover, now with flowability characteristics similar to small cereal grains, is moved by

standard grain loading and unloading systems into large corrugated steel bins for intermediate

storage. In this harvest format, the stover is handled by only two machines before it reaches the

roadside and never hits the ground, significantly reducing dry matter losses. The biofuel

producer would then pick up its feedstock from the elevator/depot in trucks and trailers for

transport to the facility. We believe stover feedstock in the ground format could have a

significantly higher bulk-density than baled stover, which should translate into lower

transportation costs.

1.3.3 Cellulosic Feedstock Transportation and Storage Tool

In order to better estimate the impacts of transport and secondary storage on the overall

price of cellulosic ethanol, we have developed a tool that estimates the location of future

cellulosic ethanol plants. Using these locations, we can estimate the average cost for transport of

feedstock material both locally (within the plant-containing county) as well as imported from

other areas in the country. The tool also provides us an estimate of the type of feedstock material

used by each plant, allowing us to determine the average cost of secondary storage for these

materials.

1.3.3.1 Basis and Assumptions for Transport Tool

Feedstock densities and locations have been compiled on a county basis for use within

the tool. This information has been provided by a variety of sources, including the National

Forestry Service for forestry residue, the National Agricultural Statistical Service (NASS, 2007)

for agricultural residues and Elliot Campbell from Stanford University for energy crops.

Municipal solid waste is also considered for this tool. For more information regarding the

sources of data used for the tool, see Section 1.1.2 of this RIA. Data for agricultural residues

provided by NASS reported harvested grain values, and needed to be modified to reflect

agricultural residue values, using grain to residue ratios193. Data provided from FASOM

modeling was used in this tool for total feedstock usage as well as farmside cost.

In order to simplify the location of plants within the tool, we have assumed that plants

will be constructed at a county centroid. Therefore, transport within a county to a plant is based

on the transport of feedstock material from farmside to the county centroid, with consideration

for feedstock density within the county as well as the total county area. Furthermore, transport of

feedstock between counties (for plants importing feedstock outside the county they are located

in) is based on the distance between county centroid locations, with an additional factor to
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account for the added distance of using on-road transportation. Information regarding the costs of

this transportation can be found in Section 4.1.1.2 of this RIA.

Assumptions for secondary storage used in the tool closely follow the determinations

made in Section 1.3.2 above. Secondary storage costs in the tool are based on the amount of

feedstock to be stored, the density of the feedstock being stored, as well as the type of feedstock

itself. As discussed in Section 1.3.2, the tool assumes that no secondary storage is necessary for

either municipal solid waste, which would be transported to the plant directly by waste removal

services; or for forest residue, which can be harvested year-round and transported on as-needed

to the plant. Capital costs used in the tool for plant selection are based on current refinery

modeling, broken down by PADD location. These capital costs are based on the total production

volume of the plant and the PADD that it is located within. For more information on transport,

secondary storage, and capital costs, please refer to Section 4.1.1.2 in this RIA.

1.3.3.2 Transport Tool Operation

The tool begins operation by compiling feedstock availability (by county) based on the

data sources discussed in Section 1.3.3.1. Using county locations, it builds a list for each county

that contains the locations of other counties within a set maximum range (these other counties

will be referred to as neighbors for the remainder of this section). This list will serve as the basis

for county to county feedstock transport further on in the tool operation. The tool then adds

feedstock information such as feedstock densities, total amount of feedstock available, and

feedstock type specific to each county. Using this feedstock information, the tool generates a list

of all feedstocks available for each county; both within the county itself as well as feedstock

available for import from other neighboring counties. At the end of this step in the tool operation,

each county has a datapoint in the tool which contains a complete list of all feedstock available

to that location.

Using the list of feedstocks available to each county generated in the last step, as well as

the transportation and secondary storage cost assumptions discussed in Section 1.3.3.1, the tool

calculates and adds complete cost information for each feedstock available to a county. These

costs include the farmside cost of the feedstock, the transportation required to move the

feedstock to the centroid of its own county and the secondary storage of that feedstock. If the

feedstock is available by import from a neighboring county, the transportation cost of moving

that feedstock from the neighboring county is also added to the complete feedstock cost for that

source. At the end of this step in the tool operation, each county datapoint contains a list

detailing the total cost of each feedstock available as they would be delivered to that county.

In the next step of the tool operation, the list of feedstock availability cost is used to

choose feedstocks that a plant located at each county centroid would processes. For each county,

the cheapest feedstock from the list is selected for the plant. The volume of feedstock available at

this price is then converted to gallons (based on feedstock conversion modeled by FASOM) and

added to a running count of the total volume of feedstock processed by that county. Capital costs

associated with the increased volume are also added to the total cost of the feedstock processing

for that county. The tool continues adding feedstock sources to a county by selecting the next

cheapest feedstock on the list. Selection proceeds until either the county either reaches a set
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maximum processing volume, or when adding another feedstock would produce a result more

expensive on a price per gallon basis. At the end of this step, each county datapoint contains

information regarding the cheapest total cost to produce cellulosic ethanol at that location.

The tool proceeds by scanning the entire list of county locations for the cheapest

processing location that could be constructed. This location is added as an estimated cellulosic

ethanol plant location for the final output of the tool. The feedstock used in by location is

removed as a source from any feedstock availability list used by other counties. The tool then

repeats using this modified feedstock data, starting from the step involving the selection of

feedstocks used in a county. In this way, a list of estimated plant locations is chosen by always

selecting the next cheapest location in which a plant can be built; this provides the final output of

the tool. The tool stops operation when the total processed volume of all locations selected

reaches the sixteen billion gallon maximum discussed in Section 1.3.3.1.

1.3.3.3 Final Tool Output and Interpretation

Not only does the tool provide estimated plant locations, it also provides supplementary

information we have used to estimate average transportation and storage costs for feedstocks

used by each plant, and subsequently all plants estimated by the tool. Since both the farmside

feedstock cost as well as the contribution of capital cost is known for each of the estimated plants

(as these are inputs to the tool), the transportation and storage costs can be calculated for each

ton of feedstock processed by that plant, including county to county transport. The cost of

transportation for each plant can then be averaged with the other plants selected by the tool to

arrive at a total transportation and storage cost average across all plants selected by the tool. For

more information about how these transportation and storage costs are used, see Section 4.1.2 in

this RIA.
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Figure 1.3-1

Illustration of Estimated Plant Sites Selected by Plant Siting Tool

Counties in black show active plant locations, counties in grey show active feedstock use

In Figure 1.3-1 above, an illustration of the plant locations selected by the tool can be

seen. It is important to note that the above average number of plants selected for the southern

region of the United States is most likely due to the lower capital costs associated within the this

region. The tool takes into account regional variations of plant construction and operation costs.

The lower capital cost in the southern region (most likely due to existing construction

infrastructure for conventional oil refineries) reduces the overall price of plants selected in these

locations, and we feel explains why the tool has a preference for this region of the country. More

specific information on these capital cost regions can be found in Section 4.1.1.2 of this RIA.

The tool was run multiple times using differing values for total feedstock availabilities as

well as the percentage of feedstock associated with each type. We have selected the tool output

that most closely matches the output for feedstock usage provided by the FASOM model, as we

feel that it is important to keep the feedstock usage quantities consistent across our analyses.

However, as improved input factors and estimates are developed over time, the tool can be easily

adjusted and updated to take into account this new information.

1.4 Biofuel Production Technologies

Biofuel production technologies continue to evolve with research and development

efforts focused on reducing costs and increasing efficiencies. Improvements include increasing

conversion yields for various feedstocks, reducing energy and materials usage, eliminating or

reducing wastes, finding alternative uses for by-products, etc. For those technologies not yet
92

commercial, researchers are combining their innovative ideas to develop cost-effective processes

to produce biofuel at low enough costs to compete with their petroleum counterparts. The

following sections describe both proven and new technologies which may be used to produce

renewable fuels to meet the EISA 36 billion gallon standard by 2022.

1.4.1 Corn Ethanol

There are two primary processes for converting corn (and other similarly processed

grains) into ethanol: wet milling and dry milling. The main difference between the two is in the

treatment of the grain. Dry mill plants grind the entire kernel (shown below in Figure 1.4-1) and

generally produce only one primary co-product: distillers grains with solubles (DGS). The coproduct is sold wet (WDGS) or dried (DDGS) to the agricultural market as animal feed. Wet

mill ethanol plants separate the grain kernel prior to processing into its component parts and

produce other co-products (usually gluten feed, gluten meal, and food-grade corn oil) in addition

to DGS. Each process is described in greater detail in the subsections that follow.

Figure 1.4-1. Components of the Corn Kernel

ENDOSPERM

Contains nearly all the

starch used to make

ethanol and also

contains protein used in

animal feed.

TIP CAP

Attachment point of the

kernel to the cob.

PERICARP/BRAN

The protective covering of

the kernel. Contains fiber

used in human and animal

food products.

GERM

Contains corn oil used in food

applications as well as

biodiesel production.

1.4.1.1 Dry Milling Technology194

In traditional dry mill plants, first the corn is screened to remove any unwanted debris.

Then, it goes through a hammer mill where it is ground into course flour also know as “meal.”

Next the meal is cooked to physically and chemically prepare the starch for fermentation.

The first step of the cooking process is to form a hot slurry. The meal is mixed with

water, the pH is adjusted, and an alpha-amylase enzyme is added. The slurry is heated to 180–

190°F for about 30–45 minutes to reduce viscosity.

The second step in the cooking process is liquefaction, which occurs in two steps. First

the hot slurry is pumped through a pressurized jet cooker at approximately 220°F and held for
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about 5 minutes. The mixture is then cooled by an atmospheric or vacuum flash condenser.

After cooling, the mixture is held for 1–2 hours at 180–190°F to give the alpha-amylase enzyme

time to break down the starch into short-chain carbohydrates also know as “dextrins.” Once

cooking is complete, a pH and temperature adjustment is made, a second enzyme (glucoamylase)

is added, and the resulting mixture (also know as “mash”) is pumped into the fermentation tanks.

During the fermentation process, the glucoamylase enzyme breaks down the dextrins to

form simple sugars. Yeast is added to convert the sugar into ethanol and carbon dioxide. The

mash is then allowed to ferment for 50–60 hours. The result is a mixture that contains 10-15%

ethanol by volume (20 to 30-proof) as well as solids from the grain and added yeast.

From here, the fermented mash is pumped into a multi-column distillation system where

additional heat is added. The columns utilize the differences in the boiling points of ethanol and

water to boil off and separate the ethanol. By the time the product stream leaves the distillation

columns, it contains about 95% ethanol by volume (190-proof). The residue from this process,

called stillage, contains non-fermentable solids and water and is pumped out from the bottom of

the columns into the centrifuges.

The final step in the ethanol production process is dehydration to remove the remaining

5% water. The ethanol is passed through a molecular sieve to physically separate the water from

the ethanol based on the different sizes of the molecules. The result is 200-proof anhydrous

(waterless) ethanol. At this point, a denaturant, which typically is natural gas liquids, is added

(making it unfit for human consumption) and the ethanol is placed into storage.

During the ethanol production process, two primary co-products are created: carbon

dioxide and distillers grains. As yeast ferment the sugar, they release large amounts of carbon

dioxide gas. In some plants it’s released into the atmosphere, but where local markets exist, it’s

captured and purified with a scrubber and sold to the food processing industry for use in

carbonated beverages and flash-freezing applications.

The stillage from the bottom of the distillation columns contains solids from the grain and

added yeast as well as liquid from the water added during the process. It is separated via

centrifuge into thin stillage (a liquid with 5–10% solids) and wet distillers grain.

Some of the thin stillage is routed back to the cooking tanks as makeup or “backset”

water, reducing the amount of fresh water required by the cooking process. The rest is sent

through a multiple-effect evaporation system where it is concentrated into a condensed distillers

solubles or “syrup” containing 25–50% solids. This syrup, which is high in protein and fat

content, is then mixed back in with the distillers grain to make wet distillers grains with solubles.

Wet distillers grains with solubles (WDGS) contain most of the nutritive value of the

original feedstock (plus added yeast) and can be easily conveyed as a wet cake for transport. As

such, WDGS makes an excellent cattle ration for local feedlots and dairies. However, WDGS

must be used soon after it’s produced because the wet grains spoil easily. Since many ethanol

plants are located in areas where there are not enough nearby cattle to utilize all the feed, a

portion or all of the WDGS is sent through a drying system to remove moisture and extend the
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shelf life. The resulting dried distillers grains with solubles (DDGS) are commonly used as a

high-protein ingredient in cattle, swine, poultry, and fish diets. Distillers grains are also being

researched for human consumption. A schematic of a typical dry-mill ethanol plant is shown

below in Figure 1.4-2.

Figure 1.4-2. Dry Milling Process

1.4.1.2 Wet Milling Technology195

In wet mill plants, first the corn is soaked or "steeped" in a dilute sulfurous acid solution

for 24-48 hours. The steeping process facilitates the separation of the corn kernel into germ,

fiber, gluten, and starch.

After steeping, the corn slurry is processed through a series of grinders to separate out the

germ. The germ is either extracted on-site or sold to crushers who extract the corn oil. The corn

oil in its crude state can be sold to the biodiesel or renewable diesel industry. However, most

wet mill plants refine the product into food-grade corn oil for use in cooking applications. The

remaining fiber, gluten and starch components are further segregated using centrifugal, screen,

and hydroclonic separators.
95

The steeping liquor is concentrated in an evaporator. This concentrated product, heavy

steep water, is co-dried with the fiber component and is then sold as corn gluten feed to the

livestock industry. Heavy steep water is also sold by itself as a feed ingredient and is used as a

component in Ice Ban, an environmentally-friendly alternative to salt for removing ice from

roads.

The gluten component (protein) is filtered and dried to produce the corn gluten meal coproduct. This product is highly sought after as a feed ingredient in poultry broiler operations.

The starch and any remaining water from the mash is generally processed in one of three

ways: fermented into ethanol, dried and sold as dried or modified corn starch, or processed into

corn syrup. If made into ethanol, the fermentation process is very similar to the dry mill ethanol

production process described above. A schematic of the wet milling process is shown below in

Figure 1.4-3.

Figure 1.4-3. Wet Milling Process

1.4.1.3 Advanced Technologies

A number of corn ethanol plants are exploring new technologies with the potential to

increase their profits by producing higher value co-products and reducing the ethanol plants

energy requirements. Dry fractionation and corn oil extraction seek to recover the oil in the corn

kernel for sale in the food, feed, or biodiesel markets. Cold starch fermentation and membranes

that reduce ethanol distillation energy requirements are two of several new technologies focusing

on reducing the energy usage of ethanol production facilities. Finally a growing number of

companies are utilizing alternative boiler fuels and/or incorporating combined heat and power

(CHP) technology into their facilities to reduce to plant energy requirements, and in some cases,
96

produce excess power for the grid. The advanced technologies currently being pursed by the

corn ethanol industry are described in more detail below.

Dry Fractionation

Dry fractionation is a mechanical separation of the corn kernel into its three component

pieces, the germ, bran, and endosperm before fermentation. This separation decreases the

amount of non-fermentable material sent through the process and allows each of the components

to be processed separately to produce new, higher-value co-products. As shown in Figure 1.4-1,

the germ is the small, non-fermentable part of the kernel consisting primarily of protein and oil.

Food grade corn oil can be extracted from the germ. After the oil has been extracted, the

remainder of the germ can then be blended into the DGS to increase its protein content. The

bran, or pericarp, is the protective outer covering of the kernel. The bran can be sold as cattle

feed, human fiber additive, or corn fiber. It can also be burned to reduce the amount of coal or

natural gas required for ethanol production. The endosperm, which contains approximately 98%

of the starch, and is the only fermentable portion of the kernel, is sent to the fermentation vessels.

Decreasing the amount of non-fermentable materials (germ and bran) in the process has many

beneficial effects, including increasing the production capacity of the plant, decreasing the

energy required to dry the DGS, and potentially decreasing the enzyme requirement of the plant

by up to 30%.

While the production capacity of the plant increases with the addition of dry

fractionation, the amount of corn used to produce a gallon of ethanol increases by approximately

2-3% due to starch loss in the fractionation process. Dry fractionation is also a capital intensive

process, costing an estimated $35 million to add to an existing 100 million gallon per year

ethanol plant. Dry fractionation is currently able to recover 50% or more of the corn oil

contained in the corn kernel. For our economic analyses we have assumed an oil recovery rate of

50% for ethanol plants that use dry fractionation. Several companies, including ICM, Delta-T,

and POET currently offer dry fractionation options for new or existing plants.

Corn Oil Extraction196

An alternative method to recovering the oil contained in the corn kernel is corn oil

extraction. Corn oil extraction is a method of mechanical separation, often by centrifuge, used to

extract the crude corn oil from the thin stillage (the non-ethanol liquid left after fermentation),

the DGS before it has been dried, or a combination of both. While the corn oil is of a lower

quality and value than that produced from corn fractionation, the equipment can be easily added

to existing ethanol production facilities and is relatively inexpensive. We estimate that adding

corn oil extraction equipment to an existing 100 million gallon per year corn ethanol plant would

cost between $5 million and $12 million, depending on the type of equipment used and the

percentage of oil recovered. The starch losses associated with dry fractionation do not occur

with corn oil extraction as the whole kernel still goes through the fermentation process. The

gains in plant capacity and reduced enzyme usage of the dry fractionation process are similarly

not realized.

The oil recovered using the corn oil extraction process is distressed oil and cannot be sold

as a food grade product. Markets for this product do exist, however, as an additive to cattle feed
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or as a biodiesel feedstock. In addition to generating an additional revenue stream, extracting the

corn oil has several other benefits for the ethanol producer. Because the oil is an insulator,

removing it improves the heating efficiency of the DGS dryers and reduces the energy demand

of the ethanol plant. Reducing the oil content of the DGS also improves its flowability and

concentrates its protein content. The de-fatted DGS is potentially more marketable than DGS

containing corn oil, as higher quantities may be able to be included in the diets of poultry and

swine. Several ethanol producers are currently using corn oil extraction technology and have

reported oil recovery rates of greater than 33%. Technology providers have indicated that in the

near future they expect to be able to extract up to 75% of the oil contained in the kernel. For our

economic analyses we have assumed that by 2022 ethanol production plants using oil extraction

technology will be able to extract 66% of the oil in the corn.

Cold Starch Fermentation197198

POET Biorefining, the United States’ largest corn ethanol producer23, has developed a

cold starch fermentation process that uses raw-starch hydrolysis to convert starch to sugar, which

then ferments to ethanol without heat. The patent-pending POET technology eliminates the

cooking process that has been part of ethanol production for years. According to POET, the

BPXTM process not only reduces energy costs, but also releases additional starch for conversion

to ethanol, increases protein content and quality of co-products, increases co-product flowability,

potentially increases plant throughput, and significantly decreases plant emissions. The benefits

of the process include reduced energy costs, increased ethanol yields, increased nutrient quality

in the distillers grains and decreased plant emissions. At least 20 POET plants currently utilize

the BPXTM cold starch fermentation technology. According to POET, the BPXTM process, which

yields 20% ethanol in fermentation, increases theoretical ethanol yields from the industry

standard of 2.7 gallons of ethanol per bushel of corn up to 3 gallons per bushel. POET also

recently announced that it was funding a research collaboration with Iowa State University to

help improve the efficiency of the BPXTM process.

As with any new process there are several potential drawbacks to cold starch

fermentation. Because heat is not used to aid in the hydrolysis of starch, more enzymes may be

required. These additional enzymes may cost the ethanol producer more than $500,000 per year

for a 100 million gallon per year plant. An additional benefit of the cooking process is that it

sterilizes the starch slurry before fermentation, killing microorganisms and neutralizing toxins

that are often contained in the corn. Without this step, the microorganisms may compete with

the yeast, lowering ethanol yields. Toxins may pass through the process to the DGS and cause

problems with the animals that eat it. One way to minimize these problems is to treat the starch

slurry with antibiotics, however recently this practice has been criticized for contributing to

antibiotic tolerant or resistant bacteria. Any ethanol producer considering using cold starch

fermentation must first determine whether the potential gains in ethanol yields and energy

savings outweigh these risks.

23 At the time of our November 2009 plant assessment. For more information, refer to Section 1.5.1.1.
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Membrane Replacement

Several companies are currently working to produce commercially viable polymeric

membranes that could potentially reduce the energy used in distillation and eliminate the need

for molecular sieve units currently used in most ethanol plants. One such company, Vaperma,

has partnered with GreenField Ethanol to prove the viability of its SiftekTM technology. SiftekTM

membranes have been successfully installed in GreenField’s Tiverton, Ontario demonstration

plant and are scheduled to be installed in their Chatham, Ontario plant, which produces 187

million liters of ethanol per year, by the end of 2008. Vaperma claims its SiftekTM membranes

are capable of producing a fuel grade ethanol product from an ethanol/water mixture that

contains as much as 60% water. These membranes would replace the rectifier unit as well as the

molecular sieves used in a conventional ethanol plant, potentially reducing the energy

consumption of the ethanol dehydration process by up to 50%. Another way for these

membranes to be used is to treat the ethanol/water vapor collected when the molecular sieve

units are regenerated. This stream is usually recycled to the rectifier and makes up

approximately one third of the feed to the rectifying column. Using SiftekTM technology to treat

this stream reduces the feed to the rectifier, reducing energy consumption and increasing

production rate by 20% or more. While membrane replacement technology has the potential to

significantly reduce the energy demands of an ethanol plant, they are likely at least a couple of

years from being commercially available. It is not expected that membrane replacement units

would be retrofitted into existing plants due to the significant capital costs. These two factors

will effectively limit the use of membrane separation units to new ethanol plants built in 2010 or

later.

An alternative method of membrane replacement is to use ethanol-permeating

membranes to eliminate the need for the beer column, followed by a water-selective membrane

for final dehydration. Eliminating the need for the beer column as well as the rectifier and

molecular sieve units would significantly reduce the capital costs of an ethanol plant, as well as

lowering the energy requirements of ethanol separation. While this technology has the potential

to significantly lower the cost and energy demands of an ethanol plant, it is highly unlikely that it

will be available for near term commercialization. It has therefore not been considered section

1.5.1.3 on the forecasted growth of advanced ethanol technologies.

Combined Heat and Power199

Ethanol production is a relatively resource-intensive process that requires the use of

water, electricity, and steam. In most cases, water and electricity are purchased from the

municipality and steam is produced on-site using boilers fired by natural gas, coal, or in some

cases, alternative fuels (described in more detail below).24 However a growing number of

ethanol producers are pursuing combined heat and power (CHP) technology. CHP, also known

as cogeneration, is a mechanism for improving overall plant efficiency by using a single fuel to

generate both power and thermal energy. The most common configuration in ethanol plants

involves using the boiler to power a turbine generator unit that produces electricity, and using

waste heat to make process steam. In some cases, the generator produces excess electricity that

can be sold to the grid. While the thermal energy demand for an ethanol plant using CHP

24 Some plants pull steam directly from a nearby utility.
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technology is slightly higher than that of a conventional plant, the additional energy used is far

less than what would be required to produce the same amount of electricity in a central power

plant. The increased efficiency is due to the ability of the ethanol plant to effectively utilize the

waste heat from the electricity generation process.

The CHP system can be owned and operated solely by the ethanol plant, or jointly

operated with the local utility company. In these cases it is common for the utility company to

purchase the generator and to split the cost of the generator fuel with the ethanol plant. The

utility company receives the electricity produced, while the ethanol plant uses the waste heat.

These arrangements reduce the energy costs for both parties, as well as reducing the green house

gas emissions that would be produced by operating the generator and boiler separately. An

illustration of the more common CHP configuration typically seen in ethanol plants is shown

below in Figure 1.4-4. Grants are available for industries looking to use CHP at both the state

and national level. These grant programs will likely encourage a greater adoption of CHP among

ethanol producers than would have otherwise been expected. We project that 26% of ethanol

plants will use CHP in the future under the RFS2 program. For more information, refer to

Section 1.5.1.3.

Figure 1.4-4. Steam Boiler with Steam Turbine

Alternative Boiler Fuels

In addition to CHP (or sometimes in combination), a growing number of ethanol

producers are turning to alternative fuel sources to replace traditional boiler fuels (i.e., natural

gas and coal), improve their carbon footprint, and/or become more self-sustainable. Alternative

boiler fuels currently used or being pursued by the ethanol industry include biomass (wood and

other organic feedstocks), co-products from the ethanol production process (bran, thin stillage or

syrup), manure biogas (methane from nearby animal feedlots), and landfill gas (generated from

the digestion of municipal solid waste). One potential alternative boiler fuel is biogas produced
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by the anaerobic digestion of the stillage in the ethanol production process. Sending the stillage

to an anaerobic digester rather than drying it and selling it as DGS would produce sufficient

biogas to exceed the energy requirements of the ethanol production facility. Excess methane

could be sold to provide an additional revenue stream, however all revenue from DGS sales

would be lost. Whether or not these systems are adopted in the future is likely to be dependent

on the relative prices of electricity, natural gas, and DGS, as well as the capital costs of these

systems.

For a breakdown of current and near-term25 utilization of CHP technology and alternative

boiler fuels, refer to Sections 1.5.1.1 and 1.5.1.2. For our 2022 projections of the potential

utilization of these and other advanced technologies, refer to Section 1.5.1.3.

1.4.2 Ethanol from Sugarcane Juice and Molasses

The production of ethanol from sugarcane juice or molasses is the least complicated

method to use biomass to produce ethanol since sugarcane contains six-carbon sugars that can be

directly fermented. This is currently the method used to produce ethanol in Brazil. In contrast,

starch or cellulosic feedstocks require additional steps before sugars are released for use in the

fermentation step.

In the production of sugarcane ethanol and sugar from sugarcane juice or molasses, the

cane stalks are shredded and the juice is extracted across tandem mills or a diffuser. The juice

contains most of the soluble sugars and the leftover sugarcane fiber is bagasse. Next, the cane

juice is filtered then heated and limed to precipitate impurities during the clarification process.

The resultant clarified juice is then concentrated across an evaporation station (14-16°Brix up to

65°Brix). The syrup produced is then further evaporated in vacuum pans and seed crystallized,

leading to a mixture of sucrose crystals surrounded by molasses with a concentration of 91-

93°Brix. The sugar crystals and molasses are subsequently separated by centrifugation. In

ethanol production in Brazil, the sugars in the juice are fermented into ethanol by the addition of

yeast. Fermentation varies from 4-12 hours, with ethanol yields ranging from 80-90%. The

fermented mixture is then distilled to produce hydrous (96 % ethanol) or anhydrous ethanol (99.7

% ethanol). The production of anhydrous ethanol is done by addition of cyclohexane or by the

use molecular sieves. See Figure 1.4.5 for a diagram of the sugarcane ethanol and sugar

production process.200 The production of sugar (for food and export) or ethanol depends on the

supply and demand changes for both products.

25 Based on current company plants.
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Figure 1.4-5. Simplified Overview of Sugarcane Ethanol and Sugar Production Process

In addition to ethanol, sugarcane also yields trash, bagasse, filter cake mud, and vinasse.

These by-products are described below:

Trash (Leaves and Tops)

The tops, brown and green leaves of sugarcane are commonly referred to as trash.

Sugarcane trash in Brazil is not currently harvested (it is typically burned in the field); however,

it is anticipated to be collected for use in the future (i.e. 2020 and beyond) since the burning of

sugarcane in Brazil is being phased-out and there is an increased mechanization of harvesting

sugarcane. In the U.S., sugarcane trash is currently mechanically harvested and delivered to the

factory with stalks. The collection and use of trash at the sugarcane ethanol facility is beneficial

as greater electricity can be produced and potentially sold to the grid.

Bagasse

Bagasse is the fibrous material left over after juice is extracted from the crushed stalk of

the sugarcane plant. It mainly consists of hemicellulose, cellulose and lignin valued mainly for

its use to produce steam for electricity. U.S. factories and other industrial units have used

bagasse mainly for steam production, but a few are producing electricity (co-generation) as well.

In Brazil, most facilities are able to produce more energy than needed and have exported excess

electricity to the grid. This is further discussed in Chapter 2 in the lifecycle section, as surplus

electricity production displaces primarily fossil-based electricity production.

Filter Cake Mud

Filter cake is the dried, leftover solid material from precipitated mud after sugarcane juice

clarification (via lime addition) at the facility. It is sometimes reapplied to sugarcane fields as a

fertilizer.
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Vinasse

Vinasse is the liquid waste product from the ethanol distillation process. It is rich in

minerals, organic material, and water. Some countries are allowed to spray vinasse on sugarcane

crops as fertilizer. For instance, it is produced and used throughout the harvest in Brazil but is

not allowed in the U.S. Environmental legislation prohibits inappropriate disposal of vinasse

into rivers, lakes, the ocean, and soils.

1.4.3 Cellulosic Biofuel

The following sections contain descriptions of cellulosic ethanol and cellulosic diesel

production technologies. Section 1.4.3.1 introduces the two primary pathways for the production

of cellulosic ethanol, through biochemical and thermochemical processes while Section 1.4.3.2

discusses cellulosic diesel which is produced through thermochemical processes. We end the

section with specific company descriptions of cellulosic biofuel technologies and briefly describe

how they differ from generic process discussions.

1.4.3.1 Cellulosic Ethanol

Cellulosic biomass has long been recognized as a potential source of mixed sugars for

fermentation to fuel ethanol. The Germans may have been one of the earliest to try

commercializing a process to produce ethanol from a cellulosic feedstock, probably from wood

in the late 1890s. They used dilute acid to hydrolyze the cellulose to glucose and xylose, but

were able to only produce a little less than 20 gallons per ton of feedstock; they soon improved

the process enough to generate yields of around 50 gallons per ton. Eventually, two commercialsized plants that used dilute sulfuric acid hydrolysis were constructed in the U.S. Lumber

production decreased following World War I, which resulted in the closing down of cellulosic

plants.201, 202 Although corn-grain ethanol was used in the early 20th Century, especially by highperformance race cars and as an additive to raise gasoline octane, petroleum-derived gasoline

eventually replaced it as the primary fuel for automobiles and light-duty trucks. From the early

1970’s and up through the present, ethanol from corn, has been increasingly used as a fuel;

however, recently, ethanol from cellulose is being viewed with increasing interest.

Several processing options are currently available to convert cellulosic biomass into

ethanol. These conversion technologies generally fall into two main categories: biochemical and

thermochemical. Biochemical conversion refers to the fermentation of sugars liberated from the

breakdown of biomass feedstock. Thermochemical conversion includes the gasification and

pyrolysis of biomass material into a synthesis gas or liquid oil for subsequent fermentation or

catalysis. The main benefit of gasification/pyrolysis over the biochemical route is that

thermochemical processes can more easily convert low-carbohydrate or “non-fermentable”

biomass materials such as forest and wood residues to alcohol fuels and can more readily accept

a wider variety of feedstocks.203 However, the thermochemical process does have some

drawbacks, such as tar production and clean-up gas procedures that require additional capital

investment.
103

Since commercial production of cellulosic ethanol has not yet begun, it is unclear which

process options will prove most viable or whether additional variations will emerge. At least in

the near future, there have been plans to build both stand-alone biochemical and thermochemical

ethanol processing plants. In addition, some investors are currently supporting research and

development in both cellulosic processing procedures, neither choosing one conversion over the

other.204 The following subsections describe the process steps, current challenges, and targeted

areas for improvement for each conversion method.

1.4.3.1.1 Biochemical Conversion

Unlike grain feedstocks where the major carbohydrate is starch, lignocellulosic biomass

is composed mainly of cellulose (40-60 %) and hemicellulose (20-40 %). The remainder

consists of lignin, a complex polymer which serves as a stiffening and hydrophobic (waterrepelling) agent in cell walls.205 Cellulose and hemicellulose are made up of sugars linked

together in long chains called polysaccharides. Once hydrolyzed, they can be fermented into

ethanol. Currently, lignin cannot be fermented into ethanol, but could be burned as a by-product

to generate electricity.

Both starch (corn grain) and cellulosic feedstocks must be hydrolyzed prior to

fermentation. Structural differences at the molecular level make it far more difficult, and

therefore more costly, to hydrolyze cellulosic biomass than it is to hydrolyze starch. Glucose,

C6H12O6, the repeating monomer in both starch and cellulose, is a six-sided ring, similar in

conformation to the classic ‘chair’ conformation of cyclohexane or benzene, except one carbon

atom in the ring is replaced by an oxygen atom. For uniformity (and ease) of discussion, it is

generally assumed that the first carbon atom next to the oxygen, is carbon #1; the numbering, 2-

5, continues around the ring with oxygen in the 6th position; one of the four bonds of the fifth

carbon atom is attached to the oxygen atom to complete the ring, one is attached to hydrogen

atom and the fourth to a -CH2OH group. Thus, a glucose molecule/monomer is a six-sided

molecule, but not a six-carbon ring (although there are six-carbon molecules present, one of

which is in the –methylhydroxy group).

The main difference between starch and cellulosic plant matter is that starch

polysaccharides are made up of -glucose monomers, uniformly strung together by -linked 1,4-

glucosidic bonds whereas cellulosic polysaccharides are made up of β-glucose monomers, strung 

together through β-linked 1,4-glucosidic bonds. In starch with the -conformation, the hydroxyl

group on carbon #1 is in the axial or -position, which causes the -OH’s on each successive

glucose monomer to end up on the same side of the polymer. There are also 1,6-linked glucose

branches that occur irregularly on approximately one in twenty-five glucose units.206 The -OH

groups on the same side of the polymer, along with the randomly attached 1,6-glucose branches,

leaves starch polymers relatively weak, flexible, and able to easily wrap and twist together to

form tiny granules ( e.g., common, everyday corn starch),

 Cellulosic polysaccharides are in the β-conformation with the hydroxyl group on carbon

#1 is positioned away from the ring, in the equatorial or β-position, which causes the -OH’s on

each successive glucose monomer, added to the chain, to end up on opposite sides of the

polymer. The hydroxyl groups lined up evenly and uniformly along opposite sides of each
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polymer strand allow intra-molecular hydrogen bonds to develop within each monomer. They

also allow inter-molecular hydrogen bonds to develop between adjacent polymers to form tight,

rigid, strong, mostly straight polymer bundles called microfibrils that act as the core constituent

in the formation of plant cell walls that are also insoluble in water and resistant to chemical

attack. The β-conformation and the resulting hydrogen bonds stabilize the glucose chair 

structure to help minimize the polymer’s flexibility (which hinders hydrolysis) and to add to its

strength.

The second cellulosic component is called hemicellulose. It consists mainly of a random

mixture of highly branched and heavily substituted five- and six-carbon rings. The five-carbon

residues are usually D-xylose and L-arabinose; the six-carbon residues are usually D-galactose,

D-glucose, and D-mannose, and uronic and acetic acid. Hemicellulose is not as rigid or strong as

cellulose, but does contribute additional strength and helps protect the plant cell wall against

attack by microbes or water. Hemicellulose is relatively easy to hydrolyze, due to its highly

branched, somewhat random or non-uniform structure.

Lignin, the third principle component, is a complex, cross-linked polymeric, high

molecular weight substance derived principally from coniferyl alcohol by extensive condensation

polymerization. Covalently bonded to the hemicellulose, it is essentially a glue-like polymer that

covers the cellulose and hemicellulose polymer cell walls and helps hold them together, provides

additional strength, helps resist microbial decay, and perhaps most importantly, for this

discussion, inhibits hydrolysis. Its molecular weight is around 10,000.207 While both cellulose

and hemicellulose contribute to the amount of fermentable sugars for ethanol production, lignin

does not, but can be combusted to provide process energy in a biochemical plant or used as

feedstock to a thermochemical process.208

To review, a significant part of the reason it is more difficult and more costly to produce

ethanol from cellulosic feedstocks, has to do with the differences in the molecular structures of

simple starch and those of cellulosic plant matter. That is, as a plant grows, glucose monomers

are added to the polysaccharide chains of the plant cell walls through condensation reactions. In

general, condensation is a chemical process by which two molecules are joined together to make

a larger, more complex molecule, and a molecule of water is a byproduct of the reaction. In the

formation of polysaccharides, and enzyme catalyzes the reaction wherein the -OH group on

carbon #1 of one monomer, or glucose residue, reacts with the -OH on carbon #4 or #6 of

another residue. An H-OH (H2O or water) molecule is removed leaving an -O- that links the

monomers together to form the polysaccharide chain. Again, depending on the direction of the –

OH group at carbon 1, it may be called an alpha (as in starch) or a beta (as in cellulose)

linkage.209

Hydrolysis is the reverse reaction. The -H from an H-OH (water) molecule is added to

one monomer and the remaining -OH is added to its pair, e.g., to the next monomer on the chain,

to regenerate separate glucose monomers. During starch hydrolysis, water and water borne

hydrolyzing enzymes can easily penetrate the randomly formed polymers (the tiny granular

particles or bundles) in order to break the bonds to release glucose monomers. However, the

cellulosic or glucan polymers formed in tightly packed, dense, rigid microfibrils are especially

resistant to water and hydrolyzing enzymes. Xylan, the main constituent of hemicellulose, is
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more easily hydrolyzed than cellulose, but not easily fermented. Cellulose is not easily

hydrolyzed, but readily ferments into alcohol. These are two of the major problems that must be

satisfactorily resolved for biochemical conversion of cellulosic feedstocks.

Biochemical conversion processes typically use dilute acid with enzymes or concentrated

acid to convert cellulosic biomass to sugar for fermentation to ethanol. Concentrated acid

hydrolysis is fairly well developed and is being pursued to commercialization in certain niche

situations. For example, concentrated acid hydrolysis is suitable for feedstocks such as

municipal solid wastes which have largely heterogeneous mixtures.210 Concentrated acid

hydrolysis is typically much faster than enzymatic approaches, albeit at the cost of reduced sugar

yields due to undesirable side reactions.211 Enzymatic hydrolysis is mostly suitable for

homogeneous mixtures because specific enzymes are needed to convert a given type of

feedstock. The cost to enzymatically hydrolyze cellulose is expected to decline significantly as

these technologies continue to improve.212

In general, steps of the biochemical process include: feedstock pretreatment, hydrolysis,

saccharification and fermentation, ethanol dehydration, and lignin recovery. Refer to Figure 1.4-

6 for an illustration of the enzymatic biochemical production process. We used NREL’s study as

a guide to describe, somewhat generically, how such a process might work. Refer to the NREL

technical documents for greater detail.213,214

Figure 1.4-6.

Cellulosic Ethanol Biochemical Production Process (Enzymatic)
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Stage 1 – Feedstock Pretreatment

Lignocellulosic biomass must undergo at least some pretreatment prior to hydrolysis.

During the early years of cellulosic ethanol production (e.g., 2010 to 2015), we anticipate that

this stage will likely occur within the facility. In the out years covered by this rule (2022) we

believe that this stage may be moved outside the plant gate (e.g., upstream of the ethanol plant)

to reduce transportation costs that are typically high due to the low density of this type of

biomass. The biomass is pretreated with either a physical or chemical pretreatment method to

help the polysacharides become more accessible to hydrolysis. Studies have shown a direct

correlation between the removal of lignin and hemicellulose and the digestibility of cellulose.215

Physical pretreatment nearly always includes size reduction by some type of grinding,

shredding, or chopping. For example, in order to biochemically process wood chips, e.g., poplar

trees or willows, the chips must be reduced in size to 1-mm or less in order to increase the

surface area for contact with acid, enzymes, etc. Breaking up a 5-in tree stem into 1-mm pieces

would consume a large amount of energy. On the other hand, corn stover chips for a

biochemical process can range up to a maximum size of 1.5 inches.216

Chemicals are also used for pretreatment. The most common chemical pretreatment

methods for cellulosic feedstocks are dilute acid, hot water, alkaline, organic solvent, ammonia,

sulfur dioxide, carbon dioxide, or other chemicals to make the biomass more digestible by the

enzymes.217,218 These chemicals cause the biomass to react quite differently.219 For example,

instead of hydrolyzing the hemicellulose (as in acidic pretreatments), an alkaline approach tends

to leave the hemicellulose and cellulose intact. Enzymes are therefore required to digest both

hemicellulose and cellulose at the same time when a basic pretreatment is used.

Different pretreatment approaches also affect the amounts of degradation products (e.g.

furfurals, acetates) that occur from the decomposition of hemicellulose and lignin. This is

important since these degradation products can inhibit microorganisms in the fermentation step.

A well known pretreatment method that does not degrade biomass sugars or produce

fermentation inhibitors is ammonia fiber expansion (AFEX). During AFEX, liquid ammonia is

added to the cellulosic material followed by a rapid pressure release.

Each type of feedstock, whether softwoods, corn stover or bagasse, requires a particular

combination of pretreatment methods to optimize the yields of that feedstock, minimize the

degradation of the substrate, and maximize the sugar yield. Pretreatment of cellulosic biomass in

a cost-effective manner is a major challenge of cellulose-ethanol technology research and

development.220 For more information on feedstock considerations and their impacts on

biorefining refer to the NREL report completed for the final rule.221

Stage 2 – Pretreatment and Hydrolyzate Conditioning

NREL refers to this stage as a combination of pretreatment and hydrolysis. In their

process flow diagram, the washed and sized-reduced feed is directly heated with steam and

mixed with dilute sulfuric acid. The process converts, primarily, the hemicellulose

polysaccharides xylan, mannan, arabinan and galactan, to produce the mixed sugars and further
107

helps prepare the cellulose for hydrolysis. A small amount of glucan in the hemicellulose and in

the cellulose is converted into glucose. The rundown from the acid hydrolysis reactor is fed to a

blowdown tank that subsequently feeds a filter press. The filter press produces two main

streams, a filter cake and a liquid filtrate, also called hydrolyzate. The filter cake carries the

unhydrolyzed portions of the feed (e.g., glucans) among other insolubles, while the liquid carries

that part of the feed that was hydrolyzed, mainly the xyloses.

The liquid portion is neutralized to remove gypsum and other contaminants that would be

toxic to downstream enzymes. The cake is washed, mixed back with the detoxified liquid

hydrolyzate, and fed to the saccharification reactors to hydrolyze the glucan polysaccharides.

Stage 3 – Saccharification and Co-Fermentation

We should point out that this is not ‘Simultaneous Saccharification and Fermentation’

(SSF). Saccharification, in the process we’re discussing, takes place primarily in several reactors

along with other intermediate treatments such as filtering and detoxifying. Using a cellulase

enzyme cocktail, saccharification of the cellulose to glucose occurs first at an elevated

temperature to take advantage of increased enzyme activity, which reduces the quantity of

required enzyme as well as the reaction time.

The cellulase enzymes used to convert cellulose to sugars can be obtained in two ways.

The first option is for a plant to produce it on-site. The second option requires the plant to

purchase the enzymes from off-site enzyme manufacturers. Due to a joint research effort by

DOE, Genencor International, and Novozymes Biotech, the cost for production of cellulase

enzymes has been drastically reduced. Such research and development in areas of enzyme

production have reduced the cost of cellulolytic enzymes by a factor of 10 to 30, down to 20 to

30 cents per gallon of ethanol produced.222,223,224 It is estimated, however, that enzyme costs

will have to be further reduced to a level comparable to those used to produce ethanol from corn

grain at a cost of 3 to 4 cents per gallon of ethanol. The current challenge is to develop the

correct enzyme “cocktails” to reflect differences in the physical and chemical characteristics of

all the various types of cellulosic materials. It may be easier, therefore, to process single

feedstocks (more homogeneous) rather than multiple feedstocks, in which variations are more

likely.

Following cellulose saccharification, both the glucose and xylose sugars are cofermented. Although xylan, the hemicellulose polysaccharide, is more easily hydrolyzed than

glucan (cellulose polysaccharides), the xylose sugar is more difficult to ferment than is the

glucose sugar. Different microbes as well as different residence times and process conditions

may be required for each.

Because xylan can make up as much as 25% of plant matter it is imperative that as much

of it as possible be fermented; the economic viability of biochemically produced ethanol depends

heavily on it. This continues to be high on the list of challenges researchers are working on, but

good progress has been made toward fermenting a higher percentage of xylose during the past

few years.225
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Stage 3A – Consolidated Bioprocessing e.g., Simultaneous Saccharification and Fermentation

(SSF)

During the past few years, researchers have been looking for ways to combine

saccharification and fermentation into a single step through the use of enzyme/microbe cocktails.

If successful, we expect there could be significant capital cost savings in that fewer reactors and

other support equipment and piping would be necessary. Also, it may be possible to reduce

processing times if hydrolysis reactions can take place simultaneously, rather than sequentially.

Such strategies are known as consolidated bioprocessing (CBP). CBP, however, is currently

hampered by the relative inability of yeast to process recombinant cellulases (enzymes that help

convert cellulose to sugars), and the relative lag in the development of molecular biological

methods to manipulate organisms that secrete cellulases naturally.226

Stage 4 – Ethanol Dehydration

NREL’s process model indicates that the fermentation reactor rundown stream, now

called ‘beer,’ runs down the beer column feed surge tank. The beer column feed consists of

about 83% water and only 5.5% ethanol; the balance of the mixture is very complex, but consists

mostly of lignin. The beer column removes the dissolved CO2 overhead and produces a

water/ethanol bottom stream that is fed to a rectification column. According to NREL’s model,

the rectification column bottoms would be mostly water with about 0.05% ethanol that’s

recycled back to the process. The rectification column overhead that consists of about 92.5%

ethanol and 7.5% water, is fed to a molecular sieve that produces a 99.5 wt.% ethanol product

stream with about 0.5 wt.% water. Gasoline, a denaturant, is added to produce ethanol fuel.

Stage 5 – Lignin Recovery

Following the saccharification and fermentation of the xylan and glucan to ethanol, the

lignin is gradually concentrated with other solids into a moist cake-like product that is about 48%

insoluble solids. About 80% of the 48% insoluble solids is essentially lignin microbial cells, and

other unconverted biomass remnants, (e.g., cellulose, xylose, glucan, xylan, other oligomers,

etc.,) from the process. This material can be either combusted to provide process heat for the

biochemical operation for a co-located starch ethanol plant, or as we discuss in the following

section, could be used as feedstock for a thermochemical unit.

1.4.3.1.2 Thermochemical Conversion

Thermochemical conversion involves biomass being broken down into intermediates

using heat and upgraded to fuels using a combination of heat and pressure in the presence of

catalysts.227 Thermochemical processes include pyrolysis (absence of oxygen), gasification

(partial oxidation in the presence of a gasifying agent, usually air, oxygen, and/or steam), and

combustion (complete oxidation). The former two conversion processes, pyrolysis and

gasification, can be used to convert biomass into energy carriers for transportation use. It is

important to note that these processing steps are also applicable to other feedstocks (e.g., coal or

natural gas); the only difference is that a renewable feedstock is used (i.e. biomass) to produce

cellulosic biofuel. A thermochemical unit can also complement a biochemical processing plant
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to enhance the economics of an integrated biorefinery by converting lignin-rich, non-fermentable

material left over from high-starch or cellulosic feedstocks conversion.228 We discuss the

gasification and pyrolysis processes below.

Gasification

Compared to corn ethanol or biochemical cellulosic ethanol plants, the use of biomass

gasification may allow for greater flexibility to utilize different biomass feedstocks at a specific

plant. Mixed biomass feedstocks may be used, based on availability of long-term suppliers,

seasonal availability, harvest cycle, and costs. Agricultural residue, energy crops, wood residues,

and municipal solid waste are all being considered as potential feedstocks. Geographic location,

availability of biomass, the existence of biomass suppliers, and costs would all likely influence

the mix of biomass feedstocks utilized. The general steps of the gasification thermochemical

process include: feedstock handling, gasification, gas cleanup and conditioning, fuel synthesis,

and separation. Refer to Figure 1.4-7 for a schematic of the thermochemical cellulosic ethanol

production process through gasification. For greater detail on the thermochemical mixedalcohols route refer to NREL technical documentation.229

Figure 1.4-7.

Cellulosic Ethanol Thermochemical Gasification Process

Stage 1 – Feedstock Handling

The particle size requirement for a thermochemical process is around 10-mm to 100-mm

in diameter.230 Once the feed is ground to the proper size, flue gases from the char combustor
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and tar reformer catalyst regenerator dry the feed from the as received moisture level of around

30% to 50% moisture to the level required by the gasifier.

Stage 2 – Gasification

There are two general classes of gasifiers. First, partial oxidation (POx) gasifiers

(directly-heated gasifiers) use the exothermic reaction between oxygen and organics to provide

the heat necessary to devolatilize biomass and to convert residual carbon-rich chars. In POx

gasifiers, the heat to drive the process is generated internally within the gasifier. A disadvantage

of POx gasifiers is that oxygen production is expensive and typically requires large plant sizes to

improve economics.

The second general class, called indirect gasification, uses steam gasifiers to accomplish

gasification through heat transfer from a hot solid or through a heat transfer surface. Either the

byproduct char and/or a portion of the product gas can be combusted with air (external to the

gasifier itself) to provide the energy required for gasification. Although steam gasifiers have the

advantage of not requiring oxygen, most operate at low pressure and therefore require product

gas compression for downstream purification and synthesis unit operations. 231,232

There are different subcategories of gasifiers which are either directly or indirectly

heated. One subcategory is termed a bubbling fluidized bed gasifier and it employes a bubbling

fluidized bed of inert material and the reactant (biomass) is also bubbled through the fluidized

bed. A second variant is the circulating fluidized bed gasifier which is similar to the bubbling

fluidized bed reactor except that a high feedstock and air flow rate circulates the fluidized bed

out of and back into the reactor. For the fluidized bed, the bed material may either be inert

alumina or sand which helps the heat transfer. There are also fixed bed reactors which either

feed the reacting gas (oxygen or air) upward or downward through a fixed bed of the reactant

(biomass). Because of the tar formed when using biomass as a feedstock, a second reactor is

sometimes added which solely targets converting the tar to syn-gas. If the biomas feedstock is

ground to a sufficiently small particle size, or liquefied, the biomass is considered to be

“entrained” in the reactor, and the reactor is defined as an entrained flow reactor.

Indirect gasification using an entrained flow gasifier is described for this example. The

gasification process begins as the biomass is fed to the reactor containing a heat transfer media,

such as sand, and is partially reacted with air (or oxygen) which is introduced to the bottom of

the reactor. The air serves as the carrier-gas and as the oxidant for partially oxidizing the

biomass to syn-gas, carbon monoxide and hydrogen. In addition to the syngas produced, char

and coke are also formed. The heat for the endothermic gasification reactions is supplied by

circulating heat transfer media (e.g. sythetic sand) between the gasifier and the char combustor.

The heat generated by the combustion of the char and coke heats the heat transfer media to over

1800°F. The syngas is separated from the sand and ash and sent to gas cleanup.

Stage 3 – Gas Cleanup & Conditioning

Once the biomass is gasified and converted to syngas, the syngas must be cleaned and

conditioned. This raw syngas has a low to medium energy content depending on the gasifying
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agent and consists mainly of CO, H2, CO2, H2O, N2, and hydrocarbons. The minor components,

tars, sulfur, nitrogen oxides, alkali metals, and particulates have the potential to negatively affect

the syngas conversion steps. Therefore, unwanted impurities are removed in a gas cleanup step

and the gas composition is further modified during gas conditioning. Gas conditioning steps

include sulfur polishing to remove trace levels of H2S and water-gas shift to adjust the final

H2/CO ratio for optimized fuel synthesis.

Stage 4 – Fuel Synthesis

After cleanup and conditioning, the “clean” syngas is comprised of essentially CO and

H2. The syngas is then converted into a liquid fuel by either a catalytic process or through the

use of a microorganism. The fuel producer has the choice of producing diesel fuel or alcohols

from syngas by optimizing the type of catalyst used and the H2/CO ratio. Diesel fuel has

historically been the primary focus of such processes, as it produces a high quality distillate

product, however, with the 45 cent tax subsidy currently available for ethanol production, it may

be economically advantageous for fuel producers to convert syngas to ethanol instead of to diesel

fuel. Production of cellulosic diesel is discussed in further detail in the following Section

1.4.3.2.

Conceptual designs and techno-economic models have been developed for ethanol

production via mixed alcohol synthesis using catalytic processes. The proposed mixed alcohol

process produces a mixture of ethanol along with higher normal alcohols (e.g., n-propanol, nbutanol, and n-pentanol). The by-product higher normal alcohols have value as commodity

chemicals and fuel additives. Typically the mixed alcohol products are high in methanol, but

contain a wide distribution of several different alcohols. One concept proposed in literature is to

completely recycle this methanol in order to increase the production of ethanol and higher

alcohols which are generally more valuable. This concept was modeled by NREL for the

thermochemical production of ethanol for the year 2012. Total mixed alcohol yield was 94.1

gallons per dry ton, in which 85% of the total alcohol product was ethanol. This was made

possible through the addition of an almost complete recycle of methanol within the process.233

For the final rule, we worked with NREL to develop the thermochemical mixed-alcohols model

for the 2015 and 2022 timeframe, as discussed in greater detail in the technical document.234 The

analyses were used to inform us of the materials and energy use for these technologies for our

lifecycle analyses discussed in Chapter 2.

In contrast to the catalytic processing of syngas to produce fuels there is also a

fermentation process being pursued that utilizes a special microorganism (Clostridium

ljungdahlii) to convert the syngas to ethanol.235 This combined syngas and fermentation process

has the benefit of having a significantly faster processing time, on the order of minutes, as

compared to the typical biochemical process on the order of days.236

Stage 5 – Alcohol Separation

The liquid rundown from the low-pressure separator is dehydrated in vapor-phase

molecular sieves, producing the dehydrated mixed alcohol feed into a methanol/ethanol overhead
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stream and a mixed, higher molecular weight alcohol bottom stream. The overhead stream is

further separated into a methanol stream and an ethanol stream.

Heat & Power

A carefully integrated conventional steam cycle produces process heat and electricity

(excess electricity is exported). Pre-heaters, steam generators, and super-heaters generate steam

that drives turbines on compressors and electrical generators. The heat balance around a

thermochemical unit or thermochemical/biochemical combined unit must be carefully designed

and tuned in order to avoid unnecessary heat losses.237

1.4.3.2 Cellulosic Diesel

Cellulosic diesel fuel tecnologies convert cellulosic feedstocks to diesel fuel. There

could be a whole set of technologies which fall in this category including thermochemical and

other chemical processes and biochemical processes.

BTL Technology

One important cellulosic diesel fuel technology is a thermochemical process which is also

termed biomass-to-liquids (BTL). Like the thermochemical ethanol process described

previously, the BTL process produces a syngas from biomass. However, instead of reacting the

syngas to alcohol, the syngas is fed to a Fischer-Tropsch (FT) reactor to primarily produce diesel

fuel and some naphtha.238

The BTL method removes contaminants from the gasification stream prior to the

reactions that form the liquid compounds. The resulting liquid fuel is essentially contaminantfree and is very similar to petroleum-based diesel fuel – in fact, its cetane number is higher than

petroleum-based diesel fuel making it somewhat better in quality. Thus it can be easily blended

with or used interchangeably with petroleum-based diesel fuel.

Figure 1.4-8 is a block diagram of a BTL process.
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Figure 1.4-8.

Biomass to Liquids (BTL) Thermochemical Gasification Process

BTL plants, like thermochemical ethanol plants, are capital intensive plants with many

subunits associated with them. The first couple of steps of BTL plants, including biomass

processing and gasification, are similar to the thermochemical cellulosic ethanol plants described

above. However, once the syngas is produced, it is then cooled producing high pressure steam,

and is scrubbed to remove particulate matter. Impurities such as mercury, arsenic and trace

metals are removed by a sulfur impregnated carbon reactor. The syngas is further treated in

either a Selexol or Rectisol unit to remove hydrogen sulfide and concentrated carbon dioxide

(CO2). The syngas is sent to a water gas shift reactor (WGS) to which causes a shift to more

hydrogen and less carbon monoxide, which is necessary to establish an optimal mix of hydrogen

and carbon for the downstream Fischer-Tropsch (FT) reactor.

The cleaned and water-shifted syngas is sent to the FT reactor where the carbon

monoxide and hydrogen are reacted over a FT catalyst. The FT catalyst is either iron-based or

cobalt-based. The cobalt catalyst is more expensive, although it does not require a recycle, while

the less expensive iron catalyst does require a recycle. The FT reactor creates a syncrude, which

is a variety of hydrocarbons that boil over a wide distillation range (a mix of heavy and light

hydrocarbons). The syncrude from the FT reactor is sent to a distillation column where it is

separated into various components based on their vapor pressure, mainly liquid petroleum gas

(LPG), naphtha, distillate and wax fractions. The heavier compounds are hydrocracked to

maximize the production of diesel fuel. The distillate boiling compounds have high cetane and

thus are of high quality for blending into diesel fuel. Conversely, the naphtha material is very

low in octane thus, it would either have to be upgraded, or blended down with high octane

blendstocks (i.e., ethanol), or be upgraded to a higher octane blendstock to have much value for

use in gasoline. The naphtha could also be sold as feedstock for the petrochemical market for

manufacturing chemical products such as ethylene and benzene.
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The unreacted carbon monoxide and hydrogen and any gaseous hydrocarbon material are

burned to produce electricity in a turbine. The waste heat from the gas turbine along with the

steam created to cool the syn-gas, may be sent to steam turbines to produce additional electricity.

Most of the electricity would be used within the BTL plant, however, some could be sold to raise

additional revenues.

Pyrolysis Diesel Fuel and Gasoline

Pyrolysis oils, or bio-oils, are produced by decomposing cellulosic biomass at lower

temperatures than the gasification process, thus producing a liquid oil instead of a synthesis

gas.239 The reaction can occur either with or without the use of catalysts, but it occurs without

any additional oxygen being present. The oil produced varies in oxygen content or viscosity

according to the feedstock used. The oil must have particulates and ash removed in filtration to

create a homogenous product and is further upgraded to hydrocarbon fuels via hydrotreating and

hydrocracking processing, which reduces its total oxygen content. Some believe that pyrolysis

could have a significant economic advantage over other cellulosic ethanol approaches, however,

very little has been done in terms of optimizing the process, and as such, there are still many

possibilities yet to be explored.240 One of the finished fuels produced by the pyrolysis process is

diesel fuel, however, a significant amount of gasoline would likely be produced as well.

1.4.3.3 Developing Technologies

When evaluating the array of biofuel technologies which could produce one or more fuels

that could qualify under RFS2, we found that it is helpful to organize them into fuel technology

categories. Organizing them into categories eases the task of understanding the costs and life

cycle impacts of these technologies because like technologies likely have similar cost and life

cycle impacts. The simplest organization is by the fuel produced. However, we frequently

found that additional subdivisions were also helpful. Table 1.4-1 provides a list of technologies,

the cellulosic fuels produced and a list of many of the companies which we learned are pursuing

the technology (or something very similar to the technology listed in the category).
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Table 1.4-1.

List of Biofuel Categories, the Fuels Produced and the Companies Pursuing the Technologies

Technology Category Fuels Produced Companies

Biochemical from Corn Grain Ethanol ICM, Delta T, Broin

Biochemical Cellulosic Ethanol Ethanol Abengoa, AE Fuels, Cornell Univ., Citrus Energy,

DuPont/Danisco, Florida Crystals, Novenzymes,

Poet, Western Biomass, ICM, Alltech/Ecofin,

IOGEN, Qteros, and Raven Biofuels, BPI, New Age

Energy, Universal, Fiberight, KL Energy.

Thermochemical/Catalytic

conversion of Cellulose

Ethanol Range Fuels, Pearson Technologies, Fulcrum

Bioenergy, Enerkem, and Gulf Coast Energy.

Thermochemical/Biochemical

conversion of Cellulose

Ethanol Coskata and INEOS Bio.

Strong Acid Hydrolysis of

Cellulose/Biochemical

Ethanol Blue Fire, Arkenol, Pencor, Pangen, Auburn Univ.,

Agresti.

Dilute Acid, Steam Explosion of

Cellulose/Biochemical

Ethanol Verenium, BP, Central Minnesota Ethanol Coop.

Consolidated Bioprocessing (one

step hydrolysis and fermentation) of

Cellulose/Biochemical

Ethanol Mascoma

Biochemical conversion of Cellulose

via carboxylic acid

Ethanol, Gasoline,

Jet Fuel, Diesel

Fuel

Terrabon, Swift Fuels, Zeachem

Thermochemical/Fischer Tropsch Diesel Fuel and

Naphtha

Choren, Flambeau River Biofuels, Baard, Clearfuels,

Gulf Coast Energy, Rentech, TRI.

Thermochemical/Fischer Tropsch DME Chemrec, New Page.

Catalytic Depolymerization of

Cellulose

Diesel, Jet Fuel or

Naphtha

Cello Energy

Biochemical conversion of Cellulose Diesel, Jet Fuel or

Naphtha

Bell Bioenergy

Catalytic Reforming of Sugars Gasoline Virent

Biochemical conversion of Sugars Diesel, Jet Fuel or

Gasoline

Amyris, Gevo, LS9.

Biochemial of Sugars – converted

corn ethanol plants

Isobutanol Gevo/ICM.

Pyrolysis of Cellulose Diesel, Jet Fuel, or

Gasoline,

Envergent (UOP/Ensyn), Dynamotive, Petrobras,

Univ. of Mass, KIOR.

Hydrotreating of Plant Oils Renewable Diesel

Fuel

UOP, Neste, Eni,Conoco-Phillips, Dynamic Fuels

(Syntroleum/Tyson).

Fatty Acid Methyl Ester (FAME) Biodiesel Many

Free Fatty Acid to Biodiesel Biodiesel Endicott

Production of Algae Oils via

Photobioreactor or open pond

Algae Oil

(Biodiesel or

Renewable Diesel

Fuel)

Solazyme, Algenol, Aurora Biofuels, Petrosun,

Sapphire Energy, Livefuels, Solix, HR Biopetroleum

(Cellana), XL Renewables, Petroalgae, Synthetic

Genomics, GreenFuel.

Of the technologies listed above, many of them are considered to be “second generation”

biofuels or new biofuel technologies capable of meeting either the advanced biofuel or cellulosic

biofuel RFS standard. The following sections describe specific companies and the new biofuel

technologies which the companies have developed or are developing. This summary is not

meant to be an unabridged list of new biofuel technologies, but rather a description of some of

the more prominent or interesting of the new biofuel technologies that serve to provide a sense of

the technology categories listed above. The process technology summaries are based on
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information provided by the respective companies. EPA has not been able to confirm all of the

information, statements, process conditions, and the process flow steps necessary for any of these

processes and companies.

Sugar to Diesel Fuel - Amyris Biotechnologies

The Amyris technology produces hydrocarbon fuels from sugars through biochemical

reactions. The technology uses custom designed yeast cells and is modular in design and can be

collocated with existing ethanol plants to produce diesel fuel and gasoline.

Amyris’s yeast cells are the key drivers of their conversion process. The process uses the

same feedstocks that are currently used to make corn starch ethanol, which could be sugar cane

or corn grain. Amyris has a 100 gallon per week pilot plant operating in Emeryville, CA, and in

mid-2009 has completed construction of a pilot plant as well as a commercial demonstration

plant in Brazil to showcase their technology. Amyris intends to convert its own sugar cane mills

over with this technology starting in 2011. In 2012, Amyris expects to begin converting sugar

cane mills owned by others with its sugar to hydrocarbon technology.

The diesel fuel capable of being produced from the process is compatible with the

existing petroleum distribution system and provides better storage and cold flow properties than

biodiesel.

Biochemical to Diesel Fuel - Bell Bio-Energy

Bell Bio-Energy has developed a biochemical technology which uses genetically

engineered bacteria to convert cellulosic feedstocks directly to synthetic hydrocarbon fuels and

compost. Depending on the types of bacteria used, this process can produce specific

hydrocarbon types which can either be methane or other light hydrocarbons, gasoline, diesel or

jet fuel type hydrocarbon compounds. For example, if a bacterium is chosen to produce

gasoline, the bacteria may only produce octane, an eight carbon hydrocarbon molecule that boils

within the distillation temperature range of gasoline.

After the inventors of this process completed their development work, they discussed

their technology with the Department of Defense which became interested in this technology for

providing fuels to their land and air based vehicles. The military agreed to partially fund the

establishment of pilot plants at different military bases, however, of the original 7 conceived

pilot plants, only one pilot plant was built at Fort Stewart in Georgia. The Fort Stewart pilot

plant began operating in late 2008. Bell Bioenergy intends on starting up two demonstration

plants – one associated with the University of California in Fresno, the other with the City of

Atlanta. The primary output of these plants will be compost, however, these two plants are also

expected to produce 1 to 2 million gallons of diesel fuel on an annual basis.

The technology works by first grinding the cellulosic feedstock into a smaller size and

then immersing the ground cellulose with bacteria into water. The bacteria begin to digest the

cellulose after only several hours, but require 30 to 60 days to fully digest the cellulose. The

produced fuel is constantly removed from the reaction vessel, and a significant amount of
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organic material is also produced which will be marketed as potting soil. The process is

expected to produce 30 to 40 gallons of renewable product per ton of feedstock and the

simplicity of the process results in low capital costs per volume produced.

Strong Acid Hydrolysis/Biochemical - BlueFire Ethanol

BlueFire Ethanol has a commercial strong acid hydrolysis technology process that

converts cellulosic materials into ethanol. The technology can make ethanol from urban trash,

rice and wheat straws, wood waste and other agricultural residues. Acid hydrolysis is the main

reaction mechanism to convert cellulosic and hemicellulosic material into simple sugars such as

hexose and pentose or "C6 and C5" sugars. Fermentation of these sugars with microbes converts

these sugars into ethanol. This process for converting cellulosic and hemicellulosic material into

ethanol via acid hydrolysis and fermentation has been around for many decades; though it has

not been economically competitive as the cost was not competitive with transport fuel made from

petroleum. BlueFire’s process is claimed to offer several improvements to existing acid

hydrolysis technology, giving higher ethanol yields and lower production costs.

BlueFire uses a proprietary concentrated acid hydrolysis system and several other process

improvements to make ethanol production more economically attractive than older acid

hydrolysis methods. Some of BlueFire’s stated improvements include a more efficient acid

recovery system; higher sugar purities and concentrations; use of more efficient microbes to

ferment C6 and C5 sugars into ethanol; the processes ability to use biomass feedstock’s

containing silica. The BlueFire process consists of the following main components; feedstock

preparation; decrystallization/hydrolysis reaction; filtration of solids and liquids; separation of

the acid and sugars; fermentation of the sugars and product separation. For product separation,

ethanol effluent is separated using distillation and then dehydrated with molecular sieve

technology.

BlueFire has successfully operated a pilot plant for six years near their headquarters in

Southern California. BlueFire is in the process of building its first commercial facility which

will be located in Lancaster California. As of the third quarter of 2009, BlueFire had obtained

the permits to build this facility and was seeking additional funding and bids for the construction

of the plant. The plant is expected to start up in 2011 or 2012 and will produce up to 3.9 million

gallons of cellulosic ethanol per year from municipal solid waste (MSW). BlueFire is planning

to start up another cellulosic ethanol plant which they call their Mecca or El Sobrante plant also

using MSW as feedstock. Although this plant was initially envisioned to be located in California,

it is likely that this plant will be built elsewhere in the U.S. No start up date has been announced

for their Mecca plant.

Chemical Depolymerization - Cello-Energy

The Cello-Energy process is a catalytic depolymerization technology. At moderate

pressure and temperature, the Cello-Energy process catalytically removes the oxygen and

minerals from the hydrocarbons that comprise cellulose. This results in a mixture of short chain

(3, 6 and 9 carbon) hydrocarbon compounds. These short chain hydrocarbon compounds are

polymerized to form compounds that boil in the diesel boiling range, though the process can also
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be adjusted to produce gasoline or jet fuel. The resulting diesel fuel meets the ASTM standards,

is in the range of 50 to 55 cetane and typically contains 3 ppm of sulfur. The resulting diesel fuel

has been tested in Caterpiller engines to demonstrate the viability of the fuel.

The Cello-Energy process is reported to convert 94% of the hydrocarbon material to

diesel fuel, although a very small amount of heavier hydrocarbons is also produced. The Cello

Energy Process could be totally self-sufficient by routing 12% of the product to run generators to

produce the electricity that the process needs. The only energy input is electricity - no natural

gas or water is used in the process. The Cello process is on the order of 82 % efficient at

converting the feedstock energy content into the energy content of the product, which is very

high compared to most of today's biochemical and thermochemical processes which are on the

order of 50 % efficient, or less.

Because of the simplicity of the process, the capital costs are very low. A 50 million

gallon per year plant is claimed to only incur a total cost of $45 million. This is typical of the

capital costs incurred when refiners expand their refineries, a very low cost for a grassroots plant.

Because of its high efficiency in converting feedstocks into liquid fuel, the production and

operating costs are estimated to be very low. By using some waste feedstocks today, production

costs are reported to be less than $0.50 per gallon. However, even with feedstock costs in the

$70 per ton range, which is the cost we used in our cost analysis, total costs would remain less

than $1.00 per gallon of diesel fuel.

Cello-Energy was founded 16 years ago and after the chemistry was worked out, they

built their first pilot plant in 1998. They next converted their pilot plant in 2004 to a larger

continuously-operating demonstration plant that produced 4 million gallons per year of diesel

fuel. In December 2008, Cello started up a 20 million gallon per year commercial demonstration

plant. As of late 2009, the plant is operational, however, the production volumes are still very

low. Cello is working to increase the production volume of its plant. According to the company,

they are currently working to resolve materials handling and processing issues that surfaced

when they attempted to scale up production to 20 MGY from a previously operated

demonstration plant. As of November 2009, they had ordered new equipment and are waiting

for it to arrive and be installed which they hoped would allow for operations to be restarted as

early as February or March, 2010. Cello energy already has chosen locations to construct and

start up two 50 million gallons per year plants by early 2011, though these are on hold until the

Bay Minette facility is operational. This includes a facility in conjunction with the State of

Georgia Energy Innovation Center, and one additional plant in Alabama. Cello explained that

they will use prefabrication techniques so that these plants can readily be constructed, shipped

and installed anywhere in the U.S.

Thermochemial/Fischer Tropsch - Choren

Choren has a technology called Carbo-V, which is a Fischer-Tropsch process that can be

used to make diesel fuel. The process can process a wide variety biomass and recycled material

materials as feedstocks. The process converts agriculture biomass, forestry biomass, biogenic

waste and recycling substances into a synthesis gas which can be further converted to a diesel

fuel using a Fischer-Tropsch reactor. The Carbo-V process can also be configured without the
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Fischer-Tropsch hydrocracking technology, so as to produce electricity, heat and power,

methanol, and other chemical feedstocks.

The principal aspect of the Carbo-V Process is a three-stage gasification process

consisting of low temperature gasification, high temperature gasification and endothermic

entrained bed gasification. In the first stage, biomass is partially oxidized with air or oxygen at

temperatures between 400 and 500 °C. This breaks down the feedstock into a gas containing tar

and solid carbon. In the second stage, the tar is oxidized at temperatures higher than the ash’s

melting point, converting the tar into a synthesis gas. In the third stage, solid carbon is

mechanically pulverized and blown into the hot gasification stream. The fluidized carbon

endothermicly reacts with the gasification stream and is converted into a synthesis gas. In the

next Fischer-Tropsch stage of the process, the synthesis gas (CO and H2) reacts with the aid of a

catalyst to form hydrocarbons. The resulting hydrocarbons produced from the three stages can

then be sent to a hydrocracking process to produce primarily diesel fuel.

Choren will be building a commercial Plant in Freiberg/ Saxony Germany that is

expected to be operational in 2011 or 2012. Initially, the plant will use biomass from nearby

forests, the wood-processing industry and straw from farmland.

Thermochemical/Biochemical - Coskata

The Coskata process is a gasification-based technology which produces ethanol from

biomass and other forms of carbon through a biofermentation route. A wide variety of

feedstocks can be used, municipal waste, agriculture waste and other carbonaceous containing

material. Since this process uses combustion and biofermentation, it is not easily classifiable as

either a biochemical or thermochemical production method. This process requires that the

biomass or carbonaceous material be processed to a small particle size and then it is injected into

a gasifier.

The gasifier combusts any dry carboneous feed stocks into syngas, comprised primarily

of carbon monoxide and hydrogen. The syngas produced is fermented in a reactor by microorganisms, which convert the carbon monoxide and hydrogen directly into ethanol. The microorganisms are low cost and can process a wide range of carbon monoxide and hydrogen molar

ratios in the syngas, providing feedstock processing flexibility. No other enzymes are required

by this process for producing ethanol, providing significant cost savings over current cellulosic

and corn based fermentation production methods. The Coskata process is conducted at low

pressures, which offers savings on capital and energy costs. Additional energy savings can be

realized by employing membrane technology to separate ethanol from the reactor decant liquid.

This technology uses gravity and filtration to recover ethanol, resulting in significant savings on

distillation capital and energy costs used in other cellulosic and corn based production methods.

Initial ethanol production cost estimates are lower than the biochemical and thermochemical

cellulosic technologies described in previously in Subsections 1.4.3.1 and 1.4.3.2.

For woody biomass, Coskata estimates that each ton of this feedstock would generate

about 100 gallons of ethanol and small amounts of ash which would be burned to supply energy

needs for the process. Corn stover is expected to provide similar ethanol yields as woody
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biomass feed stocks, though details about yields from the various feed supply stocks are not yet

public.

Coskata has a bench scale pilot plant in Warrenville, IL, and its larger 40,000 gallon per

year pilot plant became operational in 2009 in Madison, Pennsylvania. Coskata is targeting to

design and build a 50 million gallon per year commercial demonstration plant that it expects to

be operational in 2011.

Pyrolysis - Dynamotive Energy Systems

Dynamotive Energy Systems Corporation has announced a pyrolysis technology that uses

medium temperatures and oxygen free reactions to convert dry waste biomass and energy crops

into fuels that can be used in power/heat generation and transportation vehicles. Additionally,

the process can make feedstock’s that can be used to produce chemicals. The process is flexible

on the types of biomass feedstock’s that can be processed. The fuel produced from the

Dynamotive process is called “BioOil” and contains up to 25% water, though the water is

intimately mixed and does not easily separate into another phase with time. Since the BioOil

contains significant amounts of water, it is not directly useable as fuel in conventional vehicles

and would have to be converted via another catalytic conversion processing step. The additional

catalytic step envisioned for this would combust the material into a synthesis gas which would

then be converted into diesel fuel or bio-methanol via a catalytic reaction (the BTL process).

The diesel fuel produced is expected to be compatible with existing petroleum diesel fuels.

Three products are produced by the Dynamotive process, BioOil (60-75% by weight),

char (15-20% wt.) and non-condensable gases (10-20% wt.). The char produced is similar to

coke and can be used as fuel by other industries while the gases yielded from the process can be

used to supply about 75% of the energy requirements of the pyrolysis process. The pyrolysis

process operates at reactor temperatures of about 400-500 degrees Celsius.

Dynamotive has two small demonstration plants. One demonstration plant is located in

Guelph, Ontario, Canada and its capacity is 66,000 dry tons of biomass a year with an energy

output equivalent to 130,000 barrels of oil. The other of its demonstration plants is located in

West Lorne, Ontario, Canada. This plant started operation in early 2005 using waste sawdust as

a feedstock. The West Lorne plant has a capacity to convert 130 tonnes of biomass into BioOil

per day which, if proportional to the Guelph plant, translates to an energy-equivalent of 84,500

barrels of oil per year. The BioOil production capacity between the two plants is estimated at

around 9 MGY of BioOil, but both plants are currently operating at a fraction of their rated

capacity. However, according to a recent press release, Dynamotive has contracts in place to

supply a U.S.-based client with at least nine shipments of BioOil in 2010. Although Dynamotive

has been working on a technology for converting BioOil to a transportation fuel, they have not

announced plans for building such a facility
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Biochemical Ethanol - POET

POET has over twenty years of producing conventional ethanol in 23 plants in seven

states with production capability of one billion gallons of ethanol annually. POET has expanded

their production capability to include cellulosic ethanol technology. POET’s cellulosic

technology will make ethanol from plant materials like corn stalks, switch grass, wood chips and

refuse. In February 2007, POET was selected by DOE for an award totaling $80 million for

federal funding for a commercial cellulosic ethanol plant, which will be located in Emmetsburg,

Iowa. As such, POET will be one of the first to build a cellulosic plant on a commercial scale.

POET’s commercial demonstration plant is projected to produce 25 million gallons per year and

start up in 2011. It will make cellulosic ethanol from plant materials such as corn cobs and

perhaps other cellulosic feedstocks.

Biochemical Ethanol – Iogen, KL Energy, DuPont Danisco, Fiberight

Like Poet, Iogen is pursuing a biochemical cellulosic ethanol technology very similar to

the biochemical pathway described in previously in Section 1.4.3 utilizing their own proprietary

enzymes. Iogen opened the first commercial demonstration cellulosic ethanol plant in North

America. Iogen’s plant located in Ottawa, Canada has been producing cellulosic ethanol from

wheat straw since 2004. Iogen has slowly been ramping up production at its 0.5 MGY plant.

According to the company’s website, they produced approximately 24,000 gallons in 2004 and

34,000 gallons in 2005. Production dropped dramatically in 2006 and 2007 but came back

strong with 55,000 gallons in 2008. Up to the last quarter of 2009, Iogen has produced over

127,000 gallons of ethanol from their demonstration plant.

Iogen also recently became the first cellulosic ethanol producer to sell its advanced

biofuel at a retail service station in Canada. Their cellulosic ethanol was blended to make E10

available for sale to the consumers at an Ottawa Shell station. Iogen also recently announced

plans to build its first commercial scale plant in Prince Albert, Saskatchewan, in the 2011/2012

timeframe.

KL Energy Corporation (KL Energy), through its majority-owned Western Biomass

Energy, LLC (WBE) located in Upton, WY, is designed to convert wood products and wood

waste products into ethanol using a biochemical pathway similar to that described previously in

Section 1.4.3. Since the end of construction in September 2007, equipment commissioning and

process revisions continued until the October 2009 startup. The plant was built as a 1.5 MGY

demonstration plant and was designed to both facilitate research and operate commercially. It is

KL Energy’s intent that WBE’s future use will involve the production and sale of small but

commercial-quality volumes of ethanol and lignin co-product. The company’s current 2010

production goal is for WBE to generate RINs under the RFS2 program.

DuPont Danisco Cellulosic Ethanol, LLC (DDCE), a joint venture between Dupont and

Danisco, is another company pursuing biochemical conversion of cellulosic material into

ethanol. DDCE received funding from the State of Tennessee and the University of Tennessee to

build a small 0.25 MGY demonstration plant in Vonore, TN, to pursue switchgrass-to-ethanol

production. According to DDCE, construction commenced in October 2008 and the plant is now
122

mechanically complete and currently undergoing start-up operations. The facility is scheduled to

come online in January and the company hopes to operate at or around 50% of production

capacity in 2010. According to the DDCE, the objective in Vonore is to validate processes and

data for commercial scale-up.

Fiberight, LLC (Fiberight) is yet another company pursing cellulosic ethanol from a

biochemical process, but using MSW as a feedstock. According to Fiberight, they have been

operating a pilot-scale facility in Lawrenceville, VA, for three years. They have developed a

proprietary process that not only fractionates MSW but biologically converts the non-recyclable

portion into cellulosic ethanol and biochemicals. Fiberight recently purchased a shut down corn

ethanol plant in Blairstown, IA, and plans to convert it to become MSW-to-ethanol capable.

According to the company, construction is currently underway and the goal is to bring the 2

MGY demo plant online by February or March, 2010. Fiberight’s long-term goal is to expand

the Blairstown plant to a 5-8 MGY capacity and build other small commercial plants around the

country that could convert MSW into fuel.

Thermochemical Ethanol - Range Fuels and Enerkem

Range Fuels produces cellulosic ethanol via a two step thermochemical process. Their

technology converts biomass to syngas followed by catalytic conversion of the syngas to

alcohols. Range claims that their technology is capable of producing more ethanol than other

cellulosic technologies based on yields per energy input. They utilize a two step process which

can use many forms of non food biomass, such as agriculture waste, wood, and corn stocks.

Additionally, the technology can process feed stocks with variable water content.

In the Range process, biomass feedstock are converted by heat, pressure and steam into

syngas, which is then scrubbed and cleaned before entering into the second stage. The second

stage uses catalyst to convert the syngas into methanol, which are then converted in an additional

reactor into ethanol. Overall, the Range process is simple as no enzymes or living organisms are

used for the main conversion reactions.

Range has operated a pilot plant for over 7 years using over 20 different nonfood

feedstocks. Range broke ground building its first commercial plant late in late 2008 and is

expected to be operational in 2010. This plant will be located in Soperton, Georgia and is

partially funded from proceeds of a DOE grant. The plant will use wood, grasses, and corn

stover as feedstocks. In its initial phase, the Range plant is expected to produce 4 million gallons

per year of methanol. After the company is confident in its operations, Range will begin efforts

to expand the plant and add additional reaction capacity to convert the methanol to ethanol.

Enerkem is another company like Range Fuels pursuing cellulosic ethanol production via

the thermochemical route. The Canadian-based company was recently announced as a recipient

of a $50 million grant from DOE to build a 10 MGY woody biomass-to-ethanol plant in

Pontotoc, MS. The U.S. plant is not scheduled to come online until 2012, but Enerkem is

currently building a 1.3 MGY demonstration plant in Westbury, Quebec. According to the

company, plant construction in Westbury started in October 2007 and the facility is currently

scheduled to come online around the middle of 2010. While it’s unclear at this time whether the
123

cellulosic ethanol produced will be exported to the United States, Enerkem has expressed interest

in selling its fuel commercially.

Reforming of Sugars to Gasoline - Virent Bioreforming

Virent is pursuing a process called “Bioforming” which functions similarly as the

gasoline reforming process used in the refining industry. While refinery-based reforming raises

natural gasoline’s octane value and produces organic chemicals, benzene, xylene and toluene as

a byproduct, Bioforming reforms biomass-derived sugars into hydrocarbons for blending into

gasoline and diesel fuel. The process however, operates at much lower temperatures and

pressures than reforming used by the refining industry. The Bioforming process is being

developed through a partnership with Shell, Cargill, Honda and the University of Wisconsin.

Virent currently has 16 pilot plants in operation. At this stage, though, the data is limited. It

appears that Bioforming is a promising technology, as production costs estimates are low in

comparison to many other renewable and biomass production processes while the products are

compatible with traditional petroleum stocks.

Biomass feedstocks for the Bioforming process are sugar feeds, such a corn syrup,

sucrose, glycerol, sorbitol, xylose, glucose, cellulose and hemi cellulose. These are primarily

converted into gasoline and diesel fuel, though other hydrocarbons such as jet fuel, LPG,

benzene, toluene, xylene, hydrogen, natural gas can also be produced. Water is also produced, as

the reforming process removes oxygen from the sugar feeds. The resulting properties and energy

content of gasoline and diesel produced though are physically comparable to those yielded from

refining industry. Variable operating costs are low because no distillation equipment is needed

to separate the produced gasoline, diesel and other hydrocarbons, as these separate naturally

from the aqueous solutions generated in the reforming process. The net energy costs are also

low due to low operating pressures and temperatures.

1.4.4 Biodiesel & Renewable Diesel Production

1.4.4.1 Biodiesel

Plant oils and animal fats are triglycerides, a molecule consisting of a group of three

hydrocarbon chains (saturated or olefinic) linked to a three-carbon backbone via carboxylic acid

esters (see Figure 1.4-9). Biodiesel is made by removing the chains from the triglyceride

molecules and adding methanol to their ends to form methyl esters. Glycerin is formed as a coproduct from the three-carbon backbones that remain. For relatively pure triglycerides, such as

virgin plant oils, the primary reaction is catalyzed by an alkaline pH and takes place in a stirred

vessel at mild temperature and pressure conditions.
124

Figure 1.4-9. Overview of biodiesel conversion process

In the case of feedstocks containing more than a few percent free fatty acids (FFAs), such

as rendered fats, waste greases, or corn oil extracted after ethanol production, addition of base

will result in the formation of soap, an undesirable process contaminate when present above trace

levels. To avoid this, these feedstocks must first undergo an acid pre-treatment step to esterify

the FFAs before proceeding to the base-catalyzed triglyceride transesterification reaction.

Feedstocks with small amounts of FFAs may be converted in a basic environment if the soaps

can be removed from the fuel product.

Once the chemical conversions are complete, the mixture is neutralized, washed, and coproduct and unreacted alcohol and catalyst are recovered. At that point the biodiesel is subjected

to quality control testing and then released for sale. Figure 1.4-10 shows a process flow diagram

for a typical biodiesel production process that uses virgin plant oil as feedstock; processes using

waste fats or greases would include an acid esterification step upstream of the transesterification

reactor shown here. Plants that also produce other oleochemicals often have distillation

equipment at the end of the process capable of purifying the methyl esters to a high degree or

separating them by molecular weight. These plants may use this equipment to produce a very

high purity biodiesel product. We estimate that only a very small fraction of biodiesel

production is distilled. 241

+

transesterification

methyl esters

(biodiesel)

glycerin

triglyceride

+

transesterification

methyl esters

(biodiesel)

glycerin

triglyceride
125

Figure 1.4-10. Schematic of typical biodiesel production from virgin plant oil

Some differences exist between large and small plants that are worth mentioning given

the very wide range of plant capacities existing in this industry. Larger plants (greater than 10

million gallons per year) are more likely to employ continuous flow processes, which afford

certain efficiencies of scale and steady-state operation. On the other hand, small plants (less than

one million gallons per year) are most likely to produce fuel batch-by-batch, which may give

them more flexibility to change feedstock types or slow output on short notice. Smaller plants

are less likely to be able to afford an on-site laboratory or quality control specialist, which may

cause them hardship as fuel quality standards tighten and/or are more stringently enforced.

Third-party labs exist for this purpose, but they pose challenges such as significant per-test costs

and multi-day turnaround times that require holding of product batches until results are received.

The biodiesel production process is relatively simple and economical, and there is already

sufficient existing U.S. capacity to produce all the biodiesel required to meet the biomass-based

diesel standard put forth in EISA. Thus, we do not expect large changes in the process

technology used to make biodiesel going into the future. That said, it is worth noting some

potential changes as existing plants strive to comply with changing fuel quality standards, or as

new plants are occasionally built to take advantage of specific market niches.

Transesterification

Reactor

Acid

Vegetable

Oils

Methanol

Catalyst (Base)

Acidulation

Separator

Acidulation

Glycerin Stripper

Column

Methanol Rectification

Column

Glycerin

Product

Methanol /

Water

Biodiesel

Wash Tank

Crude

Biodiesel

To Methanol

Storage

Glycerin

Methanol

Water

Water

Separator

Gray

Water

Biodiesel /

Methanol

Stripper

Biodiesel

Methanol / Water

Filter

Biodiesel

Filter Product

Transesterification

Reactor

Acid

Vegetable

Oils

Methanol

Catalyst (Base)

Acidulation

Separator

Acidulation

Glycerin Stripper

Column

Methanol Rectification

Column

Glycerin

Product

Methanol /

Water

Biodiesel

Wash Tank

Crude

Biodiesel

To Methanol

Storage

Glycerin

Methanol

Water

Water

Separator

Gray

Water

Biodiesel /

Methanol

Stripper

Biodiesel

Methanol / Water

Filter

Biodiesel

Filter Product
126

One such change may be an increase in distillation of biodiesel. EPA requires biodiesel

to meet the ASTM D-6751 specification for B100 in order to be legally blended into diesel fuel

for use in vehicles. Earlier this year, ASTM amended this specification to require a cold filter

plugging test, which effectively mandates very low levels of FFAs, sterol glucosides, and

partially-converted triglycerides in the finished biodiesel. There are a variety of process

parameters a biodiesel producer can adjust to reduce the levels of these compounds in the

finished fuel, but one very effective way to ensure a high purity product is through distillation.

At this time it is unclear to what extent distillation will be relied upon for compliance with the

amended biodiesel specification. An increase in distillation would increase per-gallon energy

inputs to the process significantly.

Some industry forecasts suggest animal fats and waste greases will make up an increasing

share of biodiesel feedstocks due to their lower costs and lower upstream GHG impacts.

Because most fats and greases contain significant levels of FFAs, this shift will cause more

plants to use acid pre-treatment, increasing process complexity and per-gallon energy use.

1.4.4.2 Renewable Diesel

The renewable diesel production process converts vegetable oils and animal fats into

diesel fuel using thermal depolymerization, which is similar to hydrotreating used in petroleum

refining to remove sulfur. The process uses hydrogen and catalyst to remove oxygen from the

triglyceride molecules in the feedstocks oils via a decarboxylation and hydro-oxygenation

reaction, yielding some light petroleum products and water as co-products. The reactions can

also saturate the olefin bonds in the feedstock oils, converting them to paraffins; additional steps

can also be taken to isomerize a portion of the paraffins to create fuels with varying properties.

All of these reactions consume significant amounts of hydrogen. The yield of these reactions to

the primary product (diesel) depends on the process conditions, as some of the carbon backbone

of the oils can be cracked to naphtha and lighter products with higher severity. For our analysis

we assume approximately 90% yield to diesel, with the remainder split between light fuel gas

and naphtha. Figure 1.4-11 shows a flow diagram of the primary steps of renewable diesel

production.
127

Figure 1.4-11. Process flow diagram of primary steps in renewable diesel production

Renewable diesel can be produced either at a stand-alone facility or within the boundaries

of an existing petroleum refinery. For the stand-alone facility, feedstock is brought in and

finished fuel is transported out to market. This type of facility may be co-located with a

rendering facility or a chemical operation with excess hydrogen to minimize feedstock

transportation and storage costs. For production within the boundaries of a refinery, the feed

material may either be processed in a segregated unit (new or revamped), or co-processed with

petroleum in an existing unit. In any case, the feedstock will require pre-treatment in a unit that

removes contaminates such as sulfur, nitrogen, and trace metals that may poison hydrotreating

catalysts.

For a period during 2007 and 2008, ConocoPhilips produced some (300-500 bbl/day)

renewable diesel at their Borger, Texas, refinery from beef tallow generated by Tyson Foods,

Inc. in Amarillo, Texas. 242 In fall of 2008, Dynamic Fuels, LLC (a joint venture of Syntroleum

Corp. and Tyson Foods, Inc.) announced construction of a 75 million gallon per year plant

(5,000 bbl/day) in Geismar, Louisiana, that will use Tyson meat processing fats as feedstock to

Syntroleum’s Bio-Synfining process. Start-up is scheduled for mid-2010, with the primary

product being high-quality diesel fuel that will be fungible within the existing petroleum supply

system. 243 This facility plans to utilize supplies of hydrogen available in the industrial area

where it will be located, as well as rail and shipping infrastructure already in place nearby. 244

Syntroleum Corp was founded in 1984 and holds a number of patents in gas-to-liquids

and biomass-to-liquids conversion processes. One such process has the trade name Synfining,

and upgrades Fischer-Tropsch paraffins to isomers with properties more favorable for diesel fuel.

S, N,

metal

removal

Mixed grease

feedstock

Hydrogen

Heat in

Flash

separator

Heat out

Liquid fuel for

blending or further

separation

Water, light

hydrocarbons

Hydrotreating

reactor

S, N,

metal

removal

Mixed grease

feedstock

Hydrogen

Heat in

Flash

separator

Heat out

Liquid fuel for

blending or further

separation

Water, light

hydrocarbons

Hydrotreating

reactor
128

They have further adapted this process to use a variety of fats and oils as feedstocks, calling it

Bio-Synfining. It is this technology that will be used in the Geismar facility.

Looking internationally, the Finnish company Neste Oil began operating a 3,200 bbl/day

process in Finland in 2007 to convert vegetable oils into renewable diesel. This company has

plans to construct similar facilities in Singapore and the Netherlands by 2010, and eventually

plans to bring on-line plants that will convert biomass to liquid fuels using gasification. 245

Since thermochemical production of hydrocarbon fuels from fats and biomass is a

relatively new endeavor to conduct on a commercial scale, we expect continued innovation and

fine-tuning of the technology as these processes evolve from their roots in Fischer-Tropsch and

petroleum hydrotreating processes. (This discussion ties in with cellulosic diesel in Section

1.4.3.2.)

1.5 Biofuel Industry Characterization & Projected Growth

In this section we discuss the current state of the biofuel industry and how production

might grow in the future under the RFS2 program based on our volume assumptions. The bulk

of the discussion focuses on corn ethanol, imported sugarcane-based ethanol and conventional

FAME-based biodiesel, today’s most established U.S. biofuel sources. However, we also

discuss renewable diesel, cellulosic diesel, algae-based biodiesel and other up-and-coming

second generation biofuels that are likely to develop during the course of the RFS2 program.

In the subsections that follow, we’ll discuss corn ethanol and how the industry might look

once it finishes building out production capacity to 15 billion gallons and employs more

advanced processing technologies. From there we will discuss the availability of imported

ethanol from Brazil and Caribbean Basin Initiative (CBI) countries to help meet the advanced

biofuel standard. Domestic sugarcane- and sweet sorghum-based ethanol plants could also

contribute to meeting the advanced biofuel standard in EISA. Following this discussion, we will

characterize the present state of the cellulosic biofuel industry and talk about the potential

timeline for commercialization based on projected industry plans and technological

breakthroughs aided by state and federal grants, tax incentives, and loan guarantee programs. As

part of this discussion we will describe our assessment of the cellulosic industry in the context of

setting the standard for 2010. Finally, we will conclude our industry characterization by

discussing the present state of the biomass-based diesel industry and how we expect biodiesel

production to grow in the future along with renewable diesel and algae-based biodiesel.

1.5.1 Corn Ethanol

The majority of domestic biofuel production currently comes from plants processing corn

and other similarly-processed grains in the Midwest. However, there are a handful of plants

located outside the Corn Belt and a few plants processing simple sugars from food or beverage

waste. In this subsection, we will talk about the present state of the corn ethanol industry and

how we expect things might change in the future under the RFS2 program.
129

1.5.1.1 Historic/Current Production

The United States is currently the largest ethanol producer in the world. In 2008, the U.S.

produced nine billion gallons of fuel ethanol for domestic consumption, the majority of which

came from locally-grown corn.26,246 The nation is currently on track for producing over 10

billion gallons by the end of 2009.27,247 Although the U.S. ethanol industry has been in existence

since the 1970s, it has rapidly expanded in recent years due to the phase-out of methyl tertiary

butyl ether (MTBE), elevated crude oil prices, state mandates and tax incentives, the introduction

of the Federal Volume Ethanol Excise Tax Credit (VEETC)28, the implementation of the existing

RFS1 program29 and the new volume requirements established under EISA248. As shown in

Figure 1.5-1, U.S. ethanol production has grown exponentially over the past decade.

Figure 1.5-1.

Historical Growth in U.S. Corn/Starch Ethanol Production249

1.4 1.6 1.7

2.0

2.7

3.3

3.8

4.6

6.3

9.0

0

1

2

3

4

5

6

7

8

9

10

1999

2000

2001200220032004

2005

200620072008

Year

Corn/Starch Ethanol Production (Bgal)

26 Based on historical transportation ethanol use less imports reported by EIA.

27 Based on projected transportation ethanol use less imports reported by EIA. Actual year-end data for 2009 for

unavailable at the time of this FRM assessment.

28 On October 22, 2004, President Bush signed into law H.R. 4520, the American Jobs Creation Act of 2004 (JOBS

Bill), which created the Volumetric Ethanol Excise Tax Credit (VEETC). The $0.51/gal ethanol blender credit

replaced the former fuel excise tax exemption, blender’s credit, and pure ethanol fuel credit. However, the 2008

Farm Bill modified the alcohol credit so that corn ethanol gets a reduced credit of $0.45/gal and cellulosic biofuel

gets a credit of $1.01/gal.

29 On May 1, 2007, EPA published a final rule (72 FR 23900) implementing the Renewable Fuel Standard (RFS)

required by the Energy Policy Act of 2005. The RFS requires that 4.0 billion gallons of renewable fuel be blended

into gasoline/diesel by 2006, growing to 7.5 billion gallons by 2012.
130

As of November 2009 there were 180 corn/starch ethanol plants operating in the U.S.

with a combined production capacity of approximately 12 billion gallons per year.30,250 This

does not include idled ethanol plants, discussed later in this subsection. The majority of today’s

ethanol production (91.5% by volume) is produced exclusively from corn. Another 8.3% comes

from plants processing a blend of corn and/or similarly-processed grains (milo, wheat, or barley).

The remainder comes from small plants processing waste beverages or other waste sugars and

starches. A summary of U.S. ethanol production by feedstock is presented in Table 1.5-1.

Table 1.5-1.

Current Corn/Starch Ethanol Production Capacity by Feedstock

Plant Feedstock

(Primary Listed First)

Capacity

MGY

% of

Capacity

No. of

Plants

% of

Plants

Corna

10,994 91.5% 155 86.1%

Corn, Milob

817 6.8% 15 8.3%

Corn, Wheat 130 1.1% 1 0.6%

Milo 3 0.0% 1 0.6%

Wheat, Milo 50 0.4% 1 0.6%

Waste Beveragesc

20 0.2% 5 2.8%

Waste Sugars & Starchesd

7 0.1% 2 1.1%

Total 12,020 100% 180 100%

a

Includes one facility processing seed corn, one facility operating a pilot cellulosic butanol

plant, one facility with plans to build a pilot cellulosic ethanol plant, and two facilities with

plans to build small commercial cellulosic ethanol plants in the future.

b

Includes one facility processing a small amount of molasses in addition to corn and milo.

c

Includes two facilities processing brewery waste.

d

Includes one facility processing potato waste that intends to add corn in the future.

As shown in Table 1.5-1, of the 180 operating plants, 173 process corn and/or other

similarly processed grains. Of these facilities, 162 utilize dry-milling technologies and the

remaining 11 plants rely on wet-milling processes. Dry mill ethanol plants grind the entire

kernel and generally produce only one primary co-product: distillers’ grains with solubles

(DGS). The co-product is sold wet (WDGS) or dried (DDGS) to the agricultural market as

animal feed. However, there are a growing number of plants using front-end fractionation to

produce food-grade corn oil or back-end extraction to produce fuel-grade corn oil for the

biodiesel industry. A company called GreenShift has corn oil extraction facilities located at five

30 Our November 2009 corn/starch ethanol industry characterization was based on a variety of sources including

plant lists published online by the Renewable Fuels Association and Ethanol Producer Magazine, information from

ethanol producer websites including press releases, and follow-up correspondence with producers. The baseline

does not include ethanol plants whose primary business is industrial or food-grade ethanol production nor does it

include plants that might be located in the Virgin Islands or U.S. territories. Where applicable, current/historic

production levels have been used in lieu of nameplate capacities to estimate production capacity.
131

ethanol plants in Michigan, Indiana, New York and Wisconsin.31,251 Collectively, these facilities

are designed to extract in excess of 7.3 million gallons of corn oil per year. Primafuel Solutions

is another company offering corn oil extraction technologies to make existing ethanol plants

more sustainable. For more information on corn oil extraction and other advanced technologies

being pursued by today’s corn ethanol industry, refer to Section 1.4.1 of the RIA

In contrast to traditional dry mill plants, wet mill facilities separate the kernel prior to

processing into its component parts (germ, fiber, protein, and starch) and in turn produce other

co-products (usually gluten feed, gluten meal, and food-grade corn oil) in addition to DGS. Wet

mill plants are generally more costly to build but are larger in size on average. As such, 11.4%

of the current grain ethanol production comes from the 11 wet mill facilities listed in Table 1.5-2.

Table 1.5-2.

Existing Wet Mill Corn Ethanol Plants

Ethanol Plant/Company Location

Capacity

MGY

% of Tot

Capacity

Archer Daniels Midlanda

Cedar Rapids, IA 250 2.1%

Archer Daniels Midlanda

Clinton, IA 190 1.6%

Archer Daniels MidlandaColumbus, NE 95 0.8%

Archer Daniels Midlanda Decatur, IL 290 2.4%

Archer Daniels Midlanda Marshall, MN 40 0.3%

Aventine Renewable Energy Pekin, IL 100 0.8%

Cargill, Inc. Eddyville, IA 35 0.3%

Cargill, Inc. Blair, NE 185 1.5%

Grain Processing Corp Muscatine, IA 20 0.2%

Penford Products Cedar Rapids, IA 45 0.4%

Tate & Lyle Loudon, TN 126 1.0%

Total 1,376 11.4%

aEstimated plant capacities.

The remaining seven ethanol plants process waste beverages or sugars/starches and

operate differently than their grain-based counterparts. These small production facilities do not

require milling and operate a simpler enzymatic fermentation process. Due to their limited

feedstock supplies and niche markets, these plants have much smaller ethanol production

capacities than traditional dry and wet mill corn ethanol plants. A summary of today’s average

ethanol plant size by processing technology is found in Table 1.5-3 below.

31 Two plants in Michigan and one in each of the other three states. All company information based on GreenShift’s

Q2 2009 SEC filing.
132

Table 1.5-3.

Average Corn/Starch Plant Sizes

Processing

Technology

Capacity

MGY

% of

Capacity

No. of

Plants

% of

Plants

Avg. Size

MGY

Dry Millinga

10,618 88.3% 162 90.0% 65.5

Wet Millinga1,376 11.4% 11 6.1% 125.1

Otherb

26 0.2% 7 3.9% 3.8

Total 12,020 100.0% 180 100.0% 66.8

a

Includes a total of three corn ethanol plants with plans to process cellulosic feedstocks in the

future. To the extent that cellulosic facilities are integrated with existing processes, these

plants will need additional front-end technology to supplement existing dry milling equipment.

b

Facilities that do not process traditional grain-based crops and thus do not require milling.

Includes plants processing waste beverages or sugars and starches.

Ethanol production is a relatively resource-intensive process that requires the use of

water, electricity, and steam. Steam needed to heat the process is generally produced on-site or

by other dedicated boilers.32 The ethanol industry relies primarily on natural gas. Of today’s

180 ethanol production facilities, an estimated 151 burn natural gas33 (exclusively), three burn a

combination of natural gas and biomass, one burns natural gas and coal (although natural gas is

the primary fuel), one burns a combination of natural gas, landfill biogas and wood, and two

burn natural gas and syrup from the process. We are aware of 17 plants that burn coal as their

primary fuel and one that burns a combination of coal and biomass.34 Our research suggests that

three corn ethanol plants rely on a combination of waste heat and natural gas and one plant does

not have a boiler and relies solely on waste heat from a nearby power plant. Overall, our

research suggests that 27 plants currently utilize cogeneration or combined heat and power

(CHP) technology, although others may exist.35,252 CHP is a mechanism for improving overall

plant efficiency. Whether owned by the ethanol facility, their local utility, or a third party, CHP

facilities produce their own electricity and use the waste heat from power production for process

steam, reducing the energy intensity of ethanol production.36 A summary of the energy sources

and CHP technology utilized by today’s ethanol plants is found in Table 1.5-4.

32 Some plants pull steam directly from a nearby utility.

33 Facilities were assumed to burn natural gas if the plant boiler fuel was unspecified or unavailable on the public

domain.

34 Includes corrections from NPRM based on new information obtained on Cargill plants and Blue Flint ethanol

plant.

35 CHP assessment based on information provided by EPA’s Combined Heat and Power Partnership, literature

searches and correspondence with ethanol producers.

36 For more on CHP technology, refer to Section 1.4.1.3.
133

Table 1.5-4.

Current Corn/Starch Ethanol Production Capacity by Energy Source

Plant Energy Source

(Primary Listed First)

Capacity

MGY

% of

Capacity

No. of

Plants

% of

Plants

CHP

Tech.

Coala1,758 14.6% 17 9.4% 8

Coal, Biomass 50 0.4% 1 0.6% 0

Natural Gasb

9,627 80.1% 151 83.9% 13

Natural Gas, Biomassc115 1.0% 3 1.7% 1

Natural Gas, Coal 35 0.3% 1 0.6% 1

Natural Gas, Landfill Biogas, Wood 110 0.9% 1 0.6% 0

Natural Gas, Syrup 101 0.8% 2 1.1% 0

Waste Heatd

50 0.4% 1 0.6% 1

Waste Heatd

, Natural Gas 175 1.5% 3 1.7% 3

Total 12,020 100.0% 180 100.0% 27

a

Includes four plants that are permitted to burn biomass, tires, petroleum coke, and wood waste in addition to coal

and one facility that intends to switch to biomass in the future.

b

Includes two facilities that might switch to biomass, one facility that intends to burn thin stillage and biogas, and

two facilities that were once considering switching to coal in the future.

c

Includes one facility processing bran in addition to natural gas.

dWaste heat from utility partnerships.

During the ethanol fermentation process, large amounts of carbon dioxide (CO2) gas are

released. In some plants, the CO2 is vented into the atmosphere, but where local markets exist, it

is captured, purified, and sold to the food processing industry for use in carbonated beverages

and flash-freezing applications. We are currently aware of 40 fuel ethanol plants that recover

CO2 or have facilities in place to do so. According to Airgas, a leading gas distributor, the U.S.

ethanol industry currently recovers 2 to 2.5 million tons of CO2 per year which translates to

about 5-7% of all the CO2 produced by the industry.253

Since the majority of ethanol is made from corn, it is no surprise that most of the plants

are located in the Midwest near the Corn Belt. Of today’s 180 ethanol production facilities, 163

are located in the 15 states comprising PADD 2. For a map of the Petroleum Administration for

Defense Districts or PADDs, refer to Figure 1.5-2.
134

Figure 1.5-2.

Petroleum Administration for Defense Districts

As a region, PADD 2 accounts for over 94% (or 11.3 billion gallons) of today’s estimated

ethanol production capacity, as shown in Table 1.5-5.

Table 1.5-5.

Current Corn/Starch Ethanol Production Capacity by PADD

PADD

Capacity

MGY

% of

Capacity

No. of

Plants

% of

Plants

PADD 1 150 1.3% 3 1.7%

PADD 2 11,329 94.2% 163 90.6%

PADD 3 294 2.4% 4 2.2%

PADD 4 152 1.3% 7 3.9%

PADD 5 95 0.8% 3 1.7%

Total 12,020 100.0% 180 100.0%

Leading the Midwest in ethanol production are Iowa, Nebraska, Illinois, Minnesota, and

South Dakota. Together, these five states’ 109 ethanol plants account for over two-thirds (or

about 8.2 billion gallons) of the nation’s ethanol production capacity. However, although the

majority of ethanol production comes from PADD 2, there are a growing number of plants

situated outside the traditional Corn Belt. Our November 2009 industry assessment indicates

that Arizona, California, Colorado, Georgia, Idaho, Mississippi, New York, Oklahoma, Oregon,

Texas and Wyoming all have one or more operational ethanol plants. Some of these facilities

ship in feedstocks (namely corn) from the Midwest, others rely on locally grown/produced

feedstocks, while others rely on a combination of the two. A summary of the online ethanol

production capacity by state is presented in Table 1.5-6.
135

Table 1.5-6.

Current Corn/Starch Ethanol Production Capacity by State

State

Capacity

MGY

% of

Capacity

No. of

Plants

% of

Plants

Iowa 3,214 26.7% 39 21.7%

Nebraska 1,560 13.0% 23 12.8%

Illinois 1,342 11.2% 13 7.2%

Minnesota 1,113 9.3% 20 11.1%

South Dakota 987 8.2% 14 7.8%

Indiana 716 6.0% 9 5.0%

Wisconsin 529 4.4% 9 5.0%

Kansas 439 3.6% 11 6.1%

North Dakota 355 3.0% 5 2.8%

Ohio 320 2.7% 5 2.8%

Missouri 261 2.2% 6 3.3%

Texas 240 2.0% 3 1.7%

Tennessee 236 2.0% 2 1.1%

Michigan 217 1.8% 4 2.2%

Colorado 138 1.1% 5 2.8%

Georgia 100 0.8% 2 1.1%

Mississippi 54 0.4% 1 0.6%

New York 50 0.4% 1 0.6%

Arizona 50 0.4% 1 0.6%

Kentucky 40 0.3% 2 1.1%

Oregon 40 0.3% 1 0.6%

Wyoming 9 0.1% 1 0.6%

California 5 0.0% 1 0.6%

Idaho 5 0.0% 1 0.6%

Oklahoma 2 0.0% 1 0.6%

Total 12,020 100.0% 180 100.0%

The U.S. ethanol industry is currently comprised of a mixture of company-owned plants

and locally-owned farmer cooperatives (co-ops). The majority of today’s ethanol production

facilities are company-owned and, on average, these plants are larger in size than farmer-owned

co-ops. Accordingly, these facilities account for about 80% of today’s online ethanol production

capacity.254 Furthermore, nearly 30% of the total domestic product comes from 40 plants owned

by just three different companies – POET Biorefining, Archer Daniels Midland (ADM), and

Valero Renewables. For a summary of ethanol production capacity by company, refer to Figure

1.5-3 below.
136

Figure 1.5-3.

Current Corn/Starch Ethanol Production Capacity by Company

Green Plains Renewable

Energy, 480

Valero Renewables, 770

Archer Daniels Midland,

1,100

POET Biorefining, 1,535

Hawkeye Renewables,

460

The Andersons, 275

Glacial Lakes Energy,

LLC, 250

Aventine Renewable

Energy, 207

BioFuel Energy Corp., 230

Other, 6,714

Valero entered the ethanol industry in March of 2009 when it acquired seven ethanol

plants from former ethanol giant, Verasun. The oil company currently has agreements in place

to purchase three more ethanol plants that would bring the company’s ethanol production

capacity to 1.1 billion gallons per year.

37,255 However, ethanol plants are much smaller than

petroleum refineries. Valero’s smallest petroleum refinery in Ardmore, OK has about twice the

throughput of all its ethanol plants combined.256Still, as obligated parties under RFS1 and

RFS2, the refining industry continues to show increased interest in biofuels. Suncor and Murphy

Oil recently joined Valero as the second and third oil companies to purchase idled U.S. ethanol

plants. Many refiners are also supporting the development of cellulosic biofuels and algaebased biodiesel.

1.5.1.2 Forecasted Production Under RFS2

As highlighted earlier, domestic ethanol production is projected to grow to over 10 billion

gallons in 2009. And with over 12 billion gallons of capacity online as of November 2009,

ethanol production should continue to grow in 2010, provided plants continue to produce at or

above today’s production levels. In addition, despite current market conditions (i.e., poor

ethanol margins), the ethanol industry is expected to grow in the future under the RFS2 program.

Although there is not a set corn ethanol requirement, EISA allows for 15 billion gallons of the

36-billion gallon renewable fuel standard to be met by conventional biofuels. We expect that

37 Valero recently announced that it has purchase agreements in place to acquire the last two Verasun plants in

Linden, IN and Bloomington, OH and the former Renew Energy plant in Jefferson Junction, WI.
137

corn ethanol will fulfill this requirement, provided it is more cost competitive than imported

ethanol or cellulosic biofuel in the marketplace.

In addition to the 180 aforementioned corn/starch ethanol plants currently online, 27

plants are presently idled.38 Some of these are smaller ethanol plants that have been idled for

quite some time, whereas others are in a more temporary “hot idle” mode, ready to be restarted.

In response to the economic downturn, a number of ethanol producers have idled production,

halted construction projects, sold off plants and even filed for Chapter 11 bankruptcy protection.

Some corn ethanol companies have exited the industry all together (e.g., Verasun) whereas

others are using bankruptcy as a means to protect themselves from creditors as they restructure

their finances with the goal of becoming sustainable.

Crude oil prices are expected to increase in the future making corn ethanol more

economically viable. According to EIA’s AEO 2009, crude oil prices are projected to increase

from about $80/barrel (today’s price) to $116/barrel by 2022.257 As oil and gas prices rebound,

we expect that the biofuels industry will as well. Since our April 2009 industry assessment used

for the NPRM, at least nine corn ethanol plants have come back online.

For analysis purposes, we assumed that all 27 idled corn/starch ethanol plants would

resume operations by 2022 under the RFS2 program. We also assumed that a total of 11 new

ethanol plants and two expansion projects currently under construction or in advanced stages of

planning would come online.258 This includes two large dry mill expansion projects currently

underway at existing ADM wet mill plants and two planned combination corn/cellulosic ethanol

plants that received funding from DOE. While several of these projects are delayed or on hold at

the moment, we expect that these facilities (or comparable replacement projects) would

eventually come online to get the nation to approximately 15 billion gallons of corn ethanol

production capacity as shown below in Table 1.5-7.

Table 1.5-7.

Potential Corn/Starch Ethanol Industry Expansion Under RFS2

Plants

Currently

Online

Idled

Plantsa

Under

Constructionb

Planned Exp. or

DOE-Funded

ProjectscTotal

Plant Capacity (MGY) 12,020 1,440 1,301 166 14,927

No. of Plants 180 27 10 new 1 new, 2 exp 218

aAssumes all idled plants come back online in the future.

b

Includes construction projects that are currently on hold. Considers two dry mill expansion projects currently

underway at existing ADM wet mill sites to be new plants.

c

Includes an expansion project at an existing corn ethanol plant and two planned combination corn/cellulosic ethanol

plants that received funding from DOE.

38 Based on our November 2009 corn/starch ethanol industry characterization. We are aware of at least one plant

that has come back online since then.
138

While theoretically it only takes 12-18 months to build a corn ethanol plant39, the rate at

which new plant capacity comes online will be dictated by market conditions, which will in part

be influenced by the RFS2 requirements. As explained in Section 1.2.2, today’s program will

create a growing demand for corn ethanol reaching 15 billion gallons by 2016. However, it is

possible that market conditions could drive demand even higher. Whether the nation produces

additional corn ethanol is uncertain and will be determined by feedstock availability/pricing,

crude oil pricing, and the relative ethanol/gasoline price relationship. To measure the impacts of

the RFS2 program, we assumed that corn ethanol production would not exceed 15 billion

gallons. We also assumed that all growth would come from new plants or plant expansion

projects (in addition to idled plants being brought back online). However, it is possible that

some of the required growth could come from minor process improvements (e.g.,

debottlenecking) at existing facilities. Allowing a 5% tolerance on the baseline volume for

grandfathering facilities (per §80.1403) could promote such growth.

Once the aforementioned capacity expansion is complete, we estimate that there will be

218 corn/starch ethanol plants operating in the U.S. with a combined production capacity of

around 15 billion gallons per year. Much like today’s ethanol industry, the overwhelming

majority of new plant capacity (almost 88% by volume) is expected to come from corn-fed

plants. Another 12% is forecasted to come from plants processing a blend of corn and/or other

grains, and a tiny capacity increase is projected to come from an idled cheese whey plant coming

back online. A summary of the forecasted ethanol production by feedstock under the RFS2

program is found in Table 1.5-8.

39 For more information on our estimated plant build rates, refer to Section 1.5.3.4.
139

Table 1.5-8.

Projected RFS2 Ethanol Production Capacity by Feedstock

Capacity

MGY

No. of

Plants

Capacity

MGY

No. of

Plants

Barley 65 1 65 1

Corna

2,549 30 13,543 185

Corn, Milob

173 3 990 18

Corn, Wheat 0 0 130 1

Corn, Wheat, Milo 110 2 110 2

Corn, Whey 7 1 7 1

Milo 0 0 3 1

Wheat, Milo 0 0 50 1

Cheese Whey 3 1 3 1

Waste Beveragesc

0 0 20 5

Waste Sugars & Starchesd

0 0 7 2

Total 2,907 38 14,927 218

Additional Production Total RFS2 Estimate

Plant Feedstock

(Primary Listed First)

a

Includes one facility processing seed corn, one facility operating a pilot cellulosic butanol

plant, two facilities with plans to build pilot cellulosic ethanol plants, and three facilities with

plans to build small commercial cellulosic ethanol plants.

b

Includes one facility processing a small amount of molasses in addition to corn and milo.

c

Includes two facilities processing brewery waste.

d

Includes one facility processing potato waste that intends to add corn in the future.

With the exception of one facility40, all new corn/grain ethanol plants are expected to

utilize dry milling technologies and the majority of new production is expected to come from

plants burning natural gas. However, we anticipate that two manure biogas plants41, one

biomass-fired plant, and two coal-fired ethanol plants will be added to the mix.42 Of these new

and returning idled plants, we’re aware of five facilities currently planning to use CHP

technology, bringing the U.S. total to 32 as shown in Table 1.5-9.

40 Tate and Lyle is currently in the process of building a 115 MGY wet mill corn ethanol plant in Fort Dodge, IA.

41 One manure biogas plant that is currently idled and another that was under construction but is now on hold.

42 The two coal fired plants are the aforementioned dry mill expansion projects currently underway at existing ADM

sites. These projects commenced construction on or before December 19, 2007 and would therefore should likely be

grandfathered under the RFS2 rule. For more on our grandfathering assessment, refer to Section 1.5.1.4 of the RIA.
140

Table 1.5-9.

Projected Near-Term Corn/Starch Ethanol Production Capacity by Energy Source

Capacity

MGY

No. of

Plants

Capacity

MGY

No. of

Plants

CHP

Tech.

Biomass 88 1 88 1 1

Coala

550 2 2,308 19 10

Coal, Biomass 0 0 50 1 0

Manure Biogas 139 2 139 2 0

Natural Gasb2,130 33 11,757 184 15

Natural Gas, Biomassc

0 0 115 3 1

Natural Gas, Coal 0 0 35 1 1

Natural Gas, Landfill Biogas, Wood 0 0 110 1 0

Natural Gas, Syrup 0 0 101 2 0

Waste Heatd0 0 50 1 1

Waste Heatd, Natural Gas 0 0 175 3 3

Total 2,907 38 14,927 218 32

Total RFS2 Estimate

a

Includes six plants that are permitted to burn biomass, tires, petroleum coke, and wood waste in addition to

coal and one facility that intends to switch to biomass in the future.

b

Includes four facilities that might switch to biomass in the future, one facility that intends to burn thin stillage

and biogas, and a total of five facilities that were once considering switching to coal in the future.

c

Includes one facility processing bran in addition to natural gas.

dWaste heat from utility partnerships.

Plant Energy Source

(Primary Listed First)

Add'l Production

The information presented in Table 1.5-9 is based on the industry’s current near-term

production plans. However, we anticipate growth in advanced ethanol production technologies

under the RFS2 program. Forecasted fuel prices are projected to drive corn ethanol producers to

transition from conventional boiler fuels to biomass feedstocks. In addition, fossil

fuel/electricity prices will likely drive a number of ethanol producers to pursue CHP technology.

For more on our projected 2022 utilization of these technologies under the RFS2 program, refer

to Section 1.5.1.3 of the RIA.

Under the RFS2 program, the majority of new ethanol production (almost 70% of added

capacity) is expected to originate from PADD 2, close to where the corn is grown. However,

there are a number of “destination” ethanol plants being built outside the Midwest in response to

state production subsidies, retail pump incentives, and state mandates. A summary of the

forecasted ethanol production by PADD under the RFS2 program can be found in Table 1.5-10.
141

Table 1.5-10.

Projected RFS2 Corn/Starch Ethanol Production Capacity by PADD

Capacity

MGY

No. of

Plants

Capacity

MGY

No. of

Plants

PADD 1 349 4 499 7

PADD 2 2,011 25 13,340 188

PADD 3 145 2 439 6

PADD 4 50 1 202 8

PADD 5 352 6 447 9

Total 2,907 38 14,927 218

PADD

Additional Production Total RFS2 Estimate

Based on current production plans, we project that Iowa, Nebraska, Illinois, Minnesota

and South Dakota will continue to dominate ethanol production with a collective production

capacity of about 9.5 billion gallons per year. Ethanol production is expected to grow in other

Midwest states and there are also a growing number of plants that are being built outside the

Corn Belt. After the proposed RFS2 program is fully implemented, we estimate that more than

half of the United States will have corn/starch ethanol production. Table 1.5-11 shows our

predictions of ethanol production capacity by state (from greatest to smallest) after the RFS2

program is fully implemented.
142

Table 1.5-11.

Projected RFS2 Corn/Starch Ethanol Production Capacity by State

Capacity

MGY

No. of

Plants

Capacity

MGY

No. of

Plants

Iowa 545 3 3,759 42

Nebraska 453 4 2,013 27

Illinois 178 3 1,520 16

Minnesota 28 2 1,141 22

South Dakota 61 1 1,048 15

Indiana 286 3 1,002 12

Kansas 168 3 607 14

Ohio 224 3 544 8

Wisconsin 7 1 536 10

North Dakota 11 1 366 6

Texas 115 1 355 4

Michigan 50 1 267 5

Missouri 0 0 261 6

California 239 5 244 6

Tennessee 0 0 236 2

New York 114 1 164 2

Oregon 113 1 153 2

Colorado 0 0 138 5

Pennsylvania 110 1 110 1

Georgia 0 0 100 2

Virginia 65 1 65 1

North Carolina 60 1 60 1

Idaho 50 1 55 2

Mississippi 0 0 54 1

Arizona 0 0 50 1

Kentucky 0 0 40 2

New Mexico 30 1 30 1

Wyoming 0 0 9 1

Oklahoma 0 0 2 1

Total 2,907 38 14,927 218

Additional Production

State

Total RFS2 Estimate

The majority of future ethanol plants are expected to be company-owned. Of the 38

plants we are expecting to be built or brought back online under the RFS2 program, 36 are

expected to be owned by corporations. The leading ethanol producers will likely continue to be
143

Archer Daniels Midland and POET Biorefining, each with over 1.5 billion gallons of annual corn

ethanol production capacity. Valero Renewables is expected to be the third largest ethanol

producer with over 1.1 billion gallons of production capacity, provided the most recent ethanol

plant acquisition goes through. A summary of the projected ethanol plant ownership under the

RFS2 program is found in Figure1.5-4.

Figure 1.5-4.

Forecasted Corn/Starch Ethanol Production Capacity by Company

Other, 7,874

Tate & Lyle, 241

Green Plains Renewable

Energy, 480

Valero Renewables, 1,120

POET Biorefining, 1,580

Archer Daniels Midland,

1,650

Hawkeye Renewables,

460

Abengoa, 457

AltraBiofuels Plants, 290

Glacial Lakes Energy, 250

The Andersons, 275

White Energy Plants, 250

1.5.1.3 Forecasted Growth in Advanced Processing Technologies

While we can get a good idea of what the ethanol industry will look like in the near term

by looking at existing ethanol plants and those planned or under construction, further analysis is

needed to forecast what the ethanol industry will look like in 2022. Significant changes in the

primary fuel source and overall energy efficiency of ethanol production plants are likely to

occur. The high price of natural gas, projected to be $7.75/MMBTU in 2022 in the EIA 2009

Annual Energy Outlook, has many ethanol plants considering alternative fuel sources. Greater

biofuel availability and potential low life cycle green house gas emissions incentives may further

encourage ethanol producers to transition from fossil fuels to biomass based fuels.

As ethanol plants become more efficient and require less energy, their ability to use

biofuels increases. Two of the biggest drawbacks to using biofuels currently are handling and

storage costs. Due to the lower density of biofuels, as compared to coal, a larger area is required

to store biomass with an equivalent heating value. Handling costs are also increased as a larger

volume of fuel must be moved. These negative impacts would be less significant in an ethanol
144

plant using less energy. Lower overall energy use would also allow the energy needs of the

ethanol plant to be met entirely, or to a greater extent, by waste products and locally produced

biofuels. This would greatly reduce the purchase and transportation costs of the biofuels. If

ethanol producers do decide to make a transition to biofuels, is likely that plants currently using

natural gas would transition to biogas, and those using coal would transition to solid biomass.

This is primarily due to their ability to make these transitions without investing in new boiler

equipment. The same factors that may cause ethanol producers to increase biofuels usage, higher

fossil fuel costs and lower lifecycle green house gas emissions, are expected to increase the

number of ethanol producers using combined heat and power (CHP) technology. Projections for

the primary feedstock and use of CHP technology from 2020 to 2030 are summarized in Table

1.5-12 below.

Table 1.5-12.259

Projected Primary Fuel Sources and CHP Usage

2020 2022 2025 2030

Natural Gas Boiler 54% 49% 42% 31%

Natural Gas CHP 11% 12% 13% 15%

Coal Boiler 0% 0% 0% 0%

Coal CHP 4% 4% 4% 4%

Biomass Boiler 10% 11% 12% 15%

Biomass CHP 9% 10% 12% 15%

Biogas Boiler 12% 14% 16% 20%

The energy efficiency of ethanol plants is also expected to change significantly. New

technologies are expected to both increase the efficiency of units currently used in ethanol

production, as well as provide energy-saving alternatives to conventional production practices.

Increasing energy efficiency is a priority in many ethanol plants as is can dramatically increase

profitability by reducing energy costs, the second highest cost of ethanol production behind raw

materials. Several groups are currently working on technologies that could impact the ethanol

industry. The Department of Energy’s (DOE) Super Boiler program is expected to produce

boilers with an efficiency of 94% by 2020. The National Electrical Manufacturers Association’s

(NEMA) premium efficiency motors are expected to be adopted more widely in the coming

years. Electricity generation efficiency is also expected to increase at plants with CHP

technology. The projected energy savings from the energy efficiency improvements to units

used in conventional ethanol plants in 2022 relative to 2007 is 32.1%. The projected energy

savings from 2015 to 2030 are summarized in Table 1.5-13 below.
145

Table 1.5-13.260

Projected Energy Savings from Conventional Production Equipment

2007 2015 2020 2022 2025 2030

Boiler, Efficiency 82.0% 86.0% 90.0% 91.6% 94.0% 94.0%

Energy Savings Relative to 2007 - 1.2% 8.9% 10.5% 12.8% 12.8%

Motor, Efficiency 90.0% 92.0% 93.0% 93.8% 95.0% 95.0%

Energy Savings Relative to 2007 - 2.2% 3.2% 4.0% 5.3% 5.3%

10 MW Industrial Turbine, Efficiency 31.0% 33.0% 34.0% 34.0% 34.0% 34.0%

Energy Savings Relative to 2007 - 6.1% 8.8% 8.8% 8.8% 8.8%

The same factors that drive ethanol producers to increase the energy efficiency of their

equipment may also move them to consider energy saving changes to the ethanol production

process. Several process changes, including raw starch hydrolysis, corn fractionation, corn oil

extraction, and membrane separation, are likely to be adopted to varying degrees. The degree to

which they are adopted will depend on many factors, including technology availability, capital

cost of implementation, energy cost savings, and co-product revenue generation. A description

of each of these technologies, including the challenges and benefits of their implementation, can

be found in Section 1.4.1.3. The adoption of these technologies are expected to decrease the

average thermal energy use of dry mill ethanol plants by 11.8% and to increase the average

electrical energy use by 13.1%. These numbers are based on a plant that is drying 100% of its

distillers’ grains with solubles (DGS). Plants that dry less than 100% of their DGS would likely

realize smaller benefits from these technologies. The projected penetration of these

technologies, and the associated energy use impact, is summarized in Table 1.5-14 below.

Table 1.5-14.261

Projected Energy Savings from Process Changes

Percent of all Plants Adopting Process

Process Improvement 2020 2022 2025 2030

Raw Starch Hydrolysis 20% 22% 25% 30%

Corn Fractionation 18% 20% 24% 30%

Corn Oil Extraction 65% 70% 70% 70%

Membrane Separation 3% 5% 5% 5%

Energy Reduction from Base Process (Thermal)

Raw Starch Hydrolysis 16% 16.7% 17% 17%

Corn Fractionation 17.6% 17.6% 17.6% 17.6%

Corn Oil Extraction 5.4% 5.4% 5.4% 5.4%

Membrane Separation 15.7% 15.7% 15.7% 15.7%

Weighted Average Savings (Thermal) 10.3% 11.8% 13.0% 14.9%

Energy Reduction from Base Process (Electrical)

Raw Starch Hydrolysis 0% 0% 0% 0%

Corn Fractionation -29% -29% -29% -29%

Corn Oil Extraction -9.9% -9.9% -9.9% -9.9%

Membrane Separation -7.6% -7.6% -7.6% -7.6%

Weighted Average Savings (Electrical) -11.8% -13.1% -14.3% -16.0%
146

Another factor that plays a significant role in determining the energy usage of ethanol

plants is the treatment of the main co-product of the dry mill ethanol production process,

distillers’ grains with solubles (DGS). The DGS, which is most often sold as feed for cattle,

poultry, or swine, can be sold either dry or wet. Wet distillers’ grain with solubles (WDGS) can

often only be sold locally, as it is difficult to transport and is susceptible to spoilage. Drying the

DGS avoids these problems and allows the DGS to be sold in a much wider market; however

drying the DGS is an energy intensive process. USDA models suggest that 40.4% of the thermal

energy used in an ethanol plant that produces dry DGS is used in the drying process. Plants that

do not dry their DGS, or dry only a portion of it, could experience energy savings up to 40.4%.

According to a recent industry survey, 37% of all DGS produced by the dry mill ethanol industry

is sold wet. We have assumed that this percentage remains constant through 2022 for our energy

use projections.

Combining the impacts of these four factors (primary fuel sources, energy savings from

efficiency improvements, new technology and process changes, and DGS drying rates) allows us

to project the average energy usage of a dry mill ethanol plant in 2022. Table 1.5-15 below

outlines the projected average energy usage of dry mill ethanol plants in 2022. The first two

lines take into account the projected primary fuel types and energy efficiency improvements.

The next two lines adjust the totals to include new technologies and process changes. Finally,

the total is calculated by weighting the values for dry and wet DGS according to the production

ratio we expect in 2022 (63% dry DGS, 37% wet DGS)43.

Table 1.5-15

2022 Dry Mill Ethanol Plant Average Energy Usage

Thermal Energy Electrical Energy

Dry DGS, includes efficiency

improvements 28,977 BTU/Gal 1,515 BTU/Gal

Wet DGS, includes efficiency

improvements 17,271 BTU/Gal 1,515 BTU/Gal

Dry DGS, includes process changes 25,570 BTU/Gal 1,714 BTU/Gal

Wet DGS, includes process changes 16,255 BTU/Gal 1,714 BTU/Gal

2022 Average Energy Usage 22,123 BTU/Gal 1,714 BTU/Gal

In addition to projecting the average energy usage of a dry mill ethanol plant in 2022 we

have also projected the energy usage of a “best case scenario” plant. This plant was defined as a

plant that used the combination of all the technologies considered that resulted in the lowest

overall energy usage, as well as all the energy efficiency improvements discussed above. The

technologies used by the best case scenario plant were CHP, dry fractionation, membrane

separation, and raw starch hydrolysis. Corn oil extraction was not considered as plants would

have either corn oil extraction or dry fractionation but not both, and dry fractionation resulted in

greater energy savings. Best case scenario energy usage numbers were calculated for both

natural gas and coal/biomass fired plants producing both dry and wet DGS. The results are

shown below.

43 An Excel spreadsheet has been added to the docket showing the energy impact calculations of the technology

improvements (EPA-HQ-OAR-2005-0161-2729).
147

Table 1.5-16

2022 Best Case Natural Gas Dry Mill Plant Energy Usage

2022 Best Case Scenario (Dry DGS)

Thermal Electrical

2022 Base Plant 28,660 BTU/Gal 2,251 BTU/Gal

2022 Best Case Scenario 16,568 BTU/Gal 1,682 BTU/Gal

2022 Best Case Scenario (Wet DGS)

2022 Base Plant 17,081 BTU/Gal 2,251 BTU/Gal

2022 Best Case Scenario 9,932 BTU/Gal 1,682 BTU/Gal

1.5.1.4 Projected Grandfathered Corn Ethanol Volume

As explained in the Section II.B.3 of the preamble, renewable fuel produced from new

facilities which commenced construction after December 19, 2007 must achieve at least a 20%

reduction in lifecycle greenhouse gas emissions compared to baseline lifecycle greenhouse gas

emissions in order to generate RINs under the proposed RFS2 program.44 However, facilities

that commenced construction on or before December 19, 2007 are exempt or “grandfathered”

from the 20% GHG reduction requirement. In addition, facilities that commenced construction

in 2008 or 2009 are grandfathered if they burn natural gas, biomass, or any combination thereof.

The volume of ethanol that is grandfathered under today’s RFS2 rule will be determined

based on information received during the expanded registration process (refer to Section II.C of

the preamble). However, as part of this final rulemaking, we analyzed the volume of corn

ethanol that could potentially be grandfathered based on our knowledge of the industry.

To do so, we started with our assessment of corn ethanol plants that were operational,

idled or under construction at the time of our November 2009 assessment. As shown in Table

1.5-7, excluding the planned facilities, this amounts to about 14.8 billion gallons of ethanol

production capacity. Provided all the plants meet the definition of “commence construction”

under §80.1403, the potentially grandfathered volume of ethanol falls just shy of meeting the 15

billion gallon conventional biofuel standard. However, actual baseline volumes established

during registration could easily exceed 15 billion gallons. Furthermore, by allowing a 5%

tolerance on the baseline volume to account for minor changes during ongoing maintenance of

the facilities under §80.1403(a)(1), these plants could readily exceed 15 billion gallons of

production.

Further examination suggests that all of today’s corn ethanol plants will likely be

grandfathered under the RFS2 program because they are either fired with natural gas, biomass or

a combination thereof and commenced construction by December 31, 2009 or they burn coal but

commenced construction on or before December 19, 2007. A summary of grandfathering

assessment logic we applied is found in Figure 1.5-5. The 20 coal-fired plants we considered in

greater detail are presented in Table 1.5-17.

44 In accordance with Section 211(o)(2)(A)(i) of the Clean Air Act as amended by EISA.
148148

Figure 1.5-5.

Potential Grandfathered Volume of Corn Ethanol Under RFS2

Is plant fired with natural gas, biomass, or any

combination thereof and commenced

construction by December 31, 2009?

Grandfathered or deemed compliant

under RFS2 rule.

Yes No, plant burns coal

Not grandfathered under

the RFS2 rule.

No

Was the corn ethanol plant (or expansion

project) existing, idled or under construction

as of November 2009 industry assessment?

14.8

Bgal

0

Bgal

12.4

Bgal

2.4

Bgal

2.4

Bgal

0

Bgal

Did the facility commence construction

on or before December 19, 2007?

Yes

Yes

14.8

Bgal

Would need to meet

the 20% threshold

requirement

0.2

Bgal

No
149149

Table 1.5-17.

Coal-Fired Corn Ethanol Plants

Plant/Company Location

Capacity

MGY

On-Line

Date

Ag Processing Inc. Hastings, NE 52 1992

Archer Daniels Midland (ADM)a

Cedar Rapids, IA 250 1981

Archer Daniels Midland (ADM)a

Cedar Rapids, IA 275 Aug-10

Archer Daniels Midland (ADM)a

Clinton, IA 190 1981

Archer Daniels Midland (ADM)a Columbus, NE 275 Aug-10

Archer Daniels Midland (ADM)aColumbus, NE 95 1994

Archer Daniels Midland (ADM)a Decatur, IL 290 1976

Archer Daniels Midland (ADM) Marshall, MN 40 1988

Archer Daniels Midland (ADM) Peoria, IL 210 1980

Archer Daniels Midland (ADM) Walhalla, ND 25 1990

Aventine Renewable EnergybPekin, IL 100 1981

Cargillc

Eddyville, IA 35 1992

Chief Ethanol Fuels Inc. Hastings, NE 62 1985

Corn LPd Goldfield, IA 50 Dec-05

Grain Processing Corp Muscatine, IA 20 May-00

Heron Lake BioEnergy, LLC Heron Lake, MN 50 Oct-07

Lincolnway Energy LLC Nevada, IA 50 May-06

Red Trail Energy, LLC Richardton, ND 50 Jan-07

Riverland Biofuelse Canton, IL 38 Oct-08

Southwest Iowa Renewable Energy Council Bluffs, IA 110 Feb-09

Tate & Lyle Loudon, TN 126 1982

Total Coal-Fired Capacity 2,393

a

Permitted to burn biomass, tires, petroleum coke, and wood waste in addition to coal .

bRecently filed for bankruptcy protection.

cBurns a combination of natural gas and coal.

dBurns a combination of coal and biomass.

e

Formerly Central Illinois Energy.

As shown above, most of the coal-fired ethanol plants were built well before 2007 and

thus should have little problem qualifying as grandfathered under the RFS2 rule. There are

essentially four plants that could potentially pose a challenge with respect to the construction

cutoff date set by EISA. These facilities, bolded in Table 1.5-17 above, include two dry-mill

ADM plant expansion projects currently underway in Cedar Rapids, IA and Columbus, NE as

well as Riverland Biofuels in Canton, IL, and Southwest Iowa Renewable Energy in Council

Bluffs, IA. However, research and communications with these companies suggest that these

plants commenced construction on or before December 19, 2007 and thus should be

grandfathered and exempt from the 20% threshold requirement under RFS2.262
150

1.5.2 Imported Ethanol

In order to assess the potential for U.S. imported ethanol, we examined the chief

countries that are currently producing or consuming relatively large volumes of ethanol. In

particular, we chose to focus on Brazil, the European Union (EU), Japan, India, and China to

determine whether each country will likely be an importer or exporter of ethanol in the future.

The following sections first describe the ethanol demands of each of these countries due to

enacted or proposed mandates and goals as well as their ability to supply those demands with

domestically produced ethanol. With the exception of Brazil, we show that the majority of

countries analyzed could likely be importers of ethanol in the future and therefore could compete

with the U.S. for supplies of ethanol. We conclude our analysis by examining the most likely

pathways for imported ethanol to the U.S., namely through the Caribbean Basin Initiative (CBI)

and directly from Brazil.

1.5.2.1 Historic/Current Imports and Exports

Brazil

Much of the potential of imported ethanol will depend on the ability for Brazil to supply

ethanol to the United States and other countries. This is because Brazil has been a top producer

and is the top exporter of ethanol in the world. In fact, many countries are interested in Brazilian

produced sugarcane ethanol because it is currently the least costly method for producing ethanol.

Brazil has been steadily increasing its exports of ethanol, with total exports escalating

from under 700 million gallons in 2005 to over 1200 million gallons in 2008. As seen in Figure

1.5-6, Brazil exports ethanol to many different countries around the globe. Prior to 2006, the

majority of Brazilian ethanol exports flowed to the EU and Caribbean due to favorable

economics. In 2006, the majority of Brazilian ethanol exports (52%) went to the U.S as a result

of the withdrawal of MTBE from the U.S. gasoline fuel pool and high oil prices. The EU,

Caribbean, and U.S. have continued to be major importers of Brazilian ethanol in recent years.
151

Figure 1.5-6. Brazil Ethanol Exports (Includes all types of ethanol).263,264,265

0

200

400

600

800

1000

1200

1400

2005 2006 2007 2008 2009*

Million Gallons

Others

Venezuela

Japan

India

Mexico

Nigeria

South Korea

Caribbean

EU

U.S.

*January 2009-October 2009

Brazil currently produces both hydrous and anhydrous ethanol. Hydrous ethanol contains

96% ethanol and 4% water by volume, whereas anhydrous ethanol is made up of 99.5% ethanol

and 0.5% water.266 While hydrous ethanol is used in Brazil directly in Otto-cycle motors (as

100% ethanol by volume), anhydrous ethanol is mixed with gasoline at 20-25% by volume.

Production of anhydrous ethanol to be mixed with gasoline has fallen since the 2005/2006

harvest, on account of the smaller share of cars running exclusively on gasoline. This was

especially due to the success of flex vehicles with Brazilian customers.267 In fact, sales of flexfuel vehicles (FFVs) in Brazil, those that can use any mixture of gasoline and ethanol from 0 to

100%, have grown dramatically, with domestic FFV sales representing 85% of vehicles sold

between January 2009 and October 2009.268 Hydrous ethanol accounted for 65% of ethanol

produced in Brazil in 2008, and 73% of ethanol produced as of December 1, 2009. Figure 1.5-7

shows the historical production of hydrous and anhydrous ethanol in Brazil.
152

Figure 1.5-7.

Historical Ethanol Production of Hydrous and Anhydrous Ethanol in Brazil.269

0

1,000

2,000

3,000

4,000

5,000

6,000

1997199819992000200120022003200420052006200720082009

Million Gallons

Hydrous Ethanol

Anhydrous Ethanol

*2009 data is up to date as of 12/01/2009

In contrast to Brazil, ethanol consumed in the U.S. must first be converted to anhydrous

ethanol before it can be used in conventional or FFV vehicles. This differs from Brazil because

Brazilian FFVs have been designed to use hydrous ethanol, or E100 (100% ethanol by volume)

for the conditions in Brazil, whereas U.S. FFVs can only use up to E85 (85% ethanol and 15%

gasoline by volume). As a result, if hydrous ethanol is exported from Brazil, it must be

dehydrated somewhere else before it can be used in the U.S. This is the case for the majority of

ethanol exported from Brazil to the Caribbean, where it is dehydrated and often re-exported to

the U.S. for consumption.

In terms of future ethanol production, however, there has been much speculation about

Brazil’s ability to increase production. Sugarcane analyst Datagro recently stated that Brazil’s

ethanol fuel production would have to grow by approximately 800 million gallons a year through

2025 to keep up with demand at home and abroad.270 Estimates of future ethanol production in

Brazil vary greatly, see Figure 1.5-8. Brazil’s government has adopted plans to meet global

demand by tripling production by 2020.271 This would mean a total capacity of approximately

12.7 billion gallons, to be achieved through a combination of efficiency gains, greenfield

projects, and infrastructure expansions. Estimates for the required investment tend to range from

$2 billion to $4 billion a year. Other estimates indicate that, based on current projects, the

required investment in capacity expansion is $3-4 billion annually.272 If global demand were to

increase much more than Brazil is planning, then capacity would need to expand even further

and greater investment would be required.
153

Figure 1.5-8. Estimated Brazilian Ethanol Production Volumes273

0

5000

10000

15000

20000

25000

2000 2005 2010 2015 2020 2025 2030 2035

Million Gallons

FAPRI 2009 U.S. and World

Agricultural Outlook

OECD-FAO Agricultural Outlook

Bioenergia (2007)

Rothkopf (2006)

EPE (2007)

Unica

To meet the growing demand, the Brazilian sugar and ethanol industry is already rapidly

expanding and numerous mills have been planned. Brazil currently has nearly 400 sugar and

ethanol mills, with more anticipated over next few years.274,275,276 Brazil’s state-owned

development bank BNDES said the country is set to invest $13.1 billion between 2007 and 2011

in 89 new sugar and ethanol mills.277 Some estimate even more, where investments in sugarcane

processing factories are expected to top $23 billion over the next four years.278 Investments

include a project by Odebrecht, a Brazilian engineering company that will invest $2.6 billion

dollars over the next decade to build 12-15 plants with a combined capacity to produce ~ 400

million gallons per year of ethanol.279 Even U.S. ethanol producer ADM is preparing to enter the

sugarcane business in Brazil. A recent quote by ADM’s senior vice president of strategy, Steve

Mills, said that sugarcane ethanol is now “a key component” of ADM’s short-term strategy and,

“We’re devoting a lot of time and energy to this area. We’re not talking about something 10

years down the road. It’s on the front burner.”280

In addition to expanding sugarcane production and ethanol plant capacity, Brazil will

need to improve its current ethanol distribution infrastructure. Brazil’s transport system is

predominantly road-based.281 Railroad infrastructure and use of a waterway system is lacking, as

well as very low availability of multi-mode terminals. Logistics represent approximately 22% of

the export expenses and is one of the areas where costs need to be reduced in order for Brazilian

ethanol to become more competitive abroad.282
154

One way to deal with the lack of infrastructure is to expand the pipeline network.

Petrobras, Brazil’s largest petroleum refiner is planning to build a pipeline to transport ethanol

destined for export from the states of Sao Paulo, Minas Gerais, Mato Grosso, Mato Grosso do

Sul, Goias, and Parana. The pipeline is anticipated to go online in October 2010, with $232

million invested in the project. By 2012, Petrobras will spend more than $1.6 billion to improve

logistics infrastructure to transport Brazilian ethanol. By 2011, Petrobras has the goal of

exporting 920 million gallons per year.283 One of the pipelines will run from Goias state in

Brazil’s center-west to Petrobras’s Paulinia refinery in Sao Paulo State. The project is called

PMCC Projetos de Transporte de Alcool. The line is expected to have the capacity to ship 3.2

million gallons of ethanol annually.284

Other competitors include the joint venture from Cosan, Copersucar, and Crystalsev

which will make initial investments of $11.5 million apiece to install an ethanol-only pipeline

between the oil refinery in Paulinia, to an ethanol offloading terminal on the state’s coast. In

addition, at least three major private equity groups (Infinity, Clean Energy Brazil, and Brenco)

plan to invest $1 billion in a 683-mile pipeline expected to be completed by 2011 with a capacity

to deliver 1.1 million gallons of ethanol a year. In total, it is estimated that Brazil will need to

invest $1 billion each year for the next 15 years in infrastructure to keep pace with capacity

expansion and export demand.285

Another area that requires investment is in R&D and education. Currently, Brazil

produces only 0.08 engineers for every 1000 people, compared to 0.2 in the U.S., 0.33 in the EU,

and 0.8 in Korea.286 Since certain types of education require a long lead time (e.g., scientific

training) Brazil will need to continue to invest in training and professional development for the

sector’s labor pool to meet the growing demand in the biofuels industry.

Before ethanol can be exported to other countries, Brazil’s own domestic fuel

consumption must be met. Brazil currently has an ethanol mandate of 25%.287 The ethanol to

gasoline mix is set by the Brazilian government, which has the flexibility to adjust the ethanol

mandate from 20-25% by volume.

At some point in the future, Brazil’s light vehicle fleet may become saturated with FFVs

in preference to mainly gasoline fueled vehicles. As such, the rate in domestic demand for

ethanol is expected to begin to slow.288 Thus, as domestic demand begins to level off, some

experts believe that there is a significant possibility that exports will become more relevant in

market share terms. Figure 1.5-9 shows various estimates for future Brazilian ethanol domestic

consumption.
155

Figure 1.5-9. Estimated Brazilian Ethanol Consumption Volumes 289

0

5,000

10,000

15,000

20,000

25,000

2000 2005 2010 2015 2020 2025 2030 2035

Million Gallons

Canaplan, Director Carvalho

FAPRI 2009 U.S. and World

Agricultural Outlook

EPE (2007)

Unica

After domestic consumption is met, the rest of the ethanol can be available for exports to

other countries. Potential worldwide exports basically equal the total production minus the total

consumption. Given the available data, only three sources estimated both production and

consumption for some of the years during 2010-2022. As such, these values were used to

compute reasonable export volumes from Brazil as seen in Figure 1.5-10. Estimates from EPE

and Unica indicate that as much as 3.8-4.2 billion gallons could be exported by Brazil in the

2020/2022 timeframe. Longer timeframe estimates from sugarcane analyst Datagro project

international ethanol sales to grow to 6.6 billion gallons by 2025.290
156

Figure 1.5-10. Estimated Brazilian Export Volumes

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

2010 2012 2014 2016 2018 2020 2022

Million Gallons

EPE (2007)

Unica

FAPRI 2009 U.S. and World

Agricultural Outlook

The European Union (EU)

Although the EU market has largely focused on biodiesel, ethanol has become

increasingly important. Fuel ethanol production in the EU has grown from 140 million gallons

in 2004 to 754 million gallons in 2008.291 Germany, Spain, France, Poland, and Hungary

represent almost 80% of the production in 2008. Historically, however, ethanol production

volumes have been lower than mandatory blending targets.

The EU has set several targets for biofuel consumption over the past few years. In 2001,

an EU Directive established that by 2005 biofuels should cover 2% of the total fuel consumption

(energy basis), while the target for 2010 was set at 5.75%. However, in recent years the average

biofuel contribution has been much less (0.5%, 0.6% and 1% in 2003, 2004, and 2005,

respectively).292 It is also not expected that the EU will achieve its target of 5.75% of road

transport by 2010.293

In April 2009, the EU Energy and Climate Change Package (CCP) was adopted. This

package includes a minimum target requiring 10% renewable energy use in transport by 2020.

Most, if not all of this 10% is expected to come from increased biofuel use.294 The biofuels used

must meet certain criteria to be taken into account for the 10% goal, e.g., meet GHG emissions

reduction thresholds. The International Energy Outlook (IEO) 2009 forecasts OECD European

countries will consume 16.5 quadrillion Btu of transport fuel in 2010, growing to 17.6

quadrillion Btu by 2030.295 Assuming a split of nearly 70% fuel volume consumed as diesel and
157

30% consumed as gasoline (current use), a 10% by energy requirement would require roughly

6.8 billion gallons of ethanol in 2022.45,296 However, this may be a slight overestimate of

gasoline use since the overall gasoline consumption in the EU is declining as a result of the

increasing popularity of more economic diesel-powered cars. Other sources indicate smaller

gasoline consumption volumes are possible by the 2020/2022 timeframe which when translated

equals 5.2-5.4 billion gallons of ethanol assuming a 10% energy requirement.297,298 According to

the “FAPRI 2009 U.S. and World Agricultural Outlook”, ethanol production in the EU is

expected to grow to 1.6 billion gallons by 2018. Taking this into account, the EU would need to

import approximately 4-5 billion gallons of ethanol in order to meet the 10% volume

requirement if only traditional crops are used.

As of September 2009, the installed capacity of the EU ethanol industry is 1.7 billion

gallons, while 0.6 billion gallons are under construction and another 3 billion gallons has been

announced.299,300 Totaling these capacity estimates, the EU would have 5.3 billion gallons

ethanol capacity. In the EU, these facilities mainly process wheat, corn and sugar beet

derivatives, with a limited amount produced from barley, rye and the surplus of wine alcohol.

While not all the announced projects in the EU will be completed, this gives an estimate of how

fast and large ethanol production in the EU could grow. If we assumed that the EU could

produce this volume by 2022, as little as 1.5 billion gallons would need to be imported from

other countries assuming a 6.8 billion gallon demand due to the 10% mandate. Thus, it appears

likely from the above analysis that the EU will continue to be a net importer of biofuels under

most future scenarios.

Japan

Historically, Japan has not produced much ethanol (24,000 gallons in 2008) and has

imported the majority of its consumption.301,302 Now the government is showing signs of

encouraging biofuels production by promoting (not mandating) a 3% blend of ethanol in

gasoline. At the very least, a non-mandatory 3% blend will create a demand of 106-132 million

gallons of ethanol.303 This is similar to Japan’s Agency for Natural Resources and Energy target

to replace 132.1 million gallons of transportation fuel by 2010, using ethanol and biodiesel.304

With a 3% ethanol blend, ethanol trade may increase substantially with Japan (this may

even reach over 1.5 billion gallons annually if a 10% ethanol blend is implemented nationwide in

Japan, approximately 500 million gallons with 3% ethanol blends).305 While the use of greater

than 3% blends in Japan may be unlikely, the Japanese government has mandated that all

gasoline powered vehicles are able to run on 10% blends by 2030 and may also enact legislation

to require all new vehicles to be E10 compatible by 2012.306,307

One challenge with the use of ethanol in Japan is its distribution. As E5 and higher

ethanol blends have been shown to be corrosive to aluminum and rubber car parts, Japan is

looking into using ETBE blends of 7% and even 20-25% instead of ethanol.308 The Petroleum

Association of Japan has announced that gasoline containing ETBE blends of 7% will be

available for general public consumption by 2010. As ETBE is produced using ethanol as a

45 Assuming energy contents 115,000 Btu/gal for gasoline and 77,012 Btu/gal for denatured ethanol and 17.5

quadrillion Btu in 2022
158

feedstock, this could create a domestic ethanol demand of 90-100 million gallons.309 Imports of

ETBE were roughly 1.7 million gallons in 2008.310

Table 1.5-18 details select ethanol model plants and facilities in Japan. In total, there are

close to a dozen ethanol facilities nationwide, however, details on each facility were not fully

available and most are considered small-scale.

Table 1.5-18. Select Ethanol Model Plants and Facilities in Japan311

Plant Capacity Feedstock

Nippon Steel Plant 38,000

gallons/year

Food waste from: supermarkets,

restaurants, schools, hospitals

Mitsui Engineering

& Shipbuilding Co.

Not available Agricultural wastes: felled oil

palm trunks, empty fruit bunches,

fibrous fruit wastes, kernel shells

Shimizucho,

public-private

partnership

between Mitsubishi

Corp. and Hokuren

4 million

gallons/year

Off-spec wheat and sugarbeets

Tomakomai 4 million

gallons/year

Rice

Obihiro City,

Hokkaido run by

Tokachi

Foundation

Small volumes Wheat

Niigata, joint

operation with Zennoh

Small volumes Rice

Historically, Japan has relied on nations such as Brazil to supply ethanol, although it is

almost all for industrial use. Imports of ethanol for transportation use are currently negligible;

however, future imports may be possible from Brazil given the joint ventures established

between Japanese and Brazilian firms. In early 2005, Japan and Brazil signed an agreement for a

bilateral biofuels program to export Brazilian ethanol and biodiesel to Japan. Japan’s investment

will be used to install new ethanol facilities, increase acreage of sugarcane production, and

modernize the infrastructure necessary for the transportation of ethanol.

One such partnership is between Brazilian oil company, Petrobras, and trading house

Mitsui & Co., with financial support from Japan Bank for International Cooperation. The

companies are in the process of analyzing 40 projects evaluated at $8 billion which produce

alcohol and sugar from sugarcane. According to Paulo Roberto Costa, head of Petrobras’ supply

division, “Our target is to produce ethanol to be exported only to Japan.” Petrobras plans to

produce a total of 1 billion liters (264 million gallons) of alcohol annually at five processing

plants in the states of Mato Grosso, Goias, and Minas Gerais. Each of the five processing plants

will produce approximately 50 million gallons per year within the next 2 1⁄2 years, and the whole

production will be exported to Japan. In order to convince Japan that Petrobras has adequate
159

ethanol supplies it was noted that their processing facilities will not be able to produce sugar,

only alcohol.312 With this amount (264 million gallons) slated for Japan only, other countries

may have to either develop their own contracts with Brazil to ensure a stable supply.

Petrobras also recently bought a 90 percent stake in Exxon Mobil’s Okinawa oil refinery

that may serve as a staging point for Brazilian ethanol exports to Japan and the rest of Asia. This

may help mitigate one of the main problems for Petrobras and other major exporters, a lack of

offloading infrastructure.313

The prospect for large domestic production of ethanol in Japan appears to be small due to

limitations on feedstock. In fact, Japan’s first biomass plan, “Biomass Nippon Strategy”

unveiled in December 2002 and updated in 2008 reveals that the Government of Japan’s (GOJ’s)

current thinking, given limited agricultural resources, is to focus on cellulosic biofuel as the

future for Japan’s biofuel production.314 The Agriculture Ministry states that Japan has enough

feedstock to produce 26.4 million gallons per year, however, the Ministry of Environment

(MOE) expects Japan to meet only 10% of the 132.1 million gallon target (or 13.2 million

gallons) with domestic ethanol production.315 The Ministry of Agriculture and Fisheries

(MAFF), on the other hand, predicts that Japan could reasonably expect to supply approximately

95 million gallons. Even with these higher domestic production estimates, Japan would still be a

net importer of fuel ethanol if the biofuels target is met. Thus, the potential estimated demand

for imported ethanol ranges from 11 million gallons to 1572 million gallons depending on the

type of mandate assumed and the differences in the estimates of domestic ethanol production.

India

India has continued to focus on the use of non-food sources (e.g., sugar molasses) for the

production of ethanol for blending with gasoline. The amount of ethanol blended into gasoline

in India has fluctuated in the past few years. The government’s current target of 5% blending of

ethanol with gasoline has been partially successful in years of surplus sugar production, but

falters when sugar production declines.316 Commercial production and marketing of ethanolblended gasoline started in January 2003 when the Ministry of Petroleum and Natural Gas

launched the first phase of the ethanol blended petrol (EBP) program that mandated blending of

5% ethanol in gasoline in 9 states (out of a total of 28) and 4 union territories (UT) (out of a total

of 7). In 2004, ethanol blending in gasoline had to be halted because of a lower sugar output due

to a drought, which increased prices. However, production started back up in late 2005 when a

fuller sugarcane molasses crop became available. Then in September 2006 the government

announced the second phase of the EBP that mandates 5% blending ethanol with gasoline in 20

states and 8 union territories.46 The mandate was effective starting in November 2006 and would

have required about 159 million gallons to be used. However, the program only started with 10

states and was not implemented in other states due to high state taxes, excise duties and levies.

Industry sources report that ethanol supplies for the EBP program have come to a virtual

halt in most states since October 2008.317 In fact, industry sources estimate that only 143 million

gallons of ethanol have been supplied to the EBP program by the end of April 2009 during the

past two and a half years. The government has had plans to extend the ethanol blend ratio to

46 The number of union territories appears to have changed since 2006.
160

10% in a third stage once the program is extended to all target states. The original plan was a

minimum 10% ethanol blend by October 2008; however, this was put on hold because of the

sharp fall in crude oil prices and because of technical concerns raised by the Society of Indian

Automobile Manufacturers (SIAM). The main concern is that vehicles with older engines may

not be able to use 10% blends without engine modifications (e.g., two-wheelers). In December

2009, India’s government has indicated the urgency to kickstart the 5% blending program

because the requirement of ethanol has increased to 225 million gallons in the course of the delay

in implementation (since 2006).318 Regardless, the government announced a draft National

Biofuel Policy in September 2008 to raise the blending level to 20% of total fuel usage by 2017

(includes biodiesel).319 Industry sources expect the National Biofuel Policy may be reviewed

again soon, however, approval by the Parliament may take some time.

India has about 320 distillers with a production capacity of about 925 million gallons.

Due to the government’s ethanol policy, over 115 distilleries have modified their plants to

include an ethanol production line, with a total production capacity of 396 million gallons per

year, enough to meet the estimated demand for E5. Under an E10 mandate, however, the current

ethanol production capacity would need to be enhanced.

Some oil companies are instead pushing for imports of ethanol. However, there is an

import duty of 28.64% on the cif value for denatured ethanol. The c.i.f. (cost, insurance, and

freight) value represents the landed value of the merchandise at the first port of arrival in a given

country. In comparison to the U.S. which has a tariff of 54 cents per gallon (with 45 cents per

gallon offset by the ethanol blending subsidy) and a smaller ad valorem tax of 2.5% for

denatured ethanol, import duties in India are much higher.

The analysis of India’s biofuels developments appears to indicate that it will be selfsustaining if E5 is mandated (as noted by the sugar industry). However, as India strives to meet

its E10 goal, it may need to rely on imports from other countries. India’s own domestic

production may grow from its current estimated production of 26 million gallons of ethanol

(marketing year 2008/09), with production capacity expanding to 396 million gallons per year.

To meet current E5 and E10 mandates, approximately 225 million gallons to 450 million gallons

per year of ethanol, respectively, is required (note that this will continue to increase as fuel

demands increase). Therefore, depending on the amount of ethanol that India chooses to

mandate, India could either be an importer of ethanol or be able to meet its goals with

domestically produced ethanol.

China

In 2008, China was the world’s fourth largest fuel ethanol producer, producing around

500 million gallons.320 The majority of fuel ethanol in China is made from corn.321,322 However,

concerns in China about the security of their food supply and the inflationary impact of biofuels

which use grains as feedstock have influenced the feedstocks to be used in the future. With a

population of 1.3 billion people, corn growers have to meet the demand for food while also

providing feedstock for fuel. In addition, they supply livestock feed for which demand is

estimated to rise.323
161

In response to these food and feed demands for corn, according to the National

Development and Reform Commission (NDRC), China stopped approvals for industrial corn

processing for three years and suspended approved projects which had not yet started

construction.324 Since 2007, corn consumption by the deep-processing sector (i.e.,

transformation of corn into industrial products like ethanol) will be restricted to about 26 percent

of China's total corn consumption.

The National Development and Reform Commission (NDRC) stated in their 11th Five

Year Plan (2006-2010) that the production of approximately 2 billion gallons of grain-based

ethanol will not threaten the country’s grain security. In 2005, there were four fuel ethanol

plants operating in the country with a production capacity of approximately 300 million gallons:

Jilin Fuel Ethanol Co., Anhui BBCA Biochemical Co., Henan Tian Guan Fuel Ethanol Co., and

the China Resources Alcohol Co.325,326 These plants were established after 2000 to address a

surplus of grains in China at the time. Since then, total production in 2008 has increased to an

estimated 521 million gallons, see Table 1.5-19.327

Table 1.5-19. Fuel Ethanol Production in China

Location (Province,

City) Company Name

Principal

Feedstock

Estimated 2008

Production (Mgal)

Estimated 2009

Production Capacity

(Mgal)

Heilongjiang,

Zhaodong

China Resources

Alcohol Co. Corn/Rice 59 59

Jilin, Jilin Jilin Fuel Ethanol Co. Corn 155 165

Henan, Nanyang

Henan Tian Guan

Fuel-Ethanol Co. Wheat 135 149

Anhul, Bengbu

Anhui BBCA

Biochemical Co. Corn 132 145

Guangxi

Guangxi COFCO BioEnergy Co. Cassava 40 66

Total 521 584

As seen in the above table, several distilleries have been looking into alternative

feedstocks.328 Examples of alternative feedstocks include sorghum, wheat, cassava, and sweet

potato. These crops, however, are grown in much smaller quantities than corn. As such, if

China ethanol production expands, China may have to rely on imported feedstocks.329

China began mandating fuel ethanol blending in gasoline in June 2002.330 In 2004, the

Chinese government introduced an ethanol mandate of 10% (E10) in several provincesHelongjiang, Jilin, Liaoning, Henan, and Anhui. This mandate was further expanded to 27 cities

in the provinces of Shandong, Jiangsu, Hebei, and Hubei in 2006. To keep up with fuel demand,

a National Plan calls for fuel ethanol production to rise from approximately 330 million gallons

of ethanol per year to 660 million gallons by 2010 and 3.3 billion gallons by 2020.47,331

47 Assuming a conversion of 1 million tonnes of ethanol equals 330 million gallons.
162

China may soon become a major importer of ethanol, especially if the E10 blend is

extended across the country. With a nationwide E10 blend in 2020, biofuels demand would be

approximately 7.6 billion gallons of ethanol.332 Even if the National Plan which calls for China’s

domestic fuel ethanol production to reach 3.3 billion gallons by 2020 is met, a nationwide E10

blend would result in a supply shortfall of about 4.3 billion gallons of ethanol.333 Another study,

the “FAPRI 2009 U.S. and World Agricultural Outlook” also indicates that China would be a net

importer of ethanol in the future (out to 2018), where domestic production only reaches

approximately 600 million gallons. Assuming a possible E10 mandate nationwide and the

projections for domestically produced ethanol, China would need to import approximately 4.3-

7.0 billion gallons of ethanol per year.

Other Countries

Although Brazil is the largest exporter of ethanol, there may still be other countries that

could provide additional ethanol to the U.S. In fact, trace amounts of ethanol entered the U.S.

market from Argentina, Canada, Netherlands, and Pakistan in the past.334 The North American

Free Trade Agreement (NAFTA) is similar to the Caribbean Basin Initiative (CBI) in that it

welcomes tariff-free ethanol imports from Canada and Mexico.

In addition, there may also be other countries that are beginning biofuels programs and

could demand smaller volumes of ethanol in the future. We provide a list of the potential

mandates and goals for other countries below in Table 1.5-20. This list is not meant to be allinclusive, but rather a look at biofuel initiatives in other countries.
163

Table 1.5-20

Potential Mandates and Goals for Various Countries335,336,337,338,339,340,341,342

Argentina Former Argentine President Nestor Kirchner signed a law in February 2007 implementing tax breaks

and fuel-content mandates for biofuels. The Biofuels Act includes tax breaks for companies

investing in the biofuels sector and mandates 5% ethanol in gasoline by 2010. Analysts estimate

that the country will need 270 million liters per year of ethanol (71 million gallons per year) to satisfy

the E5 requirements in 2010, which some believe will not be fully complied. In January 2008,

Congress passed a law that promotes production of bioethanol from sugarcane, allowing sugar mills

to participate under the biofuel promotional regime.

Australia

The Australian government has set a biofuels target of 93 Mgal by 2010 according to the 'Biofuels

for Cleaner Transport' 2001 election policy. This target was never mandated in legislative form.

Queensland- In early August 2006 a mandate for a minimum of 5% ethanol from December 21,

2010.

New South Wales (NSW)- Beginning in September 2007, fuel supplied to wholesalers in New South

Wales will be required to contain 2% ethanol. Proponents of ethanol in the region want to increase

the mandate to 4% in 2009 and 10% in 2010.

Australian Capital Territory (ACT)- The ACT does not plan to mandate ethanol. Generally this

territory follows the policies of NSW because most of their fuel supplies are sourced from NSW.

Victoria- Biofuels target of 5% of fuel market by 2010 (106 Mgal), this includes biodiesel.

South Australia- No plans to mandate of set a target for biofuels use.

Northern Territory- No plans to mandate or set a target for biofuels known.

Western Australia- Biofuels target of 5% of fuel market by 2010.

Tasmania- The alternative fuels policy is currently based on CNG use.

No plans to mandate or set a target for biofuels known.

Canada On June 26, 2008, the Canadian Senate passed Bill C-33, which will require the use of 5%

renewable content in gasoline by 2010. Canada's Government General Michaelle Jean signed the

bill after it was passed in the senate, making it official.

Saskatchewan- Enacted in October 2006 a 7.5% ethanol mandate in gasoline (approximately 131

Mgal)

Ontario- Enacted in January 2007 a 5% ethanol mandate in gasoline, tentative increase to 10% by

2010

British Columbia- Bill C-16 to pass soon, 5% ethanol by 2010 to support federal plan

Alberta- Has not set its own standard as it prefers a national approach

Manitoba- Beginning April 1, 2008, 8.5% in gasoline (approximately 130 Mgal)

Quebec- 5% ethanol in gasoline by 2012, expects source to be met with cellulosic ethanol

production

Nova Scotia- No goals for biofuels

New Brunswick- No goals for biofuels

Newfoundland Labrador,PIE- Interest on the East Coast, but nothing as of May 2008

North West Territories, Yukon, Nunavut- No goals for biofuels

Columbia In September 2001, the Colombian Government issued Law 693, which made it mandatory to use

10% ethanol blends in gasoline in cities with populations larger than 500,000 inhabitants by the year

2008. The law went into effect in September 2005. Ethanol production, however, could not cover

the entire country's demand, and thus the government established a phase-in period throughout the

country for mandatory ethanol use.

Mexico On February 1, 2008, the Mexican Government published the Biofuels Promotion and Development

Law (LPDB) establising legal framework from which all biofuel public policies will develop. The law

does not currently state specific mandates for biofuels.

Summary of Potential Import/Export Demands

For the main countries we have analyzed from above, there appears to be a large potential

demand from the EU, Japan, India, and China for imported ethanol. See Table 1.5-21 for a

summary of potential import demand by 2020/2022. Total import potential demand from all
164

these countries could range from approximately 4.4-14.3 billion gallons. If these countries

decide to meet their mandated ethanol blends or enact new mandates, this could greatly increase

the amount that each country would demand from other countries. As discussed above, Brazil is

only expected to export a total of 3.8-4.2 billion gallons by 2022. This is significantly below the

volume we estimated that could be potentially demanded by other countries in the future.

Therefore, it is likely that unless Brazil increases production much more than its government

projects, the EU, Japan, India, and China will not be able to meet their stated goals. This also

indicates that the U.S. will likely compete with other foreign countries for exports from Brazil.

This analysis, however, only considers non-cellulosic biofuel potential. If cellulosic biofuel

production develops in these countries, it is entirely possible that the biofuel demands could be

lower due to greater supplies. We briefly discuss the potential for imported cellulosic feedstocks

or biofuels in Section 1.1.2.6.

Table 1.5-21. Potential Import Demand:

EU, Japan, India, and China by 2020/2022 (billion gallons).48

Country EU Japan India China Total

Potential Domestic Production 1.6-5.3 0-0.1 0-0.4 0.6-3.3

Potential Consumption

Petrobras Contract n/a 0.3 n/a n/a

E3 n/a 0.5 n/a n/a

E5 n/a n/a 0.2 n/a

E10 (or 10% by energy for EU) 5.2-6.8 1.6 0.5 7.6

7% ETBE n/a 0.1 n/a n/a

Potential Import Demand 0-5.2 0.1-1.6 0-0.5 4.3-7.0 4.4-14.3

1.5.2.2 Projected Growth Under RFS2

As long as imported ethanol is cost-competitive with gasoline, there will continue be a

demand for it. As our analysis from above shows, Brazil is the only country that will likely be

able to provide a significant volume of ethanol to the U.S. Accordingly, Brazil will ship ethanol

to the U.S. and other countries in the most cost-effective way.

The pathway Brazil chooses to ship ethanol will likely depend on the tariffs and taxes put

in place by receiving nations. Specifically, the U.S. places a 54 cent tariff on all imported

ethanol (as well as a 2.5 percent ad valorem tax for un-denatured ethanol and a 1.9 percent tax

for denatured ethanol). A key reason for establishing a tariff was to offset a tax incentive for

ethanol-blended gasoline, which is currently set at 45 cents per gallon of pure ethanol.49 This

analysis assumes that both the tax subsidy and the tariff will continue in the future.

The tariff can be avoided by first shipping ethanol to countries under the Caribbean Basin

Initiative (CBI) and then to the U.S. Historically, the majority of CBI ethanol to the U.S. comes

from dehydrating ethanol from Brazil. Legislation and agreements since the 1980s have waived

or significantly reduced the tariff on imports from Canada, Mexico, and those nations covered

under the CBI. There are currently nineteen countries that can benefit from the CBI program.

These countries are: Antigua and Barbuda, Aruba, Bahamas, Barbados, Belize, British Virgin

48 Ranges are calculated assuming the potential values for production and consumption

49 Prior to the 2008 Farm Bill, the tax incentive was set at 54 cents per gallon
165

Islands, Costa Rica, Dominica, Grenada, Guyana, Haiti, Jamaica, Montserrat, Netherlands

Antilles, Panama, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, and Trinidad

and Tobago.343

Under the Caribbean Basin Economic Recovery Act (CBERA), which created the CBI,

countries in Central America and the Caribbean have had duty-free access to the United States

since 1989 for ethanol produced from regional feedstocks. Although most analysts believe there

is sufficient land available for sugarcane production in some CBI nations, there has been

insufficient economic potential to spur sugarcane planting for ethanol production.344 Ethanol

derived from non-regional feedstocks has been limited to 7 percent of total U.S. ethanol

consumption (based on figures from the previous year). There are also country-specific

allocations for El Salvador (5.2 million gallons in first year (2006) and an annual increase of 1.3

million gallons per year, not to exceed 10% of CBI quota) and Costa Rica (31 million gallons

annually) established by the U.S. Free Trade Agreement with Central America and the

Dominican Republic (CAFTA-DR).345 Since 2007, Costa Rica, El Salvador, Jamaica, Trinidad

and Tobago, and the U.S. Virgin Islands are the only countries that have exported ethanol to the

U.S. under the CBI quota.

Historically, the CBI nations have had little ethanol production capacity of their own but

have supplemented it by importing hydrous Brazilian ethanol where it is further dehydrated

before being re-exported to the U.S. duty-free. CBI countries have also relied on surplus wine

alcohol from France, Italy, Spain and other Mediterranean countries in the past.346 According to

the United States International Trade Commission, the majority of fuel ethanol imports to the

United States came through CBI countries between 1996 and 2003. However, in 2006, CBI

ethanol imports to the U.S. totaled over 170 million gallons while imports to the U.S. from Brazil

totaled 3 times that amount, or approximately 430 million gallons. This data indicates that in

2006 it was economical to import significant quantities of ethanol directly from other nations due

to the withdrawal of MTBE and high oil prices. However, it is not clear on how much of this

volume the tariff was paid, as there have been other means for importers to avoid the tariff.

In the past, companies have also imported ethanol from Brazil through a duty

drawback.347 The drawback is a loophole in the tax rules which allowed companies to import

ethanol and then receive a rebate on taxes paid on the ethanol when jet fuel is sold for export

within three years. The drawback considered ethanol and jet fuel as similar commodities

(finished petroleum derivatives).348,349 However, Senate Representative Charles Grassley from

Iowa recently included a provision into the 2008 Farm bill that ended such refunds. The

provision states that “any duty paid under subheading 9901.00.50 of the Harmonized Tariff

Schedule of the United States on imports of ethyl alcohol or a mixture of ethyl alcohol may not

be refunded if the exported article upon which a drawback claim is based does not contain ethyl

alcohol or a mixture of ethyl alcohol.”350 The provision became effective on October 1, 2008

and companies have until October 1, 2010 to apply for a duty drawback on prior transactions.

With the loophole closed, it is anticipated that there may be less ethanol directly exported from

Brazil in the future.351 World sugar prices are also attributing to a reduction in Brazilian imports.

CBI countries have not yet exceeded the tariff rate quotas (TRQs) for ethanol imports.

The TRQ has been limited to 7 percent of total U.S. ethanol consumption (based on figures from
166

the previous year). The fill rate, or percent of the TRQ used, has ranged from 22-77% between

1990 and 2009. See Figure 1.5-11. Thus, there is still considerable room for growth in CBI

imported ethanol.

Figure 1.5-11. U.S. Fuel Ethanol CBERA TRQ, 1990-2009*

0

100

200

300

400

500

600

700

1990199219941996199820002002200420062008

Million Gallons

TRQ Used TRQ Available

*2009 TRQ Used data is preliminary. 2010 TRQ Available is 739.8.

In October 2003, the California Energy Commission (CEC) reported four active CBI

ethanol dehydration plants, two in Jamaica, one in Costa Rica, and one in El Salvador. At the

time, CEC concluded that reprocessing capacity was the limiting factor on CBI imports, with a

total of 90 million gallons per year.352 Since then, several companies have expanded plants or

announced new plants as described below:

 Jamaica- In 2005, Petrojam Ethanol Limited (PEL), upgraded and expanded their ethanol

dehydration plant in Jamaica to a capacity of 40 million .U.S gallons. Currently, the

production of anhydrous (fuel grade) ethanol at the plant is based on a marketing

agreement with the Brazilian company, Coimex Trading, where the feedstock - hydrous

ethanol is supplied from Brazil.

353,354 Jamaica Broilers Group (JBG) launched fuel

ethanol production at its 60 million gallon dehydration plant. The first shipment of 5.5

million gallons of ethanol , which arrived in June 2007, was converted to anhydrous

ethanol for export to the U.S. JBG had a deal with Bauche Energy for the supply of 50

million gallons of hydrous ethanol out of Brazil for the first year of operation.355 Jamaica
167

Ethanol Processing Ltd, which is ED & F Man’s subsidiary on the island, has a small

plant that dehydrates ethanol from Brazil at a capacity of 55 million gallons.

 Costa Rica- LAICA (cane co-op) has a plant currently dehydrating ethanol at 38 million

gallons.356

 El Salvador and Panama- In 2004, it was reported that Cargill and Chevron Texaco had

announced plans to construct new dehydration plants in El Salvador and Panama. These

plants could produce 60 million gallons per year and between 50 and 100 million gallons

per year, respectively.357 Plants currently in operation include Gasohol de El Salvador

(Liza/Vitol) at 100 million gallons per year and ARFS (CASA/Cargill/Crystalsev) at 60

million gallons per year.358

 Trinidad- EthylChem Inc. has reported plans to build an ethanol dehydration operation at

the Petrotrin Refinery in Point-a-Pierre, a southern port city in Trinidad.359 The cost to

build the plant is estimated at $20 million.360 It is probable, however, that not all the

ethanol would be exclusively for U.S. consumption. According to Ron White, the

executive director of Ethylchem, "While EthylChem intends to export the fuel to the

United States the company is examining the possibility of shipping the product to other

markets in the world".361 Another company, Angostura Ltd., started processing ethanol in

2005.362 The plant has an overall capacity of 100 million gallons per year, with 50

million gallons per year in the first phase.363

 Others- An idled ethanol plant in Haiti has attracted some investors and there are also

projects in the works in Guyana, the Dominican Republic and Aruba. The U.S. Virgin

Islands has one plant dehydrating ethanol at 100 million gallons per year capacity

(Geonet).364 A new ethanol dehydration plant is proposed to be built at the Bulk Terminal

Facility near Spring Garden Highway in Barbados.365 There is a proposal to build a

US$36 million ethanol plant near Bridgetown, Barbados. The plant is expected to

produce about 132 million gallons by refining ethanol imported from Brazil.366

In total, fuel ethanol plant capacity for dehydration in the Caribbean is estimated at 500

million gallons per year. Plans to expand total approximately 200 million gallons.367 This

means that there could be 700 million gallons per year of fuel ethanol capacity in the next few

years.

Some stakeholders, however, have expressed concern that the CBI countries are not as

stable for investment. Both Brazilian ethanol and European wine alcohol are susceptible to

factors including availability, price fluctuations, trade regulations, currency movements and

freight rates. Availability of European surplus wine alcohol has diminished since the World

Trade Organization (WTO) placed limitations on export subsidies and has found new markets in

Spain and Sweden.368 CBI countries also need to compete for Brazilian ethanol. For example,

Angostura’s ethanol subsidiary, Trinidad Bulk Traders Ltd., was not profitable in 2006 because it

could not get enough fuel from Brazil.369

There are other prohibitive factors to CBI ethanol production that exist. For instance,

many of the CBI countries have no oil, natural gas or coal. Permitting is often a huge challenge

and fresh water is typically scarce.370
168

In addition, increasing significantly beyond the 7% limit may be challenging. Few

Caribbean countries are in a position to produce ethanol from domestic feedstocks such as

sugarcane. Currently, all three plants exist in Central America (CATSA in Costa Rica, Pantleon

Group in Guatemala, and Pellas Group in Nicaragua). Capacity for each plant is approximately

10 million gallons per year. The majority of this domestic fuel ethanol is shipped to the EU for

fuel use rather than the U.S. due to higher opportunity prices and similar tariff free treatment.371

In addition, the governments of Trinidad, St. Kitts and Barbados have already decided the sugar

sectors of their islands are not worth further investment. Rum distillers such as Trinidad’s

Angostura and Jamaica’s Appleton Ltd. have also had to import molasses from Fiji for their

spirits.372 Thus, it may take years before Caribbean countries are able to domestically produce

large volumes of ethanol. As noted above, however, as dehydration capacity gets close to the

U.S. CBI quota, processors may need to consider blending indigenous ethanol.

As a result of the economic benefit of shipping ethanol through CBI nations, we

anticipate that the majority of the TRQ will be met in the future. If we assume that 90 percent of

the TRQ is met and that total domestic ethanol (corn and cellulosic ethanol) consumed in 2021

was 19.2 Bgal (under the primary control case), then approximately 1.21 Bgal of ethanol could

enter the U.S. through CBI countries in 2022.50 The rest of the Brazilian ethanol exports not

entering the CBI will compete on the open market with the rest of the world demanding some

portion of direct Brazilian ethanol. As shown in Table 1.5-22, to meet our advanced biofuel

standard, we assumed 1.03 billion gallons of sugarcane ethanol would be imported directly to the

U.S. in 2022. The total imported ethanol required by the Act was projected for each year based

on the required volumes needed to meet the advanced biofuel standard after accounting for the

volumes from cellulosic biofuel, biodiesel, and renewable diesel.

Table 1.5-22.

Projected Contribution of Ethanol from CBI Countries and

Direct Brazilian exports in 2022 (billion gallons)

Ethanol

From CBI

Countries

Ethanol

Directly

From Brazil

Total

Imported

Ethanol

1.21 1.03 2.24

The amount of Brazilian ethanol available for direct shipment to the U.S. will be

dependent on the biofuels mandates and goals set by other foreign countries (e.g., the EU, Japan,

India, and China). Our estimates show that there could be a potential demand for imported

ethanol of 4.4-14.3 billion gallons by 2020/2022 from these countries as noted in Section 1.5.2.1.

This is due to the fact that some countries are unable to produce large volumes of ethanol

because of e.g. land constraints or low production capacity. Therefore, unless Brazil or other

countries increase biofuels production significantly, there may be a limited supply for imported

ethanol to satisfy all foreign country mandates and goals.

1.5.3 Cellulosic Biofuel

50 Total Domestic Ethanol is based on the amount needed to meet EISA (i.e. for the primary control case in 2021: 15

Bgal Corn Ethanol, 4.15 Cellulosic Ethanol)
169

The majority of the biofuel currently produced in the United States comes from plants

processing first-generation feedstocks like corn, plant oils, sugarcane, etc. Non-edible cellulosic

feedstocks have the potential to greatly expand biofuel production, both volumetrically and

geographically. Research and development on cellulosic biofuel technologies has exploded over

the last few years, and plants to commercialize a number of these technologies are already

beginning to materialize. The $1.01/gallon tax credit for cellulosic biofuel that was introduced in

the Food, Conservation, and Energy Act of 2008 (“2008 Farm Bill”) and recently became

effective, is also offering much incentive to this developing industry.373 In addition to today’s

RFS2 program which sets aggressive goals for cellulosic biofuel production, the Department of

Energy (DOE), Department of Agriculture (USDA), Department of Defense (DOD) and state

agencies are helping to spur industry growth.

1.5.3.1 Current State of the Industry

There are a growing number of biofuel producers, biotechnology companies, universities

and research institutes, start-up companies as well as refiners investigating cellulosic biofuel

production. The industry is currently pursuing a wide range of feedstocks, conversion

technologies and fuels. There is much optimism surrounding the long-term viability of cellulosic

ethanol and other alcohols for gasoline blending. There is also great promise and growing

interest in synthetic hydrocarbons like gasoline, diesel and jet fuel as “drop in” petroleum

replacements. Some companies intend to start by processing corn or sugarcane and then

transition to cellulosic feedstocks while others are focusing entirely on cellulosic materials.

Regardless, cellulosic biofuel production is beginning to materialize.

We are currently aware of 36 small cellulosic biofuel plants operating in North America.

This includes process development units with fuel production capabilities, pilot plants,

demonstration plants, as well as commercial demonstration plants.51 These facilities are

summarized by fuel type in Tables 1.5-23 and 1.5-24 below. The lists below do not include

plants currently processing grains or sugars with plans to transition to cellulosic feedstocks in the

future, e.g., Amyris, Gevo/ICM, and Virent.52 However, we will continue to track these

companies during future cellulosic biofuel assessments.

Regardless of their size, the main focus at these facilities is research and development,

not commercial production. As shown below, most of the plants are rated at less than 250,000

gallons of cellulosic biofuel per year and that’s if they were operated at capacity. However, most

only operate intermittently for the purpose of demonstrating that the technologies can be used to

produce transportation fuels. As such, some don’t even report production capacities. The

industry as a whole is still working to increase efficiency, improve yields, reduce costs and prove

51 Based on research of information available on the public domain and follow-up correspondence with cellulosic

biofuel companies.

52 Both Amyris and ICM have received federal funding to further their cellulosic biofuel efforts. On January 29,

2008, DOE announced that it had awarded ICM a $40 million grant to help build a small cellulosic ethanol plant at

an existing corn ethanol plant in St. Joseph, MO. The company is currently piloting butanol production from corn

with Gevo. On December 4, 2009, DOE and USDA awarded ICM with another $25 million to further cellulosic

ethanol production at the St. Joseph plant. In the same announcement, DOE and USDA awarded Amyris with a $25

million grant to help further cellulosic research at its pilot plant in Emmeryville, CA.
170

to the public, as well as investors, that cellulosic biofuel is both technologically and

economically feasible.
171

Table 1.5-23. Current Cellulosic Alcohol Plants

AR EC W UW

Abengoa Bioenergy Corporationd York, NE Pilot 0.02 Sep-07 Ethanol Bio X X

AE Biofuels Butte, MT Demo 0.15 Aug-08 Ethanol Bio X X

Arkenol Technology Center Orange, CA Pilot N/A 1994 Ethanol Bio X

Auburn University / Masada Auburn, AL Pilot N/A 1995 Ethanol Bio X

Chemrec & Weyerhaeuser New Bern, NC Pilot N/A 1996 Ethanol Thermo X

ClearFuels / Hawaii Natural Energy Institute Honolulu, HI Pilot N/A 2004 Ethanol Thermo X

Cobalt Biofuels Mountainview, CA Pilot 0.01 N/A Butanol Bio X X

Cornell University Biofuels Research Laboratory Ithaca, NY Pilot N/A Jan-09 Ethanol Bio X X

Coskatae Warrenville, IL Pilot N/A Mar-08 Ethanol Thermo X

Coskatae Madison, PA Demo 0.04 Oct-09 Ethanol Thermo X X X X

DOE National Renewable Energy Laboratory Golden, CO Pilot N/A 2001 Ethanol Bio X

Enerkem Sherbrooke (CAN) Pilot N/A 2003 Ethanol Thermo X X

FiberightfLawrenceville, VA Demo (C) N/A 2005 Ethanol Bio X

Fulcrum Bioenergy - Turning Point Ethanol Plant Durham, NC Demo N/A Mar-09 Ethanol Thermo X

Gulf Coast Energy Livingston, AL Demo 0.20 Sep-08 Ethanol Thermo X

INEOS Bio (formerly BRI)e

Fayetteville, AR Pilot 0.04 1998 Ethanol Thermo X X X

Iogen Corporation Ottawa (CAN) Pilot N/A 1985 Ethanol Bio X X

Iogen Corporation Ottawa (CAN) Demo (C) 0.50 2004 Ethanol Bio X

KL Energy Corp / WBE Upton, WY Demo (C) 1.50 Sep-07 Ethanol Bio X

Lignol Energy Burnaby (CAN) Pilot N/A Jun-09 Ethanol Bio X X

Mascoma Corporation Rome, NY Pilot 0.20 Dec-08 Ethanol Bio X

Pan Gen Global (formerly Colusa Biomass) Colusa County, CA Pilot N/A 1995 Ethanol Bio X

Pearson Technologies Inc. Aberdeen, MS Pilot N/A 2001 Ethanol Thermo X X

POET Project BelldScotland, SD Pilot 0.02 Nov-08 Ethanol Bio X

PureVision Technology, Inc.f Fort Lupton, CO PDU N/A Mar-09 Ethanol Bio X X

Range Fuels K2A Optimization Plant Broomfield, CO Pilot N/A Mar-09 Ethanol Thermo X

SunOpta BioProcess Inc. Norval (CAN) Pilot N/A 2003 Ethanol Bio X

Verenium Jennings, LA Pilot 0.05 2006 Ethanol Bio X

Verenium Jennings, LA Demo 1.40 Feb-09 Ethanol Bio X X X

Company/Plant Name

a

PDU = Process development unit, Pilot = pilot-scale plant, Demo = demonstration-level plant, Demo (C) = Commercial demonstration plant.

bConversion technology. Bio = Biochemical, Thermo = Thermochemical.

cCellulosic feedstocks. AR = Ag residues, EC = Energy crops, W = Wood waste, chips, mill waste, etc., UW = Urban waste including sorted MSW and C&D debris.

dCellulosic ethanol plant is co-located with an existing corn ethanol plant.

e

Plant also processes non-cellulosic/renewable feedstocks, e.g., natural gas, coal.

f

Plant is not currently operational and/or producing fuel at this time.

Cell.

Tech.b

Online

Date

Max Cap

(MGY)

Plant

TypeaPlant Location

Cell.

Biofuel

Cell. Feedstocksc
172

Table 1.5-24. Current Cellulosic Hydrocarbon Fuel & Pyrolysis Oil Plants

AR EC W UW

Bell Bio-Energyd Fort Stewart, GA Pilot 0.01 Dec-08 Diesel Bio X X

Cello Energyd Bay Minette, AL Demo (C) 20.00 Dec-08 Diesel Cat X X

Clearfuels / RentecheCommerce City, CO PDU N/A 2008 Diesel, Jet Thermo X X

Dynamotive West Lorne (CAN) Demo (C) 3.55 N/A Py Oil Thermo X

Dynamotive / Evolution Biofuels Guelph (CAN) Demo (C) 5.46 Sep-07 Py Oil Thermo X

Terrabon Advanced Biofuels Research Center Bryan, TX Pilot 0.13 Apr-09 Gasoline Bio X X X

ThermoChem Recovery International (TRI) Durham, NC Pilot 0.02 Jun-09 Diesel Thermo X X X

Cell. Feedstocksc

Company/Plant Name

a

PDU = Process development unit, Pilot = pilot-scale plant, Demo (C) = Commercial demonstration plant.

b

Conversion technology. Bio = Biochemical, Cat = Catalytic depolymerization, Thermo = Thermochemical.

cCellulosic feedstocks. AR = Ag residues, EC = Energy crops, W = Wood waste, chips, mill waste, etc., UW = Urban waste including sorted MSW and C&D debris.

dPlant is not currently operational and/or producing fuel at this time.

e

Currently in the process of expanding natural gas-based PDU to a pilot plant that can process biomass feedstocks.

Cell.

Tech.b

Online

Date

M ax Cap

(MGY)

Plant

Typea Plant Location

Cell.

Biofuel
173

As shown in Tables 1.5-23 and 1.5-24, today’s cellulosic biofuel plants are run by a

combination of academic, government, and private organizations. Some of the privately-owned

companies are existing biofuel producers, but many are start-up companies entering the industry

for the first time. The following companies were awarded federal funding to help build their

small plants and/or facilitate cellulosic research – Bell Bio-Energy ($1.1 million from the

Department of Defense), Clearfuels / Rentech ($2.5 million from the DOE) and Verenium ($10

million from the DOE).374

As indicated above, a variety of feedstocks are being investigated for cellulosic biofuel

production. There is a great deal of interest in urban waste (MSW and C&D debris) because it is

virtually free and abundant in many parts of the country, including large metropolitan areas

where the bulk of fuel is consumed. There is also a lot of interest in agricultural residues (corn

stover, rice and other cereal straws) and wood (forest thinnings, wood chips, pulp and paper mill

waste, and yard waste). However, researchers are still working to find viable harvesting and

storage solutions. Others are investigating the possibility of growing dedicated energy crops for

cellulosic biofuel production, e.g., switchgrass, energy cane, sorghum, poplar, miscanthus and

other fast-growing trees. While these crops have tremendous potential, many are starting with

the feedstocks that are available today with the mentality that once the industry has proven itself,

it will be easier to secure growing contracts and start producing energy crops. For more

information on cellulosic feedstock availability, refer to Section 1.1.2.

The industry is also pursuing a number of different cellulosic conversion technologies

and biofuels. Most of the technologies fall into one of two categories: biochemical or

thermochemical. Biochemical conversion involves the use of acids and/or enzymes to hydrolyze

cellulosic materials into fermentable sugars and lignin. Thermochemical conversion involves the

use of heat to convert biomass into synthesis gas or pyrolysis oil for upgrading. A third

technology pathway is emerging that involves the use of catalysts to depolymerize or reform the

feedstocks into fuel. The technologies currently being considered are capable of producing

cellulosic alcohols or hydrocarbons for the transportation fuel market. Many companies are also

researching the potential of co-firing biomass to produce plant energy in addition to biofuels.

For a more in-depth discussion on cellulosic technologies, refer to Section 1.4.3.

1.5.3.2 Setting the 2010 Standard

The Energy Independence and Security Act (EISA) set aggressive cellulosic biofuel

targets beginning with 100 million gallons in 2010. However, EISA also supplied EPA with

cellulosic biofuel waiver authority. For any calendar year in which the projected cellulosic

biofuel production is less than the minimum applicable volume, EPA can reduce the standard

based on the volume expected to be available that year. EPA is required to set the annual

cellulosic standard by November 30th each year and should consider the annual estimate made by

EIA by October 31st of each year. We are setting the 2010 standard as part of this final rule.

Setting the cellulosic biofuel standard for 2010 represents a unique challenge. As

discussed above, the industry is currently characterized by a wide range of companies mostly

focused on research, development, demonstration, and financing their developing technologies.

In addition, while we are finalizing a requirement that producers and importers of renewable fuel
174

provide us with production outlook reports detailing future supply estimates (refer to §80.1449),

we do not have the benefit of this valuable cellulosic supply information for setting the 2010

standard. Finally, since today’s cellulosic biofuel production potential is relatively small, and the

number of actual potential producers few (as described in more detail below), the overall volume

for 2010 can be heavily influenced by new developments, either positive or negative associated

with even a single company, which can be very difficult to predict. This is evidenced by the

magnitude of changes in cellulosic biofuel projections and the potential suppliers of these fuels

since the proposal.

In the proposal, we did a preliminary assessment of the cellulosic biofuel industry to

arrive at the conclusion that it was possible to uphold the 100 million gallon standard in 2010

based on anticipated production. At the time of our April 2009 NPRM assessment, we were

aware of a handful of small pilot and demonstration plants that could help meet the 2010

standard, but the largest volume contributions were expected to come from Cello Energy and

Range Fuels.

Cello Energy had just started up a 20 million gallon per year (MGY) cellulosic diesel

plant in Bay Minette, AL. EPA staff visited the facility twice in 2009 to confirm that the first-ofits-kind commercial plant was mechanically complete and poised to produce cellulosic biofuel.

It was assumed that start-up operations would go as planned and that the facility would be

operating at full capacity by the end of 2009 and that three more 50 MGY cellulosic diesel plants

planned for the Southeast could be brought online by the end of 2010.

At the time of our assessment, we were also anticipating cellulosic biofuel production

from Range Fuels’ first commercial-scale plant in Soperton, GA. The company received a $76

million grant from DOE to help build a 40 MGY wood-based ethanol plant and they broke

ground in November 2007. In January 2009, Range was awarded an $80 million loan guarantee

from USDA.53 With the addition of this latest capital, the company seemed well on its way to

completing construction of its first 10 MGY phase by the end of 2009 and beginning production

in 2010.

Since our April 2009 industry assessment there have been a number of changes and

delays in production plans due to technological, contractual, financial and other reasons. Cello

Energy and Range Fuels have delayed or reduced their production plans for 2010. Some of the

small plants expected to come online in 2010 have pushed back production to the 2011-2012

timeframe, e.g., Clearfuels Technology, Fulcrum River Biofuels, and ZeaChem. Alltech/Ecofin

and RSE Pulp & Chemical, two companies that were awarded DOE funding back in 2008 to

build small-scale biorefineries appear to be permanently on hold or off the table. In addition,

Bell Bio-Energy, a company that received DOD funding has since abandoned plans to build

additional cellulosic diesel plants at U.S. military bases.54

At the same time, there has also been an explosion of new companies, new business

relationships, and new advances in the cellulosic biofuel industry. Keeping track of all of them

53 For more information on federal support for biofuels, refer to Section 1.5.3.3.

54 Bell Bio-Energy is currently investigating other location for turning MSW into diesel fuel according to an October

14, 2009 conversation with JC Bell.
175

is a challenge in and of itself as the situation can change on a daily basis. EIA recently provided

EPA with their first cellulosic biofuel supply estimate required under CAA section

211(o)(7)(D)(i). In a letter to the Administrator dated October 29, 2009, they arrived at a 5.04

million gallon estimate for 2010 based on publicly available information and assumptions made

with respect production capacity utilization.375 A summary of the plants they considered is

shown below in Table 1.5-25.

Table 1.5-25.

EIA’s Projected Cellulosic Biofuel Plant Production Capacities for 2010

Online Company Location Product

Capacity

(million

gallons)

Expected

Utilization

(%)

Production

(million

gallons)3

2007 KL Process Design Upton, WY Ethanol 1.5 10 0.15

2008 Verenium Jennings, LA Ethanol 1.4 10 0.14

2008 Terrabon Bryan, TX Bio-Crude 0.93 10 0.09

2010 Zeachem Boardman, OR Ethanol 1.5 10 0.15

2010 Cello Energy Bay Minette, AL Diesel 20.0 1012.00

2010 Range Fuels Soperton, GA Ethanol 5.0250 2.5

Total 30.35 5.04

Notes: 1. Cello Energy is assigned a 10-percent utilization factor as they have not been able to run on a continuous

basis long enough to apply for a Synthetic Minor Operating Permit or produce significant amounts of fuel during

2009. 2. It is estimated that only half the 2010 projected capacity (10 million gallons per year) will be a qualified

fuel. 3. The production from these facilities in 2009 is not surveyed by EIA or EPA.

In addition to receiving EIA’s information and coordinating with them and other offices

in DOE, we have initiated meetings and conversations with over 30 up-and-coming advanced

biofuel companies to verify publicly available information, obtain confidential business

information, and better assess the near-term cellulosic biofuel production potential for use in

setting the 2010 standard. What we have found is that the cellulosic biofuel landscape has

continued to evolve. Based on information obtained, not only do we project significantly

different production volumes on a company-by-company basis, but the list of potential producers

of cellulosic biofuel in 2010 is also significantly different than that identified by EIA.

Overall, our industry assessment suggests that it is difficult to rely on commercial

production from small pilot or demonstration-level plants. The primary purpose of these

facilities is to prove that a technology works and demonstrate to investors that the process is

capable of being scaled up to support a larger commercial plant. Small plants are cheaper to

build to demonstrate technology than larger plants, but the operating costs ($/gal) are higher due

to their small scale. As a result, it’s not economical for most of these facilities to operate

continuously. Most of these plants are regularly shut down and restarted as needed as part of the

research and development process. Due to their intermittent nature, most of these plants operate

at a fraction of their rated capacity, some less than the 10% utilization rate assumed by EIA. In

addition, few companies plan on making their biofuel available for commercial sale.

However, there are at least two cellulosic biofuel companies currently operating

demonstration plants in the U.S. and Canada that could produce fuel commercially in 2010. The

first is KL Energy Corporation, a company we considered for the NPRM with a 1.5 MGY
176

cellulosic ethanol plant in Upton, WY. This plant was considered by EIA and is included in their

final plant summary presented in Table 1.5-25. The second is Iogen’s cellulosic ethanol plant in

Ottawa, Canada with a 0.5 MGY capacity. Iogen’s commercial demonstration plant was

referenced by EIA as a potential foreign source for cellulosic biofuel but was not included in

their final table. In addition to these online demonstration plants, there are three additional

companies not on EIA’s list that are currently building demonstration-level cellulosic biofuel

plants that are scheduled to come online in 2010. This includes DuPont Danisco Cellulosic

Ethanol and Fiberight, companies currently building demonstration plants in the U.S. and

Enerkem, a company building a demonstration plant in Canada. Cello Energy’s plant in Bay

Minette, AL continues to offer additional potential for cellulosic biofuel in 2010. And finally,

Dynamotive, a company that currently has two biomass-based pyrolysis oil production plants in

Canada is another potential source of cellulosic biofuel in 2010. All seven aforementioned

companies are discussed in greater detail below along with Range Fuels.

KL Energy Corporation (KL Energy), through its majority-owned Western Biomass

Energy, LLC (WBE) located in Upton, WY, is designed to convert wood products and wood

waste products into ethanol. Since the end of construction in September 2007, equipment

commissioning and process revisions continued until the October 2009 startup. The plant was

built as a 1.5 MGY demonstration plant and was designed to both facilitate research and operate

commercially. It is KL Energy’s intent that WBE’s future use will involve the production and

sale of small but commercial-quality volumes of ethanol and lignin co-product. The company’s

current 2010 goal is for WBE to generate RINs under the RFS2 program.55

Iogen is responsible for opening the first commercial demonstration cellulosic ethanol

plant in North America. Iogen’s plant located in Ottawa, Canada has been producing cellulosic

ethanol from wheat straw since 2004. Like KL Energy, Iogen has slowly been ramping up

production at its 0.5 MGY plant. According to the company’s website, they produced

approximately 24,000 gallons in 2004 and 34,000 gallons in 2005. Production dropped

dramatically in 2006 and 2007 but came back strong with 55,000 gallons in 2008. Iogen recently

produced over 150,000 gallons of ethanol from the demonstration plant in 2009. Iogen also

recently became the first cellulosic ethanol producer to sell its advanced biofuel at a retail service

station in Canada. Their cellulosic ethanol was blended to make E10 available for sale to

consumers at an Ottawa Shell station.376 Iogen also recently announced plans to build its first

commercial scale plant in Prince Albert, Saskatchewan in the 2011/2012 timeframe. Based on

the company’s location and operating status, Iogen certainly has the potential to participate in the

RFS2 program. However, at this time, we are not expecting them to import any cellulosic

ethanol into the U.S. in 2010.56

DuPont Danisco Cellulosic Ethanol, LLC (DDCE), a joint venture between Dupont and

Danisco, is another potential source for cellulosic biofuel in 2010. DDCE received funding from

the State of Tennessee and the University of Tennessee to build a small 0.25 MGY

demonstration plant in Vonore, TN to pursue switchgrass-to-ethanol production. According to

55 Based on information provided by Lori Litzen, Environmental Permit Engineer at KL Energy on December 10,

2009.

56 Based on website information, comments submitted in response to our proposal, and a follow-up phone call with

Iogen Executive VP, Jeff Passmore on December 17, 2009.
177

DDCE, construction commenced in October 2008 and the plant is now mechanically complete

and undergoing start-up operations. The facility is scheduled to come online by the end of

January and the company hopes to operate at or around 50% of production capacity in 2010.

According to the DDCE, the objective in Vonore is to validate processes and data for

commercial scale-up, not to make profits. However, the company does plan to sell the cellulosic

ethanol it produces.57

Enerkem is another company pursuing cellulosic ethanol production. The Canadianbased company was recently announced as a recipient of a joint $50 million grant from DOE and

USDA to build a 10 MGY woody biomass-to-ethanol plant in Pontotoc, MS.377 The U.S. plant is

not scheduled to come online until 2012, but Enerkem is currently building a 1.3 MGY

demonstration plant in Westbury, Quebec. According to the company, plant construction in

Westbury started in October 2007 and the facility is currently scheduled to come online around

the middle of 2010. While it’s unclear at this time whether the cellulosic ethanol produced will

be exported to the United States, Enerkem has expressed interest in selling its fuel

commercially.58

Additional cellulosic biofuel could come from Fiberight, LLC (Fiberight) in 2010. We

recently became aware of this start-up company and contacted them to learn more about their

process and cellulosic biofuel production plans. According to Fiberight, they have been

operating a pilot-scale facility in Lawrenceville, VA for three years. They have developed a

proprietary process that not only fractionates MSW but biologically converts the non-recyclable

portion into cellulosic ethanol and biochemicals. Fiberight recently purchased a shut down corn

ethanol plant in Blairstown, IA and plans to convert it to become MSW-to-ethanol capable.

According to the company, construction is currently underway and the goal is to bring the 2

MGY demonstration plant online by February or March, 2010. If the plant starts up according to

plan, the company intends on making cellulosic ethanol commercially available in 2010 and

generating RINS under the RFS2 program. Fiberight’s long-term goal is to expand the

Blairstown plant to a 5-8 MGY capacity and build other small commercial plants around the

country that could convert MSW into fuel.59

Cello Energy, a company considered in the proposal, continues to be another viable

source for cellulosic biofuel in 2010. Despite recent legal issues which have constrained the

company’s capital, Cello Energy is still pursuing cellulosic diesel production. According to the

company, they are currently working to resolve materials handling and processing issues that

surfaced when they attempted to scale up production to 20 MGY from a previously operated

demonstration plant. As of November 2009, they were waiting for new equipment to be ordered

and installed which they hoped would allow for operations to be restarted as early as February or

March, 2010. Cello’s other planned commercial facilities are currently on hold until the Bay

Minette plant is operational.60

57 Based on a December 16, 2009 telephone conversation with DDCE Director of Corporate Communications,

Jennifer Hutchins and follow-up e-mail correspondence.

58 Based on an October 14, 2009 meeting with Enerkem and follow-up telephone conversation with VP of

Government Affairs, Marie-Helene Labrie on December 14, 2009.

59 Based on a December 15, 2009 telephone conversation with Fiberight CEO, Craig Stuart-Paul and follow-up email correspondence.

60 Based on a November 9, 2009 telephone conversation with Cello Energy CEO, Jack Boykin.
178

Another potential supplier of cellulosic biofuel is Dynamotive Energy Systems

(Dynamotive) headquartered in Vancouver, Canada. As shown in Table 1.5-24, Dynamotive

currently has two plants in West Lorne and Guelph, Ontario, Canada, that produce biomassbased pyrolysis oil (also known as “BioOil”) for industrial applications. The BioOil production

capacity between the two plants is estimated at around 9 MGY, but both plants are currently

operating at a fraction of their rated capacity.61 However, according to a recent press release,

Dynamotive has contracts in place to supply a U.S.-based client with at least nine shipments of

BioOil in 2010. If Dynamotive’s BioOil is used as heating oil or upgraded to transportation fuel,

it could potentially count towards meeting the cellulosic biofuel standard in 2010.

As for the Range Fuels plant, construction of phase one in Soperton, GA, is about 85%

complete, with start-up planned for mid-2010. However, there have been some changes to the

scope of the project that will limit the amount of cellulosic biofuel that can be produced in 2010.

The initial capacity has been reduced from 10 to 4 million gallons per year. In addition, since

they plan to start up the plant using a methanol catalyst they are not expected to produce

qualifying renewable fuel in 2010. During phase two of their project, currently slated for mid2012, Range plans to expand production at the Soperton plant and transition from a methanol to

a mixed alcohol catalyst. This will allow for a greater alcohol production potential as well as a

greater cellulosic biofuel production potential.62

Overall, our most recent industry assessment suggests that there are six companies that

could potentially produce cellulosic biofuel next year. Together these seven plants, summarized

in Table 1.5-26, could have over 30 MGY of cellulosic biofuel plant capacity online by the end

of 2010. However, the actual volume of cellulosic biofuel realized under the RFS2 program will

likely be much lower, as explained in more detail below.

61 According to Dynamotive’s website, the Guelph plant has a capacity to convert 200 tonnes of biomass into BioOil

per day. If all modules are fully operational, the plant has the ability to process 66,000 dry tons of biomass per year

with an energy output equivalent to 130,000 barrels of oil. The West Lorne plant has a capacity to convert 130

tonnes of biomass into BioOil per day (which, if proportional to the Guelph plant, translates to an energy-equivalent

of 84,500 barrels of oil. According to a November 3, 2009 press release, Dynamotive has contracts in place to

supply a U.S.-based client with at least nine shipments of BioOil in 2010.

62 Based on a November 5, 2009 telephone conversation with Range Fuels VP of Government Affairs, Bill Schafer.
179

Table 1.5-26

EPA’s Cellulosic Biofuel Plant Assessment – Projected Plants/Capacity Online by End of 2010

Cello Energy Bay Minette, AL Demo (C) 20.00 Currently Off-Line Mar-10 Diesel Wood chips, hay

DuPont Danisco (DDCE) Vonore, TN Demo 0.25 Undergoing Start-Up Jan-10 Ethanol Corn cobs then switchgrass

Dynamotive West Lorne (CAN) Demo (C) 3.55 On-Line Py Oil Waste wood

Dynamotive Guelph (CAN) Demo (C) 5.46 On-Line Py Oil Waste wood, wood chips

Enerkem Westbury (CAN) Demo 1.30 Under Construction Jun-10 Ethanol Treated wood

Fiberight Blairstown, IA Demo (C) 2.00 Under Construction Mar-10 Ethanol Sorted MSW

KL Energy Corp / WBE Upton, WY Demo (C) 1.50 On-Line Ethanol Wood chips

Company/Plant Name

Max Cap

(MGY)

Plant

Type Cellulosic Feedstocks

Cell.

Plant Location Biofuel

Operational

Status

Proj. Op.

Date
180

Since most of the plants in Table 1.5-26 are still under construction today, the amount of

cellulosic biofuel produced in 2010 will be contingent upon when and if these plants come online

and whether the projects get delayed due to funding or other reasons. In addition, based on our

discussions with the developing industry, it is clear that we cannot count on demonstration plants

to produce at or near capacity in 2010, or in their first few years of operation for that matter. The

amount of cellulosic biofuel actually realized will depend on whether the process works, the

efficiency of the process, and how regularly the plant is run. As mentioned earlier, most small

plants, including commercial demonstration plants, are not operated continuously. As such, we

cannot base the standard on these plants running at capacity - at least until the industry develops

further and proves that such rates are achievable. We currently estimate that production from

first-of-it’s kind plants could be somewhere in the 25-50% range in 2010. Together, the

implementation timelines and anticipated production levels of the plants described above brings

the cellulosic biofuel supply estimate to somewhere in the 6-13 million gallon range for 2010.

In addition, it is unclear how much was can rely on Canadian plants for cellulosic biofuel

in 2010. Although we currently receive some conventional biofuel imports from Canada and

many of the aforementioned Canadian companies have U.S. markets in mind, the country also

has its own renewable fuel initiatives that could keep much of the cellulosic biofuel produced

from coming to the United States, e.g., Iogen. Finally, it’s unclear whether all fuel produced by

these facilities will qualify as cellulosic biofuel under the RFS2 program. Several of the

companies are producing fuels or using feedstocks which may not in fact qualify as cellulosic

biofuel once we receive their detailed registration information. Factoring in these considerations,

the cellulosic biofuel potential from the seven plants summarized in Table 1.5-26 could result in

several different production scenarios in the neighborhood of the recent EIA estimate. We

believe this estimate of 5 million gallons or 6.5 ethanol-equivalent million gallons represents a

reasonable yet achievable level for the cellulosic biofuel standard in 2010 considering the degree

of uncertainty involved with setting the standard for the first year. As mentioned earlier, we

believe standard setting will be easier in future years once the industry matures, we start

receiving production outlook reports and there is less uncertainty regarding feasibility of

cellulosic biofuel production.

1.5.3.3 Current Outlook for 2011 and Beyond

Since the proposal, we have also learned about a number of other cellulosic biofuel

projects in addition to those described above. This includes commercial U.S. production plans

by Coskata, Enerkem and Vercipia. However, production isn’t slated to begin until 2011 or later

and the same is true for most of the other larger plants we’re aware of that are currently under

development. Nonetheless, while cellulosic biofuel production in 2010 may be limited, it is

remarkable how much progress the industry has made in such a short time, and there is a

tremendous growth opportunity for cellulosic biofuels over the next several years.

Most of the cellulosic biofuel companies we’ve talked to are in different stages of

proving their technologies. Regardless of where they are at, many have fallen behind their

original commercialization schedules. As with any new technology, there have been delays

associated with scaling up capacity, i.e., bugs to work out going from pilot to demonstration to

commercialization. However, most are saying it’s not the technologies that are delaying
181

commercialization, it is lack of available funding. Obtaining capital has been very challenging

given the current recession and the banking sector's financial difficulties. This is especially true

for start-up companies that do not have access to capital through existing investors, plant profits,

etc. From what we understand, banks are looking for cellulosic companies to be able to show

that their plants are easily “scalable” or expandable to commercial size. Many are only

considering companies that have built plants to one-tenth of commercial scale and have logged

many hours of continuous operation.

The government is currently trying to help in this area. To date, the Department of

Energy (DOE) and the Department of Agriculture (USDA) have allocated over $720 million in

federal funding to help build pilot and demonstration-scale biorefineries employing advanced

technologies in the United States.63,378 The largest installment from Recovery Act funding was

recently announced on December 4, 2009 and includes funding for a series of larger commercial

demonstration plants including cellulosic ethanol projects by Enerkem and INEOS New Planet

BioEnergy, LLC. DOE has also issued grants to help fund some of the first commercial

cellulosic biofuel plants. Current recipients include Abengoa Bioenergy, BlueFire Ethanol64 and

POET Biorefining in addition to Range Fuels.379 The DOE is also in the process of issuing loan

guarantees.

The Energy Policy Act of 2005 (EPAct) authorized DOE to issue loan guarantees to

eligible projects that "avoid, reduce, or sequester air pollutants or anthropogenic emissions of

greenhouse gases" and "employ new or significantly improved technologies as compared to

technologies in service in the United States at the time the guarantee is issued."380 On October 4,

2007, DOE issued final regulations for its loan guarantee program and invited 16 pre-applicants

to submit applications for federal support of innovative clean energy projects. Five of the preapplicants are/were pursuing cellulosic biofuel production.381

Passage of the Recovery Act in 2009 created a new Section 1705 under Title XVII of the

Energy Policy Act of 2005 for the rapid deployment of renewable energy projects and related

manufacturing facilities, electric power transmission projects and leading edge biofuels projects

that commence construction before September 30, 2011.382 On December 7, 2009, Energy

Secretary Steven Chu announced the issue of a final rule amending the Department of Energy’s

regulations for its Loan Guarantee Program.383 The revised rule will allow for increased

participation in the program by financial institutions and other investors and enable the support

of more innovative energy technologies in the United States. Although, to date, DOE has issued

a number of solicitations and invited pre-applicants to submit full applications, no cellulosic

63 On January 29, 2008 DOE announced that it would provide $114 million to fund 4 small scale cellulosic

biorefineries. On April 18, 2008, DOE announced that it would provide another $86 million to help fund three

additional small-scale plants. On July 14, 2008, DOE announced another $40 million to help fund two more small

cellulosic plants. On December 4, 2009, DOE and USDA announced that up to $483 million would be made

available to fund 14 pilot-scale and 4 demonstration-scale biorefineries across the country, the majority of which are

pursuing cellulosic biofuel production.

64 Although BlueFire is still working on obtaining financing to build its first demonstration plant, it has received two

installments of federal funding towards its first planned commercial-scale plant. The 19 MGY plant in Fulton, MS

(originally planned for Southern California) was awarded $40 million from DOE on February 28, 2008 and another

$81.1 million from DOE and USDA on December 4, 2009.
182

biofuel companies have been issued loan guarantees at this time.65 However, the USDA has

begun issuing loan guarantees under the 2008 Farm Bill (explained in more detail below).

The Farm Bill is assisting the cellulosic biofuel industry in many ways. First, it modified

the $0.51/gal alcohol blender credit to give preference to ethanol and other biofuels produced

from cellulosic feedstocks. Effective January 1, 2009, corn ethanol receives a reduced tax credit

of $0.45/gal while cellulosic biofuel earns a credit of $1.01/gal.66 In addition, the Farm Bill

contains provisions that enable USDA to assist with the commercialization of second-generation

biofuels, explained in more detail below.

Section 9003, also known as the Biorefinery Assistance Program, promotes the

development of new and emerging technologies for the production of advanced biofuels -

defined as fuels that are not produced from food sources. The program provides loan guarantees

to develop, construct and retrofit viable commercial-scale biorefineries producing advanced

biofuels. The maximum loan guarantee is $250 million per project. The program is designed to

create energy-related jobs and economic development in rural America. On January 16, 2009, the

USDA Rural Development approved its first ever loan guarantee to Range Fuels.384 As

mentioned earlier, Range received an $80 million loan from USDA to help build its Soperton,

GA plant.67 Section 9004 of the 2008 Farm Bill provides payments to biorefineries to replace

fossil fuels with renewable biomass. Section 9005 provides payments to producers to support

and ensure production of advanced biofuels. And finally, Section 9008 provides competitive

grants, contracts and financial assistance to enable eligible entities to carry out research,

development, and demonstration of biofuels and biomass-based based products.

In addition to helping fund a series of small cellulosic biofuel plants, the DOE and USDA

are helping to fund critical research to help make cellulosic biofuel production more

commercially viable. In March 2007, DOE awarded $23 million in grants to four companies and

one university to develop more efficient microbes for ethanol refining.385 In June 2007, DOE

and USDA awarded $8.3 million to 10 universities, laboratories, and research centers to conduct

genomics research on woody plant tissue for bioenergy.386 Later that same month, DOE

announced its plan to spend $375 million to build three bioenergy research centers dedicated to

accelerating research and development of cellulosic ethanol and other biofuels. The centers,

which will each focus on different feedstocks and biological research challenges, will be located

in Oak Ridge, TN, Madison, WI, and Berkeley, CA.387 In December 2007, DOE awarded $7.7

million to one company, one university, and two research centers to demonstrate the

thermochemical conversion process of turning grasses, stover, and other cellulosic materials into

biofuel.388

In February 2008, DOE awarded another $33.8 million to three companies and one

research center to support the development of commercially-viable enzymes to support cellulose

hydrolysis, a critical step in the biochemical breakdown of cellulosic feedstocks.389 In March

65 To the best of our knowledge based on an assessment of DOE press releases.

66 Refer to Part II, Subparts A and B (Sections 15321 and 15331).

67 USDA also recently issued a $54.5 million loan guarantee to Sapphire Energy to help demonstrate an integrated

algal biorefinery process in Columbus, NM. For more information on Sapphire and other algae-based biodiesel

projects, refer to Section 1.5.4.3 of the RIA.
183

2008, DOE and USDA awarded $18 million to 18 universities and research institutes to conduct

research and development of biomass-based products, biofuels, bioenergy, and related

processes.390 In July 2008, DOE and USDA awarded $10 million to 10 universities and research

centers to advance biomass genomics to further the use of cellulosic plant material for bioenergy

and biofuels.391 In August 2008, DOE announced the availability of $7 million to seven DOE

National Laboratories to accelerate clean energy technologies, including biofuels.392 In

September 2008, DOE announced plans to invest another $4.4 million in six universities to

support research and development for cost-effective, environmentally-friendly biomass

conversion technologies for turning non-food feedstocks into advanced biofuels.393 On October

7, 2008, USDA and DOE released the National Biofuels Action Plan (NBAP), an interagency

plan detailing the collaborative efforts of Federal agencies needed to accelerate the development

of a sustainable biofuels industry.394 The plan focuses on seven critical areas including

sustainability, feedstock production, feedstock logistics, and conversion technology. On the

same day, DOE announced a $7 million investment in five research organizations and

institutions to advance technologies needed for stabilization of biomass-based fast pyrolysis

oils.395

In July 2009, DOE and USDA announced the joint selection of two research centers and

five universities to receive $6.3 million towards fundamental genomics-enabled research leading

to the improved use of plant feedstocks.396 In August 2009, DOE announced awards totaling

$377 million for 46 Energy Frontier Research Centers.397 The recipients, funded by the

Recovery Act, include at least six centers focused on advanced biofuels (totaling more than $100

million). Later that month, DOE announced that $21 million would be made available to five

projects to develop supply systems to handle and deliver high tonnage biomass feedstocks for

cellulosic biofuels production.398 In November 2009, DOE and USDA announced 12 projects

selected for over $24 million in grants to research and develop technologies to produce biofuels,

Bioenergy, and high-value biobased products.399

Numerous states are also offering grants and tax incentives to help encourage biofuel

production. Most of the efforts are currently centered on expanding existing production and

developing sustainable, second-generation feedstocks, technologies and fuels. According to a

recent assessment of DOE’s Energy Efficiency and Renewable Energy (EERE) website, over 20

states currently offer some form of production incentive for advanced biofuels including, but not

limited to, those made from cellulosic materials. The incentives range from grants, loan

guarantees and tax breaks for advanced biofuel producers to support for technology and

feedstock development.

In addition to the production incentives described above, a group of states in the Midwest

have joined together to pursue ethanol and other biofuel production and usage goals as part of the

Midwest Governors Association (MGA). States that have signed on to the MGA goals include

Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Ohio, South Dakota and

Wisconsin. In 2007, the MGA adopted the Midwest Energy Security and Climate Stewardship

Platform.400 The Platform goals are to produce cellulosic ethanol on a commercial level by 2012

and to have E85 offered at one-third of refueling stations by 2025. They also want to reduce the

energy intensity of ethanol production and supply 50% of their transportation fuel needs by

regionally produced biofuels by 2025. In 2009, the MGA approved a follow-up infrastructure
184

initiative called the Midwestern Energy Infrastructure Accord which includes the governors’

support for building out a bio refueling system throughout the region.401

The refining industry is also helping to further cellulosic biofuel R&D efforts and fund

some of the first commercial plants. Many of the major oil companies have invested in advanced

second-generation biofuels over the past 12-18 months. A few refiners (e.g., BP and Shell) have

even entered into joint ventures to become cellulosic biofuel producers. General Motors and

other vehicle/engine manufacturers are also providing financial support to help with research and

development.

A summary of some of the cellulosic biofuel companies with near-term

commercialization plans in North America is provided in 1.5-27. The capacities presented

represent maximum annual average throughput based on each company’s current production

plans. However, as noted, capacity does not necessarily translate to production. Actual

production of cellulosic biofuel will likely be well below capacity, especially in the early years

of production. We will continue to track these companies and the cellulosic biofuel industry as a

whole throughout the duration of the RFS2 program. In addition, we will continue to collaborate

with EIA in annual standard setting. A more detailed description of the new (commercial

demonstration and larger) plants corresponding to these company estimates is provided in Tables

1.5-28 and 1.5-29.
185185

Table 1.5-27. Potential Growth in Cellulosic Biofuel Capacity by Company and Year*

Today Dec-10 Dec-11 Dec-12 Dec-13 2014+

Abengoa Ethanol 0.02 0.02 0.02 16.02 16.02 16.02

AE Biofuels Ethanol 0.15 0.15 15.15 20.15 20.15 20.15

BlueFire Ethanol Ethanol - - - - - 22.90

Cello Energy Diesel - 20.00 20.00 20.00 20.00 120.00

CMEC / SunOpta Ethanol - - - - - 10.00

Coskata Ethanol 0.04 0.04 0.04 50.04 50.04 100.04

DynamotiveaBioOil 9.00 9.00 9.00 9.00 9.00 9.00

Enerkem Ethanol - 1.30 11.30 21.30 21.30 41.30

Fiberight Ethanol - 2.00 6.50 6.50 6.50 6.50

Flambeau River Biofuels Diesel - - - 8.00 8.00 8.00

Fulcrum Bioenergy Ethanol - - - 10.50 10.50 10.50

Inbicon / Great River Energy Ethanol - - - - 20.00 20.00

INEOS Bio / New Planet Energy Ethanol - - 8.00 8.00 8.00 8.00

Iogen Ethanol 0.50 0.50 0.50 23.50 23.50 23.50

KL Energy Ethanol 1.50 1.50 1.50 1.50 1.50 6.50

Mascoma Corporation Ethanol 0.20 0.20 0.20 2.20 20.20 80.20

New Page Diesel - - - 2.50 2.50 2.50

Ohio River Clean Fuels / Baardb Diesel, Naphtha - - - - - 17.00

Pacific Ethanol Ethanol - - - - - 2.70

POET Biorefining Ethanol 0.02 0.02 25.02 25.02 25.02 25.02

Range Fuels Methanol, Ethanol - 4.00 4.00 30.00 30.00 100.00

Rentechc Diesel - - 0.15 7.15 7.15 7.15

Vercipia (Verenium/BP JV) Ethanol 1.40 1.40 1.40 37.40 37.40 37.40

Maximum Plant Capacity (MGY) 12.83 40.13 102.78 298.78 336.78 694.38

Capacity Expansion Plans (MGY)

Cellulosic Company Biofuel(s)

aCapacity has been estimated.

b

Plant will co-process biomass and coal. It is unclear at this time how much fuel would come from biomass and potentially qualify as

cellulosic biofuel.

c

Includes Clearfuels demo plant and Silvagas commercial plant.

*Capacity, not actual production
186

Table 1.5-28.

Promising New Cellulosic Alcohol Plants

Cap

(MGY)

Op

Date

Cap

(MGY)

Op

Date AR EC W UW

Abengoa Bioenergy Corporationac Hugoton, KS 16.00 2012 Ethanol Bio X X

AE Advanced Fuels - Keyesc,d Keyes, CA 15.00 2011 20.00 2012 Ethanol Bio X X

BlueFire Ethanol Lancaster, CA 3.90 TBD Ethanol Bio X X

BlueFire Ethanol Fulton, MS 19.00 TBD Ethanol Bio X

Central Minnesota Cellulosic Ethanol Partnersc

Little Falls, MN 10.00 TBD Butanol Bio X

Coskata / U.S. Sugar Corp. Clewiston, FL 50.00 2012 100.00 TBD Ethanol Thermo X

Enerkem Pontotoc, MS 10.00 2012 20.00 2015 Ethanol Thermo X X

Enerkem GreenField Alberta Biofuels (EGAB) Edmonton (CAN) 10.00 2011 20.00 TBD Ethanol Thermo X

Fiberight (former Xethanol plant) Blairstown, IA 2.00 End-2010 6.50 2011 Ethanol Bio X

Fulcrum Bioenergy - Sierra BioFuels Plant McCarran, NV 10.50 Mid-2012 Ethanol Thermo X

Inbicon / Great River Energy Spiritwood, ND 20.00 2013 Ethanol Bio X

INEOS Bio / New Planet Bioenergy, LLC Vero Beach, FL 8.00 End-2011 Ethanol Thermo X X

Iogen Corporation Prince Albert (CAN) 23.00 2012 Ethanol Bio X

KL Energy Corp Kremmling, CO 5.00 TBD Ethanol Bio X

Mascoma Corporation / Frontier Resources Kinross, MI 2.00 2012 20.00 2013 Ethanol Bio X

Pacific Ethanol Boardman, OR 2.70 TBD Ethanol Bio X X

POET Project Libertyc

Emmetsburg, IA 25.00 End-2011 Ethanol Bio X

Range Fuelse

Soperton, GA 30.00 2012 100.00 TBD Methanol Thermo X X

Vercipia (Verenium/BP JV) Highland County, FL 36.00 2012 Ethanol Bio X

Cell. Feedstocksb

Cell.

Biofuel

Cell.

Tech.a

a

Conversion technology. Bio = Biochemical, Thermo = Thermochemical.

bCellulosic feedstocks. AR = Ag residues, EC = Energy crops, W = Wood waste, chips, mill waste, etc., UW = Urban waste including sorted MSW and C&D debris.

cCellulosic ethanol plant will be co-located with an existing corn ethanol plant.

dWill start off processing corn and then transition to cellulosic feedstocks.

eWill start off producing methanol and then switch catalysts and shift to producing a mix of methanol and ethanol.

Company/Plant Name

Current Plan Production Goal

Plant Location
187

Table 1.5-29

Promising New Cellulosic Hydrocarbon Plants

Cap

(MGY)

Op

Date

Cap

(MGY)

Op

Date AR W UW

Cello Energy Georgia (TBA) 50.00 TBD Diesel Cat X X

Cello Energy Alabama (TBA) 50.00 TBD Diesel Cat X X

Flambeau River Biofuelsc

Park Falls, WI 8.00 2012 Diesel Thermo X

New Page - Project Independencec Wisconsin Rapids, WI 2.50 Early-2012 Diesel Thermo X

Ohio River Clean Fuels, LLC / Baardd Wellsville, OH 17.00 2014 Diesel, Naphtha Thermo X X

Rentech / Rialto Renewable Energy Center Rialto, CA 7.00 End-2012 Diesel Thermo X

Cell. Feedstocksb

Cell.

Biofuel

Cell.

Tech.a

aConversion technology. Cat = Catalytic depolymerization, Thermo = Thermochemical.

bCellulosic feedstocks. AR = Ag residues, W = Wood waste, chips, mill waste, etc., UW = Urban waste including sorted MSW and C&D debris.

cCapacities exclude heavy distillate/wax production.

d

Plant will co-process biomass and coal. It is unclear at this time how much fuel would come from biomass and potentially qualify as cellulosic biofuel.

Company/Plant Name

Current Plan Production Goal

Plant Location
188

1.5.3.4 Construction Feasibility for Cellulosic Biofuel Industry

Start-up of cellulosic biofuel plants (alcohol or hydrocarbon) is expected to begin in

earnest with a few small plants in 2010-11, followed by addition of industry capacity continuing

at an increasing pace due to more plant starts per year as well as increasing plant size. This is

typical as an industry progresses up the learning curve, and investors become more confident and

are willing to fund larger, more efficient plants. During the period from 2010-12, we also expect

a slowing of starch ethanol plant construction, such that engineering and construction personnel

and equipment fabricators would potentially be able to transition to work on cellulosic biofuel

facilities.

Here we examine the build rate required to construct cellulosic plants in time to meet the

standards in Table 1.2-1, and we compare this to the historic build rate of capacity in the starch

ethanol industry. Figure 1.5-12 depicts these construction trends.

Figure 1.5-12.

Historic and projected plant starts and projection capacity, 2001-2022.a

0

5

10

15

20

25

30

20022004200620082010

2012

20142016

201820202022

Plant starts/yr

0

5

10

15

20

25

30

35

Bgal capacity/yr

Starch Plant Starts Cellulosic Plant Starts Total Biofuel Capacity

2009 and later data are projected

a Volumes do not include biodiesel or renewable diesel.

Historical plant build rates for starch ethanol were derived from capacity information in

Figure 1.5-1. Average plant capacity figures were estimated from existing capacity and plant

counts, and we project that the recent trend toward larger plant sizes continues going forward.

Approximately 200 starch ethanol plants are expected to be operating by 2022.
189

For cellulosic biofuel plant construction, we assumed new plant size would begin

relatively small at 40 million gal/yr for any builds during 2010-13, increasing to 80 million gal/yr

for 2014-17, and 100 million gal/yr afterwards. Given the volume standards laid out in the

EISA, as well as the volume of cellulosic biofuel projected, we arrive at a maximum required

build rate of approximately 2 billion gal/yr from 2018-2022. This is similar to the rate of starch

ethanol construction in recent years. Table 1.2-30 shows a summary of the figures used in the

analysis.

Table 1.5-30.

Summary of figures used in the cellulosic biofuel plant construction rate analysis,

2001-2022.a

Build Avg Plant Capacity Industry Build Avg Plant Capacity Industry

Rate b Capacity c Change Capacity Rate b Capacity c Change Capacity

Year Starts/yr Mgal/yr Bgal/yr Bgal/yr Starts/yr Mgal/yr Bgal/yr Bgal/yr

2001 1.7

2002 6 50 0.3 2.0

2003 14 50 0.7 2.7

2004 12 50 0.6 3.3

2005 10 50 0.5 3.8

2006 16 50 0.8 4.6

2007 17 100 1.7 6.3

2008 26 100 2.6 8.9

2009 13 100 1.3 10.2

2010 10 100 1.0 11.2 2 40 0.1 0.1

2011 9 100 0.9 12.1 3 40 0.1 0.2

2012 7 100 0.7 12.8 4 40 0.2 0.4

2013 6 100 0.6 13.4 9 40 0.4 0.7

2014 14.0 7 80 0.5 1.3

2015 15.0 11 80 0.9 2.1

2016 15.0 11 80 0.9 3.0

2017 15.0 11 80 0.9 3.9

2018 15.0 11 100 1.1 5.0

2019 15.0 11 100 1.1 6.1

2020 15.0 14 100 1.4 7.5

2021 15.0 21 100 2.1 9.7

2022 15.0 18 100 1.8 11.4

Starch Ethanol Cellulosic Biofuel

a

Figures for 2009 and later are projected; volumes do not include biodiesel or renewable diesel. Year-by-year

industry capacity figures were taken from RIA Table 1.2-1.

b Build rate is an approximate figure, derived from other figures used in this analysis.

c Average plant capacity is an approximate figure based on historical ethanol industry trends.

This analysis suggests that it is feasible to construct plants quickly enough to meet the

cellulosic standard if plant starts can reach a rate similar to that of starch ethanol plants in recent
190

years. Given that cellulosic biofuel technology is still developing, some types of plants may be

considerably more complex and expensive to construct than starch ethanol plants. Therefore, we

believe the market will need to react even more enthusiastically with capital funding, design and

construction resources.

1.5.4 Biodiesel & Renewable Diesel

1.5.4.1 Biodiesel

The biodiesel industry differs significantly in profile from the ethanol industry, in that it

is comprised of plants with a wide variety of sizes, ranging from less than one million gallons to

more than 50 million gallons per year production capacity, using feedstock ranging from virgin

soy oil to recycled cooking grease and rendered fats. The industry capacity has expanded

rapidly, going from a sparse network of small businesses selling locally to one with large

companies selling internationally in less than a decade. As of November 2009, the aggregate

production capacity of biodiesel plants in the U.S. was estimated at 2.8 billion gallons per year

across approximately 191 facilities, with a mean size of 16 million gallons per year and a median

size of just 6 million gallons per year.402 Table 1.5-31 shows historical aggregate capacity, sales

volumes, and other information related to biodiesel production and use.

Table 1.5-31. Recent biodiesel industry production and use trends.

403

Year Domestic

production

capacity

Domestic

total

production

Apparent

capacity

utilization

Net domestic

biodiesel use

Net domestic use

as percent of

production

2004 245 28 11% 27 96%

2005 395 91 23% 91 100%

2006 792 250 32% 261 104%

2007 1,809 490 27% 358 73%

2008 2,610 776 30% 413 53%

2009 2,806 475 (est.) 17% 296 (est.) 62%

The average capacity utilization had been steady around 30% during 2006-2008 due to

continued expansion of on-line capacity despite apparently adequate existing capacity. Reasons

for this include various state incentives to build plants, along with state and federal incentives to

blend and sell biodiesel, which have given rise to an optimistic industry outlook over the past

several years. However, in 2009 utilization was about half this level, due to a steep decline in

exports as a result of European trade barriers enacted early in the year, as well as a drop in U.S.

diesel prices which has made biodiesel relatively more expensive.

We can speculate that sustained low capacity utilization has been feasible for this

industry because of the relatively low capital cost (typically 5-10% of total per-gallon production

cost) of these plants, which enables them to operate only part of the year or at reduced capacity,

depending on feedstock prices or other market conditions. Besides fuel, some plants may also

produce oleochemicals for use in detergents, lubricants or other products, providing additional

sources of revenue for part of the industry.
191

In order to conduct our emissions and distribution analyses, we needed to have an

industry characterization at the time of the fully phased-in program, the year 2022. This was not

a simple task because of the apparent feasibility of sustained over-capacity and the variety of

useable feedstocks. As discussed in Section 1.2, we project under our primary control case that

in order to meet the RFS2 standards, 1.67 billion gallons of biodiesel will be produced in 2022.

With this information, we estimated how many plants would continue to produce biodiesel and

where they might be located based on three factors: state incentives for production and sales,

BQ-9000 certification of existing plants, and capabilities for handling multiple feedstock types.

This information was gathered from a database of member plants maintained by the National

Biodiesel Board, and a summary of tax incentives from the Department of Energy website. 404

Existing plants with affirmative status for more of these factors were expected to be more likely

to survive over those that had fewer. We also projected that a number of very small plants

processing waste greases/fats would continue to operate based on local market niches regardless

of these criteria.

We project that between now and 2022 the number of plants will decline by about 30%,

pushing capacity utilization above 80%. It is expected that plants will continue to operate in 44

states. During this period most plants will have added the pre-treatment and feedstock

segregation capacity to process any mix of feedstock types available in their area. Multi-product

plants will retain the capacity to produce biodiesel, but it is not expected to be their primary

product due to higher margins for more specialized products like surfactants, lubricants, or

renewable oleochemical feedstocks for re-sale. Table 1.5-32 summarizes key parameters of the

industry as it is currently and in the 2022 forecast.

Table 1.5-32. Summary of Current Biodiesel Industry and Forecast.405

2008 2022

Total production capacity on-line (million gal/yr) 2,610 1,968

Number of operating plants 176 121

Median plant size (million gal/yr) 5 5

Total biodiesel production (million gal) 776 1,670

Average plant utilization 0.30 0.85

1.5.4.2 Renewable Diesel

For a period of time in 2007 and 2008, ConocoPhilips produced small quantities (300-

500 bbl/day) of renewable diesel at their Borger, Texas, refinery from beef tallow generated by

Tyson Foods, Inc. in Amarillo, Texas. This operation was stopped primarily due to changes in

tax law that reduced the subsidy for renewable diesel products being coprocessed with petroleum

at refineries. 406

In fall of 2008, Dynamic Fuels, LLC (a joint venture of Syntroleum Corp. and Tyson

Foods, Inc.) announced construction of a 75 million gallon per year plant (5,000 bbl/day) in

Geismar, Louisiana, that will use Tyson meat processing byproducts as feedstock to

Syntroleum’s Bio-Synfining process. Start-up is scheduled for 2010, with the primary product
192

being high-quality diesel fuel that will be fungible within the existing petroleum supply system.

407 The Geismar facility plans to utilize supplies of hydrogen available in the industrial park

where it will be located, as well as rail and shipping infrastructure already in place nearby.408

However, it is not co-located with existing petroleum production, and therefore would be

considered a stand-alone facility in our analyses (thus meeting the definition of non-coprocessed

fuel eligible to generate RINs counting toward the biomass-based diesel standard).

Our industry projection is based on the expectation of that Dynamic Fuels, LLC, (or

another company) will construct and operate two facilities like the one underway in Geismar,

LA, during our analysis period. 409 It is conceivable that more facilities will be built by Dynamic

Fuels or other companies (such as Neste), or that some renewable diesel will be imported into the

U.S., but we felt there was too much uncertainty to project volumes, given the large capacity for

biodiesel production already on-line. Also, considering tax subsidy and RIN incentives putting

co-processed renewable diesel at a disadvantage, we’ve chosen to assume all renewable diesel is

produced in stand-alone facilities.

1.5.4.3 Algae-Based Biofuel

Recently, there has been a renewed interest in the production of algae-based biofuels and

a growth in the number of potential technology providers. To give a sense of the size of the

industry, we’ve developed a list of over 70 companies from various locations around the world

and summarized a basic description of their technologies for algae production (Table 1.5-33).

This list is current as of November 2009 and is based mainly on biofuel magazines and articles

that are supplemented with company websites. As new information is available on a near daily

basis, it is possible that we have not included newly formed companies or those not highly

publicized.

Companies that have announced plans for algae-based biofuel production include:

Sapphire Energy for 135 MMgal by 2018 and 1 Bgal by 2025, Petrosun for a 30 MMgal/yr

facility, Solazyme for 100 MMgal by 2012/13, and U.S. Biofuels for 4 MMgal by 2010 and 50

MMgal by full scale. It is important to realize that future projections are highly uncertain, and

we have taken into account the best information we could acquire at the time. For more

information on algae as a feedstock for biofuel, refer to Section 1.1.3.4.

In recent months, there have also been grants given to technologies based on algae. On

December 4, 2009, the Department of Energy announced that it awarded several algae-based

technology providers. This included the following companies: Algenol Biofuels ($25 million

grant for a pilot scale project located in Freeport, Texas), Solazyme ($22 million grant for a pilot

scale project located in Riverside, Pennsylvania) and Sapphire Energy ($50 million grant for a

demonstration scale project located in Columbus, New Mexico).
193

Table 1.5-33. Companies Developing Algae Production Technologies68

Company Name Technology

Headquarters &

Facilities

A2BE Carbon Capture

Closed PBR algae system

recycling CO2 from industries. Boulder, Colorado

Advanced Lab Group

Polyethylene film for closed

PBRs, wants to reduce costs

for harvesting and dewatering,

heat venting for closed

systems, and reduce oil

extraction and process costs. Santa Monica, California

Alfa Laval

Algae/water separability tests

using different centrifuge test

units. Headquarters in Sweden

Algae Venture Systems

Develops harvesting,

dewatering, and drying of

algae technology. Marysville, Ohio

AlgaeLink

Uses photobioreactor (PBR)

technology and has expertise

in extracting oil and biomass.

Offers algae production

capacity for a farm of 250 ton

dry algae per day.

Dutch-based, plant in the

Netherlands

Algenol Biofuels

Direct to ethanol process,

using algae, sunlight, CO2,

and seawater. Produces

ethanol at rate of 6,000

gallons per acre per year,

targeting 10,000 gallons per

acre per year. Ethanol is

produced inside each algae

cell. Uses hybrid algae in

sealed, clear plastic

photobioreactors.

Plans first US plant in

Florida or Texas. One in

development in Sonora,

Mexico with company

called BioFields.

Corporate headquarters in

Naples, Florida. Goal is to

have 4 sites in US by

2010, target Florida,

Texas, Arizona, New

Mexico. Announced on

June 29, 2009, demo

plans of 3,100 bioreactors

on a 24-acre site at Dow's

Freeport, Texas site.

Aquaflow Bionomic Corporation

Produce biofuel from wild

algae harvested from open air

environments, clean-up algaeinfested polluted water

systems. New Zealand

Aquatic Energy

Proprietary strain of algae for

continuous outdoor growth,

filed patents for growth and

harvesting techniques.

Interested in developing,

constructing, and operating

open pond algae farms.

Headquarters in Lake

Charles, Louisiana;

Couple of acre pilot facility

in Lake Charles

68 Although we provide this summary here, we caveat that we have not confirmed the statements made on the

company websites or on the data collected from news magazine/articles. For latest information please refer to the

company’s website or contact the company’s representatives. Blanks occur where information was not available or

found.
194

Aurora Biofuels

Use genetically-modified

algae to generate oil for

production of biodiesel. Uses

seawater-fed, open ponds.

Has produced slightly under

1,000 gallons oil per year from

1/8th acre surface area. The

company estimates that will

translate into 6,000

gallons/yr/acre at commercial

size. The company uses

waste water technology and a

wet extraction process instead

of the traditional process of

centrifugation and drying. On

August 18, 2009 announced

that it had optimized particular

algae strains to more than

double their uptake of carbon

dioxide.

Headquarters in Alameda,

California ;Developed at

the University of California

at Berkeley; Pilot-Scale

facility in Florida

AXI LLC

Developing various strains of

algae for the production of

biofuels. Quincy, Massachusetts

BARD, LLC

BARD's closed loop photobioreactor technology can

produce 66 million gallon of

algae oil in 7 acres of land,

which is 8,571,428 gallon of

algae oil per acre. The pilot

facility will begin by producing

43,070 gallons of algae oil /

biodiesel per annum using

only six modules of photobioreactors covering 84

square feet.

Commercial scale algae

system pilot facility located

in the Commonwealth of

Pennsylvania also plans

for plant in Ohio.

Bellona

Supports algae in

photobioreactors which can

deliver food, fodder and fuel. Norway

Bio Algene

Use algae to generate oil for

production of biodiesel and

extract oil by breaking cell

wall. Algae cultivation to

remediate pollution, produce

fuel and other bioproducts.

Company has developed

methods to accelerate algae

growth and is investigating

different harvesting methods. Headquarters near Seattle

Biocentric Energy

Manufactures and sells closed

loop algae bioreactor systems

for commercialization. California

Biofuel Systems Group Limited

Use phytoplankton to produce

biodiesel. Design and build

biodiesel processing systems. England
195

Biolight Harvesting

Develop renewable fuels and

chemicals from blue-green

algae. Biolight is focused on

brackish water and agricultural

runoff as a long-term medium

for cultivation.

California; 40-acre pilot

facility in California's

Imperial Valley

Bionavitas

High-volume production of

algae using biofactories and

fiber-optic lights in algaculture

system. Claims to have a

cost-efficient way to deliver

light to biomass. Light

Immersion Technology (LIT). Redmond, Washington

BioProcess Algae LLC

Photobioreactor systems

coupled next to an ethanol

facility which provides water,

heat, and CO2.

Pilot project anticipated to

be in Shenandoah, Iowa

Blue Marble Energy

Convert algal biomass to

energy by creating,

centralizing, and harvesting

wild algae blooms. BME’s

proprietary AGATE (Acid,

Gas, and Ammonia Targeted

Extraction) system processes

nearly any organic feedstock,

utilizing cultured strains of

bacteria to perform

fermentation (like brewing

beer) to produce a wide

variety of biochemicals; can

utilize wet biomass, bypassing

energy-intensive drying Seattle, Washington

Bodega Algae LLC

Developer of scalable algae

photobioreactors. Developing

proprietary light technology to

enhance growth of algae.

Headquarters in Boston,

Massachusetts

Canadian Pacific Algae Inc.

Grower and producer of

phytoplankton (marine

microalgae), current research

center uses eight - 1 million

liter tanks. Nanaimo, British Columbia

Carbon Capture Corp.

Operates open algae ponds.

In the business of processing

algal-derived renewable

diesel, butanol, biomethane

and jet fuel propellant.

La Jolla, California; 40-

acre Algae Research

Center, part of a 326-acre

R&D facility in Imperial

Valley, California

Cellena

Open pond and PBR

technology. Developing

process for extracting algae oil

without chemical use, drying

or an oil press. Kona facility

will grow only non-modified,

marine microalgae in a hybrid

system.

Hawaii; Building an openpond demo facility in

Hawaii - Kona Pilot Facility

on Big Island began on

January 16, 2008.
196

Circle Biodiesel and Ethanol

Corporation

Have manufactured an algae

photobioreactor for the

production of algae. Also has

an algae harvesting system

for the extraction of algae oil

for algae biodiesel or algae

biofuel. Algae harvesting

system retails for $195,000

US dollars, can process one

gallon of algae oil per minute.

Headquarters in San

Marcos, California

Desert Sweet Biofuels

Using a combination of

gasification and pyrolysis in

such a way as to produce

biochar, a byproduct is

electricity. One low cost algae

production system currently

being developed is vectoring

algae through Daphnia. Gila Bend, Arizona

Diversified Energy Corp.

Has licensed technology from

XL Renewables under the

name Simgae for simple

algae. Gilbert, Arizona

Dynamic Biogenics Utilizes photobioreactors.

Headquarters in

Sacramento, California

ENN Hebei Province, China

General Atomics

Developing improved

processes for growing and

extracting oil from algae in

open ponds. San Diego, California

Genifuel

Licensed method to convert

algae into renewable natural

gas. Uses wet biomass like

algae in a gasifier - Catalytic

Hydrothermal Gasifier (CHG).

The gasifier was developed by

PNNL. Focus on outdoor

ponds or inexpensive troughs.

Global Green Solutions Inc.

Focused initially on biodiesel

feedstock. Developed

Vertigro, self-contained algae

growing system.

Vancouver, British

Colombia

Green Plains

Fourth largest ethanol

producer in North America.

Focus on photobioreactor

systems. The pilot plant is

planned to be used for animal

feed, at least initially.

Shenandoah, Iowa; pilot

project expected to be

operational by July 2009

Green Star Products

Developed formulas to

increase algae growth rates,

Montana Micronutrient

Booster (MMB). Developed

wet-algae stripping

technology.

Headquarters in San

Diego, California. Had

plans to move algae

facility to Utah.
197

GreenShift

Has license agreement with

Ohio University for bioreactor.

Corporate offices in New

York, New York;

Engineering located in

Alpharetta, Georgia

HeroBX (formerly Lake Erie

Biofuels)

Investigating algae as a

feedstock, conducting a

vetting process with PBRs. Erie, Pennsylvania

HR Biopetroleum Inc.

Focus on earth-marine

microalgae plants to produce

biofuel feedstocks and animal

nutrition products. The

company offers algae

products, such as algae oil,

biodiesel, and animal feed

proteins; carbohydrates for the

production of ethanol and

petroleum-based products;

and military jet fuel. The

technology is focused on

coupling PBRs with open

pond systems. Hawaii

Infinifuel Biodiesel

Focusing on algae for

biodiesel using algae ponds.

Headquarters in Dayton,

Nevada

Ingrepro

Focused on open-pond

systems. Suggests that best

business model will remediate

waters, integrate heat, and

produce multiple products.

Netherlands; Plans to

build algae facilities in

Malaysia

International Energy Inc. PBR Washington, DC

Inventure Chemical Technology

Patent-pending algae-to-jet

fuel product. The company

provides expertise in both

process conversion and plant

design and construction. Gig Harbor, Washington

Kai BioEnergy

Continuous, open pond

system that produces bio

crude oil from microalgae.

Technology claims to

overcome risk of algae

contamination and allows for

high yield growth of a

dominant species.

Del Mar, California and

Hawaii

Kelco Harvests natural kelp beds. San Diego, California

Kent BioEnergy

Develops open pond algae

farm, experience in

aquaculture.

San Diego, California;

160-acre process

development/production

facility south of Palm

Springs

Live Fuels Inc.

Open-pond algae bioreactors

to create green crude, not

ethanol or biodiesel. Up to

20,000 gallons per acre

predicted for algae yield. The

Headquarters in Menlo

Park, California; Original

plans to grow algae in

ponds at the Salton Sea,

an inland saline lake in
198

company grows a mix of

native algae species in 45

acres of open saltwater

ponds. To harvest the algae,

the company uses “algae

grazers” such as filter-feeding

fish species and other aquatic

herbivores. The fish, including

those from the Tilapia or

sardine families, collect and

clean the algae through

structures in their mouths,

according to the company.

They swallow it and the algae

is digested and concentrated

in the fish’s flesh. To extract

the oil, the fish are cooked

and pressure is applied,

resulting in Omega-3 fatty

acids and other oils used as

feedstocks for renewable

fuels.

Southern California, but

has shifted to Texas. Will

begin pilot operations at

its test facility in

Brownsville, Texas. The

results of the pilot project

will be used to

commercialize the process

along the coast of

Louisiana.

Martek Biosciences Corporation

Martek currently produces

algae in a closed, dark system

where the algae are fed

sugars in a fermentation

process similar to yeast

growing on corn sugar, in

contrast to the photosynthetic

processes being developed by

others in the algae-to-fuel

race. The sugar-to-biodiesel

pathway will use advanced

biological science to convert

sugars derived from biomass

into lipids which are then

converted into fuel molecules

through chemical or

thermocatalytic processes. Maryland

MBD Energy

Algae grown in waste water

with high concentration of

CO2 from a nearby power

plant. Algae are harvested to

produce algae oil and algae

meal. East Melbourne, Australia

Neptune Industries

Has a patented system to use

fish waste for the growth of

algae for biofuels and

methane gas. Boca Raton, Florida

Odyssey Oil and Energy Inc.

Company focuses on carbon

sequestration and generation

of renewable energy. PBR

technology, ALG Bio Oil Ltd. Pretoria, South Africa
199

OriginOil

The company's bioreactor

attempts to speed the growth

of algae in a tank by blending

light emitted from a rotating

shaft with nutrients. The

process does not require

chemicals, initial dewatering,

or high capex for heavy

machinery. The company’s

technology combines

electromagnetism and pH

modification to break down

cell walls, releasing algal oil

within the cells. The oil rises to

the top for skimming and

refining, while the remaining

biomass settles to the bottom

for further processing as fuel

and other valuable products. Los Angeles, California

Petroalgae

Developing a commercialized

system of technologies to

grow and harvest oil from

algae. Certain initial alga

strains originated at the

National Renewable Energy

Lab (“NREL”). Selected and

utilizes strains of algae to

optimize growth and harvest

characteristics for different

applications and different

geographic environments.

Based in Melbourne, FL;

Pilot plant in Fellsmere;

Plans to complete a 20-

acre demo algae farm by

end of 2009.

PetroSun

Scottsdale, Arizona,

factory in Rio Honda,

Texas

Phycal

Aims to harvest oil from algae

without killing it, by bathing in

solvents that remove the oil.

Olexal non-destructive

extraction "milking" process.

Highland Heights, Ohio;

Pilot by end of 2009. Subpilot scale in Ohio and

R&D lab in St. Louis. Pilot

facility in Hawaii planned

to begin operations in

2010.

Plankton Power

Closed ponds and integrated

PBR, continuous process with

low energy algal separation,

oil extraction. Wellfeet, Massachusetts

Primafuel

Grown in shallow ponds with

sunlight and fertilizers as

inputs; Fertilizers are grass

clippings and wood biomass.

Signal Hill, CA

Lund, Sweden

Renewed World Energies

Reportedly, the only fully

automated and modular

photo-bioreactor currently

available, yields algae oil and

cake. Captures nitrogen

oxides and CO2 from flue

Georgetown, South

Carolina
200

gases.

SAIC

Focus on creation of algaebased jet fuel

Headquarters in McLean,

Virginia; locations in 150

cities worldwide

Sapphire Energy

Plans to grow algae in open

ponds of unusable water.

Algae-based based fuels

developed include gasoline,

diesel, and aviation fuels.

San Diego, California;

Demo in Las Cruces,

California

SCIPIO Biofuels

Continuously circulating

photobioreactors and

continuous algae harvester.

The company says it will

target whatever fuel is

demanded, be it jet fuel,

ethanol, biodiesel, or

biobutanol.

Headquarters in Laguna

Hills, California

Plans for facility in

Greensburg, Kansas

Seambiotic

Produces marine algae for a

variety of applications, health

foods, chemicals, medical

products, and biofuels. Uses

raceway/paddle-wheel openpond algae cultivation. Ashkelon, Israel

Solazyme

Grows algae in the dark using

standard industrial

bioproduction equipment,

where the algae are fed a

variety of non-food and waste

biomass materials including

cellulosic biomass and lowgrade glycerol.Headquarters in San

Francisco, California

Solena Group

Plasma technology to gasify

algae and other organics into

energy outputs. Algae would

be grown in big plastic

containers and fed sunlight

and sodium bicarbonate.

Biomass is converted to

syngas to produce electricity.

Headquarters in

Washington D.C.;

European Office in

Madrid, Spain

Solix Biofuels

Harvest oil, uses PBR; After

oil is extracted the rest can be

used as animal feed and

ethanol. Claims to use less

water than other processes.

Headquarters in Fort

Collins, Colorado.

Announced in 2008 that it

will build its first large

scale facility at nearby

New Belgian Brewery,

where CO2 produced will

be used to feed the algae.

Plans for a Coyote Gulch

Demonstration Facility,

which will be operational

by late summer 2009. The

Utes chipped in more than

$20 million and the land
201

for the project in

Southwest Colorado.

StellarWind Bioenergy

The company is using its

proprietary PhycoGenic

Reactor and PhycoProcessor

systems as well as a

RecyCO2Tron system for

CO2 recovery. The

PhycoProcessor is an oil

recovery system. Their

resource recovery system

coverts algae biomass into

methane, charcoal, fertilizer,

or syngas. Indianapolis, Indiana

SunEco Energy

Harvesting and growth of

native algae species in open

ponds, claims to can produce

at least 33,000 gallons of

biocrude per acre-foot per

year.

Headquarters in Chino,

California; Operations in

Niland, California

Synthetic Genomics

Synthetic is collaborating with

Exxon Mobil to research and

develop the most advanced

algae. In the future hope to

mass farm the oil from algae. La Jolla, California

Texas Clean Fuels

Developing photobioreactors

and equipment for algae

farms. Their product line,

known as MOPS (Micro

Organism Production System)

Headquarters in Rockwall,

Texas

Univenture

Algae harvesting system that

could reduce energy cost due

to harvesting, dewatering, and

drying of algae using a novel

absorbent moving belt

harvester.

Operations in Ohio,

Ireland, China

US Biofuels PBR

Negotiating with Co-op

Greenhouse regarding

locations in Fresno, the

Imperial Valley, and

Palmdale.

Valcent Products Inc.

Creates, designs, and

develops patents e.g. vertical

bioreactors in a closed loop.

Headquarters in El Paso,

Texas

Vertigro Energy

Closed-loop vertical algae

growth system.

San Diego, California;

commercial-scale

bioreactor pilot project in

El Paso, Texas
202

W2 Energy Inc.

SunFilter technology: a tubular

algae bioreactor; Inside the

bioreactor, low-power

ultraviolet lights, in

combination with the gases,

feed the algae so it grows and

fills the tubes with blooms.

When the blooms have

reached an appropriate

density, a set of magnetic

rings inside the tubes scrapes

the blooms clean and pushes

the algae to the upper

manifold, where compressed

air pushes it out. The algae is

then compressed, dried and

then either gasified or fed into

a biodiesel reactor to produce

biodiesel. W2 also has

developed a multi-fuel reactor

to produce ultra-low sulfur

diesel, a blend of JP8 jet fuel

or gasoline; a plasma-assisted

gasifier; a SteamRay rotary

system engine that converts

energy from steam or fuel

combustion into a rotary force;

small energy generating

systems; and the NonThermal Plasmatron.

Carson City, NV; Plans for

bioreactor running in

Guelph, Ontario in midSept 2009

XL Renewables (formerly XL

Dairy Group)

Patent-pending hybrid algae

system that can operate as a

closed or open system.

Focuses on creating

renewable energy using dairy

waste streams. Wants to

produce algae biomass for

animal feeds (high omega-oil

content). Their Super Trough

System design is expected to

provide annual algae yield of

300 dry tons/acre.

Phoenix, Arizona;

Developing a 400-acre

integrated biorefinery

located in Vicksburg,

Arizona. Algae

Development Center in

Cas Grande, Arizona.

1.6 Biofuel Distribution

1.6.1 Biofuel Distribution Overview

The current motor fuel distribution infrastructure has been optimized to facilitate the

movement of petroleum-based fuels. Consequently, there are very efficient pipeline-terminal

networks that move large volumes of petroleum-based fuels from production/import centers on

the Gulf Coast and the Northeast into the heartland of the country. In contrast, the most biofuel

volumes are produced in the heartland of the country and need to be shipped to the coasts,
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flowing roughly in the opposite direction of petroleum-based fuels. The location of renewable

fuel production plants is often dictated by the need to be close to the source of the feedstocks

used rather than to fuel demand centers or to take advantage of the existing pipeline distribution

system for petroleum products.69

To varying degrees, the physical/chemical nature of some biofuels also limit the extent to

which they can be shipped/stored fungibly with petroleum-based fuels. The vast majority of

biofuels are currently shipped by rail, barge and tank truck to petroleum terminals. All biofuels

currently are blended with petroleum-based fuels prior to use. Most biofuel blends can be used

in conventional vehicles. However, E85 can only be used in flex-fuel vehicles, requires

specially-constructed retail dispensing/storage equipment, and may require special blendstocks at

terminals. These factors limit the ability of biofuels to utilize the existing petroleum fuel

distribution infrastructure. Hence, the distribution of renewable fuels raises unique concerns and

in many instances requires the addition of new transportation, storage, blending, and retail

equipment.

Significant challenges must be faced in reconfiguring the distribution system to

accommodate the large volumes of biofuels that we project would be used to meet the proposed

standards. Considerable efforts are underway by individual companies in the fuel distribution

system, consortiums of such companies, industry associations, independent study groups, and

inter-agency governmental organizations to evaluate what steps might be necessary to facilitate

the necessary upgrades to the distribution system to support compliance with the volumes of

biofuels required by the RFS2 standards.70 EPA will continue to participate in or monitor these

efforts as appropriate.

Considerations related to the distribution of ethanol, cellulosic distillate fuel, renewable

diesel fuel, and biodiesel are discussed in the following sections as well as the changes to each

segment in the distribution system that would be needed to support the volumes that we project

would be used to satisfy the RFS2 standards. The costs associated with making the necessary

changes to the fuel distribution infrastructure are discussed in Section 4.2 of this RIA. The

importation of ethanol into the U.S. is discussed in Section 1.5.2 of this RIA.

1.6.2 Biofuel Shipment to Petroleum Terminals

Pipelines are the preferred method of shipping large volumes of petroleum products over

long distances because of the relative low cost and reliability. Ethanol currently is not

commonly shipped by pipeline because it can cause stress corrosion cracking in pipeline walls

and its affinity for water and solvency can result in product contamination concerns.410 Shipping

ethanol in pipelines that carry distillate fuels as well as gasoline also presents unique difficulties

in coping with the volumes of a distillate-ethanol mixture which would typically result.71 We

69 A discussion of the projected locations of ethanol production facilities can be found in Chapter 1.5 of this RIA.

70 For example, the “Biomass Research and Development Board”, an inter-governmental group co-chaired by

USDA and DOE., includes a group that is focused on evaluating biofuels distribution infrastructure issues.

http://www.usbiomassboard.gov/distribution_infrastructure.htm

71 Different grades of gasoline and diesel fuel are typically shipped in multi-product pipelines in batches that abut

each other. To the extent possible, products are sequenced in a way to allow the interface mixture between batches
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believe that it is currently not possible to re-process this mixture in the way that diesel-gasoline

mixtures resulting from pipeline shipment are currently handled.72 The Pipeline Research

Council International (PRCI) in coordination with the Pipeline and Hazardous Materials Safety

Administration (PHMSA), and the Association of Oil Pipelines (AOPL) are conducting research

to address the safety and technical challenges to pipeline transportation of ethanol.411 A short

gasoline pipeline in Florida is currently shipping batches of ethanol and other more extensive

pipeline systems have feasibility studies underway.412 Thus, existing petroleum pipelines in

some areas of the country may play an increasing role in the shipment of ethanol. Evaluations

are also currently underway regarding the feasibility of constructing a new dedicated ethanol

pipeline from the Midwest to the East coast.413 Substantial issues would need to be addressed

before construction on such a pipeline could proceed, including those associated with securing

new rights-of-ways and establishing sufficient surety regarding the return on the several billion

dollar investment.

We expect that cellulosic distillate fuels and renewable diesel fuel will not have materials

compatibility issues with the existing petroleum fuel distribution infrastructure. Thus, there may

be more opportunity for these biofuels to be shipped by pipeline. However, the location of

ethanol and cellulosic distillate/renewable diesel production facilities relative to the origination

points for existing petroleum pipelines will be a limiting factor regarding the extent to which

pipelines can be used. The gathering of ethanol from production facilities located in the Midwest

and shipment by barge down the Mississippi for introduction to pipelines in the Gulf Coast has

been discussed by industry. This approach might also be considered for cellulosic distillate fuel

when such plants are constructed. However, the additional handling steps to bring the ethanol or

cellulosic distillate fuel to the pipeline origin points in this manner could negate the potential

benefit of shipment by existing petroleum pipelines compared to direct shipment by rail.

Biodiesel is currently not widely shipped by pipeline due to concerns that it may

contaminate jet fuel that is shipped on the same pipeline and potential incompatibility with

pipeline gaskets and seals. Segments of Kinder Morgan’s Plantation pipeline are currently

shipping B5 blends, and its Oregon Pipeline that runs from Portland to Eugene is currently

shipping B2 blends.414 These systems do not handle jet fuel. The shipment of biodiesel by

pipeline may become more widespread and might be expanded to systems that handle jet fuel.

However, the relatively small production volumes from individual biodiesel plants and the

widespread location of such production facilities may tend to limit the extent to which biodiesel

may be shipped by pipeline. Rail cars, barges, and tank trucks that transport biodiesel over long

distances will need to be heated/insulated in cold climates to prevent gelling.

Due to the uncertainties regarding the extent to which pipelines might participate in the

transportation of biofuels in the future, we assumed that biofuels will continue to be transported

by rail, barge, and truck to petroleum terminals as the vast majority of biofuel volumes are today.

to be cut into one of the adjoining products. In cases where diesel fuel abuts gasoline in the pipeline, the resulting

mixture must typically be reprocessed into its component parts by distillation for resale as gasoline and diesel fuel.

72 We believe that it is not currently possible to separate ethanol from a gasoline/diesel mixture sufficiently by

distillation. Hence, a significant amount of ethanol may remain in the gasoline and diesel fractions separated by

distillation. Gasoline-ethanol mixtures can be blended into finished gasoline provided the applicable maximum

allowed ethanol concentration is not exceeded. However, diesel-ethanol mixtures can not be used as motor fuel.
205

To the extent that pipelines do play an increasing role in the distribution of ethanol, this may

improve reliability in supply and reduce distribution costs.

Apart from increased shipment by pipeline, biofuel distribution, and in particular ethanol

distribution, can be further optimized primarily through the expanded use of unit trains. Unit

trains are composed entirely of 70-100 ethanol tank cars, and are dedicated to shuttle back and

forth to large hub terminals. In the future, unit trains might also be used for the shipment of

cellulosic distillate fuel. Unit trains can be assembled at a single production plant or if a group

of plants are not large enough to support such service individually, can be formed at a central

facility which gathers fuel from a number of producers. The Manly Terminal in Iowa, accepts

ethanol from a number of nearby smaller ethanol production facilities for shipment by unit train.

Regional (Class 2) railroad companies are an important link bringing ethanol to gathering

facilities for assembly into unit trains for long-distance shipment by larger (Class 1) railroads.

We anticipate that the vast majority of new ethanol and cellulosic distillate facilities will be sized

to facilitate unit train service. We do not expect that biodiesel facilities will be of sufficient size

to justify shipment by unit train. In the NPRM, we projected that unit train receipt facilities

would be located at petroleum terminals and existing rail terminals. Based on industry input

regarding the logistical hurdles in citing unit train receipt facilities at petroleum/existing rail

terminals, we expect that such facilities will be constructed on dedicated property with rail

access that is as close to petroleum terminals as practicable.73

Shipment of biofuels by manifest rail to existing rail terminals will continue to be an

important means of supplying biofuels to distant markets where the volume of the production

facility and/or the local demand is not sufficient to justify shipment by unit train. Manifest rail

shipment refers to the shipment of biofuel in rail tanks cars that are incorporated into trains

which are composed of a variety of other commodities. Shipments by barge will also play an

important role in those instances where production and demand centers have water access and in

some cases as the final link from a unit train receipt facility to a petroleum terminal. Direct

shipment by tank truck from production facilities to petroleum terminals will also continue for

shipment over distances shorter than 200 miles.

We project that most biofuel volumes shipped by rail will be delivered to petroleum

terminals by tank truck.74 We expect that this will always be the case for manifest rail

shipments. In the NPRM we projected that trans-loading of biofuels from rail cars to tank trucks

would be an interim measure until biofuel storage tanks were constructed.75 Based on industry

input, we now expect trans-loading will be a long-term means of transferring manifest rail car

shipments of biofuels received at existing rail terminals to tank trucks for delivery to petroleum

terminals. We also anticipate that trans-loading will be used at some unit train receipt facilities,

although we expect that most of these facilities will install biofuel storage tanks from which tank

trucks will be filled for delivery to petroleum terminals. Imported biofuels will typically be

73 Existing unit train receipt facilities have primarily followed this model. See the US Development Group’s

interactive map of their ethanol unit train receipt facilities at http://www.us-dev.com/terminals.htm

74 At least one current ethanol unit train receipt facility has a pipeline link to a nearby terminal. To the extent that

additional unit train receipt facilities could accomplish the final link to petroleum terminals by pipeline, this would

significantly reduce the need for shipment by tank truck.

75 Trans-loading refers to the direct transfer of the contents of a rail car to a tank truck without the intervening

delivery into a storage tank.
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received and be further distributed by tank truck from petroleum terminals that already have

receipt facilities for waterborne fuel shipments.

Our analysis of the shipment of ethanol and cellulosic distillate fuels to petroleum

terminals is based on the Oakridge National Laboratory (ORNL) analysis of ethanol

transportation activity under the EISA that was conducted for EPA.415 The ORNL analysis

contains detailed projections of which transportation modes and combination of modes (e.g. unit

train to barge) are best suited for delivery of ethanol to specific markets considering ethanol

source and end use locations, the current configuration and projected evolution of the distribution

system, and cost considerations for the different transportation modes. The NPRM analysis

assumed that all biofuel volumes other than biodiesel would be ethanol. For this FRM, we

analyzed three scenarios under which varying volumes of cellulosic distillate fuel would take the

place of ethanol production volumes to satisfy the RFS2 requirements. However, due to the

timing of the various analyses for the FRM, the NPRM projections of the location of ethanol

production facilities and end use areas contained in the NPRM had to be used as the inputs into

the ORNL analysis. Our use of the ORNL analysis to evaluate the distribution impacts for the

final rule assumes that cellulosic distillate production plants would take the place of some of the

ethanol production plants projected in the NPRM. It further assumes that cellulosic distillate fuel

use would coincide with the ethanol end-use areas projected in the NPRM.

The extent to which new cellulosic distillate fuel and cellulosic ethanol production

facilities are more dispersed than projected in the NPRM, distribution for ethanol from new

production facilities and from all cellulosic distillate facilities might be simplified as the fuel has

more opportunity to be used locally. Cellulosic distillate fuel distribution may also be further

simplified to the extent that in the future it is blended with petroleum-based diesel fuel in higher

blend-ratios than the 20% blends currently registered by EPA. An increased blend ratio for

cellulosic distillate fuel would tend to enhance the ability for its use close to the place of

manufacture rather than having to be spread more widely over a larger petroleum diesel pool.

We projected the volumes of biodiesel that would be used on a State-by-State basis to

meet anticipated State biodiesel mandates/incentives and the estimated demand for biodiesel as a

blending component in heating oil. Using the estimated locations of biodiesel production

facilities and their volumes, we evaluated the most efficient means of meeting this projected

demand while minimizing shipping distances (and cost). The remaining biodiesel production

volume from these production facilities that was needed to meet the RFS2 mandated volume

was assumed to be used in the same State where it was produced up to the point where the

State’s entire diesel fuel pool contained 5% biodiesel. We believe that this should provide a

somewhat conservatively high estimate of biodiesel distribution costs since biodiesel might be

used in excess of 5% even absent a State mandate. If a State was already saturated with 5%

biodiesel, the remaining volume was assumed to be shipped out of State within a 1,000 mile

shipping distance. A 1,000 mile shipping distance was selected to ensure that all biodiesel not

used to satisfy a State mandate or for bio-heat could find a market. It is likely that some fraction

would not need to travel quite as far. Therefore, this assumption is also likely to result in a

conservatively high estimate of biodiesel freight costs. It was assumed that biodiesel production

volumes will continue to be insufficiently concentrated to justify shipment by unit train. Where

distances are beyond 300 miles, shipment by manifest rail was assumed to be the preferred
207

option other than in cases on the East coast where there were apparent barge routes from

production to demand centers. In case where biodiesel is shipped by manifest rail, it was

assumed that it would be trans-loaded at a rail terminal for further shipment by tank truck to a

petroleum terminal. Additional discussion of our estimate of how increased biodiesel volumes

used to comply with the RFS2 standards would be transported to petroleum terminals can be

found in Section 4.2 of this RIA on biodiesel freight costs.

We anticipate that the deployment of the necessary distribution infrastructure to

accommodate the shipment of biofuels to petroleum terminals is achievable. We believe that

construction of the requisite rail cars, barges, tank trucks, tank truck and rail/barge/truck receipt

facilities is within the reach of the corresponding construction firms. Although shipment of

biofuels by rail represents a major fraction of all biofuel ton-miles, it is projected to account for

approximately 0.4% of all rail freight by 2022.76 Many improvements to the freight rail system

will be required in the next 15 years to keep pace with the large increase in the overall freight

demand. Given the broad importance to the U.S. economy of meeting the anticipated increase in

freight rail demand, and the substantial resources that seem likely to be focused on this cause, we

believe that overall freight rail capacity would not be a limiting factor to the successful

implementation of the biofuel requirements under EISA.

1.6.3 Changes in Freight Tonnage Movements Due to RFS2

In order to estimate the freight rail system impacts associated with biofuels transport

under RFS2, we commissioned an analysis by Oak Ridge National Laboratories (ORNL) to

examine fuel ethanol transportation, activity, and potential distribution constraints for the North

American freight rail system.416 The analysis found that biofuels transport is expected to

constitute approximately 0.4% of the total freight tonnage for all commodities transported by the

freight rail system through 2022. The results suggest that it should be feasible for the freight rail

system to accommodate the additional biofuels freight associated with the RFS2.

For the analysis, we provided the estimated location of ethanol production facilities,

sources of ethanol imports, and state-level consumption for the annual volumes of ethanol that

we estimated would be consumed in response to the EISA.77 We also provided the projected

volumes of biodiesel and non-co-processed renewable diesel fuel that would be used. Due to the

uncertainty associated with non-ethanol biofuels, biodiesel and non-co-processed renewable

diesel fuel volumes were assumed to originate from the ethanol production facilities and follow

projected ethanol use patterns in the analysis. This assumption seems reasonable, given the

relatively small volumes of these non-ethanol biofuels relative to ethanol.

Rail traffic information from the 2006 Surface Transportation Board Carload waybill

sample was incorporated into ORNL’s North American Transportation Infrastructure Network

Model to provide a baseline approximation of the current day freight rail system unstressed by

the transport of EISA-mandated biofuels volumes. Freight rail activity for the unstressed

baseline model was projected for 2012, 2014, and 2022 using information from the Commodity

76 See Section 1.6.3. of this RIA for a discussion of the increase in freight traffic due to the transport of the biofuels

needed to comply with the RFS2 standards

77 These inputs are summarized in the ORNL final report.
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Origin-Destination Database of DOT’s Freight Analysis Framework version 2 (FAF2) to identify

potential distribution constraints for the North American freight rail system. FAF2 integrates

data from a variety of sources to estimate commodity flows by different modes of transportation

and related freight transportation activity among states, regions, and major international

gateways. FAF2 provides freight transportation forecasts through 2035.

To estimate potential future constraints of the freight rail system, EISA-mandated

biofuels volumes were superimposed onto the unstressed Infrastructure Network model for 2012,

2012, and 2022. For each forecast year, total biofuels demand includes biodiesel and non-coprocessed renewable diesel fuel demand. As such, total biofuel demand for the forecast years

were assumed to be 14.6, 17.5, and 35.1 billion gallons, respectively. See the ORNL report for

additional assumptions and modeling details.

On average, 84% of the nation’s freight rail system will not be affected by biofuels

shipments under the RFS2 scenarios considered, according to the ORNL analysis. The 16%

which will be impacted will see a 2.5% increase in freight rail traffic associated with biofuels

shipments, on average.78 Approximately 85% of all ethanol shipments are expected to originate

in the Midwest, with approximately 24%, 15%, 13%, 8% and 6% of all unit train shipments of

ethanol originating from Iowa, Nebraska, Illinois, Minnesota, and Indiana, respectively. The

balance is expected to originate from the surrounding Midwestern states.

As such, the 16% of the freight rail system that is expected to see an increase in biofuels

shipments under RFS2 will see it concentrated along rail corridors radiating out of the Midwest.

Most high-volume ethanol movements are estimated to occur from the Midwest producing

regions to high-demand regions, such as the northeast, west, and south. For instance, Midwest

ethanol shipments destined for the west constitute about 19% of all ethanol shipments.

Shipments destined from the Midwest to the Northeast constitute about 10% of all ethanol

shipped while shipments to the southeast constitute another 10%. Shipments to the southwest

constitute 7% of overall ethanol shipments as do shipments to the south. Interstate shipments

account for 17% of all ethanol shipped. Shipments originating and terminating in the Midwest

constitute approximately 31% of all ethanol unit train shipments. For all scenarios, the EISArelated transport impacts on the freight rail system were negligible.

The results of the analysis suggest that any additional stress placed upon the North

American freight rail system by biofuels transport under EISA would have minimal impacts on

transportation infrastructure overall since freight associated with biofuels constitutes only a small

portion of the total freight tonnage for all commodities. The results of this analysis suggest that

it should be feasible for the distribution infrastructure upstream of the terminal to accommodate

the additional freight associated with this RFS2.

1.6.4 Rail Transportation System Accommodations

Many improvements to the freight rail system will be required in the next 15 years to

keep pace with the large increase in the overall freight demand. Much of the projected increase

in rail freight demand is associated with the expected rapid growth of inter-modal rail transport.

78 The overall increase in freight tonnage is 0.4% (2.5% x 16%)
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Most of the needed upgrades to the freight rail system are not specific to the transport of

renewable fuels and would be needed irrespective of the need for increased biofuel transport

under the EISA. The modifications required to satisfy the increase in demand include upgrading

tracks to allow the use of heavier trains at faster speeds, the modernization of train braking

systems to allow for increased traffic on rail lines, the installation of rail sidings to facilitate train

staging and passage through bottlenecks.

Some industry groups79 and governmental agencies in discussions with EPA and in

testimony provided for the Surface Transportation Board (STB) expressed concerns about the

ability of the rail system to keep pace with large increase in demand without the implementation

of the RFS2 standards. A 27% overall increase in rail fright traffic is projected by 2022 without

considering the potential impact of compliance with the RFS2 program. For example, the

electric power industry has had difficulty keeping sufficient stores of coal in inventory at power

plants due to rail transport difficulties and has expressed concerns that this situation will be

exacerbated if rail congestion worsens. One of the more sensitive bottleneck areas with respect

to the movement of ethanol from the Midwest to the East coast is Chicago. The City of Chicago

commissioned its own analysis of rail capacity and congestion, which found that the lack of rail

capacity is “no longer limited to a few choke points, hubs, and heavily utilized corridors.”

Instead, the report finds, the lack of rail capacity is “nationwide, affecting almost all the nation’s

critically important trade gateways, rail hubs, and intercity freight corridors.” This is due, in

part, to the lack of critical linkages between the 27 major rail yards located in the Chicago-land

area.

To help improve east-west rail connections through the city, federal, state, and local

officials announced an agreement in 2006 to invest $330 million over three-years in city-wide

rail infrastructure designed to improve the flow of rail traffic through the area. The State of

Illinois, the City of Chicago, and seven Class I rail carriers, as well as Amtrak and Metra, the

area's transit system, also committed $1.5 billion in improvements. Chicago is the largest rail

hub in the country with more than 1,200 trains passing through it daily carrying 75% of the

nation's freight valued at $350 billion; 37,500 rail freight cars pass through the city every day

projected to increase to 67,000 by 2020. Chicago is the only city where all six Class I railroads

converge and exchange freight. The plan calls for the creation of five rail corridors to aid in

alleviating the bottleneck.

Significant private and public resources are focused on making the modifications to the

rail system to cope with the increase in demand. Rail carriers report that they typically invest 16

to 18 billion dollars a year in infrastructure improvements.417 Substantial government loans are

also available to small rail companies to help make needed improvements by way of the Railroad

Rehabilitation and Improvement Finance (RRIF) Program80, administered by Federal Railroad

79 Industry groups include the Alliance of Automobile Manufacturers, American Chemistry Council, and the

National Industrial Transportation League; governmental agencies include the Federal Railroad Administration

(FRA), the General Accountability Office (GAO), and the American Association of State Highway Transportation

and Officials (AASHTO). Testimony for the STB public hearings includes Ex Parte No. 671, Rail Capacity and

Infrastructure Requirements and Ex Parte No. 672, Rail Transportation and Resources Critical to the Nation’s

Energy Supply.

80 The RRIF program was established by the Transportation Equity Act for the 21st Century (TEA-21) and amended

by the Safe Accountable, Flexible and Efficient Transportation Equity Act: a Legacy for Users (SAFETEA-LU).
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Administration (FRA), as well as Section 45G Railroad Track Maintenance Credits, offered by

the Internal Revenue Service (IRS).

The RRIF program offers loans to railroads for a variety of capital purposes including

track and equipment rehabilitation at “cost of money” for 25 year terms. Typically, short line

railroads cannot secure this kind of funding in the private markets. Under this program, FRA is

authorized to provide direct loans and loan guarantees up to $35.0 billion. Up to $7.0 billion is

reserved for projects benefiting freight railroads other than Class I carriers. However, the

program has lent less than $650 million to non-passenger rail carriers since 2002, according to

the FRA/RRIF website.

The American Association of State Highway Transportation Officials (AASHTO)

estimates that between $175 billion and $195 billion must be invested over a 20-year period to

upgrade the rail system to handle the anticipated growth in freight demand, according to the

report’s base-case scenario.418 The report suggests that railroads should be able to provide up to

$142 billion from revenue and borrowing, but that the remainder would have to come from other

sources including, but not limited, to loans, tax credits, sale of assets, and other forms or publicsector participation. Given the reported historical investment in rail infrastructure, it may be

reasonable to assume that rail carriers would be able to manage the $7.1 billion in annual

investment from rail carriers that AASHTO projects would be needed to keep pace with the

projected increase in freight demand.

The Association of American Railroads (AAR) estimates419 that meeting the increase in

demand for rail freight transportation will require an investment in infrastructure of $148 billion

(in 2007 dollars) over the next 28 years and that Class I railroads' share is projected to be $135

billion, with $13 billion projected for short line and regional freight railroads.

In testimony before the STB, Class I railroads committed to working with all parties in

the ethanol logistical chains to provide safe, cost-effective, and reliable ethanol transportation

services as well as to resolve past freight rail capacity difficulties. Presumably, this commitment

extends to the projected three-percent increase in overall freight tonnage envisioned herein.

However, the Government Accounting Office (GAO) found that it is not possible to

independently confirm statements made by Class I rail carriers regarding future investment

plans.81 In addition, questions persist regarding allocation of these investments, with the

Alliance of Automobile Manufacturers, American Chemistry Council, National Industrial

Transportation League, and others expressing concern that their infrastructural needs may be

RRIF funding may be used to: acquire, improve, or rehabilitate intermodal or rail equipment or facilities, including

track, components of track, bridges, yards, buildings and shops; refinance outstanding debt incurred for the purposes

listed above; and develop or establish new intermodal or railroad facilities.

81 The railroads interviewed by GAO were generally unwilling to discuss their future investment plans with the

GAO. Therefore, GAO was unable to comment on how Class I freight rail companies are likely to choose among

their competing investment priorities for the future, including those of the rail infrastructure, GAO testimony Before

the Subcommittee on Surface Transportation and Merchant Marine, Senate Committee on Commerce, Science, and

Transportation, U.S. Senate, Freight Railroads Preliminary Observations on Rates, Competition, and Capacity

Issues, Statement of JayEtta Z. Hecker, Director, Physical Infrastructure Issues, GAO, GAO-06-898T Washington,

D.C.: June, 21, 2006).
211

neglected by the Class I railroads in favor of more lucrative intermodal traffic. Moreover, the

GAO has raised questions regarding the competitive nature and extent of Class I freight rail

transport. This raises some concern that providing sufficient resources to facilitate the transport

of increasing volumes of ethanol and biodiesel might not be a first priority for rail carriers. In

response to GAO concerns, the Surface Transportation Board (STB) agreed to undertake a

rigorous analysis of competition in the freight railroad industry.82

Given the broad importance to the U.S. economy of meeting the anticipated increase in

freight rail demand, and the substantial resources that seem likely to be focused on this cause, we

believe that overall freight rail capacity would not be a limiting factor to the successful

implementation of the biofuel requirements under the RFS2 standards. Evidence from the recent

ramp up of ethanol use has also shown that rail carriers are enthusiastically pursuing the

shipment of ethanol, although there is some indication that the Class I freight rail industry will

expect ethanol to primarily be shipped by unit train from facilities that assemble unit trains

which are developed and paid for by the ethanol industry.

Class 2 railroads have been particularly active in gathering sufficient numbers of ethanol

cars to allow Class 1 railroads to ship ethanol by unit train. Based on this recent experience, we

believe that biofuels will be able to compete successfully with other commodities in securing its

share of freight rail service.

While many changes to the overall freight rail system are expected to occur irrespective

of today’s final rule, several biofuel-specific modifications will be needed. Additional unit train

and manifest rail receipt facilities will be needed to handle the volumes of ethanol and cellulosic

distillate fuel that we project will be used to comply with the RFS2 standards. In the NPRM, we

projected that unit train receipt facilities would be located at petroleum terminals and existing

rail terminals. Based on industry input regarding the logistical hurdles in citing unit train receipt

facilities at petroleum/existing rail terminals, we expect that such facilities will be constructed on

dedicated property with rail access that is as close to petroleum terminals as practicable.83 We

assumed that under the primary mid-ethanol and the low-ethanol control scenarios that all unit

train and manifest rail receipt facilities would be capable of handling the receipt of both ethanol

and cellulosic distillate fuel. There is no cellulosic distillate fuel under the high-ethanol scenario,

thus all unit train receipt facilities would be dedicated to handling ethanol under the high-ethanol

control scenario.

In the NPRM, we assumed that some new manifest rail receipt facilities for biofuels

would be located at petroleum terminals. Since the NPRM we received industry input that it is

unlikely that additional manifest rail receipt facilities could be located at petroleum terminals due

to a lack of reasonable access to a rail line. Consequently, we are now assuming that additional

manifest rail receipt facilities for biofuels would be placed at exiting rail terminals. We are

assuming that biofuels will continue to be trans-loaded directly from rail cars to tank trucks at

rail terminals for shipment to petroleum terminals as is the case today, thereby obviating the need

82 GAO, Freight Railroads: Industry Health Has Improved, but Concerns about Competition and Capacity Should

Be Addressed, GAO-07-94 (Washington, D.C.: Oct. 6, 2006); GAO, Freight Railroads: Updated Information on

Rates and Other Industry Trends, GAO-07-291R Freight Railroads (Washington, D.C.: Aug. 15, 2007).

83 Existing unit train receipt facilities have primarily followed this model.
212

for biofuel storage at rail terminals.84 Some manifest rail receipt facilities would also handle

biodiesel as well as ethanol, and cellulosic distillate fuel/renewable diesel fuel.

As part of Oakridge National Laboratory’s study for EPA on the projected patterns of

ethanol distribution from producer to terminal under the EISA, ORNL estimated the number of

unit train receipt facilities.420 The ORNL study used our NPRM estimate that all biofuel used to

comply with the EISA (other than biodiesel) would be ethanol. Because unit train receipt

facilities would handle both ethanol and cellulosic distillate fuel, the number of these facilities

that would be needed is driven by the combined volume of these fuels that we project would

used. Therefore, the ORNL estimate of the number of unit train receipt facilities for the NPRM

control case is still very useful in estimating the number of such facilities under the control cases

examined in this final rule. The NPRM control scenario assumed the use of 34.14 BGal/yr of

ethanol by 2022. Under the high-ethanol control scenario in this final rule (FRM), we estimate

that 33.24 BGal/yr of ethanol would be used by 2022. Given their similarity, we assumed that

the ORNL results for the NPRM would be applicable to the FRM high-ethanol scenario for

estimating the number of unit train receipt facilities required.

Based on our analysis of a spreadsheet used in the ORNL analysis, we determined that

ORNL estimated that there would be approximately 210 unit train receipt facilities under the

NPRM control case.421 The ORNL estimate was based on an assumption by ORNL regarding

the minimum annual throughput needed to justify the construction of a unit train facility (~20

MGal/yr) which we now believe to understate the throughput needed. Since the completion of

the ORNL study, we received input from industry experts who are familiar with the construction

of ethanol unit train receipt facilities that the minimum annual throughput for such a facility is

approximately 230 million gallons per year. This minimum throughput volume assumes a

fortuitous grouping of circumstances including low cost of the land needed, and ease of

construction of the rail spur to the facility to a rail line. To provide a more realistic estimate

under varied conditions, we assumed a minimum throughput volume of 280 MGal/yr.

We evaluated the location and annual throughput volumes of the unit train receipt

facilities projected by ORNL. We consolidated the volumes from the smaller facilities projected

by ORNL regionally to satisfy a minimum throughput volume of 280 MGal/yr while maintaining

a reasonable trucking distance (<200 miles) from unit train facilities to petroleum terminals.

Based on this analysis, we arrived at an estimate of 40 unit train receipt facilities to support the

volumes of ethanol and cellulosic distillate fuel that we project would be used under the EISA.

We estimated the additional transport by tank truck from these unit train facilities to petroleum

terminals that would be needed to compensate for the reduced number of unit train receipt

facilities compared to the ORNL study.85

84 In the NPRM, we assumed that trans-loading would only continue only until biofuel storage tanks could be

constructed at rail terminals. Input from industry indicates that trans-loading will continue to be employed in the

future. This input also indicates that construction of biofuel storage tanks at rail terminals is unlikely due to space

and other constraints.

85 See Section 1.6.6 for a discussion of the tanker trucks needed to support the distribution of biofuels under the

EISA. For a discussion of our estimation of ethanol and cellulosic distillate freight costs, see Sections 4.2.1.2 and

4.2.2.2 respectively in this RIA. The attribution of the costs of unit train facilities to the volumes of ethanol and

cellulosic distillate fuel is discussed in Sections 4.2.1.1.2 and 4.2.2.1.3
213

We assumed that 40 unit train facilities would be needed under each of the 3 control

scenarios that we evaluated. This may somewhat overstate the number needed under the primary

mid-ethanol and the low ethanol scenarios since the total volume of ethanol and cellulosic

distillate fuel is somewhat lower under these scenarios relative to the high-ethanol scenario.

However, we believe that this is an appropriate approach since it provides some margin to

compensate for the potential that there may be some instances where a unit train receipt facility

may only handle ethanol or cellulosic distillate fuel (potentially increasing the overall number of

unit train facilities needed slightly). We estimate that there would be 9 unit train receipt facilities

to support the transport of biofuels under the AEO reference case and 3 under the RFS1

reference case. For the AEO reference case, this includes those unit train receipt facilities

currently in place and those under construction. To estimate the number unit train facilities

under the RFS1 reference case, we evaluated how many of these type of facilities were in place

or under construction when historic ethanol consumption levels were consistent with the RFS1

case. Under the RFS1 reference case, we attributed the need for 37 additional biofuel unit train

receipt facilities (40-3) to the implementation of the EISA. Under the AEO reference case, we

attributed 31 additional unit train receipt facilities (40-9) to the EISA.

The construction of each of these unit train receipt facilities would require: the

acquisition of land near a rail line and within trucking distance of the petroleum terminals that

would be served, the construction of a rail spur and internal tracks to handle unit trains, facilities

for the high-speed unloading of rail cars and loading of tank trucks, biofuel storage tanks and/or

pipelines to ship biofuel to nearby petroleum terminals, and other miscellaneous biofuel handling

equipment. For our analysis, we assumed that all unit train rail receipt facilities would construct

biofuel storage tanks. Biofuels would be unloaded from unit trains into these storage tanks

before being loaded into tank trucks for shipment to petroleum terminals. To the extent that

some facilities are able to link to nearby petroleum terminals by pipeline or employ transloading, there would be less need for storage tanks at unit train receipt facilities. A large

petroleum fuel terminal and transportation company recently announced a joint venture with a

leading biofuel unit train receipt facility developer to facilitate the rapid expansion of ethanol

logistics facilities throughout the U.S.422

A spreadsheet used in the ORNL analysis indicates that ORNL estimated that there

would be 56 manifest rail receipt facilities for biofuels under the NPRM control case.423 To

provide some margin to compensate for the potential need for additional manifest rail receipt

facilities beyond that indicated by the ORNL analysis, we used the estimate of 56 manifest rail

facilities for each of the 3 FRM control scenarios relative to the RFS1 reference case.86 We

estimated that an additional 43 manifest rail receipt facilities would be needed to support the

transport of biofuels for the three FRM control cases relative to the AEO reference case. We

arrived at this estimate by subtracting the number of manifest rail receipt facilities that could be

attributed to the incremental increase in biofuel shipment volumes in going from the RFS1 to the

AEO reference case from the number of facilities attributed to the EISA under the RFS1

86 No deduction to the number of manifest rail receipt facilities attributed to the EISA was made based on the

number of such facilities that would have been in place to support the transport of the volumes of biofuels

corresponding to the RFS1 reference case.
214

reference case (56-13).87 The construction of a new manifest rail receipt facilities at a rail

terminal would involve the acquisition of a mobile trans-loading platform including fuel and fuel

vapor transfer hoses, the preparation of spill containment for the area where trans-loading would

take place, accommodations for recordkeeping and the preparation of bills of lading, and the

installation of other miscellaneous equipment to support the trans-loading process.

A substantial number of additional rail cars would be needed to transport the volumes of

ethanol, cellulosic distillate fuel, renewable fuel, and biodiesel that are projected to be used in

response to the RFS2 standards. Biodiesel rail cars typically have a deliverable volume of

25,600 gallons, whereas the deliverable volume for ethanol rail cars is typically 29,000. We

assumed that rail cars similar to those used for the transport of ethanol would be used to handle

cellulosic distillate and renewable diesel fuels. Our estimation of the rail cars needed to transport

ethanol and cellulosic distillate fuel under the 3 control scenarios is based on an interpolation of

the results from the ORNL analysis for the NPRM control case (34.14 BG/yr of ethanol by 2022)

and AEO reference case (13.18 BG/yr of ethanol by 2022). The underlying assumption in this

approach is that the overall number of rail cars needed varies by the total volume of biofuel

projected to be used under a given control scenario. Based on this approach, we estimate that

40,400 rail cars would be needed to transport the volumes of ethanol and cellulosic distillate

fuel/renewable diesel fuel under the high-ethanol scenario, 36,200 under the primary mid-ethanol

scenario, and 34,400 under the low-ethanol scenario. We subtracted the number of rail cars

needed under the two reference cases to determine the incremental number of rail cars attributed

to compliance with the EISA (see Table 1.6-1).

Table 1.6-1.

Additional Rail Cars Needed by 2022 for Shipment of the Incremental RFS2 Volumes of

Ethanol, and Cellulosic Distillate Fuel/Renewable Diesel Fuel

Number of Rail Cars

Reference Case used for Comparison

RFS1 AEO 2007

Low-Ethanol Scenario 24,600 12,600

Mid-Ethanol Scenario 20,400 8,300

High Ethanol Scenario 18,500 6,500

We estimated the number of rail cars that would be needed to transport biodiesel using

the projected volume of biodiesel that we expect would be shipped by manifest rail and the

assumed rail car volume and cycle time. We assumed a cycle time of one month for shipment by

manifest rail car. We believe this is a conservatively high estimate given current industry

experience and the potential for improvement in the future. We estimate that 1,370 rail cars

would be needed by 2022 to transport the volume of biodiesel that we project will be used to

satisfy the RFS2 standards. We estimate that 250 rail cars would be needed by 2022 to transport

87 The number of manifest rail receipt facilities attributed to the incremental increase in biofuel shipment volumes in

going from the RFS1 to the AEO reference case was calculated by volume weighting.
215

the volume of biodiesel projected under the RFS1 reference case and 310 rail cars under the

AEO reference case. Consequently, we attribute the construction of an additional 1,130

biodiesel rail cars to the implementation of the EISA under the RFS1 reference case and 1,060

under the AEO reference case. The total additional number of rail cars for the transport of

ethanol, cellulosic distillate fuel/renewable diesel fuel, and biodiesel that we attribute to the

implementation of the EISA is presented in Table 1.6-2.

Table 1.6-2.

Additional Rail Cars Needed by 2022 for Shipment of

All Incremental RFS2 Biofuel Volumes

Number of Rail Cars

Reference Case used for Comparison

RFS1 AEO 2007

Low-Ethanol Scenario 25,800 13,700

Mid-Ethanol Scenario 21,500 9,400

High Ethanol Scenario 19,700 7,500

Our analysis of ethanol, biodiesel cellulosic distillate, and renewable diesel fuel rail car

production capacity indicates that access to these cars should not represent a serious impediment

to meeting the requirements under the RFS2 standards. Ethanol tank car production has

increased approximately 30% per year since 2003, with over 21,000 tank cars expected to be

produced in 2007. To accommodate the increased demand for ethanol tank cars, rail car

producers converted existing boxcar production facilities to tank production facilities and

brought on additional work shifts to adjust to rapidly changing to market conditions.

With the recent economic downturn, the backlog for railcars has decreased significantly.

For example, the backlog for railcars of a major producer was approximately 7,000 railcars in

2009, but dropped to approximately 1,200 railcars scheduled for delivery in 2010. This has led

to the closure of several railcar production facilities. We believe that the excess railcar

production capacity will allow the industry to rapidly respond to potential increases in railcar

demand due to ethanol, biodiesel, cellulosic distillate, and renewable diesel fuels, when the need

arises.

1.6.5 Marine Transportation System Accommodations

The American Waterway’s Association expressed concerns about the need to upgrade

the inland waterway system in order to keep pace with the anticipated increase in overall freight

demand. The majority of these concerns have been focused on the need to upgrade the river lock

system on the Mississippi river to accommodate longer barge tows and on dredging inland

waterways to allow for movement of fully loaded vessels. We do not anticipate that a substantial

fraction of biofuels will be transported via these arteries. Thus, we do not believe that the ability

to ship biofuels by inland marine will represent a serious barrier to the implementation of the

requirements under RFS2 standards. Substantial quantities of the corn ethanol co-product dried
216

distiller grains (DDG) is expected to be exported from the Midwest via the Mississippi river as

the US demand for DDG becomes saturated. We anticipate that the volume of exported DDG

would take the place of corn that would be shifted from export to domestic use in the production

of ethanol. Thus, we do not expect the increase in DDG exports to result in a substantial increase

in river freight traffic.

A number of new barges would be needed to transport the volumes of biofuels that are

projected to be used in response to the RFS2 standards. We assumed the use of tank barges with

a carrying capacity of 10,000 barrels (42,000 gallons). We understand that the tank barge

industry is trending towards the use of tank barges with a carrying capacity of 30,000 barrels.

Thus, our assumed use of 10,000 barrel barges may overstate the number of barges that would be

needed. Our estimation of the barges needed to transport ethanol and cellulosic distillate fuel

under the 3 control scenarios is based on an interpolation of the results from the ORNL analysis

for the NPRM control case (34.14 BG/yr of ethanol by 2022) and AEO reference case (13.18

BG/yr of ethanol by 2022). The underlying assumption in this approach is that the over all

number of barges needed varies by the total volume of ethanol and cellulosic distillate fuel

projected to be used under a given control scenario. Based on this approach, we estimate that

167 barges would be needed to transport the volumes of ethanol and cellulosic distillate

fuel/renewable diesel fuel under the high-ethanol scenario, 150 under the primary mid-ethanol

scenario, and 143 under the low-ethanol scenario. We subtracted the number of barges needed

under the two reference cases to determine the incremental number of rail cars attributed to

compliance with the EISA (see Table 1.6-3).

Table 1.6-3.

Additional Barges Needed by 2022 for Shipment of the Incremental RFS2 Volumes of

Ethanol, and Cellulosic Distillate Fuel/Renewable Diesel Fuel

Number of Barges

Reference Case used for Comparison

RFS1 AEO 2007

Low-Ethanol Scenario 95 45

Mid-Ethanol Scenario 78 28

High Ethanol Scenario 71 21

We estimated the number of barges that would be needed to transport biodiesel using the

projected volume of biodiesel that we expect would be shipped by barge and the assumed barge

volume and cycle time. We assumed a 2 week barge cycle time, which we understand to be

typical given the markets where we expect most barge shipments would occur.88 We estimate

that 41 barges would be needed by 2022 to transport the volume of biodiesel that we project will

be used to satisfy the RFS2 standards. We estimate that 7 barges would be needed by 2022 to

transport the volume of biodiesel projected under the RFS1 reference case and 9 barges under the

88 We believe most barge shipments of biofuels would originate and terminate in the Northeast. Cycle time refers to

the time needed to complete one delivery and return to the origin including the time to prepare for the next shipment.
217

AEO reference case. Consequently, we attribute the construction of an additional 34 biodiesel

barges to the implementation of the EISA under the RFS1 reference case and 32 under the AEO

reference case. The total additional number of barges for the transport of ethanol, cellulosic

distillate fuel/renewable diesel fuel, and biodiesel that we attribute to the implementation of the

EISA is presented in Table 1.6-4.

Table 1.6-4.

Additional Barges Needed by 2022 for Shipment of All Incremental RFS2 Biofuel Volumes

Number of Barges

Reference Case used for Comparison

RFS1 AEO 2007

Low-Ethanol Scenario 129 67

Mid-Ethanol Scenario 112 60

High Ethanol Scenario 105 53

The U.S. tank barge fleet currently numbers 3,600.424 In 2004, over 500 barges of all

types were added to the U.S. barge fleet. Given the gradual ramp up in demand for shipment of

biofuels by barge over time, we believe that the addition to the fleet of the barges estimated to be

needed to transport biofuels can be accommodated by the industry.

As discussed in Section 1.5.2. of this RIA, we are projecting significant imports of

ethanol by 2022. To estimate which ports would receive ethanol imports we gave priority to

ports that have a history of receiving ethanol imports from Brazil and Caribbean Basin Initiative

Counties89 according to company-level historical fuel import data from the Energy Information

Administration (EIA).425 Additional ports were selected from those that have a history of

receiving finished gasoline imports. Ports were selected in States that could not satisfy their

internal ethanol demand from in-State production and from those ports that were closest to large

demand centers. We estimate that a total of 30 ports would receive imported ethanol by 2022.

The list of ethanol import ports was provided to ORNL as an input to the ethanol transportation

analysis that they conducted for EPA.426 Under the high-ethanol option, we estimate that the 18

ports which did not receive ethanol in the past would need to install/modify ethanol receipt

facilities including piping, pumps, vapor handling systems, and ethanol storage tanks while ports

that had received ethanol in the past would primarily need to install additional ethanol storage

tanks. We project that under the primary mid-ethanol scenario that 15 new ethanol import

locations would be added and that under the low ethanol scenario there would be 14 new ethanol

import locations. We used these estimates relative to both the RFS1 and AEO reference cases

since we expect that the increase in ethanol imports would most appropriately be attributed to the

incremental increase in ethanol use levels above those reflected under both the AEO and RFS1

reference cases. We believe that all the ports where ethanol would be imported would be

89 Caribbean Basin Initiative countries receive special exemptions from U.S. ethanol import tariffs (See Section 1.5

of this RIA regarding the source of ethanol imports and for additional discussion regarding how we estimated where

ethanol imports would enter the U.S..
218

incorporated into existing petroleum terminals. Hence, the need for additional ethanol storage as

well as outgoing ethanol shipping facilities would be covered within the context of our

estimation of the upgrades needed to petroleum terminal facilities.

As part of Oakridge National Laboratory’s study for EPA on the projected patterns of

ethanol distribution from producer to terminal under the EISA, ORNL estimated the number of

barge receipt facilities that would be needed to support biofuel shipments within the U.S.427

Based on our analysis of a spreadsheet used in the ORNL analysis, we determined that ORNL

estimated that there would be approximately 57 barge facilities under the NPRM control case.428

Since the NPRM control case has a somewhat higher total biofuel volume than under the FRM

high-ethanol control scenario, we believe that the ORNL estimate of the number of barge receipt

facilities needed for the NPRM control scenario provides a reasonable (although perhaps

conservatively high) estimate of the number of such facilities that would be needed under the

high-ethanol scenario.

We assumed that all biofuel barge receipt facilities would handle ethanol and cellulosic

distillate fuel and that some of these facilities would handle biodiesel. To compensate for the

potential that there may be some instances where a manifest rail receipt facility might handle

ethanol but not cellulosic distillate fuel or vice-versa (perhaps increasing the number of unit train

facilities slightly), we assumed that 57 manifest rail receipt facilities would also be needed under

the mid-ethanol and low-ethanol scenarios. Our analysis of the aforementioned ORNL

spreadsheet indicates that ORNL estimated there would be approximately 4 barge receipt

facilities under the RFS1 reference case. Therefore, we estimate that an additional 53 barge

receipt facilities would need to be configured to receive biofuels in order to facilitate compliance

with the RFS2 program relative to the RFS1 reference case. By interpolating between the ORNL

results for the RFS1 reference case and the NPRM control case, we estimated that 16 barge

receipt facilities would be needed under the AEO reference case. Therefore, we estimate that an

additional 41barge receipt facilities would need to be configured to receive biofuels in order to

facilitate compliance with the RFS2 program relative to the AEO reference case

We believe that barge receipt facilities that receive shipments of biofuels would be those

that already handle the receipt of petroleum-based fuels and which are incorporated into

petroleum terminals or would be linked to unit train receipt facilities. Such facilities would need

to install/modify piping, pumps, vapor handling systems. The need for biofuel storage tanks and

other facilities to handle the storage and transfer of biofuels to other means of distribution at such

is addressed within the context of the additional facilities needed at petroleum terminals and unit

train facilities.

1.6.6 Road Transportation System Accommodations

A substantial number of tank trucks would be needed to distribute the additional volume

of biofuels that we project would be used to meet the RFS2 volumes. In all cases, a tank truck

capacity of 8,000 gallons was assumed. Larger tank trucks are permitted in some areas, so this

assumption will tend to overestimate of the number of tank trucks needed. We assumed that tank

trucks similar to those used for the transport of ethanol would be used to handle cellulosic

distillate and renewable diesel fuels.
219

Our estimation of the tank trucks needed to transport ethanol and cellulosic distillate fuel

under the 3 control scenarios is based on an interpolation of the results from the ORNL analysis

for the NPRM control case (34.14 BG/yr of ethanol by 2022) and AEO reference case (13.18

BG/yr of ethanol by 2022). The underlying assumption in this approach is that the overall

number of tank trucks needed varies by the total volume of ethanol and cellulosic distillate fuel

projected to be used under a given control scenario. We increased the estimated number of tank

trucks needed from that which we arrived at from this interpolation to compensate for our

reduction in the number of unit train facilities that would be constructed from the estimate in the

ORNL study.

The volume of biofuels shipped to the unit train facilities under the ORNL analysis which

we consolidated into larger unit train receipt facilities represents 41% of the total volume shipped

to unit train facilities (12.6 BG/yr out of 21.4 BG/yr in 2022 under the NPRM control case). We

compared the location of the 170 unit train facilities that we consolidated into the remaining 40

such facilities from the ORNL analysis to the location of the petroleum terminals that these

facilities were intended to service. Based on this comparison, we estimated that 41% of the

volume of biofuels shipped by unit train would need to be shipped 3 times farther on average to

reach the petroleum terminals serviced than under the ORNL analysis. We assumed that this

would result in a 3 fold increase in the number of trucks needed to take this volume from the unit

train facility to the petroleum terminal.90 The majority of the number tank trucks which ORNL

estimated would be needed are attributed to the transport of biofuels from rail receipt facilities to

petroleum terminals. Consequently, we believe that a reasonable (albeit conservatively high)

estimate of the increase in the number of tank trucks that would be needed due to our decrease in

the number of unit train facilities can be arrived at by multiplying the fraction of biofuels shipped

by unit train that is attributed to consolidated unit train terminals (41% of the total volume

shipped by unit train) by the average increase in shipping distance for the affected volume (factor

of 3). By so doing, we arrived at an estimate that the reduction in the number of unit train receipt

facilities would result in a 23% increase in the number of tank trucks needed compared to that

indicated by interpolation of the results from the ORNL study

Based on this approach, we estimate that 1,940 tank trucks would be needed to transport

the volumes of ethanol and cellulosic distillate fuel/renewable diesel fuel under the high-ethanol

scenario, 1,720 under the primary mid-ethanol scenario, and 1,620 under the low-ethanol

scenario. We subtracted the number of tank trucks which ORNL estimated would be needed

under the two reference cases to determine the incremental number of tank trucks attributed to

compliance with the EISA (see Table 1.6-5).

90 This may somewhat overstate the number of additional tank trucks needed given that the tank truck

loading/unloading time remains constant. ORNL assumed a relatively short shipping distance from rail receipt

facility to petroleum terminal.
220

Table 1.6-5.

Additional Tank Trucks Needed by 2022 for Shipment of the Incremental RFS2 Volumes

of Ethanol, and Cellulosic Distillate Fuel/Renewable Diesel Fuel

Number of Biofuel Tank Trucks

Reference Case used for Comparison

RFS1 AEO 2007

Low-Ethanol Scenario 1,490 1,080

Mid-Ethanol Scenario 1,230 820

High Ethanol Scenario 1,120 710

To estimate the number of tank trucks needed to transport biodiesel to petroleum

terminals we assumed 6 shipments per day per truck from production facilities to terminals. We

believe that a short shipping distance for tank truck transport from biodiesel production facilities

is justified based on the widespread dispersion and the fact that some would be located at

petroleum terminals. We estimate that 150 tank trucks would be needed by 2022 to transport the

volume of biodiesel that we project will be used to satisfy the RFS2 standards. We estimate that

30 tank trucks would be needed by 2022 to transport the volume of biodiesel projected under the

RFS1 reference case and 35 tank trucks under the AEO reference case. Consequently, we

attribute the construction of an additional 130 biodiesel tank trucks to the implementation of the

EISA under the RFS1 reference case and 120 under the AEO reference case. The total additional

number of tank trucks for the transport of ethanol, cellulosic distillate fuel/renewable diesel fuel,

and biodiesel that we attribute to the implementation of the EISA is presented in Table 1.6-6.

Table 1.6-6.

Additional Tank Trucks Needed by 2022 for Shipment

of All RFS2 Incremental Biofuel Volumes

Number of Biofuel Tank Trucks

Reference Case used for Comparison

RFS1 AEO 2007

Low-Ethanol Scenario 1,610 1,200

Mid-Ethanol Scenario 1,350 940

High Ethanol Scenario 1,240 830

In Section 1.6.8 of this RIA we discuss our estimation of the number of tank trucks than

might potentially be needed to transport butane to terminals for E85 blending. The results of this

analysis are presented in Table 1.6-7.
221

Table 1.6-7.

Estimated Number of Tank Trucks Needed for Shipment of Butanea

Number of Tank Trucks Needed to Transport Butane

Low-Ethanol

Scenario

Primary

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Tank Truck (8,200 gallons) 2,165 3,280 5,530

a

If a solution to the current difficulty in blending E85 to meet minimum volatility specifications can not be arrived

upon by ASTM International to allow the use of commonly available gasoline blendstocks.

Concerns have been raised in the trade press regarding the ability of the trucking industry

to attract a sufficient number of drivers to keep pace with demand. We used estimates of the

number of truck drivers required to transport biofuels from the ORNL report as a basis for our

estimate of the number of truck drivers that would be needed to transport the additional volume

of biofuels attributed to the RFS2 program. Given the volume of butane required for blending

into E85, typical travel distances, etc., we estimated that the number of truck drivers required to

transport butane was approximately 1,500. Similar inputs were used to estimate the number of

truck drivers required to transport non-ethanol biofuels; this number was approximately 300.

When combined with the estimates from ORNL, the number of truck drivers required to

transport biofuel feedstocks and finished product is approximately 5,300 drivers.91

According to a 2005 study commissioned by the American Trucking Association (ATA),

the motor carrier industry will face a shortage of qualified professional long-haul truck drivers by

2014.429 In the study, ATA found that the long-haul, heavy-duty truck transportation industry in

the United States is currently experiencing a national shortage of 20,000 truck drivers and, if the

current trend continues, that shortage of long-haul truck drivers could increase to 111,000 by

2014. ATA projected the need for additional 54,000 drivers each year. The trucking industry is

active in a number of efforts to attract and retrain a sufficient number of new truck drivers

including ATA’s National Truck Driver Recruiting Campaign and Driver Tuition Finance

Program.

As discussed above, we estimate that the growth in the transportation of biofuels by truck

through 2022 due to the RFS2 standards would result in the need for a total of approximately

5,300 additional trucks drivers for the transport of biofuel feedstocks and finished products.

Given the relatively small number of new truck drivers needed to transport the volumes of

biofuels projected to be used to comply with the RFS2 standards through 2022 compared to the

total expected increase in demand for drivers over the same time period (>750,000), we do not

expect that the implementation of the RFS2 standards would substantially exacerbate the

potential for an overall shortage of truck drivers. Discussions with transport industry officials

support this conclusion. However, specially-certified drivers are required to transport biofuels

because these fuels are classified as hazardous liquids. Thus, there may be a heightened level of

concern about the ability to secure a sufficient number of such specially-certified drivers to

91 This is the maximum number of drivers that would be needed under any control scenario. Somewhat fewer

drivers would be needed under the mid-ethanol and low-ethanol scenarios.
222

transport biofuels. The trucking industry is involved in efforts to streamline the certification of

drivers for hazardous liquids transport. We do not anticipate that the need for special hazardous

liquids certification for biofuels truck drivers of would substantially interfere with the ability to

transport the projected volumes of biofuels by tank truck. We project that tank truck deliveries

of biofuels would typically be accomplished within an 8 hour shift allowing the driver to return

home each evening.92 The ATA sponsored study indicated that there was particular difficulty in

attracting and retaining drivers for long haul routes the keep the driver away from home

overnight. Thus, driving a tank truck (with typical 8 hour shift) may be relatively more attractive

compared to a long haul truck driving position.

Truck transport of biofuel feedstocks to production plants and finished biofuels and coproducts from these plants naturally is concentrated on routes to and from these production

plants. This may raise concerns about the potential impact on road congestion and road

maintenance in areas in the proximity of these facilities. We do not expect that such potential

concerns would represent a barrier to the implementation of the RFS2 standards. Distant truck

traffic associated with the plant will be diffuse. Hence, we expect that impacts associated with

such distant traffic are negligible. Routes in close proximity to plants may require repaving as a

result of construction traffic associated with the facility. As such, the repaved routes would be

more capable of handling additional truck traffic associated with production at the plants. The

improved routes can also be expected to provide benefits for communities in close proximity to

the production plant as well as lower maintenance costs. The potential impact on local road

infrastructure and the ability of the road net to be upgraded to handle the increased traffic load is

an inherent part in the placement of new biofuel production facilities. Consequently, we expect

that any issues or concerns would be dealt with at the local level. The transport of biofuel

feedstocks is discussed in Section 1.3.3 of this RIA.

1.6.7 Petroleum Terminal Accommodations

Petroleum terminals will need to install additional storage capacity to accommodate the

volume of ethanol, cellulosic distillate fuel/renewable diesel fuel, and biodiesel that we anticipate

will be used in response to the RFS2 standards. We estimate that it would be necessary to

maintain an inventory level of 15% of the annual consumption of a given biofuel at the terminal

level in order to provide a sufficient downstream buffer to ensure consistent supply. We chose a

working inventory level of 15% rather than the 10% that is typical for petroleum-based fuels to

compensate for the potential increase in temporary disruptions in biofuel delivery compared to

petroleum-based fuels. We believe that this is appropriate due to the reliance on rail, barge, and

truck for the transport of biofuels in our analysis as opposed to use of pipelines for the shipment

of petroleum-based fuels. The need for additional biofuel storage volume at terminals to provide

a buffer for interruptions in delivery may be reduced somewhat to the extent that pipelines play a

role in the distribution of biofuels. We further estimate that an additional 30% of storage

capacity would be needed as working space to accommodate biofuel deliveries.93 Our estimates

of the biofuel storage capacity needed at petroleum terminals by 2022 to facilitate the

92 A small fraction of biofuels deliveries may require a sleep-over on the road of the driver due to limitations on the

amount of time a driver can spend behind the wheel in a day.

93 Petroleum terminals typically allow an additional 30 percent of storage capacity (in relation to the amount

provided for working inventory) to accommodate the receipt of petroleum products.
223

distribution of the volume of biofuels that we project would be used to meet the RFS2 volumes

are based on the application of these working inventory and working space estimates. These

estimates are presented in Table 1.6-8.

Table 1.6-8.

Total Biofuel Storage Capacity needed at Petroleum Terminals by 2022

to Handle the RFS2 Volumes

Biofuel Tankage (Mbbl)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Ethanol 81.2 103.9 149.7

Cellulosic Distillate Fuel/

Renewable Diesel Fuel

43.0 30.3 NA

Biodiesel 7.2 7.2 7.2

To estimate of the additional biofuel storage tank capacity that should be attributed to the

incremental RFS2 biofuel volumes relative to the 2 reference cases, we subtracted the volume

which would have been in place regardless of the RFS2 program under the 2 reference cases.

The same working inventory and working space estimates were used to estimate the volume of

biofuel storage under the reference cases.

Overall demand for the gasoline motor vehicle fuel is expected to remain relatively

constant through 2022 whereas demand for compression ignition vehicle fuel is anticipated to

increase by over 10% over the same time period.430 We expect that much of the demand for new

ethanol storage capacity could be accommodated by modifying storage tanks that had previously

been used for the gasoline that would be displaced by ethanol. Due to the lower energy density

of ethanol relative to gasoline (67%), we project that only 67% of the demand for new ethanol

storage might potentially be accommodated by modifying existing gasoline tanks for ethanol

service. Likewise, we anticipate that much of the demand for cellulosic distillate fuel/renewable

diesel fuel storage capacity might be satisfied by dedicating storage tanks that would have been

constructed to store petroleum-based diesel fuel to instead store these biofuels. Due to the

anticipated lower energy density of cellulosic distillate fuel relative to petroleum-based diesel

fuel (~90% of petroleum-based diesel fuel), we project that only 90% of the demand for new

cellulosic distillate/renewable diesel fuel storage might potentially be accommodated by

modifying existing gasoline tanks for cellulosic distillate/renewable diesel fuel service. To

provide some margin to compensate for the need for a greater degree of new tank construction

than that indicated by the above analysis, we assumed that 5% of the tanks which might have

been rededicated tanks previously used for petroleum-based fuels would instead be new

construction. The rededication to ethanol service of storage tanks previously used to store

gasoline involves lining the tank and other miscellaneous modifications to ensure the tank is

compatible with ethanol. We assume that no changes would be needed to petroleum-based

diesel fuel storage tanks to allow them to be used to store cellulosic distillate fuel/renewable

diesel fuel. Since biodiesel storage tanks need to be insulated and heated under cold conditions,
224

we assumed that all of the need for additional biodiesel storage capacity would be satisfied

through new construction.

The volume of new biofuel storage capacity that we project would be needed as a result

of the implementation of the EISA under the 2 reference cases is presented in Tables 1.6-9 and

1.6-10.
225

Table 1.6-9.

Additional Biofuel Storage Capacity at Petroleum Terminals by 2022

to Meet the EISA Volumes Relative to the RFS1 Reference Casea

Biofuel Tankage (Mbbl)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Ethanol, Total 48.5 70.2 116.9

Ethanol, New Construction 17.6 25.5 42.5

Ethanol, Retrofitted Tanks 30.9 44.7 74.4

Cellulosic Distillate Fuel/Renewable

Diesel Fuel, Total

43.0 30.3 NA

Cellulosic Distillate Fuel/Renewable

Diesel Fuel, New Construction

6.2 4.4 NA

Cellulosic Distillate Fuel/Renewable

Diesel Fuel, Rededicated Tanks

36.8 25.9 NA

Biodiesel, New Construction 5.9 5.9 5.9

All Biofuels, New Construction 29.7 35.8 48.4

All Biofuels, Retrofitted Tanks 30.9 44.7 74.4

All Biofuels, Redicated Tanks 36.8 25.9 0

a “Retrofitted” refers to tanks that need significant changes to be made suitable for biofuel storage.

“Rededicated” refers to tanks that need essentially no changes to be made suitable for biofuel storage.

Table 1.6-10.

Additional Biofuel Storage Capacity at Petroleum Terminals by 2022

to Meet the EISA Volumes Relative to the AEO Reference Casea

Biofuel Tankage (Mbbl)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Ethanol, Total 20.0 41.7 88.5

Ethanol, New Construction 12.7 26.5 56.3

Ethanol, Retrofitted Tanks 7.3 15.2 32.2

Cellulosic Distillate Fuel/Renewable

Diesel Fuel, Total

43.0 30.3 NA

Cellulosic Distillate Fuel/Renewable

Diesel Fuel, New Construction

6.2 4.4 NA

Cellulosic Distillate Fuel/Renewable

Diesel Fuel, Rededicated Tanks

36.8 25.9 NA

Biodiesel, New Construction 5.5 5.5 5.5

All Biofuels, New Construction 24.4 36.4 61.8

All Biofuels, Retrofitted Tanks 7.3 15.2 32.2

All Biofuels, Rededicated Tanks 36.8 25.9 0

a“Retrofitted” refers to tanks that need significant changes to be made suitable for biofuel storage. “Rededicated”

refers to tanks that need essentially no changes to be made suitable for biofuel storage.
226

Concerns have been raised by terminal operators in the Eastern U.S. about the ability of

some terminals to install the needed storage capacity due to space constraints and difficulties in

securing permits.431 We acknowledge that it may not be possible for some terminals that have

become surrounded by urban growth over time to install additional storage tanks within the

boundaries of their existing facilities. However, we believe that there are ways to manage this

situation. The areas served by existing terminals often overlap. In such cases, one terminal

might be space constrained while another serving the same area may be able to install the

additional capacity to meet the increase in demand. Terminals with limited biofuel storage could

receive truck shipments of ethanol from terminals with more substantial biofuel storage capacity.

In cases where it is impossible for existing terminals to sufficiently expand their storage capacity

due to a lack of adjacent available land or difficulties in securing the necessary permits or to

make arrangements to sufficiently reduce the need for such additional storage, new satellite

storage or new separate terminal facilities may be need for additional biofuel storage. However,

we believe that there will be few (if any) such situations.

As discussed below, we project that all terminals that distribute gasoline would install

ethanol blending capability in response to the RFS2 standards. We estimate that approximately

91% of terminals that distribute diesel would install biodiesel blending/storage capability under

the RFS2 standards. Therefore, in the case of biodiesel, those terminals that would experience

that most difficulty in installing new storage capacity would have some opportunity to forgo

bringing biodiesel into their terminal

Another question is whether the storage tank construction industry would be able to keep

pace with the increased demand for new tanks that would result from today’s proposal. The

storage tank construction industry recently experienced a sharp increase in demand after years of

relatively slack demand for new tankage. Much of this increase in demand was due to the

unprecedented increase in the use of ethanol. Storage tank construction companies have been

increasing their capabilities which had been pared back during lean times. Given the projected

gradual increase in the need for biofuel storage tanks, it seems reasonable to conclude that the

storage tank construction industry would be able to keep pace with the projected demand.

Petroleum terminals would need to install additional equipment to blend ethanol,

cellulosic distillate fuel/renewable diesel fuel, and biodiesel into petroleum-based fuels. In the

case of ethanol other miscellaneous upgrades to piping, pumps, seals, and vapor recovery

systems would also be needed to ensure ethanol compatibility. In the case of biodiesel, piping

and blending systems would need to be heated/insulated under cold conditions. All terminals

with biofuel blending capability would need to provide facilities for receipt of biofuels via tank

truck.

There are currently 1,063 petroleum terminals that carry gasoline.432 We project that 899

of these terminals (85% of the total) would install E10 blending equipment absent the

implementation of the RFS2 requirements in order to support the consumption of 13.18 BGY of

ethanol by 2022 under the AEO reference case. This is based on 85% of the gasoline needing to

be blended with ethanol in order to consume 13.18 BGY of ethanol considering the projected use

levels of E10 versus E85 and total motor vehicle fuel consumption in 2022.433 We project that

essentially all gasoline would be either E10 or E85 by 2022 under the RFS2 standards. Thus, we
227

estimate that all terminals would need to have ethanol blending capability to support the use of

the volume of ethanol we project would be used under the RFS2 standards. Based on our

projection that 899 terminals would install ethanol blending capability absent the RFS2 standards

under AEO reference case, we estimate that 164 terminals would need to install ethanol blending

equipment to meet the RFS2 volumes relative to the AEO reference case.

The estimated number of terminals that would need to install ethanol blending capability

as a result of the RFS2 standards relative to the RFS1 reference case is based on an extrapolation

of the estimate for the AEO reference case. The volume of ethanol projected to be used under

the RFS1 reference case is 53% of the volume projected to be used under the AEO reference

case. We estimated that the number of terminals that blend ethanol under the RFS1 reference

case is 53% of the number under the AEO reference case (899 x 53% = 481). Based on this, we

estimate that an additional 582 terminals would install ethanol blending capability to meet the

RFS2 volumes under the RFS1 reference case.

We estimate that E85 would need to be reasonably available in 70% of the nation in order

to support the use of the projected volume of E85 needed to comply with the RFS2 standards

under the high-ethanol scenario.94 To provide a conservatively high estimate, we are projecting

that 90% of all gasoline terminals (931) would need to install E85 blending capability by 2022

under the high-ethanol scenario. The remaining terminals (132 out of a total of 1,063) would

only have E10 blending capability in 2022 under the high-ethanol scenario.

Under the primary mid-ethanol scenario, we estimate that 60% of the nation would need

to have reasonable access to E85 in order to support the use of the projected volume of E85

needed to comply with the RFS2 standards. Our estimate of the number of terminals that would

need to install E85 blending capability under the mid-ethanol scenario is based on the ratio of the

percent of the country which would need to have reasonable access to E85 under the mid-ethanol

scenario relative to the high-ethanol scenario. By multiplying our 90% estimate of the number of

terminals that would need to install E85 under the high-ethanol scenario by 60%/70%, we

arrived at an estimate of 77% of all gasoline terminals (820) having E85 access under the midethanol scenario. Under the low-ethanol scenario, we estimate that 40% of the nation would

need to have reasonable access to E85 in order to support the use of the projected volume of E85

needed to comply with the RFS2 standards. We used the same approach outlined above to

estimate that 51% of all gasoline terminals (547) would install E85 blending capability under the

low-ethanol scenario.

We estimate that the terminals which would have installed E10 blending capability absent

the RFS2 standards would upgrade their E10 blending facilities to accommodate E85 as well as

E10. This is based on the assumption that those terminals that were the first to blend E10 would

also be the first to begin blending E85. Input from terminal operators indicates that the

modification of E10 blending equipment to handle E85 primarily involves an upgrade to the

blending equipment software.95 We estimate that the vapor recovery systems at all terminals that

had not received ethanol before would need to be upgraded to handle ethanol-blended gasoline.

94 A discussion of our E85 use projections is contained in chapter 1.7 of this RIA.

95 Additional ethanol storage and modifications to terminal piping would also be needed to supply additional

quantity of ethanol needed to blend E85.
228

The potential need to provide special blendstocks at petroleum terminals for the manufacture of

E85 is discussed in Section 1.6.8 of this RIA.

Our estimate of the number of terminals that would install biodiesel blending capability

under the RFS2 standards is based on an extrapolation of the analysis conducted for the NPRM.

We estimate that 853 terminals handle diesel fuel.434 We estimate that approximately 62.5

billion gallons of diesel fuel would be used in 2022.96 Thus, the average diesel throughput per

terminal would be approximately 73.2 MGY. In the NPRM analysis, we estimate that on a

national average basis biodiesel would represent approximately 2.9% of the diesel fuel pool. For

the purposes of our calculation of the number of terminals that would carry biodiesel, we

assumed that 2.9% of the diesel fuel they dispense would be biodiesel. This is likely to result in

a conservatively high number of terminals that would need to carry biodiesel, since those

terminals that do carry biodiesel would be expected too blend at higher than the national average

concentration. Assuming that 2.9% of a terminal’s diesel fuel throughput would be biodiesel, we

arrive at an estimate that 377 terminals would need to blend biodiesel to support the projected

use of 810 MGY of biodiesel assumed to be used by 2022 under the RFS2 standards in the

NPRM.

We estimated the number of terminals that would need to blend biodiesel for our FRM

analysis by increasing the NPRM estimate in proportion to volume of biodiesel that we project

would be used in the FRM by 2022 relative to that projected in the NPRM (1,671 Mgal/yr / 810

Mgal/yr). By so doing, we estimate that 777 terminals will be needed to blend biodiesel by 2022

to support the use of the biodiesel volume projected to be used in this FRM. We estimate that

200 terminals would need to store/blend biodiesel in order to support the use of volume of

biodiesel that we estimate would be used as a result of the RFS2 standards relative to the AEO

380 MGY 2022 baseline. Thus, we project that 637 additional terminals would blend biodiesel

as a result of the RFS2 standards under the RFS1 reference case and 600 under the AEO

reference case.

The Independent Fuel Terminals Operators Association (IFTOA) stated that terminals are

concerned that the market would not be able to adapt in time to ensure that the necessary

distribution infrastructure accommodations are in place to support compliance with the timetable

for the implementation of the RFS2 standards.435 Based on this concern, in a presentation at the

recent SAE government-industry conference IFTOA suggested that EPA should consider

reducing and or slowing the pace of the implementation of the RFS2 standards in order to allow

the market sufficient time to adjust. 436 We believe that given the time over which biofuel

volumes ramp up under the RFS2 standards, it should be feasible for terminals to adapt

sufficiently within the time frame established by the EISA.

1.6.8 Potential Need for Special Blendstocks at Petroleum Terminals for E85

ASTM International is considering a proposal to lower the minimum ethanol

concentration in E85 to facilitate meeting ASTM minimum volatility specifications in cold

96 A discussion of our estimate of biodiesel use in relation to the use of petroleum-based diesel is contained in

Section 1.5.4 of this RIA.
229

climates and when only low vapor pressure gasoline is available at terminals.97 Commenters on

the ASTM proposal have stated that the current proposal to lower the minimum ethanol

concentration to 68 volume percent may not be sufficient for this purpose. ASTM International

may consider an additional proposal to further decrease the minimum ethanol concentration.

Absent such an adjustment, a high-vapor pressure petroleum-based blendstock such as butane

would need to be supplied to most petroleum terminals to produce E85 that meets minimum

volatility specifications. In such a case, butane would need to be transported by tank truck from

petroleum refineries to terminals and storage and blending equipment would be needed at

petroleum terminals.

Automated inline butane blending systems located at terminals can be used to blend

butane into gasoline before it is blended with denatured ethanol. Such systems consist of inline

RVP analyzers which sample gasoline being transferred from storage tanks to loading racks

where it is to be mixed with ethanol to produce E85.

The analyzers determine the RVP of the incoming gasoline stream and use this

information to determine the volume of butane which must be blended with the gasoline down

stream of the analyzer required to meet the volatility specification for the finished product. The

analyzer, variable frequency butane pump, and supporting equipment are self-contained on a

skid-mounted unit, and require at least one 60,000 gallon butane storage tank.

We estimated the number of automated inline butane blending systems, butane storage

tanks, tanks trucks, railcars, trans-loading facilities, and other facility changes needed for butane

blending as follows. Of the existing 1,063 terminals, two-thirds (709 terminals) are assumed to

require butane in order to blend E85 that is complaint with ASTM International volatility

specifications. All 709 terminals are assumed to require new butane blending equipment. Of

these terminals, twenty-five percent (177) are assumed to receive butane via railcar and seventyfive percent (532) are assumed to receive butane via tank truck. Of the 177 terminals that

receive butane via railcar, fifty-percent are assumed to have butane directly off-loaded to tank

storage. In the case of the other fifty-percent of the terminals, butane is assumed to be transloaded from railcars to tank trucks for final delivery to terminals. This requires that each

terminal have a skid-mounted inline butane blending system and two 60,000 gallon butane tanks.

Usable tank volumes are assumed to be 51,000 gallons per tank. Tank trucks are assumed to

carry 8,200 gallons of butane. Railcars are assumed to carry 31,500 gallons of butane.

Our estimates of the number of tank trucks and railcars required to deliver butane varies

by control scenario (see Table 1.6-11).

97 Minimum volatility specifications were established by ASTM to address safety and vehicle driveability

considerations.
230

Table 1.6-11.

Estimated Number of Tank Trucks and Rail Cars Needed for Shipment of Butane

Number of Tank Trucks and Rail Cars Needed to

Transport Butane

Low Case Medium Case High Case

Tank Truck (8,200 gallons) 2,165 3,280 5,530

Railcar (31,500 gallons) 236 358 602

Instead of lowering the minimum ethanol concentration of E85, some stakeholders are

discussing establishing a new high-ethanol blend for use in flex-fuel vehicles. Such a fuel would

have a minimum ethanol concentration that would be sufficient to allow minimum volatility

specifications to be satisfied while using finished gasoline that is already available at petroleum

terminals.98 E85 would continue to be marketed in addition to this new fuel for use in flex-fuel

vehicles when E85 minimum volatility considerations could be satisfied.

We believe that industry will resolve the concerns over the ability to meet the minimum

volatility needed for high-ethanol blends used in flex-fuel vehicles in a manner that will not

necessitate the use of high-vapor pressure blendstocks in their manufacture. Nevertheless,

petroleum terminals may find it advantageous to blend butane into E85 because of the low cost

of butane relative to gasoline provided that the cost benefit outweighs the associated butane

distribution costs.99

1.6.9 Need for Additional E85 Retail Facilities

The number of additional E85 retail facilities needed to consume the volume of ethanol

used under EISA varies substantially depending on the control case. As discussed in Section

1.7.1.2 of this RIA, we estimate that end-users would need to have reasonable access to E85 in

70% of the nation by 2022 under the high-ethanol scenario given our projections regarding the

population of flexible fuel vehicles (FFVs) and E85 refueling frequencies.437 Under the primary

mid-ethanol scenario we estimate that reasonable access would be needed in 60% of the nation,

and 40% under the low-ethanol scenario.

We define reasonable access as one in four gasoline retail facilities offering E85 in a

fashion consistent with the way they currently offer gasoline. We selected one in four based on a

review of the number of facilities that have been postulated to be needed to support the

introduction of alternative fuels vehicles such as hydrogen and natural gas vehicles, the number

of facilities that currently offer diesel fuel, and industry estimates regarding the number of E85

facilities that would be needed. One-in-five to one-in-three retail facilities has been discussed as

a reasonable rule of thumb regarding the number of retail facilities needed to support the

widespread introduction of alternative fuel vehicles.

98 Such a new fuel might have a lower ethanol concentration of 60% and a maximum ethanol concentration of 85%.

99 EPA may consider reevaluating its policies regarding the blendstocks used in the manufacture of E85 to facilitate

this practice.
231

We estimate that approximately one in three fuel retail facilities (32%) offered diesel fuel

in 1999 based on our review of fuel retailer survey data.438 The National Association of

Convenience Stores (NACS) reported that in 2006, 36.6% of the respondents to their survey

offered diesel fuel.439 We believe that given that NACS members typically do not include truck

stop operators (who all offer diesel fuel) that that it is most likely that the number of diesel fuel

retailers has increased since 1999. Since fuel retailers make most of their money from in-store

sales as opposed to fuel sales, it seems likely that more retailers recognized an opportunity to

attract additional customers by offering diesel fuel since 1999. In any event, the number of

diesel fuel refueling facilities available in 1999 or 2006 has not hindered the use of diesel fuel

vehicles. Unlike diesel fuel vehicles that can refuel only on diesel fuel or alternative fuel

vehicles that can only be fueled on the alternative fuel, flex fuel vehicles can refuel on gasoline

as well as E85. Thus, we believe that fewer E85 stations should be necessary than were provided

for diesel fuel.100

At the same many time fleet operators were divesting of their in-house fueling facilities

because of new environmental regulations, most retailers were installing equipment to blend

mid-grade gasoline at the pump rather than store a separate mid-grade gasoline. This allowed for

a significant number of retailers to begin offering diesel fuel at relatively low capital cost by

converting storage tanks that had been dedicated to mid-grade gasoline storage to diesel fuel

service. A number of retail facilities (40% of the total that installed diesel fuel tanks had low

annual diesel throughput volumes of less than 60,000 gallons per year in 2000.440 Only 5% of

total diesel retail sales are estimated to be sold at these low-volume retailers. Given that the

installation of some diesel retail facilities was not strictly driven on the expectation or realization

of substantial throughput, it seems reasonable to assume that some fraction of low-volume

retailers may not be absolutely necessary to ensure adequate diesel availability. Therefore,

somewhat less than 32% of retail facilities might actually be needed to ensure adequate diesel

fuel availability. We believe that this comparison to the number of diesel fuel retail facilities

available supports our estimate that one in four retail facilities would be sufficient to provide

reasonable access to E85.

The National Petroleum News (NPN) estimates that there were a total of 161,768

gasoline retail facilities in the United States in 2008.441 We multiplied the one-in-four

reasonable access assumption by the percentage of the retail market that would need to have

reasonable access to E85 and the total number of retail facilities to arrive at our estimate of the

number of E85 retail facilities needed under a given RFS2 control scenario. Under the highethanol scenario, we estimate that a total of 28,309 E85 refueling facilities would be needed.

Under the primary mid-ethanol scenario, we estimate that 24,265 facilities would be needed, and

that 16,177 facilities would be needed under the low-ethanol scenario.

In order to provide for sufficient E85 throughput while maintaining timely access of

customers to an E85 dispenser, we estimated that all E85 retail facilities would have 3 E85

dispensers under the high-ethanol scenario.101 Under the primary mid-ethanol scenario, we

estimate that half of E85 retail facilities would have a single dispenser and the other half would

100 Particularly since we do not assume that flex-fuel vehicles would refuel on E85 all the time. A discussion of E85

refueling rates is contained in Section 1.7.1.2.4 of this RIA.

101 Each dispenser has two E85 refueling positions.
232

have 2 dispensers. Under the low-ethanol scenario, we estimate that all E85 retail facilities

would have a single dispenser. These estimates are based on ensuring that E85 throughput per

refueling position is consistent with historical data for gasoline throughput per refueling position.

We believe that this approach provides an estimate consistent with ensuring that consumers have

reasonable access to a E85 refueling position while providing the retailer with sufficient

throughput to justify their investment in installing E85 refueling facilities.

The National Association of Convenience Stores (NACS) reports throughput per

refueling position.442 For all types of fuel dispensed, NACS reports that from 2001 through

2006, the annual throughput varied from approximately 142,000 to 164,000 gallons per refueling

position. These data include reports on the sales of all fuels including premium, mid-grade, and

regular gasoline, diesel fuel and other fuels. The most appropriate comparison would be made to

throughput from refueling positions that dispense only regular gasoline since the use of E85

would primarily displace regular gasoline sales. However, this is not possible given that most

gasoline is dispensed from blender pumps that can dispense any gasoline grade. Hence, we

choose to make the comparison to throughput over dispensers that offer all gasoline grades,

which may tend to underestimate the potential utilization rate of dispenser that dispenses only

regular grade gasoline.

NACS reports that there is an average of 8.6 refueling positions at the retail facilities

that responded to their survey. NACS reports that 36.6% of stores sold diesel fuel and 15.1%

sold “other” fuels (i.e. not diesel, regular, mid-grade, or premium gasoline).102 To estimate how

many refueling positions are dedicated to diesel fuel and “other” fuels, we assumed that retailers

offer diesel fuel from one pump with two nozzles, and other fuels from one pump with one

nozzle. By multiplying the percentage of retailers that offer diesel fuel/other fuel by the assumed

refueling positions for these fuels where they are present, we arrived at an estimate of 0.9

refueling positions per facility on average dedicated to diesel fuel and other fuel. This translates

to an average of 7.7 refueling positions per facility that dispenses gasoline. NACS reports that

92.7% of fuel volumes sold by respondents to their survey is gasoline (of all grades). By

dividing 92.7% of the total average throughput for all fuels per facility reported by NACS by 7.7

refueling positions, we arrived at an estimate of annual gasoline throughput per nozzle of

177,000 gallons for 2003.103

The National Ethanol Vehicle Coalition (NEVC) estimates there are currently 2,095 E85

refueling facilities.443 However, the NEVC estimate includes E85 refueling facilities that are not

open to the general public. “NEAR85” estimates that there are currently 1,293 E85 retail

facilities.444 The Near85 estimate includes only retail facilities. Based on these data, we are

assuming that there are approximately 1,300 E85 retail facilities currently in service. By

increasing the number of E85 retail facilities by the same proportion as the growth in ethanol use

under the AEO reference from now until 2022, we estimate that 4,500 E85 refueling facilities

would be in place by 2022 absent the RFS2 standards. We estimate that there would be 1,210

E85 refueling facilities under the RFS1 reference case. We arrived at this estimate by a review

of historical data regarding the number of E85 retail facilities that were in place when ethanol

use levels matched those under the RFS1 reference case. We assume that all E85 retail facilities

102 In many cases, we expect that the “other” fuel is kerosene.

103 The year 2003 had the highest average throughput per refueling position over the years 2001- 2006.
233

under the REFS1 and AEO reference cases would have a single E85 dispenser (with 2 refueling

positions).

To estimate the E85 refueling facility changes which that may be needed to reach the

RFS2 volumes, we compared the changes needed to support the use of the total volume of E85

projected to be used under the 3 control scenarios to the E85 refueling facilities needed under the

2 reference cases. Our estimates of the of the E85 facility changes that will take place to reach

the RFS2 volumes are contained in Tables 1.6-12 and 1.6-13

Table 1.6-12.

Additional E85 Retail Facilities Needed by 2022 to Reach the RFS2 Volumes Relative to the

RFS1 Reference Case

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

New E85 Installation with 1 Dispenser 15,000 10,900 0

New E85 Installation with 2 Dispensers 0 12,100 0

New E85 Installation with 3 Dispensers 0 0 27,100

Addition of 2 Dispensers to Retail

Facility that had 1 Dispenser

0 0 1,200

Table 1.6-13.

Additional E85 Retail Facilities Needed by 2022 to Reach the RFS2 Volumes Relative to the

AEO Reference Case

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

New E85 Installation with 1 Dispenser 11,700 7,600 0

New E85 Installation with 2 Dispensers 0 12,100 0

New E85 Installation with 3 Dispensers 0 0 23,800

Addition of 2 Dispensers to Retail

Facility that had 1 Dispenser

0 0 4,500

On average, approximately 1,520 additional E85 facilities will be needed each year from

2010 through 2022 under our primary scenario relative to the AEO reference case. Under the

high and low-ethanol scenarios, an additional 1,820 and 900 E85 retail facilities per year would

be needed respectively. Under the high-ethanol scenario, 4,500 facilities would also need to be

upgraded to provide 3 E85 dispensers rather than a single dispenser. Under the high ethanol case

and to a lesser extent under the primary case, this represents an aggressive timeline for the

addition of new E85 facilities given that the small number of E85 retail facilities in service

today. Nevertheless, we believe the addition of these numbers of new E85 facilities may be

possible for the industries that manufacture and install E85 retail equipment. Underwriters

Laboratories requires that E85 refueling dispenser systems must be certified as complete units.104

To date, no complete E85 dispenser systems have been certified by UL. We understand that all

104 See http://ulstandardsinfonet.ul.com/outscope/0087A.html
234

the fuel dispenser components with the exception of the hoses that connect to the refueling

nozzle have successfully passed the necessary testing. There does not appear to be a technical

difficulty in finding hoses that can pass the required testing. Therefore, we anticipate this

situation will be resolved once the demand for new E85 facilities is demonstrated. Hence, we

believe that the current lack of a UL certification for complete E85 dispenser systems will not

impede the installation of the additional E85 facilities that we projected will be needed.

Petroleum retailers expressed concerns about their ability to bear the cost of installing the

needed E85 refueling equipment given that most retailers are small businesses and have limited

capital resources. They also expressed concern regarding their ability to discount the price of

E85 relative to E10 sufficiently to persuade flexible fuel vehicle owners to choose E85 given the

lower energy density of ethanol. Today’s rule does not contain a requirement for retailers to

carry E85. We understand that retailers will only install E85 facilities if they can be assured of

sufficient E85 throughput to recover their capital costs and that this could become an issue.

However, if obligated parties are going to comply with the RFS2 standards, they will have to

find a way to get the appropriate incentives to retailers. In addition, the projections regarding the

future cost of gasoline relative to ethanol indicate that as crude oil prices rise it may be possible

to price E85 more profitably. While the $3 billion total cost for E85 refueling facilities is a

substantial sum under our primary E85 facility scenario, it equates to 3 cents per gallon of E85

throughput.105 We expect that larger fuel retailers would be most likely to install new E85

refueling facilities. Therefore, the smallest retailers would not need to install E85 facilities.

Government incentives are also available to help defer the cost of installing E85 retail equipment

and expansions of these incentives are under consideration.106 Given the projections regarding

ethanol pricing relative to gasoline and other factors that may tend to encourage ethanol

consumption, we believe that it may be possible for retailers to price E85 in such a way as to

facilitate the sale of the E85 volumes that we estimate would be used to facilitate meeting the

RFS2 volumes.107

1.6.10 Fuel Distribution Accommodations to Support the Introduction of E15 Should a

Waiver be Granted

We evaluated the changes to the fuel distribution system that might be needed to support

the introduction of E15 if a waiver is granted by EPA in order to provide the basis for a

preliminary cost analysis regarding such changes. Our nation’s system of gasoline fuel

regulation, fuel production, fuel distribution, and fuel use is built around gasoline with ethanol

concentrations limited to E10. As a result, while a waiver may legalize the use of mid-level

ethanol blends under the CAA, there are a number of other actions that would have to occur to

bring mid-level blends to retail. This discussion focuses on the changes which may impact the

costs associated with the introduction of E15. A number of changes/accommodations would also

be needed to federal, state, and local regulations.

105 Our estimates of the cost of the E85 retail facilities that would be needed to support the use of the volume of

ethanol that we project would be used under the RFS2 standards is contained in Section 4.2 of this RIA. E85 retail

costs were amortized over 15 years at a 7% cost of capital.

106 See Section 1.7.1.2.3 of this RIA for a discussion of government incentives to install E85 retail refueling

equipment.

107 This issue is discussed in Section 1.7.1.2.5 of this RIA.
235

The CAA provides a 1 pound RVP waiver for ethanol blends of 10 volume percent or

less. This waiver was granted at a time when ethanol use was not widespread. Thus, the

environmental considerations at the time were relatively minor. Now that the nation is moving

to E10 nationwide, the 1 psi waiver may have significant environmental implications for all

conventional gasoline. Lacking a similar RVP waiver, a special low-RVP gasoline blendstock

would be needed at terminals to allow the formulation of mid-level ethanol blends that are

compliant with EPA RVP requirements. Providing such a separate gasoline blendstock would

present significant logistical challenges and costs to the fuel distribution system. It should be

possible for refiners to formulate a gasoline blendstock that would be suitable for manufacturing

both mid-level ethanol blends and E10 at the terminal. While this would avoid the logistical

problems associated with maintaining separate blendstocks, there could be additional refining

costs.

Assuming that refiners develop a common gasoline blendstock for both E10 and E15, the

accommodations that would be needed to the fuel distribution infrastructure to facilitate the

introduction of a mid-level ethanol blend would primarily be limited to vehicle refueling

facilities. Some terminal operators may need to modify their ethanol blending facilities to allow

the in line blending of a mid-level ethanol blend. However, in most if not all cases this would

only involve a modification to the software for the blending system to allow a mid-level as well

as an E10 or E0 blend rate rather than necessitating a physical change to the system. Terminal

operators would also need to provide for the receipt and storage of the greater volumes of ethanol

needed to manufacture a mid-level ethanol blend.108

Fuel retailers would need to ensure that the equipment used to store and dispense E15 is

suitable for this purpose. EPA’s Office of Underground Storage Tanks (OUST) requires that

underground storage tank (UST) systems must be compatible with the substance stored in the

system. A number of authorities require that fuel retailers use equipment that has been certified

as compatible with the fuel being sold. Such a certification is required by the Occupational

Safety and Health Administration (OSHA), many local fire marshals, tank insurance and state

tank fund policies, and the provisions contained in many business loan agreements.

Underwriters Laboratories (UL) is the leading safety certification organization and is

often specifically referenced in regulations and insurance policies. UL stated that they have data

which indicates that the use of fuel dispensers certified for up to E10 blends could dispense

blends up to a maximum ethanol content of 15 volume percent without causing critical safety

concerns.109 Based on these data, UL stated that it would support a decision by Authorities who

Have Jurisdiction (AHJs, e.g. state and local fire marshals) to permit equipment originally

certified for up to E10 blends to be used to dispense up to 15 volume percent ethanol.110

However, UL stated that it could not recertify equipment that was originally certified for up to

108 The need for additional facilities to receive, store, and blend ethanol is anticipated in any event due to the

projected need for expanded use of E85 to meet the renewable fuel volume requirements under EISA.

109 The UL announcement can be found at http://www.ul.com/newsroom/newsrel/nr021909.html

110 The reference of up to 15 volume percent ethanol by UL does not equate to E15. Variability in the test method

for ethanol content and other factors mean that in-use fuel blends with a nominal ethanol content of 15 % could at

times exceed 15 volume percent.
236

E10 blends for a higher ethanol blend.111 Furthermore, the UL announcement did not address

underground storage systems (storage tank, piping, valves, pumps, fittings, leak detection, etc.).

Evaluations are currently underway by EPA’s Office of Underground Storage Tanks

(OUST) in coordination with the Department of Energy (DOE) and UL regarding the

compatibility of existing UST systems to store mid-level ethanol blends. Based on this

evaluation, OUST could prepare guidance to states on how facilities with UST systems that store

a mid-level ethanol blend could demonstrate compliance with the EPA requirement that such

systems are compatible with the substance stored in the system.112 The Department of Energy in

coordination with UL is conducting testing to evaluate the suitability of existing retail fuel

dispensing equipment to accommodate a mid-level ethanol blend.113 Depending on the results of

the OUST and DOE/UL efforts, the authorities referenced above may be encouraged to allow the

use of certain existing equipment originally certified for E10 to handle a mid-level ethanol blend.

One potential approach in lieu of requiring a UL certification might be for AHJs to require that

fuel retailers have records to establish what type of equipment is present and to obtain

manufacture certifications that the equipment is suitable for a mid-level ethanol blend.

Documenting the manufacturer and model number of the various components of their

fuel storage and dispensing equipment may be a relatively simple undertaking for newer stations

that have records readily on hand. However, for older stations that may have had multiple

owners, it may be difficult to assemble a full list of their fuel handling components. For above

ground components (i.e. the dispenser), a potential gap in the records could be resolved by a

visual inspection. However, with respect to underground components there may no be practical

way to identify certain components without breaking concrete. The most difficulty is likely to be

faced in identifying the type of seals, gaskets, pipe joints, and bonding materials used by the

contractors who installed the equipment.114 Many UST installation companies and components

manufactures may have gone out of business, further complicating the process of identifying

what hardware is installed and obtaining a manufacture certification of compatibility. This may

tend to limit the ability to introduce a mid-level ethanol blend to newer fuel retailers and larger

chain retailers who may have more complete records. However, such retailers are also likely to

have a relatively high fuel sales compared to the fuel retailer population as a whole. Thus, the

ability to introduce a mid-level ethanol blend at such retailers could potentially support the sale

of a substantial volume of such a fuel.

If a partial waiver is granted which provides for the use of a mid-level ethanol blend in a

subset of vehicles, then E10 would need to continue to be made available for use in

vehicles/equipment not covered by the waiver.115 We believe that this might be most practicably

111 UL announced a separate retail dispenser certification pathway for ethanol blends up to E25 in August of 2009

(http://www.ul.com/global/eng/pages/corporate/newsroom/newsitem.jsp?n=ul-announces-new-certification-pathfor-ethanol-fuel_20090810122400). This is addition to the UL certification pathways to cover up to E10 blends and

to cover E85 and lesser ethanol blends.

112 The EPA OUST requirement is located at 40 CFR Part 280.32. Enforcement of this requirement is typically

delegated to the State level.

113 This is the above ground equipment commonly referred to as the fuel pump stand or fuel dispenser.

114 These are the UST components where there may be the most concern regarding compatibility with a mid-level

ethanol blend.

115 E0 will also be needed for use in gasoline piston engine aircraft.
237

accomplished by switching some or all dispensers of regular gasoline at a retail facility to handle

the mid-level ethanol blend.116 The premium dispenser could continue to handle E10 (or E0) for

use in legacy vehicles/equipment.117 Some of the nonroad equipment currently requires the use

of a premium grade fuel. Thus, premium gasoline would continue to be the “universal fuel” as it

is today, capable of being used in any gasoline vehicle or equipment. Some retailers who have

multiple regular grade storage tanks may choose to offer both an E15 and E10 regular grade in

order to offer a less expensive E10 fuel to customers that do not require the use of premium but

are not covered by a partial waiver. In most cases this would likely involve breaking concrete to

separate tanks that are currently interconnected.

If the OUST and DOE evaluations show that current retail fuel equipment is largely

compatible with a mid-level ethanol blend, it may be possible for a substantial number of retail

facilities to introduce a mid-level ethanol blend at a modest cost. If some components of the

above ground existing retail hardware are found to be incompatible with a mid-level ethanol

blend, it may be possible for them to be replaced through normal attrition. For example the

“hanging hardware” which includes the nozzle and hose from the dispenser is typically replaced

every 3 to 5 years. If more extensive modifications are shown to be necessary, the costs could

approach those necessary to introduce E85. If this is the case, the costs would tend to inhibit the

rapid introduction of a mid-level ethanol blend. The potential costs to the fuel distribution

system associated with the introduction of E15 are discussed in Section 4.2.1.1.

1.7 Ethanol Consumption Feasibility

1.7.1 Background

Over the past decade, ethanol use has grown rapidly due to oxygenated fuel requirements,

MTBE bans, tax incentives, state mandates, the first federal renewable fuels standard (“RFS1”),

and rising crude oil prices. Although the cost of crude has come down since reaching record

levels in 2008, uncertainty surrounding pricing and the environmental implications of fossil fuels

has continued to drive ethanol use.

As shown in Table 1.7-1, a record 9.5 billion gallons of ethanol were blended into U.S.

gasoline in 2008 and EIA is forecasting additional growth in the years to come. According to

their recently released Short-Term Energy Outlook (STEO), EIA is forecasting 0.7 million

barrels of daily ethanol use in 2009, which equates to 10.7 billion gallons. The October 2009

STEO projects that total ethanol usage (domestic production plus imports) will reach 12.1 billion

gallons by 2010.445

116 Commenters stated that this arrangement could encourage misfueling if the “premium grade” E10 was

substantially more costly than the “regular grade” E15.

117 The state of Oregon recently amended its requirement that all gasoline contain 10 percent ethanol to allow

premium grade gasoline which does not contain ethanol to be sold for use in specified equipment/vehicles which

may not be ethanol tolerant (including gasoline piston engine aircraft)

http://www.leg.state.or.us/09reg/measures/hb3400.dir/hb3497.en.html
238

Table 1.7-1.

U.S. Ethanol Consumption

Year Production Net Importsb

Totala

1999 1.4 0.0 1.4

2000 1.6 0.0 1.6

2001 1.7 0.0 1.7

2002 2.0 0.0 2.0

2003 2.7 0.0 2.8

2004 3.3 0.1 3.5

2005 3.8 0.1 4.0

2006 4.6 0.7 5.3

2007 6.3 0.4 6.7

2008 9.0 0.5 9.5

Ethanol Usage (Bgal)

a

EIA Monthly Energy Review September 2009 (Table 10.2)

bEIA website (http://tonto.eia.doe.gov/dnav/pet/hist/mfeimus1a.htm)
239

The National Petrochemical and Refiners Association (NPRA) estimates that ethanol is

currently blended into about 75 percent of all gasoline sold in the United States.446 The vast

majority is blended as E10 or 10 volume percent ethanol, although a small amount is blended as

E85 for use in flexible fuel vehicles (FFVs). California, the largest U.S. consumer of gasoline is

yet to reach 100% E10 saturation. Historically, the state has only blended ethanol into gasoline

at 5.7 vol%, limited by its Predictive Model blending constraints. However, California has since

adjusted its model and effective January 1, 2010, ethanol blending is expected to increase to

10%.118 A publication by Hart Energy Consulting estimating ethanol penetration by state is

provided in Figure 1.7-1.447

Figure 1.7-1.

E10 Market Penetration by State

Complete saturation of the gasoline market with E10 is referred to as the ethanol “blend

wall.” The height of the blend wall in any given year is directly related to gasoline demand. In

AEO 2009, EIA projects that gasoline energy demand will peak around 2013 and then start to

taper off due to vehicle fuel economy improvements. As shown below in Figure 1.7-2, not only

is EIA forecasting a flattening of gasoline energy demand in the future due to vehicle

improvements, AEO 2009 also shows an additional decline due to the recent economic

downturn. This is a considerably different projection of the future than EIA made in their prior

forecasts. Although we have presented AEO 2008 and AEO 2007 for illustrative purposes, the

final release of AEO 2009 (April 2009 – ARRA Update) is the basis for all energy and ethanol

consumption calculations utilized in this analysis.
240

Figure 1.7-2.

Projected Gasoline Energy Demand448

0

5

10

15

20

25

20102011201220132014

2015

2016201720182019

2020

20212022

Year

Motor Gasoline + E85 Energy (Quadrillion BTUs)

AEO 2007

AEO 2008

AEO 2009 Final

Based on the gasoline demand projections in AEO 2009, the maximum amount of ethanol

that can be blended into gasoline as E10 will be around 14-15 billion gallons, depending on the

year (refer ahead to Figure 1.7-3). There are many challenges associated with getting beyond the

ethanol blend wall and consuming more than 14-15 billion gallons including rapid growth in

FFV/E85 infrastructure, problems with meeting ASTM specs, testing and potential approval of

mid-level blends, etc. As such, as discussed in Sections 1.4.3 and 1.5.3, a growing number of

companies are investigating non-ethanol biofuels (e.g., cellulose-based diesel, gasoline, etc.) as a

mechanism for meeting the cellulosic biofuel standard. The benefit of synthetic hydrocarbon

fuels is that there is virtually no blend wall issue, they are fungible with existing fuel

infrastructure and they can be priced at parity with petroleum at retail. In many ways, they are

essentially drop-in replacements for gasoline and diesel. However, like all second-generation

biofuels, there are technological and financial hurdles that need to be overcome before biomassbased synthetic hydrocarbon fuels can be brought to market.

These factors make it difficult to project the mix of renewable fuels types that will be

used in the future to meet the RFS2 standards. To address the uncertainty of which fuels will be

used, we have analyzed three control cases with varying levels of ethanol as part of this final

rule. As shown below in Table 1.7.2, total ethanol usage (corn, imported and cellulosic) could

range from 17.5 to 33.2 billion gallons in 2022.
241

Table 1.7-2.

Potential Ethanol Usage Scenarios Under RFS2

Scenario

2022 Total

Ethanol Use

RFS1 Reference Case 7.1

AEO 2007 Reference Case 13.2

Low-ETOH Control Case 17.5

Mid-ETOH Control Case (Primary) 22.2

High-ETOH Control Case 33.2

Under the primary control case, ethanol consumption will need to be about three times

higher than RFS1 levels, more than twice as much as today’s levels, and 9 billion gallons higher

than the ethanol consumption predicted to occur in 2022 absent RFS2 (according to AEO 2007).

A summary of the projected ramp up in ethanol usage in each of these three cases compared to

the blend wall is provided in Figure 1.7-3. For more information on how the control case

volumes were derived, refer to Section 1.2 of the RIA.
242

Figure 1.7-3.

Projected Increase in Ethanol Under RFS2

0

5

10

15

20

25

30

35

20102011

20122013

2014201520162017

2018

2019202020212022

Year

Billion Gallons of Ethanol

High-ETOH Control Case

Mid-ETOH Control Case

Low-ETOH Control Case

E10 Blendwall

As shown above in Table 1.7-2, all three ethanol usage scenarios modeled require the

nation to get beyond the E10 blend wall. As expected, the more aggressive the ethanol usage,

the sooner the nation will hit the blend wall. As shown above, the nation is expected to hit the

blend wall in 2013 under our high-ethanol control case, in 2014 under our primary mid-ethanol

control case and closer to 2015 under our low-ethanol control case. Regardless, to meet today’s

RFS2 requirements using increased volumes of ethanol we are going to need to see growth in

FFV and E85 infrastructure and increases in FFV E85 refueling rates (consideration of mid-level

blends is discussed below in Section 1.7.6 below). However, the amount of change needed is

proportional to the amount of ethanol we rely on versus other renewable fuels. As expected, the

low-ethanol case would require only moderate changes in FFV/E85 infrastructure and refueling

whereas the high-ethanol case would require very dramatic changes and likely a mandate.

Once the nation gets past the blend wall, more ethanol will need to be blended as E85 and

less as E10. FFV owners who were formerly refueling on E10 will need to start filling up on

E85. As shown in Figure 1.7-4, under our primary mid-ethanol control case, we project that 12.9

billion gallons of ethanol would be blended as E10 and 9.3 billion gallons would be blended as

E85 to reach the 22.2 billion gallons in 2022.
243

Figure 1.7-4.

Ethanol by Blend in 2022

0

5

10

15

20

25

30

35

Low-ETOH Mid-ETOH High-ETOH

RFS2 Control Case

Total Ethanol

Ethanol as E85

Ethanol as E10

In the subsections that follow, we will present the FFV and E85 infrastructure

assumptions made for the final regulatory impact analysis and the corresponding FFV E85

refueling rates that would be required to reach the ethanol volumes described above. We will

also discuss some of the retail and other changes that might be needed to encourage E85 usage.

It is possible that conventional gasoline (E0) could co-exist with E10 and E85 for some

time. However, for analysis purposes, we have assumed that E10 would replace E0 as

expeditiously as possible and that all subsequent ethanol growth would come from E85.

Furthermore, we assumed that no ethanol consumption would come from the mid-level ethanol

blends (E15 or E20) since they are not currently approved for use in non-FFVs. However, in

light of the Growth Energy waiver request449, we discuss how approval of E15 for use in

conventional vehicles could help the nation postpone the blend wall in Section 1.7.6.

1.7.2 Projected Growth in Flexible Fuel Vehicles

Over the years there have been several policy attempts to increase FFV sales including

Corporate Average Fuel Economy (CAFE) credits119 and government fleet alternative-fuel

119 Under the CAFE program, the production of FFVs provides credits toward meeting the required standards.

However, the EPCA incrementally phases out these credits through MY 2019, after which they are no longer

available to help demonstrate CAFE compliance. EPA recently proposed similar FFV credits as part of their

Rulemaking to Establish Light-Duty Vehicle GHG Emission Standards and Corporate Average Fuel Economy
244

vehicle requirements. As a result, there are an estimated 8 million FFVs on the road today, up

from just over 7 million in 2008.120,450 While this is not insignificant in terms of growth, FFVs

continue to make up less than 4 percent of the total gasoline vehicle fleet.

According to EPA certification data, over one million FFVs were sold in both 2008 and

2009. Despite the recession and current state of the auto industry, automakers are incorporating

more and more FFVs into their light-duty production plans. While the FFV system (i.e., fuel

tank, sensor, delivery system, etc.) used to be an option on some vehicles, most are moving in the

direction of converting entire product lines over to E85-capable systems. Still, the number of

FFVs that will be manufactured and purchased in future years is uncertain.

To measure the impacts of increased volumes of renewable fuel, we considered three

different FFV production scenarios that might correspond to the three biofuel control cases

analyzed for the final rule. For all three cases, we assumed that total light-duty vehicle sales

would follow AEO 2009 trends. The latest EIA report suggests lower than average sales in

2008-2013 (less than 16 million vehicles per year) before rebounding and growing to over 17

million vehicles by 2019 as shown below in Figure 1.7-5.451 These vehicle projections are

consistent with EPA’s recently proposed Light-Duty Vehicle GHG Rule.452

We also applied the AEO 2009 projected car/truck sales split adjusted for NHTSA’s new

car definition beginning in 2011.121 Accordingly, by 2022, cars are expected to comprise over

70% of new light-duty vehicle sales. With respect to in-use vehicle stock, we relied on historical

car/truck sales reported by DOE’s Energy Efficiency and Renewable Energy (EERE) combined

with vehicle survival rates taken from the proposed Light-Duty Vehicle GHG Rule.453,454

Standards (74 FR 49454 September 28, 2009). Under the proposed program, FFV credits would remain available

for 2016 and later model years, but the credits would be based on demonstrated E85 usage.

120 FFV sales based on DOE’s Energy Efficiency and Renewable Energy (EERE) for 1998-2005 and EPA’s vehicle

certification data for 2006-2008. In-use FFV estimates based on vehicle survival rates taken from EPA’s proposed

Light-Duty Vehicle GHG Rule.

121 According to NHTSA’s Final Rule on 2011 MY Vehicles (74 FR 14196 March 30, 2009), starting in MY 2011,

2WD versions of SUVs are no longer classified as off-highway capable light trucks under 49 CFR § 523.5(b),

simply because the SUV also comes in a 4WD version. Based on an estimate used in EPA’s Proposed Light-Duty

Vehicle GHG Rule, approximately 22% of the forecasted AEO 2009 light-duty truck sales are cars based on the new

NHTSA definition.
245

Figure 1.7-5.

Assumed Light Duty Vehicle Production

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

16,000,000

18,000,000

20062007200820092010201120122013201420152016201720182019202020212022

Model Year

Light-Duty Vehicle Sales

Trucks

Cars

NPRM

Although we assumed that total vehicle and car/truck sales would be the same in all three

cases, we assumed varying levels of FFV production. For our low-ethanol control case, we

assumed steady FFV growth according to AEO 2009 predictions.455 For our primary midethanol control case, we assumed increased FFV sales under the presumption that GM, Ford and

Chrysler (referred to hereafter as the “Detroit 3”) would follow through with their commitment

to produce 50% FFVs by 2012. Despite the current state of the economy and the hardships

facing the auto industry, the Detroit 3 appear to still moving forward with their voluntary FFV

commitment.456 And finally, for our high-ethanol control case, we assumed a theoretical 80%

FFV mandate based on the Open Fuel Standard Act of 2009 that was reintroduced in Congress

on March 12, 2009.457 Based on reduced vehicle sales and gasoline demand, we believe an FFV

mandate would be the only viable means for consuming the 32.2 billion gallons of ethanol in

2022 required under the high-ethanol control case.

For the two reference cases, we assumed more modest, business-as-usual FFV sales. For

the RFS1 reference case, we assumed that automakers would continue to make about 8% of all

light-duty vehicles FFVs (current 2008 marketshare based on EPA certification data). For the

AEO 2007 reference case, we assumed FFV growth according to EIA’s AEO 2007.458 The

annual FFV sales assumptions for our three control cases and two reference cases are presented

below in Table 1.7-3. More information on FFV cost and assumptions made with respect to our

primary mid-ethanol control case is presented below.
246

We estimate that the cost to produce FFVs could be anywhere from $50 to $100 per

vehicle, depending on the vehicle and how many FFV-capable systems the automaker is

producing. Current estimates suggest that the per-FFV cost could easily be as high as $100.122,459

However, in the event of a hypothetical mandate, automakers would likely find a more

economical way to mass produce the necessary ethanol-compatible fuel tanks, sensors, etc. As

such, we assigned higher per-vehicle FFV production costs in the low-ethanol control case and

lower production costs in the high-ethanol case. For more on this rationale and the resulting

FFV production costs, refer to Section 4.2 of the RIA.

Table 1.7-3.

Annual FFV Sales Assumptions

RFS1

Based on

Today's

Marketshare

AEO 2007

Based on AEO

2007

Predictions

Low-ETOH

Based on AEO

2009

Predictions

Mid-ETOH

Based on 50%

Domestic 3

Commitment

High-ETOH

Based on OFS

Mandate in

Congress

2010 983,267 1,669,998 1,253,426 1,848,835 3,617,298

2011 1,083,940 1,746,847 1,598,610 2,661,252 5,439,471

2012 1,162,875 1,768,321 1,903,862 3,523,548 7,393,103

2013 1,234,554 1,795,684 2,251,284 3,740,737 9,418,573

2014 1,281,162 1,826,871 2,523,575 3,881,960 11,403,172

2015 1,306,173 1,817,706 2,693,557 3,957,744 13,286,614

2016 1,309,814 1,817,699 2,761,794 3,968,776 13,323,649

2017 1,321,421 1,826,073 2,804,322 4,003,948 13,441,727

2018 1,334,395 1,834,957 2,929,336 4,043,259 13,573,697

2019 1,348,016 1,855,352 2,825,574 4,084,529 13,712,247

2020 1,358,903 1,899,794 2,771,285 4,117,519 13,822,998

2021 1,352,943 1,913,799 2,669,883 4,099,459 13,762,369

2022 1,351,996 1,913,938 2,607,584 4,096,590 13,752,738

Reference Cases Control Case FFV Production

For our primary mid-ethanol control case, we assumed that the Detroit 3 would continue

to comprise 45% of total light-duty vehicle sales – 2008 production levels less Hummer,

Landrover, Jaguar, Saab, Saturn, and Volvo (brands that were recently or are in the process of

being sold off). We assumed that domestic automakers would continue to dominate truck sales

and car sales would gradually increase to allow the Detroit 3 to continue to maintain 45%

marketshare in future years. With respect to FFV sales, we assumed that the Detroit 3 would

follow through with their FFV commitment and increase FFV production from 16% of total sales

in 2008 to 50% of total sales in 2012. With respect to vehicle type, we assumed that about twothirds of the Detroit 3’s FFV sales would be trucks – based on historical sales and 2009 MY

offerings.

122 According to DOE and others, conventional gasoline engines need to be slightly modified (at an additional cost

of about $100) to handle higher blends of ethanol.
247

We assumed that non-domestic automakers would continue to maintain 55% marketshare

in 2009 and beyond (based on adjusted 2008 production levels). Although non-domestic

automakers have not made any official FFV production commitments, Nissan, Toyota,

Mercedes, Izuzu, and Mazda all included at least one flexible fuel vehicle in their 2009 model

year offerings.460 We do not currently anticipate that the non-domestic automakers will follow

through with an FFV commitment. However, it seems reasonable that we could expect a small

amount of FFV growth in the future. As such, for our primary mid-ethanol control case, we

assumed that non-domestic FFV production would grow from 1% in 2008 to 2% in 2009 and

future years based on current FFV offerings. With respect to FFV vehicle type, we assumed

about equal car and truck FFV sales (52% and 48%, respectively) based on 2008 sales.

Under our primary mid-ethanol scenario, as shown in Table 1.7-3, Detroit 3 and nondomestic FFV sales amount to just over 4 million per year in 2017 and beyond. This is less

aggressive than the assumptions made in the NPRM. At that time, we were expecting more

cellulosic ethanol which could justify higher FFV production assumptions. We assumed that not

only would the Detroit 3 fulfill their 50% by 2012 FFV production commitment, non-domestic

automakers might follow suit and produce 25% FFV in 2017 and beyond. We also assumed that

annual light-duty vehicle sales would continue around the historical 16 million vehicle mark

resulting in 6 million FFVs in 2017 and beyond.

Based on our revised vehicle/FFV production assumptions coupled with vehicle survival

rates, VMT and fuel economy estimates applied in the recently proposed Light-Duty Vehicle

GHG Rule, we estimate that the maximum percentage of fuel (gasoline/ethanol mix) that could

feasibly be consumed by FFVs in 2022 would be about 20% under our mid-ethanol control case.

Under our low-ethanol control, the 2022 fuel fraction was estimated at 14%. And under the

high-ethanol control case, with the FFV mandate, the fuel fraction was 56% in 2022. A summary

of the FFV fuel fraction over time for each of these scenarios is presented in Figures 1.7-6

through 1.7-8.
248

Figure 1.7-6

Low-ETOH / Low-FFV Fuel Fraction

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Year

Gasoline Fuel Fraction

Nonroad, Motorcycles, etc. Legacy NonFFVs 2001+ NonFFVs FFVs

Figure 1.7-7

Mid-ETOH / Mid-FFV Fuel Fraction

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Year

Gasoline Fuel Fraction

Nonroad, Motorcycles, etc. Legacy NonFFVs 2001+ NonFFVs FFVs
249

Figure 1.7-8

High-ETOH / High-FFV Fuel Fraction

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Year

Gasoline Fuel Fraction

Nonroad, Motorcycles, etc. Legacy NonFFVs 2001+ NonFFVs FFVs

As shown above, we split the non-FFV fuel fraction into multiple categories to help

determine the number of engines/vehicles that might be capable of handling E15 in the event of a

waiver. The basis for these assumptions and more information on the data sources is presented

in Section 1.7.6.

1.7.3 Projected Growth in E85 Access

According to the National Ethanol Vehicle Coalition (NEVC), there are currently 2,100

gas stations offering E85 in 44 states plus the District of Columbia.461 While this represents

significant industry growth, it still only translates to 1.3% of U.S. retail stations nationwide

carrying the fuel.123 As a result, most FFV owners clearly do not have reasonable access to E85.

For our FFV/E85 analysis, we have defined “reasonable access” as one-in-four pumps offering

E85 in a given area.124 Accordingly, just over 5% of the nation currently has reasonable access

to E85, up from 4% in 2008 (based on a mid-year NEVC pump estimate).125

123 Based on National Petroleum News gasoline station estimate of 161,768 in 2008.

124 For a more detailed discussion on how we derived our one-in-four reasonable access assumption, refer to Section

1.6 of the RIA. For the distribution cost implications as well as the cost impacts of assuming reasonable access is

greater than one-in-four pumps, refer to Section 4.2 of the RIA.

125 Computed as percent of stations with E85 (2,101/161,768 as of November 2009 or 1,733/161,768 as of August

2008) divided by 25% (one-in-four stations).
250

There are a number of states promoting E85 usage by offering FFV/E85 awareness

programs and/or retail pump incentives. A growing number of states are also offering

infrastructure grants to help expand E85 availability. Currently, 10 Midwest states have adopted

a progressive Energy Security and Climate Stewardship Platform.126,462 The platform includes a

Regional Biofuels Promotion Plan with a goal of making E85 available at one third of all stations

by 2025. In addition, the American Recovery and Reinvestment Act of 2009 (ARRA or

Recovery Act) recently increased the existing federal income tax credit from $30,000 or 30% of

the total cost of improvements to $100,000 or 50% of the total cost of needed alternative fuel

equipment and dispensing improvements.463

Given the growing number of subsidies, it is clear that E85 infrastructure will continue to

expand in the future. However, like FFVs, we expect that E85 station growth will be somewhat

proportional to the amount of ethanol realized under the RFS2 program. As such, we analyzed

three different E85 growth scenarios for the final rule that could correspond to the three different

RFS2 control cases. As an upper bound for our high-ethanol control case, we maintained the

70% access assumption we applied for the NPRM. This translates to about 1:6 stations

nationwide.

126 The following states have adopted the plan: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri,

Ohio, South Dakota and Wisconsin.
251

As explained in the NPRM, one way to provide 70% of the nation with reasonable 1-in-4

access would be to make it available in urban areas. For analysis purposes, we defined “urban”

areas as:

 The top 150 metropolitan statistical areas according to the U.S. Census Bureau and/or

counties with the highest 150 VMT projections according the EPA MOVES model.

 Federal RFG areas

 Winter oxy-fuel areas

 Summertime low-RVP areas

 Other relatively populated cities in the Midwest. Cities with populations greater than

100,000 people in states with a potential ethanol surplus in 2022.

For an illustration of the urban areas representing about 70% of the nation’s VMT, refer to

Figure 1.7-9

Figure 1.7-9.

A Look at 70% E85 Access - Concentrating Pumps in Urban Areas

.

For our other control cases we assumed access to E85 would be lower with the logic that

retail stations (the majority of which are independently owned and operated and net around
252

$30,000 per year) would not invest in more E85 infrastructure than what was necessary to meet

the RFS2 requirements. As explained in Section 4.2.1.1.9 of the RIA, the cost to install E85

could be anywhere from $131,000 to $177,000 per station depending on the configuration and

number of dispensers. For our primary mid-ethanol control case we assumed reasonable access

would grow from 4% in 2008 to 60% in 2022 and for our low-ethanol control case we assumed

that access would only grow to 40% by 2022. As a simplifying assumption, we assumed a linear

phase-in as shown below in Figure 1.7-10. As discussed in Section 1.6, we believe these E85

growth scenarios are possible based on our assessment of distribution infrastructure capabilities.

For more on the number of new E85 stations compared to the reference cases and the associated

cost, refer to Section 4.2.1.1.9 of the RIA.

Figure 1.7-10.

Projected Growth in 1-in-4 Station Access to E85

0%

10%

20%

30%

40%

50%

60%

70%

80%

2010201120122013201420152016201720182019202020212022

Year

% of Country with 1-in-4 Access to E85

High-ETOH: 70% by 2022

Mid-ETOH: 60% by 2022

Low-ETOH: 40% by 2022

1.7.4 Required Increase in E85 Refueling Rates

As mentioned earlier, there were just over 7 million FFVs on the road in 2008. If all

FFVs refueled on E85 100% of the time, this would translate to about 8.3 billion gallons of E85

use. This is based on the assumption that the average FFV in 2008 traveled about 16,500 miles

and got about 19 miles per gallon of gasoline under actual in-use driving conditions.127,464 The

127 Fleet average VMT and MPG estimates based on modeling assumptions used in the proposed Light-Duty Vehicle

GHG Rule.
253

estimate also assumes is takes about 1.3 gallons of E85 for an FFV to travel the same distance as

a gallon of gasoline due to the difference in energy density of the fuels.128

Although we computed the theoretical E85 usage potential to be around 8.3 billion

gallons in 2008, according to EIA, actual E85 usage was only about 12 million gallons in

2008.129,465 This means that, on average, FFV owners were only tapping into about 0.15% of

their vehicles’ E85/ethanol usage potential. Assuming only 4% of the nation had reasonable

one-in-four access to E85 in 2008 (as discussed in Section 1.7.3), this equates to an estimated 4%

E85 refueling frequency for those FFVs that had reasonable access to the fuel.

There are several reasons behind today’s low E85 refueling frequency. For starters,

many FFV owners may not know they are driving a vehicle that is capable of handling E85. As

mentioned earlier, more and more automakers are starting to produce FFVs by engine/product

line, e.g., all 2008 Chevy Impalas are FFVs.466 Consequently, consumers (especially brand loyal

consumers) may inadvertently buy a flexible fuel vehicle without making a conscious decision to

do so. And without effective consumer awareness programs in place, these FFV owners may

never think to refuel on E85. In addition, FFV owners with reasonable access to E85 and

knowledge of their vehicle’s E85 capabilities may still not choose to refuel on E85. They may

feel inconvenienced by the increased refueling requirements. Based on its lower energy density,

FFV owners will need to stop to refuel 22% more often when filling up on E85 over E10 (and

24% more often when refueling on E85 over conventional gasoline).130 In addition, some FFV

owners may be deterred from refueling on E85 out of fear of reduced vehicle performance or just

plain unfamiliarity with the new motor vehicle fuel. However, as we move into the future, we

believe the biggest determinant will be price – whether E85 is priced competitively with gasoline

based on its reduced energy density (discussed in more detail below).

To comply with the RFS2 program and consume 17.5 to 33.2 billion gallons of ethanol

by 2022, not only will we need more FFVs and more E85 retailers, we’ll also need to see a

dramatic increase in the FFV E85 refueling frequency relative to today. Based on the FFV and

retail assumptions presented in Sections 1.7.2 and 1.7.3, our analysis suggests that FFV owners

with reasonable access to E85 would need to refuel on it 42-70% of the time, depending on the

scenario (refer to Figure 1.7-11). This is a significant increase from today’s estimated 4%

refueling frequency.

128 Assuming E85 contains approximately 74 vol% denatured ethanol on average (77,012 BTU/gal) and 26 vol% E0

gasoline (115,000 BTU/gal) based on EIA’s AEO 2009 assumption.

129 0.007 quadrillion BTUs of ethanol from E85 (from AEO 2009) converted into Bgal using EIA’s HHV (84,262

BTU/gal) and divided by 0.74 (EIA’s assumed average ethanol content of E85).

130 Assuming E85 contains approximately 74 vol% denatured ethanol on average (77,012 BTU/gal) and 26 vol% E0

gasoline (115,000 BTU/gal) based on EIA’s AEO 2009 assumption. For analysis purposes, E10 was assumed to

contain 10 vol% denatured ethanol and 90 vol% E0 gasoline.
254

Figure 1.7-11.

Necessary FFV E85 Refueling Rates

(Given 1-in-4 Access to Fuel)

0%

10%

20%

30%

40%

50%

60%

70%

80%

20102011

2012

2013201420152016

201720182019202020212022

Year

% of Time FFVs Refuel on E85 (Given Access)

Low-ETOH: AEO 2009 FFVs, 40% Access to E85

Mid-ETOH: Committed FFVs, 60% Access to E85

High-ETOH: Mandated FFVs, 70% Access to E85

Max = 70%

Max = 42%

Max = 58%

As shown above, modeling an FFV mandate and E85 station access reaching 70% by

2022, results in the lowest required FFV E85 refueling frequency (42%) for the high-ethanol

control case. Similarly, the infrastructure assumptions modeled for the low-ethanol control case

resulted in the highest required FFV E85 refueling frequency (70%). While this may seem

counter-intuitive, the result is a product of the competing and variable modeling assumptions

used. Had we elected to hold FFV production and E85 access constant for all three control cases

(i.e., applied more aggressive infrastructure assumptions across the board), we would have come

up with the lowest required FFV E85 refueling frequency for low-ethanol case and the highest

requirements for the high-ethanol case. The computed required refueling frequency would also

look more linear. However, this would mean large investments in FFV production and E85

refueling infrastructure despite low demand for E85. We figured that, at costs of up to $100 per

FFV and as much as $177,000 per E85 station, the nation would not build more FFV/E85

infrastructure than what was needed to meet the RFS2 requirements Regardless, in order for

any significant increase in FFV E85 refueling rates to occur, there will need to be an

improvement in the current E85/gasoline price relationship.

1.7.5 Market Pricing of E85 Versus Gasoline

According to an online fuel price survey, E85 is currently priced almost 40 cents per

gallon or about 15% lower than regular grade conventional gasoline.467 But this is still about 30

cents per gallon higher than conventional gasoline on an energy-equivalent basis. To increase
255

our nation’s E85 refueling frequency to the levels described above, E85 needs to be priced

competitively with (if not lower than) conventional gasoline based on its reduced energy content,

increased time spent at the pump, and limited availability. Overall, we estimate that E85 would

need to be priced about 25% lower than E10 at retail in 2022 in order for it to make sense to

consumers (as outlined below).

First, E85 needs to be priced lower than E10 based on its reduced energy density. For

our ethanol consumption analysis and this E85/gasoline price assessment, denatured ethanol was

assumed to have a lower heating value of 77,012 BTU/gal based on the new 2% denaturant

requirement.131,468 Conventional gasoline (E0) was assumed to have an average lower heating

value of 115,000 BTU/gal. E10 was assumed to contain 10 vol% denatured ethanol and 90 vol%

gasoline and E85 was assumed to contain 74 vol% denatured ethanol and 26 vol% gasoline on

average (based on EIA’s AEO 2009 report).469 As shown below, E85 would need to be priced

about 78% lower than E10 based on its reduced energy density.

%1.78

/000,11590.0/012,7710.0

/000,11526.0/012,7774.0

10

85



 

  



galBTU galBTU

galBTU galBTU

E EnergyDensity

E EnergyDensity

In 2022, based on EIA’s $116/barrel crude oil projections, wholesale gasoline (E10) is

expected to be priced at $3.42/gallon.132 Factoring in transportation costs, taxes, and mark-up at

retail (about $0.60/gallon total), gasoline can be expected to be priced at $4.02/gallon at retail in

2022. To be cost-competitive with gasoline, E85 would have to be priced at least 78% lower

than E10 at retail, or around $3.14/gallon.

In addition, we need to take the value of FFV owners’ time into consideration because

they could be spending 22% more time at the pump if they are refueling exclusively on E85. In

the U.S., a person’s time is currently valued at around $30 per hour. This value of time (VOT)

estimate was based on an average of values identified in a review of economics literature and is

consistent with 2005 Brownstone and Small VOT estimates.470 Adjusting the 2005 VOT

estimate to 2007 dollars, yields a $31.61 per hour estimate. Assuming it takes about six minutes

for a 15-gallon refill, E85 needs to be priced an additional $0.05 per gallon less than E10

Finally, we accounted for the fact that, as an alternative fuel, it is unlikely that E85 will

ever be available nationwide. As mentioned above, the greatest access we anticipate FFV

owners will have to E85, is one-in-four stations offering the fuel. And that will likely only be in

select areas of the country. And unlike diesel fuel, FFV owners are not required to fill up on it.

So in order to get consumers to want to refuel on E85 over gasoline, there needs to be an

additional price incentive at the pump according to a 1997 Oakridge National Lab report.133,471

131 The 2008 Farm Bill1 contained a provision that stipulates the full value of the Volumetric Ethanol Excise Tax

Credit (VEETC) is only available to blenders when using fuel ethanol denatured at a maximum of 2%.

132 Refer to Table 4.4-9 in Section 4.4 of the RIA.

133 Although the 1997 David Greene study was based on asking consumers about a hypothetical fuel that “works

just as well as gasoline”, we assumed that Figure 6 from the report (pictured) could also be used to determine the

retail price incentive given to E85 to account for its limited availability . As explained in the preceding text, this

was in addition to the incentives assigned to E85 to account for its reduced energy density and additional time spent

at the pump.
256

As shown below in Figure 1.7-12, if an alternative fuel is only available at 25% of stations and

you want people to refuel on it about 58% of the time in 2022 (as is, under our primary midethanol control case), it needs to be given an $0.11/gallon price advantage (1997$). Inflating the

1997 David Greene estimate to 2007 dollars, E85 would need to be priced an additional 14 cents

per gallon lower than E10.

Figure 1.7-12.

Required Price Incentive for Alternative Fuels with Limited Availability

Refueling frequency given

“reasonable access”

Percent of stations offering E85 in areas of the

country with “reasonable access”, i.e., 25%

$0.11/gal Price Advantage (1997$)

X

Overall, our retail price analysis suggests that E85 would need to be priced around $2.95

per gallon ($3.14/gal - $0.05/gal - $0.14/gal) in order for it to be competitive with $4.02 gasoline

in 2022. Essentially, E85 would need to be priced at least 25% lower than gasoline at retail

outlets in order for consumers to want to choose it regularly.

However, ultimately it comes down to what refiners are willing to pay for ethanol

blended as E85. The more ethanol you try to blend as E85, the more devalued ethanol becomes

as a gasoline blendstock. Changes to state and Federal excise tax structures could help promote

ethanol blending as E85. But for the most past, as long as crude oil prices remain high (as

projected by AEO 2009), it should look attractive to refiners as a blendstock. Based on our retail

cost calculations, summarized in Figure 1.7-13 below, ethanol would have to be priced at

$1.97/gallon in order for it to be attractive to refiners for E85 blending in 2022.
257

Figure 1.7-13.

Required Ethanol Pricing Needed in 2022 to Encourage E85 Blending

TAX

402 c/gal 47 c/gal 3 c/gal 342 c/gal

Gasoline Pricing

E85 Pricing

TAX

402 c/gal

47 c/gal 3 c/gal 235 c/gal E85

X 0.78 FE effect

- 5 c/gal (refueling time)

295 c/gal for E85 234 c/gal = 0.26*342 + 0.74*197

Ethanol would have to be priced at

197 c/gal or less to be attractive to

refiners in 2022.

10 c/gal

10 c/gal

E10

- 14 c/gal (limited availability)

According to the DTN Ethanol Center, the current rack price for ethanol is around

$2.20/gallon.472 However, as explained in Section 4.4 of the RIA, we project the average ethanol

delivered price (volume-weighted average production cost of corn, cellulosic and imported

ethanol plus distribution) will come down to around $1.67/gallon in 2022 under our mid-ethanol

primary control case.134 Therefore, while gasoline refiners and markets will always have a

greater profit margin selling ethanol in low-level blends to consumers based on volume, they

should be able to maintain a profit selling it as E85 in the future.

1.7.6 Consideration of >10% Ethanol Blends

On March 6, 2009, Growth Energy and 54 ethanol manufacturers submitted an

application for a waiver of the prohibition of the introduction into commerce of certain fuels and

fuel additives set forth in section 211(f) of the Act. This application seeks a waiver for ethanolgasoline blends of up to 15 percent ethanol by volume.473 On April 21, 2009, EPA issued a

Federal Register notice announcing receipt of the Growth Energy waiver application and

soliciting comment on all aspects of it.474 On May 20, 2009, EPA issued an additional Federal

Register notice extending the public comment period by an additional 60 days.475 The comment

period ended on July 20, 2009, and EPA is now evaluating the waiver application and

considering the comments which were submitted.

In a letter dated November 30, 2009, EPA notified the applicant that, because crucial

vehicle durability information being developed by the Department of Energy would not be

available until mid-2010, EPA would be delaying its decision on the application until a sufficient

amount of this information could be included in its analysis so that the most scientifically

supportable decision could be made.476 As the current Growth Energy waiver application is still

134 Refer to Table 4.4-4 in Section 4.4 of the RIA.
258

under review, EPA believes it is appropriate to address aspects of the mid-level blend waiver in

its decision announcement on the waiver application as opposed to dealing with the comments

and evaluation of the potential waiver in today’s final rule.

Although EPA has yet to make a waiver decision, since its approval could have a

significant impact on our analyses that are based on the use of E85, as a sensitivity analysis, we

have evaluated the impacts that E15 could have on ethanol consumption feasibility. More

specifically, we have assessed the impacts of a partial waiver for newer technology vehicles

consistent with the direction of EPA’s November 30, 2009 letter.

For our analysis, we assumed that E10 would need to continue to co-exist for legacy and

non-road equipment based on consumer demand regardless of any waiver decision. As shown in

Figures 1.7-5 through 1.7-7, we assumed that the percentage of gasoline energy consumed by

nonroad, heavy-duty gasoline vehicles, and motorcycles would be about 8% based on

information obtained from ORNL’s Transportation Energy Data Book.477 For analysis purposes,

we assumed E10 would be marketed as premium-grade gasoline (the universal fuel), E15 would

be marketed as regular-grade gasoline (to maximize ethanol throughput) and, like today,

midgrade would be blended from the two fuels to make a 12.5 vol% blend (E12.5). In addition,

we assumed that some E15-capable vehicles would continue to choose E10 or E12.5 based on

today’s premium and midgrade sales shown below in Table 1.7-4.

Table 1.7-4.

Mid-level Ethanol Blend Assumptions

Grade of Gasoline

% of CG

Sales*

Ethanol

Content

Regular 86.5% 10.0%

Midgrade 5.0% 12.5%

Premium 8.5% 15.0%

*Petroleum Marketing Annual 2008, Table 45

In the event of a partial waiver, it is unclear how long it would take for E15 to be fully

deployed or whether it would ever be available nationwide. For analysis purposes, we made the

simplifying assumption that E15 would be fully phased in and available at all retail stations

nationwide by the time the nation hit the blend wall, or by around 2014 for our primary midethanol control case shown in Figure 1.7-14.
259

Figure 1.7-14

Max E15 Ethanol Consumption Compared to Mid-Ethanol Control Case

0

5

10

15

20

25

30

35

40

2010

20112012

2013

20142015

20162017

201820192020

2021

2022

Year

Billion Gallons of Fuel (ETOH-Eq)

Non-Ethanol Biofuels

Cellulosic Ethanol

Imported Ethanol

Corn Ethanol

E15 Blendwall (Partial Waiver)

E10 Blendwall

As modeled, a partial waiver for E15 could increase the ethanol consumption potential

from conventional vehicles to about 19 billion gallons. Under our primary control case (shown

in Figure 1.7-14), E15 could postpone the blend wall by up to five years, or to 2019. Although

E15 would fall short of meeting the RFS2 requirements under this scenario, it could provide

interim relief while the country ramps up non-ethanol cellulosic biofuel production and/or

expands E85/FFV infrastructure.

Under our low-ethanol case, a partial waiver for E15 could eliminate the need for

additional FFV/E85 infrastructure all together. Similarly, for our high-ethanol case, E15 could

eliminate the need for FFV or E85 infrastructure mandates or postpone the blend wall by about 3

years from about 2013 to 2016. These scenarios are shown in Figures 1.7-15 and 1.7-16.
260

Figure 1.7-15

Max E15 Ethanol Consumption Compared to Low-Ethanol Control Case

0

5

10

15

20

25

30

35

40

2010

20112012

2013

20142015

20162017

201820192020

2021

2022

Year

Billion Gallons of Fuel (ETOH-Eq)

Non-Ethanol Biofuels

Cellulosic Ethanol

Imported Ethanol

Corn Ethanol

E15 Blendwall (Partial Waiver)

E10 Blendwall
261

Figure 1.7-16

Max E15 Ethanol Consumption Compared to High-Ethanol Control Case

0

5

10

15

20

25

30

35

40

2010201120122013201420152016

2017201820192020

2021

2022

Year

Billion Gallons of Fuel (ETOH-Eq)

Non-Ethanol Biofuels

Cellulosic Ethanol

Imported Ethanol

Corn Ethanol

E15 Blendwall (Partial Waiver)

E10 Blendwall

1.8 Inputs Used for the Air Quality Modeling

The information presented in Section 1.5 reflects our most current assessment of the

renewable fuels industry and our projections through 2022 to meet the RFS2 standards. In

addition, Section 1.7 reflects our most current assessment on how the renewable fuel might be

consumed and the associated challenges, e.g., E10 blend wall, etc. The information presented in

these sections serves as the basis for various final rulemaking impact analyses, including cost.

However, the air quality modeling and some of the fuel distribution analyses had to begin prior

to this assessment being completed. As a result, they relied on industry assessments carried out

for the NPRM. This section presents the relevant NPRM assessment which served as the basis

for these analyses.

1.8.1 Ethanol Inputs

1.8.1.1 Corn Ethanol Inputs

1.8.1.1.1 Existing Corn/Starch Ethanol Production

At the time of our May 2008 corn ethanol plant assessment used for air quality modeling,

there were 158 fuel ethanol plants operating in the U.S. with a combined production capacity of
262

9.2 billion gallons per year.135478479 The majority of ethanol (nearly 89% by volume) was

produced exclusively from corn. Another 11% came from a blend of corn and/or similarly

processed grains (milo, wheat, or barley) and less than half a percent was produced from cheese

whey, waste beverages, and sugars/starches combined. A summary of the feedstocks utilized by

the U.S. ethanol industry as of May 2008 is found in Table 1.8-1.

Table 1.8.1

May 2008 Corn/Starch Ethanol Production Capacity by Feedstock

Plant Feedstock

(Primary Listed First)

Capacity

MGY

% of

Capacity

No. of

Plants

% of

Plants

Corna8,141 88.8% 131 82.9%

Corn, Milob704 7.7% 14 8.9%

Corn, Wheat 130 1.4% 1 0.6%

Corn, Wheat, Milo 115 1.3% 2 1.3%

Milo 3 0.0% 1 0.6%

Wheat, Milo 50 0.5% 1 0.6%

Cheese Whey 8 0.1% 2 1.3%

Waste Beveragesc13 0.1% 4 2.5%

Waste Sugars & Starchesd7 0.1% 2 1.3%

Total 9,169 100% 158 100%

a

Includes one facility processing seed corn, one facility also operating a pilot-level cellulosic ethanol

plant, and six facilities with plans to build pilot-level cellulosic ethanol plants or incorporate biomass

feedstocks in the future.

b

Includes one facility processing small amounts of molasses in addition to corn and milo.

c

Includes two facilities processing brewery waste.

d

Includes one facility processing potato waste that intends to add corn in the future.

The corn ethanol industry relies primarily on natural gas. At the time of our May 2008

plant assessment, 134 of the 158 corn/starch ethanol plants burned natural gas (exclusively).136

In addition, three burned a combination of natural gas and biomass, one burned a combination of

natural gas, landfill syngas and wood, while one burned a combination of natural gas and syrup

from the process. In addition, 18 plants burned coal as their primary fuel and one burned a

combination of coal and biomass. Our research suggested that 24 plants utilized cogeneration or

combined heat and power (CHP) technology at the time of our assessment. A summary of the

135 Our May 2008 corn/starch ethanol industry characterization was based on a variety of data sources including:

Renewable Fuels Association (RFA) Ethanol Biorefinery Locations (updated April 2, 2008); Ethanol Producer

Magazine (EPM) Current plant list (last modified on April 14, 2008), and ethanol producer websites. The baseline

does not include ethanol plants whose primary business is industrial or food-grade ethanol production. Where

applicable, ethanol plant production levels were used in lieu of nameplate capacities to estimate plant production.

The baseline does not include U.S. plants that were idled as of May 2008 or plants that might be located in the

Virgin Islands or U.S. territories.

136 Facilities were assumed to burn natural gas if the plant boiler fuel was unspecified or unavailable on the public

domain.
263

energy sources and CHP technology utilized by the U.S. ethanol industry as of May 2008 is

found in Table 1.8-2.

Table 1.8.2.

May 2008 Corn/Starch Ethanol Production Capacity by Energy Source

Plant Energy Source

(Primary Listed First)

Capacity

MGY

% of

Capacity

No. of

Plants

% of

Plants

CHP

Tech.

Coala

1,720 18.8% 18 11.4% 8

Coal, Biomass 50 0.5% 1 0.6% 0

Natural Gasb

7,141 77.9% 134 84.8% 15

Natural Gas, Biomassc

113 1.2% 3 1.9% 1

Natural Gas, Landfill Syngas, Wood 100 1.1% 1 0.6% 0

Natural Gas, Syrup 46 0.5% 1 0.6% 0

Total 9,169 100.0% 158 100.0% 24

a

Includes four plants that are permitted to burn biomass, tires, petroleum coke, and wood waste in addition to

coal and one facility that intends to transition to biomass in the future.

b

Includes one facility that intends to burn thin stillage biogas, five facilities that intend to transition to coal, and

one facility that intends to switch to biomass in the future.

c

Includes one facility processing bran in addition to natural gas.

Besides a few plants located outside of the Corn Belt, the majority of ethanol is produced

in PADD close to where the corn is grown. At the time of our May 2008 ethanol industry

characterization, PADD 2 accounted for 94% (or 8.6 billion gallons) of the estimated ethanol

production capacity as shown in Table 1.8.-3 below.

Table 1.8-3.

May 2008 Corn/Starch Ethanol Production Capacity by PADD

PADD

Capacity

MGY

% of

Capacity

No. of

Plants

% of

Plants

PADD 1 50 0.5% 2 1.3%

PADD 2 8,619 94.0% 140 88.6%

PADD 3 170 1.9% 3 1.9%

PADD 4 160 1.7% 7 4.4%

PADD 5 171 1.9% 6 3.8%

Total 9,169 100.0% 158 100.0%

Leading the Midwest in ethanol production were Iowa, Nebraska, Illinois, South Dakota

and Minnesota. Together, these five states’ 93 ethanol plants accounted for 67 percent of the

nation’s ethanol production capacity in May 2008. For a map of the ethanol plant locations and a

summary of ethanol production capacity by state, refer to Figure 1.8.1 and Table 1.8.4 below.
264

Figure 1.8.1.

May 2008 Corn/Starch Ethanol Plant Locations
265

Table 1.8-4

May 2008 Corn/Starch Ethanol Production Capacity by State

State

Capacity

MGY

% of

Capacity

No. of

Plants

% of

Plants

Iowa 2,282 24.9% 30 19.0%

Nebraska 1,278 13.9% 22 13.9%

Illinois 941 10.3% 9 5.7%

South Dakota 892 9.7% 14 8.9%

Minnesota 749 8.2% 18 11.4%

Indiana 540 5.9% 7 4.4%

Wisconsin 479 5.2% 8 5.1%

Kansas 464 5.1% 12 7.6%

Ohio 345 3.8% 4 2.5%

Michigan 214 2.3% 4 2.5%

Missouri 202 2.2% 5 3.2%

Colorado 146 1.6% 5 3.2%

Texas 140 1.5% 2 1.3%

North Dakota 125 1.4% 3 1.9%

California 81 0.9% 4 2.5%

Tennessee 66 0.7% 1 0.6%

New York 50 0.5% 1 0.6%

Arizona 50 0.5% 1 0.6%

Kentucky 40 0.4% 2 1.3%

Oregon 40 0.4% 1 0.6%

New Mexico 30 0.3% 1 0.6%

Wyoming 9 0.1% 1 0.6%

Idaho 5 0.1% 1 0.6%

Oklahoma 2 0.0% 1 0.6%

Georgia 0 0.0% 1 0.6%

Total 9,169 100.0% 158 100.0%
266

1.8.1.1.2 Forecasted Growth in Corn/Starch Ethanol Production Under RFS2

According to our industry assessment, there were 59 ethanol plants under construction or

expanding as of May 2008 with a combined production capacity of 5.2 billion gallons per

year.137 These projects were at various phases of construction from conducting land stabilization

work, to constructing tanks and installing ancillary equipment, to completing start-up activities.

We assumed that all this capacity would eventually come online as well as a number of other

projects that were at advanced stages of planning at the time of our May 2008 industry

assessment.

Once all the aforementioned projects are complete, we projected that there would be 216

corn/starch ethanol plants operating in the U.S. with a combined production capacity of about 15

billion gallons per year. Much like today’s ethanol production facilities, the overwhelming

majority of new plant capacity (95% by volume) was expected to come from corn-fed plants.

The remainder was forecasted to come from plants processing a blend of corn and milo. A

summary of the forecasted ethanol production by feedstock under the RFS2 program based on

our May 2008 plant assessment is found in Table 1.8-5.

Table 1.8-5.

Projected RFS2 Ethanol Production Capacity by Feedstock

(Based on May 2008 Ethanol Industry Characterization)

Capacity

MGY

No. of

Plants

Capacity

MGY

No. of

Plants

Corna

5,526 54 13,666 185

Corn, Milob

303 4 1,007 18

Corn, Wheat 0 0 130 1

Corn, Wheat, Milo 0 0 115 2

Milo 0 0 3 1

Wheat, Milo 0 0 50 1

Cheese Whey 0 0 8 2

Waste Beveragesc

0 0 13 4

Waste Sugars & Starchesd

0 0 7 2

Total 5,829 58 14,998 216

a

Includes one facility processing seed corn, one facility also operating a pilot-level cellulosic

ethanol plant, and six facilities with plans to build pilot-level cellulosic ethanol plants or

incorporate biomass feedstocks in the future.

b

Includes one facility processing small amounts of molasses in addition to corn and milo.

c

Includes two facilities processing brewery waste.

d

Includes one facility processing potato waste that intends to add corn in the future.

New Plants/Exp. Total RFS2 Est.

Plant Feedstock

(Primary Listed First)

137 Based on Renewable Fuels Association (RFA), Ethanol Biorefinery Locations – Under Construction/Expansions

(updated April 4, 2008); Ethanol Producer Magazine (EPM), Under Construction plant list (last modified on April

14, 2008), ethanol producer websites, and follow-up correspondence with ethanol producers.
267

Based on May 2008 industry plans, the majority of new corn/grain ethanol production

capacity (82% by volume) was predicted to come from new or expanded plants burning natural

gas. Additionally, we forecasted one new plant burning a combination of natural gas and syrup

(from the process) and an expansion at an existing facility burning natural gas and biomass. Our

predictions also suggest two new coal-fired ethanol plants and three expansions at existing coalfired plants.138 Finally, we projected three new plants burning alternative fuels – one relying on

manure biogas, one burning biomass, and one burning a combination of biomass and thin stillage

from the process.139 Our research indicated that nine of the 58 new plants would utilize

cogeneration, bringing the total number of CHP facilities to 33. A summary of the forecasted

ethanol plant energy sources in 2022 under the RFS2 program is found in Table 1.8-6.

Table 1.8-6.

Projected Near-Term Corn/Starch Ethanol Production Capacity by Energy Source

(Based on May 2008 Ethanol Industry Characterization)

Capacity

MGY

No. of

Plants

Capacity

MGY

No. of

Plants

CHP

Tech.

Biomass 88 1 88 1 0

Coala

740 4 2,460 22 12

Coal, Biomass 0 0 50 1 0

Manure Biogas 115 1 115 1 0

Natural Gasb4,776 50 11,917 184 19

Natural Gas, Biomassc40 0 153 3 1

Natural Gas, Landfill Biogas, Wood 0 0 100 1 0

Natural Gas, Syrup 50 1 96 2 0

Thin Stillage Biogas, Biomass 20 1 20 1 1

Total 5,829 58 14,998 216 33

a

Includes four existing plants and two under construction facilities that are permitted to burn biomass, tires,

petroleum coke, and wood waste in addition to coal. Also includes one facility that intends to transition to biomass

in the future.

b

Includes one facility that intends to burn thin stillage biogas, six facilities that intend to transition to coal, and one

facility that intends to switch to biomass in the future.

c

Includes one facility processing bran in addition to natural gas.

New Plants/Exp. Total RFS2 Est.

Plant Energy Source

(Primary Listed First)

The information presented in Table 1.8-6 is based on near-term production plans at the

time of our May 2008 industry assessment. However, we anticipate additional growth in

advanced ethanol production technologies in the future under the RFS2 program. For more on

our projected 2022 utilization of these technologies under the RFS2 program, refer to Section

1.5.1.3.

138 We anticipate that all the coal-fired corn ethanol plants would be grandfathered under the RFS2 program. For

more on our grandfathering assessment, refer to Section 1.5.1.4.

139 Thin stillage is a process liquid with 5–10 percent solids taken out of the distillers grains via centrifuge.

However, construction on this alternatively fuel ethanol plant near Heyburn, ID was since terminated. Accordingly,

this plant was not included in our November 2009 RFS2 projections.
268

Based on our May 2008 assessment, 85% of new ethanol production capacity under

RFS2 is expected to originate from PADD 2. For a summary of this and other forecasted PADDlevel production projections, refer to Table 1.8-7.
269

Table 1.8-7.

Projected RFS2 Corn/Starch Ethanol Production Capacity by PADD

(Based on May 2008 Ethanol Industry Characterization)

Capacity

MGY

No. of

Plants

Capacity

MGY

No. of

Plants

PADD 1 214 2 264 4

PADD 2 5,002 47 13,620 187

PADD 3 215 2 385 5

PADD 4 70 2 230 9

PADD 5 328 5 499 11

Total 5,829 58 14,998 216

New Plants/Exp.

PADD

Total RFS2 Est.

Our May 2008 assessment suggested that Iowa, Nebraska, and Illinois would continue to

dominate ethanol production under RFS2 with a collective annual production capacity of about

7.5 billion gallons. Minnesota and Indiana were projected to be the fourth and fifth largest

ethanol producers. A map of the forecasted corn ethanol plant locations based on our May 2008

assessment is provided in Figure 1.8-2 and a summary of the ethanol production capacity by state

is presented in Table 1.8-8.
270

Figure 1.8-2

Projected RFS2 Corn/Starch Ethanol Plant Locations

(Based on May 2008 Ethanol Industry Characterization)
271

Table 1.8-8.

Projected RFS2 Corn/Starch Ethanol Production Capacity by State

(Based on May 2008 Ethanol Industry Characterization)

Capacity

MGY

No. of

Plants

Capacity

MGY

No. of

Plants

Iowa 1,573 13 3,854 43

Nebraska 959 7 2,237 29

Illinois 465 4 1,406 13

Minnesota 440 4 1,189 22

Indiana 470 5 1,010 12

South Dakota 100 1 992 15

Kansas 203 4 667 16

Wisconsin 70 1 549 9

Ohio 185 3 530 7

Texas 215 2 355 4

North Dakota 210 2 335 5

Michigan 107 1 321 5

Missouri 60 1 262 6

California 160 3 241 7

Tennessee 160 1 226 2

New York 114 1 164 2

Oregon 113 1 153 2

Colorado 0 0 146 5

Georgia 100 1 100 2

Idaho 70 2 75 3

Washington 55 1 55 1

Arizona 0 0 50 1

Kentucky 0 0 40 2

New Mexico 0 0 30 1

Wyoming 0 0 9 1

Oklahoma 0 0 2 1

Total 5,829 58 14,998 216

New Plants/Exp. Total RFS2 Est.

State

1.8.1.2 Projected Ethanol Import Locations

A discussion of the sugarcane ethanol imports that might come directly from Brazil

versus through the CBI countries is contained in Section 1.5.2. However, to provide upstream

inputs for AQ modeling and distribution purposes, we needed to estimate imports based on their

country of origin and projected U.S. destination, i.e., port location.
272

1.8.1.2.1 Origin of Projected Imports

To estimate the future breakdown of ethanol imports from CBI countries by country of

origin, we evaluated historical ethanol import data from the International Trade Commission

(ITC) and trends regarding potential growth in such imports. Table 1.8-9 contains 2005-2007

data from the ITC on ethanol imports from CBI countries.480 Table 1.8-10 contains January –

March 2008 data from the ITC on ethanol imports from CBI countries.481

Table 1.8-9. Ethanol Imports from CBI Countries 2005-2007

2005 2006 2007

% of CBI

imports

Volume

(Million

Gallons)

% of CBI

imports

Volume

(Million

Gallons)

% of CBI

imports

Volume

(Million

Gallons)

Costa Rica 32% 33.4 22% 35.9 17% 39.3

El Salvador 23% 23.7 23% 38.5 32% 73.3

Jamaica 35% 36.3 40% 66.8 33% 75.2

Trinidad

and Tobago

10% 10 15% 24.8 19% 42.7

Source: International Trade Commission

Table 1.8-10. Ethanol Imports from CBI Countries, January through March 2008

January February March

% of CBI

imports

Volume

(Million

Gallons)

% of CBI

imports

Volume

(Million

Gallons)

% of CBI

imports

Volume

(Million

Gallons)

Costa Rica 26% 5.4 27% 5.4 0 0

El Salvador 13% 2.6 0 0 23% 4.6

Jamaica 19% 4.0 32% 6.4 39% 7.9

Trinidad

and Tobago

20% 4.1 21% 4.2 29% 6

Virgin

Islands

22% 4.6 21% 4.2 9% 1.9

Source: International Trade Commission

Based on our review of the January through March 2008 data, we assumed that ethanol

exports from the Virgin Islands would continue to grow to equal those of Trinidad and Tobago in

2022. By accommodating this assumption into our review of 2005 though 2007 historical

ethanol import data, we arrived at our projections regarding the future breakdown of ethanol

imports from CBI countries which is contained in Table 1.8-11
273

Table 1.8-11.

Projected Future Breakdown of

Ethanol Imports from CBI Countries

% of Total Ethanol Imports from

CBI Countries

Costa Rica 20%

El Salvador 20%

Jamaica 30%

Trinidad and Tobago 15%

Virgin Islands 15%

1.8.1.2.2 Destination of Projected Imports

As explained above, to determine where imported ethanol might enter the United States,

we started by looking at historical ethanol import data and made assumptions as to which

countries would likely contribute to the CBI ethanol volumes and to what extent.

From there, we looked at 2006-2007 import data and estimated the general destination of

Brazilian ethanol and the five contributing CBI countries’ domestic imports.482 Based on these

countries’ geographic locations and import histories, we estimated that in 2022 82% of the

ethanol would be imported to the East and Gulf Coasts and the remaining 18% would go to the

West Coast and Hawaii. The destination of imports from Brazil and the CBI countries in 2022 is

detailed in Table 1.8-12.

Table 1.8-12

2022 Projected Destination of Ethanol Imports from Brazil

and CBI Countries Based on 2006-2007 Import Data

Origin Destination of Ethanol Imports (% of imported volume)

West Coast Hawaii East & Gulf Coasts

Costa Rica 83% 35% 47%

El Salvador 18% 9% 88%

Jamaica 3% 0% 17%

Trinidad & Tobago 0% 32% 68%

Virgin Islands 3% 9% 88%

Brazil (direct) 7% 0% 93%

Total 11% 7% 82%

Source: Energy Information Administration historical gasoline and ethanol import data:

http://www.eia.doe.gov/oil_gas/petroleum/data_publications/company_level_imports/cli_historical.html

To estimate the 2022 ethanol import locations on a finer level, we looked at coastal ports

that had received ethanol or finished gasoline imports in 2006. We chose to include ports which

imported finished gasoline (in addition to ethanol) because we believe finished gasoline will be

one of the first petroleum products to be replaced under the proposed RFS2 rule. And

presumably, these ports cities already have existing gasoline storage tanks that could be
274

retrofitted to accommodate fuel ethanol. All together, we arrived at 28 potential ports in 16

coastal states that could receive ethanol imports in 2022 (refer to Figure 1.8-3 below).140

To determine how much ethanol would arrive at each port location, we started by

examining each receiving state’s imported ethanol consumption potential. To do this, we

considered each state’s maximum ethanol consumption potential (based on projected gasoline

energy demand) and deducted the projected 2022 corn and cellulosic ethanol production

(detailed in Sections 1.8.1.1 and 1.8.1.3, respectively). Once we determined the amount of

imported ethanol that each state would receive in 2022 under RFS2, for states with multiple

ethanol ports, we allocated the ethanol among port locations based on each port county’s relative

energy demand - using projected 2022 vehicle miles traveled (VMT) from EPA’s MOVES

model 2022 VMT. A summary of the projected ethanol imports volumes by port location is

found in Figure 1.8-3.

Figure 1.8-3.

Projected RFS2 Ethanol Import Locations and Volumes (Million Gallons)141

140 We are considering adding Hampton Roads, VA and Baltimore, MD to the list of future ethanol import locations

and may adjust our analysis for the final rule accordingly.

141 We are considering adding Hampton Roads, VA and Baltimore, MD to the list of future ethanol import locations

and may adjust our analysis for the final rule accordingly.
275

1.8.1.3 Cellulosic Ethanol Plant Siting

As explained in Section 1.5.3, cellulosic biofuel production capacity needs to expand

greatly in order to meet the cellulosic biofuel mandate of 16 billion gallons by 2022. While

current production plans provide an initial idea of the types of feedstocks and potential plant

locations that are being considered by biofuel producers, future production will be highly

dependent on acquiring relatively cost-effective feedstocks in sufficient quantities.

A wide variety of feedstocks can be used for cellulosic biofuel production, including

agricultural residues, forestry biomass, the certain renewable portions of municipal solid waste

and construction and demolition waste and energy crops. These feedstocks are currently much

more difficult to convert into ethanol than traditional starch/corn crops or at least require new

and different processes because of the more complex structure of cellulosic material.

1.8.1.3.1 Summary of Plant Siting Results

As long lead times were required for our air quality modeling, it was necessary to use

available data at the time on the likely cellulosic feedstocks and projected locations of cellulosic

facilities for production of 16 billion gallons cellulosic biofuel by 2022. Our original plant siting

analysis for cellulosic ethanol facilities used the most current version of outputs from FASOM at

the time, which was from April 2008. Therefore, the version used for the majority of other

analyses in the rest of this package is different from the results presented below.

Our cellulosic ethanol plant siting analysis assumed that the following cellulosic

feedstock and volumes would be used, as shown in Table 1.8-13.

Table 1.8-13.

Cellulosic Feedstocks Assumed to Meet EISA in 2022

(NPRM version for AQ Modeling)

Feedstock Volume (Ethanolequivalent Bgal)

Agricultural Residues 9.1

Corn Stover 7.8

Sugarcane Bagasse142 1.2

Sweet Sorghum Pulp 0.1

Forestry Biomass 3.8

Urban Waste 2.2

Dedicated Energy Crops

(Switchgrass)

0.9

Total 16.0

142 Bagasse is a byproduct of sugarcane crushing and not technically an agricultural residue. Sweet sorghum pulp is

also a byproduct of sweet sorghum processing. We have included it under this heading for simplification due to

sugarcane being an agricultural feedstock.
276

Future cellulosic biofuel plant siting was based on the types of feedstocks that would be

most economical as shown in Table 1.8-13, above. As cellulosic biofuel refineries will likely be

located close to biomass resources in order to take advantage of lower transportation costs,

we’ve assessed the potential areas in the U.S. that grow the various feedstocks chosen. To do

this, we used data on harvested acres by county for crops that are currently grown today, such as

corn stover and sugarcane (for bagasse).483 In some cases, crops are not currently grown, but

have the potential to replace other crops or pastureland (e.g., dedicated energy crops). We used

the output from our economic modeling (FASOM) to help us determine which types of land are

likely to be replaced by newly grown crops. For forest residue biomass, the U.S. Forest Service

provided supply curve data by county showing the available tons produced. Urban waste (MSW

wood, paper, and C&D debris) was estimated to be located near large population centers. Refer

to Section 1.8.1.3.2 below for more detailed information.

Using feedstock availability data by county/city, we located potential cellulosic sites

across the U.S. that could justify the construction of a cellulosic plant facility. Table 1.8-14

shows the volume of cellulosic facilities by feedstock by state projected for 2022. Table 1.8-15

lists the 180 cellulosic ethanol facilities that we project could potentially be used to produce 16

Bgal of cellulosic biofuel by 2022. The total volumes given in Table 1.8-14 match the total

volumes given in Table 1.8-15 within a couple hundred million gallons. As these differences are

relatively small, we believe the cellulosic facilities sited are a good estimate of potential

locations. See Figure 1.8-4 for a visual representation of the locations of these facilities.
277

Table 1.8-14.

Projected Cellulosic Ethanol Volumes by State (million gallons in 2022)

State

Total

Volume Ag Volume

Energy Crop

Volume

Urban Waste

Volume

Forestry

Volume

Alabama 532 0 0 140 392

Arkansas 298 0 0 0 298

California 450 0 0 221 229

Colorado 28 0 0 28 0

Florida 421 390 0 31 0

Georgia 437 0 0 67 370

Illinois 1,525 1,270 0 198 58

Indiana 1,109 948 0 101 60

Iowa 1,697 1,635 0 32 30

Kansas 310 250 0 29 32

Kentucky 70 70 0 0 0

Louisiana 1,001 590 0 103 308

Maine 191 0 0 2 189

Michigan 505 283 0 171 51

Minnesota 876 750 0 50 76

Mississippi 214 0 0 22 192

Missouri 654 504 0 78 72

Montana 92 0 0 9 83

Nebraska 956 851 0 31 75

Nevada 17 0 0 17 0

New Hampshire 171 0 35 29 107

New York 72 0 0 72 0

North Carolina 315 0 0 98 217

Ohio 598 410 0 156 32

Oklahoma 793 0 777 0 16

Oregon 244 0 0 44 200

Pennsylvania 42 0 0 42 0

South Carolina 213 0 0 57 156

South Dakota 434 350 0 6 78

Tennessee 97 0 0 19 78

Texas 576 300 0 131 145

Virginia 197 0 0 95 102

Washington 175 0 0 17 158

West Virginia 149 0 101 0 48

Wisconsin 581 432 0 43 106

Total Volume 16,039 9,034 913 2,139 3,955
278

Table 1.8-15.

Projected Cellulosic Facilities

(million gallons in 2022)

County State

Total Volume

(million gallons/yr)

Escambia Alabama 112

Greene Alabama 108

Morgan Alabama 96

Russell Alabama 101

Talledega Alabama 115

Cleveland Arkansas 99

Howard Arkansas 97

Woodruff Arkansas 102

Butte California 94

Orange California 133

San Joaquin California 120

Siskiyou California 102

Adams Colorado 28

Broward Florida 31

Hendry Florida 90

Palm Beach Florida 100

Palm Beach Florida 100

Palm Beach Florida 100

Glynn Georgia 108

Grady Georgia 130

Richmond Georgia 101

Treutlen Georgia 98

Bureau Illinois 130

Carroll Illinois 77

Champaign Illinois 89

Coles Illinois 77

De Witt Illinois 100

Du Page Illinois 128

Grundy Illinois 77

Iroquois Illinois 80

Knox Illinois 89

Menard Illinois 99

Montgomery Illinois 78

Morgan Illinois 67

Ogle Illinois 95

Richland Illinois 81

Shelby Illinois 68

Tazewell Illinois 107

Washington Illinois 85

Benton Indiana 92

Clinton Indiana 80

Daviess Indiana 93

De Kalb Indiana 91

Fulton Indiana 74

Jasper Indiana 82
279

Jennings Indiana 94

Madison Indiana 78

Morgan Indiana 100

Parke Indiana 92

Union Indiana 82

Vanderburgh Indiana 74

Wells Indiana 77

Benton Iowa 69

Buchanan Iowa 83

Buena Vista Iowa 84

Cerro Gordo Iowa 79

Chickasaw Iowa 82

Des Moines Iowa 87

Dubuque Iowa 70

Franklin Iowa 80

Grundy Iowa 83

Guthrie Iowa 85

Ida Iowa 88

Mahaska Iowa 80

Muscatine Iowa 83

O Brien Iowa 80

Page Iowa 81

Palo Alto Iowa 75

Pottawattamie Iowa 84

Sioux Iowa 72

Story Iowa 89

Union Iowa 76

Webster Iowa 86

Logan Kansas 75

Nemaha Kansas 78

Sedgwick Kansas 71

Stevens Kansas 87

Webster Kentucky 70

Bienville Louisiana 115

E. Baton Rouge Louisiana 106

E. Carroll Louisiana 103

Jeff Davis Louisiana 87

Allen Louisiana 50

Avoyelles Louisiana 100

Iberville Louisiana 90

La Fourche Louisiana 50

Lafayette Louisiana 100

Pt. Coupe Louisiana 100

St Landry Louisiana 100

Penobscot Maine 100

Piscataquis Maine 91

Calhoun Michigan 109

Ionia Michigan 117

Tuscola Michigan 105

Van Buren Michigan 89
280

Wayne Michigan 85

Chippewa Minnesota 92

Dakota Minnesota 114

Dodge Minnesota 86

Faribault Minnesota 88

Lyon Minnesota 84

Martin Minnesota 95

Rock Minnesota 73

Sibley Minnesota 102

Stearns Minnesota 68

Stevens Minnesota 76

Forrest Mississippi 107

Grenada Mississippi 107

Audrain Missouri 86

Chariton Missouri 74

Clark Missouri 89

Gentry Missouri 95

New Madrid Missouri 84

Ray Missouri 100

St. Louis Missouri 125

Sanders Montana 92

Boone Nebraska 98

Custer Nebraska 84

Harlan Nebraska 78

Hitchcock Nebraska 83

Holt Nebraska 91

Lancaster Nebraska 74

Lincoln Nebraska 81

Nuckolls Nebraska 76

Saunders Nebraska 100

Wayne Nebraska 96

York Nebraska 94

Clark Nevada 17

Carroll New Hampshire 136

Carroll New Hampshire 35

West Chester New York 72

Cumberland North Carolina 110

Forsyth North Carolina 104

Martin North Carolina 102

Auglaize Ohio 80

Clinton Ohio 100

Franklin Ohio 77

Logan Ohio 75

Portage Ohio 98

Richland Ohio 83

Wood Ohio 85

Craig Oklahoma 130

Grady Oklahoma 108

Hughes Oklahoma 91

Kingfisher Oklahoma 110
281

Lincoln Oklahoma 120

Muskogee Oklahoma 118

Osage Oklahoma 116

Lane Oregon 126

Yamhill Oregon 118

Montgomery Pennsylvania 42

Berkeley South Carolina 105

Spartanburg South Carolina 108

Day South Dakota 85

Edmunds South Dakota 80

Kingsbury South Dakota 98

Lake South Dakota 83

Turner South Dakota 89

Monroe Tennessee 97

Angelina Texas 114

Bexar Texas 16

Cameron Texas 100

Dallas Texas 52

Harris Texas 80

Hidalgo Texas 100

Travis Texas 14

Willacy Texas 100

Halifax Virginia 98

Prince George Virginia 99

Chelan Washington 78

Thurston Washington 97

Harrison West Virginia 149

Calumet Wisconsin 91

Dane Wisconsin 76

Dunn Wisconsin 63

Eau Claire Wisconsin 65

Grant Wisconsin 68

Jefferson Wisconsin 94

Marquette Wisconsin 65

Wood Wisconsin 59

Total 16039
282

Figure 1.8-4. Projected Cellulosic Facilities

1.8.1.3.2 Assumptions and Details of Plant Siting Analysis

An important assumption in our siting analysis is that an excess of feedstock would have

to be available for producing the biofuel. Banks are anticipated to require excess feedstock

supply as a safety factor to ensure that the plant will have adequate feedstock available for the

plant, despite any feedstock emergency, such as a fire, drought, infestation of pests etc. For our

analysis we assumed that twice the feedstock of MSW, C&D waste, and forest residue would

have to be available to justify the building of a cellulosic ethanol plant. For corn stover, we

assumed 50 percent more feedstock than necessary. We used a lower safety factor for corn

stover because it could be possible to remove a larger percentage of the corn stover in any year

(usually only 50 percent or less of corn stover is assumed to be sustainably removed in any one

year).143

Another assumption that we made is that if multiple feedstocks are available in an area,

each would be used as feedstocks for a prospective cellulosic ethanol plant. For example, a

particular area might comprise a small or medium sized city, some forest and some agricultural

land. We would include the MSW and C&D wastes available from the city along with the corn

stover and forest residue for projecting the feedstock that would be processed by the particular

cellulosic ethanol plant.

Each of the cellulosic plants was chosen to produce approximately 100 million gallons

per year of ethanol. In some cases we had to resort to lower volumes due to limited resources in

a given area. In other cases, we used greater than 100 million gallons per year because relatively

close materials were available that would otherwise go unused. In addition, we limited biomass

transport distances to be approximately 100 miles each way or less (radius from proposed

facility), as large transport distances are economically prohibitive. We found that the majority of

143 The FASOM results do not take into consideration these feedstock safety margins. Safety margins were used,

however, for the plant siting analysis described in this section.

Forest Residue Plant

Ag. Residue Plant

Switchgrass Plant

MSW Plant

Bagasse Plant
283

corn stover cellulosic facilities required smaller transport distances than the assumed 100 mile

limit due to relatively close proximity to available feedstocks. Forest residues, on the other hand,

typically required greater distances as collectable material appeared to be sparser.

Our analyses also take into account the locations of planned cellulosic facilities as well as

any corn facilities or pulp and paper mills when we project where cellulosic plants are located

into the future. While not all planned cellulosic facilities will likely come to fruition, it was

important to look at the locations of these facilities as their locations are likely to be chosen for

good reasons (i.e. close to resources, infrastructure in place, etc.). We analyzed current corn

facilities and pulp and paper mill sites as well since they are likely to be close to their respective

feedstocks (i.e. corn stover and wood residues) and could have many synergies with cellulosic

biofuel production, such as shared steam and electricity production. However, this does not

mean that we placed cellulosic facilities at all the locations where there are current corn facilities

and pulp and paper mills. The locations are only used to help select areas that could be

preferential towards building a cellulosic facility.

It is important to note, that there are many more factors other than feedstock availability

to consider when eventually siting a plant. We have not taken into account, for example, water

constraints, availability of permits, and sufficient personnel for specific locations. Nevertheless,

our plant siting analysis provides a reasonable approximation for analysis purposes since it is not

intended to predict precisely where actual plants will be located. Other work is currently being

done that can help address some of these issues.484

For this analysis, we estimated MSW and C&D wood waste by state (similar to the

analysis described in Section 1.1.2.4) and calculated the tons of MSW and C&D wood waste

material generated per person per state. We used the estimate of MSW and C&D wood waste

material generated per person per state (i.e. tons/person) along with data on the population sizes

of the largest cities within the state to allocate the total waste material in a state to specific cities.

Assuming that the majority of this waste is of negligible cost to a potential ethanol producer, we

calculated a minimum size for a cellulosic plant dedicated to MSW and C&D wood waste for

various locations in the U.S. Sizes ranged from 9-60 million gallons per year.

We did not consider small cities that might be able to justify a cellulosic ethanol plant

because some other source of biomass is also available that, when combined with the MSW and

C&D wood waste, can supply the cellulosic ethanol plant with sufficient feedstock. However,

where non-MSW and C&D wood waste feedstocks are not available, we needed to estimate what

the minimum plant size that would be competitive with other cellulosic ethanol plants.

We conducted this analysis early on before NREL provided us with the cost information

for a biochemical cellulosic ethanol plant. Instead we used a representation made by NREL in

2007 for of a thermochemical ethanol plant. Using that cellulosic plant model we estimated the

production cost for a 100 million gallon per year thermochemical plant which processed a

cellulosic feedstock. We conducted this analysis in different parts of the country using different

capital cost factors that account for how capital costs vary in different parts of the country. The

different regions were Petroleum Administration for Defense Districts (PADDs) for which we

have plant installation costs. In each part of the country, we estimated the cost of the ethanol
284

produced processing the cellulosic feedstock assuming that the feedstock cost about $70 per dry

ton. Next, we set the feedstock costs to zero cost in our cost spreadsheet and determined at what

plant size, when scaling the capital costs as the plant size became smaller, the resulting cellulosic

production costs matched those of the non-MSW and C&D wood waste plants. See Table 1.8-16.

Table 1.8-16.

Breakeven Plant Size for MSW and C&D Wood Waste Cellulosic Ethanol Plants

PADD 1 PADD 2 PADD 3 PADD 4 PADD 5 CA

Ethanol Production

Cost (c/gal)

1.33 1.24 1.10 1.29 1.19 1.57

Breakeven Plant

Size (million

gals/yr)

28 19 9 23 15 60

We then identified the cities that had large enough MSW and C&D wood waste to justify

a dedicated cellulosic facility. By dedicated cellulosic facility, we mean that only MSW and

C&D wood waste is used as a feedstock, as opposed to a facility that has multiple mixed

feedstocks. Nineteen facilities were identified to meet such criteria, as shown in Table 1.8-17.

The total contribution from dedicated cellulosic MSW and C&D wood waste is approximately

640 million gallons.
285

Table 1.8-17.

Projected Dedicated Cellulosic MSW and C&D

Wood Waste Facilities by Location and Size for 2022

State County City PADD Size of Facility (Mgal)

1 Alabama Jefferson Birmington 3 11

2 Arizona Maricopa Phoenix 5 20

3 California Los Angeles Los Angeles 5 56

4 California Riverside Riverside 5 24

5 California San Francisco San Francisco 5 17

6 Colorado Adams Denver 4 28

7 Florida Miami Fort Lauderdale 1 31

8 Georgia Cobb Atlanta 1 43

9 Illinois Cook Chicago 2 79

10 Michigan Oakland Detroit 2 33

11 Nevada Clark Las Vegas 5 17

12 New York New York City New York 1 72

13 Oregon Clackamas Portland 4 15

14 Pennsylvania Philadelphia Philadelphia 1 42

15 Texas Dallas Dallas 3 52

16 Texas Fort Bend Houston 3 49

17 Texas Bexar San Antonio 3 16

18 Texas Travis Austin 3 14

19 Washington King Seattle 5 17

We did assume that in areas with other cellulosic feedstocks (forest and agricultural

residue), that the MSW would be used even if the MSW could not justify the installation of a

plant on its own. Therefore, we estimated that urban waste could help contribute to the

production of approximately 2.2 billion gallons of ethanol.144

The results from the April 2008 version of the agricultural modeling (FASOM) suggested

that corn stover will make up the majority of agricultural residues used by 2022 to meet the

EISA cellulosic biofuel standard (approximately 83 million dry tons used to produce 7.8 billion

gallons of cellulosic ethanol).145 Smaller contributions were expected to come from bagasse,

which is a by-product from the production of sugarcane, (1.2 bgal ethanol) and sweet sorghum

pulp (0.1 bgal ethanol). At the time of the proposal, FASOM was able to model agricultural

residues but not forestry biomass as potential feedstocks. As a result, we had relied on the U.S.

Forest Service for information on the forestry sector for our plant siting analysis.

Using the assumptions from FASOM on residue and ethanol yields, we determined if it is

possible to site potential cellulosic plants based on the acres currently harvested. We identified

that there are enough harvested acres to produce 7.8 Bgal of ethanol from corn stover by 2022

without having to rely on new lands. Therefore, the siting of many of the cellulosic facilities will

likely be located where corn is typically grown today. See Table 1.8-18 for a summary of the

144 Assuming approximately 90 gal/dry ton ethanol conversion yield; Note that this is slightly different from the 2.3

billion gallons of ethanol assumed in other analyses in this package.

145 Assuming 94 gal/dry ton ethanol conversion yield for corn stover in 2022
286

states producing corn stover, and their projected volume contribution to meeting the EISA

cellulosic requirement by 2022.

Table 1.8-18.

Projected Ethanol Produced to Meet EISA in 2022 from Corn Stover

(NPRM version for AQ Modeling)146

Total

Harvested

Acres

Total Residue

Yield

Total Residue

Available Residue Used

Percent

Residue

Used

Ethanol

Produced

State (in 2022) (tons/acre) (Million tons) (Million tons) (Million gallons)

Illinois 12,994,100 5.43 71 15 21% 1444

Indiana 6,209,463 5.58 35 10 29% 922

Iowa 14,482,313 5.47 79 17 21% 1557

Kansas 3,026,615 5.33 16 3 19% 261

Kentucky 1,473,023 5.08 7 1 13% 63

Michigan 2,238,321 4.30 10 3 31% 246

Minnesota 7,509,658 5.37 40 8 20% 750

Missouri 2,732,875 4.73 13 5 39% 434

Nebraska 10,135,162 5.88 60 9 15% 840

Ohio 3,712,612 4.91 18 5 27% 453

South Dakota 4,268,425 4.01 17 4 23% 350

Wisconsin 3,001,454 4.74 14 5 35% 432

Total 71,784,020 n/a 380 82 22% 7752

Sugarcane, on the other hand, is grown mainly in Florida, Louisiana, and Texas, although

plans are underway to also grow sugarcane in California as well. See Section 1.1.1.2 of the RIA

for more discussion on sugarcane ethanol produced in the U.S. If all the sugarcane acres

harvested in the U.S. in 2007 were used to produce ethanol from the bagasse, using the

assumptions from FASOM on residue and ethanol yields, only approximately 700 million

gallons could be produced, see Table 1.8-19. FASOM, however, predicted that the production of

1.2 billion gallons of ethanol could be economically feasible from sugarcane bagasse. This

means that between now and 2022, more sugarcane may be grown, allowing for more

availability of bagasse in the future.

146 Corn stover is given in dry tons/acre and assumes an ethanol yield of 94 gal/dry ton (this was updated in the final

rule to 92.3 gal/dry ton based on NREL estimates); This table gives approximate averages by state based on our

April 2008 version of the agricultural modeling, actual yields will vary greatly depending on specific soil type,

slope, etc. The values above are calculated using the FASOM data outputs from April 2008 and thus are different

from those found in other sections of this package which use more updated runs from 2009.
287

Table 1.8-19.

Projected Ethanol Produced to Meet EISA in 2022 from Sugarcane Bagasse

State

Total Harvested

Acres (in 2007)

Total Bagasse

Yield

(tons/acre)

Residue Used

(Million tons)

Ethanol

Produced

(Million gallons)

Total 810,800 n/a 10 707

Florida 382,000 14.71 6 389

Louisiana 389,600 10.25 4 277

Texas 39,200 15.23 1 41

Using FASOM, we analyzed the types of land likely to be supplanted by additional

sugarcane acres in 2022 in the states of Florida, Louisiana, and Texas. In Florida, sugarcane

crops appear to replace mainly corn, soy, and hay acres. In Louisiana, sugarcane crops appear to

have replaced mainly corn, soy, wheat, sorghum, and hay acres. In Texas, sugarcane crops

appear to have replaced mainly soy and sorghum crops. For these three states we gathered

available data on corn, soy, wheat, and sorghum acres currently harvested by county (data on hay

acres were unavailable and appeared to show small changes compared to corn and soy).485

We then identified the top counties (in terms of acres available) in close proximity to each other

that could potentially be converted from corn to sugarcane crops, soy to sugarcane, wheat to

sugarcane, etc. in order to produce enough ethanol for half a billion gallons.

Sweet sorghum pulp is predicted to be used to produce approximately 0.1 billion gallons

of ethanol. According to the National Agriculture and Statistics Service (NASS) of the

Department of Agriculture, there is not current available data on sweet sorghum acres grown in

the United States. Therefore, we used FASOM to predict the type of crops that sweet sorghum is

mainly replacing, which is corn and soybeans. Similar to the analysis done for sugarcane, we

identified the top counties (in terms of acres available) in close proximity to each other that could

potentially be converted from corn to sweet sorghum crops and soy to sweet sorghum crops in

order to produce enough ethanol for 0.1 billion gallons.

For forestry biomass, we utilized data provided by the U.S. Forest Service (biomass

supply curves for various sources i.e., logging residues, other removal residues, thinnings from

timberland, etc.). This information suggested that a large portion of forest material could be

available for producing biofuels (excluding forest biomass material contained in national forests

as required under the Act). See Section 1.1.2.3 for more information on forest residue feedstock

availability. However, much of the forest material is in small pockets of forest which because of

its regional low density, could not help to justify the establishment of a cellulosic ethanol plant.

After conducting our availability analysis, we estimated that approximately 44 million dry tons

of forest material could be used, which would make up approximately one fourth, or 3.8 billion

gallons, of the 16 billion gallons of cellulosic biofuel required to meet EISA.

The April 2008 version of the FASOM results projected that 0.9 billion gallons of

cellulosic ethanol from switchgrass is economically feasible by 2022. The majority of

switchgrass is projected to likely be grown in Oklahoma, where the majority of acres are
288

replacing wheat and hay. A smaller portion is expected to come from West Virginia and New

Hampshire where hay is mainly replaced. Similar to the analysis done for sugarcane and sweet

sorghum, we identified the top counties (in terms of acres available) in close proximity to each

other that could potentially be converted from wheat to switchgrass or hay to switchgrass in

order to produce enough ethanol for 0.9 billion gallons.

1.8.1.4 Ethanol Usage Assumptions

To understand the impacts of increased ethanol use on air quality, we estimated where the

ethanol might be used in the future under the RFS2 program. For this analysis, discussed in

more detail in Chapter 3 of the RIA, we measured the impacts of 34.1 billion gallons of ethanol

use in 2022, the total volume of ethanol assumed to be produced and consumed in the NPRM.

For this analysis, we also applied NPRM assumptions with respect to FFV and E85 availability,

described in more detail below.

With respect to FFVs, we assumed that the Detroit 3 would follow through with their

50% by 2012 FFV commitment and the non-domestic automakers would follow suit and produce

25% FFVs by 2017. This corresponded to the primary Optimistic FFV Production Scenario

outlined in the NPRM. The annual FFV sales by vehicle type are summarized in Table 1.8-20

below. For analysis purposes, we made the simplifying assumption that all FFVs would be

distributed homogeneously and total vehicle sales would remain constant around 16 million units

per year. This differs from vehicle assumptions made for the final rule, outlined in Section

1.7.1.2.
289

Table 1.8-20.

Optimistic FFV Production Scenario – FFV Production Assumptions

Tot FFVs FFV-Cars FFV-Trucks Tot FFVs FFV-Cars FFV-Trucks

2002 1,000,000 200,000 800,000 0 0 0

2003 1,000,000 200,000 800,000 0 0 0

2004 1,000,000 200,000 800,000 0 0 0

2005 1,000,000 200,000 800,000 0 0 0

2006 1,000,000 200,000 800,000 0 0 0

2007 1,000,000 200,000 800,000 0 0 0

2008 1,600,000 320,000 1,280,000 80,000 0 80,000

2009 2,200,000 440,000 1,760,000 160,000 0 160,000

2010 2,800,000 560,000 2,240,000 240,000 0 240,000

2011 3,400,000 680,000 2,720,000 320,000 0 320,000

2012 4,000,000 800,000 3,200,000 400,000 0 400,000

2013 4,000,000 800,000 3,200,000 720,000 0 720,000

2014 4,000,000 800,000 3,200,000 1,040,000 0 1,040,000

2015 4,000,000 800,000 3,200,000 1,360,000 0 1,360,000

2016 4,000,000 800,000 3,200,000 1,680,000 0 1,680,000

2017 4,000,000 800,000 3,200,000 2,000,000 0 2,000,000

2018 4,000,000 800,000 3,200,000 2,000,000 0 2,000,000

2019 4,000,000 800,000 3,200,000 2,000,000 0 2,000,000

2020 4,000,000 800,000 3,200,000 2,000,000 0 2,000,000

2021 4,000,000 800,000 3,200,000 2,000,000 0 2,000,000

2022 4,000,000 800,000 3,200,000 2,000,000 0 2,000,000

Year

GM, Chrysler & Ford Non-Domestic Automakers

Based on these FFV production assumptions and forecasted vehicle phase-out, VMT, and

fuel economy estimates provided by an earlier version of EPA’s MOVES Model, we calculated

that the maximum percentage of fuel (gasoline/ethanol mix) that could feasibly be consumed by

FFVs in 2022 would be about 30%. The resulting gasoline energy consumption by vehicle type

under the Optimistic FFV Production Scenario is shown below in Figure 1.8-5. For analysis

purposes, we assumed that the percentage of gasoline energy consumed by nonroad, heavy-duty

gasoline vehicles (HDGVs), and motorcycles would be about 8% based on historical information

provided by DOE.486
290

Figure 1.8-5.

Optimistic FFV Production Scenario - Gasoline Consumption by Vehicle Type

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010

20112012

2013201420152016

20172018

2019

20202021

2022

% of Gasoline Consumption

FFVs

Tier 2 Vehicles (Non-FFVs)

Pre-Tier 2 Vehicles

Nonroad, HDGVs & Motorcycles
291

For the primary ethanol usage scenario analyzed in the NPRM and used for the AQ

modeling work, we assumed practical, yet aggressive growth in E85 access. We considered the

possibility that 70% of the nation could have reasonable one-in-four-station access to E85 by

2022. This is roughly equivalent to all urban areas in the United States offering E85 as explained

in Section 1.7.3 of the RIA.

We are not concluding that E85 would only be offered in urban areas in the future. In

fact, most E85 stations are currently located in the Midwest. However, we believe that this

would be one possible way to provide 70% of the population with reasonable access to E85.

From a fuel price standpoint, it makes sense that E85 might be offered in areas of the country

with relatively high gasoline prices (e.g., RFG and low-RVP areas). Additionally, from an

infrastructure cost standpoint, it makes sense that E85 might be offered in more populated

metropolitan areas with high gasoline throughput. For more on fuel distribution logistics and

costs, refer to Sections 1.6 and 4.2 of the RIA.

Assuming that reasonable E85 access grows linearly to 70% by 2022, we iteratively

computed the corresponding nationwide E0 and E10 access assuming that a) each fuel retailer

only carries one type of conventional gasoline (E0 or E10) and b) the nation does not exceed the

RFS2 ethanol volume requirements analyzed for the NPRM. Under a very aggressive FFV

production scenario, we estimate that E0 could theoretically remain in existence until 2016 as

shown below in Figure 1.8-6. However, we anticipate that E10 will likely replace E0 sooner

based on current market trends.
292

Figure 1.8-6.

Assumed Phase-Out of E0 and Phase-In of E10 & E85

0%

20%

40%

60%

80%

100%

201020112012

20132014201520162017201820192020

2021

2022

Year

Nationwide Access to Fuel

E0

E10

E85

To comply with the proposed RFS2 program and consume 34.1 billion gallons of ethanol

by 2022, not only would we need more FFVs and more E85 retailers, we’ll need to see a

significant increase in FFV E85 refueling. Under the Optimistic FFV Production Scenario

(assuming practical growth in E85 access), our analysis suggests that FFV owners with

reasonable one-in-four access to E85 would need to fill up on it 74% of the time in 2022 - a

significant increase from today’s refueling frequency.

To estimate where E85 might be consumed under the proposed RFS2 program in 2022,

we conducted a cost effectiveness study. For each area of the county, we began by looking at

gasoline delivered prices. We started with state-level gasoline prices (excluding taxes) provided

by EIA’s Petroleum Marketing Annual 2006.487 We relied on Table 31 for average gasoline

prices, looked to Table 34 for RFG prices and back-calculated CG prices by applying the

respective gasoline fuel volumes provided in Table 48. For states requiring 7 or 7.8-lb gasoline

in the summertime, we applied PADD-average low-RVP gasoline production costs derived from

the Mobile Source Air Toxics (MSAT) rule488 to come up with the respective low-RVP and 9-lb

conventional gasoline prices in these states. From there, we added in the corresponding gasoline

taxes (state plus federal) according to the American Petroleum Institute (API).489 This gave us

the average retail cost of gasoline by state and fuel type.
293

Next we converted the gasoline prices into competitive retail E85 prices by adjusting for

the reduced energy density of E85, the increased refueling time, and E85’s presumed limited

availability in 2022. For a more on this general methodology, refer to Section 1.7.1.2.5 of the

RIA. From there, we deducted fuel taxes (assumed to be the same as gasoline), backed out

marketing costs and retail profits (assumed to be $0.10 per gallon) and subtracted the terminalto-retail transportation costs (assumed to be $0.03) to arrive at the estimated retail value of E85,

and ultimately, the retail value of ethanol.147 Once we computed the retail value of ethanol, we

compared it to the estimated ethanol delivered price (based on transportation costs presented in

Section 4.2 of the proposal) to come up with the respective E85 profit margin.

To conclude, we assigned E85 to the areas of the county with the highest E85 profit

margins, or in some cases, the least negative E85 profit margins until we arrived at

approximately 34 billion gallons of ethanol in 2022. For a graphical representation of the areas

of the country we assumed would receive/consume E85, refer to Figure 1.8-7.

147 For analysis purposes we assumed that E85 was taxed at the same rate as gasoline. We acknowledge that a

number of states currently have reduced excise taxes or excise tax exemptions for E85. However, the extent of the

tax breaks is somewhat unknown and the potential that these tax breaks will exist in the future is uncertain.
294

Figure 1.8-7

Projected E85 Availability in 2022 Under RFS2

1.8.2 Biodiesel & Renewable Diesel Inputs

1.8.2.1 Upstream Production Inputs

In order to generate county-level emissions inputs for the control case, we needed

projected locations of biodiesel production facilities. This task was complicated by the fact that

the current aggregate industry production capacity is significantly larger than the volume of

biodiesel projected to be consumed in our primary control case, a fact which suggests the

industry may downsize in the long term.

We developed a method to determine where biodiesel producers were most likely to

remain based on state incentives to biodiesel producers and for biodiesel sales or use. Data on

state incentives was taken from an online database maintained by the Department of Energy

Office of Energy Efficiency and Renewable Energy.490 Two other criteria we considered were

the BQ-9000 status of individual plants and their ability to process multiple feedstock types, as

listed by the National Biodiesel Board. 491 Based the volume of the primary control case,

assuming a capacity utilization factor of approximately 80%, a list of plants for the 2022
295

scenarios was generated choosing first from those plants with most favorable status of the four

criteria and working downward. We projected that a number of very small plants processing

waste greases/fats would continue to operate based on local market niches regardless of these

criteria. In an effort to be realistic in this forecast, other practical considerations were made,

such as avoiding siting several plants in the same state (except in the Midwest).

We project that between now and 2022 plants will continue to compete and consolidate to

make fewer plants of larger size. During this period most plants will have added the pretreatment and feedstock segregation capacity to process any mix of feedstock types available in

their area.

From the projected list of plant locations, emission quantities were generated for each

county based on each plant’s biodiesel production rate. Spreadsheets showing lists of the

representative plants and their emission factors as input in the inventory and air quality models

can be found in the docket. This information is summarized here in Table 1.8-21 and Figure 1.8-

9.

Table 1.8-50. Summary of biodiesel industry and forecast used for AQM.492

2008 2022

Total production capacity on-line (million gal/yr) 2,610 1,050

Number of operating plants 176 35

Median plant size (million gal/yr) 5 30

Total biodiesel production (million gal) 700 810

Average capacity factor 0.27 0.77
296

Figure 1.5-18. Biodiesel industry forecast for 2022

1.8.2.2 Downstream Consumption Inputs

Biodiesel, like ethanol, is generally blended at the end of the distribution chain, just

before delivery to retail outlets. Because of its chemical properties, it is not currently considered

fungible with diesel fuel, and thus its blend level in fuels offered for sale is typically deliberate

and explicit. Renewable diesel, on the other hand, is a fuel or hydrocarbon blendstock which can

be blended into fungible fuel at any point in the distribution system, such that the blend level at

the final point of use is not typically of concern and, in fact, would probably be difficult to

determine. Because of its nature, and the relatively small volumes we are projecting (less than

0.5 billion gallons per year), we have not analyzed distribution or use impacts for renewable

diesel. The remainder of this section addresses biodiesel use.

Vehicle and engine manufacturers recognize biodiesel as a lubricity improver at low

levels, something that is useful with ultra-low sulfur diesel fuel now phasing in across the

country. Therefore, most state that their products are compatible with blends up to 5%, and a

few suggest blends up to 20% can be used without problems. Therefore, our analysis assumes

blends up to 5% can find widespread use.

In order to conduct our distribution and emissions analyses, we needed to forecast

approximate volumes of biodiesel to be used in each state. We considered transportation diesel

fuel and home heating oil as the primary uses for biodiesel. For transportation fuel estimates, we

13/15

105/123

0.8/1

0.4/0.5

35/85 41/60

113/158

56/60

9.2/22

75/80

13/30

17/20

38/45

NJ: 38/40

CT: 3.3/4

MA: 0.4/0.5

RI: 0.2/0.3

42/50

67/80

25/30

FL: 17/18

8.3/10

88/105

0.4/0.5

7.5/8

Biodiesel Production/Capacity

(million gal/yr) in 2022

13/15

105/123

0.8/1

0.4/0.5

35/85 41/60

113/158

56/60

9.2/22

75/80

13/30

17/20

38/45

NJ: 38/40

CT: 3.3/4

MA: 0.4/0.5

RI: 0.2/0.3

42/50

67/80

25/30

FL: 17/18

8.3/10

88/105

0.4/0.5

7.5/8

13/15

105/123

0.8/1

0.4/0.5

35/85 41/60

113/158

56/60

9.2/22

75/80

13/30

17/20

38/45

NJ: 38/40

CT: 3.3/4

MA: 0.4/0.5

RI: 0.2/0.3

42/50

67/80

25/30

FL: 17/18

8.3/10

88/105

0.4/0.5

7.5/8

Biodiesel Production/Capacity

(million gal/yr) in 2022
297

assumed that biodiesel would be preferentially used in states that have blend mandates or

significant per-gallon incentives. Table 1.8-21 shows the states with such mandates and

incentives on record as of summer 2008, as well as the associated potential biodiesel volumes

based on 2005 diesel fuel use.493 State-level forecasts were not available for transportation fuel

use, thus the reliance on historical data for this estimate.

Table 1.8-21.

State biodiesel incentives as of summer 2008 and potential volumes based on 2005 data.494

Diesel fuel use 2% biodiesel 5% biodiesel

(million gal/yr) (million gal/yr) (million gal/yr)

IL per-gallon tax incentive(s) for B11+,

state fleet requirement

1,660 33.2

KS per-gallon tax incentive(s) for B2+ 816 16.3

LA B2 mandate with some conditions 1,734 34.7

MA B2 mandate, increasing to B15 with

some conditions

491 24.5

MI per-gallon tax incentive(s) for B5+ 1,071 53.5

MN B2 mandate; state fleet requirement 999 20.0

NC per-gallon tax incentive(s), B2 school

bus requirement

1,234 24.7

ND per-gallon tax incentive(s) 358 7.2

NE per-gallon tax incentive(s) 547 10.9

NM B5 mandate with some conditions 475 23.7

OH per-gallon tax incentive(s) 1,556 31.1

OR B2 mandate, increasing to B5 with

some conditions

738 36.9

SC per-gallon tax incentive(s) 764 15.3

SD per-gallon tax incentive(s) 263 5.3

TX per-gallon tax incentive(s) 5,339 106.8

WA B2 mandate, increasing to B5 with

some conditions

1,230 24.6

Total biodiesel 468.7

State Incentive or mandate

Table 1.8-22 shows home heating oil use in 2005. We estimate potential biodiesel use in

heating oil at 89 million gallons per year based on a 2% blend in all heating oil north of the

Washington, DC, area (i.e., PADD 1A and 1B). This area was chosen because it is where the

majority of heating oil is used, and should have adequate biodiesel access from New Jersey,

Pennsylvania, and Connecticut in our forecasted production scenarios. To the extent that heating

oil use declines over time, the blend levels may increase in some areas or in the shoulder

seasons, such that the total biodiesel volume used in this market would not decline drastically.
298

Table 1.8-22.

Potential biodiesel use in heating oil based on 2005 data.495

Heating oil 2% biodiesel Volume Used

(million gal/yr) (million gal/yr) (million gal/yr)

U.S. 5,565,489 111.3

PADD 1 4,759,198 95.2

PADD 1A 1,923,405 38.5 38.5

CT 545,910 10.9

ME 308,464 6.2

MA 674,324 13.5

NH 175,484 3.5

RI 136,618 2.7

VT 82,604 1.7

PADD 1B 2,529,106 50.6 50.6

DE 33,221 0.7

DC 12,832 0.3

MD 149,919 3.0

NJ 322,088 6.4

NY 1,282,899 25.7

PA 728,147 14.6

PADD 1C 306,687 6.1

FL 3,608 0.1

GA 1,520 0.0

NC 81,528 1.6

SC 8,810 0.2

VA 197,255 3.9

WV 13,966 0.3

Total used for biodiesel in heating oil 89.1

Area

Combining these volumes gives 558 million gallons per year potential biodiesel

consumption, leaving approximately 250 million gallons to be sold in blends above the projected

levels shown here, or in states not included here. For more on biodiesel-related distribution

issues and costs, refer to Section 4.2.2.2.
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Chapter 2: Lifecycle GHG Analysis

2.1 Chapter Overview

This chapter describes each component of the analysis undertaken by EPA as part of the

RFS2 rulemaking to determine lifecycle GHG emissions impacts for renewable and petroleumbased transportation fuels. The chapter is organized as follows:

 Section 2.2 provides background about lifecycle analysis for RFS2 and key modeling

updates EPA has made since the proposed rule.

 Section 2.3 lays out the goals and scope of our analysis.

 Section 2.4 provides a detailed explanation of each component in EPA’s lifecycle

analysis of renewable fuels.

o Section 2.4.1 summarizes the Agency’s overall biofuel modeling approach.

o Section 2.4.2 focuses on domestic agricultural sector GHG emissions impacts,

including our evaluation of changes in agricultural inputs and livestock

production.

o Section 2.4.3 discusses international agricultural impacts.

o Section 2.4.4 explains EPA's assessment of GHG emissions impacts from biofuelinduced domestic and international land conversions, including our quantification

of uncertainty in international land conversion GHG emissions impacts.

o Section 2.4.5 describes our accounting for lifecycle GHG emissions over time.

o Section 2.4.6 explains EPA’s analysis of biofuel feedstock transport.

o Section 2.4.7 discusses energy use and GHG emissions from biofuel processing.

o Section 2.4.8 includes our updated analysis of fuel transport and distribution.

o Section 2.4.9 covers renewable fuel tailpipe emissions.

o Section 2.4.10 discusses other potential indirect impacts from biofuel production.

o Section 2.4.11 describes other modeling approaches that EPA considered for

lifecycle GHG analysis.

 Section 2.5 presents EPA’s analysis of baseline gasoline and diesel lifecycle GHG

emissions for comparison with biofuels.

 Section 2.6 discusses the fuel-specific lifecycle GHG emissions results, including

sensitivity analyses.

 Section 2.7 includes our analysis of the overall GHG impacts of the rulemaking volumes.

 Section 2.8 concludes the chapter with a discussion of the effects of the RFS2 on global

temperature and sea level.
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2.2 Background for Estimating Fuel Lifecycle Greenhouse Gas Emissions

2.2.1 Lifecycle Analysis for the RFS2 Proposal

Lifecycle modeling of transportation fuels, often referred to as fuel cycle or well-towheel analysis, assesses the net impacts of a fuel throughout each stage of its production and use

including production / extraction of the feedstock, feedstock transportation, fuel production, fuel

transportation and distribution, and tailpipe emissions. Use of a lifecycle approach to analyze

different transportation fuels requires modeling and evaluation of many different input factors.

Lifecycle assessments can be divided into two major methodological categories:

attributional and consequential.496

An attributional approach to GHG emissions accounting in products provides information

about the GHG emitted directly by a product and its life cycle. The product system includes

processes that are directly linked to the product by material, energy flows or services following a

supply-chain logic.

A consequential approach to GHG emissions accounting in products provides

information about the GHG emitted, directly or indirectly, as a consequence of changes in

demand for the product. This approach typically describes changes in GHG emissions levels

from affected processes, which are identified by linking causes with effects.

The definition of lifecycle greenhouse gas emissions established by Congress states that:

The term ‘lifecycle greenhouse gas emissions’ means the aggregate quantity of

greenhouse gas emissions (including direct emissions and significant indirect

emissions such as significant emissions from land use changes), as determined by

the Administrator, related to the full fuel lifecycle, including all stages of fuel and

feedstock production and distribution, from feedstock generation or extraction

through the distribution and delivery and use of the finished fuel to the ultimate

consumer, where the mass values for all greenhouse gases are adjusted to

account for their relative global warming potential.497

This definition and specifically the clause “(including direct emissions and significant

indirect emissions such as significant emissions from land use changes)” requires the Agency to

consider a consequential lifecycle analyses and to develop a methodology that accounts for all of

the important factors that may significantly influence this assessment, including the secondary or

indirect impacts of expanded biofuels use.

Furthermore, independent of the statutory language the Agency believes it is important to

include secondary, indirect, or consequential impacts of biofuel use, specifically:

 Capturing secondary market driven agricultural sector impacts, such as changes in

other crop patterns and livestock production as a response to changing prices in

biofuel feedstocks.
301

 Production of co-products from biofuel production requires some type of

allocation, either splitting emissions of fuel production between fuel and coproducts or examining the use of co-products in other markets. For example in

the case of corn ethanol, the co-product of ethanol production is a feed product

that is assumed to replace the use of corn and soybean meal. Therefore, the

emissions of producing an equivalent amount of corn and soybean meal to these

co-products are subtracted from the lifecycle assessment. This requires modeling

of the co-product economic markets.

 To the extent that they are included in attributional lifecycle analyses, land use

impacts are typically confined to direct impacts, e.g., land converted to produce

corn directly used for ethanol production. This does not capture effects of land

converted to produce crops that are indirectly impacted by increased biofuel

production. One specific example of this is increased corn ethanol production in

the U.S. could lead to decreased crop exports resulting in increased crop

production and land use impacts internationally. Another example is corn

production increases resulting in less rice production and lower CH4 emissions.

 Consideration of specific policies and interaction between different fuel volumes

could have very distinct impacts especially in the agricultural sector.

The lifecycle methodology developed for the RFS2 rulemaking analysis included the use

of economic models to perform a consequential type of lifecycle analysis.

The consequential approach of incorporating economic models into a lifecycle

assessment is not a new concept. Most notably the Economic Input-Output Lifecycle

Assessment (EIO-LCA) method has been employed in the past. The EIO-LCA method estimates

the materials and energy resources required for, and the environmental emissions resulting from,

activities in the overall economy. The EIO-LCA method was theorized and developed by

economist Wassily Leontief in the 1970s based on his earlier input-output work from the 1930s

for which he received the Nobel Prize in Economics. Researchers at the Green Design Institute

of Carnegie Mellon University operationalized this method in the mid-1990s, once sufficient

computing power was widely available to perform the large-scale matrix manipulations required

in real-time. This work relies on static input-output tables of the U.S. economy to determine the

full economy wide impacts of producing a product or service.

Mark Delucchi at the Institute of Transportation Studies of the University of California

Davis has developed the Lifecycle Emissions Model (LEM) that looks at transportation fuels.

He has also highlighted the need to look at market impacts when considering biofuel production

and specifically to consider land use changes.498 There have also been several studies examining

the consequential or economic-based life cycle assessment including several focusing on the

agricultural sector.

Currently, no single model captures all of the complex interactions associated with

estimating lifecycle GHG emissions for biofuels, taking into account the "significant indirect

emissions such as significant emissions from land use change" required by EISA. For example,
302

some lifecycle analysis tools typically used in the past focused on process modeling—the energy

and resultant emissions associated with the direct production of a fuel at a petroleum refinery or

biofuel production facility. But this is only one component in the production of the fuel. Clearly

in the case of biofuels, impacts from and on the agricultural sector are important, because this

sector produces feedstock for biofuel production. Commercial agricultural operations make

many of their decisions based on an economic assessment of profit maximization. Assessment

of the interactions throughout the agricultural sector requires an analysis of the commodity

markets using economic models. However, existing economy wide general equilibrium

economic models are not detailed enough, on their own, to capture the specific agricultural sector

interactions critical to our analysis (e.g., changes in acres by crop type) and would not provide

the types of outputs needed for a thorough GHG analysis. As a result, EPA has used a set of

tools that are best suited for each specific component of the analysis to create a more

comprehensive estimate of GHG emissions. Where no direct links between the different models

exist, specific components and outputs of each are used and combined to provide an analytical

framework and the composite lifecycle assessment results.

To estimate the changes in the domestic agricultural sector (e.g., changes in crop acres

resulting from increased demand for biofuel feedstock or changes in the number of livestock due

to higher corn prices) and their associated emissions, we used the Forestry and Agricultural

Sector Optimization Model (FASOM), developed by Texas A&M University and others.

FASOM is a partial equilibrium economic model of the U.S. forest and agricultural sectors. EPA

selected the FASOM model for this analysis for several reasons. FASOM is a comprehensive

forestry and agricultural sector model that tracks over 2,000 production possibilities for field

crops, livestock, and biofuels for private lands in the contiguous United States. It accounts for

changes in CO2, methane, and N2O from most agricultural activities and tracks carbon

sequestration and carbon losses over time. Another advantage of FASOM is that it captures the

impacts of all crop production, not just biofuel feedstock. Thus, as compared to some earlier

assessments of lifecycle emission, using FASOM allows us to determine secondary agricultural

sector impacts, such as crop shifting and reduced demand due to higher prices. It also captures

changes in the livestock market (e.g., smaller herd sizes that result from higher feed costs) and

U.S. export changes. FASOM also has been used by EPA to consider U.S. forest and

agricultural sector GHG mitigation options.499

The output of the FASOM analysis includes changes in total domestic agricultural sector

fertilizer and energy use. These are calculated based on the inputs required for all the different

crops modeled and changes in the amounts of the different crops produced due to increased

biofuel production. FASOM output also includes changes in the number and type of livestock

produced. These changes are due to the changes in animal feed prices and make-up due to the

increase in biofuel production. The FASOM output changes in fertilizer, energy use, and

livestock are combined with GHG emission factors from those sources to generate biofuel

lifecycle impacts. The GHG emission factors for fuel and fertilizer production come from the

Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) spreadsheet

analysis tool developed by Argonne National Laboratories, and livestock GHG emission factors

are from IPCC guidance.
303

GREET includes the GHG emissions associated with the production and combustion of

fossil fuels (diesel fuel, gasoline, natural gas, coal, etc.). GREET also estimates the GHG

emissions associated with electricity production required for agriculture and biofuel production.

For the agricultural sector, we also relied upon GREET to provide GHG emissions associated

with the production and transport of agricultural inputs such as fertilizer, herbicides, pesticides,

etc. GREET has been under development for several years and has undergone extensive peer

review through multiple updates. Of the available data sources of information on lifecycle GHG

emissions of fossil energy and agricultural sector inputs consumed, we believe that GREET

offers the most comprehensive treatment of emissions from the covered sources. GREET

version 1.8c was the primary version used in this analysis.

To estimate the domestic impacts of N2O emissions from fertilizer application, we used

the CENTURY and DAYCENT models, developed by Colorado State University. The

DAYCENT model simulates plant-soil systems and is capable of simulating detailed daily soil

water and temperature dynamics and trace gas fluxes (CH4, N2O, NOx and N2). The CENTURY

model is a generalized plant-soil ecosystem model that simulates plant production, soil carbon

dynamics, soil nutrient dynamics, and soil water and temperature. Model results for N2O

emissions from different crop and land use changes were combined with FASOM output to

generate overall domestic N2O emissions.

FASOM output also provides changes in total land use required for agriculture and land

use shifting between crops, and interactions with pasture, and forestry. This output is combined

with emission factors from land use change to generate domestic land use change GHG

emissions from increased biofuel production.

To estimate the impacts of biofuels feedstock production on international agricultural and

livestock production, we used the integrated Food and Agricultural Policy and Research Institute

international models, as maintained by the Center for Agricultural and Rural Development

(FAPRI-CARD) at Iowa State University. These models capture the biological, technical, and

economic relationships among key variables within a particular commodity and across

commodities. FAPRI-CARD is a worldwide agricultural sector economic model that was run by

the Center for Agricultural and Rural Development (CARD) at Iowa State University on behalf

of EPA. The FAPRI models have been previously employed to examine the impacts of World

Trade Organization proposals, changes in the European Union’s Common Agricultural Policy,

analyze farm bill proposals since 1984, and evaluate the impact of biofuel development in the

United States. In addition, the FAPRI models have been used by the USDA Office of Chief

Economist, Congress, and the World Bank to examine agricultural impacts from government

policy changes, market developments, and land use shifts.

The output of the FAPRI-CARD model included changes in crop acres and livestock

production by type and by country globally. Unlike FASOM, the FAPRI-CARD output did not

include changes in fertilizer or energy use or have land type interactions built in. These were

developed outside the FAPRI-CARD model and combined with the FAPRI-CARD output to

generate GHG emission impacts.
304

Crop input data by crop and country were developed and combined with the FAPRICARD output crop acreage change data to generate overall changes in fertilizer and energy use.

These fertilizer and energy changes along with the FAPRI-CARD output livestock changes were

then converted to GHG emissions based on the same basic approach used for domestic sources,

which involves combining with emission factors from GREET and IPCC.

The FAPRI-CARD model does predict how much crop land will change in other

countries but does not predict what type of land such as forest or pasture will be affected. We

used data analyses provided by Winrock International to estimate what land types will be

converted into crop land in each country and the GHG emissions associated with the land

conversions. Working with Winrock, we used recent satellite data to analyze recent land use

changes around the world that have resulted from the social, economic, and political forces that

drive land use. In our assessment, we are assuming that these recent drivers of land use change

will remain in relative affect through our 2022 modeling time frame such that the recent trends in

land use change are indicative of land use changes likely to result in 2022 due to biofuel

production. We combined the recent land use change patterns with various estimates of carbon

stocks associated with different types of land at the state level. This international land use

assessment is an important consideration in our lifecycle GHG assessment and is explained in

more detail later in Section 2.4.4 in this chapter.

Additional modeling and data sources used to determine the GHG emissions of other

stages in the biofuel lifecycle include studies and data on the distance and modes of transport

needed to ship feedstocks from the field to the biofuel processing facility and the finished biofuel

from the facility to end use. These distances and modes are used to develop the amount and type

of energy used for transport which are combined with GREET factors to generate GHG

emissions.

We also calculate energy use needed in the biofuel processing facility from industry

sources, reports, and process modeling. This energy use is combined with emissions factors

from GREET to develop GHG impacts of the biofuel production process

To test the robustness of the FASOM, FAPRI-CARD and Winrock results, we also

examined biofuel land use change impacts with the Global Trade Analysis Project (GTAP)

model, a multi-region, multi-sector, computable general equilibrium model that estimates

changes in world agricultural production. Maintained through Purdue University, GTAP

projects international land use change based on the economics of land conversion, rather than

using the historical data approach applied by FAPRI-CARD/Winrock. GTAP is designed to

project changes in international land use as a result of the change in U.S. biofuel policies, based

on the relative land use values of cropland, forest, and pastureland. The GTAP design has the

advantage of explicitly modeling the competition between different land types due to a change in

policy. As further discussed in Section 2.4.11, the GTAP model results were generally

consistent with our FAPRI-CARD/satellite data analysis, in particular supporting the significant

impact on international land use.

Figure 2.2-1 graphically shows the different models used and what parts of the lifecycle

they are used to represent.
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Figure 2.2-1 System Boundaries and Models Used

2.2.2 Updates for this Final Rulemaking

Throughout the development of EPA’s lifecycle analysis, the Agency has employed a

collaborative, transparent, and science-based approach. EPA’s lifecycle methodology, as

developed for the RFS2 proposal, required breaking new scientific ground and using analytical

tools in new ways. The work was generally recognized as state of the art and an advance in

lifecycle modeling, specifically regarding the indirect impacts of biofuels.

However, the complexity and uncertainty inherent in this work made it extremely

important that we seek the advice and input of a broad group of experts and stakeholders. In

order to maximize stakeholder outreach opportunities, the comment period for the proposed rule

was extended to 120 days. In addition to this formal comment period, EPA made multiple

efforts to solicit public and expert feedback on our approach. Beginning early in the NPRM

process and continuing throughout the development of this final rule, EPA held hundreds of

meetings with stakeholders, including government, academia, industry, and non-profit

organizations, to gather expert technical input. Our work was also informed heavily by

consultation with other federal agencies. For example, we have relied on the expert advice of

USDA and DOE, as well as incorporating the most recent inputs and models provided by these

Agencies. Dialogue with the State of California and the European Union on their parallel, ongoing efforts in GHG lifecycle analysis also helped inform EPA’s methodology. As described
306

below, formal technical exchanges and an independent, formal peer review of the methodology

were also significant components of the Agency’s outreach. A key result of our outreach effort

has been awareness of new studies and data that have been incorporated into our final rule

analysis.

Technology Exchanges: Immediately following publication of the proposed rule, EPA

held a two-day public workshop focused specifically on lifecycle analysis to assure full

understanding of the analyses conducted, the issues addressed, and the options discussed. The

workshop featured EPA presentations on each component of the methodology as well as

presentations and discussions by stakeholders from the renewable fuel community, federal

agencies, universities, and environmental groups. The Agency also took advantage of

opportunities to meet in the field with key, affected stakeholders. For example, the Agency was

able to twice participate in meetings and tours in Iowa hosted by the local renewable fuel and

agricultural community. As described in this section, one of the many outcomes of these

meetings was an improved understanding of agricultural and biofuel production practices.

As indicated in the proposal, our lifecycle results were particularly impacted by

assumptions about land use patterns and emissions in Brazil. During the public comment

process we were able to update and refine these assumptions, including the incorporation of new,

improved sources of data based on Brazil-specific data and programs. In addition, the Agency

received more recent trends on Brazilian crop productivity, areas of crop expansion, and regional

differences in costs of crop production and land availability. Lastly, we received new

information on the effectiveness of current efforts to curb deforestation allowing the Agency to

better predict this impact through 2022.

Peer Review: To ensure the Agency made its decisions for this final rule on the best

science available, EPA conducted a formal, independent peer review of key components of the

analysis. The reviews were conducted following the Office of Management and Budget’s peer

review guidance that ensures consistent, independent government-wide implementation of peer

review, and according to EPA's longstanding and rigorous peer review policies. In accordance

with these guidelines, EPA used independent, third-party contractors to select highly qualified

peer reviewers. The reviewers selected are leading experts in their respective fields, including

lifecycle assessment, economic modeling, remote sensing imagery, biofuel technologies, soil

science, agricultural economics, and climate science. They were asked to evaluate four key

components of EPA’s methodology: (1) land use modeling, specifically the use of satellite data

and EPA’s proposed land conversion GHG emission factors; (2) methods to account for the

variable timing of GHG emissions; (3) GHG emissions from foreign crop production (both the

modeling and data used); and (4) how the models EPA relied upon are used together to provide

overall lifecycle estimates. The full peer review records, including all of the charge questions

and peer reviewer responses, are available in the public docket for this rulemaking.

The advice and information received through this peer review are reflected throughout

this chapter. The reviewers also provided recommendations that have helped to inform the larger

methodological decisions presented in this final rule. For example, the reviewers in general

supported the importance of assessing indirect land use change and determined that in general

EPA used the best available tools and approaches for this work. However, the review also
307

recognized that no existing model comprehensively simulates the direct and indirect effects of

biofuel production both domestically and internationally, and therefore model development is

still evolving. The uncertainty associated with estimating indirect impacts and the difficulty in

developing precise results also were reflected in the comments. In the long term, this peer

review will help focus EPA’s ongoing lifecycle analysis work as well as our future interactions

with the National Academy of Science and other experts.

Altogether, the many and extensive public comments we received to the rule docket, the

numerous meetings, workshops and technical exchanges, and the scientific peer review have all

been instrumental to EPA’s ability to advance our analysis between proposal and final and to

develop the methodological and regulatory approach described in this section.

Based on peer review results as well as other comments received we have made several

updates to our modeling since the NPRM analysis as shown in Table 2.2-1.

.
308

Table 2.2-1. Key Lifecycle Modeling Updates

Update Source

Updates to Domestic Agricultural Sector Modeling:

 Incorporated the FASOM forestry module  Updated FASOM Forestry component

 Added new land classifications: cropland,

cropland-pasture, rangeland, forest-pasture,

forest, CRP, developed land

 U.S. land cover databases

 Reflected new data on projected switchgrass

yields

 New data from PNNL on switchgrass yields

 Updated N2O / soil carbon emissions factors  DAYCENT/CENTURY model updates by

Colorado State University

 Updated emission factors for farm input

production

 New version of GREET (version 1.8c)

Updates to International Agricultural Sector Modeling:

 Incorporated a Brazil module into the

international model framework

o Regional crop and pasture modeling

 FAPRI-CARD Brazil Module148

 Added price induced yield changes (e.g., long

term elasticity for the Corn Belt in the U.S.

0.07)

 FAPRI-CARD 2010 U.S. And World

Agricultural Outlook

 Updated international agricultural GHG

emission estimates

 International Fertilizer Industry Assoc.

(2009)500 and pesticide consumption from

FAOStat501

 Updated Brazil sugarcane production based on

recent studies

 Macedo (2008)502

Updates to Biofuel Processing in Both Domestic and International Agricultural Sector Modeling:

 Built in corn fractionation pathways (with coproduct markets, etc.) USDA

 Adjusted DGS co-product replacement rates

o Reflected studies that indicate more

efficient use of co-product

 Empirical studies by Argonne Laboratory and

University of Minnesota: Arora, Wu and Wang

(2008)503 and Shurson (2009)504

 Added biodiesel glycerin co-product credit  Based on data from NBB and GREET

 Updated process energy use  New studies by USDA505, NREL506,507,508 and

Energy Resources Center509

Updates to Land Use Change Modeling:

 Used more recent / longer time coverage /

higher resolution satellite data - 2001-2007

 MODIS V5 (2009)510

 Augmented satellite data with region specific

data where available (e.g., data from Brazil on

pasture intensification)

 FAPRI-CARD Brazil module

 New soil carbon data  Harmonized World Soil Database (2009)511

 New studies monitoring long-term forest

growth rates

 Lewis et al. (2009)512 and Phillips et al.

(2008)513

Petroleum Baseline Updates:

 Updated 2005 petroleum baseline  DOE/NETL (2009)514

148 Iowa State University working with Brazilian experts developed this module which has been incorporated into

the FAPRI-CARD 2010 U.S. And World Agricultural Outlook, released date early 2010
309

Furthermore, in the proposal, we asked for comment on whether and how to conduct an

uncertainty analysis to help quantify the magnitude of this uncertainty and its relative impact on

the resulting lifecycle emissions estimates. The results of the peer review, and the feedback we

have received from the comment process, supported the value of conducting such an analysis.

Therefore, working closely with other government agencies as well as incorporating feedback

from experts who commented on the rule, one of the main changes we made since the proposal

was that we have quantified the uncertainty associated with specifically the international indirect

land use change emissions associated with increased biofuel production. More discussion of

treatment of uncertainty is found in Section 2.4.4.2.8.

2.3 Goals and Scope of This Analysis

Lifecycle analysis is used in several ways for this rulemaking. Fuel-specific GHG

reductions are used to develop threshold determinations for specific fuels. Lifecycle analysis is

also used to determine the overall impact of the rulemaking on GHG emissions worldwide. The

first step was to establish the goals and scope for this analysis, as summarized below.

2.3.1 Goal

The RFS2 rulemaking involves determining lifecycle GHG impacts of specific fuels and

fuel pathways for comparison with thresholds as defined in the legislation. Obligated parties will

be required to use mandated quantities of renewable fuels, but only fuels that meet the GHG

thresholds can qualify under the program. (Fuels produced at grandfathered facilities are exempt

from these GHG threshold requirements.) The lifecycle GHG reductions represent the GHG

differences between renewable fuels relative to the petroleum-based gasoline and diesel that they

displace. The lifecycle methodology described here is used to determine the GHG displacement

values for different renewable fuels to be compared to the thresholds. Therefore this analysis

will provide:

 Amount of GHG emissions (on a mass basis) per amount of fuel produced (on an

energy content basis) for both conventional petroleum based fuels and renewable

fuels.

 Results are combined to quantify the emission change per energy unit (i.e., per BTU)

of renewable fuel compared to that for the conventional fuel replaced.

2.3.2 Scope

2.3.2.1 Scenario Analysis

To quantify the lifecycle GHG emissions associated with the increase in renewable fuel

mandated by EISA, we needed to compare the impacts of renewable fuels with EISA to a

reference case without EISA. Since it is not practical or workable to conduct such an analysis

and come up with factors for every year, to carry out this analysis we chose to look at the final

year of the RFS2 standards when they are fully phased in. For our reference case we assumed a

“business as usual” volume of a particular renewable fuel based on what would likely be in the
310

fuel pool in 2022 without EISA as predicted by the Energy Information Agency’s Annual Energy

Outlook (AEO) for 2007 (which took into account the economic and policy factors in existence

in 2007 before EISA). For our control case we assumed the higher volumes of renewable fuels

as mandated by EISA for 2022. For each individual biofuel, we analyzed the incremental impact

of increasing the volume of that fuel to the total mix of biofuels needed to meet the EISA

requirements while holding volumes of other fuels constant. Any changes between now and

2022 in factors such as crop yields, energy costs, or production plant efficiencies, both

domestically and internationally, are reflected in both scenarios. Rather than focus on the

impacts associated with a specific gallon of fuel and tracking inputs and outputs across different

lifecycle stages, we determined the overall aggregate impacts across sections of the economy in

response to a given volume change in the amount of biofuel produced. We then normalize those

impacts to a gallon of fuel by dividing total impacts over the given volume change. In the case

of overall rule impacts, we analyze the change in reference vs. control case volumes for all fuels

together and take the absolute GHG results (e.g., do not normalize the overall rule impacts).

We did not calculate the emission impacts for each gallon of fuel based upon its unique

production characteristics which could vary widely across the nation (e.g., a gallon of ethanol

produced using corn grown in Iowa may have different direct lifecycle emissions impacts than a

gallon of ethanol produced at an identical facility in Nebraska using corn grown in Nebraska due

to regional differences in agricultural practices. However, on a lifecycle basis, considering the

indirect impacts in the context of the entire corn market they are not different). Rather, we

determined the overall aggregate impacts across sections of the economy in response to a given

volume change in the amount of biofuel produced. In the case of agricultural impacts, we

assessed the impact on the entire U.S. agricultural system that would result from expanded

demand for biofuel feedstock. We then normalized those impacts to a gallon of fuel by dividing

total impacts over the renewable fuel volume change between our business as usual case and the

EISA volumes. Similarly, we estimated the typical emissions impact of a type of biofuel

production facility (e.g., a plant that uses the dry mill process to turn corn starch into ethanol).

The emissions assessment from a typical facility was then ascribed to all biofuel produced across

facilities using that same basic technology.

We focus our final rule analyses on 2022 results for two main reasons. First, it would

require an extremely complex assessment and administratively difficult implementation program

to track how biofuel production might continuously change from month to month or year to year.

Instead, it seems appropriate that each biofuel be assessed a level of GHG performance that is

constant over the implementation of this rule, allowing fuel providers to anticipate how these

GHG performance assessments should affect their production plans. Second, it is appropriate to

focus on 2022, the final year of ramp up in the required volumes of renewable fuel as this year.

Assessment in this year allows the complete fuel volumes specified in EISA to be incorporated.

This also allows for the complete implementation of technology changes and updates that were

made to improve or modeling efforts. For example, the inclusion of price induced yield

increases and the efficiency gains of DDGS replacement are phased in over time. Furthermore,

these changes are in part driven by the changes in earlier years of increased biofuel use.

Several of the lifecycle emission impacts for one fuel are interrelated with those of

another fuel, in particular the land-use changes. For our analysis of the overall GHG impacts of
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the program (discussed in Section 2.7), we modeled all of the fuel changes simultaneously to

determine the land-use impact. However, from that analysis it is not possible to differentiate the

contribution of the land-use change to one fuel vs. another. As a result, for this analysis we had

to model the impacts of just one fuel change at a time. In doing this we have held the other fuel

volumes constant at their mandated levels in order to best approximate the impacts a single fuel

change would have in the context of the full RFS2 standard volumes.

We used the same approach to determine the lifecycle GHG emissions for corn ethanol,

cellulosic ethanol, biodiesel produced from soybean (and other vegetable) oils, and biodiesel

produced using waste oils as feedstock. For waste oils, we note that no land use changes are

included in the FASOM assessment, because any land use impacts are attributed to the original

purpose of the feedstock (e.g.., the use of the vegetable oil for cooking or the production of

animals for their meat), rather than the biofuel produced from the recovered waste material.

FASOM does not model feedstocks for fuels produced outside the U.S. We addressed

imported ethanol by analyzing the difference in total GHG emissions based on two 2022

scenarios using only the results from FAPRI-CARD modeling runs: (1) the business as usual

reference case volume of 0.6 Bgal and (2) an RFS2 projected volume of 2.2 Bgal of imported

sugarcane ethanol.

Current models present some challenges in estimating GHG lifecycle emissions for

cellulosic biofuels. For example, the FAPRI-CARD model used for this analysis did not include

switchgrass or similar energy crops, and could only use corn stover or other food crop residues

as feedstock in predicting cellulosic biofuel impacts. To overcome this limitation we ran the

FASOM model with a switchgrass scenario to generate domestic land use and crop change

results. We then applied these domestic crop changes by region to the FAPRI-CARD model to

generate the international land use change and crop shifting due to the domestic impacts

predicted by the FASOM switchgrass scenario.

For biofuels made from wastes and byproducts (e.g., MSW, rendered fats and waste oils

and corn stover feedstock), we assumed no land use changes, because these biofuel feedstocks do

not compete for domestic crop acreage. For corn stover, we analyzed only the change in

domestic GHG emission resulting from an increase in fertilizer replacement application rates to

compensate for the removal of stover from the land. Table 2.3-1 shows the different fuel

scenarios considered.

Table 2.3-1. Fuel Volume Scenarios Considered in This Analysis (Billions of Gallons)

Biofuel Reference Case –

Low Volume

Control Case –

High Volume Change

Corn Ethanol 12.3 15.0 2.7

Switchgrass Cellulosic Ethanol 0 7.9 7.9

Corn Residue Cellulosic Ethanol 0 4.9 4.9

Imported Sugarcane Ethanol 0.6 2.2 1.6

Soybean Oil Biodiesel 0.1 0.6 0.5
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2.3.2.2 System Boundaries

It is important to establish clear system boundaries in lifecycle analysis. By determining

a common set of system boundaries, different fuel types can then be validly compared. As

described in the previous section, we have assessed the direct and indirect GHG impacts in each

stage of the full fuel lifecycle for biofuels and petroleum fuels.

Figure 2.3-1Figure 2.3-1provides a simplified diagram describing the system studied.

Figure 2.3-1. Simplified Lifecycle System Diagram

International

Agricultural

Sector

Domestic

Agricultural

Sector

Conventional

Petroleum Fuel

Production

Food / Feed Uses Transportation Sector

Fuel Demand

Biofuel

Production

The different fuel volume scenarios were compared based on delivery of the same

functions, in this case providing for both the agricultural sector market and transportation fuels

markets. Within the overall system shown in

Figure 2.3-1 the unit process listed in Table 2.3-2 will be considered.

Table 2.3-2. Unit Processes Considered

Biofuel Petroleum-Based Fuel

Feedstock Agriculture Crude Oil Extraction

Feedstock Transport Crude Oil Transport

Feedstock Processing &

Biofuel Production

Refining

Biofuel Transport and

Distribution

Fuel Transport and

Distribution

Biofuel Tailpipe Emissions Fuel Tailpipe Emissions

Included in each unit process shown in Table 2.3-2 are the emissions and energy use

associated with each operation as well as upstream components that feed into them. For

example, the feedstock agriculture stage includes emissions from fuel used in tractors as well as

from producing and transporting the fertilizer used in the field. Electricity production emissions

are included in almost all of the stages shown. For direct impacts, as was the case in the

proposal analysis, this results in system boundaries that include operation-related activities, but

not infrastructure-related activities. As such, while we do include the emissions associated with
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the operation of farm equipment and trucks used for feedstock / fuel transportation we do not

include the emissions associated with the production of the equipment or vehicles. Furthermore,

we include the emissions from the operations of biofuel production plants and petroleum

refineries but we do not include emissions from producing the material used to construct the

facilities.

In determining what indirect impacts to include in the system boundaries of this analysis

we focus on the goal and scope of the analysis as specified by the statutory language in EISA.

The Act specifies different categories of renewable fuels, conventional renewable fuel,

advanced biofuel, cellulosic biofuel, and biomass-based diesel. The categories of fuel are

defined in part based on their GHG emissions. For example for cellulosic biofuel:

The term ‘cellulosic biofuel’ means renewable fuel derived from any cellulose,

hemicellulose, or lignin that is derived from renewable biomass and that has lifecycle

greenhouse gas emissions, as determined by the Administrator, that are at least 60

percent less than the baseline lifecycle greenhouse gas emissions.

So, the main goal of this analysis is to determine the lifecycle GHG emissions of different

biofuel feedstock and fuel pathways for determination of compliance against the GHG thresholds

as defined and mandated in the Act. More specifically the language stipulates that the analysis

compares biofuel “lifecycle greenhouse gas emissions” against the “baseline lifecycle

greenhouse gas emissions”.

Biofuel lifecycle greenhouse gas emissions are further defined as:

The term ‘lifecycle greenhouse gas emissions’ means the aggregate quantity of

greenhouse gas emissions (including direct emissions and significant indirect emissions

such as significant emissions from land use changes), as determined by the

Administrator, related to the full fuel lifecycle, including all stages of fuel and feedstock

production and distribution, from feedstock generation or extraction through the

distribution and delivery and use of the finished fuel to the ultimate consumer, where the

mass values for all greenhouse gases are adjusted to account for their relative global

warming potential.149

This definition forms the basis of defining the system boundaries for the biofuels

lifecycle analysis. As the language specifically mandates that lifecycle GHG emissions include

“direct emissions and significant indirect emissions such as significant emissions from land use

changes” the system boundaries modeled include indirect impacts as determined through our

economic modeling discussed in Section 2.4.

EISA defines baseline lifecycle greenhouse gas emissions as:

The term ‘baseline lifecycle greenhouse gas emissions’ means the average lifecycle

greenhouse gas emissions, as determined by the Administrator, after notice and

149 Clean Air Act Section 211(o)(1).
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opportunity for comment, for gasoline or diesel (whichever is being replaced by the

renewable fuel) sold or distributed as transportation fuel in 2005.

Therefore, the petroleum production component of the system boundaries is specifically

mandated by EISA to be based on the 2005 average for crude oil used to make gasoline or diesel

sold or distributed as transportation fuel, and not the marginal crude oil that will be displaced by

renewable fuel. Furthermore, as the EISA language specifies that the baseline emissions are to

be only “average” lifecycle emissions for this single specified year and volume, it does not allow

for a comparison of alternative scenarios. Indirect effects can only be determined using such an

analysis; therefore, there are no indirect emissions to include in the baseline lifecycle greenhouse

gas emissions. More discussion on the petroleum fuel baseline and potential impact of

considering indirect impacts on the petroleum baseline are discussed in Section 2.5.

2.3.2.3 Environmental Flows Considered

The lifecycle analysis discussed here evaluates the impacts of increased renewable fuel

use on greenhouse gas emissions. EISA specifies a definition of greenhouse gases to include in

the analysis:

The term ‘greenhouse gas’ means carbon dioxide, hydrofluorocarbons, methane,

nitrous oxide, perfluorocarbons, sulfur hexafluoride. The Administrator may

include any other anthropogenically emitted gas that is determined by the

Administrator, after notice and comment, to contribute to global warming.

EISA also specifies that the mass values for all greenhouse gases are adjusted to account

for their relative global warming potential.

The relative global warming contribution of emissions of various greenhouse gases is

dependant on their radiative forcing, atmospheric lifetime, and other considerations. For

example, on a mass basis, the radiative forcing of CH4 is much higher than that of CO2, but its

effective atmospheric residence time is much lower. The relative warming impacts of various

greenhouse gases, taking into account factors such as atmospheric lifetime and direct warming

effects, are reported on a ‘CO2-equivalent’ basis as global warming potentials (GWPs). The

GWPs used in this analysis were developed by the UN Intergovernmental Panel on Climate

Change (IPCC) as listed in their Second Assessment Report, and are shown in Table 2.3-3.

Second assessment report values are used to be consistent with current standards for international

reporting of GHG emissions.

Table 2.3-3. 100 Year Global Warming Potentials for Greenhouse Gases

Greenhouse Gas GWP

CO2 1

CH4 21

N2O 310

Greenhouse gases are measured in terms of CO2-equivalent emissions (CO2e), which

result from multiplying the GWP for each of the three pollutants shown in the above table by the
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mass of emissions for each pollutant. The sum of impacts for CH4, N2O, and CO2, yields the

total effective GHG impact. Other GHGs like HFCs, PFCs and SF6 are not released in

significant amounts over the lifecycle of renewable or petroleum fuels, and are therefore not

tracked in this analysis. Other non-GHG climate impacts like albedo (light reflectance), land

surface roughness, hydrologic and energy flux, and loss of forest aerosols, while potentially an

important aspect of climate impacts associated with land use change, are currently outside the

scope of this analysis.

Other environmental flows besides GHG emissions are also considered in our analysis for

this rulemaking. Criteria and toxic air pollutants are modeled and results are described in

Chapter 3 of the RIA. Water use and impacts are also considered and are described in Chapter 6

of the RIA.

2.3.2.4 Data Quality

Lifecycle analysis is a data intensive process and the results are affected by data quality.

Data quality may be defined by specific characteristics that describe both quantitative and

qualitative aspects of data, as well as the methods used to collect and integrate those data into the

analysis. The quality of data used can be characterized by how well the geographic, technical

and temporal aspects of the data match the goals and scope of the analysis in question.

The quality of the data used in this analysis was classified based on its geographic,

technical and temporal relevance to the goals of the study as follows:

Geographic coverage – this analysis was conducted without any regard to the

geographic attributes of where emissions or energy use occurs. The benefits of this

proposed rule represent global reductions in GHG emissions and energy use, not just

those occurring in the U.S. For example, the savings associated with reducing overseas

crude oil extraction and refining are included here, as are the international emissions

associated with producing imported ethanol. Data for agricultural sector impacts include

both U.S. and international defaults. Agricultural commodity production in other

countries was based on data specific to those areas (e.g., fertilizer production in other

countries). Land use change was specifically modeled in different countries; impacts of

land use change were based on factors representing sub-country level land characteristics,

and for areas where data was not available averages were used.

Technology coverage – this analysis models industries that do not exist yet – cellulosic

ethanol and renewable diesel for example. Therefore assumptions based on existing

information and modeling were made to represent these industries rather than relying on

existing facility data. Even for industries that currently exist there is expected to be a

range of technology development over time. For this analysis we have made our best

projections for what the industry may look like by 2022. There is expected to be

considerable variation in the technologies used, for example combined heat and power

and corn oil fractionation in a dry mill ethanol plant. To account for this we have looked

at different fuel technology pathways as discussed in Section 2.4.7.
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Temporal coverage – this analysis considered impacts in 2022. Therefore we modeled

future data; we projected ethanol production in 2022 based on process models –

consistent with cost analysis used in this rulemaking. For example, this assumed that

future plants will be more energy efficient then current plants. Agricultural sector models

also represented 2022 values including improvements in yields and cropping patterns.

2.3.2.5 Addressing Uncertainty

The peer review, the public comments we have received, and the analysis conducted for

the proposal and updated here for the final rule, indicate that it is important to take into account

indirect emissions when looking at lifecycle emissions from biofuels. It is clear that, especially

when considering commodity feedstocks, including the market interactions of biofuel demand on

feedstock and agricultural markets is a more accurate representation of the impacts of an increase

in biofuels production on GHG emissions than if these market interactions are not considered.

However, it is also clear that there are significant uncertainties associated with these

estimates, particularly with regard to indirect land use change and the use of economic models to

project future market interactions. Reviewers highlighted the uncertainty associated with our

lifecycle GHG analysis and pointed to the inherent uncertainty of the economic modeling.

Therefore, working closely with other government agencies as well as incorporating

feedback from experts who commented on the rule, we have quantified the uncertainty

associated with specifically the international indirect land use change emissions associated with

increased biofuel production. There are four main areas of uncertainty in our modeling

approach:

 Economic Modeling Inputs

 Types of Land Converted and GHG Emission Factors

 Methodology Choices

 Other GHG Factors and Input Data

Although there is uncertainty in all portions of the lifecycle modeling, we focused our

uncertainty analysis on the factors that are the most uncertain and have the biggest impact on the

results. For example, the energy and GHG emissions used by a natural gas-fired ethanol plant to

produce one gallon of ethanol can be calculated through direct observations, though this will

vary somewhat between individual facilities. The indirect domestic emissions are also fairly

well understood, however these results are sensitive to a number of key assumptions (e.g.,

current and future corn yields). The indirect, international emissions are the component of our

analysis with the highest level of uncertainty and have particularly significant impact on our

overall assessment results. For example, identifying what type of land is converted

internationally and the emissions associated with this land conversion are critical issues that have

a large impact on the GHG emissions estimates.

Therefore, we focused our efforts on the international indirect land use change emissions

and worked to manage the uncertainty around those impacts in three ways: (1) getting the best

information possible and updating our analysis to narrow the uncertainty, (2) performing

sensitivity analysis around key factors to test the impact on the results, and (3) establishing
317

reasonable ranges of uncertainty and using probability distributions within these ranges in

threshold assessment. The following sections outline how we have incorporated these three

approaches into our analysis.

Economic Modeling Inputs: The use of economic models and the uncertainty of

those models to accurately predict future agricultural sector scenarios was one of the main

comments we received on our analysis. While the comments and specifically the peer review

supported our need to use economic models to incorporate and measure indirect impacts of

biofuel production they also highlighted the uncertainty with that modeling approach, especially

in projecting out to the future.

However, it is important to note that while many factors impact the certainty in

predicting total land used for crop production, making accurate predictions of many of these

factors are not relevant to our analysis. For example different assumptions about economic

growth rates, weather, and exchange rates will all impact future agricultural projections including

amount of land use for crops. However, we are interested only in the difference between two

biofuel scenarios holding all other changes constant. So the absolute values and projections for

crops, etc. in the model projections are not as important as the difference the model is projecting

due to an increase in biofuels production. This limits the uncertainty of using the economic

models for our analysis.

The main factors impacting the economic modeling and land use results due to

biofuels are overall crop / commodity demand and yields (and the responsiveness of these

parameters to price changes). To examine the impact of changes in yield on the overall biofuel

lifecycle GHG results, we have made two main changes in the economic modeling used for the

proposal. In order to update our analysis and reduce uncertainty we have included a price

induced yield impact, as discussed in RIA Chapter 5. Furthermore we also include a sensitivity

analysis of a high yield scenario to test the impact of higher yields on the results, as discussed in

Section 2.6.2.

Types of Land Converted and Land Conversion GHG Emissions Factors: The

international indirect land use change impacts of biofuels were determined based on the results

of the economic models that provide the total amount of new land needed. The results of the

economic models were combined with recent satellite data to predict the types of land converted

to meet the increased land demand. GHG emissions factors were then applied to the type of land

to calculate GHG emissions from land use change. As this is one of the areas of greatest

uncertainty we specifically incorporated an approach to quantify the uncertainty in our satellite

data and GHG emissions estimates and incorporated these results into our analysis.

Methodology Choices: A main underlying methodological decision that impacts the

overall lifecycle GHG results is how to deal with the timing of emissions. This is manifested in

two main ways, the first is how to deal with short term land use change emissions versus ongoing

benefits of the use of biofuels, and the second is what timeframe to consider the analysis for.

The main approach for addressing this uncertainty was to conduct sensitivity analysis with

various methodology choices, as presented in Section 2.6.2.1. For example, we used a 30-year

time period for our lifecycle analysis, but we also present results with different time periods, as
318

well as the payback periods for each fuel, which is a metric that does not require the analyst to

choose a specific time period.

Other GHG Factors and Input Data: Non-economic modeling inputs and assumptions

impact overall GHG results, for example crop production inputs (energy use for tractors, etc.),

and agricultural sector GHG emissions (livestock, soil N2O, etc.). These factors are applied on

top of economic modeling to determine mainly the non-land use GHG impacts of agriculture.

While there is some uncertainty inherent in the factors, most of them do not have a significant

impact on the overall results.

For the final rule analysis, instead of developing uncertainty profiles and ranges

around these other input factors, we focused on reducing the uncertainty through updates to

improve our data and modeling. For example, N2O emissions from soil as part of crop

production is a key component of agricultural sector GHG emissions so we focused on updating

our analysis to include the most up to date information on this source of emissions. We also had

our analysis of international agricultural sector GHG emissions peer reviewed and have updated

our analysis in response to the peer review comments.

2.4 Biofuels Analysis

2.4.1 Modeling Approach

As mentioned in Section 2.2, our methodology includes the use of agricultural sector

economic models. Our methodology involves the use of the FASOM model to determine

domestic agriculture sector-wide impacts of increased biofuel production, and the FAPRI-CARD

model to determine international changes in crop production and total crop. Agricultural sector

GHG emissions are estimated by FASOM, and FAPRI-CARD results were converted to GHG

emissions based on GREET defaults and IPCC emission factors. Biofuel process energy use and

associated GHG emissions were based on process models for the different pathways considered.

Feedstock and co-product transportation GHG emissions were based on GREET defaults.

The agricultural sector models were used to determine the impacts associated with

biofuels production by comparing two similar scenarios in both models. Both agricultural sector

models were run with two similar volumes of the specific fuel in question, while other fuel

volumes were held constant to isolate the fuel-specific impacts. Table 2.4-1 shows the 2022 fuel

volumes modeled in FASOM in order to isolate the incremental impacts of each type of

renewable fuel. Section 2.3 includes more discussion of the fuel volume scenarios.
319

Table 2.4-1. 2022 Fuel Volumes Modeled with FASOM (Billions of Gallons)

Control Case

Biodiesel Only

Case

Corn Ethanol

Only Case

Corn Stover

Ethanol Only

Case

Switchgrass

Ethanol Only

Case

Soybean

Biodiesel 0.6 0.1 0.6 0.6 0.6

Corn Ethanol 15.0 15.0 12. 3 15.0 15.0

Corn Stover

Ethanol 4.9 4.9 4.9 0.0 4.9

Switchgrass

Ethanol 7.9 7.9 7.9 7.9 0.0

The total impacts from changes in biofuel production were calculated by taking the

difference in total GHG emissions from the two scenarios considered. Per gallon or per million

British Thermal Units (mmBTU) impacts were calculated by dividing the total GHG emission

changes by the increase in volume of fuel represented in the scenarios. Therefore, the results

presented in this proposed rulemaking represent the per mmBTU “average marginal” impact of

the change in fuel volumes considered. In other words, the GHG impacts were estimated for a

marginal increase in fuel production, and the average impact of a marginal gallon was calculated.

2.4.2 Domestic Agriculture

GHG emissions from the domestic agricultural sector were estimated with the FASOM

model, a partial equilibrium economic model of the U.S. forest and agricultural sectors. As

discussed in Section 2.2, FASOM accounts for changes in GHG emissions from most

agricultural activities, including the total amount of fertilizer, chemicals, gasoline, diesel and

electricity used on farms for the entire domestic agricultural sector. It also captures changes in

the soil management, livestock production and U.S. agricultural exports. More detail on the

FASOM model can be found in Chapter 5 of the RIA. For all figures and tables in Section 2.4.2,

we report results for the biochemical pathway under the “Corn Stover Ethanol” and “Switchgrass

Ethanol” scenarios.

Figure 2.4-1 shows the total harvested crop acres in the different fuel-specific pathway

scenarios. The projected changes in total harvested acres are modest, because we modeled the

incremental difference in renewable fuel volumes between the scenarios.
320

Figure 2.4-1. FASOM Projected Domestic Harvested Acres, 2022

0

50

100

150

200

250

300

350

Reference Case Control Case Corn Only Case Biodiesel Only

Case

Sw itchgrass

Only Case

Corn Res Only

Case

Millions of Acres

Other

Sw itchGrass

Sugarcane

Sugarbeet

Soybeans

Sorghum

Silage

Rye

Rice

Oats

Hay

Wheat

Cotton

Corn

Barley
321

Figure 2.4-2 includes the projected changes in harvested crop acres by field crop for the

fuel volume cases considered (acreage changes are normalized per thousand gallons of

renewable fuel production). In the corn ethanol scenario, corn acreage increased; area planted

with soybeans, wheat, switchgrass, sorghum and rice decreased; and harvested acres of other

crops were practically unchanged. As anticipated, soybean acreage decreased the most when

corn ethanol production increased, because corn and soybeans are often in direct competition for

fertile land.

Figure 2.4-2.

Normalized Changes in Domestic Cropland by Crop, 2022

(acres per thousand gallons of renewable fuel)
322

-4

-3

-2

-1

0

1

2

3

4

5

6

Corn Ethanol Soy Biodiesel Sw itchgrass Ethanol Corn Residue Ethanol

Acres per 1000 gallons (EtOH Equiv)

Other Crops

Cotton

Hay

Oats

Sorghum

Rice

Wheat

Sw itchgrass

Soybeans

Corn + Silage

Soy-based biodiesel production induced a large increase in harvested soybean acres,

largely due to the low yield of soy-based biodiesel in terms of gallons produced per acre. Cotton

was the only other crop that increased substantially along with biodiesel production. The

competition between corn and soybeans was evident again, as corn acreage saw a steep decline.

However, switchgrass acres declined by nearly the same amount as corn, showing the relative

competition between switchgrass and soybeans. Wheat, rice, barley, sorghum and rye also

declined when biodiesel volumes increased.

In the scenario where switchgrass ethanol production increased, switchgrass was the only

field crop to gain acreage, with the exception of a small increase in corn and sugarbeet acres.

New switchgrass plantings displaced a wide variety of other crops (
323

Figure 2.4-2). As discussed more in RIA Chapter 5, the FASOM runs for the proposed rule

project that switchgrass will primarily be grown in Kansas, Missouri, Texas, Oklahoma, and

Arkansas

Production of ethanol from corn residue had a very small effect on the acreage of other

crops. This was expected because corn stover production does not displace other crop

production, as corn stover is a residual product of corn cultivation. FASOM did project minor

amounts of crop shifting in the corn stover scenario, because using corn stover for ethanol can

increase the profitability of corn production in certain regions, with subsequent impacts. The

effects of corn stover harvesting on agricultural inputs, such as the need to use more fertilizer

after stover removal, are discussed below.

2.4.2.1 Domestic Crop Inputs

FASOM utilizes data about crop inputs to build crop budgets for field crops across 11

market regions and 63 sub-regions. FASOM crop budgets include data on yields, fertilizer,

chemicals, and energy use needed to grow crops in each of the different regions. The crop

budgets are based on USDA historic data and are also projected into the future. The crop

budgets represent an average for each region, and do not specifically calculate input or yield

changes that could result from the use of marginal croplands or altered crop rotation patterns

(e.g., continuous corn production).150 Table 2.4-2 defines the 11 market regions in FASOM.

RIA Chapter 5 includes a detailed discussion of the FASOM crop budgets, including

assumptions about crop yields and yield growth rates. Below we provide a summary of some of

the key FASOM assumptions that were used to estimate domestic agricultural GHG emissions.

150 FASOM does not explicitly model the selection of alternative crop rotations. Because the model operates in 5-

year time steps, it has not generally been applied to shorter-term decisions such as changes in rotation patterns.

Rather, the model data implicitly reflect average conditions for crop production (e.g., yields, input use, etc.)

associated with historical rotation patterns on a regional level.
324

Table 2.4-2. Definitions of 11 Market Regions in FASOM

Key Market Region Production Region (States/Subregions)

NE Northeast Connecticut, Delaware, Maine, Maryland, Massachusetts, New

Hampshire, New Jersey, New York, Pennsylvania, Rhode Island,

Vermont, West Virginia

LS Lake States Michigan, Minnesota, Wisconsin

CB Corn Belt All regions in Illinois, Indiana, Iowa, Missouri, Ohio (IllinoisN, IllinoisS,

IndianaN, IndianaS, IowaW, IowaCent, IowaNE, IowaS, OhioNW, OhioS,

OhioNE)

GP Great Plains Kansas, Nebraska, North Dakota, South Dakota

SE Southeast Virginia, North Carolina, South Carolina, Georgia, Florida

SC South Central Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Tennessee, Eastern

Texas

SW Southwest (agriculture

only)

Oklahoma, All of Texas but the Eastern Part (Texas High Plains, Texas

Rolling Plains, Texas Central Blacklands, Texas Edwards Plateau, Texas

Coastal Bend, Texas South, Texas Trans Pecos)

RM Rocky Mountains Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah,

Wyoming

PSW Pacific Southwest All regions in California (CaliforniaN, CaliforniaS)

PNWE Pacific Northwest—

East side (agriculture

only)

Oregon and Washington, east of the Cascade mountain range

PNWW Pacific Northwest—

West side (forestry

only)

Oregon and Washington, west of the Cascade mountain range

The crop budgets included in the FASOM model include data on input use that varies by crop,

management practices, and region. There is often considerable regional variation in the inputs

used per acre, which suggests that total input use (and the associated GHG emissions and other

environmental impacts) will be affected as biofuel production causes crop shifting and alters
325

crop management practices. For example, nitrogen fertilizer use is an important factor for

lifecycle GHG analysis because of GHG emissions from fertilizer production and use.

Figure 2.4-3 includes FASOM assumptions about average nitrogen fertilizer use by crop in 2022

for non-irrigated production without residue harvesting. Regions that have a zero nitrogen

fertilizer use rate are not included in the averages.

Figure 2.4-3 illustrates the relative fertilizer intensity of major crops. Corn, hay and

silage are relatively fertilizer-intense crops; whereas soybeans require less than 10 pounds of

nitrogen per acre (soybeans naturally fix nitrogen in the soil as they grow).

Figure 2.4-3.

FASOM Average Nitrogen Fertilizer Use by Crop, 2022

Non-Irrigated, No Residue Harvesting

(lbs per acre)

0

20

40

60

80

100

120

140

160

180

Corn Soybeans Switchgrass Wheat (hard

red winter)

Rice

(irrigated)

Sorghum Oats Hay Cotton

Nitrogen Fertzilier Use (lbs per acre)

Note: The range indicates the regions with the highest and lowest average nitrogen fertilizer use rates.

Mechanized agriculture requires many forms of energy including diesel, gasoline, natural gas

and electricity. The FASOM crop budgets include detailed energy use information by crop and

region.
326

Figure 2.4-4 includes FASOM assumptions for average diesel use by crop in 2022, for

non-irrigated production without residue harvesting.

Figure 2.4-4.

FASOM Average Diesel Use by Crop, 2022

Non-Irrigated, No Residue Harvesting

(gallons per acre)
327

0

5

10

15

20

25

Corn Soybeans Switchgrass Wheat (hard

red winter)

Rice (irrigated) Sorghum Oats Hay Cotton

Diesel Use (gal per acre)

Note: The range indicates the regions with the highest and lowest average diesel use rates.

Figure 2.4-5 shows FASOM assumptions for average gasoline use by crop in 2022, for

non-irrigated production without residue harvesting. The FASOM crop budgets do not include

gasoline use for switchgrass production.
328

Figure 2.4-5

FASOM Average Gasoline Use by Crop, 2022

Non-Irrigated, No Residue Harvesting

(gallons per acre)

0

1

2

3

4

5

Corn Soybeans Switchgrass Wheat (hard

red winter)

Rice

(irrigated)

Sorghum Oats Hay Cotton

Gasoline Use (gal per acre)

Note: The range indicates the regions with the highest and lowest average gasoline use rates.

FASOM crop budgets include electricity and natural gas use for irrigation water

pumping. Rice and sugarbeets are the only crops assumed to use natural gas for water pumping

(See Table 2.4-3.). Therefore, overall natural gas use in each scenario is dependent on changes

in these crops. For the rest of the irrigated crops that have private energy use for water pumping,

electricity is the assumed energy source, with the largest electricity consumption in the Great

Plains region (See Figure 2.4-6).

Table 2.4-3.

Natural Gas Usage for Irrigated Crop by Region, 2022

(1000 cu ft/acre)
329

Crop CB GP LS NE PNWE PSW RM SC SE SW

Rice 23.3 NA NA NA NA 0.0 NA 20.1 NA 0.0

Sugarbeet 26.1 9.7 26.1 NA 3.8 0.0 0.0 NA NA 0.0

Figure 2.4-6.

FASOM Electricity Use by Crop, 2022

Irrigated, No Residue Harvesting

(kWh per acre)

0

2

4

6

8

10

12

14

Corn Soybeans Wheat, Hard Red

Winter

Sorghum Oats Hay Cotton

Electricity Use (kWh per acre)

Note: The range indicates the regions with the highest and lowest average gasoline use rates.

Energy use for grain drying is calculated in FASOM based on assumptions that removing

10 percentage points of moisture from 100 bushels of grain requires 17.5 gallons of propane and

9 kWh of electricity. Thus, energy use per acre is calculated as the number of percentage points

of moisture to be removed multiplied by the yield per acre and the energy use per percentage

point and yield unit for each crop that is dried. Emissions are then calculated based on assumed

emissions factors per unit of energy use by energy type. Table 2.4-4 shows the average

emissions associated with grain drying that are used in FASOM. Drying rice is a relatively

energy intensive process, as reflected in the grain drying GHG emissions per acre. Emissions

from grain drying are included in the overall domestic agricultural GHG emissions estimates.
330

Table 2.4-4. FASOM Average Carbon Dioxide Emissions from Grain Drying by Region

(kgCO2e / acre)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Dryland

Corn 161.4 135.9 202.2 160.5 NA NA 66.1 24.5 43.8 15.2

Sorghum 99.4 22.3 NA 54.3 NA 17.7 NA NA NA NA

Soybeans 26.0 7.0 24.1 14.3 NA NA NA NA NA NA

Wheat, Durham NA 5.1 23.4 NA NA NA NA NA NA NA

Wheat, Hard Red Spring NA 6.7 25.4 NA 9.1 NA NA NA NA NA

Wheat, Hard Red Winter 51.3 11.1 51.6 34.5 NA 11.6 NA NA NA NA

Wheat, Soft White NA NA NA NA NA NA 11.6 NA NA NA

Irrigated

Corn NA 185.1 NA NA 132.6 121.6 103.2 21.0 NA 30.7

Rice 1,216.6 NA NA NA NA 1,667.3 NA 1,254.8 NA 1,400.8

Sorghum NA 33.0 NA NA NA NA NA NA NA NA

Soybeans NA 10.3 NA NA NA NA NA NA NA NA

Wheat, Durham NA 11.3 NA NA NA 21.0 NA NA NA NA

Wheat, Hard Red Spring NA 10.2 NA NA NA NA 17.6 NA NA NA

Wheat, Hard Red Winter NA 15.4 NA NA NA 22.6 NA NA NA NA

Wheat, Soft White NA NA NA NA NA NA 18.3 NA NA NA

Based on input data for each individual crop and the associated costs of production and

projected prices, the model predicts how the total U.S. agricultural sector will change with

increased feedstocks used for biofuel production. The results for total agricultural sector inputs

of the different fuel scenarios considered are shown in Table 2.4-5 through Table 2.4-8.
331

Table 2.4-5. Change in Domestic Agricultural Inputs under Corn Ethanol Scenario, 2022

Units

per mmBTU

Corn Ethanol Only

Scenario

Control

Scenario Difference

Percent

Change

Total N use Pounds 136.6 138.8 2.1 1.5%

Total P2O5 use Pounds 31.2 31.7 0.5 1.5%

Total K2O use Pounds 38.8 39.5 0.7 1.9%

Total Lime Use Pounds 104.2 104.7 0.5 0.5%

Herbicide Use Pounds 1.9 2.0 0.0 2.2%

Pesticide Use Pounds 0.4 0.4 0.0 2.8%

Total Diesel Fuel use Gal 14.3 14.2 -0.1 -0.5%

Total Gasoline use Gal 1.7 1.7 0.0 -0.9%

Total Electricity Use kWh 1.0 1.0 0.0 0.3%

Total Natural Gas Use BTU 248,002 234,746 -13,257 -5.6%

Table 2.4-6. Change in Domestic Agricultural Inputs in the Soy Biodiesel Scenario, 2022

Units

Per

mmBTU

Soy Biodiesel Only

Scenario

Control

Scenario Difference

Percent

Change

Total N use Pounds 437.1 435.3 -1.8 -0.4%

Total P2O5 use Pounds 99.2 99.4 0.2 0.2%

Total K2O use Pounds 123.3 124.0 0.7 0.6%

Total Lime Use Pounds 325.6 328.5 2.9 0.9%

Herbicide Use Pounds 6.2 6.2 0.0 0.3%

Pesticide Use Pounds 1.4 1.4 0.0 1.5%

Total Diesel Fuel use Gal 45.0 44.7 -0.4 -0.9%

Total Gasoline use Gal 5.3 5.4 0.1 1.6%

Total Electricity Use kWh 3.2 3.2 0.0 1.4%

Total Natural Gas Use BTU 833,308 736,362 -96,946 -13.2%

Table 2.4-7.

Change in Domestic Agricultural Inputs in the Corn Stover Ethanol Scenario, 2022

Units

per

mmBTU

Corn Stover Only Ethanol

Scenario

Control

Scenario Difference

Percent

Change
332

Units

per

mmBTU

Corn Stover Only Ethanol

Scenario

Control

Scenario Difference

Percent

Change

Total N use Pounds 74.6 75.8 1.2 1.5%

Total P2O5 use Pounds 16.8 17.3 0.5 3.1%

Total K2O use Pounds 19.3 21.6 2.3 10.8%

Total Lime Use Pounds 57.3 57.2 -0.1 -0.2%

Herbicide Use Pounds 1.1 1.1 0.0 0.6%

Pesticide Use Pounds 0.2 0.2 0.0 0.4%

Total Diesel Fuel use Gal 7.8 7.8 -0.1 -0.8%

Total Gasoline use Gal 0.9 0.9 0.0 0.0%

Total Electricity Use kWh 0.6 0.6 0.0 0.0%

Total Natural Gas Use BTU 133,037 128,201 -4,836 -3.8%

Table 2.4-8.

Change in Domestic Agricultural Inputs in the Switchgrass Ethanol Scenario, 2022

Units

per

mmBTU

Switchgrass Ethanol Only

Scenario

Control

Scenario Difference

Percent

Change

Total N use Pounds 44.5 45.9 1.4 3.1%

Total P2O5 use Pounds 9.8 10.5 0.7 6.2%

Total K2O use Pounds 12.5 13.1 0.6 4.3%

Total Lime Use Pounds 34.7 34.7 0.0 -0.1%

Herbicide Use Pounds 0.7 0.7 0.0 -1.1%

Pesticide Use Pounds 0.1 0.1 0.0 -3.0%

Total Diesel Fuel use Gal 4.8 4.7 -0.1 -1.1%

Total Gasoline use Gal 0.6 0.6 0.0 -4.2%

Total Electricity Use kWh 0.3 0.3 0.0 -0.2%

Total Natural Gas Use BTU 90,890 77,690 -13,200 -17.0%

The amounts shown in Table 2.4-5 through Table 2.4-8 were combined with GREET

defaults for GHG emissions from production of fertilizer and chemicals to calculate GHG

emissions changes. Fuel use emissions included both the upstream emissions associated with

production of the fuel as well as combustion emissions, also from GREET. Emissions from

electricity production represented average U.S. grid electricity production.

In addition to the GHG emissions associated with fertilizer and chemical production, and

fuel production and use, there are several other non-fossil fuel combustion related GHG sources

of emissions from the agricultural sector that would be impacted by the increased use of corn for

ethanol and associated changes to the agricultural sector. FASOM provides directly the GHG

emissions from these additional sources.

2.4.2.2 Domestic Nitrous Oxide Emissions

An important GHG impact from the agricultural sector is releases of nitrous oxide (N2O)

emissions. N2O can be released from a number of different N-input sources including inorganic
333

fertilizer, nitrogen fixing crops (e.g., soybeans), crop residues, and manure management. N2O

can be released either directly or indirectly through N leaching offsite.

Figure 2.4-7Figure 2.4-7 highlights some of the major sources of agricultural N2O

emissions.

Figure 2.4-7. Agricultural Sources of N2O Emissions

Crutzen et al. show that, as long as it includes both direct and indirect emissions, topdown accounting for N2O emissions are not inconsistent with the IPCC bottom –up approach to

N2O accounting.515 Since the publication of the NPRM, the N2O emission factors in FASOM

have been updated with the DAYCENT/CENTURY model by Colorado State University (CSU)
334

to more accurately estimate direct and indirect N2O emissions in cropland. The FASOM

modeling captures both direct N2O emissions from fertilizer application and N-fixing crops and

indirect emissions from leaching, volatilization well as from crop residue emissions and residue

burning, capturing all sources of N2O emissions and reflecting the most recent available science.

This section discusses the changes made using the CSU DAYCENT/CENTURY work. It details

the direct and indirect emissions from synthetic fertilizer, N fixing crops, and crop residue.

Figure 2.4-8 summarizes FASOM average direct and indirect N2O emissions per acre by

crop. Livestock N2O emissions and N2O emissions associated with international agriculture are

discussed in more detail in Section 2.4.2.3 and 2.4.3, respectively.

Figure 2.4-8.

FASOM Average N2O Emissions by Crop (Non-Irrigated, No Residue Harvesting)

(kgCO2e per acre)

0

100

200

300

400

500

600

700

N2O Emissions (kgCO2eq per acre)

Residue Burning

Crop Residues

Leaching

Volatilization

Direct -- Fertilizer Application
335

EPA worked with CSU to use the DAYCENT/CENTURY model to refine FASOM

accounting of direct N2O emissions from fertilizer application and indirect emissions associated

with nitrogen leaching, volitization, and surface runoff. Specifically, DAYCENT simulations

account for all N inputs to agricultural soils, including mineral N fertilizer, organic amendments,

symbiotic N fixation, asymbiotic N fixation, crop residue N, and mineralization of soil organic

matter.

CSU used the DAYCENT/CENTURY model to simulate a suite of domestic U.S. land

use and crop management in the 11 FASOM market regions (Table 2.4-2). The

DAYCENT/CENTURY simulations provided regression equations with coefficients for N2O

estimation that vary by region, crop type, irrigation status, and crop residue treatment. Each of

the 63 FASOM regions was assigned the coefficients for its respective super-region. The

regression equations were incorporated into FASOM to calculate N2O emissions per acre

according to region, crop, irrigation status, and crop residue treatment.

FASOM estimates N2O emissions from crop residues and residue burning using IPCC

guidelines, taking into account variation across regions in

 N content by crop based on yield,

 residue-to-crop ratio,

 percent dry matter,

 percentage of rice area burned in each state,

 burn and combustion efficiency,

 percent of residue burned by crop.

For crop residue emissions, FASOM assumes that 1% of N residing in crop residues that remain

on the field is emitted as N2O emissions, following IPCC guidelines.

Field burning of crop residues is not considered a net source of CO2, because the carbon

released to the atmosphere as CO2 during burning is assumed to be reabsorbed during the next

growing season. Field burning of crop residues, however, does emit N2O and CH4, which are

released during combustion. Field burning is not a common method of agricultural residue

disposal in the United States. The primary crop types whose residues are typically burned in the

United States are wheat, rice, sugarcane, corn, barley, soybeans, and peanuts.

FASOM assumes that a certain fraction of fields are burned each year, which results in

N2O emissions as well as CH4 emissions. Using the IPCC default value for burned residue,

FASOM assumes that, on average, 0.7% of N contained in the burned residue is emitted as N2O.

FASOM predicts minor reductions in GHG emissions from residue burning under the full RFS2

policy due to reductions in crop production with residues that are typically burned. In addition,

CH4 emissions are calculated based on the average methane emissions per acre; however, CH4

emissions are typically quite small relative to the other emissions tracked in FASOM.

2.4.2.3 Domestic Rice Production Emissions
336

Methane (CH4) emissions associated with rice production are also a source of noncombustion GHG emissions from the domestic agricultural sector. When rice fields are flooded,

aerobic decomposition of organic material gradually depletes most of the oxygen present in the

soil, causing anaerobic soil conditions. Once the environment becomes anaerobic, CH4 is

produced through anaerobic decomposition of soil organic matter by methanogenic bacteria.

Some of this CH4 is transported from the soil to the atmosphere through the rice plants via

diffusive transport. Minor amounts of CH4 also escape from the soil via diffusion and bubbling

through floodwaters.

FASOM assumes that all rice produced in the United States is grown in flooded fields

and emits CH4. Although there are potentially changes in water and soil management practices

that could be implemented to reduce methane emissions, FASOM assumes that reduction of rice

acreage is the only available method for reducing CH4 emissions from rice cultivation. Thus,

changes in CH4 emissions from rice cultivation result only from changes in the acreage planted

to rice in the model.

Methane emissions per acre are calculated based on regional emissions factors per acre

calculated for each region based on 2001 data from the EPA GHG inventory for 1990–2003 (see

Table 2.4-9).516 The model then calculates emissions from rice production based on emissions

factors for each region and the distribution of rice acreage in the model solution.

Table 2.4-9. FASOM Average Methane Emissions from Irrigated Rice Cultivation by

Region (kg CO2e / acre)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Rice 1,826.1 NA NA NA NA 1,783.4 NA 2,249.2 NA 4,375.0

Note: NA indicates not applicable, i.e., those crops were not cultivated under that irrigation status in that FASOM

region. In addition, there is no dryland rice or sugarcane production or irrigated hybrid poplar, switchgrass, or

willow production in FASOM.

As with other sources of emissions different management methods and other factors

(such as soil type and amounts of fertilization) will impact CH4 emissions from rice production.

With the exception of corn stover ethanol, FASOM projects that rice methane emissions will

decrease for all fuel pathways analyzed due to decreased domestic rice acreage (Table 2.4-10).

Table 2.4-10.

Change in Domestic Rice Emissions by Scenario, 2022

Corn

Ethanol

Soybean

Biodiesel

Corn Stover

Ethanol

Switchgrass

Ethanol

Rice Methane Emissions

(‘000 tons CO2e) -42 -506 159 -938

2.4.2.4 Domestic Livestock Emissions

Livestock production and management also contribute significant non-combustion GHG

emissions from the agricultural sector. GHG emissions from livestock come from two main

sources: enteric fermentation and manure management. Enteric fermentation produces CH4
337

emissions as a by-product of normal digestive processes in animals. During digestion, microbes

resident in an animal’s digestive system ferment food consumed by the animal. The amount of

CH4 produced and excreted by an individual animal depends primarily upon the animal’s

digestive system, and the amount and type of feed it consumes. Ruminant animals (e.g., cattle,

buffalo, sheep, goats, and camels) are the major emitters of CH4 because of their unique

digestive system.

FASOM projects changes in CH4 emissions associated livestock enteric fermentation due

to change in livestock herd number. Changes in production of crops used for feeds, such as corn

or soybeans, can impact feed prices which, in turn, drive livestock production and demand.

Enteric fermentation emissions from livestock are calculated based on the number of each

livestock type and on the average emissions per head. Average emissions per head are based on

2001 emissions values by livestock type and the number of livestock in each livestock category

reported in the EPA GHG inventory report for 1990–2003.517 There are emissions mitigation

options included within the FASOM model, but these options do not enter the market in the

absence of incentives for reducing CH4 emissions. Thus, enteric fermentation emissions are

affected only by the number of animals in each livestock category in this model. The FASOM

model generally predicts reductions in livestock herds as shown in Table 2.4-11.

Table 2.4-11. Change in Domestic Livestock Herd Size by Scenario, 2022

Corn Ethanol Soy-based

Biodiesel

Livestock Corn Stover Ethanol Switchgrass Ethanol

Type

mmHead % change mmHead % change mmHead % change mmHead % change

Dairy -0.02 -0.31% -0.01 -0.17% 0.00 -0.01% -0.02 -0.36%

Beef 0.09 0.14% -0.11 -0.18% 0.95 1.56% 0.21 0.34%

Poultry -58.84 -0.79% -58.84 -0.79% -58.84 -0.79% -58.84 -0.79%

Swine -0.22 -0.17% 0.24 0.19% 9.15 7.27% 7.80 6.20%

Enteric fermentation emissions increase across fuel pathway scenarios with the exception

of the soybean biodiesel scenario. Cattle numbers increase under the corn ethanol, corn stover

ethanol, and switchgrass ethanol scenarios. Cattle are ruminants, and therefore, increase in cattle

number results in increased CH4 emissions (Table 2.4-12). Cattle number decreases under the

soy-based biodiesel scenario, resulting in decreased methane emissions due to enteric

fermentation.

Table 2.4-12. Change in Domestic Livestock Emissions by Scenario, 2022

Corn Ethanol Soybean

Biodiesel

Corn Stover

Ethanol

Switchgrass

Ethanol

Enteric CH4 Emissions

(‘000 tons CO2e) 21 -128 1,129 338

Manure CH4 and N2O Emissions

(‘000 tons CO2e) -94 -5 2,194 1,751

Total Livestock Emissions

(‘000 tons CO2e) -73 -133 3,322 2,089

Use of DGS has been shown to decrease methane produced from enteric fermentation if

replacing corn as animal feed. This is due to the fact that the DGS are a more efficient feed

source. Consistent with our assumptions regarding the efficiency of DGS as an animal feed in

our agricultural sector modeling, we have also included the enteric fermentation methane
338

reductions of DGS use in our final rule analysis. Based on default factors in GREET, the model

assumed a decrease in CH4 (-3,381 g CO2e/mmBTU ethanol) per head of cattle and cows that

were fed with DGS. The reduction in CH4 is based on the same Argonne report used to

determine DGS feed replacement efficiency (discussed in RIA Chapter 5). This assumption

resulted in a reduction in the lifecycle GHG emissions for corn ethanol compared to the proposal

assumptions.

The management of livestock manure can also produce anthropogenic CH4 and N2O

emissions. CH4 is produced by the anaerobic decomposition of manure. N2O is produced

through the nitrification and denitrification of the organic nitrogen in livestock manure and urine.

The type of manure management methods impacts the quantity of GHG emissions emitted.

FASOM bases manure management emissions calculations on emissions factors for livestock

types and livestock management methods as reported in the EPA GHG inventory report for

1990–2003.518 Manure management emissions are projected to decrease as a result of lower

livestock herd values.

Under the corn ethanol and soybean biodiesel scenario, manure-associated GHG

emissions slightly decrease. Under the corn stover and switchgrass scenarios, swine production

markedly increases leading to an increase in total livestock emissions (Table 2.4-12).

2.4.2.5 Domestic Agriculture Sector Results (Excluding Land Use Change)

Table 2.4-13 provides a summary of FASOM projections for total GHG emissions

impacts for the domestic agricultural sector for each fuel pathway scenario analyzed. Land use

change impacts are discussed in Section 2.4.4.

Table 2.4-13.

Domestic Agriculture GHG Emission Changes by Scenario, 2022

(g CO2e/mmBTU)

Emission Source Corn Ethanol Soybean Biodiesel Corn Stover

Ethanol

Switchgrass

Ethanol

Fuel and Feedstock

Transport 4,265 3,461 2,418 2,808

Farm Inputs 10,313 6,482 2,770 4,890

Livestock (Manure and

Enteric Fermentation) -3,746 -2,100 9,086 3,462

Rice Methane -209 -7,950 434 -1,555

Total Domestic

Agriculture 10,623 -107 14,708 9,605

With the exception of soybean biodiesel, FASOM projects that increased biofuel

production in 2022 in the scenarios analyzed will lead to increased GHG emissions in the

domestic agricultural sector, excluding land use change. With increased volumes of each

biofuel, fuel and feed transport and farm inputs increase and thereby increase GHG emissions.

No one domestic agricultural sector emission source (excluding land use change) emerges as the

specific driver of GHG emissions across all fuel pathway scenarios. Rather, emission sources act

with varying degrees of importance in each scenario.
339

Overall the small impact in the domestic agricultural sector is due to the indirect effects

and demand changes, specifically demand changes in U.S. exports. For example, the sources of

corn used in ethanol production in the FASOM model are shown in. Some of the additional corn

comes from increased corn production; however, the increase in corn acres is mostly offset by

reductions in other crop acres as shown in Figure 2.4-10. Some of the corn used for ethanol

comes from decreased corn used for feed. During the corn ethanol production process, one of

the byproducts produced are distillers grains with solubles (DGS). DGS can be used as a feed

source for beef cattle, dairy cows, swine and poultry, and partially offsets the use of corn directly

as feed.

Figure 2.4-9.

Sources of Corn for Ethanol

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Other

Exports

Feed Use

Net Production

However, as seen from Figure 2.4-9, one of the sources of corn for ethanol production is

projected to come from reductions in corn exports. Therefore, the domestic agricultural sector

impacts are only a portion of the total impacts due to increased ethanol production in the U.S.

The change in corn and other crop exports will have impacts on the international agricultural
340

sector that need to be accounted for when determining lifecycle GHG impacts of biofuel

production in the U.S.

2.4.2.5 Translation of Domestic Impacts into International Impacts

In order to estimate the impact on international agricultural sector GHG emissions, the

FAPRI-CARD model was run with the same domestic biofuel volume scenarios, with the

exception of cellulosic ethanol, as was run in the FASOM model for the domestic agriculture

sector analysis. In the FAPRI-CARD models, links between the U.S. and international models

are made through commodity prices and net trade equations. In general, for each commodity

sector, the economic relationship that quantity supplied equals quantity demanded is achieved

through a market-clearing price for the commodity. In each country domestic prices are modeled

as a function of the world price using a price transmission equation. Since econometric models

for each sector can be linked, changes in one commodity sector will impact the other sectors.

The model for each commodity consists of a number of countries/regions, including a

rest-of-the-world aggregate to close the model. The models specify behavioral equations for

production, use, stocks, and trade between countries/regions. The models solve for representative

world prices by equating excess supply and demand across countries. Using price transmission

equations, the domestic price for each country is linked with the representative world price

through exchange rates. It is through changes in world prices that change in worldwide

commodity production and trade is determined.

When analyzing the impact of the RFS2 biofuel requirements in the U.S., there are two

primary domestic effects that directly affect a commodity’s worldwide use and trade: change in

exports, and changes in domestic U.S. prices. For example, as discussed above, the corn ethanol

biofuel requirement places an additional demand for corn used for ethanol, and this corn comes

not just from additional production, but also from decreases in other uses including exports. In

addition, as corn production expands, it places pressure in terms of relative demand on other

crops in a particular region in the U.S., which in turn affects their prices and use (including

exports). As the level of exports from the U.S. of a particular commodity decreases, other

countries will adjust their production and trade to satisfy the demand for that commodity.

Figure 2.4-10 shows the change in U.S. exports per by major commodity per thousand gallons of

biofuel, as projected by FASOM.

Figure 2.4-10. Normalized Changes in U.S. Exports by Crop, 2022

(tons per thousand gallons of renewable fuel)
341

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

Corn Ethanol Soy Biodiesel Sw itchgrass Ethanol Imported Ethanol

Tons per Thousand Gallons of Biofuel (Ethanol Equiv)

Wheat

Sorghum

Rice

Cotton

Barley

DG

Soybean Meal

Soybean Oil

Soybeans

Corn

As expected, an increase of a particular biofuel will have the greatest impact on U.S.

exports of that biofuel’s feedstock. For instance, with an increase of one thousand gallons of

corn ethanol, corn exports decrease by four tons, and with an increase of one thousand gallons of

soy biodiesel, soybean oil exports decrease by two tons. Increases in corn ethanol and soy

biodiesel will not only affect the crop area and export levels of its primary feedstock (corn and

soybean oil, respectively), but it will also affect other crops as increased demand for these

commodities change the relative demand, and therefore production and use, between different

commodities.

Although switchgrass and other cellulosic ethanol sources are not explicitly modeled in

FAPRI-CARD, the changes in acres for various crops as a result from an increase in switchgrass

ethanol as modeled by FASOM were applied to FAPRI-CARD on a regional basis in the U.S.

This provides a reasonable approximation of the effects of an increase of switchgrass acres in the

FAPRI-CARD model, and the affect it has on other crop area, production, prices and trade for

other crops.

In addition, we have modeled the impact of increased production of Brazilian sugarcane

ethanol for use in the U.S. market. The FAPRI-CARD model has been used to determine the

international impacts of Brazilian sugarcane ethanol production. The increase in Brazilian

sugarcane ethanol production is assumed to have no impacts on domestic U.S. agriculture

emissions.
342

As well as the change in U.S. exports, the FAPRI-CARD model relies on price

transmission equations, therefore changes in the U.S. price for a commodity will have a direct

impact on the world price for that commodity. This, in turn, will have an impact on the demand

for that commodity worldwide, and affect production and trade levels in other countries.

Additional information on the changes in the world price for commodities and the coordination

of assumptions between the FASOM and FAPRI-CARD models can be found in Chapter 5 of the

RIA.

2.4.3 Evaluation of International Agricultural GHG Emissions Impacts

For this analysis we used the FAPRI-CARD model to estimate the impacts on

international crop production due to changes in biofuel production. These results were used to

generate GHG emissions from the international agricultural sector, similar to what was done to

determine domestic agricultural GHG emission changes.

2.4.3.1 International Agricultural Inputs

The FAPRI-CARD model does not directly provide an assessment of the GHG impacts

of changes in international agricultural practices (e.g., changes in fertilizer load and energy use).

However, it does predict changes in crop area and production by crop type and country. We,

therefore, determined international fertilizer and energy use based on data collected by the Food

and Agriculture Organization (FAO) of the United Nations and the International Energy Agency

(IEA). For the final rule, we have also incorporated more up-to-date fertilizer consumption

statistics provided by a recent International Fertilizer Industry Association (IFA) report,

Assessment of Fertilizer Use by Crop at the Global Level, 2006/07 – 2007/08.519 For more

details refer to the memorandum to EPA from ICF International.520 Where country and crop

specific energy use data was available (in the case of Brazilian sugarcane), we used that data as

further discussed in Section 2.4.3.3.

We took the FAPRI-CARD provided activity data on changes in crop acres, by crop and

country, and multiplied by regional fertilizer use rate factors (kg per ha) to determine the global

impacts of biofuel production on fertilizer application. Historical fertilizer application rates for

nitrogen, phosphorus, and potassium were updated using more recent data available from the

IFA report as recommended by expert reviewers during our peer review.521 IFA data are

preferred over FAO’s Feristat data because estimates are more current and years of available data

are consistent across all countries. Also, FAO has altered its survey methodology since 2004.522

The IFA dataset covers 23 countries (considering the European Union-27 as one country)

and 11 crop groups including: wheat, rice, corn, other coarse grains, soybean, palm oil, other

oilseeds, cotton, sugar crops, fruits and vegetables, and other crops. IFA consumption data were

averaged over the two reported time periods: 2006 or 2006/07, and 2007 or 2007/08 to account

for seasonal variations in crop production. Fertilizer application rates were calculated by

dividing IFA total consumption values by FAOStat agricultural area harvested data from the

FAOStat database.523
343

The FAO Feristat dataset was also updated to the most recent version since the proposal.

Feristat data are used for country/crop and region/crop combinations not covered by the IFA

dataset. In addition, Feristat data is preferred to calculate fertilizer consumption for “rest of the

world” regions since the dataset provides for a greater number of countries and greater detail for

a variety of crops. Feristat fertilizer application rates (kg per ha) are calculated by dividing total

Feristat fertilizer consumption by Feristat agricultural area fertilized.

Figure 2.4-11 compares the nitrogen fertilizer application rates for major corn produces

around the word as determined with IFA and FAO data, with the U.S. as a reference for

comparison.

Figure 2.4-11. Nitrogen Application Rates for Corn in Select Regions

0

20

40

60

80

100

120

140

160

180

US Brazil Argentina China Mexico EU

Nitrogen Fertzilier Use (kg per ha)

Herbicide and pesticide use data have been updated using the most current data available

from FAO’s FAOStat dataset for pesticide consumption.524 FAO’s pesticide consumption

dataset did not provide values for China, and thus data was used from the U.S. Department of

Agriculture’s (USDA) Economic Research Service (ERS).525

We acknowledge that there may be other country and crop specific sources of fertilizer

data available for example for Brazilian sugarcane in addition to the IFA data, however, it was

not available in time for incorporation in the final rule analyses and furthermore the data used

provides a consistent dataset for all crops and countries.526 This is an area for future research for

any future analysis.

IFA does not collect data on lime use, however for the final rule we include lime use for

sugarcane based on data from Macedo (2008), estimated at 333.3 kg/ha.

We then used GREET factors for emissions from production of agricultural chemicals to

estimate the upstream GHG impacts of fertilizer and chemical production to calculate total

impacts for each fuel scenario with the exception of lime where we used data from Macedo to

represent lime production in Brazil. Table 2.4-14 provides the total change in fertilizer and

chemical use for the different fuel scenarios, per mmBTU renewable fuel.
344

Table 2.4-14.

International Change in Fertilizer and Chemical Use by Scenario, 2022

(kg/mmBTU)

Input Corn

Ethanol

Soy-Based

Biodiesel

Switchgrass Biochemical

Ethanol

Sugarcane

Ethanol

N Application 0.3683 0.0526 0.0774 0.4451

P Application 0.1780 0.6216 0.1302 0.1520

K Application 0.1245 0.6288 0.1179 0.5735

Herbicide Application 0.0006 0.0021 0.0006 0.0006

Pesticide Application 0.0009 0.0024 0.0008 0.0008

2.4.3.2 International N2O Emissions

For international N2O emissions we considered both direct and indirect emissions from

synthetic fertilizer application, crop residue N, and manure management. Manure management

emissions are discussed in the following section. Direct and indirect emissions from synthetic

fertilizer application and crop residues were calculated based on IPCC guidance as shown in

Table 2.4-15.527
345

Table 2.4-15. Calculations of N2O Emissions from Synthetic Fertilizer and Crop Residues

Direct N2O Emissions

Direct Emissions (Overall Equation) Equation

Emissions =

(N additions to soils from mineral fertilizer + N additions to soils from crop residues) ×

EF

EF for N additions from mineral fertilizer and crop residues = 0.01 kg N2O-N / kg N added

N Additions from Mineral Fertilizers

N additions to soils from mineral fertilizers = Kg fertilizer N applied to soils (i.e., change in fertilizer N applications from Table 2.4-14)

N Additions from Crop Residues

N additions to soils from crop residues =

above-ground residue dry matter × Crop Area × [N content of aboveground residues +

ratio of belowground residues to aboveground biomass × N content of belowground

residues]

where,

Above-ground residue dry matter and N additions to soils from crop residues = Taken from IPCC default values by crop

Indirect N2O Emissions

Note that for indirect emissions, the calculation of N applied to soils from fertilizers or crop residues is the same as for direct emissions

Indirect Emissions from Volatilization Equation

Emissions = N additions to soils from mineral fertilizers × N lost through volatilization × EF

N lost (from synthetic fertilizer additions) through volatilization = 0.1 (kg NH3–N + NOx–N) / kg N applied

EF for N lost through volatilization = 0.010 kg N2O–N / (kg NH3–N + NOX–N volatilised)

Indirect Emissions from Leaching/Runoff Equation

Emissions =

(N additions to soils from mineral fertilizers + N additions to soils from crop residues) ×

N lost through leaching or runoff × EF

N lost through leaching/runoff (from all N sources) = 0.3 N losses by leaching or runoff / kg N addition

EF for N lost through leaching/runoff = 0.0075 kg N20-N / kg N leaching or runoff
426

The proposal did not include N2O emissions from the Direct and Indirect Emissions from

Crop Residues for cotton, palm oil, rapeseed, sugar beet, sugarcane, or sunflower. These were

not included for these crops because default crop-specific IPCC factors used in the calculation

were not available.

Comments from our peer review process suggested that we include proxy emissions from

these crops based on similar crop types that do have default factors. Therefore, for our final rule

analysis we have included crop residue N2O emissions from sugarcane production based on

perennial grass as a proxy. Perennial grass is chosen as a proxy based on input from N2O

modeling experts. Emissions for cotton, palm oil, rapeseed, sugar beet, and sunflower were also

included based on root crops, other as a proxy.

Figure 2.4-12 summarizes N2O emissions by crop for a sample of crops and countries by

the four categories of N2O emissions.

Figure 2.4-12. Sources of N2O Emissions by Crop for Select Regions

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

corn

soybean

sugarcane

corn

soybean

corn

soybean

corncorn

Brazil Argentina China EU Mexico

N2O Emissions (kg CO2-eq/ha)

Indirect Crop Res N

Direct Crop Res N

Indirect Fertilizer N

Direct Fertilizer N

Based on the equations in Table 2.4-15
427
426

Table 2.4-15and the crop production changes projected by FAPRI-CARD, we estimated the

total change in N2O emissions for each fuel scenarios, as shown in Table 2.4-16.

Table 2.4-16.

International Crop Change in N2O Emissions in 2022 from Different Fuel Scenarios

(kg CO2e/mmBTU)

Corn

Ethanol

Soybean

Biodiesel

Switchgrass

Ethanol

Brazilian Sugarcane

Etanol

Direct and Indirect

N2O Emissions 3.38 2.09 0.95 29.25

2.4.3.3 International Fuel Combustion Emissions

In terms of evaluating international agriculture energy use data, we continued to use IEA

data as these are the best available for this purpose (providing data by end use and fuel type for

over 130 countries representing the major energy users of the world). We collected data from

IEA on total CO2 emissions from agricultural fuel combustion by country.528 We also collected

IEA data on agricultural electricity and fuel use by country, which was combined with emissions

factors to estimate country-level GHG emissions from agricultural electricity and fuel use.

Historical trends were used to project chemical and energy use in 2022. These total GHG

emissions were only combustion related, so we scaled them to represent full lifecycle GHG

emissions from fuel production, based on the ratio of combustion to full lifecycle GHG

emissions from U.S. fuel and electricity use. Country-level GHG emissions from agricultural

energy use were then divided by the area of agricultural land in each country, from the

FAOSTAT land area database to derive a per acre GHG emissions factor from agricultural

energy use by country. Our estimates use average energy consumption and GHG emissions per

acre for all crops in each foreign country. We multiplied these agricultural energy consumption

emissions factors by the country-level crop acreage changes projected by FAPRI-CARD to

determine the change in GHG emissions from foreign agricultural energy use for each fuel

scenario.

In the case of Brazilian sugarcane, we had country and crop specific data available to

estimate agricultural energy use.529 For sugarcane farming, energy use includes the diesel fuel

used to power farming equipment and energy use for sugarcane preparation. The energy used to

perform other activities and small services during productive operation was also included.

Energy embedded in farming equipment was not included in this calculation, as consistent with

other renewable fuel pathways. Table 2.4-17 shows how diesel consumption is expected to

increase in the future mainly due to increased use of diesel consumption with the growth of

mechanical harvesting and trash recovery.
427

Table 2.4-17. Energy Use (BTU/MT sugarcane)151

Activity 2002 2005/2006 2020

Ag Operations 15544 12606 14028

Harvesting 20568 31562 44453

Other Activities 36491 42462

Seeds 5592 5592 6256

Total 41704 86251 107198

For the final rule we assumed the energy use estimated in 2020 for agricultural

operations, harvesting, other activities and seed production would be similar to that in 2022, and

have adopted these estimates for our GHG calculations for the case where 40% of the trash

(sugarcane leaves and tops) are assumed to be collected as predicted in literature. When trash is

not assumed to be collected, we used 2005/2006 energy use data to account for the fact that less

energy use would be required as the collection of trash is not needed. We assume the energy use

is from 100% diesel.

Table 2.4-18 provides the total change in agricultural energy use GHG emissions for the

different fuel scenarios.

Table 2.4-18.

International Change in Agricultural Energy Use GHG Emissions by Scenario, 2022

(kg CO2e / mmBTU)

Corn

Ethanol

Soybean

Biodiesel

Switchgrass

Ethanol

Brazilian

Sugarcane

Ethanol

Agricultural Sector Energy

Use GHG Emissions 1.7 1.88 -0.16 5.14

2.4.3.4 International Rice Methane Emissions

To estimate rice emission impacts internationally, we used the FAPRI-CARD model to

predict changes in international rice production as a result of the increase in biofuels demand in

the U.S. Since FAPRI-CARD does not have GHG emissions factors built into the model, we

applied IPCC default factors by country.

Calculating emissions from rice cultivation, per the IPCC 2006 guidelines, requires the

following data: area of rice harvested, an emissions factor, and planting to harvesting season

length. Area of rice harvested by country was provided by the FAPRI-CARD results. The

default IPCC emission factors were used scaled for each cropping regime: irrigated, rainfed

lowland, upland and deepwater by country. Rice cultivation season lengths were available from

the International Rice Research Institute (IRRI).530

151 Converted from Macedo (2008) Table 9.
426

2.4.3.5 International Crop Residue Burning Emissions

International crop residue burning on the field, and specifically changes in residue

burning emissions could occur due to changes in U.S. biofuel policy. We specifically included

for the final rule analysis an estimate of sugarcane field burning and mechanical harvesting

emissions. We also incorporate emissions from land clearing for crop production as discussed in

Section 2.4.4.

Sugarcane leaves and tops are typically burned in the field before and after harvest. Per

metric ton of sugarcane, there is 280 kg of leaves and tops (with 50% moisture content) or 140

kg of dry leaves and tops, which we assume for modeling purposes. In the case where trash is

collected, it was assumed that 40% (i.e. 56 kg dry leaves and tops per metric ton of cane would

be recovered to be used as fuel at the mill).

Current trends in Brazil are moving from burned cane with manual harvesting to unburned cane

with mechanical harvesting. This change is related to the gradual reduction of cane trash preburning at both Federal and State levels in Brazil. See

Figure 2.4-13 for the phase out schedule for trash burning practices.

Figure 2.4-13. Phase Out Schedule for Trash Burning Practices

According to Brazil’s Sugarcane Research Center (CTC), about 47.5% of all sugarcane in

Brazil is already mechanically harvested, and 35.3% of all sugarcane in Brazil is mechanically

harvested without being burned in the field.531 These percentages have increased since 2002, see

Table 2.4-19.
427

Table 2.4-19. Sugarcane Harvest –2002 Situation

Type of harvest Sao Paulo (%) Center-South (%)

Manual 63.8 65.2

Mechanical 36.2 34.8

Burned sugarcane 75.0 79.1

Unburned sugarcane 25.0 20.9

UNICA states that in 2008 about half of the sugarcane fields in Sao Paulo were

mechanically harvested, up from 36.2% in 2002. In the future, Sao Paulo state law requires that

sugarcane field burning be phased-out by 2021 for areas where mechanical areas are possible

with existing technologies (over 85% of existing sugarcane fields) and where mechanical

harvesting may not be possible, 30% will be required to phase-out burning. This implies that by

2022 considering all areas in Sao Paulo about 90% of sugarcane fields could be unburned.

Sao Paulo currently accounts for 60% of all national production of sugarcane, with the

Center-South producing about 89% of all sugarcane. Considering that 89% of Brazilian

Production occurs in the Center-South, and Sao Paulo consists of a considerable portion of that

production, the following situation in Table 2.4-20 as assumed for Brazil in 2022.

Table 2.4-20. Sugarcane Harvest – Projected for 2022

Type of harvest Brazil (%)

Manual 0

Mechanical 100

Burned sugarcane 10

Unburned sugarcane 90

We took into account emissions from open-field burning from two pollutants, methane

(CH4) and nitrous oxide (N2O). Carbon dioxide (CO2) emissions were not taken into account

because the CO2 is considered to be taken from the air during sugarcane growth.

Table 2.4-21. Emission Factors of Open-Field Burning of Sugarcane Leaves and Tops532

Pollutant g/kg of dry leaves and tops burned

CH4 2.7

CO 92

N2O 0.07

NOx 2.5

PM10 7.8

PM2.5 3.9

SOx 0.4

VOC 7.0

2.4.3.6 International Livestock GHG Emissions

Similar to domestic livestock impacts associated with an increase in biofuel production,

internationally the FAPRI-CARD model predicts changes in livestock production due to changes

in feed prices. The GHG impacts of these livestock changes, enteric fermentation and manure
426

management GHG emissions, were included in our analysis. Unlike FASOM, the FAPRICARD model does not have GHG emissions built in and, therefore, livestock GHG impacts were

based on activity data provided by the FAPRI-CARD model (e.g., number and type of livestock

by country) multiplied by IPCC default factors for GHG emissions.

Table 2.4-22

Table 2.4-22 shows the changes in livestock predicted by the FAPRI-CARD model in

2022 for each of the fuel scenarios considered.

Table 2.4-22.

Foreign Livestock Changes by Region and Renewable Fuel, 2022

(head / billion BTU)

Corn Ethanol Dairy Beef Swine Sheep Poultry

Canada 0.00 0.05 -0.17 0.00 1.37

Western Europe 0.00 -0.07 0.12 0.02 1.58

Eastern Europe 0.00 -0.83 0.01 0.00 17.72

Oceania -0.02 0.11 0.01 0.07 3.53

Latin America -0.15 3.44 0.48 0.00 -0.46

Asia -0.09 0.17 -0.04 -1.19 -1.53

Africa and Middle East -0.03 -0.45 0.32 0.00 -3.01

Indian Subcontinent 0.00 0.12 0.02 0.00 -3.66

Soy-Based Biodiesel Dairy Beef Swine Sheep Poultry

Canada 0.02 0.27 1.62 0.00 -8.07

Western Europe -0.01 0.57 -1.16 -0.45 29.30

Eastern Europe 0.00 -0.59 -0.44 0.00 -114.88

Oceania 0.05 0.03 0.11 -0.20 -30.92

Latin America 0.45 -8.38 0.18 0.00 -0.06

Asia 0.33 0.92 0.60 6.67 -55.37

Africa and Middle East 0.01 2.11 0.82 0.00 2.65

Indian Subcontinent 0.00 0.27 0.06 0.00 -34.31

Sugarcane Ethanol Dairy Beef Swine Sheep Poultry

Canada 0.00 0.00 -0.10 0.00 0.47

Western Europe 0.00 -0.01 0.07 0.01 -0.32

Eastern Europe 0.00 -0.12 0.02 0.00 4.56

Oceania 0.00 0.01 -0.01 0.00 0.96

Latin America -0.03 0.22 -0.08 0.00 -0.09

Asia -0.01 -0.01 -0.14 -0.32 0.13

Africa and Middle East -0.01 0.00 -0.06 0.00 -0.58

Indian Subcontinent 0.00 -0.01 0.00 0.00 -0.43

Switchgrass Ethanol

(Biochemical) Dairy Beef Swine Sheep Poultry

Canada 0.00 0.00 -0.11 0.00 0.72

Western Europe 0.00 -0.01 0.06 0.02 -0.34

Eastern Europe 0.00 -0.12 0.05 0.00 6.36

Oceania 0.00 0.02 0.00 0.00 1.10

Latin America -0.03 0.04 0.07 0.00 -0.11

Asia -0.02 0.00 0.13 -0.47 0.48

Africa and Middle East -0.01 0.05 0.03 0.00 -0.77

Indian Subcontinent 0.00 -0.01 0.00 0.00 -0.44
427

The enteric fermentation GHG impacts of livestock changes were calculated by applying

regional default factors for enteric fermentation CH4 emissions by livestock type. These factors

are shown in Table 2.4-23.

Table 2.4-23. Enteric Fermentation Emission Factors

Enteric Fermentation

(kg CH4/head/year) Dairy Cattle Swine Sheep

North America 121 53 1.5 8

Western Europe 109 57 1.5 8

Eastern Europe 89 58 1.5 8

Oceania 81 60 1 5

Latin America 63 56 1 5

Asia 61 47 1 5

Africa and Middle East 40 31 1 5

Indian Subcontinent 51 27 1 5

Manure management GHG impacts of livestock changes for each fuel scenario were

calculated by applying regional default factors for manure management CH4 and N2O emissions

by livestock type. Manure management CH4 emission factors are shown in Table 2.4-24.

Manure management N2O values were based on default IPCC nitrogen produced per livestock

type and IPCC default manure management practices by region.

Table 2.4-24. Manure Management Methane Emission Factors

Manure Management (kg CH4/head - year) Dairy Cattle Swine Sheep Poultry

North America 78 2 23.5 0.28 0.02

Western Europe 51 15 15.5 0.28 0.02

Eastern Europe 27 13 6.5 0.28 0.02

Oceania 29 2 18 0.15 0.02

Latin America 1 1 1 0.15 0.02

Asia 18 1 4 0.15 0.02

Africa and Middle East 1.5 1 2 0.15 0.02

Indian Subcontinent 5 2 4 0.15 0.02

Based on the peer review of the methodology used for the proposal it was determined that

the calculations for manure management did not include emissions from soil application. These

emissions were included for our final rule analysis but do not cause a significant change in the

livestock GHG emission results.

2.4.3.7 International Agriculture Sector Results (Excluding Land Use Change)

Table 2.4-25 provides an overview of the total GHG emissions impacts from the

international agricultural sector based on the results of the FAPRI-CARD modeling. As

discussed above, emissions from farm inputs include the production, transport and fate of

agricultural inputs including pesticide, fertilizer and other chemicals. The farm inputs category

also includes energy used in crop production processes. Land use change impacts are discussed

in Section 2.4.4.
426

Table 2.4-25.

Foreign Agriculture GHG Emission Changes in 2022 from Different Fuel Scenarios

(g CO2e/mmBTU)

Corn Ethanol Soy Biodiesel Sugarcane Ethanol Switchgrass Ethanol

Farm Inputs 6,601 5,402 37,884 1,310

Livestock Production 3,458 -6,436 -128 -245

Rice Methane 2,089 2,180 485 -920

2.4.4 Land Conversion GHG Emissions Impacts

Our lifecycle GHG estimates include emissions from domestic and international land use

conversions induced by increased renewable fuels consumption in the United States. To estimate

land conversions GHG emissions we answered six key questions:

1. How much land is converted?

2. Where does land conversion occur?

3. What types of land are converted?

4. What are the GHG emissions impacts from that land conversion?

5. How do we account for the variable timing of land conversion GHG releases?

6. What is the level of uncertainty in our land conversions GHG emissions estimates?

This section describes our approach for answering these questions about land use change.

We used the FASOM model to project land conversions in the United States. FASOM was

designed to simulate domestic land use interactions and land use change GHG impacts. We used

the FAPRI-CARD model to project international cropland expansion in response to increased

United States biofuel consumption. We used the FAPRI-CARD international models to project

changes in the area of land used for crop production and pasture. FAPRI-CARD does not,

however, project which types of land would be cleared to make room for additional agricultural

land uses, or where within in each country or region agricultural expansion would likely take

place. To fill this information gap we used MODIS satellite data provided by Winrock

International, Inc. (from now on referred to as Winrock), that shows recent land use change

patterns from 2001 to 2007. To determine the GHG impacts of the projected land conversions

we applied GHG emissions factors prepared by Winrock following IPCC guidelines. To account

for the variable timing of land use change GHG impacts, we annualized land use change GHG

impacts over 30 years (with a 0% discount rate).

To quantify the uncertainty in our quantification of GHG emissions from international

land conversions, we focused on two areas: uncertainty in the MODIS satellite data used to

determine the types of land affected (e.g., forest or grassland), and uncertainty in the our land

conversion GHG emissions factors (i.e., the GHG emissions per unit area of land conversion).

To reduce and quantify the uncertainty in the MODIS satellite data we utilized extensive data

validation efforts by NASA, which we used to correct systematic errors in the MODIS data set

and to quantify the remaining uncertainty. To quantify the uncertainty in land conversion GHG

emissions factors, we evaluated the uncertainty in every data input based on the quality, quantity,

resolution and variability in the underlying data sources. Correlation groups were assigned based
427

on the sources of underlying data. Finally, the total uncertainty in international land use change

GHG emissions was quantified a Monte Carlo analysis following Tier 2 IPCC guidelines.

This section describes the data sources and methods used for the analysis summarized

above, with key results illustrated throughout.

2.4.4.1 Evaluation of Domestic Land Conversion GHG Emissions Impacts

We used FASOM to project U.S. land use change under each fuel-specific pathway

scenario due to the increase in respective renewable fuels and then the change in GHG emissions

that result from the changes in land use. FASOM was designed to simulate land use interactions

to predict the types of land converted in the U.S. (See RIA Chapter 5 for more details). In this

section we discuss FASOM modeling of land conversion and related GHG emissions as well as

final calculations for GHG emissions on a per mmBTU basis for each fuel-specific pathway

scenario.

2.4.4.1.1 Area and Location of Domestic Land Conversions

How land is used in FASOM is determined through the relative profits from various

activities. This varies not only between crops, but also between different land uses, such as

pasture for livestock production. A number of updates have been made to the FASOM model

since the analysis for the Proposal in order to have a more complete assessment of land use in the

U.S. One of these updates includes the incorporation of the forestry component of the FASOM

model. Running both the forestry and agriculture components of the model for the final

rulemaking analysis shows the interaction between these two sectors as they compete for land in

various regions, as well as the effect on products and prices in each respective sector.

In addition, FASOM also includes a representation of seven major land use categories,

including cropland, cropland pasture, forestland, forest pasture, rangeland, developed land, and

acres enrolled in the Conservation Reserve Program (CRP). These categories are based on the

USDA National Agriculture Statistics Service (NASS), and enable the FASOM model to

explicitly link the interaction between livestock, pasture land, cropland, and forest land, as well

as have a detailed accounting of acres in the U.S. across different land uses. Cropland is actively

managed cropland, used for both traditional crops (e.g., corn and soybeans) and dedicated energy

crops (e.g., switchgrass). Cropland pasture is managed pasture land used for livestock

production, but which can also be converted to cropland production. Forestland contains a

number of sub-categories, tracking the number of acres both newly and continually harvested

(reforested), the number of acres harvested and converted from other land uses (afforested), as

well as the amount of forest acres on public land. Forest pasture is unmanaged pasture land with

varying amounts of tree cover that can be used to raise livestock. A portion of this land may be

used for timber harvest. Rangeland is unmanaged land that can be used for livestock grazing

production. While the amount of rangeland idled or used for production may vary, rangeland

may not be used for any other purpose than for animal grazing. For each of these categories,

FASOM accounts for how much is actively used in production, and how much idled, in a

particular time period.
426

Another update to the FASOM model is the distillers grains and solubles (DGS)

replacement rates for corn and soybean meal in animal feed. These replacement rates are based

on recent research published by Argonne National Laboratory533 that demonstrate higher

replacement rates than what was used in the analysis for the Proposal. This means that DGS, as

a byproduct of corn ethanol production, are relatively more efficient compared to the Proposal’s

analysis, and results in less corn and soybean meal needed for animal feed. This, in turn, results

in less corn and soybean production needed for use in animal feed relative to the Proposal.

Further discussion of changes made to the FASOM model can be found in Chapter 5.

For the corn ethanol scenario, the FASOM model estimates that total cropland area used

for production increases by 1.4 million acres in 2022. This is a result of an increase of 3.7

million acres of corn, a decrease of 1.3 million acres in soybeans, as well as changes in other

crop acres. Similarly, total cropland area increases by 1.9 million acres in the soybean biodiesel

scenario, which consists of an increase of 3.5 million acres of soybeans, decreases of 0.6 million

acres each of corn and switchgrass, as well as a variety of other changes. In the switchgrass

ethanol scenario, total cropland acres increases by 4.2 million acres, including an increase of

12.5 million acres of switchgrass, a decrease of 4.3 million acres of soybeans, a 1.4 million acre

decrease of wheat acres, a decrease of 1 million acres of hay, as well as decreases in a variety of

other crops. Table 2.4-26 summarizes the change in total cropland acres used in production, both

total and normalized by changes in biofuel volume, and Figure 2.4-14 shows the changes for

each crop in each fuel-specific volume scenario.

Table 2.4-26.

Change in total area of domestic cropland used for production by scenario, in 2022

Scenario Total Cropland Increase

(million acres)

Normalized Cropland Increase

(acres per thousand gallons, ethanol equivalent)

Corn Ethanol 1.4 0.12

Soybean Biodiesel 1.9 0.39

Switchgrass Ethanol 4.2 0.04

Corn Stover Ethanol 0.6 0.06
427

Figure 2.4-14. Normalized Domestic Crop Acreage Changes by Scenario, 2022

(acres per thousand gallons, ethanol equivalent)

-4

-3

-2

-1

0

1

2

3

4

5

6

Corn Ethanol Soy Biodiesel Sw itchgrass Ethanol Corn Residue Ethanol

Acres per 1000 gallons (EtOH Equiv)

Other Crops

Cotton

Hay

Oats

Sorghum

Rice

Wheat

Sw itchgrass

Soybeans

Corn + Silage

2.4.4.1.2 Types of Domestic Land Conversions

Based on relative demand for crop and livestock production changes that result from

increased demand for biofuels, there are direct effects on land used for crop and livestock

production, as well as indirect effects on other land types in the U.S. For instance, in 2022, as

demand for corn ethanol increases in the corn ethanol scenario, total cropland (used for

production and idled) increases by 0.9 million acres, total cropland pasture decreases by 0.9

million acres, total forest pasture increases by 0.2 million acres, forestland decreases by 0.03

million acres. As soybean biodiesel increases (in the biodiesel scenario), total cropland increases

by 1.2 million acres, cropland pasture decreases by 1.0 million acres, forest pasture increases by

0.2 million acres, and forestland decreases by 0.2 million acres. With an increase in switchgrass

ethanol, cropland increases by 2.2 million acres, cropland pasture decreases by 1.5 million acres,

forest pasture increases by 0.6 million acres, and forestland decreases by 0.6 million acres.

In the corn stover scenario, an increase in ethanol from corn stover does not directly

result in crop acre changes, merely an increase in the harvesting of residue from existing corn

acres. However, an increased demand for ethanol from corn stover does inherently give more

value per acre of corn with residue removal. Based on corn residue removal possibilities by

region, there are relatively small changes to land uses with an increase in corn stover ethanol

production. Specifically, cropland increases by 0.07 million acres, cropland pasture increases by

0.05 million acres, forest pasture acres do not change, and forestland acres decrease by 0.2

million acres.
426

The number of acres enrolled in CRP does not vary between volume scenarios because in

each scenario, the maximum amount is taken from the program and converted to cropland for

production, all leaving the assumed minimum limit of 32 million acres, in accordance with the

2008 Farm Bill. The number of acres in rangeland does not vary because rangeland acres are not

suitable for any other use than unmanaged land for livestock production. The only change in

rangeland is whether or not a certain number of acres are actively used for production, or

whether they remain idle. Lastly, developed land is assumed to be of higher value than all other

land categories, and FASOM assumes that the amount of developed land increases at a steady

rate over time, and does not vary with changes in demand for biofuel.

Figure 2.4-15. Change in Domestic Land Use by Type, 2022

-3

-2

-1

0

1

2

3

Corn Ethanol Soy Biodiesel Sw itchgrass Ethanol Corn Stover Ethanol

Millions of Acres

Developed Land

Rangeland

CRP

Forestland

Forest Pasture

Cropland Pasture

Cropland

Note: Some of these land use categories are not used in GHG emission calculations

2.4.4.1.3 Quantification of GHG Emissions from Domestic Land Conversions

Domestic land use change GHG emissions are based on outputs of the FASOM model.

FASOM models the changes in GHG emissions and sequestration due to changes in land

management. FASOM explicitly models change in soil carbon due to change in crop

production acres and in crop type. In addition, FASOM’s forestry module models the change in

above-ground biomass and below-ground biomass carbon stock and soil carbon in the forestry

sector due to land conversion. With the addition of the forestry module for the final rulemaking,

we have used FASOM to model changes in soil carbon and biomass carbon due to land use

conversion between cropland, pasture, forestland, and developed land.
427

In addition to quantifying GHG emissions and sinks, FASOM distinguishes the unique

time dynamics and accounting issues of carbon sequestration options. These include issues such

as saturation of carbon sequestration over time (i.e., carbon sequestration in a particular sink

reaches an equilibrium such that carbon storage is maintained, but is no longer increasing),

potential reversibility of carbon benefits (e.g., due to changes in tillage, forest harvests,

wildfires), and fate of carbon stored in products after forest harvest.

GHGs, generally in the form of carbon, can be sequestered in soils, standing trees, other

vegetation, and wood products. Sequestration refers to storage of the GHGs for more than one

year. As a consequence, the sequestration definition used in the model for standing vegetation is

limited to carbon storage in trees, understory, and litter within both forests and plantations of

woody biofuel feedstocks (poplar and willow) but excludes, for instance, carbon stored in

annually cultivated crops. Carbon sequestration is also modeled within cropland soils,

pastureland soils, soils in idled lands, timberland soils, and harvested wood products. In

addition, changes in sequestration for lands that move out of forestry and agricultural production

into some form of developed usage such as housing, shopping centers, and roads are tracked in

the model.

In the subsections below, we detail FASOM accounting of carbon stock changes from

agricultural land and forestry land. We also describe EPA’s use of FASOM GHG accounting to

project the changes in GHG emissions associated with domestic land use change for each

renewable fuel-specific pathways for the year 2022.

2.4.4.1.4 Domestic Agricultural Soil GHG Accounting

FASOM models the change in agricultural soil carbon due to land conversion and

changes in crop patterns. The FASOM GHG factors for agricultural land conversion are based

on factors for different crops, management practices, and land conversion effects (e.g.,

converting pasture to crop production). As EPA committed in the NPRM, the FASOM

agricultural land GHG emission factors were updated with new DAYCENT/CENTURY model

runs to reflect the most recent science available.

Agricultural soil carbon sequestration depends on management activities that influence

carbon storage per acre. Baseline carbon storage is estimated from the baseline distribution of

land across tillage practices, irrigation status, land use, and cropping patterns, assuming carbon

sequestration rates are equal to those at equilibrium.

Intensity of agricultural tillage. Agricultural soils have traditionally been tilled; however,

tillage breaks up soil aggregates and increases the exposure of soil organic matter to

oxygen, which speeds oxidation and results in reduced soil carbon with an associated

release of CO2 into the atmosphere. The use of tillage alternatives that reduce soil

disturbance and therefore reduce oxidation of soil organic matter will increase soil

carbon sequestration. Reduced tillage practices also leave crop residues on the soil,

thereby potentially increasing carbon inputs.
426

Irrigation status. The DAYCENT/CENTURY model shows differences in soil carbon

sequestration per acre for a given region between irrigated and dryland cropland

systems.152 For irrigated sites, the increased yields are expected to increase biological

activity and therefore increase soil carbon sequestration compared to dryland

cropland.

Relative abundance of grasslands. Generally, pastureland and CRP land experience less

soil disturbance than actively tilled croplands and store more carbon per acre. Thus,

changes in the distribution of land between pastureland, cropland, and land in the

CRP will affect agricultural soil carbon sequestration.

Mix of annuals versus perennials. Perennial crops are not tilled on an annual basis

typically show a reduction in soil disturbance relative to actively tilled annual crops.

By definition in FASOM, perennial crops such as switchgrass, hybrid poplar, and

willow are produced under zero tillage.

Changes in agricultural soil carbon due to changes in tillage, irrigation status, or land use

are generally assumed to take place over a number of years as the soil carbon levels adjust to a

new equilibrium. In FASOM, soil carbon levels are assumed to reach a new equilibrium after 25

years, although almost 94% of the adjustment takes place within 15 years (see Figure 2.4-16).153

Figure 2.4-16. Percentage Adjustment over Time to New Soil Carbon Equilibrium

Following Change in Land Use or Management

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Year 0 Year 5 Year 10 Year 15 Year 20 Year 25

% Adjustment over Time

Because movement of soil carbon sequestration towards equilibrium levels is not

constant over time, FASOM yields non-uniform changes in soil carbon consistent with the

152 All pastureland and CRP land in FASOM are assumed to be produced in dryland systems.

153 There is an immediate jump in carbon storage in year 0 due to changing tillage, irrigation, and/or land use that

depends on the initial state and the new state. The dynamics discussed and shown in Figure A-5 refer to the change

over time from the initial state under new management/land use conditions to the equilibrium for that state.
427

generally accepted scientific finding that carbon sequestered in an ecosystem approaches steadystate equilibrium under any management alternative. The rate of change in carbon storage

decreases over time and eventually reaches zero at the new equilibrium (saturation). See Figure

2.4-16.534 Soil carbon per acre may increase or decrease depending on the land use change or

change in land management taking place.

FASOM also estimates N2O emissions from cropland and pastureland due to land use

change based on Colorado State University DAYCENT/CENTURY models. See Section 2.4.2.2

for a full discussion.

2.4.4.1.5 Evaluation of GHG Emissions Impacts from Domestic Forests

One of the largest carbon pools is carbon sequestered in forests. Carbon is stored not just

in the live and standing dead trees, but also in understory, forest floor and coarse woody debris,

and forest soil. Harvesting timber causes a reduction in carbon sequestration, although some of

the carbon that was in the harvested trees will continue to be stored in forest products for some

time afterward. If harvested stands are replanted, then there is little loss in forest soil carbon, and

carbon sequestration in trees planted in that stand will increase over time.

The FASOM model estimates change in carbon stock of above-ground and below-ground

biomass in continuous and afforesting forestland. It accounts for carbon storage in forest

products and emission streams from these products over time. It also takes into account

changing management practices (e.g., harvest cycles). Land converted from forestry to

agricultural or other uses, however, will have a much greater permanent reduction in carbon

sequestration. We summarize the forest carbon accounting procedures used in FASOM

below.535

Forest carbon accounting in FASOM follows the FORCARB model developed by the

U.S. Forest Service and used in the periodic aggregate assessments of forest carbon

sequestration. Tree carbon is the largest forest carbon pool and is modeled as a function of three

factors: (1) merchantable volume, (2) the ratio of growing stock volume to merchantable

volume, and (3) and parameters of a forest volume-to-biomass model developed by U.S. Forest

Service researchers.536 Harvest age is allowed to vary; thus, the growth of existing and

regenerated/afforested stands must be modeled. Timber growth and yield data are included for

existing stands, reforested stands, and afforested lands that track the volume of wood in each

unharvested stand, which, in turn, is used in computing forest carbon sequestration. These data

indicate the wood volume per acre in unharvested timber stands for each timber stand strata (e.g.,

a stand giving location, forest type, management intensity class) by age cohort. The data used

are derived largely from the U.S. Forest Service RPA modeling system.537 Merchantable

volume, by age, on each representative stand is obtained from the timber growth and yield tables

included in FASOM. The volume factors and biomass model parameters vary by species and

region and are obtained from538,539 and Smith et al. (2003).540

Carbon in live and standing dead trees is calculated using the parameters of the forest

volume-to-biomass model equations for live and dead tree mass densities (above- and

belowground) in Smith et al.,541 weighted for the FASOM region/forest type designations.

Forest land area data reported by the RPA assessment542 are used to calculate the appropriate
426

weights. Birdsey’s assumption that the mass of wood is approximately 50% carbon is used to

derive the associated levels of carbon.543

Soil carbon is the second-largest pool of carbon. Treatment of soil carbon follows

Birdsey544,545 and recent work by Heath, Birdsey, and Williams.546 FASOM computes soil

carbon profiles using soil carbon data over time from Birdsey.547,548 As Heath, Birdsey, and

Williams noted, little change in soil carbon occurs if forests are regenerated immediately after

harvest. 549 As a result, FASOM assumes soil carbon on a reforested stand remains at a steadystate value. Currently, the age that this value is reached is assumed to be the minimum harvest

age for FASOM region/forest type. This assumption is generally consistent with the ages at

which steady-state levels of soil carbon are achieved in Birdsey.550,551 Afforested land coming

from crop or pasture use start with the initial soil carbon value for that land/region combination

reported by the Century Model, which was developed by Colorado State University.154 The land

then accumulates carbon until reaching the steady-state value for forests of the type planted in

the region afforestation takes place (where steady state is assumed to be reached at the minimum

harvest age in FASOM for that region/forest type).

Forest floor carbon constitutes the third largest carbon storage pool, but is much smaller

than tree or soil carbon pools. FASOM bases its forest floor carbon estimates on the model

developed by Smith and Heath552 to estimate forest floor carbon mass. The model’s definition of

forest floor excludes coarse woody debris materials; that is, pieces of down dead wood with a

diameter of at least 7.5 cm that are not attached to trees.553 In order to account for this material,

coarse woody debris is assumed to be a fixed fraction of live tree carbon based on ratios of

coarse woody debris carbon to live tree carbon.554 This value is then added to the forest floor

carbon values generated by Smith and Heath’s forest floor model. The model for net

accumulation of forest floor carbon is a continuous and increasing function of age, although the

rate of accumulation eventually approaches zero (i.e., forest floor carbon reaches a steady state).

Understory vegetation comprises the smallest component of total carbon stock and

includes all live vegetation except trees larger than seedlings. FASOM makes the assumption

that understory carbon is a fixed fraction of live tree carbon and uses published ratios reported in

U.S. EPA Inventory of U.S. GHG Emissions and Sinks555 as the basis for these calculations.

When timber is harvested, FASOM tracks the fate of the carbon that had been sequestered on the

harvested land.

154 The current version of the CENTURY agroecosystem model simulates carbon, nitrogen, phosphorus, and sulfur

dynamics through an annual cycle over time scales and centuries and millennia. CENTURY is capable of modeling

a wide range of cropping system rotations and tillage practices for analysis of the effects of management and climate

on agroecosystem productivity and sustainability. The model has undergone numerous enhancements since the

original version developed in Parton, W.J., D.S. Schimel, C.V. Cole, and D.S. Ojima. 1987. Analysis of factors

controlling soil organic matter levels in Great Plains grasslands. Soil Science Society of America Journal 51:1173-

1179.
427

Figure 2.4-17Figure 2.4-17 summarizes the disposition of carbon following harvest. To

calculate carbon in harvested logs, cubic feet of roundwood (the units in which timber is

quantified in the model) is converted into metric tons of carbon using factors reported in Skog

and Nicholson.556 These factors vary by region and are reported for logs coming from an

aggregate softwood and hardwood stand. They exclude carbon in logging residue left onsite.

Logging residue is tracked separately in the forest floor carbon pool described above.

Harvested logs removed from site are converted into three types of outputs through

primary manufacturing processes: wood and paper products, mill residues, and fuel wood (See

Table 2.4-27 for a list of products tracked by FASOM). The fate of each of these outputs is

discussed below.

Table 2.4-27. Wood and paper products tracked by FASOM

Product

softwood sawlogs for export

hardwood sawlogs for export

softwood lumber

softwood plywood

oriented strand board

hardwood lumber

hardwood plywood

softwood miscellaneous products

hardwood miscellaneous products

softwood used in non-OSB reconstituted panel

hardwood used in non-OSB reconstituted panel

softwood pulpwood

hardwood pulpwood

The distribution of product carbon changes over time. FASOM tracks the fate of product

carbon with two carbon pools: carbon remaining in-product and carbon leaving the product

(Figure 2.4-17). Carbon that leaves the product ultimately is emitted or permanently sequestered

in landfills.
426

Figure 2.4-17. Carbon Disposition after Timber Harvest557

FASOM determines the fraction of carbon that remains in products using specified halflife values for a set of end-use categories (Table 2.4-28).558 The half-life represents the time it

takes for approximately half of the product to decompose. Skog and Nicholson559 assumed that

Carbon Disposition After

Harvest

Harvested Logs Removed from

site

Primary FASOM

Manufacturing

Wood and Paper Products (i =

1,...,13)

Mill Residue

Fuelwood

Logging Residue

Down Dead Wood (Coarse Woody

Debris)

Accounted for in on-site module

Wood and Paper Product

Disposition over Time

Emission Fuel Offset

Emission

In-product or Landfill

Secondary Manufacturing
427

67% of carbon leaving the wood product pool and 34% of carbon leaving the paper product pool

goes to landfills (Figure 2.4-18). The remainder of the carbon leaving the wood and paper

product pools is emitted as CO2 into the atmosphere.

Table 2.4-28. Half-life for Forest Products in End Uses560

End Use or Product Half-Life in Years

Paper 2

New residential construction

Single family 100

Multifamily 70

Mobile homes 12

Residential upkeep & improvement 30

New nonresidential construction

All ex. railroads 67

Railroad ties 12

Railcar repair 12

Manufacturing

Household furniture 30

Commercial furniture 30

Other products 12

Shipping

Wooden containers 6

Pallets 6

Dunnage etc. 6

Other uses for lumber and panels 12

Uses for other industrial timber products 12

Exports 12

Figure 2.4-18. Wood and Paper Product Carbon Disposition561
426

FASOM tracks the fate of mill residue using two different pools. The first is for mill

residue that is used as an intermediate input in the production of wood and paper products. This

carbon is tracked using the appropriate product category as described above. The second pool is

for carbon in mill residue that is burned for fuel. The fraction burned in each region based on

Smith et al.562 It was assumed that one-third of mill residue burned is used to offset fossil fuels.

Harvested fuel logs and the associated carbon are used as to produce energy at mills. For fuel

wood, FASOM assumes that 100% of fuel wood burned in the sawtimber and pulpwood

production process is used to offset fossil fuels.

In FASOM, land used in forestry can move to agriculture or developed use, resulting in a

dynamic change in carbon storage levels on the previously forested land. When land moves

from forestry to agricultural use or developed use, FASOM tracks carbon in residual forest floor

carbon and in soil carbon. FASOM’s model of forest floor decay is based on the average forest

floor of mature forests and regional averages for decay rates, as described in Smith and Heath.563

When forested land is converted to agricultural use, soil carbon levels are consistent with

DAYCENT/CENTRUY model data on agricultural soil carbon for the appropriate category of

agricultural land and do not vary over time. When forest is converted to developed land uses,

FASOM assumes that soil carbon levels are consistent with the steady-state value of the

minimum harvest age.

2.4.4.1.6 Aggregate GHG Emissions Impacts from Domestic Land Conversions in 2022

In the FASOM model the difference in GHG emissions and sequestrations from

agricultural and forest lands related to land use changes are not only a function of the difference

between the land use change and management practices in 2022 under two scenarios, but are also

dependent on previous changes in land use and practices under the two scenarios. For instance,

different land use patterns under two scenarios may result in differing harvesting cycles.

Total Carbon in Wood and

Paper Products

Emission

Carbon

remaining in

product

Landfill

Landfill

(Permanent)

Landfill

(Temporary)

Carbon

leaving

product
427

Ideally, an emissions comparison of land use patterns in two scenarios would capture the

changes and associated emissions that lead to the 2022 land use status in both scenarios.

Because FASOM generates GHG emissions estimates associated with land use change for every

five year period over the time horizon of the model run, EPA was able to calculate the

cumulative GHG emissions change for each fuel-specific pathway and for the RFS2 policy. We

then annualized the cumulative change.

To calculate the annualized cumulative GHG emissions due to land use change for a

specific fuel, we first summed all emissions associated with agricultural land (CO2 and N2O from

cropland, pastureland, CRP land) and forestland (CO2 from biomass, soil, and forest products)

between the years 2000 and 2022 for the control and fuel-specific scenarios. Emissions from

soil, decaying biomass, and forest products can occur over several years or decades. FASOM

tracks such emission streams over time. We included in the cumulative GHG emissions from

land use change all emission streams due to changes that occurred between 2000 and 2022 for

the thirty year time horizon (See Section 2.4.5) after 2022.

We report these results as CO2 equivalent mass and then normalize the results on an

mmBtu basis (Figure 2.4-19).

Figure 2.4-19. Change in GHG Emissions Due to Domestic Land Use Change by Scenario,

2022, Annualized Over 30 Years

2.4.4.2 International Land Conversion GHG Emissions Impacts

2.4.4.2.1 Area and Location of International Land Conversions

We used the FAPRI-CARD international agricultural models to determine the amount of

international land use change resulting from the renewable fuel volumes mandated by RFS2.

The FAPRI-CARD model provides a dynamic projection of how policy or economic shocks will
426

affect international agricultural commodity markets, and the resulting area of land used to

produce agricultural goods. FAPRI-CARD accounts for several key factors that affect the

amount/area of international land use change: crop yield growth rates over time, price-induced

crop yield changes, crop yields on marginal/new land, the efficiency of renewable fuel coproducts over time, supply and demand in the livestock sector, and many other significant

variables. More details about the FAPRI-CARD model and our assumptions are provided in

RIA Chapter 5.

2.4.4.2.2 Area and Location of International Cropland Conversions

To determine the area of land use change caused by increased consumption of each of the

renewable fuels analyzed (i.e., corn ethanol, soy-based biodiesel, sugarcane ethanol and

switchgrass ethanol) we used the FAPRI-CARD model to simulate the scenarios outlined in

Table 2.3-12.4-1. By varying only one type of renewable fuel in each scenario we isolated the

impacts for each fuel type. The land use change results are the difference between each scenario

and the control case, and are normalized by dividing by the incremental increase in renewable

fuel production in a given scenario and year, on an energy-content basis. Table 2.4-29 shows

foreign (i.e. not including the United States) crop area changes in thousands of harvested

hectares (000s ha), and the normalized changes in hectares per billion British Thermal Units (ha/

billion BTU), for each of the scenarios considered.155 Note that we focus on the change in land

use between scenarios in 2022. 156

Table 2.4-29. Changes in International Crop Area Harvested by Renewable Fuel, 2022

Scenario

International Crop Area

Change (000s ha)

Normalized Crop Area

Change (ha / billion BTU)

Corn Ethanol 789 3.94

Soy-Based Biodiesel 678 10.65

Sugarcane Ethanol 430 4.38

Switchgrass Ethanol 1,358 2.25

The location of land use changes is a critical factor in the determination of land use

change GHG impacts, because the GHG impacts of land conversions varies substantially by

region. For example, deforestation in the tropics releases substantially more carbon than

deforestation in drier regions. The FAPRI-CARD model allocates crop area changes across 54

regions based on a number of factors, including existing trade patterns, regional costs of

production, and the potential for agricultural expansion in each region. Normalized crop area

changes by region and renewable fuel are shown in

Figure 2.4-20, with 12 aggregated regions for purposes of illustration. Once again, land use

changes in the United States are excluded from the figure, except for the case of sugarcane

ethanol.

155 The sugarcane ethanol scenario includes land use changes in the United States as projected by FAPRI-CARD.

For all of the other renewable fuels, domestic land use changes were determined with FASOM as described in the

previous section.

156 We assumed 76,000 BTU/gallon of ethanol; 115,000 BTU/gallon of biodiesel and 2.471 acres/hectare.
427

Figure 2.4-20. Normalized Harvested Crop Area Changes by Renewable Fuel, 2022

(ha / billion BTU)

-2

0

2

4

6

8

10

12

Africa &

Middle

East

Asia Oceania Brazil Canada Eastern

Europe &

Russia

India Other

Latin

America

Rest of

World

United

States

Western

Europe

World

Soy Biodiesel

Corn Ethanol

Sugarcane Ethanol

Switchgrass

Ethanol

The projected net changes in crop area are the result of many factors, including shifting

among different types of crops in each region. For example, for scenarios where corn ethanol

production increases in the United States, we project a domestic shift from soybean production to

corn production, and a shift toward oilseeds production in other countries to fill the gap in lost

U.S. output. The following figures illustrate projected changes in harvested area by crop type

and region for each renewable fuel scenario. All results are from the FAPRI-CARD model, with

changes in the United States are included for illustrative purposes.
426

Figure 2.4-21. Harvested Crop Area Changes by Crop and Region

Corn Ethanol Scenario, 2022 (000s ha)

-1,000

-500

0

500

1,000

1,500

2,000

Africa &

Middle East

Asia Brazil Canada Eastern

Europe

India Oceania Other Latin

America

Rest of World US Western

Europe

World

Corn

Soybeans

Rapeseed

Wheat

Sugarcane

Rice

Palm

Sorghum

Cotton

Oats

Sunflower Seed

Sugar Beet

Peanut

Dry Beans

Barley

Hay

All Crops

Figure 2.4-22. Harvested Crop Area Changes by Crop and Region

Soy-Based Biodiesel Scenario, 2022 (000s ha)

-400

-200

0

200

400

600

800

1,000

1,200

Africa &

Middle East

Asia Brazil Canada Eastern

Europe

India Oceania Other Latin

America

Rest of World US Western

Europe

World

Corn

Soybeans

Rapeseed

Wheat

Sugarcane

Rice

Palm

Sorghum

Cotton

Oats

Sunflower Seed

Sugar Beet

Peanut

Dry Beans

Barley

Hay

All Crops

Figure 2.4-23. Harvested Crop Area Changes by Crop and Region

Sugarcane Ethanol Scenario, 2022 (000s ha)
427

-300

-200

-100

0

100

200

300

400

500

600

700

Africa &

Middle East

Asia Brazil Canada Eastern

Europe

India Oceania Other Latin

America

Rest of World US Western

Europe

World

Corn

Soybeans

Rapeseed

Wheat

Sugarcane

Rice

Palm

Sorghum

Cotton

Oats

Sunflower Seed

Sugar Beet

Peanut

Dry Beans

Barley

Hay

All Crops

Figure 2.4-24. Harvested Crop Area Changes by Crop and Region

Switchgrass Ethanol Scenario, 2022 (000s ha)

-3,500

-3,000

-2,500

-2,000

-1,500

-1,000

-500

0

500

1,000

1,500

Africa &

Middle East

Asia Brazil Canada Eastern

Europe

India Oceania Other Latin

America

Rest of World US Western

Europe

World

Corn

Soybeans

Rapeseed

Wheat

Sugarcane

Rice

Palm

Sorghum

Cotton

Oats

Sunflower Seed

Sugar Beet

Peanut

Dry Beans

Barley

Hay

All Crops

Note: Switchgrass harvested area is not included in the figure.

2.4.4.2.3 Area and Location of International Pasture Land Conversions
426

In addition to considering international changes in crop area, our analysis also accounts

for changes in pasture area, i.e., land used for livestock grazing. Accounting for pasture area is

essential to understand the land use change impacts of renewable fuels, because renewable fuel

production can affect the livestock sector which uses pasture. Furthermore, more land is used

globally for pasture than for crop production.564 The new, more detailed, representation of Brazil

in the FAPRI-CARD model (see RIA Chapter 5 for more details) explicitly accounts for changes

in pasture area, therefore, accounting for the competition between crop and pasture land uses.

Furthermore, the FAPRI-CARD Brazil module allows for livestock intensification, i.e., the

increasing the heads of cattle per unit area of land in response to higher commodity prices,

increased demand for land, or other reasons. In addition to modifying how pasture is treated in

Brazil, we also improved the methodology for calculating pasture area changes in other

countries.

In the proposed rule, we made a broad assumption that the total land area used for pasture

would stay constant in each country or region. Thus, in the proposed rule, we assumed that any

crop expansion onto pasture would necessarily require an equal amount of pasture to be replaced

on forest or shrubland. For the final rule we relaxed this assumption, and we now account for

changes in pasture area resulting from livestock fluctuations and therefore capture the link

between livestock and land used for grazing. Based on regional pasture stocking rates (i.e.,

livestock per hectare), we now calculate the amount of land used for livestock grazing. As a

result of this analytical improvement, in countries where we project decreased livestock numbers

we also project less land needed for pasture. Therefore, unneeded pasture areas are available for

cropland or allowed to revert to their natural state. In countries where livestock numbers

increase, more land is needed for pasture, which can be added on abandoned cropland or unused

grassland, or it can result in deforestation. This new methodology provides a more realistic

assessment of land use changes, especially in regions where livestock populations are changing

significantly.

A multi-step process was used to translate the FAPRI-CARD livestock projections to

pasture area changes. First, the FAPRI-CARD projections for dairy cattle and beef cattle (i.e., all

non-dairy cattle) and sheep were converted to animal unit equivalents (AUE) using IPCC data

(see Table 2.4-30).565 Next, average stocking rates for each of the 54 FAPRI-CARD regions

were determined with data on livestock populations from the UN Food and Agricultural

Organization (FAO)566 and data on pasture area measured with agricultural inventory and

satellite-derived land cover data.567 The FAO data set provides a globally consistent estimate of

livestock units per country. The estimated stocking rates are listed in Table 2.4-31. Expert

judgment was used to adjust unusually low regional stocking rates. For example, we removed

serious outliers from the "CIS, Other" FAPRI-CARD region.157 Specifically Kazakhstan and

Turkmenistan were removed from this calculation because these countries include vast stretches

of desert pasture used for rotational sheep grazing. Removing these outliers provided a more

realistic estimate of how cattle population changes would affect land use in this region. For

157 CIS, Other includes the following countries in the Commonwealth of Independent States: Armenia, Azerbajian,

Belarus, Georgia, Kazakhstanm, Krygyzstan, Moldova, Tajikistan, Turkmenistan, Uzbekistan
427

other regions that had unreasonably low stocking rate factors, we set the stocking rates equal to a

neighboring country with a more reasonable factor. 158

Based on the data sources considered, some regions had very high stocking rates due in

part to the use of intensive livestock operations, such as feedlots. We did not adjust these

stocking rates because we would not expect livestock population changes to have a large impact

on pasture area in these regions.

Table 2.4-30. Animal Unit Equivalents (Livestock Units per Head)

Region Dairy Beef Sheep

Canada 1.33 0.86 0.18

Western Europe 1.32 0.93 0.18

Eastern Europe 1.21 0.86 0.18

Oceania 1.10 0.73 0.10

Brazil 0.88 0.67 0.10

Other Latin America 0.88 0.67 0.10

Asia 0.77 0.70 0.10

Africa & Middle East 0.61 0.38 0.10

India 0.61 0.24 0.10

US 1.33 0.86 0.18

Rest of World 1.00 0.69 0.13

Source: IPCC Vol. 4, Ch.10

158 These adjustments were made after consulting experts at Iowa State University and USDA who suggested that,

although the best available data was used, the calculated stocking rates in many regions were unreasonably low.

One reason for this, and part of the justification for adjusting the stocking rates upward, is that the data used

considered all pasture land globally, including areas (e.g., Kazakhstan) with extraordinarily low stocking rates. The

adjustments help to account for the fact that we would expect biofuel-induced livestock changes to affect globally

integrated livestock regions, and these regions would likely not exhibit very low stocking rates.
426
427

Table 2.4-31. Pasture Stocking Rates by FAPRI-CARD Region

(Livestock Units / Ha)

FAPRI-CARD Regions Stocking Rate Notes/Adjustments

Algeria 0.50 equals Tunisia

Argentina 0.41

Australia 0.41 equals world average

Bangladesh 25.25

Brazil: Amazon Biome 0.95

Brazil: Central-West Cerrados 1.00

Brazil: Northeast Coast 0.87

Brazil: North-Northeast Cerrados 0.90

Brazil: South 1.62

Brazil: Southeast 0.94

from FAPRI-CARD Control Case, 2022

Canada 0.64

China 0.41

New Zealand 1.14

Colombia 0.60

Cuba 1.02

Egypt 0.44 equals Iraq

EU 1.45

Guatemala 0.74

India 9.22

Indonesia 4.11

Iran 0.22

Iraq 0.44

Ivory Coast 0.45 equals Guinea

Japan 9.63

Malaysia 1.99

Mexico 0.45

Morocco 0.50 equals Tunisia

Myanmar (Burma) 11.41

Nigeria 0.74

Africa, Other 0.33 Zambia, Chad and Botswana removed

Asia, Other 0.34 Mongolia & Singapore removed

CIS, Other 0.45 Kazakhstan & Turkmenistan removed

Eastern Europe, Other 0.37

Latin America, Other 0.52 Bolivia removed

Middle East, Other 0.30

Pakistan 3.64

Paraguay 0.35

Peru 0.37

Philippines 17.16

Rest of World 0.41 equals world average

Russia 0.41 equals China

South Africa 0.33 equals Africa, Other

South Korea 35.14

Taiwan 0.41 equals China

Thailand 17.01

Tunisia 0.50

Turkey 0.61

Ukraine 0.49

Uruguay 0.67

United States 0.46

Uzbekistan 0.45 equals CIS, Other

Venezuela 0.63

Vietnam 8.95

Western Africa 1.89

World 0.41
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As described above, pasture intensification was modeled endogenously in Brazil by the

FAPRI-CARD model. In the FAPRI-CARD model, pasture intensification was a function of

many factors, including livestock and crop prices, and competition for land between grazing and

crop production uses. In general, the FAPRI-CARD results produced pasture intensification

elasticities of 5-10% (i.e., the % change in pasture intensification resulting from a % change in

livestock population). For regions outside of Brazil we used a simple pasture intensification

factor of 10% in regions where livestock populations increased.

Table 2.4-32 shows total and normalized international pasture area changes for each of

the scenarios considered.159 The pasture area results are largely driven by the relative changes in

the livestock markets. In scenarios where beef and dairy production (which require pasture)

declined more than swine and poultry production (which do not require pasture), the pasture area

changes were larger. Section 2.4.3.6 includes international livestock production results for each

scenario, and RIA Chapter 5 discusses the determining factors for these changes, such as coproduct and livestock feed ration efficiencies.

Table 2.4-32. Changes in International Pasture Area by Renewable Fuel, 2022

Scenario

International Pasture Area

Change (000s ha)

Normalized Pasture Area

Change (ha / billion BTU)

Corn Ethanol -446 -2.23

Soy-Based Biodiesel -268 -4.20

Sugarcane Ethanol -164 -1.67

Switchgrass Ethanol -580 -0.96

Note: Only the Sugarcane ethanol scenario results include United States land use changes.

The location of pasture area changes is very important when analyzing these results. As

the above table shows, we projected that global pasture area would decrease in all of our

renewable fuel scenarios. However, pasture area changes resulted in positive net GHG

emissions in some cases because of the location of the resulting land conversions. For example,

in the corn ethanol scenario we projected a significant increase in pasture area in the Brazilian

Amazon, which causes large GHG emissions. Figure 2.4-25 illustrates pasture area changes by

region. Figure 2.4-26 includes pasture area changes by region in Brazil.

159 The sugarcane ethanol scenario includes land use changes in the United States as projected by FAPRI-CARD.

For all of the other renewable fuels, domestic land use changes were determined with FASOM as described in the

previous section. Thus, in Table 2.4-32 only the sugarcane ethanol scenario includes United States land use change

results.
427

Figure 2.4-25. Normalized Pasture Area Changes by Renewable Fuel, 2022

(ha / billion BTU)

-10

-8

-6

-4

-2

0

2

4

Africa &

Middle

East

Asia Oceania Brazil Canada Eastern

Europe

& Russia

India Other

Latin

America

Rest of

World

United

States

Western

Europe

World

Soy Biodiesel

Corn Ethanol

Sugarcane Ethanol

Switchgrass Ethanol

Figure 2.4-26. Normalized Pasture Area Changes in Brazil by Renewable Fuel, 2022

(ha / billion BTU)

-10

-8

-6

-4

-2

0

2

Amazon

Biome

CentralWest

Cerrados

Northeast

Coast

NorthNortheast

Cerrados

South Southeast Brazil

Corn Ethanol

Soy Biodiesel

Sugarcane Ethanol

Switchgrass Ethanol

2.4.4.2.4 Area and Location of International Cropland and Pasture Land Conversions

As discussed above, in the proposed rule we made a broad assumption that the total land

area used for pasture would stay constant in each country or region. Thus, in the proposed rule,

we assumed pasture area could not decrease in regions where crop area increased. In the final

rule analysis we used a more sophisticated approach that captures a wider range of potential

interactions between crop and pasture areas. For example, in regions were pasture decreases,
426

this land is available for crop expansion. Therefore, it is important to look at both the crop and

pasture area changes together to understand the land use change GHG emissions impacts.

Figure 2.4-27 shows the crop and pasture area changes for each scenario. Brazil is broken out as

a separate region because, as the figure shows, it is the most important country in terms of its

response in livestock production and pasture area.

Figure 2.4-27. Normalized International Land Use Change by Renewable Fuel

(ha / billion BTU)

-10

-5

0

5

10

15

Corn Ethanol

Soy Biodiesel

Sugarcane Ethanol

Switchgrass Ethanol

Corn Ethanol

Soy Biodiesel

Sugarcane Ethanol

Switchgrass Ethanol

Corn Ethanol

Soy Biodiesel

Sugarcane Ethanol

Switchgrass Ethanol

Brazil Rest of World World

Pasture

Crop

Total

Finally, the following figures compare the proposed rule land use change projections to

the land use change results in the final rule. Notice that in the final rule pasture area decreases in

many regions, whereas in the proposed rule we assumed that pasture area could not decline. As

discussed in the next sections, this had a large impact on the types of land conversions projected,

and on the resulting GHG emissions impacts.
427

Figure 2.4-28. Proposed Rule and Final Rule Comparison

Normalized International Land Use Changes

Corn Ethanol, 2022 (ha / billion BTU)

-4

-2

0

2

4

6

8

10

12

14

16

Proposal Final Proposal Final Proposal Final

Brazil Rest of World World

Pasture Proposal

Crop Proposal

Pasture Final

Crop Final

Figure 2.4-29. Proposed Rule and Final Rule Comparison

Normalized International Land Use Changes

Soy-Based Biodiesel, 2022 (ha / billion BTU)

-15

-10

-5

0

5

10

15

20

Proposal Final Proposal Final Proposal Final

Brazil Rest of World World

Pasture Proposal

Crop Proposal

Pasture Final

Crop Final
426

Figure 2.4-30. Proposed Rule and Final Rule Comparison

Normalized International Land Use Changes

Sugarcane Ethanol, 2022 (ha / billion BTU)

-4

-2

0

2

4

6

8

10

12

14

16

Proposal Final Proposal Final Proposal Final

Brazil Rest of World World

Pasture Proposal

Crop Proposal

Pasture Final

Crop Final

Figure 2.4-31. Proposed Rule and Final Rule Comparison

Normalized International Land Use Changes

Switchgrass Ethanol, 2022 (ha / billion BTU)

-2

0

2

4

6

8

10

Proposal Final Proposal Final Proposal Final

Brazil Rest of World World

Pasture Proposal

Crop Proposal

Pasture Final

Crop Final

2.4.4.2.5 Evaluation of the Types of International Land Conversions

As explained in the previous section, the FAPRI-CARD international models were used

to project changes in the area of land used for cropland and pasture in 54 regions. In this section

we describe the two-step procedure that was used to determine in more detail the types and

locations of land conversions:
427

1. The FAPRI-CARD output was disaggregated into 12 land conversion categories.

2. The land cover types affected (e.g., forest or grassland) and the location of land

conversions (i.e., by Administrative Unit) were evaluated with MODIS Version 5 satellite

data from 2001-2007.

2.4.4.2.5.1 Determination of International Land Conversion Categories

Based on the FAPRI-CARD model results, we determined the conversions between

annual crops, perennial crops, pasture land, and natural ecosystems in each of the 54 FAPRICARD regions. First, the FAPRI-CARD land use change projections (both positive and negative

changes in area) were broken into three categories for each region: annual crops, perennial crops

and pasture.160 We used a rule-based approach to determine the interaction of these three

agricultural land uses with natural eco-systems in each region. These rules are summarized

below in order of priority:

1. Annual and perennial crop areas interact with each other, e.g., where annual crop area

increases and perennial crop area decreases, annual crops expand onto the land

previously used for perennial crops.

2. Pasture and crop area interact with each other, e.g., where pasture area decreases and crop

area increases, crops expand onto the land previously used for pasture.

3. Changes in the total area of land used for agriculture affect previously non-agricultural

areas.

Following the 3 rules listed above, the FAPRI-CARD projections were disaggregated into

12 land conversion categories, where natural eco-systems include forests, grasslands, savannas,

shrublands, wetlands and barren land:

 Annual Crops to/from Perennial Crops

 Pasture to/from Perennial Crops

 Pasture to/from Annual Crops

 Natural Ecosystems to/from Annual Crops

 Natural Ecosystems to/from Perennial Crops

 Natural Ecosystems to/from Pasture

Table 2.4-33 illustrates the results of this process with the results in Argentina for each

scenario.161 The FAPRI-CARD Results columns show the projected change in area for annual

crops (Annl), perennial crops (Prnnl) and pasture (Pstr). Positive numbers indicate expansion

and negative numbers indicate contraction. These results were translated into the 6 Land

Conversion columns, where positive numbers indicate conversion in the direction shown in the

header row, and negative numbers indicate a conversion in the opposite direction. For example,

in the Ntrl to Annl (i.e., Natural Ecosystems to Annual Crops) column, a positive number

160 The perennial crops included in the FAPRI-CARD model are sugarcane and palm oil.

161 The results for all 54 FAPRI-CARD regions are included in the public docket.
426

indicates conversion of natural ecosystems to annual crop production. A negative number in the

Ntrl to Annl column indicates reversion of annual crops back to natural ecosystems.

Table 2.4-33. Argentina Land Conversion Categories by Renewable Fuel, 2022

(Ha / billion BTU)

FAPRI-CARD Results Land Conversions

Scenario

Annl

Crops

Prnnl

Crops Pstr

Annl to

Prnnl

Pstr to

Prnnl

Pstr to

Annl

Ntrl to

Annl

Ntrl to

Prrnl

Ntrl to

Pstr

Corn Ethanol 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00

Soy Biodiesel 0.26 0.00 -0.48 0.00 0.00 0.26 0.00 0.00 -0.22

Sugarcane Ethanol -0.18 0.01 0.00 0.01 0.00 0.00 -0.17 0.00 0.00

Switchgrass

Ethanol 0.52 0.00 0.06 0.00 0.00 0.00 0.51 0.00 0.06

2.4.4.2.5.2 Evaluation of International Land Conversion Patterns with Satellite Data

In the proposed rule analysis land use change patterns were estimated at the national scale

(and sub-national scale for key countries) using 1-km resolution MODIS products for the years

2001-2004.568 MODIS land cover products were chosen originally due to their global, multiyear coverage, low cost and homogenous classification scheme. For the final rule analysis we

used the higher resolution, and more recent, MODIS Version 5 (MODIS V5) land cover dataset

which covers the years 2001-2007 with 500-meter resolution.

To assess the accuracy/uncertainty in our use of MODIS satellite data we performed a

Monte Carlo analysis based on the underlying uncertainty in the satellite data as quantified by

NASA. The MODIS data set is routinely validated by NASA’s MODIS land validation team.162

NASA uses several validation techniques for quality assurance and to develop uncertainty

information for its products. NASA’s primary validation technique includes comparing the

satellite classifications to data collected through field and aircraft surveys, and other satellite data

sensors. The accuracy of the MODIS V5 land cover product was assessed over a significant set

of international locations, including roughly 1,900 sample site clusters covering close to 150

million square kilometers. The results of these validation efforts are summarized in a “confusion

matrix” which compares the satellite’s land classifications with the actual land types observed on

the ground.569 We used this information to assess and correct the accuracy and systematic biases

in the published MODIS data. Our analytical procedures are summarized below and discussed in

more detail in a technical report by ICF International, Inc., available on the public docket.570 The

full Monte Carlo model, with all data inputs and results, is also publicly available.

The key data source that allows us to understand the accuracy of the MODIS V5 product

is a confusion matrix published by researchers that work as part of the MODIS land validation

team. As explained by Dr. Mark Friedl:

The confusion matrix is a commonly used tool for assessment of accuracy for land

cover classifications. The matrix scores how the classification process has

162 More information about the MODIS Land Validation procedures is available from the NASA Goddard Space

Flight Center website, http://landval.gsfc.nasa.gov/
427

labelled a series of test sites or test pixels at which the correct land cover label is

known. Typically, the true class label is displayed across rows, while the actual

mapped class is displayed in columns. The diagonal of the confusion matrix

displays the number of sites or pixels for which the true class and the mapped

class agree. The overall accuracy of the entire sample is then the sum of the

diagonal elements divided by the total of all sites or pixels. For individual classes,

the marginal totals of the matrix can easily be used to estimate the producer’s

accuracy and user’s accuracy from the sample. The producer’s accuracy is the

probability that a pixel truly belonging to class i is also mapped as class i, while

the user’s accuracy is the probability that a pixel mapped as class i is truly of

class i.571

The MODIS V5 confusion matrix includes 17 land use/ land cover categories developed

by the International Geosphere-Biosphere Programme (IGBP). As shown in Table 2.4-34, we

aggregated the confusion matrix data to match the 10 land categories used in our analysis. The

resulting aggregate confusion matrix is shown below in Table 2.4-36.

Table 2.4-35Table 2.4-35 is a number key for the land cover classes presented in

confusion matrix.

Table 2.4-34. Aggregation of IGBP land cover classes into EPA land cover classes

EPA Land Cover

IGBP Land Cover Class Classes

Proposed

Rule

Final

Rule

Evergreen Needleleaf Forest Forest

Evergreen Broadleaf Forest Forest

Deciduous Needleleaf Forest Forest

Deciduous Broadleaf Forest Forest

Mixed Forest Forest Forest

Closed Shrubland Shrubland Shrubland

Open Shrubland Shrubland Shrubland

Woody Savanna Savanna Savanna

Savanna Savanna Savanna

Grasslands Grassland Grassland

Permanent Wetlands Excluded Wetland

Cropland Cropland Cropland

Cropland/Nat Veg

Mosaic Excluded Mixed

Barren/Sparse Excluded Barren

Snow and Ice Excluded Excluded

Water Excluded Excluded
426

Table 2.4-35. EPA land cover class number key

Number Key EPA Land Cover Class

1 Annual Crops

2 Forest

3 Grassland

4 Mixed

5 Savanna

6 Shrubland

7 Wetland

8 Barren

9 Perennial Crops

10 Excluded

Table 2.4-36. MODIS Version 5 confusion matrix with aggregated EPA land cover classes

Satellite Classification Label

Training Site

Label 1 2 3 4 5 6 7 8 10

Training

Total

1 6,963 0 118 84 77 73 60 2 127 7,504

2 25 7,763 5 42 564 52 482 1 5 8,939

3 414 3 1,938 26 279 570 40 77 111 3,458

4 498 103 22 402 264 69 105 0 0 1,463

5 300 422 172 102 2,331 275 233 16 0 3,851

6 148 34 341 10 279 3,135 71 111 8 4,137

7 19 59 5 0 6 0 2,406 0 0 2,495

8 4 0 14 0 27 334 0 4,802 1 5,182

10 0 0 13 0 0 1 12 4 2,411 2,441

Satellite Total 8,371 8,384 2,628 666 3,827 4,509 3,409 5,013 2,663 39,470

Note: values for perennial crops (land class 9) were assigned with a procedure described in the ICF report.572

The confusion matrix contains information about the accuracy of the satellite data which

can be used statistically to correct systematic biases. The matrix includes data from 39,470

training sites where the MODIS land team validated the satellite classification labels with on-theground training site surveys. For example, if we look at forest (land class 2) in the matrix, we

see that of the 8,384 sites that were classified by the satellite as forest (see the satellite total row)

7,763 of these sites (see the diagonal in row 2) were correctly classified. The quotient of these

figures (i.e. 7,763/8,384 = 92.6%) gives us what it is known as the producer’s accuracy for

forest. The user's accuracy for forest, 86.8%, can also be calculated by using the training site

total for forest in the denominator (i.e., 7,763/8,939 = 86.8%).
427

Furthermore, we can determine which land classes forestlands tended to be misclassified

as (i.e., confused with), which classes tended to be misclassified as forests, and the probability of

each specific misclassification. Table 4 presents the producer's accuracy matrix for MODIS

Version 5 using EPA's aggregated land classes. Each value in Table 2.4-37 gives the probability

that a pixel reported as land cover R is actually land cover A, where R is the reported land class

listed in the columns and A is the actual land cover listed in the rows. For example, the

intersection of column 2 and row 5 shows that there was a 5.0% probability that a pixel reported

as forest (land class 2) was actually savanna (land class 5).

Table 2.4-37.

MODIS Version 5 producer’s accuracy matrix with aggregated EPA land cover classes

Reported

Actual 1 2 3 4 5 6 7 8 10

1 83.2% 0.0% 4.5% 12.6% 2.0% 1.6% 1.8% 0.0% 4.8%

2 0.3% 92.6% 0.2% 6.3% 14.7% 1.2% 14.1% 0.0% 0.2%

3 4.9% 0.0% 73.7% 3.9% 7.3% 12.6% 1.2% 1.5% 4.2%

4 5.9% 1.2% 0.8% 60.4% 6.9% 1.5% 3.1% 0.0% 0.0%

5 3.6% 5.0% 6.5% 15.3% 60.9% 6.1% 6.8% 0.3% 0.0%

6 1.8% 0.4% 13.0% 1.5% 7.3% 69.5% 2.1% 2.2% 0.3%

7 0.2% 0.7% 0.2% 0.0% 0.2% 0.0% 70.6% 0.0% 0.0%

8 0.0% 0.0% 0.5% 0.0% 0.7% 7.4% 0.0% 95.8% 0.0%

10 0.0% 0.0% 0.5% 0.0% 0.0% 0.0% 0.4% 0.1% 90.5%

100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Using the information in Table 2.4-37, it is fairly straight-forward to adjust/correct the

reported land use data to provide a much better estimate of the actual land use during this time

period. The MODIS data was corrected with the following multi-step process:

 First, the confusion matrix for each country and administrative unit is scaled for each

year so that the share of reported land use of total land use matches the adjusted MODIS

estimates share.

o ACMcup = CMup /CMxp, where CMup are the values in the input aggregated

confusion matrix and CMxp = ∑uCMup where u is the user’s land use and p is the

producer’s land use163

o The coefficients from the resulting matrix sum to 1.0

 Next, the number of producer sites is scaled similarly to the approach above and the

producer’s estimate is recalculated164

 The actual land use in 2001 and 2007 is then given by the following equations165

o ALUci = ∑pBCMcyip * AMcpx for y = 2001 and where i is the initial land use

o ALUcf = ∑pBCMcyfp * AMcxp for y = 2007 and where f is the final land use

 The reported land use change is then calculated first for 2001

163

x is a placeholder that indicates that the matrix presents total land use.

164 In fact, the producer’s estimate does not change.

165

x is a placeholder that indicates that the matrix presents total land use.
426

o We find a land use category where the total land use for 2001 decreased (ALUci <

AMcix)

o We scale the land use change to all land uses down from that land use category

based on the percentage decrease in total land use

o We allocate the remaining land use change to the remaining land uses in 2001

based on the share of the increase in land use that increased

 We then use a similar process to adjust the 2007 land use

As an example of the 2nd to last step, let’s assume that, based on the confusion matrix

adjustment procedure described above, the adjusted land use for savanna has decreased in 2001

from the producer values by 30%, from 21 to 14 million hectares and that the adjusted land use

for cropland and forestland, and grassland increase by 5, 3, and 2 million hectares respectively

and all other land uses decrease or do not change. The adjusted land use change from Savanna to

all 10 land uses is then scaled to be 70% of the original land use change from Savanna. The

remaining 30% of the land use change is then allocated as coming from cropland, forestland, and

grassland with the shares equal to 5/(5+3+2) = 5/10 for cropland,3/10 for forestland, and 2/10

for grassland respectively. This means that 3.5, 2.1, and 1.4 million hectares of increased

cropland, forestland, and grassland have been accounted for and the ratios to apply to the land

use change from Savanna is 0.5*0.3, 0.3*0.3, and 0.2*0.3 respectively. If the Savanna to

Cropland land use change was 1 million hectares originally, it is now 0.7 million hectares. If the

Cropland, Forestland, and Grassland to Cropland land use change was 2, 0.1, 0.4 million hectares

originally, they are 2.0 + 1.0 * 0.5 * 0.3, 0.1 + 1.0*0.3*0.3, and 0.4 + 1.0*0.2 or 2.15, .19, and .6

respectively. We then go to the next land use with reductions in the adjusted land use for 2001

and repeat this process but in the allocation, we use (5-3.5), (3-2.1), and (2-1.4) million hectares

for the allocation for cropland, forestland, and grassland respectively.

The approach for the adjustments in land use for 2007 is similar, but it scales and adds

using (i) the change in land use in 2007, instead of change in land use in 2001, and (ii) the land

use change from the 10 land uses to Savanna, instead of the change from Savanna to the 10 land

uses.

The corrected satellite data was used to evaluate the types of land affected by the

projected land conversions in each scenario. For agricultural expansions, the types of land

affected were evaluated with the corrected land use change data from 2001-2007. We also used

this approach to determine, within each country/region, the location of land use changes, i.e. the

Administrative Units where conversions would occur. For example, in each region we looked at

the types of land converted to cropland during this time period.

To determine the types of land converted to pasture, we applied the land use change data

for cropland. This was done primarily because the MODIS data set does not classify land used

for pasture. MODIS does classify grasslands and savanna, which we know are used for livestock

grazing, but it provides no information about the share of grassland and savanna used as pasture

in each region. Thus, looking only at land types that were replaced by grassland or savanna

would ignore the fact that pasture can expand onto grassland and savanna areas. By applying the

cropland change data for pasture we included/approximated these important land conversion
427

possibilities. The justification for this approach was based, in part, on the assumption that

pasture expansion is likely to affect similar land types as cropland.166

For land reversions, land cover in 2007 was used to estimate the land types that would

likely grow back on abandoned agricultural lands in each region. For example, in a region that

was 80% forest, we assume that 80% of abandoned agricultural land would grow back as forest.

For land reversions this approach was preferable to using change data, because the time period

covered by the MODIS satellite imagery was not long enough to determine the final land

category following reversion, i.e., 30 years later. We also used this approach to determine,

within each country/region, the location of land reversions, i.e. the Administrative Units where

reversions would occur.

The contributing land use change categories and the bases of their weighting factors for

each agricultural land use change are presented in Table 2.4-38.. The first column lists the 12

land conversion categories modeled. The middle column indicates the satellite data weighting

approach used. The last column includes the resulting land conversions, which were weighted

with the approach listed in the middle column. More details about the application of satellite

data to weight land conversions is provided in a technical report by ICF International available

on the public docket.573

166 This assumption is supported to some degree by Cardille and Foley (2003) who found that cropland and pasture

expansion affected similar land types in the Brazilian Amazonia between 1980 and 1995.
426

Table 2.4-38.

Contributing Land Use Change Categories and Bases of Weighting Factors for Agricultural Land Use Change Categories

Agricultural Land Use Change Category Land Use Change or Land Use Used to Estimate

Weighted Emission Factors Land Use Change Category or Land Use Type

Annual Crops to Perennial Crops 2007 Land Use Cropland

Perennial Crops to Annual Crops 2007 Land Use Cropland

Pasture to Perennial Crops Land Use Change - 2001 to 2007 Grasslands to Perennial

Savanna to Perennial

Perennial Crops to Pasture Land Use Change - 2001 to 2007 Perennial to Grasslands

Perennial to Savanna

Pasture to Annual Crops Land Use Change - 2001 to 2007 Grasslands to Croplands

Savanna to Croplands

Annual Crops to Pasture Land Use Change - 2001 to 2007 Croplands to Grasslands

Croplands to Savanna

Forestland to Croplands

Grasslands to Croplands

Mixed to Croplands

Savanna to Croplands

Shrubland to Croplands

Wetland to Croplands

Natural Ecosystems to Annual Crops Land Use Change - 2001 to 2007

Barren to Croplands

Forestland

Grasslands

Mixed

Savanna

Annual Crops to Natural Ecosystems 2007 Land Use

Shrubland

Forestland to Perennial

Grasslands to Perennial

Mixed to Perennial

Savanna to Perennial

Shrubland to Perennial

Wetland to Perennial

Natural Ecosystems to Perennial Crops Land Use Change - 2001 to 2007

Barren to Perennial

Forestland

Grasslands

Mixed

Perennial Crops to Natural Ecosystems 2007 Land Use

Savanna
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Agricultural Land Use Change Category Land Use Change or Land Use Used to Estimate

Weighted Emission Factors Land Use Change Category or Land Use Type

Shrubland

Forestland to Grasslands

Shrubland to Grasslands

Mixed to Grasslands

Wetland to Grasslands

Barren to Grasslands

Forestland to Savanna

Shrubland to Savanna

Mixed to Savanna

Wetland to Savanna

Natural Ecosystems to Pasture Land Use Change - 2001 to 2007

Barren to Savanna

Forestland

Mixed

Pasture to Natural Ecosystems 2007 Land Use167

Shrubland

167 The model actually uses the three land uses twice, once to represent the replacement of grasslands and the other to represent the replacement of Savanna
426

Table 2.4-39 includes the regional shares of land types converted to cropland and pasture based

on the original Version 5 MODIS data.

Table 2.4-40 shows the same data after it was corrected using the confusion matrix data

and the procedure described above. In many regions, the corrections significantly reduced the

share of grassland, savanna and/or mixed land converted to cropland. This was due, in part, to

the tendency of MODIS to confuse these land types with each other and with cropland. As a

result, the share of forest affected by agricultural expansion increased for most of the regions

analyzed. Table 2.4-41 shows the land type shares for agricultural reversion with the corrected

data. Our estimates of satellite data uncertainty are presented in below in Table 2.4-49.
427

Table 2.4-39. Types of Land Converted to Cropland/Pasture by Region

Original Version 5 MODIS Data, 2001-2007

FAPRI-CARD Region Forest Grassland Mixed Savanna Shrubland Wetland Barren

Algeria 0% 12% 7% 8% 72% 0% 0%

Argentina 11% 37% 20% 17% 13% 1% 0%

Australia 1% 54% 2% 16% 27% 0% 0%

Bangladesh 21% 6% 24% 24% 11% 13% 1%

Brazil: Amazon Biome 15% 33% 12% 36% 4% 0% 0%

Brazil: Central-West Cerrados 3% 30% 17% 49% 1% 0% 0%

Brazil: Northeast Coast 0% 22% 15% 54% 9% 0% 0%

Brazil: North-Northeast Cerrados 1% 32% 7% 53% 7% 0% 0%

Brazil: South 5% 52% 22% 20% 0% 0% 0%

Brazil: Southeast 1% 20% 43% 35% 1% 0% 0%

Canada 2% 52% 5% 5% 32% 0% 3%

China 1% 67% 6% 3% 15% 0% 7%

New Zealand 30% 37% 6% 2% 24% 0% 0%

Colombia 3% 74% 5% 14% 4% 0% 0%

Cuba 2% 6% 74% 15% 2% 1% 0%

Egypt 2% 4% 50% 4% 27% 0% 13%

EU 4% 37% 36% 8% 14% 0% 1%

Guatemala 17% 3% 60% 18% 1% 0% 0%

India 2% 12% 41% 23% 22% 0% 1%

Indonesia 27% 5% 43% 22% 2% 2% 0%

Iran 0% 77% 1% 1% 16% 0% 4%

Iraq 0% 53% 4% 2% 39% 0% 2%

Ivory Coast 26% 6% 30% 30% 6% 2% 1%

Japan 8% 9% 58% 15% 10% 0% 0%

Malaysia 35% 4% 50% 5% 3% 3% 0%

Mexico 2% 36% 17% 16% 29% 0% 0%

Morocco 0% 18% 4% 4% 72% 0% 2%

Myanmar 7% 9% 46% 27% 10% 2% 0%

Nigeria 2% 73% 12% 11% 2% 0% 0%

Other Africa 0% 59% 8% 18% 11% 0% 3%

Other Asia 0% 79% 2% 1% 6% 0% 11%

Other CIS 0% 87% 2% 1% 4% 0% 5%

Other Eastern Europe 1% 48% 38% 9% 4% 0% 0%

Other Latin America 7% 40% 13% 19% 20% 0% 1%

Other Middle East 0% 13% 13% 8% 54% 0% 11%

Pakistan 0% 13% 29% 3% 51% 0% 4%

Paraguay 8% 31% 22% 39% 0% 0% 0%

Peru 1% 78% 2% 3% 15% 0% 0%

Philippines 12% 2% 78% 4% 1% 3% 0%

Rest of World 1% 54% 9% 22% 12% 0% 1%

Russia 3% 50% 25% 6% 16% 0% 0%

South Africa 1% 52% 12% 18% 17% 0% 0%

South Korea 5% 5% 82% 6% 3% 0% 0%

Taiwan 25% 6% 36% 15% 16% 1% 1%

Thailand 5% 10% 64% 15% 4% 1% 0%

Tunisia 0% 8% 10% 3% 79% 0% 1%

Turkey 0% 81% 3% 6% 9% 0% 0%

Ukraine 2% 26% 59% 7% 6% 0% 0%

Uruguay 2% 82% 13% 3% 0% 0% 0%

US 0% 84% 5% 4% 7% 0% 0%

Uzbekistan 0% 56% 3% 3% 20% 0% 18%

Venezuela 1% 38% 8% 36% 16% 0% 0%

Vietnam 16% 5% 55% 11% 4% 7% 1%

Western Africa 2% 15% 34% 46% 3% 0% 0%
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Table 2.4-40. Types of Land Converted to Cropland/Pasture by Region

Corrected Version 5 MODIS Data, 2001-2007

FAPRI-CARD Region Forest Grassland Mixed Savanna Shrubland Wetland Barren

Algeria 1% 16% 8% 10% 64% 0% 0%

Argentina 12% 26% 27% 17% 14% 1% 3%

Australia 6% 32% 11% 22% 25% 0% 4%

Bangladesh 19% 21% 24% 20% 11% 5% 1%

Brazil: Amazon Biome 54% 8% 15% 20% 2% 1% 0%

Brazil: Central-West Cerrados 11% 26% 20% 36% 6% 0% 0%

Brazil: Northeast Coast 11% 19% 19% 41% 8% 0% 1%

Brazil: North-Northeast Cerrados 15% 16% 10% 49% 9% 0% 1%

Brazil: South 13% 23% 28% 29% 6% 0% 0%

Brazil: Southeast 10% 18% 30% 36% 6% 0% 0%

Canada 8% 28% 13% 14% 31% 2% 4%

China 6% 30% 23% 20% 17% 1% 3%

New Zealand 28% 33% 15% 7% 15% 1% 1%

Colombia 33% 9% 31% 18% 8% 1% 1%

Cuba 9% 12% 49% 23% 7% 0% 0%

Egypt 2% 20% 30% 8% 33% 0% 7%

EU 6% 25% 32% 21% 14% 1% 1%

Guatemala 21% 7% 42% 24% 5% 1% 0%

India 10% 21% 30% 19% 17% 1% 2%

Indonesia 39% 5% 29% 22% 3% 2% 0%

Iran 2% 43% 5% 8% 33% 0% 9%

Iraq 1% 37% 8% 8% 43% 0% 3%

Ivory Coast 22% 8% 15% 46% 8% 0% 1%

Japan 8% 9% 47% 23% 11% 1% 1%

Malaysia 52% 3% 27% 13% 2% 2% 0%

Mexico 10% 18% 27% 21% 21% 1% 2%

Morocco 2% 28% 7% 9% 50% 0% 4%

Myanmar 14% 10% 34% 30% 9% 2% 1%

Nigeria 11% 36% 19% 25% 9% 0% 1%

Other Africa 10% 19% 14% 37% 13% 0% 6%

Other Asia 4% 42% 15% 11% 19% 0% 9%

Other CIS 1% 49% 17% 11% 18% 0% 3%

Other Eastern Europe 6% 37% 31% 16% 8% 1% 1%

Other Latin America 18% 13% 27% 26% 13% 1% 2%

Other Middle East 2% 21% 11% 11% 32% 0% 23%

Pakistan 3% 23% 28% 13% 31% 0% 2%

Paraguay 17% 20% 22% 36% 5% 1% 0%

Peru 45% 30% 4% 9% 10% 1% 1%

Philippines 16% 5% 54% 19% 2% 3% 0%

Rest of World 18% 13% 25% 27% 12% 1% 3%

Russia 8% 20% 27% 20% 22% 1% 2%

South Africa 5% 35% 19% 18% 20% 0% 3%

South Korea 5% 11% 58% 20% 5% 1% 0%

Taiwan 25% 8% 27% 21% 17% 1% 1%

Thailand 12% 10% 48% 23% 5% 1% 0%

Tunisia 3% 29% 12% 12% 43% 0% 1%

Turkey 5% 45% 15% 10% 23% 0% 3%

Ukraine 3% 31% 20% 32% 13% 2% 1%

Uruguay 3% 57% 17% 11% 12% 0% 0%

US 6% 36% 24% 18% 14% 1% 1%

Uzbekistan 2% 34% 16% 15% 32% 0% 1%

Venezuela 7% 13% 27% 43% 9% 0% 1%

Vietnam 21% 8% 39% 20% 6% 5% 1%

Western Africa 14% 12% 14% 50% 8% 0% 1%
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Table 2.4-41. Types of Land That Replace Abandoned Cropland/Pasture by Region

Corrected Version 5 MODIS Data, 2001-2007

FAPRI-CARD Region Forest Grassland Mixed Savanna Shrubland

Algeria 3% 21% 3% 10% 64%

Argentina 14% 21% 6% 13% 45%

Australia 7% 20% 3% 19% 51%

Bangladesh 32% 14% 20% 24% 9%

Brazil: Amazon Biome 83% 1% 5% 10% 1%

Brazil: Central-West Cerrados 24% 9% 11% 49% 6%

Brazil: Northeast Coast 14% 10% 15% 54% 7%

Brazil: North-Northeast Cerrados 20% 9% 11% 53% 7%

Brazil: South 32% 17% 23% 23% 5%

Brazil: Southeast 21% 7% 22% 44% 6%

Canada 43% 11% 4% 15% 26%

China 27% 34% 10% 16% 14%

New Zealand 64% 14% 2% 6% 13%

Colombia 64% 8% 11% 14% 4%

Cuba 36% 7% 33% 21% 4%

Egypt 2% 30% 8% 10% 50%

EU 45% 11% 15% 17% 11%

Guatemala 54% 3% 17% 23% 3%

India 22% 12% 24% 29% 13%

Indonesia 77% 1% 12% 9% 1%

Iran 3% 36% 3% 8% 51%

Iraq 2% 25% 3% 8% 62%

Ivory Coast 32% 5% 20% 38% 4%

Japan 77% 2% 8% 11% 2%

Malaysia 82% 1% 10% 7% 1%

Mexico 19% 17% 8% 24% 32%

Morocco 2% 18% 4% 10% 66%

Myanmar 59% 4% 12% 22% 3%

Nigeria 17% 18% 25% 33% 7%

Other Africa 24% 17% 8% 36% 15%

Other Asia 13% 52% 5% 9% 22%

Other CIS 5% 64% 4% 8% 19%

Other Eastern Europe 42% 8% 29% 17% 4%

Other Latin America 56% 7% 8% 15% 14%

Other Middle East 1% 20% 2% 8% 68%

Pakistan 5% 17% 8% 10% 60%

Paraguay 41% 8% 14% 33% 5%

Peru 62% 17% 2% 7% 12%

Philippines 54% 3% 30% 12% 1%

Rest of World 44% 11% 9% 20% 16%

Russia 43% 10% 6% 14% 27%

South Africa 8% 22% 6% 23% 42%

South Korea 67% 3% 13% 13% 3%

Taiwan 75% 3% 9% 10% 3%

Thailand 32% 5% 34% 25% 3%

Tunisia 3% 17% 5% 10% 65%

Turkey 12% 47% 7% 16% 18%

Ukraine 34% 14% 32% 16% 5%

Uruguay 4% 67% 5% 11% 13%

US 27% 31% 14% 12% 16%

Uzbekistan 2% 40% 3% 9% 46%

Venezuela 55% 8% 11% 21% 4%

Vietnam 48% 5% 21% 22% 4%

Western Africa 14% 8% 17% 54% 7%
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2.4.4.2.6 Quantification of International Land Conversion GHG Emissions Impacts

Land use change emissions factors were calculated by the non-profit organization

Winrock International following 2006 IPCC Agriculture Forestry and Other Land Use (AFOLU)

Guidelines.574 Winrock’s staff is highly regarded for their years of experience and

accomplishments in this field, including their work with the IPCC to develop the AFOLU

Guidelines. Following publication of the proposed rule, we sponsored an expert peer review on

this part of our lifecycle analysis. Based on the reviewers recommendations a number of

important improvements were made, including incorporation of more recent and higher

resolution data sets. Our analysis of land use change emissions factors has also been expanded

to provide global coverage. For the proposed rule, emissions factors were estimated for 5 land

categories in 314 regions across 35 of the most important countries, with a weighted average

applied to the rest of the world. Our analysis now includes 9 land categories in over 750 distinct

regions across 160 countries covering all significant agricultural producers. This section

describes the methods used to estimate GHG emissions from international land use change, with

a focus on updates since the proposed rule. More details are available in a technical document

by Winrock available on the public docket.575

2.4.4.2.6.1 Data Sources and Methods for International Land Conversion GHG Emissions

Factors

Emission factors were calculated using the IPCC equations explained in DRIA Chapter

2.576 The emissions factors include the sum of changes in above- and belowground biomass

carbon stocks, changes in soil carbon stocks on mineral soils, emissions from peat drainage on

peat soils cleared for agriculture, foregone forest sequestration, and non-CO2 emissions (CH4,

N2O) resulting from land clearing with fire where applicable. Methane emissions from rice

cultivation were excluded from the updated emission factors, as these emissions are accounted

for elsewhere in EPA’s lifecycle analysis. Updates to various components of the final emission

factor are described below.

2.4.4.2.6.1.1 Data Sources and Methods for International Forest Carbon Stock Estimates

Our emission factor analysis incorporated spatial maps of forest carbon stocks from

several data sources. The region-specific maps were preferred due to the use of country-specific

data (i.e., IPCC Tier 2 vs. Tier 1), and also because the only globally consistent carbon stock

map available was derived using adjusted biome-level Tier 1 default values from IPCC rather

than from country specific data sources (Ruesch and Gibbs 2008). We used regional and/or

country-level maps where available, and the global Ruesch and Gibbs (2008) data product was

used only to fill in gaps where no other information on forest carbon stocks was available. 577

Our analysis of forest carbon stocks was improved by incorporating several new data

sources. Most notable is the inclusion of a new spatially explicit map of tropical forest carbon

stocks. Winrock is working with Dr. Sassan Saatchi from NASA’s Jet Propulsion Laboratory to

create a pantropical benchmark map of above- and belowground forest carbon stocks for the year

2000 at 1-km resolution.578 The methodology uses about 4,000 ground inventory plots of forest

biomass, 150,000 biomass values estimated from heights measured by spaceborne lidar, and a
427

suite of satellite imagery products to derive a spatially refined map of aboveground forest carbon

at a 1-km grid cell resolution. Belowground carbon is added to aboveground carbon using an

equation from Mokany et al. (2006).579 The estimates are directly comparable across countries

and regions due to the consistency in the methodological approach (see Figure 2.4-32).

Figure 2.4-32.

Map of forest carbon stocks in above- and belowground biomass for the tropics.

Note: from Saatchi et al. (in prep)

Preliminary results of Saatchi et al. for forest carbon stocks in Latin America and Africa

were incorporated into the updated EPA analysis by clipping the map to MODIS forest cover in

2001 and calculating the area-weighted average forest carbon stock per country (and per

administrative unit in key countries). Preliminary results for Asia are now complete, but were

not included in the updated EPA analysis due to timing considerations. Therefore, the original

Brown et al. (2001) map was retained for forest carbon stock estimates in Asia.580 The Saatchi et

al. results represent a significant improvement over previous estimates; the maps were evaluated

for accuracy using cross validation with approximately 50% of the ground and lidar biomass data

and resulted in an overall accuracy of 76% across the three regions (Latin America: 81%, Africa:

86%, Southeast Asia: 69%).

Myneni et al. (2001) also produced a spatially-explicit map of woody biomass for

Northern (i.e., boreal and temperate) forests (Figure 2.4-33).581 Although we used carbon stock

values from other data sources for the United States, Russia and many countries of the European

Union (Blackard et al. 2007, Houghton et al. 2007, Nabuurs et al. 2003, see Figure 2.4-34), the

Myneni et al. (2001) dataset filled in the data gap for Canada and many Eastern European

countries.582,583,584,585
426

Figure 2.4-33. Aboveground biomass carbon stocks in Northern forests.

Note: reproduced from Figure 2 of Myneni et al. (2001).

Apart from the new data sources described above, the data sources used to estimate forest

carbon stocks in other regions remained unchanged in the updated analysis.586 A summary of

data sources used is shown in Figure 2.4-34.

Figure 2.4-34

Figure 2.4-34.

Data sources used for estimating forest carbon stocks in updated emission factor analysis

Note: Nabuurs et al. and Nabuurs et al. 2003 are the same data source.

In all cases where belowground biomass was not estimated, belowground biomass was

added to aboveground biomass estimates using an equation from Mokany et al. (2006).587 (This

equation represents an update to the default belowground biomass values given in the IPCC

Guidelines.) Forest carbon stock values per country or administrative unit (for key countries) are

shown in Figure 2.4-35.
427

Figure 2.4-35. Spatially averaged forest carbon stocks in above- and belowground biomass

(tCO2e/ha)

< 50

51 - 100

101 - 150

151 - 300

301 - 500

501 - 750

> 750

2.4.4.2.6.1.2 Data Sources and Methods for International Cropland Carbon Stock Estimates

In the proposed rule emission factor analysis, all cropland conversion was assumed to be

conversion to annual cropland. In the updated analysis, emission factors were estimated

separately for conversion to annual cropland and conversion to perennial cropland. Perennial

cropland in Indonesia and Malaysia was assumed to be oil palm, while perennial cropland in all

other countries was sugarcane. Carbon stocks in oil palm plantations after one year of growth

were estimated as 15 t CO2e/ha. Table 5.3 of the 2006 IPCC Guidelines for AFOLU gives

biomass stocks on oil palm plantation as 136 t/ha (68 t C/ha), and if this value is divided by an

assumed 15-year growth period, a linear growth rate of 4 t C/ha/yr (15 t CO2e/ha/yr) was

assumed. This value is also nearly identical to the average carbon stock in biomass after one year

of growth averaged across all tropical climate regions and all perennial crop types, as given in

Table 5.9 of the 2006 IPCC Guidelines.

Carbon stocks in sugarcane after one year of growth were assumed to be 44 t CO2e/ha.

(Carbon stocks for long-lived tree species such as oil palm accumulate carbon more slowly in the

early phases of growth.) The value for sugarcane was derived from estimates of carbon stocks in

sugarcane in aboveground biomass (17 t C ha-1 or 62 t CO2, Amaral et al. 2008)588 and in

belowground biomass (7 t C/ha or 26 t CO2/ha, Smith et al. 2005)589 for a total of 88 t CO2e/ha.

We assumed a growth period of two years to achieve full carbon stocks, therefore the carbon

stock in sugarcane after one year of growth was assumed to be 44 t CO2/ha.

All biomass accumulated after Year 1 would have been harvested over the course of 30

years in the case of both sugarcane and oil palm, leading to little net sequestration during the

time period for which emission factors were estimated (30 years). Over the long term (e.g., 100

years), oil palm plantations may have a long-term average carbon stock higher than that at Year
426

1, but the land use after 30 years is highly uncertain and there is no guarantee of future rotations.

Therefore, the average carbon sequestration at any given time over 30 years was assumed to be

the carbon stock in vegetation after one year of growth.

Figure 2.4-36 illustrates this concept by showing an example of carbon stock growth for

perennial crops with different rotation lengths.

Figure 2.4-36. Perennial Crop Carbon Stocks Over Time

2.4.4.2.6.1.3 Data Sources and Methods for International Grassland, Savanna and Shrubland

Carbon Stock Estimates

The approach for estimating carbon stocks in grassland, savanna and shrubland land

cover categories was unchanged from the proposed rule, as there were no significant comments

on this aspect of our analysis from the peer reviewers. Above- and belowground carbon stocks of

grassland, savanna and shrublands in Brazil were estimated using values from de Castro and

Kauffman (1998)590 who report biomass along a vegetation gradient from campo limpo (pure

grassland), campo sujo (a savanna with a sparse presence of shrubs), campo cerrado (a

dominance of shrubs with scattered trees and a grass understory), cerrado sensu stricto (a

dominance of trees with scattered shrubs and a grass understory) and cerradão (a closed canopy

forest) (Coutinho 1978, Eiten 1972, Goodland & Pollard 1973).591,592,593 Shrubland carbon

stocks in Brazil were estimated as the average of biomass values reported for cerrado aberto and

cerrado denso. Savanna carbon stocks in Brazil were estimated as the average biomass value

reported for campo sujo and grassland carbon stocks in Brazil were estimated as the average

value reported for campo limpo.

To maintain a consistent approach, for all countries except Brazil (explained in the

paragraph above), carbon stocks in grasslands were estimated based on default biomass values

given in Table 6.4 of the IPCC AFOLU Guidelines. These default values are presented by

ecological zone. Therefore, grassland C stocks within each country reflect the area-weighted

value based on the proportions of each ecological zone present within each country. Carbon

stocks of savanna and shrubland land cover types in all countries except Brazil were estimated
427

using a proportional approach based on the Brazil dataset, which indicates an increasing trend in

carbon stocks from grassland to savanna to shrubland in a ratio of 1 to 1.8 to 3.4. These ratios

were applied to other countries for estimating carbon stocks in savanna and shrubland based on

the estimated carbon stocks of grassland within each country.

Grassland carbon stock estimates for each country and administrative unit (for key

countries) are shown in Figure 2.4-37.

Figure 2.4-37. Grassland carbon stock estimates for each country and administrative unit

(t CO2e/ha)

< 5

6 - 10

11 - 15

16 - 20

21 - 25

26 - 30

> 30

2.4.4.2.6.1.4 Data Sources and Methods for International Wetland, Barren and Mixed Carbon

Stock Estimates

In line with recommendations for the expert peer reviewers, the updated analysis

included land cover change to/from the wetland and barren land cover categories, and therefore

emission factors were estimated for these conversions. According to the IGBP land cover

description, the permanent wetlands category can consist of herbaceous and/or woody

vegetation. However, after confirming that Indonesian peat swamp forests (a type of permanent

forested wetland) are classified as forest and not wetland in the MODIS land cover maps, the

carbon stocks of permanent wetlands in a given country or administrative unit were calculated as

the average of carbon stocks in shrubland and grassland land cover categories. Carbon stocks on

barren lands were assumed to be zero. In accordance with the IGBP land cover definitions,

mixed carbon stocks were calculated as the average of forest, shrubland, grassland and cropland

carbon stocks.

2.4.4.2.6.2 Evaluation of Changes in Biomass Carbon Stocks from International Land

Conversions
426

Initial changes in biomass carbon stocks on land converted to another land category (e.g.,

from forest to cropland) were calculated the same way as in the proposed rule analysis, i.e. based

on Equation 2.16 in the IPCC AFOLU:

   

i

CCONVERSION AFTER BB BEFORE CF i i( )

where:

CCONVERSION = initial change in biomass carbon stocks on land converted to

another land category, tonnes C ha-1yr

-1

AFTERi

B

= biomass stocks on land type i immediately after the conversion,

tonnes d.m. ha-1

BEFOREi

B

= biomass stocks on land type I before the conversion, tonnes d.m.

ha-1

CF = carbon fraction of dry matter, tonne C (tonnes d.m.)-1

i = type of land use converted to another land-use category

2.4.4.2.6.3 Evaluation of GHG Emissions from International Land Clearing with Fire

In the proposed rule we used expert judgment to determine the regions where land is

cleared with fire prior to conversion to crop production. In general, it was assumed that clearing

with fire takes place in tropical regions. Several of the expert peer reviewers suggested the use

of fire products, such as those derived from MODIS and AVHRR sensors, to determine which

regions typically use fire to clear land for another land use. However, the use of these products in

isolation would not allow a distinction between fire that occurs for land conversion versus fire

that occurs due to wildfires, especially for temperate regions. Therefore, we considered an

approach in which various fire maps could be overlain onto land cover change maps to determine

fires that occurred on changed pixels (land conversion) versus fires that occurred on pixels that

remained in the same land cover category (e.g., forest fire, annual burning of cropland residues,

etc.). However, the time needed to do this analysis exceeded the time available. Therefore, we

maintained the approach used in the proposed rule analysis, whereby expert judgment was used

to determine the regions where fire is commonly used when land is cleared for agricultural

production.

Figure 2.4-38 shows the countries where fire is assumed to occur as part of site

preparation for crop production.

Figure 2.4-38. Countries that Clear with Fire in Preparation for Crop Production
427

As in the proposed rule analysis, in countries where fire is used commonly as a land

clearing practice for conversion to agriculture, non-CO2 emissions were estimated using

emission factors in Table 2.5 and Equation 2.27 of the IPCC AFOLU. Fire for land clearing was

assumed to occur in all countries included in the analysis except China and Argentina.

Non-CO2 emissions from land clearing with fire were estimated as:

3

10

fire GCMAL effB



Where:

L fire = amount of greenhouse gas emissions from fire, MT of each GHG (i.e.,

CH4, N2O)

A = area burnt, ha

M B = mass of fuel available for combustion, MT ha-1

.

Cf = combustion factor, dimensionless

Gef = emission factor, g kg-1 dry matter burnt

The mass of fuel available for combustion was conservatively assumed to be equal to the

above- and belowground biomass only; dead wood and litter pools were not included in the fuel

load estimates. IPCC defaults were used for the forest combustion factor. Values from de Castro

and Kaufmann (1998) were used for clearing other land cover types (grassland, savanna,

shrubland).

2.4.4.2.6.4 Evaluation of International Soil Carbon Stocks

For the initial analysis, soil carbon stocks were estimated using the FAO/UNESCO Soil

Map of the World. In March 2009, a new soil database was released (Harmonized Soil Map of

the World v.1.1) with 1-km resolution grid cells and therefore this improved dataset was used for

the updated analysis.594 Attribute values of the database did not include average soil carbon

stocks, but values were included instead for bulk density (g cm-3) and carbon content (%C) in
426

both the top 30 cm and top meter of soil in each grid cell. Therefore, we calculated average soil

carbon stocks in the top 30 cm of soil – assumed to be the depth to which soil carbon stocks

would be affected when converted to agriculture – by multiplying the volume of soil in a given

hectare (1 ha x 30 cm depth = 3,000 m3) by the bulk density to calculate the mass of soil in a

given hectare, then multiplied the soil mass by the carbon content to derive an average soil

carbon stock value per hectare (t C ha-1). Soil carbon stocks estimated per country (and per

administrative unit in key countries) are shown in Figure 2.4-39. The soil carbon stocks for

annual cropland (i.e., after conversion) are based on long-term cultivated annual cropland with

full tillage and medium inputs.

Figure 2.4-39.

Soil carbon stocks in the top 30 cm of soil for each country and administrative unit

(t CO2e/ha)

< 50

51 - 100

101 - 150

151 - 242

243 - 500

501 - 750

> 750

Note: estimates were derived from the Harmonized World Soil Database v. 1.1

2.4.4.2.6.4.1 Evaluation of Changes in Soil Carbon Stocks from International Land Conversions

Identical to the approach used in the proposed rule analysis, changes in soil carbon stocks

on land converted to cropland were calculated based on Section 5.3.3.4 of the IPCC AFOLU.

Soil carbon stocks after conversion to cropland were based on specific soil stock change factors

for land use, management and inputs (FLU, FMG, FI, respectively) listed in Table 5.10 of the IPCC

AFOLU. Stock change factors were selected for each land cover type (before and after

conversion) and multiplied by reference soil carbon stocks. Following the IPCC AFOLU

guidelines, the total difference in carbon stocks before and after conversion was averaged over

20 years. Thus the average annual change in soil carbon stocks due to land use conversion was

calculated as:
427

20

( () ) Re ,beforeLUf ,beforeMG FFFSOC ,beforeI Re ,afterLUf , FFFSOC ,afterIafterMG

SOC

      



where:

SOC = average annual change in carbon stocks in top 30 cm of soil; t C

ha-1yr

-1

f

SOCRe = reference carbon stocks in top 30 cm of soil; t C ha-1

FLU = land use factor before or after conversion

FMG = management factor before or after conversion

FI = input factor before or after conversion

As default values for stock change factors (FLU, FMG, FI) are all one for forest soils and

non-degraded grassland soils, soil carbon stocks were assumed to remain unchanged for all

conversion types (conversion to shrubland, savanna, perennial cropland) except conversion to

cropland. Full tillage and medium inputs were assumed in all scenarios of cropland conversion.

Consistent with IPPC default guidelines, soil carbon stock changes were spread equally over 20

years.

2.4.4.2.6.5 Accounting for International Harvested Wood Products

In the updated analysis, we addressed the potential significance of the harvested wood

product pool and concluded that the amount of carbon stored in wood products long-term is

immaterial for most regions of the world, especially when considering a timeframe of 30 years.

Therefore, carbon storage in harvested wood products was not incorporated into our updated

emission factors.

We reached this conclusion as follows: the proportion of extracted timber that ends up in

long-lived (>5 yr) wood products was estimated using information presented in Winjum et al.

(1998), who related harvesting and use of wood products to carbon impacts (Table 2).595 The

proportion of timber volume extracted ending up in long-lived wood products was calculated by

dividing carbon in net production of industrial roundwood by the total carbon in commodity uses

>5 yr. We did this for the developing and developed world and calculated percentages of 53%

and 60%, respectively. The country-level percentages were generally lower than the aggregated

values. Winjum et al. (1998) also estimates inherited emissions from the retirement of past wood

products, so we also estimated the proportion of roundwood production that is re-emitted into the

atmosphere through the retirement of past wood products. These values are reproduced in Table

2.4-42 below.
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Table 2.4-42.

Calculation of the proportion of extracted timber that goes to long-lived wood products

and the proportion of extracted timber as inherited emissions. (Tg C)

All units are in Tg C. From Winjum et al. (1998). SWD=sawnwood, WBP=woodbase

panels, OIR=other industrial roundwood and P&P=paper and paperboard.

SWD WBP OIR P&P Total

Developing

Brazil 23 4 1 1 1 7 4 30 17

India 9 4 0.1 1 1 6 3 67 33

Indonesia 12 2 0.1 1 0.42 3 1 25 8

Ivory Coast 1 0.05 0.03 0.2 0.01 0.3 0.2 30 20

Developed

Canada 39 3 1 1 2 7 1 18 3

Finland 9 0.6 0.2 0.1 0.4 1.2 0.3 13 3

New Zealand 2.7 0.3 0.1 0.1 0.2 0.6 0.3 22 11

U.S.A. 102 23 8 3 23 57 17 56 17

Worldwide

Developing 128 26 6 22 14 68 42 53 33

Developed 308 70 27 29 58 184 71 60 23

Total 436 96 33 51 72 252 113 58 26

Commodity use ≥ 5 yr#

Industrial

Roundwood

Production*

Category /

Country % HWP

Inherited

emissions

%

Inherited

Emissions

* From Table 4 in Winjum et al. (1998).

#

From Table 5 in Winjum et al. (1998).

Next, we analyzed per-hectare extraction volumes from 111 developing countries using

data reported to FAO for the 2005 Forest Resources Assessment. Of the countries analyzed, the

country with the highest reported extraction rate was Indonesia (50 m3/ha). This value was much

higher compared to countries in Africa and Asia, which weren’t much higher than about 20

m

3

/ha and often less.

For the 50 m3/ha Indonesia case, we converted volume to biomass using an average

conversion factor of 0.55 (Table 1 in Winjum et al. 1998, tropical aggregate), then converted

biomass to carbon using a conversion factor of 0.5. Therefore, 50 m3/ha of extracted timber

translates into 14 t C/ha. Assuming that 25% of this carbon ends up in long-lived (>5 yrs) wood

products (i.e., the value calculated in Table 3 above for Indonesia), the emission factor estimated

for forest conversion after taking into account carbon storage in wood products would be reduced

only by 3 t C ha-1, or 11 t CO2 ha-1, or approximately 1-2%.

This result of 11 t CO2/ha stored in wood products from Indonesian harvests longer than

five years is an overestimate. The calculation assumes that the carbon that ends up in these wood

products is stored forever. After taking into account the inherited emissions that emanate from

the oxidation (i.e., burning and decay) of wood products that were produced from harvests

during previous years (retirement rate, see Table 3), the Indonesia value of carbon stored in

wood products decreases even further to 2 t C/ha, or 7 t CO2e/ha.
427

Finally, Winjum et al. (1998) states that for the oxidation fractions of 0.04, 0.08 and 0.10

(representing rates for woodbase panels, other industrial roundwood and paper/paperboard), the

time period of oxidation would extend back 25, 12 and 10 yr from the base year, respectively, for

tropical regions. Therefore, much of the timber harvested today and stored as wood products

will be completely oxidized 25 years from now. Considering EPA is estimating 30-year

emission factors, carbon storage in wood products is likely insignificant, even for temperate and

boreal regions where oxidation rates are slower. The analysis outlined above is also

representative of productive forestlands only, and it is unlikely that every hectare of forest that is

cleared for another land use is stocked for timber production.

As discussed in preamble Section V, modeling the fate of international harvested wood

products is an area for future work and consideration of more data. For example, Pingoud et al.

(2001)596 and Micales and Skog (1997)597 estimate longer average lifetimes for wood products

than the assumptions used in our analysis. However, based on our research discussed above, we

believe it is very likely that carbon sequestration from harvested wood products is captured in

our estimated uncertainty ranges.

2.4.4.2.6.6 Evaluation of International Foregone Forest Sequestration

Forest sequestration rates were estimated in the proposed rule analysis using IPCC Tier 1

default values for native forests. These values are listed by ecological zone, so final rates in the

initial analysis were calculated by weighting the ecological zone-based sequestration rates by the

proportion of forest area in each ecological zone within a country or administrative unit (for key

countries). The expert peer reviewers pointed out a number of recent papers that summarize

long-term monitoring plots in old growth tropical forests across the tropics and suggested the use

of these more recent datasets for estimating annual rates of carbon sequestration in tropical

forests.

Lewis et al. (2009) published long-term aboveground carbon sequestration rates of 0.63 t

C/ha/yr for African “closed canopy mature forests” (assumed moist or rain forest) based on longterm monitoring plots.598 This is similar to the IPCC default rate for >20 yr old African tropical

moist deciduous forests (0.65 t C/ha/yr) but lower than for >20 yr old African tropical rain

forests (1.55 t C/ha/yr). Baker et al. (2004) also report an annual Amazonian C sequestration rate

of 0.61 t C ha-1yr

-1, which is lower than the IPCC default of 1.0 t C ha/yr for >20 yr old tropical

moist deciduous forests and 1.55 t C/ha/yr for >20 yr old tropical rain forests of South America.

After combining all standardized inventory data from Africa, tropical America and Asia together,

Lewis et al. (2009) estimate carbon sequestration across all tropical intact old growth forests as

0.49 t C/ha/yr. We have used this estimate for foregone sequestration across the tropics in our

updated analysis.

Myneni et al. (2001) and Nabuurs et al. also estimated the carbon sink of temperate and

boreal forests in various countries, and these values were generally higher than sequestration in

tropical forests, with rates of approximately 3-4 t CO2e/ha/yr on average but extending up to 7-8

t CO2e/ha/yr in Norway and Switzerland. These data reflect the long-term carbon sink capacity

of forests, which have long been understood to be the case in temperate forests and have more

recently been illustrated for old-growth tropical forests as well.
426

2.4.4.2.6.7 Evaluation of International Land Reversion Carbon Uptake Factors

In addition to estimating emission factors, reversion factors were developed to estimate

the carbon accumulation in biomass and soils that occurs when managed cropland and pasture

land is abandoned. All reversion factors (except reversion to forest) were estimated as the reverse

of emission factors, whereby all increases in biomass carbon stocks occur in Year 1 (analogous

to the stock change approach used to estimate emission factors) while changes in soil carbon

stocks on abandoned cropland recovers to pre-land use change levels in 20 years (analogous to

the soil emission factors, which were assumed to emit over 20 years). The only reversion factor

to include soil carbon accumulation was reversion from abandoned cropland.

While most reversion factors assumed that all biomass carbon stock changes occurred in

Year 1 (i.e., IPCC stock change approach), forest reversion factors assumed that biomass

accumulates every year over the entire 30-year time period. This was done to reflect forests’

slow but continual carbon sink capacity. Despite the fact that young (<20 year old) forests

accumulate biomass more quickly than older (>20 year old) forests, the annual rate of carbon

accumulation on abandoned croplands that revert back to forests was conservatively assumed to

be equal to the foregone forest sequestration rate (estimated for the emission factor analysis). If

the forest biomass carbon stock (estimated for the emission factor analysis, see Figure 4 above)

was less than 20 times the assumed annual foregone carbon sequestration value, then the annual

carbon sequestration rate for reversion factors was assumed to be 1/20th of the initial forest

carbon stock. Both of these assumptions provide a very conservative estimation of the carbon

accumulation that occurs on abandoned land when it reverts to forest.

2.4.4.2.6.8 International Land Conversion GHG Emissions Factor Results

Our updated analysis includes land use change emissions factors for up to 42 different

land conversions in over 750 regions across 160 countries, i.e. over 30,000 land conversion

emissions factors. In this section we use the example of the Amazon region in Brazil to illustrate

the emissions factors used in our analysis. The sample results shown below cover all of the types

of land conversions considered, but they do not cover all of the 750+ regions. For all of the

emissions factors used in our analysis, including the data inputs, refer to the results spreadsheets

available on the public docket.

The FAPRI-CARD model simulates agricultural production in 6 regions in Brazil,

including the Amazon Biome. The Amazon Biome region in FAPRI-CARD includes the

following Administrative Units: Acre, Amapa, Amazonas, Para, Rondonia, Roraima, and the

northern part of Mato Grosso which is characterized by forest biome land cover.

Figure 2.4-40 illustrates the segment of Mato Grosso included in the Amazon region). The

carbon stock data inputs for the Amazon region are shown in

Table 2.4-43.
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Figure 2.4-40. Division of Mato Grosso into North and South regions

Table 2.4-43. Carbon stocks by land type and Administrative Unit in the Amazon Region

(t CO2e/ha)

Administrative Units

Acre Amapa Amazonas

North Mato

Grosso Para Rondonia Roraima

Forest, Above

Ground 585 425 477 453 457 481 487

Forest, Below

Ground 150 112 125 119 120 125 127

Grassland 40 40 40 40 40 40 40

Savanna 72 72 72 72 72 72 72

Shrubland 137 137 137 137 137 137 137

Mixed 232 183 199 192 193 200 202

Wetland 88 88 88 88 88 88 88

Annual 18 18 18 18 18 18 18

Perennial 44 44 44 44 44 44 44

Soil 131 154 231 213 174 115 145

Soil, Annual 63 74 111 102 84 55 70

We assume that land converted to cropland in the Amazon will be cleared with fire.

Therefore, to determine non-CO2 emissions from fire in the Amazon we used the fire combustion
426

data inputs in Table 2.4-44 where the data inputs are the same for all of the Administrative

Units.

Table 2.4-44. Data inputs for non-CO2 emissions in the Amazon by land type

Land

Cover

Fire

combustion

factors

(dimensionless)

Fire CH4

emission

factors

(g/kg)

Fire N2O

emission

factors

(g/kg)

Forest 0.46 6.80 0.20

Grass 0.76 2.30 0.21

Shrub 0.72 2.30 0.21

Savanna 0.57 2.30 0.21

Wetland 0.70 2.30 0.21

Mixed 0.64 3.80 0.21

To show emissions estimates for all of the land conversions considered in our analysis,

we will focus on the North Mato Grosso, i.e. the portion of Mato Grosso in the Amazon region.

Table 2.4-45. shows land use change GHG emissions, broken out by emissions category, for

crop and pasture expansion in this region. We show emissions for conversion of land cover to

annual cropland and perennial cropland. We also show emissions for conversion to pasture,

which can be in the form of grassland or savanna. The values are presented as total GHG

emissions, or, where specified, as annual emissions (yr-1). Note that negative values signify

carbon uptake, whereas positive values denote GHG releases.

We also present the emissions results over time. For accounting purposes, emissions are

allocated to either year zero (i.e., the year when land clearing takes place), years 1-19 or to years

20-80. This procedure is not intended to be a precise accounting of the timing of emissions

releases, but it is sufficient to determine total emissions over the first 30 years following land

conversion. Carbon emissions from the changes in biomass resulting from land conversion (i.e.,

biomass combustion or decay) are assigned to year zero. Non-CO2 emissions from fire

combustion are also allocated to year zero. Lost forest sequestration continues indefinitely. The

change in soil carbon is spread evenly over the first twenty years following conversion, i.e.

ending in year 19. Thus, total emissions are presented for year zero, years 1 through 19, and

years 20-80. We also present the total emissions over 30 years.
427

Table 2.4-45. Land use change emissions factors for North Mato Grosso, Brazil

(t CO2e/ha)

Start End

Change in

biomass

Lost forest

seques. yr-1

Change in

soil yr-1

Total fire

emis.

Yr 0

emis.

Yrs 1-19

emis. Yr-1

Yrs 20-80

emis. yr-1

30-yr

emis.

Forest Annual 553.78 1.80 5.53 53.31 614.42 7.33 1.80 771.62

Shrub Annual 118.43 0.00 5.53 11.17 135.13 5.53 0.00 240.22

Savanna Annual 53.90 0.00 5.53 4.67 64.10 5.53 0.00 169.19

Grass Annual 21.63 0.00 5.53 3.42 30.59 5.53 0.00 135.68

Wetland Annual 70.03 0.00 5.53 7.01 82.58 5.53 0.00 187.67

Mixed Annual 173.46 0.00 5.53 17.88 196.87 5.53 0.00 301.96

Forest Perennial 528.12 1.80 0.00 0.00 529.91 1.80 1.80 582.02

Shrub Perennial 92.77 0.00 0.00 0.00 92.77 0.00 0.00 92.77

Savanna Perennial 28.23 0.00 0.00 0.00 28.23 0.00 0.00 28.23

Grass Perennial -4.03 0.00 0.00 0.00 -4.03 0.00 0.00 -4.03

Wetland Perennial 44.37 0.00 0.00 0.00 44.37 0.00 0.00 44.37

Mixed Perennial Crop 147.80 0.00 0.00 0.00 147.80 0.00 0.00 147.80

Forest Grass 532.15 1.80 0.00 0.00 533.95 1.80 1.80 586.05

Shrub Grass 96.80 0.00 0.00 0.00 96.80 0.00 0.00 96.80

Mixed Grass 151.83 0.00 0.00 0.00 151.83 0.00 0.00 151.83

Forest Savanna 499.88 1.80 0.00 0.00 501.68 1.80 1.80 553.78

Shrub Savanna 64.53 0.00 0.00 0.00 64.53 0.00 0.00 64.53

Mixed Savanna 119.56 0.00 0.00 0.00 119.56 0.00 0.00 119.56

Note: "Annual" refers to annual crops, and "Perennial" refers to perennial crops, i.e., sugarcane.

Table 2.4-46. is similar to the preceding table, except that emissions factors are shown

for crop and pasture abandonment, i.e. land reversion. For land reverting to forest, the change in

biomass (i.e. plant growth) is an annual factor that continues for twenty years. After twenty

years forests grow at the foregone sequestration rate, which is 1.80 tCO2e /ha/yr in the Amazon

region. For land reverting to any other land type, the change in biomass is a total uptake that is

allocated fully to year zero. That is why year zero uptake in Table 2.4-46. is larger for reversion

to shrubland than to forest, but the total forest uptake over thirty years is larger than reversion to

shrubland. These time accounting procedures were designed to provide accurate estimates of

emissions over 30 years. Soil carbon uptake is an annual factor that is constant for the first

twenty years following conversion.
426

Table 2.4-46.

Land reversion factors for North Mato Grosso, Brazil (t CO2e/ha)

Start End

Change in

biomass

Soil seques.

yr-1

Yr 0

uptake

Yr 1-19

uptake yr-1

Yr 20-80

uptake yr-1

30-yr

uptake

Annual Forest -25.93 -5.53 -31.46 -31.46 -1.80 -647.18

Annual Shrub -118.43 -5.53 -123.96 -5.53 0.00 -229.05

Annual Savanna -53.90 -5.53 -59.43 -5.53 0.00 -164.52

Annual Grass -21.63 -5.53 -27.16 -5.53 0.00 -132.25

Annual Mixed -173.46 -5.53 -178.99 -5.53 0.00 -284.08

Perennial Forest -25.93 0.00 -25.93 -25.93 -1.80 -536.56

Perennial Shrub -92.77 0.00 -92.77 0.00 0.00 -92.77

Perennial Savanna -28.23 0.00 -28.23 0.00 0.00 -28.23

Perennial Grass 4.03 0.00 4.03 0.00 0.00 4.03

Perennial Mixed -147.80 0.00 -147.80 0.00 0.00 -147.80

Grass Forest -25.93 0.00 -25.93 -25.93 -1.80 -536.56

Grass Shrub -96.80 0.00 -96.80 0.00 0.00 -96.80

Grass Mixed -151.83 0.00 -151.83 0.00 0.00 -151.83

Savanna Forest -25.93 0.00 -25.93 -25.93 -1.80 -536.56

Savanna Shrub -64.53 0.00 -64.53 0.00 0.00 -64.53

Savanna Mixed -119.56 0.00 -119.56 0.00 0.00 -119.56

Note: "Annual" refers to annual crops, and "Perennial" refers to perennial crops, i.e., sugarcane.

All of the data and calculations for the results presented above for the Amazon region,

and for all of the 750+ regions analyzed, are available in supporting material on the public

docket for this rulemaking.

2.4.4.2.7 Aggregate International Land Conversion GHG Emissions Impact Results

Figure 2.4-41 presents the 2022 international land use change GHG emissions by

renewable fuel, with land use change emissions normalized by the increment of additional

biofuel produced in each scenario and annualized over 30 years. The figure shows that, based on

our modeling, soy-based biodiesel causes the largest release of international land use change

GHG emissions. The majority of international land use change emissions originate in Brazil in

the corn ethanol and switchgrass ethanol scenarios. This is largely a consequence of projected

pasture expansion in Brazil, and especially in the Amazon region where land clearing causes

substantial GHG emissions. Of the renewable fuels analyzed, our modeling found that sugarcane

ethanol causes the least amount of land use change emissions. This was due largely to our

projection that sugarcane crops would expand onto grasslands in South and Southeast Brazil,

which results in a net sequestration because sugarcane sequesters more biomass carbon than the

grasslands it would replace.
427

Figure 2.4-41. International land use change GHG emissions by renewable fuel, 2022

(kgCO2e/mmBTU)

5

37

43

22

10

32

-1

5

4

14

2

15

-5

0

5

10

15

20

25

30

35

40

45

Brazil Rest of World World

Soy Biodiesel

Corn Ethanol

Sugarcane Ethanol

Switchgrass Ethanol

Brazil is a very prominent region in our projections of where biofuel-induced land use

changes would occur.
426

Figure 2.4-42Figure 2.4-42 shows the land use change emissions across the 6 regions of

Brazil included in the FAPRI-CARD model. All of the renewable fuels analyzed, except for

soy-based biodiesel, cause land use change emissions in the Amazon region. In the soy biodiesel

scenario we project net sequestration in the Amazon as a result of reduced pasture area in that

region. As discussed above, sugarcane ethanol results in net sequestrations in South and

Southeast Brazil.

Figure 2.4-42. Brazil land use change GHG emissions by renewable fuel, 2022

(kgCO2e/mmBTU)
427

-25

-20

-15

-10

-5

0

5

10

15

20

25

Amazon Biome Central-West

Cerrados

Northeast

Coast

NorthNortheast

Cerrados

South Southeast Brazil

Corn Ethanol

Soy Biodiesel

Sugarcane Ethanol

Switchgrass Ethanol

Finally, for reference, Table 2.4-47 presents same results broken out by all 54

international regions in the FAPRI-CARD model.

Table 2.4-47.

International land use change GHG emissions by renewable fuel and by region, 2022

(kgCO2e/mmBTU)

FAPRI-CARD Region Corn Ethanol Soy Biodiesel Sugarcane Ethanol Switchgrass Ethanol

Algeria 0.02 0.02 0.01 0.01

Argentina -0.31 0.11 -0.52 0.15
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FAPRI-CARD Region Corn Ethanol Soy Biodiesel Sugarcane Ethanol Switchgrass Ethanol

Australia 0.52 0.00 0.00 0.18

Bangladesh -0.43 -0.56 -0.12 0.10

Brazil: Amazon Biome 12.83 -18.63 4.79 5.57

Brazil: Central-West Cerrados 4.09 8.52 0.76 3.47

Brazil: Northeast Coast 0.41 0.14 0.77 0.14

Brazil: North-Northeast Cerrados 0.86 5.33 0.52 1.47

Brazil: South 1.93 8.95 -4.20 2.08

Brazil: Southeast 1.56 1.00 -3.81 0.80

Canada -0.04 0.73 -0.11 0.08

China 0.56 4.54 -0.03 0.46

New Zealand 0.05 0.60 0.01 0.03

Colombia 0.25 1.98 0.25 0.15

Cuba 0.05 0.10 0.14 0.01

Egypt -0.01 0.00 0.00 0.00

EU 0.47 1.68 0.30 0.29

Guatemala 0.22 0.17 0.11 0.06

India 0.84 2.30 0.47 -2.14

Indonesia 3.34 4.07 1.13 -0.13

Iran 0.09 0.22 0.05 0.06

Iraq 0.01 0.04 0.01 0.01

Ivory Coast 0.07 0.33 0.09 0.13

Japan 1.22 0.07 0.02 -0.74

Malaysia -0.11 2.98 0.03 0.04

Mexico 1.01 2.25 0.11 0.06

Morocco 0.04 0.05 0.02 0.03

Myanmar (Burma) -0.06 0.14 0.01 -0.01

Nigeria 0.76 0.58 0.19 0.32

Africa, Other 1.13 3.87 0.43 0.61

Asia, Other 0.12 0.34 0.00 -0.09

CIS, Other -1.50 -0.70 -0.13 -0.05

Eastern Europe, Other 0.02 0.14 0.02 0.03

Latin America, Other 0.49 2.27 0.26 0.21

Middle East, Other 0.00 0.05 0.01 0.02

Pakistan -0.07 0.39 0.06 0.14

Paraguay 0.03 -0.52 0.17 0.26

Peru -0.56 1.88 0.08 0.09

Philippines 1.25 1.26 0.51 0.34

Rest of World 1.04 2.73 0.32 0.29

Russia 0.01 0.31 0.09 0.12

South Africa 0.04 0.58 0.05 0.05

South Korea 0.00 0.02 0.00 0.01

Taiwan 0.00 0.04 0.00 0.00

Thailand 0.22 0.40 0.15 0.16

Tunisia 0.02 0.05 0.01 0.02

Turkey -0.10 0.11 0.03 0.02

Ukraine -0.13 0.18 0.01 0.02

Uruguay -0.03 0.37 0.03 0.05

United States* 1.05

Uzbekistan -0.47 -0.29 -0.06 -0.05

Venezuela -0.21 1.14 0.05 0.02

Vietnam 0.23 0.15 0.11 0.07

Western Africa 0.03 0.09 0.04 0.08

TOTAL 31.79 42.54 4.30 15.07

Note: land use change emissions in the United States were calculated by the FASOM model (see discussion above about domestic

land use change), except for in the sugarcane ethanol scenario.

2.4.4.2.8 Uncertainty Assessment for International Land Conversion GHG Emissions Impacts

For the proposed RFS rule, EPA estimated uncertainty around its lifecycle GHG emission

estimates by sensitivity analyses by which, for example, the upper bound of the emissions from
427

international land use change was estimated by assuming that all crop expansion came from

forest and the lower bound was estimated by assuming that all expansion came from idle

grassland (also by assuming that no pasture replacement is necessary). For its updated analysis,

we took a more rigorous approach towards estimating uncertainty.

Uncertainty can be expressed as a percentage confidence interval relative to a mean

value, with the confidence interval defined as a range that encloses the true value of an unknown

parameter with a specified probability. For example, if the area of forest land converted to

cropland (mean value) is 100 ha, with a 95% confidence interval ranging from 90 to 110 ha, we

can say that the uncertainty around the estimate is ±10%.

The 95% confidence interval, which is the value typically used in the context of

estimating GHG emissions and removals under the United Nations Framework Convention on

Climate Change (UNFCCC), has a 95 percent probability of enclosing the true but unknown

value of a given parameter.

The first step in our uncertainty analysis was to identify the potential sources of

uncertainty. We focused on two key sources of uncertainty in international land use change GHG

emissions:

(1) Classification errors that arise from interpretation of satellite imagery to derive land

cover maps (i.e., the types of land affected by land use change);

(2) Errors in parameters used in emission factor estimates (i.e., the magnitude of GHG

emissions per unit of land area converted).

When estimating the total uncertainty in land use change GHG emissions, the two

sources of uncertainty listed above need to be considered together, which was done with a Monte

Carlo simulation model that combined the total uncertainty in the satellite imagery and the

emissions factor estimates. Each step of our uncertainty analysis is explained in this section.

2.4.4.2.8.1 Satellite Data Uncertainty Assessment

As discussed above, MODIS validation data was used to adjust/correct systematic errors

in the MODIS land cover classifications from 2001 to 2007. These adjustments were based on

the producer's accuracies for each land cover class derived from the aggregated confusion matrix

(see Table 2.4-37 above). To estimate the uncertainty in this procedure, we calculated the

producer’s accuracy standard errors for each land category based on the number of training sites

that were used to validate the satellite classifications. For example, based on the number of

training sites that validated forest land, we calculated a standard error of +/- 1.2% around the

92.6% producer’s accuracy for forest. The most accurate approach to estimate the standard

errors from the aggregated confusion matrix would be to reassign the detailed site and pixel data

and then recalculate the standard error following Stehman (1997).599 However, detailed data

about the training site clusters were not available, so a simplification of this procedure was used

where we assumed that the number of pixels per site (or cluster) was constant for each producer
426

land use, essentially representing the random variable for the producer’s accuracy estimate as a

binomial distribution:168

 We assumed that the number of pixels per site for the reported land use is constant.

 The number of reported sites in each land category was then estimated by using the

producer’s accuracy estimate, sometimes referred to as the producer’s estimate, and the

producer’s standard error using the following equation derived from the equation for the

standard deviation for a binomial distribution:

o N = e * (1-e)/(s+a)2

o where,

 N is the number of sites

 e is the producer’s estimate

 s is the producer’s standard error reported as a percentage of the number

of sites

 a is an adjustment factor (-0.0007397) used to account for round off and so

the total number of sites estimated approximates the total number of sites

report by the input source169

o This equation is derived by assuming that the assignment of sites to land

categories is a binomial process (either assigned to the actual land category or

not) which has a standard deviation of (s+a)*N = (N *e*(1-e))0.5

 The number of sites for the 17 land categories were then aggregated to the 10 EPA land

uses

 The standard error as a percentage of the total number of reported sites for the 10 EPA

land uses was then calculated using the equations = (N*e*(1-e))^0.5/N

The producer’s accuracy and standard errors were reduced significantly by aggregating

the MODIS data from 17 land cover categories to 10 categories. As an example, the standard

error for forest estimated with 17 land categories land was high because there were less training

sites for each type of forest, and the producer estimate for each forest type was low because

different types of forest were confused with each other. Table 2.4-48

Table 2.4-48 compares the producer’s accuracy and standard errors for the 17 IGBP land

categories to the producer’s accuracies and standard errors for EPA’s aggregated land cover

classes.

168 Our simplified procedures very closely reproduced the producer accuracy standard errors reported in Friedl et al.

(2010).

169 This equation is derived from the equation from the standard deviation for a binomial distribution.
427

Table 2.4-48. Producer accuracies and standard errors for MODIS Collection 5 classes

and EPA classes based on cross-validation

Producer's accuracy

(%)

Producer's

accuracy (%)

IGBP Land Cover

Class

PA Std. err.

EPA Land

Cover Class

PA Std. err.

Evergreen Needleleaf 89.8 2.3

Evergreen Broadleaf 92.6 2.4

Deciduous Needleleaf 67.3 10.9

Deciduous Broadleaf 68.9 6.2

Mixed Forest 76.2 5.7

Forest 92.6 1.2

Closed Shrubland 63.4 5.9

Open Shrubland 48.3 6.2

Shrubland 69.5 4.0

Woody Savanna 45.2 4.1

Savanna 22.6 4.4

Savanna 60.9 3.1

Grasslands 73.6 4.1 Grasslands 73.7 4.0

Permanent Wetlands 70.6 4.2 Wetlands 70.6 4.1

Annual

Crops 83.2 2.7

Cropland 83.3 2.0

Perennial

Crops 83.2 8.6

Cropland/Nat Veg

Mosaic 60.5 5.7 Mixed 60.4 5.6

Barren/Sparse 95.8 1.4 Barren 95.8 1.3

Snow and Ice 75.6 10.9

Water 96.6 1.9

Excluded 90.5 2.7

The MODIS data was adjusted/corrected based on the producer accuracies reported

above, i.e., with the producer standard errors set to zero. To assess the uncertainty in our

correction process, and thus the MODIS data, we simulated the uncertainty in the producer’s

accuracy by generating 10 pseudo random values each for 2001 and for 2007 (RVpy), for each of

the land uses. The pseudo random values are distributed normally with a mean of zero and a

standard deviation equal to the calculated producer standard error. The stochastic model uses

these to adjust the reported land use in the confusion matrix and create an adjusted confusion

matrix, BCM, with coefficients BCMcyup as follows:

o BCMcypp = ACMcpp * (1+RVpy)

o Scale the remaining coefficients in the column so that the total of the column does

not change

 BCMcyup = ACMcup

* β 

 Where β = (1-BCMcypp)/(1-ACMcpp) when ACMcpp < 1 and 0 otherwise

o For every iteration, we repeated the steps listed above in Section 2.4.4.2.5.2 to

calculate the land conversion shares in each region.

We repeated this stochastic procedure 300 times to generate the 95% confidence intervals

for the share of land conversion types in each of the 54 FAPRI-CARD regions.

Table 2.4-49
426

30-Year Emissions Factor Satellite Data Uncertainty for Select Land Conversions

(+/- 95% confidence intervals as percent of mean)

FAPRI-CARD Region Natural to Annual Annual to Natural Natural to Pasture Pasture to Natural

Algeria 12% 1% 16% 7%

Argentina 1% 1% 5% 4%

Australia 3% 1% 6% 3%

Bangladesh 2% 1% 9% 5%

Brazil: Amazon Biome 6% 1% 8% 1%

Brazil: Central-West Cerrados 2% 1% 5% 3%

Brazil: Northeast Coast 1% 1% 4% 5%

Brazil: North-Northeast Cerrados 2% 1% 5% 4%

Brazil: South 1% 1% 6% 3%

Brazil: Southeast 1% 1% 5% 3%

Canada 5% 1% 11% 2%

China 2% 1% 5% 3%

New Zealand 3% 1% 5% 1%

Colombia 7% 1% 17% 1%

Cuba 3% 1% 8% 2%

Egypt 25% 6% 19% 14%

EU 4% 1% 11% 2%

Guatemala 3% 1% 6% 2%

India 2% 2% 4% 4%

Indonesia 2% 1% 4% 2%

Iran 3% 1% 5% 2%

Iraq 2% 3% 3% 4%

Ivory Coast 3% 1% 5% 2%

Japan 10% 1% 29% 1%

Malaysia 2% 2% 3% 2%

Mexico 1% 1% 4% 2%

Morocco 3% 1% 8% 4%

Myanmar 3% 1% 6% 2%

Nigeria 2% 1% 5% 3%

Other Africa 2% 2% 7% 4%

Other Asia 3% 1% 6% 2%

Other CIS 6% 1% 10% 2%

Other Eastern Europe 3% 1% 9% 2%

Other Latin America 5% 1% 11% 2%

Other Middle East 12% 3% 13% 5%

Pakistan 3% 1% 5% 3%

Paraguay 2% 1% 6% 2%

Peru 9% 1% 13% 1%

Philippines 2% 1% 4% 2%

Rest of World 5% 3% 8% 4%

Russia 3% 1% 9% 2%

South Africa 2% 1% 6% 3%

South Korea 2% 1% 7% 2%

Taiwan 3% 1% 6% 2%

Thailand 2% 1% 4% 2%

Tunisia 3% 1% 8% 4%

Turkey 5% 1% 15% 4%

Ukraine 10% 2% 31% 4%

Uruguay 0% 1% 5% 6%

US 4% 1% 7% 2%

Uzbekistan 11% 2% 15% 4%

Venezuela 5% 1% 15% 2%

Vietnam 2% 1% 3% 2%

Western Africa 1% 1% 5% 5%
427

Table 2.4-49 reports relatively modest levels of uncertainty from the MODIS data. There

are several potential explanations for this. First, by correcting for systematic errors in the

MODIS data, based on NASA's extensive validation efforts, the uncertainty in the satellite data

set is reduced substantially. Second, the aggregated land classes in our analysis reduced the

uncertainty compared to the 17 MODIS land cover classes. Third, the greatest uncertainty in the

satellite data is between land cover classes that sequester similar amounts of carbon (i.e.,

savanna, shrubland and mixed land). Finally, we assume that recent land use change patterns

accurately predict future patterns, and our uncertainty assessment does not quantify the potential

uncertainty from this assumption.

The overall uncertainty in our land use change GHG emissions estimates also includes

the uncertainty in the emissions factors per unit area of land use change. Before the final

uncertainty estimates are presented, our evaluation of emissions factor uncertainty is discussed in

the next section.

2.4.4.2.8.2 International Land Conversions GHG Emissions Factor Uncertainty Assessment

We assessed the uncertainties in our estimates of carbon stocks, and consequently of

carbon stock changes (i.e., the emission factors), for every land conversion included in our land

use change modelling. The final emissions factors for each land conversion were derived from a

combination of several different input parameters, each with its own uncertainty. In this section

we describe the uncertainty estimates for each input parameter and the Monte Carlo analysis

used to combine all of the individual input parameter uncertainties. At the end of this section we

present the total emissions factor uncertainty, which considers spatial correlation across

emissions factor errors. All of the uncertainty estimates, for each data input and region, are

available on the public docket.

2.4.4.2.8.2.1 Evaluation of Input Parameter Uncertainty

The foundation of our emissions factor uncertainty analysis was a rigorous assessment,

following IPCC guidelines, of the uncertainty in all of the input parameters used to calculate the

land use change emissions factors. Winrock generated 95% confidence intervals for every data

input based on the quality, quantity, resolution and variability in the underlying data sources.

The estimation of uncertainty was difficult for some parameters due to the absence of

quantitative error analyses in the source data. Therefore, where no uncertainty information was

available for a given parameter, expert judgement was used to identify an uncertainty range, and

the upper bound was assumed as the uncertainty value. This produced final emission factor

uncertainty values that are likely overestimated and thus conservative.

2.4.4.2.8.2.1.1 Evaluation of Forest Carbon Stocks Input Parameter Uncertainty

Forest carbon stocks for countries in Latin America and Africa were estimated using the

new pantropical carbon stock map of Saatchi et al. (in prep.). However, an accuracy assessment

for this new map was not completed in time for our updated analysis. Therefore, although we

used the new map to derive mean values, uncertainty around these mean values was estimated to

be 19% using accuracy information reported for a prior forest biomass product for Latin America
426

derived using a similar methodological approach (Saatchi et al. 2007). This represents a very

conservative estimate (i.e., a high estimate) of uncertainty in our analysis because the Saatchi et

al. estimates are very robust at the multi-state/regional scale. Because we used the same

pantropical carbon stock map to derive estimates for Latin America and Africa, 19% uncertainty

is also a conservative estimate for Africa.

Forest biomass carbon stocks for countries in South Asia were estimated using a map

developed by Brown et al. (2001), but no formal accuracy assessment was performed as part of

their analysis. Therefore, we consulted Table 4.7 in the IPCC 2006 Guidelines for AFOLU to

see what the reported uncertainty was around Tier 1 aboveground biomass estimates. Biomass

ranges are reported by continent and ecological zone, with extremely wide ranges in many cases

(e.g., mean biomass of subtropical humid forests in Asia is 180 t ha-1, with a range of 10 to 560 t

ha-1). Because the Brown et al. (2001) map was derived using some Tier 2 (country-level)

information, we assumed the uncertainty of the Brown et al. (2001) product to be lower than

uncertainty for Tier 1 IPCC values and therefore assigned Asia biomass carbon stocks an

uncertainty value of 50%. This was done to reflect the somewhat better prediction of biomass

over IPCC Tier 1 default values.

Some data sources provided accuracy information in the original documentation, and

therefore this information was used to assign uncertainty values to the country-level estimates

derived for our analysis. Uncertainty was estimated for biomass carbon estimates in Eastern

Europe and Canada (33%; Myneni et al. 2001), China (36%, Piao et al. 2005), Russia (40%,

Houghton et al. 2007), various EU countries (11%, Nabuurs et al. 2003), and the United States

(7-31% depending on state, Blackard et al. 2007). For other EU countries in which biomass

carbon stocks were estimated using FAO data, an uncertainty value of 50% was assumed. We

assigned an uncertainty value of 80% to the global carbon stock map developed by Reusch and

Gibbs (2008), as this was developed primarily from IPCC Tier 1 information. A summary of

uncertainty values used for forest biomass carbon stocks is presented in Table 2.4-50.

In cases where data sources reported only aboveground biomass (or aboveground carbon)

stocks only, we used regression equation information presented in Cairns et al. (1997) to add in

the uncertainty related to estimating belowground biomass. The relationship in Cairns et al.

(1997) relates belowground biomass to aboveground biomass, and the equation has an adjusted

R

2

value of 0.83. Therefore, we assumed the uncertainty (the percent of variation in

belowground biomass not explained by aboveground biomass to be 7%.
427

Table 2.4-50. Uncertainty values used for forest carbon stock estimates.

Data Source Uncertainty Value Justification

Saatchi et al. (2007) 19% (aboveground), 17% (belowground) Accuracy assessment based on past

product (Saatchi et al. 2007)

Brown et al. (2001) 50% (above- and belowground combined) No formal accuracy assessment

given; therefore expert opinion as

50% (some country-level data used

therefore better than IPCC Tier 1)

Blackard et al. (2007) 7 – 31% (aboveground), 17%

(belowground)

Pixel-level uncertainty values

averaged per state

Houghton et al. (2007) 40% (above- and belowground combined) Reported in original source

Myneni et al. (2001) 27% (aboveground), 33% (belowground) Reported in original source

Nabuurs et al. (2003) 11% (above- and belowground combined) Reported in original source

Piao et al. (2005) 36% (above- and belowground combined) Reported in original source

Reusch and Gibbs (2008) 80% (above- and belowground combined) IPCC Tier 1

FAO (2006) 50% (above- and belowground combined) No formal accuracy assessment

given; therefore expert opinion as

50% (some country-level data used

therefore better than IPCC Tier 1)

2.4.4.2.8.2.1.2 Evaluation of Cropland, Grassland, Savanna, Shrubland and Wetland Carbon

Stocks Input Parameter Uncertainty

Uncertainty around carbon stocks in annual croplands and grasslands was assumed to be

75%, based on default error margins reported in IPCC Table 5.9 and 6.4, respectively. In the

absence of any uncertainty information for savanna, shrubland, perennial cropland and wetlands,

uncertainty in carbon stock estimates for these other land cover categories was also estimated as

75% in keeping with the default uncertainty values presented in the IPCC Guidelines. The one

exception to the 75% uncertainty assumption was Brazil, for which more precise information

was available on the carbon stocks along a continuum of grasslands, savanna and shrublands.

The uncertainty for these land cover types was estimated as 0.6%, 0.9% and 16%, respectively,

derived from the standard errors reported in the original data source (de Castro and Kaufmann

1998). The higher uncertainty in shrubland carbon stocks is likely related to the comparatively

large variation in tree cover (and therefore carbon stocks) in shrublands compared to grasslands

and savannas. Uncertainty in the mixed land cover category was estimated as the average of the

uncertainty in forest, crop, shrub and grass categories in keeping with the IGBP description of

this land cover class.

2.4.4.2.8.2.1.3 Evaluation of Soil Carbon Input Parameter Uncertainty

The data source used to estimate initial (i.e., reference) soil carbon stocks was changed

from FAO/UNESCO’s Soil Map of the World to the newly released World Harmonized Soil

Database for the updated analysis. This was done because the spatial resolution of the new data

product is much improved compared to the FAO map. However, neither data source reports

information on accuracy or uncertainty. In the absence of any reliable information about the

uncertainty of the estimates, we assumed an uncertainty value of 90%, which is the default error

estimate for Tier 1 default soil organic carbon stocks in all soil-climate types (derived from

Table 2.3 of the IPCC 2006 Guidelines).
426

Carbon stocks after land use conversion to cropland were calculated using IPCC Equation

2.25 as the initial soil carbon stock modified by land use, management, and input factors that

relate to how the soil is managed. Default soil factors presented in Table 5.5 of the 2006 IPCC

Guidelines were used, assuming conversion to long-term cultivated annual cropland under full

tillage and medium inputs. The error margin for the land use factor was estimated using default

values presented in Table 5.5 of the 2006 IPCC Guidelines and ranged from 9 to 61% depending

on temperature and moisture regime. After carbon stocks after land use conversion were

estimated, changes in carbon stocks due to land conversion were calculated as the difference

between initial and final carbon stocks divided by an assumed transition period of 20 years

during which soil emissions take place.

2.4.4.2.8.2.1.4 Evaluation of Foregone Forest Sequestration Input Parameter Uncertainty

Uncertainty values for foregone forest carbon sequestration were derived from standard

errors reported in the original data sources. Uncertainty in carbon sequestration rates ranged from

20% to 50%, with higher uncertainty in tropical regions.

2.4.4.2.8.2.1.5 Evaluation of Clearing with Fire Input Parameter Uncertainty

Fire emissions were calculated in the updated analysis when land is cleared for cropland

in a region where fire is assumed to be used as a means of site preparation for the new land use.

Fire emissions were calculated following IPCC Guidelines as the product of initial carbon stocks,

a combustion factor (define) and a GHG emission factor (define). Combustion factors were

estimated per land cover type using information on standard errors reported on combustion

factors in de Castro and Kaufmann (1998). Uncertainties in combustion factors ranged from 42%

to 69%. IPCC defaults were used to estimate uncertainty in the CH4 and N2O emission factors

and ranged from 59% to 78% depending on land cover type burned.

2.4.4.2.8.2.2 Monte Carlo Analysis of Combined Emissions Factor Uncertainty

The uncertainties in individual parameters of an emission factor can be combined using

either (1) simple error propagation (IPCC Tier 1) or (2) Monte Carlo simulation (IPCC Tier 2).

We followed the Tier 2 approach.

One of the inputs required for the Monte Carlo uncertainty model is an estimate of the

degree of correlation among different variables – both the correlation of one variable across

space as well as the correlation of one variable to any others used in the analysis. This is in

contrast to the IPCC error propagation approach, which assumes no correlation. The assumed

correlations among different data inputs used to calculate uncertainty in emission factors using

the Monte Carlo approach are summarized below.

Forest carbon stocks were derived from various sources, and therefore estimates for

certain countries are correlated, i.e. errors in estimates that came from the same data source are

assumed to be correlated. The correlation groups for forest carbon stocks are shown in
427

Figure 2.4-34.Figure 2.4-34 above. All countries use the same equation that relates

belowground biomass to aboveground biomass (Mokany et al. 2006), therefore we assumed that

belowground biomass is perfectly correlated to aboveground biomass.

Although the World Harmonized Soil Database was used to estimate soil carbon stocks in

the updated analysis, the dataset was developed by compiling different data sources together, and

therefore soil carbon stocks are not correlated across all regions. The regional distribution of data

sources is shown in Figure 2.4-43 below.

Figure 2.4-43. Correlation groups for soil carbon stocks

Note: Different colors represent different correlation groups.

Grassland carbon stocks were estimated using IPCC default carbon stock values and

weighting the carbon stock based on the proportion of each country falling within each

ecological zone. Correlation groups for these land cover categories were determined by assuming

that countries with the same dominant ecological zone were correlated (i.e., all use the same

carbon stock value). In addition, carbon stocks of grassland, shrubland and savanna were all

assumed to be correlated to each other because we used a simple proportional approach to

estimate the carbon stocks of savanna and shrubland (based on the grassland value).

All annual croplands have an assumed carbon stock of 5 t C ha-1 and therefore all regions

are correlated for this input parameter. Perennial croplands in Indonesia and Malaysia are

assumed to be oil palm, and are therefore these regions are correlated, while perennial croplands

in the rest of the world are assumed to be in a different correlation group than Indonesia and

Malaysia (and have carbon stocks equivalent to that of sugarcane).

Correlation groups for lost forest sequestration are delineated by data sources, described

above, such that lost forest sequestration across the tropics is correlated.

To estimate the uncertainty in each land conversion emissions factor, a Monte Carlo

analysis was completed using the uncertainty estimates and correlation groups specified for each

data input. For the Monte Carlo analysis we treated the data inputs as variables and assumed that
426

each variable's uncertainty distribution is normal with a standard deviation equal to the

uncertainty value times the mean divided by 100, converting the uncertainty range to a fraction

then dividing by 2.170

To calculate emission factors, Monte Carlo model first generates pseudo random values

for the variables above using the mean, calculated standard deviations, and pseudo random

values for a Normal (0,1) distribution. Note that the resulting values after applying the mean and

standard deviations are all constrained to be greater than 0.

The Monte Carlo model then used these pseudo random variables to calculate

intermediate emissions variables for each country and administrative unit in each Monte Carlo

iteration. 15,000 iterations were generated to calculate the mean and 95% confidence intervals

for the land use change emissions factors. The uncertainty ranges from the emissions factor

estimates, excluding the uncertainty from the MODIS data, are presented below for select land

conversions and for all of the FAPRI-CARD regions.

Table 2.4-51 shows the contribution of uncertainty in our emissions factor estimates to

total uncertainty by land conversion category and region. The uncertainty from emissions factors

is generally larger than the uncertainty from our satellite data analysis (see Table 2.4-49 for

uncertainty from the satellite data). The uncertainty ranges, as a percent of the mean, are very

large in certain regions, such as Egypt, where the mean emissions factor estimates are very small.

170 The uncertainty range is defined as the absolute value of the 97.5th percentile of the distribution minus the 2.5th

percentile of the distribution, divided by the mean value of the distribution which is 2 standard deviations divided by

the mean for a random variable with a normal distribution.
427

Table 2.4-51.

Contribution of Emissions Factor Estimates to Uncertainty in the 30-yr Weighted Land

Conversion Emissions Factors

(+/- 95% confidence intervals as percent of mean)

FAPRI-CARD Region Natural to Annual Annual to Natural Natural to Pasture Pasture to Natural

Algeria 142% 143% 69% 69%

Argentina 74% 80% 18% 27%

Australia 59% 57% 43% 35%

Bangladesh 65% 64% 39% 49%

Brazil: Amazon Biome 23% 37% 19% 41%

Brazil: Central-West Cerrados 45% 41% 28% 34%

Brazil: Northeast Coast 48% 50% 28% 22%

Brazil: North-Northeast Cerrados 39% 38% 25% 22%

Brazil: South 56% 52% 33% 22%

Brazil: Southeast 47% 40% 38% 24%

Canada 58% 52% 32% 47%

China 57% 43% 33% 29%

New Zealand 60% 54% 75% 52%

Colombia 55% 54% 20% 43%

Cuba 53% 43% 34% 36%

Egypt 595% 401% 42% 50%

EU 56% 61% 38% 48%

Guatemala 53% 47% 24% 36%

India 57% 63% 38% 39%

Indonesia 42% 46% 47% 49%

Iran 81% 76% 70% 67%

Iraq 57% 57% 74% 72%

Ivory Coast 42% 53% 16% 49%

Japan 54% 34% 43% 32%

Malaysia 42% 44% 47% 50%

Mexico 61% 56% 29% 38%

Morocco 95% 92% 71% 74%

Myanmar 54% 47% 54% 50%

Nigeria 63% 68% 16% 37%

Other Africa 52% 57% 19% 40%

Other Asia 53% 48% 45% 38%

Other CIS 53% 49% 56% 55%

Other Eastern Europe 62% 48% 34% 39%

Other Latin America 47% 40% 21% 35%

Other Middle East 88% 87% 61% 63%

Pakistan 81% 90% 76% 70%

Paraguay 52% 50% 15% 28%

Peru 34% 45% 15% 43%

Philippines 54% 47% 53% 51%

Rest of World 37% 37% 34% 32%

Russia 60% 56% 40% 39%

South Africa 46% 43% 32% 39%

South Korea 56% 36% 23% 24%

Taiwan 59% 65% 73% 77%

Thailand 49% 48% 38% 37%

Tunisia 119% 116% 58% 66%

Turkey 72% 73% 51% 57%

Ukraine 62% 48% 51% 52%

Uruguay 83% 86% 47% 47%

US 45% 43% 28% 41%

Uzbekistan 50% 58% 59% 69%

Venezuela 57% 47% 34% 48%

Vietnam 44% 43% 42% 41%

Western Africa 62% 64% 7% 23%
426

2.4.4.2.8.3 Evaluation of Total Uncertainty in International Land Conversion GHG Emissions

Impacts

Total uncertainty in land use change GHG emissions was estimated for every renewable

fuel scenario by combining the satellite data and emissions factor uncertainty estimates. The

Monte Carlo model generated 15,000 iterations by generating 300 cases where it varied the

pseudo-random values for historic land used changes and, for each of these 300 cases, generated

50 iterations where it varied the pseudo-random values for the variables used to calculate the

emissions factors.

Within each of the 300 land use cases, the model first took the reported land use change

from MODIS and remapped it to the simulated land use change using the MODIS version 5

confusion matrix and the uncertainties derived from the confusion matrix. This provided land

use in 2007 and land use change from 2001 to 2007 for up to 10x10=100 combinations of land

use in 2001 and land use in 2007.

Next, the Monte Carlo model calculates emission factors for each of the 50 iterations

within the land use case for each of the 42 land use change possibilities (e.g., forest to cropland)

for each country and administrative unit. The model calculated annual land use change

emissions for up to 80 years, and also 30-year aggregated emissions factors. The model then

used the land use change to calculate weighted average emission factors for each of the 54

FAPRI-CARD regions and each of the 12 land conversion options (e.g., annual crops to natural

eco-systems). Finally, the model reports mean emissions as well as 95% confidence ranges. It

also produces the mean and uncertainty ranges for each of the FAPRI-CARD scenario results,

i.e., for each renewable fuel type.

Figure 2.4-44Figure 2.4-44 provides a graphical illustration of the total uncertainty

ranges for international land use change emissions. Error bars are only presented for the global

estimates because Brazil and Rest of World are aggregate regions and the uncertainty ranges are

not the sum of the sub-regions. The error bars in the figure present the low and high ends of the

95% confidence range for international land use change GHG emissions. Table 2.4-52 and

Table 2.4-53 include the low and high ends of the 95% confidence range for land conversion

GHG emissions for each region and renewable fuel scenario. Taken together, the values in these

tables form the 95% confidence intervals for land use change GHG impacts in each region. Note

that given the nature of stochastic modeling, the total low and high ends of the range are not the

sum of the regions.
427

Figure 2.4-44. International land use change GHG emissions by renewable fuel with 95%

confidence intervals, 2022 (kgCO2e/mmBTU)

-10

0

10

20

30

40

50

60

70

80

Brazil Rest of World World

Soy Biodiesel

Corn Ethanol

Sugarcane Ethanol

Switchgrass Ethanol
426

Table 2.4-52. Low end of the 95% confidence range for international land use change

GHG emissions by renewable fuel, 2022

(kgCO2e/mmBTU)

FAPRI-CARD Region Corn Ethanol Soy Biodiesel Sugarcane Ethanol Switchgrass Ethanol

Algeria 0.00 0.00 0.00 0.00

Argentina -0.46 -0.35 -0.94 0.05

Australia 0.28 -0.13 -0.02 0.10

Bangladesh -0.72 -0.95 -0.21 0.04

Brazil: Amazon Biome 10.48 -27.56 3.94 4.45

Brazil: Central-West Cerrados 2.67 0.62 0.26 1.76

Brazil: Northeast Coast 0.18 0.07 0.27 0.07

Brazil: North-Northeast Cerrados 0.49 2.19 0.37 0.87

Brazil: South 0.73 3.26 -7.83 0.79

Brazil: Southeast 0.69 -0.02 -9.71 0.41

Canada -0.11 0.50 -0.16 0.04

China 0.26 3.04 -0.17 0.11

New Zealand 0.01 0.16 0.00 0.01

Colombia 0.01 1.49 0.17 0.09

Cuba 0.02 0.06 0.09 0.00

Egypt -0.02 -0.01 -0.01 -0.01

EU 0.19 0.99 0.14 0.13

Guatemala 0.11 0.12 0.08 0.03

India 0.42 1.16 0.27 -3.52

Indonesia 1.97 2.42 0.67 -0.19

Iran 0.02 0.10 0.01 0.02

Iraq 0.00 0.02 0.00 0.01

Ivory Coast 0.04 0.22 0.06 0.08

Japan 0.55 0.03 0.01 -1.00

Malaysia -0.18 1.52 0.02 0.02

Mexico 0.36 1.55 0.01 -0.02

Morocco 0.01 0.01 0.00 0.01

Myanmar (Burma) -0.09 0.07 0.00 -0.02

Nigeria 0.34 0.34 0.09 0.15

Africa, Other 0.52 3.05 0.24 0.36

Asia, Other 0.06 0.20 0.00 -0.15

CIS, Other -2.43 -1.26 -0.24 -0.16

Eastern Europe, Other 0.00 0.09 0.01 0.02

Latin America, Other 0.23 1.71 0.18 0.13

Middle East, Other 0.00 0.02 0.00 0.01

Pakistan -0.15 0.13 0.02 0.04

Paraguay -0.03 -0.88 0.13 0.20

Peru -0.84 1.55 0.05 0.05

Philippines 0.62 0.63 0.26 0.17

Rest of World 0.66 1.81 0.21 0.19

Russia -0.01 0.13 0.04 0.05

South Africa -0.04 0.39 0.03 0.03

South Korea 0.00 0.01 0.00 0.00

Taiwan -0.01 0.01 0.00 0.00

Thailand 0.12 0.23 0.09 0.09

Tunisia 0.00 0.02 0.00 0.00

Turkey -0.18 0.05 0.01 0.01

Ukraine -0.21 0.04 0.01 0.01

Uruguay -0.05 0.20 0.01 0.02

United States 0.62

Uzbekistan -0.80 -0.51 -0.10 -0.08

Venezuela -0.38 0.73 0.02 0.01

Vietnam 0.13 0.09 0.07 0.04

Western Africa 0.01 0.04 0.02 0.04

TOTAL 23.45 21.37 -3.66 9.58

Note: given the nature of stochastic modeling, the total low and high 95% confidence ranges do not equal the sum of the regions.
427

Table 2.4-53. High end of the 95% confidence range for international land use change

GHG emissions by renewable fuel, 2022

(kgCO2e/mmBTU)

FAPRI-CARD Region Corn Ethanol Soy Biodiesel Sugarcane Ethanol Switchgrass Ethanol

Algeria 0.06 0.05 0.02 0.04

Argentina -0.18 0.64 -0.17 0.27

Australia 0.77 0.17 0.02 0.28

Bangladesh -0.18 -0.23 -0.05 0.17

Brazil: Amazon Biome 15.27 -6.87 5.68 6.80

Brazil: Central-West Cerrados 5.82 18.14 1.20 5.54

Brazil: Northeast Coast 0.69 0.23 1.27 0.22

Brazil: North-Northeast Cerrados 1.31 9.18 0.71 2.19

Brazil: South 3.40 15.85 -1.24 3.65

Brazil: Southeast 2.61 2.25 1.20 1.27

Canada 0.02 0.97 -0.06 0.12

China 0.91 6.05 0.14 0.89

New Zealand 0.08 1.05 0.02 0.05

Colombia 0.53 2.46 0.33 0.21

Cuba 0.08 0.13 0.19 0.01

Egypt 0.01 0.01 0.00 0.01

EU 0.78 2.43 0.48 0.47

Guatemala 0.35 0.22 0.14 0.10

India 1.41 3.83 0.72 -1.11

Indonesia 4.81 5.79 1.63 -0.07

Iran 0.17 0.35 0.10 0.11

Iraq 0.01 0.06 0.01 0.02

Ivory Coast 0.10 0.45 0.14 0.19

Japan 1.97 0.12 0.03 -0.48

Malaysia -0.05 4.49 0.05 0.06

Mexico 1.77 2.96 0.23 0.16

Morocco 0.09 0.10 0.05 0.07

Myanmar (Burma) -0.04 0.22 0.02 -0.01

Nigeria 1.30 0.89 0.33 0.55

Africa, Other 1.96 4.73 0.67 0.93

Asia, Other 0.21 0.49 0.00 -0.04

CIS, Other -0.58 -0.15 -0.01 0.06

Eastern Europe, Other 0.05 0.19 0.03 0.04

Latin America, Other 0.81 2.85 0.35 0.29

Middle East, Other 0.00 0.09 0.02 0.04

Pakistan -0.02 0.71 0.11 0.27

Paraguay 0.08 -0.07 0.21 0.33

Peru -0.23 2.21 0.12 0.13

Philippines 1.97 1.97 0.79 0.53

Rest of World 1.50 3.68 0.45 0.40

Russia 0.02 0.52 0.15 0.21

South Africa 0.13 0.78 0.07 0.07

South Korea 0.00 0.03 0.00 0.01

Taiwan 0.00 0.07 0.00 0.00

Thailand 0.34 0.59 0.22 0.24

Tunisia 0.06 0.08 0.02 0.04

Turkey -0.04 0.17 0.06 0.04

Ukraine -0.06 0.34 0.02 0.02

Uruguay -0.01 0.58 0.07 0.10

United States 1.03

Uzbekistan -0.15 -0.09 -0.02 -0.01

Venezuela -0.04 1.55 0.07 0.04

Vietnam 0.35 0.22 0.16 0.10

Western Africa 0.05 0.14 0.06 0.14

TOTAL 41.89 69.80 10.99 21.86

Note: given the nature of stochastic modeling, for the low and high 95% confidence ranges total emissions do not equal the sum of the

regions.
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2.4.5 Accounting for Lifecycle GHG Emissions Over Time

When comparing the lifecycle GHG emissions associated with biofuels to those

associated with gasoline or diesel emissions, it is critical to take into consideration the time

profile associated with each fuel’s GHG’s emissions stream. With gasoline, a majority of the

GHG emissions associated with extraction, conversion, and combustion are likely to be released

over a short period of time (i.e., annually) as crude oil is converted into gasoline or diesel fuel

which quickly pass to market.

In contrast, the GHG emissions from the production of a typical biofuel (e.g., corn-based

ethanol) may continue to occur over a long period of time. As with petroleum based fuels, GHG

emissions are associated with the conversion and combustion of biofuels in every year they are

produced. In addition, GHG emissions could be released through time if new acres are needed to

produce corn or other crops for biofuels. The GHG emissions associated with converting land

into crop production would accumulate over time with the largest release occurring in the first

few years due to clearing with fire or biomass decay. After the land is converted, moderate

amounts of soil carbon would continue to be released for approximately 20 years. Furthermore,

there would be foregone sequestration associated with the fact that the forest would have

continued to sequester carbon had it not been cleared for approximately 80 years (See Figure

2.4-45.).

Figure 2.4-45.

Timing of International Land Use Change Emissions in the Corn Ethanol Scenario

(gCO2e / mmBTU)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

2022203020402050

GHG Emissions (gCO2-eq / mmBtu)

International Land Use Change Emissions

Year 0 Emissions:

•Clearing with fire

•Biomass decay

•Soil carbon emissions

•Foregone forest sequestration

Years 1-19 Emissions:

•Soil carbon emissions

•Foregone forest sequestration

Years 20-79 Emissions:

•Foregone forest sequestration

While biomass feedstocks grown each year on new cropland can be converted to biofuels

that offer an annual GHG benefit relative to the petroleum product they replace, these benefits

may be small compared to the upfront release of GHG emission. Depending on the specific

biofuel in question, it can take many years for the benefits of the biofuel to make up for the large

initial releases of carbon that result from land conversion (e.g., the payback period).
427

As required by EISA, our analysis must demonstrate whether biofuels reduce GHG

emissions by the required amount relative to the 2005 petroleum baseline. A payback period

alone cannot answer that question. Since the payback period is not sufficient for our analysis, we

have developed methods for capturing the stream of emissions and benefits over time. For our

analytical purposes, it is important for us to determine how the time profiles of emission releases

of different fuels compare. It is useful to have a unitary metric that allows for a direct

comparison of biofuels compared to gasoline or diesel, which requires an accounting system for

GHG emissions over time. When considering the time profile of GHG emissions, the two

assumptions that have a significant impact on the determination of whether a biofuel meets the

emissions reduction threshold include: 1) the time period considered and 2) the discount rate we

apply to future emissions. The proposed rule presented results using a 100-year time horizon and

a 2% discount rate, as well as results with a 30-year time horizon and a 0% discount rate.

Based on input from the expert peer review and public comments, EPA has chosen to

analyze lifecycle GHG emissions using a 30 year time period, over which emissions are not

discounted, i.e., a zero discount rate is applied to future emissions.

The main reasons for why a short time period is appropriate: this time frame is the

average life of a typical biofuel production facility; future emissions are less certain and more

difficult to value, so the analysis should be confined insofar as possible to the foreseeable future;

and a near-term time horizon is consistent with the latest climate science that indicates that

relatively deep reductions of heat-trapping gasses are needed to avoid catastrophic changes due

to a warming climate.

EPA has decided not to discount (i.e., use a 0% discount rate) GHG emissions due to the

many issues associated with applying an economic concept to a physical parameter. First, it is

unclear whether EISA intended lifecycle GHG emissions to be converted into a metric whose

underpinnings rest on principals of economic valuation. A more literal interpretation of EISA is

that EPA should consider only physical GHG emissions. Second, even if the principle of tying

GHG emissions to economic valuation approaches were to be accepted, there would still be the

problem that there is a lack of consensus in the scientific community about the best way to

translate GHG emissions into a proxy for economic damages. Also, there is a lack of consensus

as to the appropriate discount rate to apply to GHG lifecycle emissions streams through time.

Finally, since EPA has decided to base threshold assessments of lifecycle GHG emissions on a

30 year time frame, the issue of whether to discount GHG emissions is not as significant as if the

EPA had chosen the 100 year time frame to assess GHG emissions impacts.

2.4.6 Feedstock Transport

The GHG impacts of transporting biofuel feedstock from the field to the biofuel facility

and transporting co-products from the biofuel facility to the point of use were included in this

analysis. The GREET default of truck transportation of 50 miles was used to represent corn and

soybean transportation from farm to plant. This includes 10 miles from farm to stacks and 40

miles from stacks to plant. Transportation assumptions for DGS transport were 14% shipped by

rail 800 miles, 2% shipped by barge 520 miles, and 86% shipped by truck 50 miles. The percent

shipped by mode was from data provided by USDA and based on Association of American
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Railroads, Army Corps of Engineers, Commodity Freight Statistics, and industry estimates. The

distances DGS were shipped were based on GREET defaults for other commodities shipped by

those transportation modes. Default GREET assumptions were also used for cellulosic ethanol

feedstock transport. Crop residues, switchgrass and forest wastes were all assumed to be shipped

by truck from point of production to plant. Crop residue distance shipped was 30 miles,

switchgrass distance was 40 miles, and forest waste was 75 miles. The GHG emissions from

transport of these feedstocks and co-products are based on GREET default emission factors for

each type of vehicle including capacity, fuel economy, and type of fuel used.

GHG emissions from the transport of sugarcane from the field to the ethanol production

facility also depend on distance and the type of truck used. The average one-way distance in

2002 was 20 km (12.4 mi)600. Over time, transport distance has increased to 23 km for

2005/2006, and is expected to be close to 30 km (18.6 mi) by 2020.601 In terms of trends for

logistics, there has been a replacement of single load trucks by trucks with lower specific fuel

consumption and higher load capacities (3 to 4 wagons).

Table 2.4-54. Sugarcane Transportation Inputs

Parameter Units GREET

Default

GREET

ISJ

(2008)602

2002 2005/

2006

Scenario

2020

Transportation distance (oneway)km 19.3 20 20 23 30

Truck diesel use efficiency ml/(t*km) 27.7 14.8 20.4 19.1 16.1

Diesel consumption ml/tonne

cane

534 296 408 439 483

As we are projecting to a 2022 case, we used the projections available for the 2020

scenario given in Table 2.4-54, along with a revised truck payload assumption based on

comments we received, to estimate GREET inputs for truck payload (tons), fuel economy of the

truck (mpg), and the average one-way distance from field to the mill (miles). Specifically, we

assumed a truck payload of 42 tons603, fuel economy of 3.8 mpg, and an average distance of 19

miles. We incorporated these revised inputs into a recent release of the GREET model to

estimate the GHG impacts of sugarcane transport. 604

2.4.7 Biofuel Processing

GHG emissions from renewable fuel production were calculated by multiplying the

BTUs of the different types of energy inputs at biofuel process plants by emissions factors for

combustion of those fuel sources. The BTU of energy input was determined based on analysis of

the industry and specific work done as part of the NPRM. The emission factors for the different

fuel types are from GREET and were based on assumed carbon contents of the different process

fuels. The emissions from producing electricity in the U.S. were also taken from GREET and

represent average U.S. grid electricity production emissions. The emissions from combustion of

biomass fuel source are not assumed to increase net atmospheric CO2 levels. Therefore, CO2

emissions from biomass combustion as a process fuel source are not included in the lifecycle

GHG inventory of the biofuel production plant. The following sections outline the assumptions

used to model biofuel production for different feedstocks and fuel pathways.
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2.4.7.1 Corn Ethanol

One of the key sources of information on energy use for corn ethanol production was a

study from the University of Illinois at Chicago Energy Resource Center. Between proposal and

final rule, the study was updated to reflect more recent data, therefore, we incorporated the

results of the updated study in our corn ethanol pathways process energy use for the final rule.

We also updated corn ethanol production energy use for different technologies in the final rule

based on feedback from industry technology providers as part of the public comment period.

The main difference between proposal and final corn ethanol energy use values was a slight

increase in energy use for the corn ethanol fractionation process, based on feedback from

industry technology providers.

The two basic methods for producing ethanol from corn are dry milling and wet milling.

In the dry milling process, the entire corn kernel is ground and fermented to produce ethanol.

The remaining components of the corn are then either left wet if used in the near term or dried

for longer term use as animal feed (dried distillers grains with solubles, or DGS). In the wet

milling process, the corn is soaked to separate the starch, used to make ethanol, from the other

components of the corn kernel. Wet milling is more complicated and expensive than dry milling,

but it produces more valuable products (ethanol plus corn syrup, corn oil, and corn gluten meal

and feeds). The majority of ethanol plants in the United States are dry mill plants, which

produce ethanol more simply and efficiently.

For this analysis the amount of corn used for ethanol production as modeled by the

FASOM and FAPRI-CARD models was based on yield assumptions built into those two models.

Assumptions were ethanol yields of 2.71 gallons per bushel for dry mill plants and 2.5 gallons

per bushel for wet mill plants (yields represents pure ethanol).

As mentioned above, in traditional lifecycle analyses, the energy consumed and

emissions generated by a renewable fuel plant must be allocated not only to the renewable, but

also to each of the by-products. However, for corn ethanol production, this analysis accounts for

the DGS and other co-products use directly in the FASOM and FAPRI-CARD agricultural sector

modeling described above. DGS are considered a partial replacement for corn and other animal

feed and thus reduce the need to make up for the corn production that went into ethanol

production. Since FASOM takes the production and use of DGS into account, no further

allocation was needed at the ethanol plant and all plant emissions are accounted for here.

In terms of the energy used at renewable fuel facilities, there is a lot of variation between

plants based on the process type (e.g., wet vs. dry milling) and the type of fuel used (e.g., coal vs.

natural gas). There can also be variation between the same type of plants using the same fuel

source based on the age of the plant and types of processes included, etc. For our analysis we

considered different pathways for corn ethanol production. Our focus was to differentiate

between facilities based on the key differences between plants, namely the type of plant and the

type of fuel used. One other key difference we modeled between plants was the treatment of the

co-products DGS. One of the main energy drivers of ethanol production is drying of the DGS.

Plants that are co-located with feedlots have the ability to provide the co-product without drying.

This has a big enough impact on overall results that we defined a specific category for wet vs.
426

dry co-product. One additional factor that appears to have a significant impact on GHG

emissions is corn oil fractionation from DGS. Therefore, this category is also broken out as a

separate category in the following section. See RIA Chapter 1.4 for a discussion of corn oil

fractionation.

Furthermore, as our analysis was based on a future timeframe, we modeled future plant

energy use to represent plants that would be built to meet requirements of increased ethanol

production, as opposed to current or historic data on energy used in ethanol production. The

energy use at dry mill plants was based on ASPEN models developed by USDA and updated to

reflect changes in technology out to 2022 as described in RIA Chapter 1. The modeling

provided energy use for the different types of dry mill ethanol plants as shown in Table 2.4-55.
427

Table 2.4-55. 2022 Energy Use at Ethanol Plants w/CHP (BTU/gal)

Type Technology NG Use Coal Use Biomass Use Purchased Elec

Corn Ethanol – Dry Mill NG Base Plant (dry DDGS) 28,660 2,251

w/ CHP (dry DDGS) 30,898 512

w/ CHP and Fractionation (dry DDGS) 25,854 1,512

w/ CHP, Fractionation and Membrane Seperation (dry DDGS) 21,354 1,682

w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (dry DDGS) 16,568 1,682

Base Plant (wet DGS) 17,081 2,251

w/ CHP (wet DGS) 19,320 512

w/ CHP and Fractionation (wet DGS) 17,285 1,512

w/ CHP, Fractionation and Membrane Seperation (wet DGS) 12,785 1,682

w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (wet DGS) 9,932 1,682

Corn Ethanol – Dry Mill Coal Base Plant (dry DGS) 35,824 2,694

w/ CHP (dry DGS) 39,407 205

w/ CHP and Fractionation (dry DGS) 33,102 986

w/ CHP, Fractionation and Membrane Seperation (dry DGS) 27,477 1,191

w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (dry DGS) 21,495 1,191

Base Plant (wet DGS) 21,351 2,694

w/ CHP (wet DGS) 24,934 205

w/ CHP and Fractionation (wet DGS) 22,390 986

w/ CHP, Fractionation and Membrane Seperation (wet DGS) 16,766 1,191

w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (wet DGS) 13,200 1,191

Corn Ethanol – Dry Mill Biomass 2022 Base Plant (dry DGS) 35,824 2,694

2022 Base Plant w/ CHP (dry DGS) 39,407 205

2022 Base Plant w/ CHP and Fractionation (dry DGS) 33,102 986

2022 Base Plant w/ CHP, Fractionation and Membrane Seperation (dry DGS) 27,477 1,191

2022 Base Plant w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (dry DGS) 21,495 1,191

2022 Base Plant (wet DGS) 21,351 2,694

2022 Base Plant w/ CHP (wet DGS) 24,934 205

2022 Base Plant w/ CHP and Fractionation (wet DGS) 22,390 986

2022 Base Plant w/ CHP, Fractionation and Membrane Seperation (wet DGS) 16,766 1,191

2022 Base Plant w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (wet DGS) 13,200 1,191

Corn Ethanol – Wet Mill Plant with NG 45,950

Plant with coal 45,950

Plant with biomass 45,950
426

In response to comments received, we included corn oil fractionation and extraction as a

potential source of renewable fuels for this final rulemaking. Based on research of various corn

ethanol plant technologies, corn oil as a co-product from dry mill corn ethanol plants can be used

as an additional biodiesel feedstock source. Dry mill corn ethanol plants have two different

technological methods to withdraw corn oil during the ethanol production process. The

fractionation process withdraws corn oil before the production of the DGS co-product. The

resulting product is food-grade corn oil. The extraction process withdraws corn oil after the

production of the DGS co-product, resulting in corn oil that is only suitable for use as a biodiesel

feedstock.

Based on cost projections outlined in Section 4, it is estimated that by 2022, 70% of dry

mill ethanol plants will conduct extraction, 20% will conduct fractionation, and that 10% will

choose to do neither. These parameters have been incorporated into the FASOM and FAPRICARD models for the final rulemaking analysis, allowing for corn oil from extraction as a major

biodiesel feedstock.

2.4.7.2 Corn Butanol

For the final rule analysis we included a scenario of converting corn starch into butanol.

The production of corn was assumed to be the same as for ethanol production and based on the

agricultural sector modeling described in the previous sections. However, the results were scaled

based on the yield of butanol produced. Corn ethanol was assumed to have a processing yield of

2.7 gal/bu and an energy content of 76,000 Btu/gal which results in an overall energy yield of

206,280 Btu/bu. Corn butanol has a slightly lower processing yield of 2.12 gal/bu but a higher

energy content of 99,827 Btu/gal for an overall energy yield of 212,153 Btu/bu. Therefore, on a

per Btu produced basis corn butanol has slightly lower emissions compared to corn ethanol.

For process energy use we assumed the same types of technology as used for corn

ethanol production. To estimate GHG emissions we used the average 2022 mix of plants and

technologies which includes fractionation and 63% dry DGS and 37% wet DGS using natural

gas as an energy source. Average energy use was 26,496 Btu NG per gallon produced and 4,642

Btu purchased electricity per gallon.

2.4.7.3 Biodiesel (including Algae)

Three scenarios for biodiesel production were considered, one utilizing soybean oil as a

feedstock, one using yellow grease, and the last using algae oil. All three were assumed to be

converted to biodiesel through the Fatty Acid Methyl Ester (FAME) process. The emissions

from soybean growing were estimated through the agricultural sector modeling described in the

previous sections. This section discusses the modeling for the production of algal oil, producing

soybean oil, and conversion of al oils into biodiesel.
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2.4.7.3.1 Algae Oil Production

We developed our lifecycle analysis of the algae pathways primarily based on Aspen

modeling provided by the National Renewable Energy Laboratory (NREL).605 As the algae

industry is still in its nascent stages and there are potentially many variations to the processing of

algae, e.g. methods used for harvesting and lipid extraction, byproduct utilization, etc., it is

challenging to say which set of technologies and configurations may be the most successful in

the future. A recent publication summarized some of the potential algae-biofuel production

pathways being considered.606

Two production pathways were evaluated at this time, one utilizing an open pond (op)

system and the other a photobioreactor (PBR) system. More details on the assumptions used for

those systems are described in the following sections as well as in the technical memorandum

from NREL. We view this assessment as a starting point for evaluating algae-to-biofuel

pathways and not as the only or preferred production configurations for algae. Nevertheless, we

believe that the assumptions and scenarios chosen to represent the production of algae by 2022

are reasonable given the expert opinion solicited. Over time we plan to evaluate different

variations of these pathways and to update the data and analyses as the algae industry grows and

commercializes.

NREL evaluated three cases: a base case, an aggressive case, and a maximum case for

each of the algae production pathways, i.e. op and PBR. A brief summary of the cases evaluated

are given below:

Base case: algae yield = 25 g/m2

/day (op), 63 g/m2/day (PBR); lipid content = 25%

(corresponds to a reasonable but still challenging target for the near future)

Aggressive case: algae yield = 40 g/m2/day (op), 100 g/m2/day (PBR); lipid content = 50%

(assumes identification of a strain with near optimal growth rates and lipid content)

Maximum case: algae yield = 60 g/m2/day (op), 150 g/m2/day (PBR); lipid content = 60%

(represents the near theoretical maximum based on photosynthetic efficiencies)

For all cases: scale of facility = 10 MMgal/yr, 10% algae lost after production, 5% lipid lost

in extraction

The production of algae-based biofuel consists of the following stages:

 Algae Cultivation

 Algae Harvesting

 Algae Oil Extraction and Recovery

 Algae Oil Transport to Biofuel Facility

 Algae Oil Conversion to Biofuel

 Biofuel Distribution
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The modeling completed by NREL covered the first three production steps. We assumed

that the biofuel facility would be co-located next to the algae oil production processes and thus

transport emissions for the fourth step would be negligible. The last two steps were assumed to

be similar to the conversion of soy oil to biodiesel and soy-based biodiesel distribution.

Algae require several inputs, including water, land, nutrients, and in most cases, light to

sustain growth. The following Table 2.4-56 summarizes these main inputs for the various open

pond and photobioreactor scenarios:

Table 2.4-56. Inputs for Algae Cultivation

Input Base Case Aggressive Case Max Case

op PBR op PBR op PBR

Water Use:

Net water demand

[MMgal/yr]

9,740 720 3,830 320 2,710 250

Net water demand

[gal/gal lipid]

974 72 383 32 271 25

Land Use [acre]:

Pond/PBR land size 4,743 1,897 1,482 593 823 329

Total plant land required 7,079 3,795 2,212 1,186 1,229 659

Nutrient Use [ton/yr]:

Fertilizer for algae 23,920 23,880 12,000 11,980 10,010 10,000

Nutrients for anaerobic

digester

2,960 3,000 1,440 1,460 1,190 1,200

CO2 Use:

CO2 consumed [lb/lb

algae produced]

2 2 2 2 2 2

Net CO2 used from

offsite flue gas [ton/yr]

290,000 290,000 150,000 150,000 120,000 120,000

The scenarios assume water use is from low-value brackish or saline water pumped from

an underground source rather than drawing from fresh water resources.171 In areas where water

is limited, fresh water may not be available at a reasonable cost and therefore may affect the

feasibility of the system. One factor that could further limit water consumption is by cultivating

algae in nutrient-rich eutrophic or mixed waters (e.g. animal litter, tertiary wastewater, and

agricultural or industrial runoffs). This in turn could limit the amount of nutrients purchased for

algae cultivation. An additional benefit to the use of wastewater is that an algae process that

treats wastewater displaces carbon that would have been generated in conventional wastewater

treatment processes.

171 Some fresh makeup water is assumed to replace evaporative losses in the cooling system for the PBR system.
427

Algae cultivation is expected to be able to use non-arable lands. As such, the conversion

of carbon-rich lands to agriculture can be avoided and thus emissions from land-use change.

Due to higher cell densities, the use of photobioreactors can lower land use in comparison to

open pond systems. The scenarios assume 330 operating days/year and a solar exposure of 12

hours/day which implies that a site location is chosen which receives high year-round solar

exposure.

The nutrients used in the process include fertilizers purchased for the algae and those

needed to operate the anaerobic digesters. The fertilizers for the algae purchased are

approximately 64% urea and 36% di-ammonium phosphate (DAP). The nutrients required to aid

anaerobic digestion are primarily caustic with some phosphoric acid, urea and micronutrients.

Algae also consume CO2 during cultivation. The scenarios assume that part of the CO2 is

recycled within the process from anaerobic digestion of spent biomass and part of the CO2 is

delivered from offsite flue gas, e.g. from power plant. Scenarios assume that CO2 is delivered

from a distance of 1.5 miles from a power plant or other emissions source. For both the open

pond and PBR “base case”, pure CO2 was chosen where instead of transporting the entire flue

gas material, the CO2 is scrubbed out and transported under pressure to the facility.

Harvesting is necessary to recover biomass from the cultivation system. Commonly used

techniques include flocculation, dissolved air flotation (DAF), centrifugation, microfiltration,

and decantation. Wet biomass may also be dewatered or dried. Dewatering decreases the

moisture content by draining or mechanical means. Additional drying can follow using e.g.

drum dryer, freeze dryer, spray dryer, rotary dryer, or by solar drying. Primary harvesting under

our scenarios occurs using natural settling to concentrate the algae from 0.05% to 1%. Secondary

harvesting concentrates the algae to 10% via DAF using chitosan as a flocculant.

Oil from algae can be extracted through chemical or mechanical processes to separate the

algal oil from the cell membrane. The TAGs (Triacylglycerides) are typically the main product

which goes to biodiesel production. The remainder consists of carbohydrates, proteins, nutrients,

and ash), usually referred to as the algal residue or spent biomass.

The extraction step is commonly regarded as the most speculative in terms of large-scale

feasibility.607 Thus extraction is a critical area of research going forward to achieve practical

algal lipid production. Some of the more common methods are solvent extraction, supercritical

fluid extraction, and mechanical extraction. Algal extraction under both op and PBR cases was

assumed here to be carried out using mechanical extraction via high-pressure homogenization to

lyse algae cells. Homogenization was chosen because it is the closest to the necessary

processing scale investigated given current technology. Other extraction techniques discussed

include solvent extraction, supercritical fluid extraction, osmotic shock, and sonication.

The lipids are assumed to be recovered via phase separation in a clarifier tank which

allows contents to settle into lipid, water and spent biomass.

The spent biomass is assumed to be used in anaerobic digestion and power generation via

gas turbine which provides power to run the plant. The other method commonly discussed is its
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use as animal feed; however, this was not assumed under these scenarios. Table 2.4-57

summarizes the net annual electricity required (purchased from grid) for the cultivation,

harvesting, oil extraction and recovery stages. We assumed that the average U.S. grid electricity

is used.

Table 2.4-57. Net Annual Electricity Required (purchased from grid) [MM kwh/yr]

for 10 MMgal/yr Lipid Production

Base Case Aggressive Case Max Case

Op PBR Op PBR op PBR

60.2 35.7 27.0 18.8 19.8 16.0

2.4.7.3.2 Soybean Oil Production

For the soybean oil scenario, the energy use and inputs for the biodiesel production

process were based on a model developed by USDA and used by EPA in the cost modeling of

soybean oil biodiesel including crushing, as discussed in Chapter 4. Soybean crushing was

modeled assuming yields of 11.2 lbs soybean oil/bu soybeans and energy use of 14,532 BTU of

natural gas and 2,843 BTU of purchased electricity per gallon of biodiesel produced.

Similar to the case with corn ethanol co-products, we analyze the aggregate GHG

emissions from soybean crushing and transesterification that occur as a result of increased

demand for a particular biofuel. Therefore, any increase in soybean meal or soybean oil

produced as a result of larger biodiesel volumes would take into account GHG emissions

reductions from a decrease in the production of other feed and vegetable oil substitutes from our

FASOM modeling.

2.4.7.3.3 Conversion of Oil to Biofuel

For the proposal we based biodiesel processing energy on a process model developed by

USDA-ARS to simulate biodiesel production from the Fatty Acid Methyl Ester (FAME)

transesterification process. In this process vegetable oil (triglyceride) is reacted with an alcohol

(e.g., methanol) and a catalyst (e.g., sodium hydroxide) to produce biodiesel and glycerin.

During the comment period USDA updated their energy balance for biodiesel production to

incorporate a different biodiesel dehydration process based on a system which has resulted in a

decrease in energy requirements. Soybean biodiesel transesterification was modeled assuming

yields of one kilogram of biodiesel from a kilogram of soybean oil and energy use of 4,381 BTU

of natural gas and 361 BTU of electricity per gallon of biodiesel produced.608

We assumed that the algae oil produced would be similar in quality as soy oil. Although

it is possible that the algae oil may require an upgrading step such as degumming to remove

phospholipids, this step was not included as there is no information at this time regarding the

process logistics specific to algal-derived oil. Algae oil is also assumed to be converted to

biodiesel through the transesterification process with the same energy and material requirements

of soybean oil.
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For the yellow grease case, no soybean agriculture emissions or energy use was included.

Soybean crushing natural gas use was included as a surrogate for yellow grease processing

(purification, water removal, etc.). Also, due to additional processing requirements, the energy

use associated with producing biodiesel from yellow grease is higher than for soybean oil

biodiesel production. The energy use for yellow grease biodiesel production was assumed to be

1.7 times the energy used for soybean oil biodiesel and yields of 0.94 kilograms of biodiesel

from a kilogram of yellow grease.

GHG emissions from other biodiesel production raw material inputs were also included

in the analysis. HCl, methanol, NaOCH3 and sodium hydroxide are used in the production of

biodiesel and GHG emissions from producing the raw material inputs were also added to the

model. Table 2.4-58 shows the values that were used to convert raw material inputs into GHG

emissions used in the anlaysis.

Table 2.4-58.

Lifecycle Factors for Biodiesel Raw Material Production

Factor Unit Methanol

Sodium

Methoxide

Sodium

Hydroxide HCl

CO2 g/g 0.401 0.966 0.923 1.011

CH4 g/g 0.003 0.002 0 0

N2O g/g 3.9E-06 2.5E-06 0 0

Total Energy BTU/g 19.05 24.10 9.67 9.35

Glycerin is a co-product of biodiesel production. Our proposal analysis did not assume

any credit for this glycerin product. We have included for the final rule analysis that glycerin

would displace residual oil as a fuel source on an energy equivalent basis. This is based on the

assumption that the glycerin market would be saturated in 2022 and that glycerin produced from

biodiesel would not displace any additional petroleum glycerin production. However, the

biodiesel glycerin would not be a waste and a low value use would be to use the glycerin as a

fuel source. The fuel source assumed to be replaced by the glycerin is residual oil.

2.4.7.4 Cellulosic Biofuel

For the cellulosic biofuel pathways, we updated our final rule energy consumption

assumptions on process modeling completed by NREL. For the NPRM, NREL estimated energy

use for the biochemical enzymatic process to ethanol route in the near future (2010) and future

(2015 and 2022).609 As there are multiple processing pathways for cellulosic biofuel, we have

expanded the analysis for the FRM to also include thermochemical processes (Mixed-Alcohols

route and Fischer-Tropsch to diesel route) for plants which assume woody biomass as its

feedstock.610,611

Cellulosic biofuel can be produced through two main types of production processes,

either fermentation or gasification. The fermentation option may show preference towards using

more homogeneous feedstock sources like farmed trees (hardwoods), switchgrass and corn

stover whereas more heterogeneous sources like forestry waste (typically softwoods) may prefer

the gasification option due to processing challenges. For more information on key biomass
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feedstock considerations and the potential impact they may have on yields and processability

within the biorefinery refer to the technical document provided by NREL.612

As discussed, we have worked with NREL to generate models of cellulosic ethanol and

diesel fuel production.
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Table 2.4-59Table 2.4-59 shows the energy use required for the different cellulosic

ethanol and F-T diesel production processes. For the biochemical pathway, process energy is

assumed to be generated through the unfermentable portion (mainly lignin) of incoming biomass

being burned for electricity production. The process is assumed to generate excess electricity per

gallon of ethanol produced.
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Table 2.4-59. 2022 Energy Use at Cellulosic Biofuel Plants (BTU/gal)

Type Technology Biomass Use Diesel Fuel Use Purchased Elec. Sold Elec.

Cellulosic Ethanol – Enzymatic Switchgrass feedstock & lignin used as fuel 61,001 -12,249

Corn stover feedstock & lignin used as fuel 61,001 -12,249

Forest waste feedstock & lignin used as fuel 64,220 -18,391

Cellulosic Ethanol – Thermochemical Switchgrass feedstock 90,935 177

Corn stover feedstock 90,935 177

Forest waste feedstock 90,935 177

Farmed trees feedstock 90,935 177

Cellulosic Diesel – F-T Switchgrass feedstock 168,220 327 17

Corn stover feedstock 168,220 327 17

Forest waste feedstock 168,220 327 17

Farmed trees feedstock 168,220 327 17
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The benefit of electricity generation is the possibility of lowering greenhouse gas

emissions by offsetting other forms of electricity production. This is captured in our analysis by

assuming that the excess electricity produced by the ethanol plant will offset U.S. grid electricity

production. Therefore, GHG emissions from U.S. grid electricity are calculated for the amount

of excess electricity produced based on GREET defaults for electricity production and subtracted

from the lifecycle results of cellulosic ethanol production.

2.4.7.5 Brazilian Sugarcane Ethanol

Under the imported sugarcane ethanol cases we updated process energy use assumptions

to reflect anticipated increases in electricity production for 2022 based on recent literature and

comments to the proposal. One major change was assuming the potential use of trash (tops and

leaves of sugarcane) collection in future facilities to generate additional electricity. The NPRM

had only assumed the use of bagasse for electricity generation. Based on comments received, we

are also assuming marginal electricity production (i.e., natural gas) instead of average electricity

mix in Brazil which is mainly hydroelectricity. This approach assumes surplus electricity will

likely displace electricity which is normally dispatched last, in this case typically natural gas

based electricity. The result of this change is a greater credit for displacing marginal grid

electricity and thus a lower GHG emissions profile for imported sugarcane ethanol than that

assumed in the NPRM. We also received public comment that there are differences in the types

of process fuel e.g. used in the dehydration process for ethanol. While using heavier fuels such

as diesel or bunker fuel tends to increase the imported sugarcane ethanol emissions profile, the

overall impact was small enough that lifecycle results did not change dramatically. We describe

these changes in further detail below.

In Brazil, the majority of mills are configured to produce both sugar and ethanol

simultaneously. To simplify the lifecycle analysis, we assumed that a sugarcane ethanol mill is

operated with 100% feed for ethanol production. In a sugarcane mill, sugarcane is cleaned,

crushed, and the cane juice extracted. The juice is then treated to produce ethanol and/or sugar,

depending on market demands. The stream for ethanol is fermented and distilled into hydrous

ethanol. From there, there are two possibilities. Hydrous ethanol may be stored as the final

product or dehydrated to anhydrous ethanol.

2.4.7.5.1 Sugarcane Ethanol Process Energy Consumption

In Brazil, the majority of energy used at the sugarcane ethanol facility is supplied by

burning bagasse, the fiber material leftover after extracting cane juice. The bagasse is combusted

in a boiler to produce steam and generate electricity to meet internal demands as well as export

surplus electricity to the grid. A smaller portion of energy is required for chemical and lubricant

use. We used a bagasse yield of 280 kg (with 50% moisture) per MT of sugarcane.

2.4.7.5.2 Bagasse Combustion Emissions

We used the IPCC guidelines (2006b) and average emission factors of CH4 and N2O from

biomass combustion, as shown in Table 2.4-60.
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Table 2.4-60. Emissions per mmBTU Bagasse Burned613

Pollutant g/mmBTU bagasse burned

CH4 31.65

N2O 4.22

2.4.7.5.3 Chemical and Lubricant Use

We assumed that the chemicals and lubricants are similar to residual oil in terms of

energy and emission profiles (see Table 2.4-61).

Table 2.4-61. Energy used for Chemicals and Lubricants614

2005/2006

Scenario

2020

Energy Use (Btu/gal); 100%

residual oil 798 766

We further assumed a 10% allocation of residual oil to ethanol to account for lubricating oil that

is used not as a combustion source but is lost during operation of the machinery in the production

of ethanol.

2.4.7.5.4 Ethanol yields

Table 2.4-62 shows a summary of ethanol yields from several studies.

Table 2.4-62. Ethanol yields in Sugarcane Mills

Year L/MT Source

1996-1997 79.5 Moreira & Goldemberg (1999)

2000 85.4 Assuncao (2000)

2001 78.58 UNICA-Carb Comments; 138.7 TRS/ton

cane and 1.7651 kg TRS/L anhydrous

2005 85 OECD (2008)

Avg. 2006-2008 84.68 UNICA-Carb Comments; 149.47 TRS/ton

cane and 1.7651 kg TRS/L anhydrous

Avg. in 2002

Best

86

91

Macedo et al. (2004)

GREET default

2006

“2020”

86.3

92.3/129*

Macedo et al. (2008)

*Includes cellulosic ethanol

2015

2025

100

109

Unicamp, as noted in OECD (2008)
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2.4.7.5.5 Ethanol dehydration

Standard distillation leaves over 4% water in ethanol, requiring a second step in the

process to remove water in order to obtain fuel grade anhydrous ethanol (>99.3 wt%).615 The

most important ethanol dehydration techniques used in the world industry include azeotropic

distillation, dehydration on molecular sieves, and more recently, pervaporation or vapor

permeation.616 Azeotropic distillation uses a third component, typically benzene or cyclohexane,

to remove the final water from ethanol. Molecular sieves use an adsorbent with a strong affinity

for water and little affinity for ethanol. This allows for separation of water from the ethanol

product. Most new ethanol plants today are built with molecular sieve dehydrators.

Pervaporation is still a fairly new technology, however, there is potential for energy consumption

savings, thus making the technology attractive for newly built facilities.617

Data was unavailable to determine the split of facilities using one type of dehydration

process over the other for import into the United States. However, we collected data on the

amount of energy required to dehydrate a gallon of hydrous ethanol into anhydrous ethanol using

primarily molecular sieve technology, see Table 2.4-63.

Table 2.4-63. Energy Required for Dehydration

BTU/gallon of

anhydrous

Source/Details

4,000 Swain

2,830-5665 Vane618

4,500 Kawaitkowski619

4186-5931 CBEPG620; Fuel

Oil (primarily

diesel)

5156-5210 CBEPG621;

Natural Gas

As noted in Chapter 1, the majority of ethanol imported into the U.S. may preferentially

come through the Caribbean Basin Initiative countries due to favorable economic conditions. As

the public comments on our rule suggest, there are differences in the type of fuels burned to run

the processes for dehydration. This depends on the location of the dehydration facility and the

fuel choices available at those locations. Fuels used to run the dehydration process include

bagasse, natural gas, #2 distillate (diesel fuel), and #6 oil (bunker fuel).622

For the final rule, we have assumed an average energy consumption for dehydration from

fuel oil use of 5059 BTU/gallon anhydrous produced and 5183 BTU/gallon anhydrous produced

if natural gas is used.

We received comment to include a pathway for the Caribbean Basin countries. We

evaluated the pathway based on the type of fuel used for dehydration, either from fuel oil or from

natural gas. For the NPRM we had already evaluated the Brazilian direct pathway assuming

dehydration used bagasse as a fuel. We calculated 1) the additional emissions from burning fuel
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oil and natural gas instead of bagasse and 2) the emissions credit from not dehydrating in Brazil

from bagasse (i.e. electricity is produced instead).

Assuming an electricity generation efficiency of 30% (the current Brazil industrial

average) and the energy consumption for dehydration as 5059 BTU/gallon anhydrous and 5183

BTU/gallon anhydrous for fuel oil and natural gas, we calculated an electricity credit of 0.44 and

0.46 kWh/gallon anhydrous produced for fuel oil and natural gas, respectively. This electricity

credit is assumed to displace electricity as it is produced in Brazil, i.e. marginal electricity

produced from natural gas. See discussion of Brazilian electricity generation in the following

section.

2.4.7.5.6 Electricity generation in Brazil

In Brazil, there has been an increasing use of bagasse to generate enough steam and

electricity to supply the whole mill energy demand while still producing electricity surpluses.

Table 2.4-64 summarizes the current and anticipated electricity generated for Brazilian

sugarcane facilities. As noted, this is highly dependent on the types of boilers used, and whether

or not there is collection of sugarcane leaves and tops (trash). Average cogeneration surplus for

all sugarcane mills in Brazil was 10.5 kwh/MT cane in 2008, and could increase above 100

kwh/MT cane with the utilization of trash.

For the final rule, we have chosen to model the low (40 kwh/MT cane) and high (135

kwh/MT cane) surplus electricity scenarios.
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Table 2.4-64. Electricity Surplus in Brazil under Various Conditions

Year Biomass Used Kwh/MT

cane

Source/Details

2006 Bagasse w/ surplus

leftover

9.2 CTC (2006); 10% mills use high press boilers, 90% 21

bar/300 C

2007 Bagasse 22.5/23 UNICA/MME/COGEN-SP/GREET default

2008 Bagasse 10.5 Avg. for all UNICA members (124 mills)

2008 Bagasse 40 OECD (2008), one standard facility; 20% of 39 mills or 4%

of all mills

2008 Bagasse 25.16 Avg. for 39 mills surveyed by UNICA

Current Bagasse 0-10 Smeets (2008), Combustion, partial steam extraction turbine,

22 bar, -300C

Current Bagasse 40-60 Smeets (2008), Combustion, partial steam extraction turbine,

80 bar, -480C

2012 Bagasse 65 COGEN-SP, Amounts contracted

Near-term Bagasse 75 UNICA, Upgrading to high-pressure steam cycle generators,

using all bagasse

2020+ Bagasse + 40% trash

for cellulosic ethanol;

Bagasse + 40 % trash

for electricity

44

135

Macedo (2008)

Mills at 65 bar/480 C, CEST systems; process steam

consumption ~340 kg steam/tonne cane

LongertermBagasse + 50% trash

for electricity

67-100 Smeets (2008), Combustion, condensing steam turbine, 80

bar, -480C

2020+ Bagasse + 50% trash

for electricity

135-200 Smeets (2008), Gasification, steam-injected gas turbine

2.4.7.5.6.1 Average Brazilian Grid Electricity versus Marginal Grid Electricity

We have factored in credit in our analyses for the excess electricity generated from the

burning of bagasse and potentially trash in the future. This, however, is dependent on the type of

electricity displaced. Several comments on our rule indicate that the cogeneration in Brazil

should displace the marginal power supplier (i.e., thermoelectric power plants running on natural

gas or heavy fuel oil) instead of average grid electricity (i.e., hydroelectricity).623 See Table

2.4-65 for the Brazil average fuel mix in 2007.
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Table 2.4-65.

Brazilian average fuel mix for electricity generation in 2007624

Fuel %

Petroleum 2.83%

Natural Gas 3.63%

Coal 1.34%

Biomass 3.47%

Nuclear 2.54%

Hydro 77.28%

Others 8.94%

Total 100.0%

We believe the use of marginal grid electricity instead of average electricity is reasonable

given that 1.) We are crediting on the basis of displacement 2.) Electricity produced at the

sugarcane ethanol facility is always dispatched when a mill is operating and this allows for

reduction of the use of other thermal power plants. Table 2.4-66 shows the average fuel mix for

Brazil’s operating margin in 2008.

Table 2.4-66. Brazilian Grid Operating Margin average fuel mix

for electricity generation in December 2008.

625

Fuel %

Petroleum 3.63%

Natural Gas 60.24%

Coal 14.37%

Biomass 0.00%

Nuclear 18.99%

Hydro 1.11%

Others 1.65%

Total 100.0%

As natural gas is the predominant fuel use, we have chosen to assume that marginal

electricity in Brazil will displace electricity derived from natural gas.

2.4.8 Fuel Transport

The greenhouse gas impacts associated with the transportation and distribution of

biofuels depend the average distance the fuel is transported from the plant to the retail location

and the mode of transport (barge, rail, truck, etc.). This section summarizes the assumptions used

in this analysis to represent the transport of biodiesel, and domestic and imported ethanol. A

recent release of GREET626 was utilized to estimate the GHG emissions based on these

assumptions.
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2.4.8.1 Biodiesel

For biodiesel transport, GREET default values were used to represent the average

distances biodiesel is transported by barge, pipeline, rail, and truck from the plant to the terminal

where it is blended with petroleum-based diesel fuel. The percentage of fuel transported by each

mode was chosen to be consistent with the cost analysis described in Chapter 4. These inputs are

summarized in Table 2.4-67.

GREET default values were used to represent the transport of biodiesel from the terminal

to the retail location. These defaults assume 100% of biodiesel shipped by truck a distance of 30

miles.

Table 2.4-67. Biodiesel Assumptions

for Transport from Plant to Terminal

Mode % Distance

(miles)

Barge 5% 520

Pipeline 0% 400

Rail 45% 800

Truck 50% 50

2.4.8.2 Corn and Cellulosic Ethanol

Oak Ridge National Laboratory (ORNL)627 recently conducted a study that models the

transportation of ethanol from production or import facilities to petroleum blending terminals by

domestic truck, marine, and rail distribution systems. We used ORNL’s transportation

projections for 2022 under the EISA policy scenario to estimate the percentage of corn and

cellulosic ethanol transported by each mode and the averaged distance traveled. These

assumptions are summarized in Table 2.4-68. More details on the ORNL study and the

transportation projections can be found in Sections 1.6 and 3.3.

Since the study did not address the transport of ethanol from the terminal to refueling

station, we used the GREET default assumptions of 100% shipped by truck a distance of 30

miles.
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Table 2.4-68. Corn and Cellulosic Ethanol Assumptions

for Transport from Plant to Terminal

Mode % Distance

(miles)

Barge 12% 336

Rail 77% 629

Truck 17% 68

Local Truck172 83% 6.5

2.4.8.3 Sugarcane Ethanol

This analysis accounts for the transportation of sugarcane ethanol within Brazil, en route

to U.S. import facilities, and within the United States. GREET default values are used to

represent the transport of ethanol from a production facility in Brazil to a Brazilian port.

Specifically, we assumed that 50% of the ethanol is transported via pipeline and the other 50%

by rail an average distance of 500 miles (for each mode).

The ethanol is then loaded onto ocean tankers for transport to the United States.

As described in Chapter 1, we projected that 46% of imported ethanol in 2022 would be shipped

directly from Brazil, while 54% would first be shipped to a country in the Caribbean Basin

Initiative (CBI) and then to the United States. For the latter case, we assumed 20% would be

imported from Costa Rica, 20% from El Salvador, 30% from Jamaica, 15% from Trinidad and

Tobago, and 15% from the Virgin Islands (see Table 1.8-11). Table 2.4-69 summarizes EPA

estimates for the average distance ethanol is transported by ocean tanker for each of these paths.

For these estimates, we used EIA data on fuel ethanol imports from 1993 to August 2009628 to

determine the fraction of ethanol shipped to different U.S. ports from Brazil and the CBI

countries. We estimated the average distance imported ethanol travels by ocean tanker,

accounting for all of these paths, to be 7,348 miles.

We received comment that assuming ocean tankers bringing ethanol from Brazil to the

United States return to Brazil empty is incorrectly attributing emissions of an ocean tanker’s

round trip to sugarcane ethanol629,630. We, therefore, assume that emissions from back-haul are

negligible for this analysis.

172 The ORNL study includes a second transportation mode for trucks, called “Local Trucks”, which transport

ethanol from dedicated ethanol terminals to blending terminals. Ethanol that travels directly from a refinery to a

petroleum blending terminal would not be transported by local truck.
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Table 2.4-69. Average Ocean Tanker Distances for Sugarcane Ethanol Transport from

Brazil and CBI Countries to the United States173

Average distance to U.S. import

facilities (miles)

Total distance, including distance

from Brazil (miles)

Costa Rica 3375 10398

El Salvador 3691 11011

Jamaica 2466 7393

Trinidad & Tobago 2766 6590

Virgin Islands 1702 5919

Brazil (direct) 6141 6141

Within the United States, ORNL’s transportation projections were used to estimate the

average distance sugarcane ethanol is transported from an import facility to a petroleum blending

terminal and the percentage that travels by each mode. Table 2.4-70 summarizes transport

assumptions for sugarcane ethanol from production facilities in Brazil to blending terminals in

the United States. As with corn and cellulosic ethanol, we used the GREET default assumptions

to represent the transport of sugarcane ethanol from the terminal to a refueling station. These

assumptions were 100% shipped by truck a distance of 30 miles.

Table 2.4-70. Sugarcane Ethanol Assumptions

for Transport from Plant to Terminal

Mode % Distance (miles)

Pipeline (in Brazil) 50% 500

Rail (in Brazil) 50% 500

Ocean Tanker 100% 7348

Barge (in U.S.) 12% 336

Rail (in U.S.) 77% 629

Truck (in U.S.) 17% 68

Local Truck (in U.S.) 83% 6.5

2.4.9 Biofuel Tailpipe Combustion

Combustion CO2 emissions for ethanol and biomass-based diesel were based on the

carbon content of the fuel. However, over the full lifecycle of the fuel, the CO2 emitted from

biomass-based fuels combustion does not increase atmospheric CO2 concentrations, assuming

the biogenic carbon emitted is offset by the uptake of CO2 resulting from the growth of new

biomass. As a result, CO2 emissions from biomass-based fuels combustion are not included in

their lifecycle emissions results. Net carbon fluxes from changes in biogenic carbon reservoirs

173 Distances between ports were calculated using www.distances.com. For Brazil and CBI Countries, the following

representative ports were used: Santos in Brazil, Puntarenas in Costa Rica, Acajutla in El Salvador, Kingston in

Jamaica; Port of Spain in Trinidad and Tobago, and St. Croix in the Virgin Islands.
426

in wooded or crop lands are accounted for separately in the land use change analysis as outlined

in the agricultural sector modeling above.

When calculating combustion GHG emissions, however, the CH4 and N2O emitted

during biomass-based fuels combustion are included in the analysis. Unlike CO2 emissions, the

combustion of biomass-based fuels does result in net additions of CH4 and N2O to the

atmosphere. Therefore, combustion CH4 and N2O emissions are included in the lifecycle GHG

emissions results for biomass-based fuels.

Combustion related CH4 and N2O emissions for biomass-based fuels are based on EPA

MOVES model results. The values used are shown in Table 2.4-71. CO2 emissions from

biofuels are shown for illustrative reasons, but as mentioned above are not included in the

analysis because they are assumed to be offset by carbon uptake from plant growth.

Table 2.4-71.

Tailpipe Combustion Emissions for Bio-Based Fuels

CO2 CH4 N2O

Fuel Type (g/mmBTU) (g/mmBTU) (g/mmBTU)

Ethanol 75,250 269 611

Biodiesel 81,044 11 689

2.4.10 Other Indirect Impacts

In the analysis of the proposed rulemaking the Agency conducted a study of the U.S.

energy sector impacts of increased biofuel production. Using an EPA version of the Energy

Information Administration’s National Energy Modeling System (NEMS) we attempted to

determine the effects of biofuel production energy use, for example increased natural gas use for

corn ethanol production and the impact that has on natural gas and other fuel sources price and

use. The EPA-NEMS is a modeling system that simulates the behavior of energy markets and their

interactions with the U.S. economy by explicitly representing the economic decision-making

involved in the production, conversion, and consumption of energy products.

There were several problems encountered with the modeling done for the proposal,

mainly in trying to isolate the impacts of a specific fuel and of the specific impact of biofuel

energy use so the results were not used in the analysis. However, we indicated that we would

continue exploring this modeling for the final rule.

Therefore, for the final rule we created a scenario in EPA-NEMS to simulate the RFS2

volumes, reaching 31.8 billion gallons of biofuels production in 2022. This scenario was

compared to AEO 09, which estimated 13.8 billion gallons of biofuel production. This allowed

us to see the energy system impacts of an increase in renewable fuels of 18 billion gallons.

The increase in renewable fuels supply triggered a decrease in gasoline demand. This led

to a 0.73 million barrel per day decrease in crude oil imports and a 0.18 million barrel per day

decrease in refined product imports. As a result of declining demand, crude oil prices decreased
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from $117.11/barrel to $116.43/barrel and petroleum product prices also decreased slightly. In

addition, prices for natural gas and electricity declined significantly. The only price increases

were a $0.10/mmbtu increase in the price of motor gasoline and a $1.49/mmbtu increase in the

price of E85.

The overall CO2 impact was a 34,736 grams CO2 decrease for each mmbtu increase in

renewable fuels over the baseline, or -34,736 grams CO2/mmbtu. Reduced consumption of

gasoline, diesel, and still gas (for refining) resulted in an overall decrease in emissions of 86.6

mmt CO2. This decrease in emissions was partially offset by natural gas consumption for

production of renewable fuels and by increased coal consumption for power generation, yielding

an overall decrease in the domestic energy sector of 61.5 mmt CO2.

The EPA-NEMS results were used in part to estimate the crude oil import reductions

from the increased renewable fuel volumes mandated by this rulemaking, as discussed in Chapter

5. However, we have not used this analysis at this point in calculations of renewable fuel

threshold analysis or for the overall rule impacts because of double counting issues regarding

GHG emissions sources.

The final rule EPA-NEMS analysis eliminated some of the problems with the proposal

modeling by considering a larger increase in biofuels consumption and by not specifically trying

to isolate the impacts of one type of fuel. However, there were still issues with how this analysis

compares to the other lifecycle modeling work conducted for this rulemaking. The main issue is

double counting between the EPA-NEMS analysis and our lifecycle work. Both account for

renewable fuel production energy use, which is difficult to separate in the EPA-NEMS modeling

(especially for purchased electricity). Both also account for gasoline and diesel fuel reduction,

both end use and refining energy. This is also difficult to back out of the EPA-NEMS modeling.

Therefore, it is difficulty to isolate only the secondary or energy sector impacts that are not

already covered elsewhere. There is also the issue that the EPA-NEMS model is only domestic

and does not capture any potential international energy sector impacts. We will continue to

study this modeling as part of any ongoing work on biofuel analysis.

2.4.11 Other Modeling Approaches Considered

2.4.11.1 Analysis with the GTAP Model

The Global Trade Analysis Project (GTAP) model is an economy-wide multi-region

general equilibrium (GE) model coordinated by the Center for Global Trade Analysis at Purdue

University. GTAP is a publicly available global model that was originally developed for

addressing international agricultural trade issues. An advantage of GE models such as GTAP is

that they take into account how changes in U.S. biofuel policies affect world prices, output, and

trading patterns for a wide variety of commodities that extend beyond the agricultural sector.

The GTAP data base is peer reviewed and updated triannually. The GTAP databases and

versions of the model are widely used internationally by a large modeling community.631 Since

its inception in 1993, GTAP has rapidly become a common "language" for many of those

conducting global economic analysis. For example, the WTO and the World Bank co-sponsored

two conferences on the so-called Millennium Round of Multilateral Trade talks in Geneva.
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Here, virtually all of the quantitative, global economic analyses were based on the GTAP

framework. The use of the GTAP data base and model has been increasing with the growing

research interests in international trade policies, energy policies, and climate change policies.

Because GTAP is publicly available, there are numerous versions of the GTAP-based model.

However, the GTAP Center has a peer review process which includes replication of results by

independent scientists. Those versions of GTAP which have been through this process, including

the versions used in this analysis, qualify as peer-reviewed, published models.

The GTAP Version 6 data base divides the global economy into 57 sectors and 87

regions, some of which have been aggregated in the results presented below for simplification.

Over the past few years, several improvements have been made to the model. For example, a

version of the model was developed to explicitly account for substitution between energy

commodities.632 Another version of the model was developed to explicitly model global

competition among different land types (e.g., forest, agricultural land, pasture) and different

qualities of land based on the relative value of the alternative land-uses.633 More recently the

above two frameworks were combined and modified to include biofuel substitutes for gasoline

and diesel.634 The California Air Resources Board (CARB) has utilized the GTAP model to

assess biofuel land use impacts in its recent rulemaking on a Low Carbon Fuel Standard.

Current research is ongoing to add additional detail on the biofuels market, some of which is

described below.

2.4.11.1.1 Partial Equilibrium versus General Equilibrium Modeling

Although we have used the partial equilibrium (PE) models FASOM and FAPRI-CARD

as the primary tools for evaluating whether individual biofuels meet the GHG thresholds, as part

of the peer review process, we explicitly requested input on whether GE models should be used.

None of the commenters recommended using a GE model as the sole tool for estimating GHG

emissions, although several reviewers discussed some of the advantages of GE models compared

to PE models. For example, GTAP captures the interaction between different sectors of the

economy. As discussed by the peer reviewers, the link between the agricultural and the energy

markets has become increasingly important given the increased production of renewable fuels

from agricultural products. Higher crude oil prices and policies to increase demand for

renewable fuels have increased the linkages between these two markets, and increased renewable

fuel production could have impacts on food security, international trade, and natural resources.

These linkages can be captured in a sufficiently detailed GE model. The literature on economic

modeling of biofuels suggest that for analyzing the long-term consequences on consumption, the

GTAP model is a suitable economic tool to link energy and crop demand.635

One of the major benefits of using the GTAP model is that it explicitly models land-use

conversion decisions. GTAP is designed with the framework of predicting the amount and types

of land needed in a region to meet demands for both food and fuel production. The GTAP

framework also allows predictions to be made about the types of land available in each region to

meet the needed demands, since it explicitly represents different land types within the model.

In theory, a detailed GE model would be the ideal modeling framework. However, as

described in other sections, there is currently no single model that captures all of the necessary
427

aspects of lifecycle GHG emissions. In their current state of development, GE models alone,

including GTAP, are not yet adequate for determining whether biofuels meet greenhouse gas

emission thresholds for the following reasons.

First, most GE models do not contain the level of detail in the agriculture sector required

to determine acreage and production changes by crop by region. Because GE models must

account for all sectors of the global economy, simplifications have been made to capture many of

the complex interactions. Therefore, some level of aggregation of regions, markets, and

relationships is necessary. For example, GTAP contains only an aggregated “coarse grain” crop

and does not provide information specific to corn acres and production. Similarly, GTAP

contains only a generic oilseed crop and does not include information about soybean-specific

production and usage data. As a result, palm oil and soybean oil are aggregated into a single

sector, even though these two crops may have very different resource implications. Furthermore,

the GTAP model does not yet contain cellulosic feedstocks such as switchgrass or corn stover.

Second, the version of GTAP used for biofuels is a static model that does not currently

capture changes over time. (The dynamic GTAP model has yet to be modified for use in energy

and land use issues.) The GTAP Version 6 data base, the version used for this analysis, is based

on a 2001 world economy. The model has been validated against historical data from 2001

through 2006 and the resulting 2006 baseline is used for biofuels policy analyses. Due to its

static nature, the GTAP biofuels model is not able to project the time path of the global economy

through 2022, which is the timeframe of primary interest for this rulemaking. Since we expect

trends such as increases in crop yields, oil prices, population growth, and GDP growth to

continue in the future, it is essential that our modeling framework captures these dynamics.

Third, the GTAP model relies on differences in land rental rates to determine which lands

will be converted to crop land as a result of increasing biofuel demand. Land rents are the

indicators of productivity in each agro-ecological zone (AEZ). In the GTAP data base, Lee et al.

(2009) determine land rents for cropland, pasture, and forest based on the yearly economic

activity in a given AEZ.636 By definition, land rents are largest in those AEZs where high value

crops are grown. For determining land rents for the livestock sector, Lee et al. draw on the direct

competition between these sectors with grazing land. For computing livestock sectors’ land rent,

Lee et al. use the average coarse grain yield in each AEZ (as there is no ‘forage crop’ sector in

the GTAP data base) and multiply it by the pasture land cover hectares. Finally, Lee et al.

compute the forest land rents by using information on timberland land rent and timberland area

offered by Sohngen et al. (2009). One of the major limitations of this methodology is that

unmanaged land, which represents approximately 34% of the land cover in the GTAP model, is

not allowed to be brought into productive use (e.g., as pasture). The unmanaged land category in

GTAP varies significantly across countries, but includes a substantial amount of shrubland,

savanna, and grassland in many areas (e.g., 20% of the land area covered in Brazil and 40% of

the land area covered in Argentina).

Fourth, although most of the behavioral parameters (e.g., international trade elasticities,

agricultural factor supply) contained in GTAP are estimated econometrically, some of the key

relationships are actually based on literature reviews, theory, and analyst judgment.637 In theory,

all the relationships in the model could be based on regionally-specific empirical data, however
426

in practice this is often not the case. For example, the elasticity of transformation (i.e., the

measure of how easily land can be converted between forest, pasture, and crop land) is an

important parameter in the GTAP model. However, the global value used for this parameter

relies on a single study that is based on U.S. data. Ideally, this value would be based on

empirical data that is specific to each region in the model, since this response is likely to be

different in different parts of the world.

Given the relative advantages of PE and GE models, we opted to use the GTAP model to

provide another estimate of the quantity and type of land conversion resulting from an increase

in corn ethanol and biodiesel given the competition for land and other inputs from other sectors

of the economy. These results help to bracket the land use changes estimated by the FAPRICARD model.

2.4.11.1.2 Comparison of GTAP and FAPRI-CARD Model Results

One of the advantages of the GTAP model is that it is an open source framework in

which many different groups can conduct research simultaneously. As a result, there are many

different “variations” of the GTAP model in existence, each of which is in a different state of

peer-review. As researchers publish papers using their updated variation of the model, the

programming code is generally published so that others may benefit from these model

enhancements. For our corn ethanol analysis, we used a slightly modified version of the GTAP

model that was extensively reviewed as part of the California Air Resources Board (CARB) for

their Low Carbon Fuel Standard rulemaking.638 However, one of the criticisms of the CARB

analysis was the treatment of biodiesel byproducts. New research by Taheripour et al639 has been

recently conducted to explicitly model the production and substitution of oilseed meal as a

byproduct of biodiesel production, which provides a more accurate representation of the soybean

biodiesel market interactions. We have therefore used this variation of the GTAP model to

conduct the soybean biodiesel analysis.174

We made three revisions to the CARB modeling inputs to make our corn ethanol and

soybean biodiesel analysis more consistent. First, we changed the elasticity of crop yields with

respect to area expansion. This parameter is a measure of how much crop yields will decrease as

agriculture expands onto new land. In theory, the most productive agricultural lands are already

in use, therefore expanding production into more marginal lands will result in a decrease in

average crop yields. CARB used a factor of 0.5 in its analysis, which implies that each new acre

of land is only 50% as productive as an existing acre of land. However, more recent analysis

suggests that a value of 0.66 may be more appropriate, indicating that for every two acres of

additional cropland needed, three acres of forest or pasture lands must be converted to new

cropland.640 Therefore, we have used 0.66 as the elasticity for our analysis of corn ethanol and

biodiesel. Second, we adjusted the 2006 baseline ethanol and biodiesel production levels. Our

modified version of the CARB model used in this study has 4.25 BG of corn-ethanol and 0.14

BG of biodiesel in the 2006 baseline. Finally, since our baseline included a crude oil price shock

174 This version of the model was in press at the time of this rulemaking. As a result, the code was not available to

use this variation of the GTAP model for the corn ethanol analysis, hence our use of the CARB model for the corn

ethanol analysis.
427

from $25/barrel in 2001 to $60/barrel in 2006, our biofuel scenarios started with a $60 oil price

laden economy versus $25 in the CARB biofuel scenarios.

Because the GTAP model is static, it was not possible to analyze the exact same corn

ethanol and soybean biodiesel scenarios in GTAP that we analyzed using the FAPRI-CARD

model. Therefore, we analyzed a 2 billion gallon increase in corn ethanol over the 2006 updated

baseline level of 4.25 BG. Similarly, our soybean biodiesel shock imposed a 1 BG increase in

U.S. soybean biodiesel production over the 2006 updated baseline level of 0.14 BG. In order to

compare the results of the GTAP model to the FAPRI-CARD model, we then “normalized” the

land cover changes to obtain an acreage change per BTU of the biofuel shock. Other

simplifications were also required. For example, GTAP aggregates regions differently than

FAPRI-CARD, therefore we have summarized the results into larger regions for comparison

purposes. Despite these shortcomings and compromises in trying to compare results from

GTAP and FAPRI-CARD, the relative impacts on land use we believe are informative in that

GTAP confirms that there are significant impacts on international land use due biofuel

production from food and feed crops.

2.4.11.1.3 Comparison of GTAP and FAPRI-CARD Corn Ethanol Results

Despite differences in the way the corn ethanol scenarios were implemented, the quantity

of total acres converted to crop land projected by GTAP were similar in scale to the changes

projected by the FAPRI-CARD results when normalized on a per BTU basis. However, the

mean estimates for land converted to crops projected by GTAP were smaller than the changes

predicted by FAPRI-CARD, which is most likely due to several important differences in the

modeling frameworks.

First, the GTAP model incorporates a more optimistic view of intensification options by which

higher prices induced by renewable fuels results in higher yields, not just for corn, but also for

other displaced crops. Second, the demands for other uses of land are explicitly captured in

GTAP. Therefore, when land is withdrawn from these uses, the prices of these products rise and

provide a certain amount of “push-back” on the conversion of land to crops from pasture or

forest. Third, none of the peer-reviewed versions of GTAP currently contain unmanaged land,

thereby omitting additional sources of land. In Figure 2.4-46 and Figure 2.4-47, the GTAP

results assume all land that is not crop or pasture is forest. However, the FAPRI-CARD results

allow land that is not crop or pasture to come from a variety of other non agricultural land such

as grassland, savanna, shrubland and wetlands. The disaggregation of FAPRI-CARD “non ag

land” is described in more detail in Section 2.4.4.2.5.

Although the global aggregated results are similar, the regional distribution of land cover

change varies between the FAPRI-CARD and GTAP models. Both models predict similar

changes in India, Oceana, non-Brazilian Latin America, and Africa/Middle East. However, the

FAPRI-CARD model predicts significant increases in crop acres in Brazil and Asia, whereas the

GTAP model projects limited land use change in those regions. In contrast, the GTAP model

projects more crop acre conversion in the U.S. for corn ethanol scenarios. These differences are

due to the result of contrasting international trade structures in the models. FAPRI-CARD

includes more flexible agricultural trade patterns, and projects agricultural expansion in lower
426

cost of production regions that show the greatest capacity for expansion. In contrast, GTAP

tends to maintain existing trade patterns, so it is more likely to project changes in countries that

are already major trading partners of the U.S. A formal econometric analysis of these

differences is offered in Villoria and Hertel (2009).641

Figure 2.4-46. Changes in Land Cover from an increase in Corn Ethanol

2022 Change in Land Cover from Corn Ethanol

-6

-4

-2

0

2

4

6

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

GTAP

FAPRI

Africa &

Middle

East

Asia Brazil Canada E.

Europe

India Oceania Other L.

America

ROW US W.

Europe

World

Ha/Billion BTU

Pasture

Crop

Forest/Non Ag Land

2.4.11.1.4 Comparison of GTAP and FAPRI-CARD Soybean Biodiesel Results

In the soybean biodiesel analysis, the total increase in crop acres aggregated at the global

level is similar in the FAPRI-CARD and GTAP results. As with the corn ethanol analysis, the

regional distribution of these changes also varies. While both models predict similar impacts in

Eastern Europe and India, once again the FAPRI-CARD model estimates much larger increases

in crop acres in Brazil than the GTAP model. The GTAP model estimates a larger increase in

crop acres in Africa and the Middle East and Canada compared to the FAPRI-CARD model.

The changes in the U.S. are also different. Whereas the FAPRI-CARD model predicts some

increase in crop and forest acres, the GTAP model predicts almost no change in crop acres.

Instead, the GTAP model estimates that there will be an increase in pasture land as a result of

increasing soybean biodiesel in the U.S. These differences appear to be based on the fact that the

GTAP model assumes the price of soybean meal will decrease significantly as a result of the

increase in soybean crushing required to produce oil for biodiesel. GTAP projects that the

decrease in soybean meal prices will lead to increased beef production, which requires additional
427

grazing land to complement the use of soybean meal for beef production. In contrast, the

FAPRI-CARD model assumes that increased biodiesel production will lead to a decrease in U.S.

beef production, since the relative price of non-grazing animals (e.g., poultry and pork) will

decrease more than the price of beef as a result of lower soybean prices. As a result, U.S. beef

production and pasture land decreases in FAPRI-CARD and we believe this is a more rational

outcome.

Figure 2.4-47. Changes in Land Cover from an increase in Soybean Biodiesel

2022 Change in Land Cover from Soybean Biodiesel

-15

-10

-5

0

5

10

15

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

GTAP-BYP

FAPRI

Africa &

Middle

East

Asia Brazil Canada E.

Europe

India Oceania Other L.

America

ROW US W.

Europe

World

Ha/Billion BTU

Pasture

Crop

Forest/Non Ag Land

2.4.11.1.5 Systematic Sensitivity Analysis with the GTAP Model

As mentioned above, there are several parameters that have a significant impact on the

amount and type of land conversions resulting from an increase in biofuel demand. Due to

uncertainty in the past and future values of these parameters, it is possible to use the GTAP

model to perform a systematic sensitivity analysis (SSA). Traditional uncertainty analysis relies

on a Monte Carlo simulation which solves for equilibrium conditions using a large number of

draws from the underlying distribution of potential parameter values. However, Monte Carlo

analysis is not generally practical for a large CGE model. Instead, previous researchers have

performed a SSA with Gaussian Quadrature numerical integration. This methodology uses a

small number of draws from the distribution of random variables to provide a robust range of

results that can be used to develop a confidence interval around the mean estimates.642

In our analysis, the parameters that appear to have the largest impact on the results

include the elasticity of crop yields, the elasticity of harvested acreage response, and the

elasticity of transformation across cropland, pasture, and forest land. The elasticity of crop
426

yields, often referred to as “price induced yields” is the measure of how much a particular crop’s

yield will increase in response to an increase in the price of that crop. The larger the value of the

elasticity, the more the increase in yields is expected to increase in response to higher prices. In

our analysis, we used Keeney & Hertel’s recommended mean value of 0.25, which indicates that

a 1% increase in coarse grain prices leads to a 0.25% increase in coarse grain yields.175 For the

SSA, the range of values analyzed was from a low end of 0 (i.e., yields do not respond to price

changes) to a high end of 0.5.643 The elasticity of transformation of crop land is a measure of

how easily crop acres can be converted between types of crops. For example, the larger the

value, the more easily coarse grain acres can be converted to oilseed acres in response to a

change in land rental rates. For our analysis, we used a mean value of -0.5 with a lower bound of

-0.1 and an upper bound of -1.0.644 The elasticity of transformation of land supply is a measure

of how easily land can be converted between land cover types (e.g., from forest to crop or

pasture). The larger the value of this elasticity, the more land will be converted to different types

of land cover in response to changes in relative land rental rates. For our SSA, we used a mean

value of -0.2, with a lower range of -0.04 and an upper range of -0.36.645

2.4.11.1.5.1 GTAP Systematic Sensitivity Analysis for Corn Ethanol

As shown in Table 2.4-48, there is a wide range of potential values for the amount of

crop cover changes by region. However, it is important to note that for almost all regions, the

range of potential values does not cross the X-axis. Thus, we interpret these results to imply that

there is a statistically significant change in crop acres in most of the GTAP regions as a result of

the increase in corn ethanol. Similarly, as shown in Figure 2.4-49, the range in potential values

of pasture cover does not generally cross the X-axis for most of the regions. We therefore

conclude that the decrease in pasture acres is statistically significant in most regions as a result of

the increase in corn ethanol. Finally, Figure 2.4-50 shows that the mean estimate for some

regions show an increase in forest acres, while other regions show a decrease in forest acres.

Again, the confidence intervals around these estimates do not generally cross the X-axis,

therefore we interpret these results to be statistically significant.

175 As discussed in RIA Chapter 5, our FAPRI-CARD projections include disaggregated price-induced yield

elasticities that vary by region, crop and time period.
427

Figure 2.4-48. Crop cover change due to U.S. corn-ethanol production (million acres)

Corn Ethanol Change in Crop Cover With Confidence Intervals

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

USA

CAN

EU27

BRAZIL

JAPAN

CHIHKG

INDIA

LAEEX

RoLAC

EEFSUEX

RoE

MEASTNAEX

SSAEX

RoAFR

SASIAEEX

RoHIA

RoASIA

Oceania

Million Acres

Figure 2.4-49. Pasture cover change due to U.S. corn-ethanol production (million acres)

-1.146

-1.60

-1.40

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

Ch in Pasture Cover
426

Figure 2.4-50. Forest cover change due to U.S. corn-ethanol production (million acres)

-0.236

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

Ch in Forest Cover

2.4.11.1.5.2 GTAP Systematic Sensitivity Analysis for Soybean Biodiesel

Similar to the corn ethanol results, the SSA for biodiesel generally shows that the land

cover changes are statistically significant for the crop, pasture, and forest acre changes predicted

by GTAP. As shown in the following figures, most of the confidence intervals do not cross the

X-axis, therefore indicating that that the results are robust.
427

Figure 2.4-51. Crop cover change due to U.S. biodiesel production (million acres)

3.214

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

USA

EU27

BRAZIL

CAN

JAPAN

CHIHKG

INDIA

C_C_Amer

S_o_Amer

E_Asia

Mala_Indo

R_SE_Asia

R_S_Asia

Russia

Oth_CEE_CIS

Oth_Europe

MEAS_NAfr

S_S_AFR

Oceania

GLOBAL

Ch in Crop Cover

Figure 2.4-52. Pasture cover change due to U.S. biodiesel production (million acres)

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

Ch in Pasture Cover
426

Figure 2.4-53. Forest cover change due to U.S. biodiesel production (million acres)

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

Ch in Forest Cover

2.4.11.2 Evaluation of International Land Conversions with Higher Resolution Satellite

Data

EPA worked with remote sensing experts from Integrity Applications Inc. (IAI) to

analyze higher resolution satellite imagery in regions that factored prominently into our land use

change analysis. The purpose of this analysis was to compare the Version 5 MODIS imagery

with 500-m resolution to an imagery data set with much higher resolution. As discussed in

preamble Section V and above, EPA only uses satellite imagery to evaluate recent land use

change patterns, which are the results of many factors. Satellite imagery is not used to determine

the amount of land conversion caused specifically by biofuel production. As discussed below,

we found that the higher resolution LandSat data set with 30-m resolution provided similar

results.

Based on resources and data availability, three regions were chosen for analysis: Brazil,

India and the Indonesian island of Sumatra. Brazil was chosen because it was, and remains, the

country with the largest agricultural land use response in our modeling of the indirect impacts of

U.S. biofuel consumption. In the proposed rule analysis, India was the most important region in

Asia. Based on modeling updates, the response in India was much smaller in our final rule

analysis. However, it is still a good country to analyze with higher resolution data because it is a

major agricultural producer with crops and land cover types distinct from what is found in Brazil.
427

Finally, Indonesia was chosen because it is a major producer of palm oil in a region with peat

soils. We narrowed our focus to the island of Sumatra because satellite imagery for Indonesia

suffered from significant cloud cover problems.

Our high resolution analysis relied on the Landsat Global Land Surveys for the years

2000 and 2005; these Global Land Surveys are mosaics (i.e., compilations) of multi-spectral

digital images produced to represent the entire earth during the growing season of a specific year.

For optimal comparison of land cover categories, the MODIS data set was used as training data

to classify the raw Landsat imagery. Table 2.4-72 summarizes the characteristics of the data

used.

Table 2.4-72. Characteristics of Satellite Data for High Resolution Analysis

Landsat MODIS

Data use IAI: land use classification and change

detection

IAI: training data to classify raw Landsat data

Winrock: land use classification and change

detection

Data source Landsat Global Land Survey MODIS V5646

Years covered Effectively 2000 and 2005 2001, 2005 and 2007

Temporal

resolution

+/- 3 years; data acquired every 16 days Every one to two days

Spatial resolution 30-m 500-m

Public Availability US Geological Survey (USGS) Archive647 LP DAAC Data Pool648

By calibrating the land cover classification definitions to those used by MODIS, we

converted the true color Landsat images into corresponding classification maps (see Figure 1).

The color key for the classification map is provided below in Figure 2.4-54.
426

Figure 2.4-54. Example of a true color Landsat image (left) and the corresponding land

cover classification map (right)

Figure 2.4-55. Land categories and corresponding colors

Classification maps from 2000 and 2005 were compared to find areas where land

changed from one category to another. For comparison with EPA’s MODIS analysis, the

resulting change matrices were aggregated by Administrative Unit and we analyzed the types of

land converted to cropland. For most regions, the higher resolution data found deforestation

rates somewhere in between the original and corrected MODIS data (see Section 2.4.4 for an

explanation of the MODIS correction process). Validation data similar to the confusion matrix

used to correct the MODIS data set was not available for the Landsat data, therefore it is difficult

to directly compare these results. However, in general, the results do suggest that the resolution

of the Version 5 MODIS data is adequate (i.e., provides similar results as higher resolution

imagery), especially after it has been corrected with data validation procedures. Figure 2.4-54.
427

Figure 2.4-54Figure 2.4-54 shows the share of deforestation from crop expansion in each region

analyzed, and

Table 2.4-73 Table 2.4-73 includes all of the land types converted to cropland. The

Indonesia results are omitted because of the cloud cover issues mentioned previously. More

details about the Landsat data analysis are provided in a technical report by IAI available on the

docket.649

Figure 2.4-56. Share of Deforestation from Crop Expansion Measured with MODIS and

Landsat Satellite Imagery

0%

10%

20%

30%

40%

50%

60%

Brazil:

Amazon

Biome

Brazil:

Central-West

Cerrados

Brazil:

Northeast

Coast

Brazil: NorthNortheast

Cerrados

Brazil: South Brazil:

Southeast

India

MODIS-Original Landsat MODIS-Corrected

Table 2.4-73. Types of Land Converted to Cropland by Data Source and Region
426

Region

Data Source Brazil:

Amazon

Biome

Brazil:

CentralWest

Cerrados

Brazil:

Northeast

Coast

Brazil:

NorthNortheast

Cerrados

Brazil:

South

Brazil:

Southeast

India

Forest Landsat 50% 6% 0% 1% 15% 2% 3%

MODIS-Corrected 54% 11% 11% 15% 13% 10% 10%

Grassland Landsat 8% 19% 8% 12% 14% 10% 5%

MODIS-Corrected 8% 26% 19% 16% 23% 18% 21%

Mixed Landsat 11% 20% 28% 14% 55% 53% 27%

MODIS-Corrected 15% 20% 19% 10% 28% 30% 30%

Savanna Landsat 26% 55% 54% 67% 14% 34% 49%

MODIS-Corrected 20% 36% 41% 49% 29% 36% 19%

Shrubland Landsat 2% 0% 9% 5% 1% 1% 14%

MODIS-Corrected 2% 6% 8% 9% 6% 6% 17%

Wetlands Landsat 2% 0% 0% 1% 1% 0% 0%

MODIS-Corrected 1% 0% 0% 0% 0% 0% 1%

Barren Landsat 0% 0% 1% 0% 0% 0% 2%

MODIS-Corrected 0% 0% 1% 1% 0% 0% 2%
427

2.5 Baseline Gasoline and Diesel Fuel

2.5.1 Background

Section 201 of the Energy Independence and Security Act (EISA) mandated that a

baseline for gasoline and diesel fuel be established against which renewable fuels were to be

compared:

The term ‘baseline lifecycle greenhouse gas emissions’ means the average lifecycle

greenhouse gas emissions, as determined by the Administrator, after notice and

opportunity for comment, for gasoline or diesel (whichever is being replaced by the

renewable fuel) sold or distributed as transportation fuel in 2005.

For the proposed rule, the Agency used the GREET model (Version 1.8b) to calculate the

baseline GHG impacts of gasoline and diesel fuel production. However, we received numerous

comments stating that GREET was not the best tool to use to calculate the petroleum baseline.

Hence, to estimate the lifecycle GHG emissions associated with baseline gasoline and diesel

transportation fuel for the final rule, we utilized the 2009 analysis performed by the National

Energy Technology Laboratory (NETL), “Development of Baseline Data and Analysis of Life

Cycle Greenhouse Gas Emissions of Petroleum-Based Fuels”, which was specifically directed at

establishing this 2005 baseline. NETL stated that the goal of their study was to “determine the

life cycle greenhouse gas emissions for liquid fuels (conventional gasoline, conventional diesel,

and kerosene-based jet fuel) production from petroleum as consumed in the U.S. in 2005 to

allow comparisons with alternative transportation fuel options on the same basis (i.e., life cycle

modeling assumptions, boundaries, and allocation procedures).” Furthermore, NETL stated that

“[t]he study goals and scope were aligned to meet the definition of “baseline lifecycle

greenhouse gas emissions” as defined in the Energy Independence and Security Act of 2007

(EISA 2007), Title II, Subtitle A, Sec. 201.” Specific detail on NETL’s analysis can be found in

their report.650

2.5.2 Crude Oil Extraction

NETL determined the emissions associated with extraction and processing for crude oil

and synthetic crude oil, natural gas liquids (NGLs), and unfinished oils as feedstocks to U.S.

petroleum refineries and to foreign refineries producing gasoline and diesel imported by the U.S.

in 2005.

2.5.2.1 U.S. Refineries

The input of crude oil, natural gas liquids, and unfinished oils to domestic refineries was

determined from EIA data and is summarized in Table 2.5-1 below.
426

Table 2.5-1. Feedstock inputs to U.S. refineries

Feedstock Input

(thousand bbl/day)

Crude Oil 15,220

Natural Gas Liquids 432

Unfinished Oils (net) 569

The crude oil mix to U.S. refineries was also determined from EIA data and is reflected

in Table 2.5-2.

Table 2.5-2. Crude oil imports to U.S. refineries

U.S. Crude Oil Sources Production/Import

as % of Refinery Crude Input (Year 2005, EIA)

U.S. Crude Oil 33.8%

Canada Crude Oil

Canada Oil Sands 10.7%

Mexico Crude Oil 10.2%

Saudi Arabia Crude

Oil

9.4%

Venezuela Crude Oil 8.1%

Nigeria Crude Oil 7.1%

Iraq Crude Oil 3.4%

Angola Crude Oil 3.0%

Ecuador Crude Oil 1.8%

Algeria Crude Oil 1.5%

Kuwait Crude Oil 1.5%

Other 9.5%

Country-specific crude oil extraction profiles were obtained by NETL from PE

International for all major oil exporters to the U.S. aside from Canada and are available in the

docket, EPA-HQ-OAR-2005-0161. Canadian crude oil extraction emissions are more difficult to

estimate, as the U.S. imports both conventional oil and oil sands from Canada. To estimate

emissions from Canadian conventional crude extraction, the U.S. conventional crude extraction

profile was utilized, while incorporating Canada-specific data on venting and flaring rates.176

For Canadian oil sands, extraction emission rates were derived using emissions reported by two

major oil sands producers. These estimated values for oil sands production were comparable to

those found by Charpentier et al. (2009)651. 9.5% of oil imports were grouped into a category

termed “other”, which consisted of imports from 31 countries. Due to the complexity and

uncertainty associated with developing estimates for each of these countries, extraction

emissions for this group were assumed to be the average of the conventional crude extraction

emissions from the other importers for which specific extraction estimates were developed.

176 The U.S. extraction profile was used as a surrogate for extraction of Canadian conventional crude oil, as most

data sources do not separate out emissions for Canadian conventional crude production from oil sands.
427

Extraction emissions for unfinished oils177 were assumed to be the same as for crude oil

extraction, with the addition of emissions for an atmospheric/vacuum distillation step after

extraction. Unfinished oils were assumed to be of the same import mix as crude oil. Emissions

associated with NGLs extraction were estimated using Canadian data for upstream oil and gas

operations.

2.5.2.2 Foreign Refineries

Countries exporting gasoline and diesel to the U.S. were determined from EIA data.

Table 2.5-3 reflects the percentage that imports made up of total U.S. consumption of gasoline

and diesel.

Table 2.5-3. Imports as a Percentage of 2005 U.S. Consumption

Product Percentage of U.S. Consumption

Conventional Gasoline 12.7%

Conventional Diesel 5.2%

Canada and the Virgin Islands were the primary liquid fuel exporters to the U.S., so

extraction emissions associated with fuels imported from those countries were estimated more

rigorously. Canada consisted of 25% of the finished motor gasoline imported to the U.S. and

32% of the diesel imported, while the Virgin Islands accounted for 17% of the gasoline and 29%

of the diesel. For both of these countries, the crude oil import mix was known, so crude oil

extraction emissions were estimated using the PE International extraction profiles.

The estimation method for other liquid fuel exporters to the U.S. depended on the origin

of the crude oil utilized. In some cases, crude oil was extracted in the same country in which it

was refined, so extraction emissions could be estimated from PE International extraction profiles.

In other cases, crude oil was imported from one country for refining in another, and the crude

import mix was not entirely clear. For most of these countries, PE International’s GaBi 4 Life

Cycle Assessment Software178 was utilized to provide estimates of extraction emissions.

This still left a handful of countries for which there was no method to estimate extraction

emissions. For these cases, “surrogate” profiles were used. For instance, for European countries

for which a country-specific profile was not available, the EU-15 or EU-25 extraction profile

was utilized. For South Korea, it is known that the source of crude oil is primarily Saudi Arabia,

so the Saudi profile was utilized to estimate extraction emissions. For the remainder of

countries, the extraction emissions were estimated to be the foreign average of all crude profiles.

In total, the foreign average profile was used for 9% of the gasoline crude oil mix and 12% of the

diesel crude oil mix.

177 “All oils requiring further processing, except those requiring only mechanical blending. Unfinished oils are

produced by partial refining of crude oil and include naphthas and lighter oils, kerosene and light gas oils, heavy gas

oils, and residuum.” Department of Energy: U.S. Energy Information Administration. “Glossary”

http://www.eia.doe.gov/glossary/glossary_u.htm.

178 “[PE International’s] GaBi software allows all the GHG emissions of your product to be captured in a systematic

and transparent way. Primary data specific to your product can then be incorporated into your analyses and

combined with secondary data on GHG emissions available from the GaBi databases.” PE International. Product

Carbon Footprint. < http://www.pe-international.com/consulting/carbon-footprint/product-carbon-footprint>
426

2.5.3 Crude Oil Transport

For domestic refineries, the NETL report states that “[c]rude oil transport to U.S.

refineries includes pipeline transport within the exporting country, ocean tanker transport to the

U.S., and domestic crude oil transport to refineries via a combination of pipeline, water carrier,

rail, and truck.” All crude is assumed to be transported by pipeline 100 miles to the U.S. border

or to a port for shipping to the U.S., with the energy intensity for pipeline transport assumed to

be 260 Btu/ton-mile. Based on EIA data, the distance from the foreign port to the U.S receiving

port was estimated for the top ten countries from which crude oil was imported. For all other

countries, the one-way travel distance was assumed to be 10,000 nautical miles.

Table 2.5-4. Travel distance for crude oil based on country of origin

Crude Oil Sources Import as % of

Refinery Crude Input

(Year 2005, EIA)

Country-Specific

Average One-Way

Travel Distance

(nautical miles)

Canada Waterborne 3.0% 675

Canada Pipeline 7.7% NA

Mexico Crude Oil 10.2% 1,061

Saudi Arabia Crude

Oil

9.4% 12,018

Venezuela Crude Oil 8.1% 1,789

Nigeria Crude Oil 7.1% 5,672

Iraq Crude Oil 3.4% 12,370

Angola Crude Oil 3.0% 6,736

Ecuador Crude Oil 1.8% 5,653

Algeria Crude Oil 1.5% 4,452

Kuwait Crude Oil 1.5% 12,526

Other 9.5% 10,000

Emissions arising from domestic transport of crude were estimated using the breakout of

crude oil transportation modes for 2004, as illustrated in Table 2.5-5.

Table 2.5-5. Domestic transportation breakout for crude oil

Pipelines Water Carriers Motor Carriers Railroads

75.9% 23.7% 0.3% 0.1%

For foreign refineries where extraction and refining occurred in the same country,

transport of 100 miles by pipeline from well to refinery was assumed. For countries which

imported crude, refined it into liquid fuels, and exported the liquid fuels to the U.S., the GaBi 4

Life Cycle Assessment Software gave estimates of the emissions associated with crude oil

transport. For Canada, the Virgin Islands, and South Korea, crude oil transport distances by

tanker were estimated, with the only exception that crude exported from the U.S. to Canada

traveled by pipeline. Transport of crude oil from the port of entry into the United States to the

petroleum refinery is not included in the model, since an analysis of petroleum refinery locations
427

indicated that most refineries are geographically located near the port of entry. The exclusion of

this transport operation was determined to have a negligible effect on the final results.

Transport of unfinished oils was modeled in the same way as crude oil transport.

Transport of NGLs was modeled in similar way to transport and distribution of petroleum

products, which is described in Section 2.5.4, “Fuel Transport and Distribution”.

2.5.4 Refining

NETL’s refining emissions estimation accounts for the following:

 Acquisition of fuels

o Indirect emissions associated with purchased power and steam

o Emissions associated with the acquisition of coal and natural gas purchased

and consumed at the refinery as fuels

o Emissions associated with production of fuels at the refinery which are

subsequently consumed as fuels (i.e. still gas, petroleum coke)

 Combustion of fuels at the refinery

 Hydrogen production (on-site and off-site)

o Upstream emissions associated with natural gas feed

o CO2 process emissions from steam methane reforming (SMR)

o Fuel combustion and upstream emissions associated with natural gas fuel and

indirect (electricity) emissions for off-site hydrogen production

 Flaring

 Venting and fugitive emissions

The NETL report indicates that, “The emissions above will be organized into a refinery

emissions pool and a hydrogen emissions pool and subsequently allocated between the various

refinery products. There are no individual assignments of energy sources to unit operations or

refinery products.”

To determine the GHG emissions from the refining of gasoline and diesel, NETL first

determined the total refining emissions from fuels combustion, fuels acquisition, flaring,

hydrogen production, and methane venting. For each of the refinery units, they then used the

capacity/throughput, energy, hydrogen consumption, and contribution to the final product slate to

allocate emissions to gasoline and diesel production.

A domestic refinery model was used as a surrogate for all foreign refinery operations. A

review of foreign refinery models from PE Americas indicated that differences in boundary

conditions and allocation procedures introduced greater uncertainty in the final results than using

the domestic refinery model as a surrogate for foreign refinery operations. The use of the

domestic refinery model for foreign refinery operations was noted by NETL as a data limitation

to the study.
426

2.5.5 Fuel Transport and Distribution

“Product transport includes transport of imported liquid fuels from the exporting nations

to the U.S. as well as domestic transport of both imported fuels and domestically produced liquid

fuels.” Foreign transport consists of tanker and/or pipeline transport of imported products to

U.S. ports. The products are assumed to be shipped 10 miles by pipeline to a port or the U.S.

border. Specific port-to-port travel distances were calculated for imports from Canada and the

U.S. Virgin Islands. All other product imports were assumed to travel 5,000 nautical miles to the

U.S. Emissions arising from domestic transport were estimated using the breakout of petroleum

product transportation modes for 2004, as shown in Table 2.5-6.

Table 2.5-6. Domestic transportation breakout for petroleum products

Pipelines Water Carriers Motor Carriers Railroads

59.8% 29.9% 6.3% 4.0%

2.5.6 Tailpipe Emissions

We updated the CO2 emission factors for gasoline and diesel to reflect revisions in the

factors made by EPA, which were used in the September 28, 2009 proposed rule to establish

GHG standards for light-duty vehicles. We have also updated the CO2 emissions factors for

ethanol and biodiesel to be consistent with those used in the October 30, 2009 final rulemaking

for the Mandatory GHG Reporting Rule. For the final rule, we have maintained the same CH4

and N2O emission factors used for the proposed rule, which were based on EPA MOVES model

run results.

Table 2.5-7. Tailpipe emissions for relevant fuels (g/mmBTU)

Fuel Type CO2 CH4 N2O

Gasoline 77,278 3 5

Diesel 78,308 1 2

Ethanol 75,885 13 2

Biodiesel 79,837 1 2

2.5.7 Land Use Change GHG Emissions

For the final rule, we performed an estimate of land use change emissions associated with

oil extraction and production to determine if the value was significant enough to be included in

our petroleum baseline calculation. As oil sands production incurs a greater degree of land use

change versus conventional crude oil production, we started with an estimate of emissions from

the conversion of Alberta forest for oil sands production.

Jordaan et al. (2009) estimated the land use change intensity for oil sands surface mining

and in-situ development, using data on project area and established reserves. They estimated an

average of 0.42 m2/m3synthetic crude oil (SCO) for surface mining and 0.11 m2/m3SCO for insitu. These intensity values were based on dividing the area of land disturbance by the total

volume of SCO produced over the lifetime of the project. Jordaan also calculated values for land
427

use change associated with upgrading of oil sands and for the extraction of natural gas utilized

for oil sands production. However, we restricted our calculation to consider only the land-use

change associated with oil sands production to be consistent with the life cycle analysis

methodology that we established for renewable fuels.179

We then utilized the Winrock database values to determine GHG emissions from land use

change of Alberta forest, the assumed area where oil sand extraction would occur. Per IPCC

Guidelines, we assumed that 20% of the soil carbon was lost from conversion, which gave an

overall value of 278.25 grams CO2/hectare over a 30 year timeframe. We multiplied the land use

change intensity and the GHG emissions from land-use change to yield a GHG intensity value.

This calculation yielded values of 1,858 (1,460-2,787) grams CO2/bbl for surface mining and

487 (310-708) grams/bbl for in-situ. These values were considerably lower (approximately

98%) than the oil sands extraction estimates determined by NETL and used in our petroleum

baseline (81,000-122,000 g CO2/bbl).

On a gasoline basis, the land-use change values were 380 (298-570) g CO2/mmbtu for

surface mining and 99 (63-145) g CO2/mmbtu for in-situ. Since oil sands only comprised about

5% of the crude oil mix to domestic refineries in 2005, these estimates were adjusted to

determine their impact on the aggregate well-to-tank petroleum baseline totals. For surface

mining, the oil sands land use change value was on the order of 0.1% of total well-to-tank CO2

emissions, and, for in-situ, it was 0.06%. Although these values likely represent the worst-case

land use impact of petroleum extraction, they are still negligible in the total well-to-tank values

and including them would not change the overall petroleum baseline values. We anticipate that

future work will help to quantify these values, and we can evaluate the appropriateness of

including a land use estimate in the future.

2.5.8 Petroleum Fuel 2005 Baseline Well-to-Tank GHG Emissions

The results for each of the lifecycle stages were combined to give a well-to-tank lifecycle

GHG value for 2005 gasoline and diesel as shown in Table 2.5-8. Tailpipe combustion

emissions for the two fuels are described in Section 2.5.6. When combined with the tailpipe

emissions values, a well-to-wheels result for gasoline of 98,205 grams CO2eq/mmBTU and

97,006 grams CO2eq/mmBTU was obtained.

Table 2.5-8. Gasoline and diesel baseline well-to-tank GHG emissions (g/mmbtu of fuel)

CO2 CH4 N2O CO2eq.

Gasoline 16,816 2,282 103 19,200

Diesel 15,838 2,066 94 17,998

179 In other words, when considering the GHG impacts of renewable fuels, we did not consider GHG emissions from

land use change associated with infrastructure or natural gas extracted and used for renewable fuels production.
494

2.6 Fuel-Specific Lifecycle Greenhouse Gas Emissions Results

In this section we present detailed lifecycle GHG analysis results, including the results of

sensitivity and scenario analyses on key assumptions. As discussed above, to implement the

EISA the crucial result that determines which renewable fuel pathways qualify for RFS2 credits

is the percent reduction in lifecycle GHG emissions compared to the average lifecycle

greenhouse gas emissions for gasoline or diesel sold or distributed as transportation fuel in 2005.

To compare lifecycle GHG emissions from renewable fuels and petroleum, we present the grams

of CO2-equivalent emissions per BTU of fuel produced (gCO2eq/mmBTU). The previous

sections in this chapter discussed our methodology for calculating lifecycle GHG emissions for

each component of the renewable fuel lifecycle, and for the 2005 petroleum baseline. In this

section we present and compare the GHG emissions results for each of these components in the

fuel lifecycle. We also discuss how key assumptions can change the GHG emissions from each

component of the fuel lifecycle, and how they influence the final GHG percent reduction

estimates.

In addition to estimating GHG emissions at every stage of the fuel lifecycle, EPA’s task

in this rulemaking is to integrate the GHG emissions estimates from all stages of the lifecycle in

order to estimate lifecycle GHG percent reductions for each renewable fuel pathway. We have

considered a number of ways to meet this challenge, and have identified several key

methodological issues that can influence whether a particular renewable fuel pathway meets the

thresholds set forth in the EISA.

2.6.1 Renewable Fuel Lifecycle GHG Results

This section presents fuel specific lifecycle GHG results for the different renewable fuels

compared to the petroleum baseline fuel replaced. Results are presented for the baseline set of

assumptions including:

 Business as usual yields

 30 year 0% discounted

 2022 year for results

Sensitivity around these assumptions are presented in Section 2.6.2. This section

presents the results as a range based on the uncertainty analysis conducted around the land use

change emissions.

2.6.1.1 Corn Ethanol Results

EPA analyzed the lifecycle GHG performance of a variety of ethanol from corn starch

pathways. The results presented here are for an average natural gas fired dry mill plant in 2022.

We predict approximately 90% of all plants will be producing corn oil as a by-product either

through a fractionation or extraction process; it is likely most if not all new plants will elect to

include such technology. We also expect that, to lower their operating costs, most facilities will

sell a portion of their co-product DGS prior to drying thus reducing energy consumption and
427

improving the efficiency and lifecycle GHG performance of the plant. The current national

average plant sells approximately 37% of the DGS co-product prior to drying.

Figure 2.6-1 shows the percent change in the lifecycle GHG emissions compared to the

petroleum gasoline baseline in 2022 for a corn ethanol dry mill plant using natural gas for its

process energy source, drying the national average of 63% of the DGS it produces and

employing corn oil fractionation technology. Lifecycle GHG emissions equivalent to the

gasoline baseline are represented on the graph by the zero on the X-axis. The 20% reduction

threshold is represented by the dashed line at -20% on the graph. The results for this corn

ethanol scenario are that the midpoint of the range of results is a 21% reduction in GHG

emissions compared to the gasoline 2005 baseline. The 95% confidence interval around that

midpoint ranges from a 7% reduction to a 32% reduction compared to the gasoline baseline

based on the uncertainty in the land use change assumptions.

Figure 2.6-1. Distribution of Results for a New Natural Gas Fired Corn Ethanol Plant

Average 2022 plant: natural gas, 63% dry, 37% wet DGS (w/ fractionation)

-50 -40 -30 -20 -10 0 10

Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel

Figure 2.6-2 below includes lifecycle GHG emissions broken down by several stages of

the lifecycle impacts for the typical corn ethanol depicted in Figure 2.6-1 compared to the 2005

baseline average for gasoline. Lifecycle emissions are normalized per energy unit of fuel

produced and presented in grams of carbon-dioxide equivalent GHG emissions per million

British Thermal Units of fuel produced (gCO2e/mmBTU). Figure 2.6-2 includes our mean
426

estimate of international land use change emissions as well as the 95% confidence range from

our uncertainty assessment, which accounts for uncertainty in the types of land use changes and

the magnitude of resulting GHG emissions. For the petroleum baseline, the fuel production stage

includes emissions from extraction, transport, refining and distribution of petroleum

transportation fuel. Petroleum tailpipe emissions include CO2 and non-CO2 gases emitted from

fuel combustion.

Figure 2.6-2. Results for a New Natural Gas Fired Corn Ethanol Plant by Lifecycle Stage

Average 2022 plant: natural gas, 63% dry, 37% wet DGS (w/ fractionation)

-21%

-20,000

0

20,000

40,000

60,000

80,000

100,000

120,000

2005 Gasoline Baseline 2022 Avg Dry Mill NG

GHG Emissions (gCO2-eq / mmBtu)

Fuel Production

International Land Use Change

International Farm Inputs and Fert

N2O

Other (fuel and feedstock

transport)

Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert

N2O

Domestic Livestock

20% Reduction Threshold

Net Emissions

We also looked at a number of different plat types, technologies and fuel types used.

Figure 2.6-3 shows the results for an average 2022 corn ethanol dry mill plant (fractionation and

63% dry DGS) but with different fuel sources, natural gas coal and biomass.
427

Figure 2.6-3. Results for New Corn Ethanol Plants by Fuel Source and Lifecycle Stage

Average 2022 plant: 63% dry, 37% wet DGS (w/ fractionation)

-21%

-1%

-38%

-20,000

0

20,000

40,000

60,000

80,000

100,000

120,000

2005 Gasoline Baseline 2022 Avg Dry Mill NG 2022 Avg Dry Mill Coal 2022 Avg Dry Mill Biomass

GHG Emissions (gCO2-eq / mmBtu)

Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O

Other (fuel and feedstock transport)

Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

20% Reduction Threshold

Net Emissions

Table 2.6-1 shows the results for all of the different corn ethanol pathways considered.

Table 2.6-1. Results for New Corn Ethanol Plants by Type

Plant Type Plant Technology Percent Change in Lifecycle GHG

Emissions

Time Horizon (years) 30 30 30

Discount Rate 0% 0% 0%

Range Low Mean High

Dry Mill NG Base Plant (dry DGS) -28% -17% -3%

Dry Mill NG w/ CHP (dry DGS) -31% -20% -6%

Dry Mill NG w/ Fractionation (dry DGS) -30% -18% -4%

Dry Mill NG w/ CHP and Fractionation (dry DGS) -33% -22% -7%

Dry Mill NG w/ Fractionation and Membrane Separation (dry DGS) -33% -22% -8%

Dry Mill NG w/ CHP, Fractionation and Membrane Separation (dry DGS) -37% -25% -11%

Dry Mill NG w/ Fractionation, Membrane Separation, and Raw Starch Hydrolysis (dry

DGS)

-38% -26% -12%

Dry Mill NG w/ CHP, Fractionation, Membrane Separation, and Raw Starch Hydrolysis

(dry DGS)

-41% -30% -15%

Dry Mill NG Base Plant (wet DGS) -39% -27% -13%

Dry Mill NG w/ CHP (wet DGS) -42% -30% -16%

Dry Mill NG w/ Fractionation (wet DGS) -38% -26% -12%

Dry Mill NG w/ CHP and Fractionation (wet DGS) -41% -29% -15%

Dry Mill NG w/ Fractionation and Membrane Separation (wet DGS) -41% -30% -16%

Dry Mill NG w/ CHP, Fractionation and Membrane Separation (wet DGS) -44% -33% -19%

Dry Mill NG w/ Fractionation, Membrane Separation, and Raw Starch Hydrolysis (wet

DGS)

-44% -33% -18%

Dry Mill NG w/ CHP, Fractionation, Membrane Separation, and Raw Starch Hydrolysis

(wet DGS)

-47% -36% -22%

Dry Mill Coal Base Plant (dry DGS) 1% 12% 26%

Dry Mill Coal w/ CHP (dry DGS) -1% 10% 24%

Dry Mill Coal w/ Fractionation (dry DGS) -7% 5% 19%

Dry Mill Coal w/ CHP and Fractionation (dry DGS) -9% 3% 17%

Dry Mill Coal w/ Fractionation and Membrane Separation (dry DGS) -14% -3% 11%
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Plant Type Plant Technology Percent Change in Lifecycle GHG

Emissions

Dry Mill Coal w/ CHP, Fractionation and Membrane Separation (dry DGS) -16% -5% 9%

Dry Mill Coal w/ Fractionation, Membrane Separation, and Raw Starch Hydrolysis (dry

DGS)

-23% -12% 2%

Dry Mill Coal w/ CHP, Fractionation, Membrane Separation, and Raw Starch Hydrolysis

(dry DGS)

-25% -14% 0%

Dry Mill Coal Base Plant (wet DGS) -21% -10% 4%

Dry Mill Coal w/ CHP (wet DGS) -23% -12% 2%

Dry Mill Coal w/ Fractionation (wet DGS) -23% -11% 3%

Dry Mill Coal w/ CHP and Fractionation (wet DGS) -25% -13% 1%

Dry Mill Coal w/ Fractionation and Membrane Separation (wet DGS) -30% -19% -5%

Dry Mill Coal w/ CHP, Fractionation and Membrane Separation (wet DGS) -32% -21% -7%

Dry Mill Coal w/ Fractionation, Membrane Separation, and Raw Starch Hydrolysis (wet

DGS)

-36% -24% -10%

Dry Mill Coal w/ CHP, Fractionation, Membrane Separation, and Raw Starch Hydrolysis

(wet DGS)

-38% -26% -12%

Dry Mill

Biomass

Base Plant (dry DGS) -51% -40% -26%

Dry Mill

Biomass

w/ CHP (dry DGS) -59% -47% -33%

Dry Mill

Biomass

w/ Fractionation (dry DGS) -49% -38% -24%

Dry Mill

Biomass

w/ CHP and Fractionation (dry DGS) -57% -45% -31%

Dry Mill

Biomass

w/ Fractionation and Membrane Separation (dry DGS) -49% -38% -24%

Dry Mill

Biomass

w/ CHP, Fractionation and Membrane Separation (dry DGS) -56% -45% -31%

Dry Mill

Biomass

w/ Fractionation, Membrane Separation, and Raw Starch Hydrolysis (dry

DGS)

-49% -38% -24%

Dry Mill

Biomass

w/ CHP, Fractionation, Membrane Separation, and Raw Starch Hydrolysis

(dry DGS)

-57% -45% -31%

Dry Mill

Biomass

Base Plant (wet DGS) -52% -41% -27%

Dry Mill

Biomass

w/ CHP (wet DGS) -59% -48% -34%

Dry Mill

Biomass

w/ Fractionation (wet DGS) -50% -39% -25%

Dry Mill

Biomass

w/ CHP and Fractionation (wet DGS) -57% -46% -32%

Dry Mill

Biomass

w/ Fractionation and Membrane Separation (wet DGS) -50% -38% -24%

Dry Mill

Biomass

w/ CHP, Fractionation and Membrane Separation (wet DGS) -57% -45% -31%

Dry Mill

Biomass

w/ Fractionation, Membrane Separation, and Raw Starch Hydrolysis (wet

DGS)

-50% -38% -24%

Dry Mill

Biomass

w/ CHP, Fractionation, Membrane Separation, and Raw Starch Hydrolysis

(wet DGS)

-57% -46% -32%

Wet Mill with NG -19% -7% 7%

Wet Mill with Coal 8% 19% 33%

Wet Mill with Biomass -59% -48% -33%

2.6.1.2 Corn Butanol Results

We analyzed corn butanol, similar to corn ethanol in terms of types of plants and

technologies.

Figure 2.6-4 shows the percent change in the lifecycle GHG emissions compared to the

petroleum gasoline baseline in 2022 for a corn butanol dry mill plant using natural gas for its

process energy source, drying the national average of 63% of the DGS it produces and

employing corn oil fractionation technology. Lifecycle GHG emissions equivalent to the

gasoline baseline are represented on the graph by the zero on the X-axis. The 20% reduction
427

threshold is represented by the dashed line at -20 on the graph. The results for this corn butanol

scenario are that the midpoint of the range of results is a 31% reduction in GHG emissions

compared to the gasoline 2005 baseline. The 95% confidence interval around that midpoint

ranges from a 20% reduction to a 40% reduction compared to the gasoline baseline based on the

uncertainty in the land use change assumptions.

The butanol results in slightly greater GHG reductions compared to corn ethanol because

of the greater energy content of the fuel. There is actually slightly more energy used in

processing but there is a greater energy production output. Therefore, on a per mmBTU basis

there are less GHG emissions produced across all the lifecycle.

Figure 2.6-4. Distribution of Results for a New Natural Gas Fired Corn Butanol Plant

Average 2022 plant: natural gas, 63% dry, 37% wet DGS (w/ fractionation)

-60 -50 -40 -30 -20 -10 0

Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel

Figure 2.6-5 below includes lifecycle GHG emissions broken down by several stages of

the lifecycle impacts for the typical corn butanol plant depicted in Figure 2.6-5 compared to the

2005 baseline average for gasoline. Lifecycle emissions are normalized per energy unit of fuel

produced and presented in grams of carbon-dioxide equivalent GHG emissions per million

British Thermal Units of fuel produced (gCO2e/mmBTU).
426

Figure 2.6-5 includes our mean estimate of international land use change emissions as

well as the 95% confidence range from our uncertainty assessment, which accounts for

uncertainty in the types of land use changes and the magnitude of resulting GHG emissions.

Figure 2.6-5. Results for New Corn Butanol Plants by Lifecycle Stage

Average 2022 plant: 63% dry, 37% wet DGS (w/ fractionation)

-31%

-20,000

0

20,000

40,000

60,000

80,000

100,000

120,000

2005 Gasoline Baseline 2022 Base Dry Mill NG

GHG Emissions (gCO2-eq / mmBtu)

Fuel Production

International Land Use Change

International Farm Inputs and Fert

N2O

Other (fuel and feedstock

transport)

Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert

N2O

Domestic Livestock

20% Reduction Threshold

Net Emissions

2.6.1.3 Biodiesel Results

Figure 2.6-6 shows the percent change in the typical 2022 soybean biodiesel lifecycle

GHG emissions compared to the petroleum diesel fuel 2005 baseline. Lifecycle GHG emissions

equivalent to the diesel fuel baseline are represented on the graph by the zero on the X-axis. The

50% reduction threshold is represented by the dashed line at -50 on the graph. The results for

soybean biodiesel are that the midpoint of the range of results is a 57% reduction in GHG

emissions compared to the diesel fuel baseline. The 95% confidence interval around that

midpoint results in range of a 22% reduction to an 85% reduction compared to the diesel fuel

2005 baseline.
427

Figure 2.6-6. Distribution of Results Soybean Biodiesel

Average 2022 plant; natural gas

-140 -120 -100 -80 -60 -40 -20 0 20

Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel

Figure 2.6-7 below includes lifecycle GHG emissions broken down by several stages of the lifecycle impacts

for the typical soybean biodiesel plant depicted in

Figure 2.6-6Figure 2.6-6 compared to the 2005 baseline average for diesel fuel.

Lifecycle emissions are normalized per energy unit of fuel produced and presented in grams of

carbon-dioxide equivalent GHG emissions per million British Thermal Units of fuel produced

(gCO2e/mmBTU).

Figure 2.6-7Figure 2.6-7 includes the mean estimate of international land use change

emissions as well as the 95% confidence range from our uncertainty assessment, which accounts

for uncertainty in the types of land use changes and the magnitude of resulting GHG emissions.
426

Figure 2.6-7Figure 2.6-7 also includes emissions from waste grease based biodiesel. The

waste grease biodiesel does not have any agricultural or land use emissions and therefore only a

point source estimate is shown for that pathway.

Figure 2.6-7. Results for Biodiesel by Lifecycle Stage

Soybean and Waste Grease Feedstock

-57%

-86%

-40,000

-20,000

0

20,000

40,000

60,000

80,000

100,000

120,000

2005 Diesel Fuel Baseline 2022 Soybean Biodiesel 2022 Waste Grease Biodiesel

GHG Emissions (gCO2-eq / mmBtu)

Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O

Other (fuel and feedstock transport)

Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

Net Emissions

50% Reduction Threshold

Figure 2.6-8 shows lifecycle GHG emissions broken down by several stages of the

lifecycle impacts for algae oil to biodiesel compared to the 2005 baseline average for diesel fuel.

Results are shown for the different cases of production described in Section 2.4.7.3.3. The algae

oil biodiesel does not have any agricultural or land use emissions and therefore only a point

source estimate is shown for each pathway.
427

Figure 2.6-8. Results for Algae Biodiesel by Lifecycle Stage

Algae Oil Feedstock

0

20,000

40,000

60,000

80,000

100,000

120,000

2005 Diesel

Fuel

Open Pond PBR Open Pond PBR Open Pond PBR

Petroleum

Baseline

Base Case Aggressive Case Max Case

GHG Emissions (gCO2-eq / mmBtu)

Fuel Production

Other (fuel and feedstock

transport)

Tailpipe

Fertilizer

50% Reduction Threshold

Net Emissions

2.6.1.4 Sugarcane Ethanol Results
426

Figure 2.6-9 shows the percent change in the average 2022 sugarcane ethanol lifecycle

GHG emissions compared to the petroleum gasoline 2005 baseline. These results assume the

ethanol is produced and dehydrated in Brazil prior to being imported into the U.S. and that the

residue is not collected. Lifecycle GHG emissions equivalent to the gasoline baseline are

represented on the graph by the zero on the X-axis. The 50% reduction threshold is represented

by the dashed line at -50 on the graph. The results for this sugarcane ethanol scenario are that

the midpoint of the range of results is a 61% reduction in GHG emissions compared to the

gasoline baseline. The 95% confidence interval around that midpoint results in a range of a 52%

reduction to a 71% reduction compared to the gasoline 2005 baseline.

Figure 2.6-9. Distribution of Results for Sugarcane Ethanol

Average 2022 plant: no residue collection
427

-90 -80 -70 -60 -50 -40 -30 -20 -10 0

Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel

We also considered pathways assuming most crop residue of the leaves as well as stalks

are collected (and therefore available for burning as process energy) or without the extra crop

residue being neither collected nor burned as fuel. We also analyzed pathways assuming the

ethanol is distilled in Brazil or alternatively being distilled in the Caribbean (“CBI”).

Figure 2.6-10 below includes lifecycle GHG emissions broken down by several stages of

the lifecycle impacts for the difference sugarcane ethanol scenarios compared to the 2005

baseline average for gasoline. Lifecycle emissions are normalized per energy unit of fuel

produced and presented in grams of carbon-dioxide equivalent GHG emissions per million

British Thermal Units of fuel produced (gCO2e/mmBTU).
426

Figure 2.6-10 includes the mean estimate of international land use change emissions as

well as the 95% confidence range from our uncertainty assessment, which accounts for

uncertainty in the types of land use changes and the magnitude of resulting GHG emissions.

Figure 2.6-10. Results for Sugarcane Ethanol by Lifecycle Stage

With and without residue collection and CBI

-91% -89%

-61% -59%

-60,000

-40,000

-20,000

0

20,000

40,000

60,000

80,000

100,000

120,000

2005 Gasoline

Baseline

No Residue Coll.

No CBI

No Residue Coll.

CBI

Residue Coll. No

CBI

Residue Coll. CBI

GHG Emissions (gCO2-eq / mmBtu)

Fuel Production

International Land Use Change

International Farm Inputs and Fert

N2O

Other (fuel and feedstock

transport)

Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert

N2O

Domestic Livestock

50% Reduction Threshold

Net Emissions

As

Figure 2.6-10 indicates, the sugarcane ethanol scenarios with residue collection have greater

GHG reductions compared to the no collection cases. For residue collection there is slightly

more energy and emissions needed for crop production due to collection and transport of the

residue. However, there are significantly more GHG savings at the plant due to more excess

electricity production from burning the collected residues.
427

The CBI cases in which the ethanol is distilled in the Caribbean add slightly more GHG

emissions from burning fossil fuels for dehydration. This is slightly offset by the additional

excess electricity from the sugarcane ethanol plant that does not need to dehydrate the ethanol.

Energy used for dehydration at the ethanol plant could then be used to generate excess electricity

that offsets grid electricity production, and results in GHG savings.

2.6.1.5 Cellulosic Biofuels Results

Figure 2.6-11 shows the percent change in the average lifecycle GHG emissions in 2022

for ethanol produced from switchgrass using the biochemical process compared to the petroleum

gasoline 2005 baseline. Lifecycle GHG emissions equivalent to the gasoline baseline are

represented on the graph by the zero on the X-axis. The 60% reduction threshold is represented

by the dashed line at -60 on the graph. The results for this switchgrass ethanol scenario are that

the midpoint of the range of results is a 110% reduction in GHG emissions compared to the

gasoline baseline. The 95% confidence interval around that midpoint ranges from 102%

reduction to a 117% reduction compared to the gasoline baseline.

Figure 2.6-11. Distribution of Results for Switchgrass Biochemical Ethanol

Average 2022 plant: biochemical process producing ethanol, excess electricity production

-140 -120 -100 -80 -60 -40 -20 0

Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel
426

We have also analyzed additional cellulosic biofuel pathways (i.e., thermochemical

cellulosic ethanol and a BTL diesel pathway) as well as considered crop residues as a cellulosic

feedstock. Figure 2.6-12 below includes lifecycle GHG emissions broken down by several

stages of the lifecycle impacts for the different cellulosic feedstock to ethanol production

scenarios compared to the 2005 baseline average for gasoline. Lifecycle emissions are

normalized per energy unit of fuel produced and presented in grams of carbon-dioxide equivalent

GHG emissions per million British Thermal Units of fuel produced (gCO2e/mmBTU).

Figure 2.6-12 includes the mean estimate of international land use change emissions as

well as the 95% confidence range from our uncertainty assessment, which accounts for

uncertainty in the types of land use changes and the magnitude of resulting GHG emissions. The

residues to ethanol scenarios do not have any international land use emissions and therefore only

a point source estimate is shown for those pathways.

Figure 2.6-12. Results for Cellulosic Ethanol by Lifecycle Stage

Biochemical and Thermochemical for Switchgrass and Corn Stover

-110%

-72%

-92%

-129%

-60,000

-40,000

-20,000

0

20,000

40,000

60,000

80,000

100,000

120,000

2005 Gasoline

Baseline

Sw itchgrass -

Biochemical

Sw itchgrass -

Thermochemical

Corn Stover -

Biochemical

Corn Stover -

Thermochemical

GHG Emissions (gCO2-eq / mmBtu)

Fuel Production

International Land Use Change

International Farm Inputs and Fert

N2O

Other (fuel and feedstock

transport)

Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert

N2O

Domestic Livestock

60% Reduction Threshold

Net Emissions
427

Figure 2.6-13Figure 2.6-13 below includes lifecycle GHG emissions broken down by

several stages of the lifecycle impacts for the different cellulosic feedstock to F-T diesel fuel

production scenarios compared to the 2005 baseline average for diesel fuel. Lifecycle emissions

are normalized per energy unit of fuel produced and presented in grams of carbon-dioxide

equivalent GHG emissions per million British Thermal Units of fuel produced (gCO2e/mmBTU).

Figure 2.6-13. Results for Cellulosic Diesel Fuel by Lifecycle Stage

F-T Diesel Fuel for Switchgrass and Corn Stover
426

-71%

-91%

-20,000

0

20,000

40,000

60,000

80,000

100,000

120,000

2005 Diesel Fuel Baseline Sw itchgrass - F-T Diesel Corn Stover - F-T Diesel

GHG Emissions (gCO2-eq / mmBtu)

Fuel Production

International Land Use Change

International Farm Inputs and Fert

N2O

Other (fuel and feedstock

transport)

Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert

N2O

Domestic Livestock

60% Reduction Threshold

Net Emissions

Biochemical ethanol production results in greater GHG savings compared to the

thermochemical or F-T diesel fuel scenarios due to the excess electricity production from the

lignin generated from the biochemical process. The corn stover scenarios have less overall

agricultural sector GHG emissions compared to the switchgrass scenario and do not have

international land use change emissions and therefore greater GHG savings.

2.6.2 Sensitivity Analysis

This section presents the results or several sensitivity analyses performed around the

different main components of the lifecycle analysis. Some of the sensitivity analysis impact all

fuels considered while some only impact specific fuels.

2.6.2.1 Timing and Discount Rate

In addition to estimating GHG emissions at every stage of the fuel lifecycle, EPA’s task

in this rulemaking is to integrate the GHG emissions estimates from all stages of the lifecycle in

order to estimate lifecycle GHG percent reductions for each renewable fuel pathway. We have

considered a number of ways to meet this challenge, and have identified several key

methodological issues that can influence whether a particular renewable fuel pathway meets the

thresholds set forth in the EISA. For example, one issue that deserves attention is the timing of

lifecycle GHG emissions.
427

Section 2.4.5 explained that the lifecycle GHG emissions associated with biofuels can

vary over time. Clearing forests, grasslands, and other types of land that sequester carbon, for

crop production can results in GHG emissions for many years. As depicted in Figure 2.6-14,

this type of land conversion produces large immediate GHG emissions, followed by a lesser

stream of emissions that can last for many years. Biomass feedstocks grown annually on new

cropland can be converted to biofuels that offer a GHG benefit relative to the petroleum product

they replace, but these benefits may be small compared to the upfront GHG emissions associated

with land clearing to expand crop production. Depending on the specific biofuel in question, it

can take many years for the benefits of the biofuel to make up for the large initial releases of

carbon that result from land conversion (e.g., the payback period).

The payback period calculation, presented graphically in Figure 2.6-14, represents the

time it takes for the emissions savings from the production of biofuels to equal the potentially

large initial emissions from land use changes. Although we do not believe it is appropriate to use

the payback period for RFS2 compliance purposes, this calculation helps to illustrate the

importance of the time dimension of renewable fuel lifecycle GHG emissions.

Figure 2.6-14. Corn Ethanol Payback Period

-1,000,000

-800,000

-600,000

-400,000

-200,000

0

200,000

400,000

600,000

800,000

2022203020402050

GHG Emissions (gCO2-eq / mmBtu)

2005 Gasoline Baseline

Corn Ethanol Lifecycle GHG Emissions (2022 Average NG Dry Mill)

Cumulative Net Corn Ethanol Life Cycle GHG Emissions

Year 0 Land Use

•Clearing with fire

•Biomass decay

•Soil carbon emissions

•Foregone forest sequestration

Years 1-19 Land Use

•Soil carbon emissions

•Foregone forest sequestration

Years 20-79 Land Use

•Foregone forest sequestration

Payback ~14 years

Figure 2.6-14 shows the baseline lifecycle GHG emissions from the 2022 average corn

ethanol natural gas fired dry mill with fractionation and drying 63% of DGS, and from the 2005

gasoline baseline. In the first year, in this case 2022, corn ethanol lifecycle GHG emissions are

more than five times greater than the gasoline it replaces. However, corn ethanol has ongoing

GHG benefits in every subsequent year. It takes approximately 14 years for the annual GHG

benefits of corn ethanol compared to gasoline to pay back the initial GHG releases from land

clearing. This tells us that unless we analyze the lifecycle GHG emissions of corn ethanol over

more than 14 years, corn ethanol from this pathway will not achieve a reduction compared to

gasoline. As we extend our analysis beyond 14 years we will see increasing GHG reductions

associated with the use of corn ethanol.

The same is true for other renewable fuels that result in land use change, soybean

biodiesel, sugarcane ethanol, and switchgrass biofuels. Furthermore, the uncertainty in the land
426

use change emissions results in a range of payback periods depending on the range of land use

change emissions. Table 2.6-2 shows the different payback periods for the different biofuels and

for the high and low range of land use change emissions.

Table 2.6-2. Payback Periods for Different Fuels

Payback Period (years)

Type of Biofuel Low Midpoint High

Corn Ethanol

(2022 average plant) 7 14 24

Soybean biodiesel 5 9 21

Sugarcane ethanol

(no residue collection, no CBI) 1 2 4

Switchgrass Ethanol

(biochemical) 0 0 1

Switchgrass Ethanol

(thermochemical) 1 1 2

Switchgrass Diesel

(F-T diesel) 1 1 2

The payback periods shown in Table 2.6-2 represent the time needed for the renewable

fuels to break even in terms of GHG emissions compared to the petroleum fuel replaced.

However, the threshold determinations needed for the rulemaking are based on the fuels reaching

a percentage reduction compared to the petroleum fuels replaced. The threshold reduction time

period is longer than just the breakeven point. Table 2.6-3 shows the threshold requirements and

time periods to reach those threshold reductions for each fuel.

Table 2.6-3. Threshold Periods for Different Fuels

Threshold Reduction Threshold Period (years)

Type of Biofuel % Low Midpoint High

Corn Ethanol

(2022 average plant)

20 15 28 43

Soybean biodiesel 50 10 24 50

Sugarcane ethanol

(no residue collection, no CBI)

50 6 12 24

Switchgrass Ethanol

(biochemical)

60 2 3 5

Switchgrass Ethanol

(thermochemical)

60 7 12 24

Switchgrass Diesel

(F-T diesel)

60 7 14 26

The payback period concept helps to demonstrate the importance of the choice of a

discount rate and time horizon for this analysis. These factors are so important because of the

variation in GHG emissions from renewable fuels over time, and the contrasting steady annual

emissions from the petroleum baseline. For the final rule threshold determinations we rely on a
427

30 year time horizon and a 0% discount rate. A longer time horizon would result in greater

benefits for biofuels, and a higher discount rate would result in lower GHG reductions.

Figure 2.6-15 includes lifecycle GHG results for the 2022 average corn ethanol produced

in a natural gas-fired dry mill over a continuum of time horizons. The horizontal axis is the

choice of time horizon. As discussed above, our results indicate that the payback period for an

average 2022 corn ethanol pathway is approximately 14 years. With a zero percent discount (the

blue line in Figure 2.6-15) corn ethanol reduces GHG emissions by 21 percent over 30 years,

and reduces emissions by 8 percent and 28 percent over 20 and 40 years respectively. With

higher discount rates, it takes longer for the future benefits of corn ethanol production to payback

earlier land clearing emissions. When we use a discount rate greater than zero, future benefits

are discounted, causing the curves in Figure 2.6-15 to flatten out over time. Results for the

midpoint of land use change uncertainty are shown in Figure 2.6-15, the high and low land use

change uncertainty results would shift the results. Low land use change results would shift the

curves down, high land use change results would shift the curves up.

Figure 2.6-15. Lifecycle GHG Results for 2022 Average Corn Ethanol

(Percent Change from Gasoline with Different Discount Rates and Time Horizons)

-8%

-28%

-3%

-15%

-21%

0%

-17%

-21%

-12%

-40%

-20%

0%

20%

40%

20222030204020502060207020802090210021102120

Lifecycle GHG Percent Change From Gasoline Baseline

Corn Ethanol Average 2022 Plant, 0% Discount Rate

Corn Ethanol Average 2022 Plant, 2% Discount Rate

Corn Ethanol Average 2022 Plant, 3% Discount Rate

20, 30 & 40 Year Results, r=0%

20, 30 & 40 Year Results, r=2%

20, 30 & 40 Year Results, r=3%

2.6.2.2 High Yield Scenario Results

There are many factors that go into the economic modeling but the yield assumptions for

different crops has one of the biggest impacts on land use and land use change. Therefore, for

this analysis we ran a base yield case and a high yield sensitivity case.

EPA’s base yield projections are derived from extrapolating through 2022 long-term

historical U.S. corn yields from 1985 to 2009. This estimate, 183 bushels/acre for corn and 48

bushels/acre for soybeans, is consistent with USDA’s method of projecting future crop yields.

During the public comment process we learned that numerous technical advancements--

including better farm practices, seed hybridization and genetic modification--have led to more

rapid gains in yields since 1995. In addition, commenters, including many leading seed

companies, provided data supporting more rapid improvements in future yields. For example,
426

commenters pointed to recent advancements in seed development (including genetic

modification) and the general accumulation of knowledge of how to develop and bring to market

seed varieties—factors that would allow for a greater rate of development of seed varieties

requiring fewer inputs such as fertilizer and pest management applications.

Therefore, in coordination with USDA experts, EPA has developed for this final rule a

high yield case scenario of 230 bushels/acre for corn and 60 bushels/acre for soybeans. These

figures represent the 99% upper bound confidence limit of variability in historical U.S. yields.

This high yield case represents a feasible high yield scenario for the purpose of a sensitivity test

of the impact on the results of higher yields. Figure 2.6-16 shows the historic data and trends for

U.S. corn yields.

Figure 2.6-16. U.S. Historic and Projected Corn Yields

Projected Corn Yields

183

194

230

0

50

100

150

200

250

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Year

Yield (bu/acre)

85 - 09 Trend

95 - 09 Trend

Historic Projected

Feedback we received indicated that corn and soybean yields respond in tandem and that

a high yield corn case would also imply a higher yield for soybeans as well. The high yield case

is therefore based on higher yield corn and soybeans in the U.S. as well as in the major corn and

soybean producing countries around the world. For international yields, it is reasonable to

assume the same percent increases from the baseline yield assumptions could occur as we are

estimating for the U.S. Thus in the case of corn, 230 bushels per acre is approximately 25%

higher than the U.S. baseline yield of 183 bushels per acre in 2022. This same 25% increase in

yield can be expected for the top corn producers in the rest of the world by 2022, as justified

improvements in seed varieties and, perhaps even more so than in the case of the U.S.,

improvements in farming practices which can take more full advantage of the seed varieties'

potential. For example, seeds can be more readily developed to perform well in the particular

regions of these countries and can be coupled with much improved farming practices as farmers
427

move away from historical practices such as saving seeds from their crop for use the next year

and better understand the economic advantages of modern farming practices. So the high yield

scenarios would not have the same absolute yield values in other countries as the U.S. but would

have the same percent increase.

Figure 2.6-17 shows the results for the 2022 average corn ethanol plant with the base and

high yield scenarios. The high yield scenario has a modest change in the overall GHG

reductions of corn ethanol. With the high yield estimates the 2022 average corn ethanol plant

reduces GHG emissions compared to the gasoline baseline by 23%, compared to reductions of

21% for the base case scenario.

Figure 2.6-17.

Distribution of High and Base Yield Results for a

New Natural Gas Fired Corn Ethanol Plant

Average 2022 plant: natural gas, 63% dry, 37% wet DGS (w/ fractionation)

-60 -50 -40 -30 -20 -10 0 10

Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel

2022 High Yield 2022 Base Yield

Figure 2.6-18Figure 2.6-18 includes lifecycle GHG emissions broken down by several

stages of the lifecycle impacts for the 2022 average corn ethanol plant for the base and high yield

scenario compared to the 2005 baseline average for gasoline. Lifecycle emissions are

normalized per energy unit of fuel produced and presented in grams of carbon-dioxide equivalent

GHG emissions per million British Thermal Units of fuel produced (gCO2e/mmBTU).
426

Figure 2.6-18. High and Base Yield Results for a New Natural Gas Fired Corn Ethanol

Plant by Lifecycle Stage

Average 2022 plant: natural gas, 63% dry, 37% wet DGS (w/ fractionation)

-21% -23%

-20,000

0

20,000

40,000

60,000

80,000

100,000

120,000

2005 Gasoline Baseline 2022 Avg Dry Mill NG - Base Yield 2023 Avg Dry Mill NG - High Yield

GHG Emissions (gCO2-eq / mmBtu)

Fuel Production

International Land Use Change

International Farm Inputs and Fert

N2O

Other (fuel and feedstock

transport)

Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert

N2O

Domestic Livestock

20% Reduction Threshold

Net Emissions

The main difference with the high yield scenario is that as ethanol production expands

there is less overall land use and crop shifting needed domestically, reflected by lower domestic

Farm input impacts. However, there is actually a greater impact on livestock compared to the

base case. There is a greater shifting to grazing livestock internationally which results in more

pasture land needed and slightly higher international land use change emissions.

Figure 2.6-19Figure 2.6-19 shows the results for soybean biodiesel with the base and

high yield scenarios. The high yield scenario has a fairly significant change in the overall GHG

reductions of soybean biodiesel. With the high yield estimates soybean biodiesel reduces GHG
427

emissions compared to the diesel fuel baseline by 70%, compared to reductions of 57% for the

base case yield scenario.

Figure 2.6-19. Distribution of High and Base Yield Results for Soybean Biodiesel

-140 -120 -100 -80 -60 -40 -20 0 20

Percentage Change in Biofuel GHG Lifecycle Emissions Compared to Petroleum Fuel

2022 High Yield 2022 Base Yield
426

Figure 2.6-20Figure 2.6-20 includes lifecycle GHG emissions broken down by several

stages of the lifecycle impacts for soybean biodiesel for the base and high yield scenario

compared to the 2005 baseline average for diesel fuel. Lifecycle emissions are normalized per

energy unit of fuel produced and presented in grams of carbon-dioxide equivalent GHG

emissions per million British Thermal Units of fuel produced (gCO2e/mmBTU).

Figure 2.6-20. High and Base Yield Results for Soybean Biodiesel
427

-57%

-70%

-40,000

-20,000

0

20,000

40,000

60,000

80,000

100,000

120,000

2005 Diesel Baseline Soy Biodiesel - Base Yield Soy Biodiesel - High Yield

GHG Emissions (gCO2-eq / mmBtu)

Fuel Production

International Land Use Change

International Farm Inputs and Fert

N2O

Other (fuel and feedstock

transport)

Domestic Land Use Change

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert

N2O

Domestic Livestock

50% Reduction Threshold

Net Emissions

Similar to the corn ethanol high yield scenario for the soybean high yield case the biggest

impact is on livestock changes compared to the base case. There is a greater shifting in the high

yield case away from grazing livestock internationally which results in less pasture land needed

and lower international land use change emissions.

2.7 Overall Lifecycle Greenhouse Gas Emissions Results of Rulemaking

Volumes Compared to AEO Projected Volumes

Our analysis of the overall GHG emission impacts of this proposed rulemaking was

performed in parallel with the lifecycle analysis performed to develop the individual fuel

thresholds described in previous sections. The same system boundaries apply such that this

analysis includes the effects of three main areas: a) emissions related to the production of

biofuels, including the growing of feedstock (corn, soybeans, etc.) with associated domestic and

international land use change impacts, transport of feedstock to fuel production plants, fuel

production, and distribution of finished fuel; b) emissions related to the extraction, production

and distribution of petroleum gasoline and diesel fuel that is replaced by use of biofuels; and c)

difference in tailpipe combustion of the renewable and petroleum based fuels.

Consistent with the fuel volume feasibility analysis and criteria pollutant emissions

evaluation, our analysis of the GHG impacts of this proposed rulemaking was conducted by

comparing the difference between a 2022 reference case and a 2022 control case with volumes of

renewable fuels meeting the RFS2 mandate. Similar to what was done to calculate lifecycle

thresholds for individual fuels we considered the change in 2022 of these two volume scenarios

of renewable fuels to determine overall GHG impacts of the rule. The reference case for the
426

GHG emission comparisons was taken from the AEO 2007 projected renewable fuel production

levels for 2022 prior to enactment of EISA. This scenario provided a point of comparison for

assessing the impacts of the RFS2 standard volumes on GHG emissions. We ran these multi fuel

scenarios through our FASOM and FAPRI-CARD models and applied the satellite data land use

change assumptions to determine to overall GHG impacts of producing this increase in

renewable fuels.

The main differences between this overall impacts analysis and the analysis conducted to

develop the threshold values for the individual fuels were that we analyzed the total change in

renewable fuels in one scenario as opposed to looking at individual fuel impacts. When

analyzing the impact of the 2022 EISA mandate, we also took into account the agricultural sector

interactions necessary to produce the full complement of feedstock.

We also considered a mix of plant types and configurations for the 2022 renewable fuel

production representing the mix of plants and feedstock we project to be in use in 2022. Table

2.7-1 shows the types of plants considered and the volumes produced by each in the analysis for

the references and control cases.
494

Table 2.7-1. Types of Plants and Volumes Considered in 2022

Plant Configuration and Energy Used (Btu/gal) Volume (Bgal)

NG Use Coal Use

Biomass

Use

Diesel Fuel

Use

Purchased

Elec Sold Elec

Reference

Case

Policy

Case Difference

Corn Ethanol – Dry Mill NG - Base Plant (dry DDGS) 25,672 2,165 4.2 5.2 1.0

- Base Plant (wet DGS) 16,320 2,165 2.5 3.1 0.6

- Integrated Biogas System (dry DGS) 11,459 231 0.9 1.2 0.2

- Integrated Biogas System (wet DGS) 7,285 231 0.6 0.7 0.1

Corn Ethanol – Dry Mill Coal - Base Plant (dry DDGS) 34,773 231 0.3 0.3 0.1

- Base Plant (wet DGS) 22,106 231 0.2 0.2 0.0

Corn Ethanol – Dry Mill Biomass - Base Plant (dry DDGS) 33,147 1,679 1.5 1.8 0.4

- Base Plant (wet DGS) 21,072 1,679 0.9 1.1 0.2

Corn Ethanol – Wet Mill - Plant with NG 45,950 0.0 0.02 0.02

- Plant with coal 45,950 1.4 1.4 0.0

Cellulosic Ethanol – Enzymatic - Switchgrass feedstock & lignin used as fuel 72,144 -12,249 0.0 1.5 1.5

- Corn stover feedstock & lignin used as fuel 68,431 -12,249 0.2 1.0 0.7

Cellulosic Ethanol – Thermochemical - Switchgrass feedstock 100,543 177 0.0 1.5 1.5

- Corn stover feedstock 95,369 177 0.0 1.0 1.0

Biodiesel - Soybean oil feedstock 18,913 3,205 0.4 1.4 1.1

- Yellow grease / tallow feedstock 21,051 494 0.0 0.2 0.2

Renewable Diesel - Yellow grease / tallow feedstock 838 0.0 0.2 0.2

Cellulosic Diesel – F-T - Farmed trees feedstock 198,429 327 13 0.0 6.5 6.5

Sugarcane Ethanol - CBI - Marginal Elec 2,592 84,241 2,606 -7,287 0.6 2.2 1.6
426

The upstream feedstock production and processing impacts for each of the different fuel

technologies were modeled based on the same assumptions used in determining the per fuel

lifecycle GHG results described in previous sections.

For this overall impacts analysis we also used a different petroleum baseline fuel that is

offset from renewable fuel use. The lifecycle threshold values are required by EISA to be based

on a 2005 petroleum fuel baseline. For this analysis of the overall impacts of the rule we

considered the crude oil and finished product that would be replaced in 2022.

For this analysis we consider that 25% of displaced gasoline will be imported gasoline

and 0% of displaced diesel fuel will be imported diesel fuel. For the types of gasoline displaced

we assume 65% of the displaced gasoline will be conventional gasoline and 35% will be RFG

blendstock gasoline. We assume 100% of the displaced diesel fuel will be low sulfur diesel fuel.

In order to come up with GHG emissions for average crude oil used in producing

gasoline and diesel fuel in 2022 we assumed 7.6% would be from tar sands and 3.8% would be

from Venezuelan heavy crude. The basis for this was EIA projections for 2022652. EIA projects

that roughly 64% of total Canadian crude oil production will be oil sand production in 2022, and

that roughly 40% of total Venezuelan crude oil production will be heavy crude production in

2022. EIA also has assumptions on how much crude oil will be imported into the U.S. from

Canada and Venezuela in 2022. We assumed the percentage of this imported Canadian and

Venezuelan crude oil that would be oil sands and heavy oil was the same percentage of total

production that is unconventional crude in those countries (~64% for Canada and ~41% for

Venezuela). Based on the percent of Canadian and Venezuelan imports to total crude oil

projected in 2022, oil sands represented 7.6% and heavy oil represented 3.8% of total crude oil

use.

For this analysis we did not assume any efficiency improvements at the petroleum

refining portion of the gasoline and diesel fuel lifecycle. Therefore the same refining energy use

and emissions was assumed that was used to represent the 2005 petroleum fuel baseline. One the

one hand this may be overestimating energy use and emission from petroleum refining, however,

this also does not factor in recent regulations that might increase energy use and emissions, such

as increased desulfurization of both gasoline and diesel fuel.

Furthermore, the tailpipe emissions changes were determined based on the specific

volumes and blends of fuel considered as opposed to looking at only the difference between the

renewable fuel and petroleum fuel replaced. For highway vehicles, the impact of this rule on

Methane (CH4) and Nitrous Oxide (N2O) emissions is primarily due to vehicles switching from

gasoline to E85 fuel. Based on available data, we projected no change in N2O or CH4 emissions

from highway vehicles that switched from conventional gasoline to E10. For diesel highway

vehicles, emissions of N2O and CH4 are almost one hundred times less than emissions from

gasoline vehicles,653 thus diesels were omitted from this analysis.

To estimate the inventory-wide impact, we used MOVES to model CH4 and N2O for

highway gasoline vehicles using reference case fuels. Because MOVES does not vary CH4 and

N2O emissions by temperature or by gasoline fuel properties, the model was run at the annual,
427

national level. FFV use was assumed to be limited to light duty cars and light duty trucks. We

multiplied the appropriate E85 factor by the emissions for that model year and then computed a

weighted average of E85 and E10 emissions for both CH4 and N2O. In order to compare the

results in a meaningful way, we also computed the CO2 equivalent by multiplying the tons for

each pollutant by the Global Warming Potential (310 for N2O, 21 for CH4

654) and summing the

products. The results are summarized in Table 2.7-2 below.

Table 2.7-2. Tailpipe Nitrous Oxide and Methane Emissions in 2022

Pollutant Reference

Case Tons

Control

Case Tons

Percent

Change

N2O 31,447 29,191 -7%

CH4 50,683 61,853 22%

LDGV &

LDGT

CO2 equiv. 10,812,803 10,348,003 -4%

N2O 33,997 31,741 -7%

CH4 55,277 66,447 20%

All Gasoline

Highway

Vehicles CO2 equiv. 11,699,809 11,235,009 -4%

Given these many differences, it is clearly not possible to simply add up the individual

lifecycle results described in Section 2.6 multiplied by their respective volumes to assess the

overall rule impacts. The two analyses are separate in that the overall rule impacts capture

interactions between the different fuels but can not be broken out into per fuels impacts, while

the threshold values represent impacts of specific fuels but do not account for all the interactions.

For example, when we consider the combined impact of the different fuel volumes the

overall land use change is less than when considering each fuel independently, as shown in Table

2.7-3.

Table 2.7-3. Comparison of International Land Use Change (‘000 Hectares)

Considering Only

Change in Soybean

Based Biodiesel

Fuel Volumes

Considering Only

Change in Corn

Ethanol Fuel

Volumes

Considering Only

Change in Brazilian

Sugarcane Ethanol

Fuel Volumes

Considering

Change of all

Fuel Volumes

Combined

Land

Use

Change

678.4 789.3 395.4 794.4

Overall rule impacts were determined for the different components of the lifecycle

analysis as described in previous sections. The domestic agricultural sector impacts include

changes in energy use GHG emissions and fertilizer / soil N2O emissions as well as changes in

livestock and rice production GHG emissions.

Our analysis indicates that overall domestic agriculture emissions would increase. There

is a relatively small increase in total domestic crop acres however, there are additional inputs

required to grow the biofuel feedstock crops. These additional inputs result in GHG emissions

from production and from N2O releases from application. This effect is somewhat offset by

reductions due to lower livestock production and reductions in rice methane.
426

As with domestic agriculture impacts, the international agricultural sector impacts

include changes in energy use GHG emissions and fertilizer / soil N2O emissions as well as

changes in livestock and rice production GHG emissions. Increased crop production

internationally resulted in increased fertilizer and fuel use emissions.

We estimate the largest overall agricultural sector impact is an increase in land use

change impacts, reflecting the shift of crop production both domestically and internationally to

meet the biofuel demand in the U.S., and land use change emissions associated with converting

land into crop production.

Other portions of the biofuel lifecycle include fuel production and feedstock and fuel

transport. We project reductions in GHG emissions from the renewable fuel production portion

of the lifecycle due to the generation of electricity along with the increased production of

cellulosic ethanol and diesel fuel.

CO2 produced in the combustion of biofuels is offset by the uptake of CO2 in the biomass

crop used to produce the fuel, resulting in a significant net reduction of CO2 compared to fossil

fuel tailpipe combustion. Net carbon fluxes from changes in biogenic carbon reservoirs in

wooded or crop lands associated with land use change are accounted for in the domestic and

international agriculture impacts shown in upstream impacts. In addition we assume biofuel use

is offsetting petroleum fuel production which also results in GHG emissions reductions from

reduced upstream emissions of petroleum fuel production (crude oil extraction and transport,

refining, transport).

The results of the individual lifecycle stage results can be summed to determine the

overall GHG impact of the proposed rulemaking. As discussed in previous sections on lifecycle

GHG thresholds there is an initial one time release from land conversion and smaller ongoing

releases but there are also ongoing benefits of using renewable fuels over time replacing

petroleum fuel use. Figure 2.7-1 shows the GHG emissions impact of the change in fuel

volumes considered over time.
427

Figure 2.7-1. GHG Impacts over time

-1,400,000,000

-1,200,000,000

-1,000,000,000

-800,000,000

-600,000,000

-400,000,000

-200,000,000

0

200,000,000

400,000,000

0 1 2 3 4 5 6 7 8 9 10

Time Since Initial Land Use Change (years)

Net Total GHG Impacts (MT CO2-eq.)

Initial Land Use Change Impacts

Ongoing Land Use Change Impacts

Ongoing Benefits (w/o Land Use Change)

Net Base Cumulative Total Emissions Over Time

Net High Cumulative Total Emissions Over Time

Net Low Cumulative Total Emissions Over Time

Figure 2.7-1 shows the baseline estimates for land use change as well as the range of

results based on the uncertainty in the international land use change modeling. The net GHG

emissions over time are also shown as a range of results based on the uncertainty in the land use

change emissions.

Based on the volume scenario considered, the one time land use change impact results in

a baseline estimate of 312.8 million metric tons of CO2-eq. emissions with a range of 296.9 to

331.9 million metric tons CO2-eq. There are however, based on the biofuel use replacing

petroleum fuels, annual reduction benefits of 150 million metric tons of CO2-eq. emissions. This

results in a less than two year payback period before the ongoing benefits of the biofuels use

offsets the initial land use impacts.

The timing of the impact of land use change and ongoing renewable fuels benefits were

discussed in the previous lifecycle results section. The issue is slightly different for this analysis

since we are considering absolute tons of emissions and not determining a threshold comparison

to petroleum fuels. However the results can be presented in a similar manner to our individual

fuels analysis in that we can determine net benefits over a 30 year time period with no

discounting. Assuming a 0% discount rate over 30 years would result in an estimate of 4.15

billion tons of discounted GHG emission reductions.

Furthermore, for the calculations of the monetized GHG benefits we calculate an

annualized NPV GHG reduction. This annualized value is based on converting a lump sum
426

present value into its annualized equivalent. For this analysis we convert the NPV results into an

annualized stream such that the NPV of the annualized emissions will equal the NPV of the

emission stream over 30 years with a 0% discount rate. This results in an annualized emission

reduction of 138.4 million metric tons of CO2-eq. emissions (ranging from 136.1 to 140.3 based

on uncertainty in the land use change results).

However, there may be additional indirect impacts associated with the production and use

of petroleum-based fuels in the real world that are not completely captured by this analysis. For

example, it is possible that renewable fuels may actually displace fuels at the margin which have

higher GHG lifecycle emissions than the average (e.g., tar sands instead of conventional crude).

To examine the question of what type of marginal crude would be displaced by biofuels

use, we performed an analysis using the Department of Energy’s Energy Technology

Perspectives (ETP) model, which is a partial equilibrium model used to analyze the international

energy system. For our analysis, we created a scenario that increased domestic gasoline demand,

as we wanted to isolate the impacts of petroleum use. The scenario roughly represented the

additional amount of gasoline that would be required if the RFS2 renewable fuel mandates were

not in effect. Our results showed that the increased gasoline demand was primarily met through

production of conventional crude oil, along with a small amount of oil sands/bitumen production.

The primary exporters of conventional crude oil to meet the additional demand were Middle

Eastern countries. Using well-to-tank GHG values for crude extracted from various countries655,

we were able to determine an approximate “marginal petroleum baseline” by applying the factors

to the countries where crude production increased. We found that the marginal baseline was, for

an average gallon of gasoline, not statistically different than the average baseline value used in

this final rulemaking. More details on this analysis can be found in the memo, “Petroleum

Indirect Impacts Analysis” at EPA-HQ-OAR-2005-0161.

There may be other indirect impacts as well. For instance, we considered whether the

displacement of petroleum fuels could also displace petroleum co-products, thus increasing the

GHG reductions associated with biofuels use. When crude oil is refined to produce gasoline and

diesel, petroleum co-products are also produced. Petroleum co-products include residual fuel oil

and petroleum coke, which are utilized as fuels in the energy system. An increase in the demand

for renewable fuels could also impact the energy system’s utilization of petroleum co-products

due to the ripple effects of price impacts.

While it is difficult to predict how the energy system would be affected in such an event,

we expect that an increase in domestic renewable fuels demand will lead to a decrease in

domestic crude oil consumption due to lower demand for gasoline and diesel. However, a

decrease in demand for gasoline and diesel is unlikely to significantly impact demand for

petroleum co-products unless the price for these co-products is significantly affected. Refiners

respond to demand for fuels, and they may choose to produce a larger percentage of petroleum

co-products per barrel of crude then they had in the past in response to lower gasoline or diesel

demand. This increased supply and possible lower refinery costs could translate into a slight

decrease in co-product cost and therefore marginally impact demand. We have not modeled this

demand increase or what its impact might be on total GHG emissions, but we expect that it
427

would have a negligible GHG effect for the rule overall. Thus, we are assuming no change in

petroleum co-products supply and no shift in the energy system as a result.

Increased renewable fuel use domestically is expected to also have the effect of lowering

the world crude oil price and therefore increase international demand for petroleum-based fuels

and increase GHG emissions. As stated above, we expect that an increase in domestic renewable

fuels demand will lead to a decrease in domestic crude oil demand. This decrease in U.S. oil

demand could cause a decline in the world oil price, which would spur increased oil consumption

abroad. This increase in demand outside of the U.S. due to price changes would partially negate

the decrease in GHG emissions domestically from reduced petroleum fuel demand due to

biofuels. This impact of biofuels use on crude oil imports and world crude oil price is included

in our Energy Security Analysis discussed in Chapter 5.

2.8 Effects of GHG Emission Reductions and Changes in Global

Temperature and Sea Level

The reductions in CO2 and other GHGs associated with this final rule will affect climate

change projections. GHGs mix well in the atmosphere and have long atmospheric lifetimes, so

changes in GHG emissions will affect future climate for decades to centuries. Two common

indicators of climate change are global mean surface temperature and global mean sea level rise.

This section estimates the response in global mean surface temperature and global mean sea level

rise projections to the estimated net global GHG emissions reductions associated with this final

rule (see Section 2.7 for the estimated net reductions in global emissions over time by GHG).

EPA estimated changes in projected global mean surface temperatures to 2050 using the

MiniCAM (Mini Climate Assessment Model) integrated assessment model180 coupled with the

MAGICC (Model for the Assessment of Greenhouse-Gas Induced Climate Change) simple

climate model.181 MiniCAM was used to create the globally and temporally consistent set of

180MiniCAM is a long-term, global integrated assessment model of energy, economy, agriculture and land use, that

considers the sources of emissions of a suite of greenhouse gases (GHGs), emitted in 14 globally disaggregated

global regions (i.e., U.S., Western Europe, China), the fate of emissions to the atmosphere, and the consequences of

changing concentrations of greenhouse related gases for climate change. MiniCAM begins with a representation of

demographic and economic developments in each region and combines these with assumptions about technology

development to describe an internally consistent representation of energy, agriculture, land-use, and economic

developments that in turn shape global emissions. Brenkert A, S. Smith, S. Kim, and H. Pitcher, 2003: Model

Documentation for the MiniCAM. PNNL-14337, Pacific Northwest National Laboratory, Richland, Washington.

For a recent report and detailed description and discussion of MiniCAM, see Clarke, L., J. Edmonds, H. Jacoby, H.

Pitcher, J. Reilly, R. Richels, 2007. Scenarios of Greenhouse Gas Emissions and Atmospheric Concentrations. Subreport 2.1A of Synthesis and Assessment Product 2.1 by the U.S. Climate Change Science Program and the

Subcommittee on Global Change Research. Department of Energy, Office of Biological & Environmental Research,

Washington, DC., USA, 154 pp.

181 MAGICC consists of a suite of coupled gas-cycle, climate and ice-melt models integrated into a single

framework. The framework allows the user to determine changes in GHG concentrations, global-mean surface air

temperature and sea-level resulting from anthropogenic emissions of carbon dioxide (CO2), methane

(CH4), nitrous oxide (N2O), reactive gases (e.g., CO, NOx, VOCs), the halocarbons (e.g. HCFCs, HFCs,

PFCs) and sulfur dioxide (SO2). MAGICC emulates the global-mean temperature responses of more sophisticated
512

climate relevant variables required for running MAGICC. MAGICC was then used to estimate

the change in the global mean surface temperature over time. Given the magnitude of the

estimated emissions reductions associated with the proposed rule, a simple climate model such as

MAGICC is reasonable for estimating the climate response.

EPA applied the estimated annual GHG emissions changes for the final rule to a

MiniCAM baseline emissions scenario.182 Specifically, the CO2, N2O, and CH4 annual emission

changes from 2022-2052 from Section 2.7 were applied as net reductions to this baseline

scenario for each GHG.

The tables below provide our estimated reductions in projected global mean surface

temperatures and mean sea level rise associated with the increase in renewable fuels in 2022

required by this final rule. We modeled three scenarios using different values for the estimated

net global GHG reduction associated with this rule; we utilized the average, low, and high values

for GHG emissions reduced, as presented in Section 2.7. To capture some of the uncertainty in

the climate system, we estimated the changes in projected temperatures and sea level across the

most current Intergovernmental Panel on Climate Change (IPCC) range of climate sensitivities,

1.5°C to 6.0°C.183

coupled Atmosphere/Ocean General Circulation Models (AOGCMs) with high accuracy. Wigley, T.M.L. and

Raper, S.C.B. 1992. Implications for Climate and Sea-Level of Revised IPCC Emissions Scenarios Nature 357, 293-

300. Raper, S.C.B., Wigley T.M.L. and Warrick R.A. 1996. in Sea-Level Rise and Coastal Subsidence: Causes,

Consequences and Strategies J.D. Milliman, B.U. Haq, Eds., Kluwer Academic Publishers, Dordrecht, The

Netherlands, pp. 11-45. Wigley, T.M.L. and Raper,

S.C.B. 2002. Reasons for larger warming projections in the IPCC Third Assessment Report J. Climate 15, 2945-

2952.

182 The reference scenario is the MiniCAM reference (no climate policy) scenario used as the basis for the

Representative Concentration Pathway RCP4.5 using historical emissions until 2005. This scenario is used because

it contains a comprehensive suite of greenhouse and pollutant gas emissions including carbonaceous aerosols. The

four RCP scenarios will be used as common inputs into a variety of Earth System Models for inter-model

comparisons leading to the IPCC AR5 (Moss et al. 2008). The MiniCAM RCP4.5 is based on the scenarios

presented in Clarke et al. (2007) with non-CO2 and pollutant gas emissions implemented as described in Smith and

Wigley (2006). Base-year information has been updated to the latest available data for the RCP process.

183 In IPCC reports, equilibrium climate sensitivity refers to the equilibrium change in the annual mean global

surface temperature following a doubling of the atmospheric equivalent carbon dioxide concentration. The IPCC

states that climate sensitivity is “likely” to be in the range of 2°C to 4.5°C and described 3°C as a "best estimate."

The IPCC goes on to note that climate sensitivity is “very unlikely” to be less than 1.5°C and “values substantially

higher than 4.5°C cannot be excluded.” IPCC WGI, 2007, Climate Change 2007 - The Physical Science Basis,

Contribution of Working Group I to the Fourth Assessment Report of the IPCC, http://www.ipcc.ch/.
513

Table 2.8-1. Estimated Reductions in Projected Global Mean Surface Temperature and

Global Mean Sea Level Rise from Baseline for the Average Case for the Final Rule in 2020-

2050

Climate Sensitivity

1.5 2 2.5 3 4.5 6

Year Change in global mean surface temperatures (degrees Celsius)

2020 0.000 0.000 0.000 0.000 0.000 0.000

2025 0.000 0.000 0.000 0.000 0.000 0.000

2030 0.000 0.000 0.000 0.000 0.000 0.000

2035 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

2040 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

2045 -0.001 -0.001 -0.001 -0.001 -0.002 -0.002

2050 -0.001 -0.001 -0.002 -0.002 -0.002 -0.002

Year Change in global mean sea level rise (centimeters)

2020 0.000 0.000 0.000 0.000 0.000 0.000

2025 0.000 0.000 0.000 0.000 0.000 0.000

2030 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

2035 -0.002 -0.002 -0.002 -0.003 -0.003 -0.003

2040 -0.003 -0.004 -0.004 -0.005 -0.005 -0.006

2045 -0.005 -0.006 -0.006 -0.007 -0.008 -0.009

2050 -0.006 -0.008 -0.009 -0.009 -0.011 -0.012
514

Table 2.8-2. Estimated Reductions in Projected Global Mean Surface Temperature and

Global Mean Sea Level Rise from Baseline for the Low Case for the Final Rule in 2020-

2050

Climate Sensitivity

1.5 2 2.5 3 4.5 6

Year Change in global mean surface temperatures (degrees Celsius)

2020 0.000 0.000 0.000 0.000 0.000 0.000

2025 0.000 0.000 0.000 0.000 0.000 0.000

2030 0.000 0.000 0.000 0.000 0.000 0.000

2035 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

2040 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

2045 -0.001 -0.001 -0.001 -0.001 -0.002 -0.002

2050 -0.001 -0.001 -0.002 -0.002 -0.002 -0.002

Year Change in global mean sea level rise (centimeters)

2020 0.000 0.000 0.000 0.000 0.000 0.000

2025 0.000 0.000 0.000 0.000 0.000 0.000

2030 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

2035 -0.002 -0.002 -0.002 -0.003 -0.003 -0.003

2040 -0.003 -0.004 -0.004 -0.005 -0.005 -0.006

2045 -0.005 -0.006 -0.006 -0.007 -0.008 -0.009

2050 -0.006 -0.008 -0.009 -0.009 -0.011 -0.012
515

Table 2.8-3. Estimated Reductions in Projected Global Mean Surface Temperature and

Global Mean Sea Level Rise from Baseline for the High Case for the Final Rule in 2020-

2050

Climate Sensitivity

1.5 2 2.5 3 4.5 6

Year Change in global mean surface temperatures (degrees Celsius)

2020 0.000 0.000 0.000 0.000 0.000 0.000

2025 0.000 0.000 0.000 0.000 0.000 0.000

2030 0.000 0.000 0.000 0.000 0.000 0.000

2035 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

2040 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

2045 -0.001 -0.001 -0.001 -0.001 -0.002 -0.002

2050 -0.001 -0.001 -0.002 -0.002 -0.002 -0.002

Year Change in global mean sea level rise (centimeters)

2020 0.000 0.000 0.000 0.000 0.000 0.000

2025 0.000 0.000 0.000 0.000 0.000 0.000

2030 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

2035 -0.002 -0.002 -0.002 -0.003 -0.003 -0.003

2040 -0.003 -0.004 -0.004 -0.004 -0.005 -0.006

2045 -0.005 -0.006 -0.006 -0.007 -0.008 -0.008

2050 -0.006 -0.007 -0.008 -0.009 -0.011 -0.012

The results in table above show small reductions in the global mean surface temperature

and sea level rise projections across all climate sensitivities. Overall, the reductions are small

relative to the IPCC’s “best estimate” temperature increases by 2100 of 1.8oC to 4.0oC.656

Although IPCC does not issue “best estimate” sea level rise projections, the model-based range

across SRES scenarios is 18 to 59 cm by 2099.184 While the distribution of potential

temperatures in any particular year is shifting down, the shift is not uniform. The magnitude of

the decrease is larger for higher climate sensitivities. The same pattern appears in the reductions

in the sea level rise projections. For instance, in 2050, the reduction in projected temperature

(for all cases) for climate sensitivities of 3 and 6 is approximately 50% and 99% greater than the

reduction for a climate sensitivity of 1.5. The same pattern appears in the reductions for the sea

level rise projections.185

Thus, we can conclude that the impact of this final rule is to lower the risk of climate

change, as the probabilities of temperature increase and sea level rise are reduced.

184 “Because understanding of some important effects driving sea level rise is too limited, this report does not assess

the likelihood, nor provide a best estimate or an upper bound for sea level rise.” IPCC Synthesis Report, p. 45

185 In 2050, the reduction in projected sea level rise (for all cases) for climate sensitivities of 3 and 6 is

approximately 45% and 86% greater than the reduction for a climate sensitivity of 1.5.
516

Chapter 3: Impacts of the Program on Non-GHG Pollutants

In addition to the GHG impacts laid out in Chapter 2, we project that the increased use of

renewable fuels required by RFS2 will affect emissions of “criteria” pollutants (those pollutants

for which a National Ambient Air Quality Standard has been established), criteria pollutant

precursors, and air toxics. Changes in these emissions would derive from the direct effect of

renewable fuels on the tailpipe and evaporative emissions of vehicles and off-road equipment;

and increased renewable fuel production and distribution including the effect of decreases in the

production and distribution of gasoline and diesel displaced by renewable fuel. For this analysis

we have focused on estimating the change in mass emissions for these pollutants across the

entire U.S. in 2022, when the program is fully implemented, and we have also conducted a fullscale air quality modeling and health impact assessment that accounts for geographic differences

in impacts at the county level. This chapter presents national emission impacts for nitrogen

oxides (NOx), volatile organic compounds (VOC), carbon monoxide (CO), particulate matter 10

microns in diameter and less (PM10), particulate matter 2.5 microns in diameter and less

(PM2.5), sulfur dioxide (SO2), ammonia (NH3), benzene, 1,3-butadiene, acrolein, formaldehyde,

acetaldehyde, naphthalene, and ethanol, including the methodology for developing these

estimates. Section 3.3 discusses the differences between the final rule emission inventories

presented in Sections 3.1 and 3.2 compared to the inventories that were used for air quality

modeling. Section 3.4 of this chapter presents the methodology and results of air quality

modeling, and Sections 3.5 and 3.6 address health and environmental impacts of today's rule.

3.1 Methodology for Calculating Non-GHG Emission Impacts

Our analysis focused on the projected impact of the renewable fuel volumes required in

2022, the first year the RFS2 program is fully implemented. The emission impacts of the 2022

RFS2 volumes are quantified in Section 3.2 for a range of renewable fuel scenarios relative to

two reference cases discussed in detail above in Section 1.2.1. In order to allow assessment of

total emission impacts of mandated renewable fuel volumes, the main reference case presented in

this analysis was the RFS1 mandate volume of 7.5 billion gallons of renewable fuel (6.7 billion

gallons ethanol). We are also presenting impacts relative to the 13.6 billion gallons of

renewable fuels projected by the Department of Energy (DOE) Annual Energy Outlook (AEO)

2007 to show the impact of the RFS2 renewable fuel volumes incremental to the projected

renewable market pre-EISA.

Our analysis of non-GHG emissions impacts was comprised of a) an analysis of direct

impacts on motor vehicles, off-road equipment and other sources from burning (or evaporating)

renewable fuels in place of petroleum-based fuels; and b) the emissions impacts from the

production and distribution of renewable fuels. These analyses are discussed separately in

Sections 3.1.1 and 3.1.2.

3.1.1 Impact on Non-GHG Emissions from Motor Vehicles and Equipment
517

The volumes of renewable fuel called for in today's rule will directly affect emissions

from most mobile source categories, and for this analysis we have quantified the effects on

exhaust and evaporative emissions of gasoline-fueled vehicles and equipment including

passenger cars, light trucks, heavy trucks, motorcycles and off-road sources such as lawn

mowers, recreational boats and all-terrain vehicles. We have also estimated the impact of

ethanol on emissions from portable fuel containers, and increased refueling emissions due to

higher volatility of ethanol-blended fuel and increased refueling events due to lower energy

content of biofuels. The emissions impacts of biodiesel were also estimated on heavy-duty

diesel vehicles, assuming additional biodiesel would be burned by on-road sources only.

A considerable source of uncertainty in estimating the emission impacts of renewable

fuels is the effect ethanol blends will have on emissions of cars and light trucks. Under today’s

action every gasoline vehicle and piece of equipment would be fueled on at least E10. For the

proposal, the uncertainty in the emission impacts of E10 was reflected by showing emission

impacts under two cases representing different levels of sensitivity in the emissions of cars and

light trucks to ethanol. In the final rule, we are reflecting preliminary results from work

sponsored by EPA and DOE which suggests that emissions from Tier 2 vehicles show little

sensitivity to E10.657 In addition to E10, many flexible-fueled passenger vehicles may need to be

operated on E85 to consume the increased volumes of renewable fuels. The amount of E85

needed will depend on the volume of ethanol as opposed to other renewable fuels utilized in the

future. Data on E85 continues to be limited, and emission results have shown large variability of

emission effects in some pollutants. As a result, for the final rule we have decided to assign no

emission effect to the use of E85, except for the emissions of acetaldehyde and ethanol.

For the analysis of all gasoline-fueled highway vehicles except motorcycles, a

preliminary version of MOVES2010 was used to generate national inventories for the control

and reference cases modeled for the RFS2 final rule. This version reflected updates to fuel

effects made to the model since the analysis for the proposal and air quality modeling versions,

based on data made available since these analyses were performed; these fuel effect updates were

eventually finalized in the recently released version of MOVES2010.658,

659 We decided to use a

draft version of MOVES for this analysis to begin to reflect significant updates in emissions, and

in particular fuel effects, from MOBILE6. As the other mobile source categories in MOVES

were still under development at the time of this analysis, all onroad diesel, motorcycle and offroad equipment emissions were calculated with the National Mobile Inventory Model (NMIM),

a platform which generates emission inventories based on EPA’s MOBILE6 and NONROAD

models.

The development of vehicle and equipment emission impact estimates for today’s rule

required: a) developing fuel supply inputs at the county level for the 2005 base year and 2022

reference and control cases which accounted for the projected change in fuel properties due to

today’s action; b) developing individual vehicle fuel effects; and c) running MOVES and NMIM

to produce raw inventory estimates and post-processing these results as needed to account for

different baselines, to apply “off model” corrections, or to estimate impacts not accounted for in

the models. Each of these steps are detailed in the following sections

3.1.1.1 Fuel Inputs
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As inputs to our emissions modeling, we developed a detailed profile of fuels for each

modeling case. We prepared county-level databases of fuel properties and fuel market shares for

the 2005 base case, the RFS1 reference case, the 2022 AEO reference case, and the 2022 control

case. These county-level databases were applied in both NMIM and MOVES for consistency in

fuel inputs across the different mobile source categories.

The 2005 base case fuel properties were derived from 2005 historical data. These data

included national summer and winter fuels surveys, studies that tracked the total amount of

ethanol produced for use in gasoline each year, and Reformulated Gasoline (RFG) surveys.

Additional data were available on the fuel properties of all gasoline produced and imported

annually by refiners, and on the distribution of gasoline to and from Petroleum Administration

for Defense Districts (PADDs). Where survey data was available, it was used to determine a

county's fuel properties for summer and winter. Where survey data was not available, fuel

properties were set to equal the average fuel properties in that PADD. Special adjustments were

made to some counties to account for local gasoline volatility control programs and winter

oxygenated gasoline programs.

For the 2022 reference and control cases, the 2005 base case fuel properties were

adjusted to account for implementation of other fuel regulations and to account for increased

ethanol use. There is a greater percentage of ethanol in both the 2022 RFS1 and AEO 2007

reference cases than in the 2005 base case because methyl tert-butyl ether (MTBE) has been

replaced with ethanol and because of increased ethanol usage mandated by RFS1 (the RFS1

reference case), and AEO-projected growth in ethanol production for 2022 (the AEO 2007

reference case). For this analysis, ethanol was allocated to the state and county level based on

the economics of distribution and blending, as well as other factors (refer to Section 1.7.1 of this

document for details). The 2022 control cases model three different approaches to meeting the

renewable fuel volume requirements of EISA. Even in the low ethanol control case (17.5 billion

gallons of ethanol), there would be enough ethanol in the fuel supply to require use of at least 10

percent ethanol (E10) in every county, while the choice of counties modeled with E85 was based

on the economics and other factors.

Future fuel properties in both the reference and control cases were adjusted to account for

widespread increases in ethanol. This was done using two assumptions: 1) ethanol has

historically been splash blended in conventional gasoline (CG), and 2) it will be match-blended

by 2022 (i.e., the changes associated with ethanol addition will be accounted for by refiners

when producing the base gasoline). We believe this is reasonable given that there will be a large

(and thus more geographically predictable) volume of ethanol used in gasoline, and that certain

property changes that take place when ethanol is blended (such as octane increase) could be

economically beneficial to refiners if they can be assumed when producing the base gasoline.

Thus, we adjusted aromatics, olefins, T50, and T90 fuel parameters by first backing out the

effects of any existing oxygenate (by reverse dilution), and then re-adjusting the properties for

ethanol blends based on refinery modeling done for the RFS1 rulemaking that projected how

gasoline properties were likely to change given widespread use of ethanol. Table 3.1-1 shows

the adjustment factors used per volume percent ethanol blended. Reid Vapor Pressure (RVP)

was increased 1.0 psi wherever ethanol was present in conventional gasoline unless there was a

local volatility control.
519

Table 3.1-1. 2022 Adjustments for Ethanol Added to Conventional Gasoline

Additive change per vol%

ethanol added

Aromatics

(vol%)

Olefins

(vol%)

E200

(vol%)

E300

(vol%)

Summer -0.69 0.00 1.10 7.52

Winter -0.68 0.00 0.78 7.21

For example, the adjusted summer aromatics value would be calculated as follows:

Current aromatics value – dilution effect of current ethanol level due to splash blending

+ (new volume percent ethanol x -0.69 for match blend effect)

For Reformulated Gasoline (RFG) areas, refiners already account for the blending of

ethanol when producing the base gasoline, and therefore the properties are not predicted to

change in the same ways as for conventional gasoline (CG). We used refinery modeling results

for each PADD (produced using the same cases and renewable fuel volumes as described above

for CG) to project the properties of fuel in RFG areas. RFG properties used in the reference and

control cases in 2022 are shown here in Table 3.1-2. The 2022 reference and control cases also

incorporate reductions in gasoline sulfur resulting from Tier 2 regulations. Fuel benzene levels

presented in this table have been updated from what was used in the NPRM to reflect the 2007

mobile source air toxics (MSAT) rule, which mandates a 0.62% fuel benzene standard.660

Table 3.1-2. 2022 Reformulated Gasoline Properties by PADD

PADD a RVP

(psi)

Aromatics

(vol%)

Benzene

(vol%)

Olefins (vol%) E200 E300

Summer

1 7.0 19.9 0.54 8.1 52 95

2 7.0 18.8 0.60 6.8 52 95

3 7.0 18.4 0.55 5.6 51 95

5 6.8 21.5 0.62 5.7 54 86

Winter

1 13.2 19.9 0.54 14.1 58 95

2 13.1 20.0 0.60 11.9 62 95

3 11.8 19.8 0.55 13.0 55 95

5 11.4 21.9 0.62 5.7 60 86

a There are no RFG areas in PADD 4.

Unlike the proposal, for the final rule we did not model the effects of flexible-fueled

vehicles running on E85 for any pollutants except acetaldehyde and ethanol.

For each of the modeled scenarios, fuel information was input into an NMIM database

and used for NMIM runs. For MOVES runs, the NMIM databases were converted into MOVES

databases using a conversion program. To reduce time needed for MOVES runs, we reduced the

size of the MOVES fuel database by processing the database with a "binner" program that
520

grouped fuels with similar properties and assigned each group to a single fuel formulation

identification number and a single set of fuel properties. A significant update to MOVES

between the proposal and final rulemaking was the inclusion of direct calculation of fuel

adjustments that allowed less aggregation in this binning approach, thus improving the resolution

of fuel-based emission impacts.

3.1.1.2 Effect of Fuels on Non-GHG Emissions from Vehicles & Equipment

The average effect of renewable fuels on an individual vehicle/equipment basis, based on

available research, is the foundation of the emission impact assessment. This section contains

discussion of the effects used in the emission impact assessment for E10 on gasoline vehicles

and equipment, for E85 on gasoline vehicles, and for biodiesel.

3.1.1.2.1 On-road Gasoline Vehicle E10 Effects

3.1.1.2.1.1 Exhaust Emissions

Ethanol blends can affect exhaust emissions from vehicles and off-road equipment. A

comprehensive analysis of E10 impacts on exhaust emissions was undertaken for the RFS1 rule,

as documented in Chapter 3 of the RFS1 Regulatory Impact Analysis.661 This analysis

considered previous EPA work in coming up with a so-called “Predictive Model” to assess

California’s request for an oxygenate waiver in 2000, as well as test data from several test

programs conducted by the auto trade associations (AAM/AIAM), ExxonMobil, Toyota, and the

Mexican Petroleum Institute. This assessment concluded that for Tier 1 and later vehicles

(nominally model year 1996 and later, comprising the majority of the fleet in 2022) there was not

enough consistency across these studies to confidently predict the impact of oxygenated fuel on

exhaust HC and NOx emissions. As a result the RFS1 analysis carried forward two sets of fuel

effects: a “primary” analysis assuming no effect of oxygen on non-methane hydrocarbon

(NMHC) and NOx emissions from Tier 1 and later vehicles, and a “sensitivity” analysis which

applied EPA’s Predictive Model effects to Tier 1 and later vehicles. For the RFS2 proposal we

characterized ethanol effect scenarios: “less sensitive” based on the “primary” case used in

RFS1, and “more sensitive” based on the RFS1 “sensitivity” case.

We are now nearing completion of a large scale testing effort aimed at quantifying the

effects on exhaust and evaporative emissions from Tier 2 vehicles of ethanol and several other

fuel properties impacted by the blending of ethanol into gasoline.662 Based on analysis of

preliminary data from this test program, we are carrying forward effects that more closely reflect

the "less sensitive" case, which does not apply any E10 effects to NOx or HC emissions for later

model year vehicles, or E85 effects for most pollutants. While the effects of E10 on individual

vehicles will vary depending on properties of the fuel (e.g., RVP, distillation, and aromatic

content), Table 3.1-3 demonstrates the effects used for conventional and reformulated gasoline

based on the fuel properties derived from Tables 3.1-1 and 3.1-2. For the “less sensitive” case

for the proposal, the effects shown for NOx, HC and toxics were applied to only Tier 0 vehicles

(mid 1990’s and older); in our analysis for this final rule, we extended these effects to Tier 1 and

NLEV cars and light trucks (through the 2003 model year) based on a recently published study
521

from CRC.663 However, our preliminary analysis of the EPA/DOE test program did not justify

attributing these effects to Tier 2 vehicles.

Table 3.1-3. Exhaust Effect of E10 Relative to E0 for Pre-Tier 2 Vehicles a

Pollutant Source CG RFG

Exhaust HC (VOC) -7.4% -9.7%

NOx

EPA Predictive

Models 7.7% 7.3%

COb MOBILE6.2 -11% / -19% -36%

Exhaust Benzene -24.9% -38.9%

Formaldehyde 6.7% 2.3%

Acetaldehyde 156.8% 173.7%

1,3-Butadiene

EPA Predictive

and Complex

Models

-13.2% 6.1%

a

Assumes summer (July) conditions

b

The first figure shown applies to normal emitters; the second applies to high emitters.

3.1.1.2.1.2 Evaporative Emissions

While E10 affects evaporative emissions from gasoline vehicles due to the increased

volatility of E10 blends, the increased permeation of fuel vapors through tanks and hoses, and

the increased vapor emissions due to the lower molecular weight of E10, for cars and light trucks

by far the largest of these effects is permeation. For the final rule, we estimated only the impact

of permeation using updated estimates in the draft MOVES model, which separates permeation

emissions from vapor venting emissions to allow better accounting for this effect.

For the proposal, permeation effects were developed from Coordinating Research

Council's (CRC) E-65 program664, which measured evaporative emissions from ten fuel systems

that were removed from the vehicles on E0 and E5.7 fuels; fuel systems were removed to ensure

that all evaporative emissions measured were from permeation of the fuel through the different

components of the fuel system. For that analysis, we estimated the effect by calculating the

percent increase in average emissions from all vehicles between E0 and E5.7 fuels over the 65 to

105 degree Fahrenheit diurnal test. That value was 46 percent. In order to estimate the effect at

E10 we simply multiplied this result by 1.75 (10/5.7), resulting in a 79 percent increase applied

to cars and light trucks from all model years. That approach heavily weighted the emission

contribution of older vehicles in the test program, and, in conjunction with lower emission rates

for vehicles certified to Enhanced Evaporative and later standards in MOVES, served to

underestimate the impact of E10 on permeation from newer vehicles.

The version of MOVES used for the FRM analysis significantly updates the permeation

estimate used in the NPRM, particularly for newer technology vehicles, based on data collected

by CRC in the followup E-65 program (E-65.3) and as part of their more recent E-77 series of

evaporative emissions programs This new data allowed us to make a distinction between the

relative impact of E10 on vehicles certified to the enhanced evaporative and later standards, vs.

older technology vehicles. The data showed a significant change in the relative impact of E10,

from a 65 percent increase for pre-enhanced vehicles, to a 213 percent increase for newer

technology vehicles.665 This analysis also confirmed the E-65.3 finding that there is no

significant difference between emission effects on E5.7 and E10.
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3.1.1.2.2 On-road Gasoline vehicle E85 effects

In the proposal, the “more sensitive” case included impacts of E85 on several pollutants,

based on analysis of limited data from EPA and Environment Canada. For the final rule we have

decided not to apply these effects to the potential increase in E85 use, with the exception of

acetaldehyde and ethanol. The rationale for this is the large range of uncertainty imposed by the

limited nature of the dataset. EPA expects more data to become available to help assess this

issue for future analyses, as CRC, EPA and DOE are all engaged in programs that will expand

this dataset. We are including the discussion of E85 data considered in this RIA to provide

documentation of available data; no new information is presented here relative to the proposal, as

no new data has been generated in the interim. The only difference between proposal and final is

that, of the effects determine in this analysis, only acetaldehyde and ethanol were included in the

final rule inventories.

For this analysis we identified three recent data sources that investigate the effects of E85

on current technology (i.e. Tier 2 and similar) vehicles186.

666, 667, 668 Two of these sources are test

programs conducted by Southwest Research Institute and Environment Canada, and the third is

EPA certification data. This section briefly describes each data source and highlights the key

findings, and explains how these data were used to generate E85 effects.

In 2006, Southwest Research Institute (SwRI) conducted a study for EPA on three model

year 2005 Tier 2 FFVs (bins 5 and 8) operating on several gasoline and ethanol blends. This

study was primarily focused on the impacts of fuel ethanol content and reduced ambient test

temperature (tests were conducted at 75oF and 20oF) on VOC and PM emissions. Multiple fuel

blends were evaluated in this program, although for this analysis we will focus only on E0 and

E85 emissions at 75o F. At this test temperature, Tier 2 certification fuel was used as the nonoxygenated test fuel (E0) as well as the base gasoline for the splash-blended E10 and E85 fuels.

Additionally, EPA certification “cold CO” wintertime gasoline was used for reduced ambient

temperature (20 oF) testing – used alone (E0) and as the base fuel for wintertime E10 and E70

blends. This base gasoline has a higher RVP than its summertime equivalent, which is necessary

to ensure proper fuel vaporization at lower ambient temperatures. Repeat tests were conducted

for the 20oF tests on the winter fuel blends, but no repeats were run for 75oF testing. In addition

to the regulated pollutants, SwRI measured CO2, CH4, benzene, 1,3-butadiene, naphthalene,

acetaldehyde, acrolein, and ethanol. This study saw reductions in PM 2.5, benzene, and 1,3-

butadiene of 55% - 70% with E85 relative to E0. HC emissions increased while NOx and CO

decreased. Emissions of methane, formaldehyde, and acetaldehyde were found to increase

significantly with E85 use. Table 3.1-6 summarizes the average percent change in emissions

with E85 vs. E0. This table also compares the findings of this dataset with the other two

programs described below.

Environment Canada released a report in 2005 in which an NLEV and an interim nonTier 2 vehicle were tested on Tier 2 certification fuel and a commercially available E85 blend.

Repeat tests were conducted in this study so that each vehicle was tested three times on each

186 EPA is aware of several test programs, either planned or underway, by CRC and others that may provide

additional test data for future fuel effects modeling and rulemaking support.
523

fuel. The pollutants measured include NMOG, NMHC, CO, NOx, CO2, CH4, N20, benzene,

1,3-butadiene, acetaldehyde, formaldehyde, acrolein, and ethanol, among others. The results,

summarized in Table 3.1-6 showed statistically significant reductions in CO and NOx (-48%

and -40%, respectively) when switching from E0 to E85. E85 caused non-methane organic gases

(NMOG) emissions to increase in one vehicle and decrease in the other. Toxics reductions were

of a similar order of magnitude as the vehicles tested in the SwRI study discussed above

EPA’s Certification and Fuel Economy Information System (CFEIS) database was

accessed to identify data from five model year 2006 Tier 2 vehicles (bins 5, 8, and 9) tested on

both E85 and Tier 2 certification gasoline. The E85 blend tested here was 85% denatured

ethanol splash blended with 15% Tier 2 certification gasoline. Each vehicle was only tested once

on each fuel. Weighted FTP results were reported for the regulated pollutants (except PM) as

well as CO2, acetaldehyde, and ethanol (formaldehyde, acetaldehyde, and ethanol were only

measured for tests where E85 was used; therefore these are expressed as fractions of NMOG

here). This data indicates that E85 causes a slight increase in NMOG emissions, a slight

decrease in NOx and CO2, and significant reductions in CO. The average percent change in

each pollutant for these vehicles when operated on E85 is shown in Table 3.1-5, below.

Table 3.1-5.

Effect of E85 on LEV and Later Per-mile Exhaust Emissions Relative to

Conventional Gasoline: Percent change separated by data source

EPA – CFEIS EPA - SwRI Env. Canada

NMOG 10% 87% 5%

CO -34% -15% -48%

NOx -3% -42% -40%

Benzene NA -61% -65%

1,3 Butadiene NA -66% -74%

Acetaldehyde 12% of NMOG 5600% 3121%

Formaldehyde 2% of NMOG 116% 98%

Acrolein

(E85 mg/mile

emissions) NA 0.023 0.010

Unburned Ethanol

(E85 mg/mile

emissions)

28.3 (55% of

NMOG)

25.4 (33% of

NMOG)

34.6 (48% of

NMOG)

PM 2.5 NA -68% NA

Viewed independently, each study provides only limited insight on the effects of E85 on

emissions relative to E0. Table 3.1-5 shows that while changes in some pollutants compare

reasonably well between studies, others can vary widely. This makes it difficult to determine

quantitative trends in emissions, since calculating an average percent change in emissions across

all three studies does nothing to address the variability of the test data. Without this assessment

of variability there is no way to estimate the statistical significance of the reported values. Only

the Environment Canada conducted the repeat tests necessary to assess the test-to-test variability
524

of a given vehicle, and none of the studies tested enough vehicles to confidently state that their

findings can be applied to the Tier 2 FFV fleet as a whole. This clearly illustrates the need for

additional testing in this area.

The fact that Environment Canada tested non-Tier 2 vehicles is noteworthy. The 2004

Chrysler Sebring was an interim non-Tier 2 bin 8 vehicle. Despite its name, however, the

standard is equivalent to the final Tier 2 bin 8 FTP standards in all areas but the full useful life

(120K miles vs. 100K miles). In fact this vehicle was cleaner than required by the standard, with

observed emissions on E0 at the level of a Tier 2 bin 7 vehicle. The second vehicle tested by

Environment Canada was a 2002 Dodge Caravan certified to the NLEV LEV LDT level. The

standards at this certification level are considerably more relaxed than Tier 2 levels for some

pollutants but not others. While these vehicles share the same NMOG certification standard

(0.100 g/mi), the CO standard is roughly 30% higher and the NOx standard nearly 4 times higher

than the Tier 2 bin 8 level. As a result of this difference in standards, the Caravan emitted about

20% more CO and 2.5 times more NOx than the Sebring. NMOG emissions were nearly the

same for both vehicles with non-oxygenated gasoline. On a relative scale, both vehicles

experienced similar percent changes in emissions between E0 and E85. The Sebring emitted

more ethanol with E85 than did the Caravan, resulting in a higher E85 NMOG emissions factor

for that vehicle.

The variability in the magnitude of these changes, however, is what weakens the

analysis. Had additional observations been made, these results may have become more

significant for more pollutants. For this final rule analysis we are only modeling emission effects

with use of E85 in flex-fueled vehicles relative to E0 for two of the pollutants: ethanol and

acetaldehyde, for which data suggests the effects are more certain. For the “more sensitive case”

presented in the NPRM, and used in the air quality modeling, we had estimated changes to

additional pollutants (including significant PM reductions) based on the limited data from the

studies discussed above. However, until such time as additional data is collected to enhance this

analysis we believe it is premature to use such assumptions.

The “more sensitive” case in the NPRM also included a 50 percent reduction in

evaporative emissions with use of E85 based on results from just one vehicle from CRC’s E-65

evaporative permeation program. Given the variability in not only vehicles, but also E85

volatility in-use, we do not believe it appropriate to rely on just one data point, and as a result

this reduction was also not applied in the final rule.

Data from the analyses discussed above, and an additional dataset from a 1995 test

program conducted by EPA’s Office of Research and Development, were used to develop inputs

for MOVES, in order to model E85 impacts on air toxics inventories. Since MOBILE6 does not

model air toxics for E85, ratios were developed to apply to E85 hydrocarbon or PM mass (Table

3.1-6). The exhaust ratios for all pollutants except naphthalene were obtained from data on

seven vehicles from the 1995 test program in EPA’s Office of Research and Development, along

with the previously discussed 2007 test program at Southwest Research Institute,669 and the

2005 test program at Environment Canada.670, 671 The data from the ORD test program is

unpublished, but is available in the docket for this rule. Naphthalene inputs for E85 were derived

from estimates from E10 values based on dilution of fuel with ethanol. The only data available
525

on evaporative emissions were results of hot soak tests from the Auto/Oil Air Quality

Improvement Research Program.672

Table 3.1-6. Toxic to THC/PM Ratios used for E85 Fuel in MOVES

Pollutant Exhaust/Ratio Type Evaporative/Ratio Type

Benzene 0.0036/THC 0.0054/THC

1,3-Butadiene 0.0005/THC N.A.

Acetaldehyde 0.0673/THC N.A.

Formaldehyde 0.0093/THC N.A.

Acrolein 0.0002/THC N.A.

Ethanol 0.3316/THC 0.6123/THC

Naphthalene 0.0126/PM 0.00006/THC

3.1.1.2.3 Spark-Ignited Off-Road Engines

Effects of E10 relative to E0 on exhaust as well as fuel tank and hose permeation

emissions from gasoline-fueled off-road engines are contained in EPA’s NONROAD model,

based on limited data. The effects on exhaust HC, NOx, and CO are shown in Table 3.1-7.

Effects on tank and hose permeation emissions vary by equipment type and were recently

updated to reflect new information on uncontrolled emissions and their control due to recently

finalized new standards.673 For most small spark-ignition engines and recreational marine

engines in 2022 E10 is estimated to double the tank and hose permeation emissions. There can

also be increases in diurnal and refueling emissions with E10 if the fuel volatility of the blend is

allowed to be greater than E0. These volatility effects are accounted for in the NMIM model that

has the county-specific fuel properties that were used to generate the emission inventory impacts

for this rule presented below in Section 3.2.

Table 3.1-7.

Exhaust Effect of Ethanol (E10) on Spark-Ignited Gasoline Emissions

4 stroke 2 stroke

HC exhaust -15.75% -2.1%

NOx +40.25% +65.1%

CO -21.7% -22.75%

EPA and the California Air Resources Board (ARB) are in the midst of additional testing

of off-road engines with gasoline and ethanol blends.674, 675 and DOE completed a report in early

2009 which included small SI emission evaluation on a variety of ethanol blend fuels.676

Although preliminary results support the type of effects listed here, there are also upcoming

allowances for manufacturers to start certifying small spark ignition engines on E10 fuel rather

than the current E0 gasoline sometime in the 2011-2012 timeframe.677 If those plans proceed as

expected, by 2022 most or all of the in-use small SI engines will have been certified on E10;

thus we would expect none of the exhaust effects that we currently assume. Much of the in-use
526

fleet of equipment will have turned over to new equipment certified on E10, and those that were

originally certified on E0 are likely to be recalibrated. As a result, emissions are expected to

result in roughly the same emissions on E10 as they currently achieve on E0. The NOx

inventory increase and HC and CO decreases associated with increasing E10 market share

estimated for the final rule will likely go away by 2022, since many of the E0 certified engines

will have been replaced by E10 certified engines by then. However, there will still likely be

effects on the mix of hydrocarbons emitted, including increased proportions of ethanol and

aldehydes in the exhaust HC.

3.1.1.2.4 Biodiesel Effects on Diesel Emissions

As discussed in Appendix A to this RIA, for the proposal we investigated the emission

impacts on NOx, PM, HC, and CO of 20 volume percent biodiesel fuels on emissions from

heavy-duty diesel vehicles.678 Average NOx emissions were found to increase 2.2 percent, while

PM, HC, and CO were found to decrease 15.6 percent, 13.8 percent, and 14.1 percent,

respectively, for all test cycles run on 20 volume percent soybean-based biodiesel fuel (Table

3.1-8). These results are generally consistent with the exhaust emission impacts for heavy-duty,

in-use diesel engines found in our previous work on this subject., 679 and we have retained these

effects for the final rule. The effects in Table 3.1-8 are for B20, while we assume biodiesel is

mostly used in concentrations of 5% or less. In applying the emission impacts to the emission

inventory we assumed that the effects were proportional to biodiesel concentration based on a

resent investigation into the issue, so the inventory impacts are proportional to the overall

biodiesel volume used. (Cite to Chien Sze et.al. SAE Paper). For our estimate of biodiesel

impacts on toxics we applied the HC emission change from Table 3.1-8 to toxic emissions.

Table 3.1-8. B20 Emission Impacts

Percent change in emissions

NOx

PM

HC

CO

+2.2%

-15.6%

-13.8%

-14.1%

3.1.1.3 Non-GHG Emission Impact Scenarios Analyzed

For today’s rule we are estimating emission impacts of three different renewable fuel

volume scenarios, as presented in Chapter 1.2, which are meant to bracket the range of likely

combinations of renewable fuel volumes, and these are each analyzed relative to two different

reference case ethanol volumes. To assess the impact of today’s rule relative to the current

mandated volumes, we analyzed impacts relative to the RFS1 mandate of 7.5 billion gallons of

renewable fuel use by 2012, which was estimated to include 6.7 billion gallons of ethanol. In

order to assess the impact of the increased use of renewable fuels needed to meet the RFS2

standards relative to a level of ethanol projected to already be in place by 2022, the AEO2007
527

projection of 13.2 billion gallons of ethanol (13.6 billion gallons of total renewable fuel) in 2022

was analyzed.

3.1.1.4 Non-GHG Emission Impact Calculation Methodology

3.1.1.4.1 On-Road Gasoline

Emissions from gasoline highway vehicles were generated with a preliminary version of

EPA’s final MOVES2010 model, which reflects significant updates in gasoline vehicle

emissions from MOBILE6. Exhaust emission rates for HC, CO and NOx were developed based

on an analysis of state inspection/maintenance and roadside remote sensing data from millions of

vehicles.680 Emissions of particulate matter are based on EPA's recent Kansas City gasoline PM

study.681,682 Evaporative emission rates have been updated based on extensive evaporative

testing conducted by EPA and the Coordinating Research Council (CRC) since the release of

MOBILE6, including investigations quantifying the effects of ethanol on permeation

emissions.683 For this assessment of toxics, MOVES applies toxic ratios from the MOBILE6.2

model to updated MOVES HC estimates within the model.684

As detailed in a memo to the docket, for the final rule, separate MOVES runs were

configured for 2022 for two reference cases (RFS1 mandate and AEO) and for a control case

that reflected 100 percent E10 (since we did not estimate E85 impacts for most pollutants, there

was no difference between 100 percent E10 and the three volume cases - low, mid, and high

ethanol - analyzed for the rule). Each of these runs required a unique “run specification” file and

bundle of input databases to allow modeling of differences in analysis year and fuel supplies.

Reference and control case runs in 2022 were run with estimates of fuel formulations and market

shares by county as we project in 2022.

MOVES allows different levels of pre-aggregation depending on the level of resolution

needed. For regional inventory applications, the finest level of aggregation the model can run is

by county for each hour of the day, which maximizes the influence of inputs such as county-level

fuel effects, hourly temperatures and activity patterns; however, since running the model at this

level for the entire nation over multiple years and scenarios would be time prohibitive, the model

was run at a higher level of aggregation to reduce run time. For the final rule inventories, new

exhaust and evaporative permeation emissions were run at the national aggregation for all cases,

meaning that county-level inputs were aggregated to a national average before being processed

into MOVES, and hourly inputs were aggregated into an average monthly value for January and

July – these monthly values were then weighted together to estimate annual emissionsWhile

aggregation does lose some resolution in the overall emission results, test runs indicated that

emissions differences are within a few percent of fully disaggregated runs and acceptable for

estimating the emission impacts of the control programs. One key aspect of this approach is that

even for higher levels of aggregation, fuel supply inputs are retained at the county level in order

to maintain the resolution of fuel effects.

Because at the time of this final rule analysis the MOVES module for automating the

calculation of E85 emissions from flexible fueled vehicles (FFVs) was not complete, we used

estimates done for the NPRM for the E85 impact on acetaldehyde and ethanol, which were
528

calculated by running a pre-draft version of MOVES2009 for all E85 and for all E10 and then

weighting the emissions in a post-processing step. To run MOVES for "all E85" we created a

special set of MOVES input files that essentially set all gasoline vehicles to run on E85. We

created MOVES fuel supply and fuel adjustment tables that applied multiplicative E85 fuel

adjustments from Table 3.1-6 to all gasoline vehicle emissions.187 Because sulfate and vapor

venting emissions are calculated using fuel properties (sulfur level and RVP) rather than fuel

adjustments, we also created a specific MOVES table of E85 fuel properties as described in

Section 3.1.1.1 In a post-processing step, we calculated a weighted average of the "all E85"

results and the 2022 control case, sensitivity analysis results (called "all E10" results here). We

chose to use the "sensitivity" results for consistency with its premise that modern vehicles are

responsive to changes in fuel characteristics. The all E85 and all E10 results were weighted

together by state, model year, and vehicle type using a weighting factor that was the product of

the FFV fraction and the E85 market share, where FFV fraction is the fraction of that vehicle

type and model year that are projected to be E85 flexible-fueled vehicles, and the E85

marketshare is the state fraction of FFV energy use that we project will be provided by E85.

These fractions were generated using the assumptions described in the sections in Chapter 1,

Section 1.7.1 pertaining to Primary FFV Growth Assumptions and Projected Growth in E85

Access. We performed this calculation for passenger cars and trucks and light commercial

trucks only since the number of heavy-duty vehicles using E85 is expected to be small.

Toxic emissions were still in development for MOVES at the time of this analysis; for

this analysis some post-processing was required to generate complete inventory estimates.

Specific toxic:hydrocarbon ratios by fuel formulation, vehicle class and model year were

developed from a series of MOBILE6 runs and fed into MOVES, which applied these ratios to

HC emissions to produce emissions of benzene, acetaldehyde, 1-3 butadiene, formaldehyde and

acrolein for all of these cases analyzed. Naphthalene from heavy-duty vehicles was ratioed to

PM 10 in MOVES. For light-duty vehicles, naphthalene emissions were calculated as the sum of

PM 2.5 elemental carbon and PM 2.5 organic carbon emissions times a ratio of 0.088.

Aggregate ratios from the running emissions were also applied to start emissions to develop

overall toxic emission inventories. E85 emissions were calculated in MOVES using the factors

in Table 3-1.6.

3.1.1.4.2 Off-Road Gasoline

Emissions from nonroad gasoline equipment were developed by running the National

Mobile Inventory Model (NMIM), a consolidated emissions modeling system for EPA’s

MOBILE6 and NONROAD models.685 The key feature of NMIM is a national county database

(NCD), which includes county-level information on temperatures, fuel properties, equipment

populations, etc. NMIM runs MOBILE6 and NONROAD based on information in the NCD.

The NCD used to produce these inventories was updated as part of the 2005 National Emission

Inventory (NEI) process.686 The NCD also included the 2005 and 2022 fuels described in

Section 3.1.1.2. The version of the NONROAD Model used included the effects of the 2008

Final Rule: Control of Emissions of Air Pollution from New Nonroad Spark-Ignition Engines,

187 The MOVES fuel adjustment table developed for this analysis contained all E85 fuel effects from Table 3.1-9,

including the not statistically significant NOx and NMHC results; however, only results pollutants identified as

statistically significant in Table 3.1-9 are reported in the sensitivity case inventory results
529

Equipment, and Vessels.687 It is also capable of modeling the effects of gasoline blends

containing 10 percent or less of ethanol.

Emissions from onroad and nonroad diesel equipment were also developed by running

NMIM (see above), using the same NCD and version of the NONROAD Model described above.

The version of MOBILE was MOBILE6.2. Diesel fuels are less fully characterized than

gasoline, since the only property used by MOBILE and NONROAD is fuel sulfur.

Most toxic emissions for off-road equipment were taken directly from NMIM. The one

exception was ethanol, which is not estimated by NMIM, so ethanol emissions were based on

VOC speciation from light-duty gasoline vehicles. Ethanol inventories for the control case were

developed by applying ratios of the aggregate MOVES ethanol exhaust, evaporative and

refueling emissions for on-road gasoline for control versus reference cases, to the reference case

ethanol emissions for off-road equipment.

3.1.1.4.3 On-Road Diesel

As it is likely that biodiesel will be consumed in a variety of blend levels (e.g. 20 percent,

5 percent, 2 percent) by light-duty diesel vehicles and off-road diesel equipment as well as

heavy-duty diesel vehicles, we assumed for this analysis that the effects of biodiesel on

emissions are linear with biodiesel concentration as demonstrated by Sze, et al,688 and that

impacts can be analyzed assuming all biodiesel is blended as B20. We applied the B20 effects

discussed in Section 3.1.1.2.4 to baseline heavy-duty emissions generated by NMIM, as MOVES

heavy-duty diesel estimates were not available in time for this analysis. Biodiesel impacts were

using the following formula:

Biodiesel Impact P = Base HD Emissions P * Effect P * (Increase in B20 Volume / Total Diesel Volume)

Where:

P = pollutant

Effect = Percent change with B20 blend from Section 3.1.1.3.3

Increase in B20 Volume= Change in B20 volume from 2022 reference case to control case

in billion gallons of B20 blend (ie, change in gallons of biodiesel * 5)

Total Volume = Total Highway Diesel Volume in 2022 in billion gallons

Toxic effects were calculated using the HC effects from Table 3.1-8.

It should be noted that the emission inventory impacts estimated for biodiesel used

baseline diesel emissions from NMIM (using MOBILE6), which are significantly lower than the

updated estimates in MOVES2010. Using MOVES, the increase in NOx and decrease in PM

from the projected biodiesel volumes may be twice the magnitude of those reported in this rule.

3.1.1.4.4 Portable Fuel Containers

There are several sources of emissions associated with portable fuel containers

(PFC) used for gasoline. These sources include vapor displacement and spillage while refueling

the gas can at the pump, spillage during transport, permeation and evaporation from the gas can
530

during transport and storage, and vapor displacement and spillage while refueling equipment. As

the calculation of emissions for refueling non-road equipment includes spillage and some vapor

displacement, these impacts are not included here. For the final rule we did not update these

estimates from the proposal.

As part of the 2007 regulation controlling emissions of hazardous pollutants from mobile

sources (MSAT2 rule), EPA promulgated requirements to control VOC emissions from gas cans.

The methodology used to develop emission inventories for gas cans is described in the regulatory

impact analysis for the rule and in an accompanying technical support document.689, 690

Based on the MSAT work, we generated two sets of hypothetical nationwide annual

estimates of PFC VOC emissions, for calendar years 2017 and 2030, based on all E0 and all E10.

Interpolation can be used to estimate PFC VOC emissions for the reference cases. Proportions of

national E0 and E10 fuel use were calculated for the 2022 reference and control cases. The

reference case featured a mix of 89.1% E10 and 10.9% E0, while the policy case featured 100%

E10. While E85 is used in flexible fueled highway vehicles, it is unlikely to be used in the near

future in non-road equipment, and is therefore unlikely to be stored or dispensed from PFCs.

MSATs found in liquid gasoline will be present as a component of VOC emissions.

These MSATs include benzene and naphthalene. Ethanol is present as well in VOC emissions

from ethanol blends. Inventories for these pollutants were estimated by the application of toxic

to VOC ratios.

For benzene emissions from all sources except permeation, the following formula was

used to calculate toxic to VOC ratios:

36.0

Re

Re





















LDGV

LDGV

fueling VOC

fueling Benzene

VOCPFC Emissions

PFC Benzene Emissions

where the ratio of refueling benzene to VOC was estimated using average nationwide fuel

properties for zero and 10 percent ethanol gasoline from refinery modeling, done for RFS rule,

and applied to EPA’s Complex Model for reformulated gasoline.691, 692 The 0.36 multiplier

corrects for the difference in the percentage of gasoline in refueling emissions at 90o F, the

temperature assumed for the algorithm in the Complex Model, versus a more typical lower fuel

temperature of 60 o F for gas cans. The basis of this adjustment is discussed in more detail in the

regulatory impact analysis for the Mobile Source Air Toxics Rule. An additional adjustment

factor is applied to the ratio for permeation emissions, based on a recent study693 that suggests

that the ratio of benzene from permeation to total VOC from permeation is about 1.77 times

higher than the ratio associated with evaporation, according to the following formula:

77.136.0

Re

Re

x

fueling VOC

fueling Benzene

VOCPFC Emissions

PFC Benzene Emissions

LDGV

LDGV 


















531

The resulting ratios for 0% and 10% ethanol did not differ at the fifth decimal place, and

were 0.0135 for all sources except for permeation, and 0.00239 for permeation. Thus, impacts of

this rule on benzene emissions are due to the overall impact of RVP changes on total VOC

emissions.

A naphthalene to VOC ratio was estimated using the following formula:

 0054.0





















LDGV

LDGV

VOCeEvaporativ

eEvaporativ Naphthalen e

VOCPFC Emissions

PFC Naphthalen Emissionse

An evaporative naphthalene to VOC ratio for light-duty gasoline vehicles of 0.0004 was

obtained from analyses done for the Mobile Source Air Toxics Rule, and did not vary by fuel

type. The 0.0054 adjustment was based on a recent analysis of average nationwide percentage of

naphthalene in gasoline vapor from gasoline distribution with an RVP of 10 psi at 60 degrees

Fahrenheit.694, 695 The resulting ratio applied to PFC emissions was 0.0000022.

For E10 fuel, we assumed 16.74 percent of the evaporative emissions were ethanol

(SPECIATE profile 1301)696 and 33.34 percent of permeation emissions were ethanol.697

3.1.1.4.5 Refueling Emissions

Refueling emissions were calculated by NMIM, based on MOBILE6 refueling module.

Emissions are impacted by the increase in RVP due to ethanol, and also because the reduced

energy density of ethanol would require more fillups. NMIM directly provides the emission

increase due to increased RVP for the areas allowing the 1.0 psi waiver, so no additional

processing was required to estimate RVP effects on refueling. For the final rule we did not

update these estimates from the proposal, except to account for the different control cases.

In order to estimate the emission impact of the increase in refueling events, we developed

ton per gallon refueling emission factors based on NMIM by dividing total refueling emissions

from NMIM for each case by the number of gallons consumed in the AEO case. The ton per

gallon emission factors were then applied to the total volume in gallons in each case. Fuel

volumes for the RFS 1 mandate and AE0 reference cases compared to the NPRM and final rule

control cases are listed in Table 3.1-9. Our estimates of total gallons were calculated from

energy balance, reflecting the various numbers of gallons needed to consume the same energy.

We assume the number of trips to the pump will increase in proportion to the increased gallons

estimated for the rule.
532

Table 3.1-9. Gasoline Volumes (Billion Gallons)

RFS 1

Mandate

AEO

2007

NPRM

Control

Case

RFS1

Mandate

2022

AEO

2008 rev

2022

FRM

RFS2

Control

Low

EtOH

FRM

RFS2

Control

Primary

FRM

RFS2

Control

High

EtOH

E0 107.51 16.03 0 65.72 6.46 0.00 0.00 0.00

E10 36.40 131.00 124.6 70.46 131.82 134.25 128.79 115.82

E85 0.00 0.11 29.3 0.00 0.00 5.49 12.54 29.26

Total

Gallons 143.91 147.14 153.9 136.18 138.28 139.74 141.32 145.08

3.1.2 Impact on Non- GHG Emissions from Fuel Production and Distribution

In addition to the effects of increased renewable fuel use on emissions from the vehicles

and equipment that use the fuels, as discussed above, there are shifts in the fuel production and

transport/distribution methods that can have substantial impacts on emissions. These "upstream"

emissions are associated with all stages of biofuel production and distribution, including biomass

production (agriculture, forestry), fertilizer and pesticide production and transport, biomass

transport, biomass refining (corn or cellulosic ethanol production facilities), biofuel transport to

blending/distribution terminals, and distribution of finished fuels to retail outlets. Additionally,

changes in agricultural economics associated with increased biomass production can result in

shifts in related agricultural production, such as livestock.

This section describes the changes in upstream emission sources and related emission

rates connected with the renewable fuel use. The emission inventory impacts resulting from

these changes are described in Section 3.2. This section is divided into two major sub-sections,

the first covering emissions of criteria pollutants, their precursors, and ammonia, and the second

covering non-criteria air toxic emissions and ethanol. The specific air toxics covered are:

benzene, acetaldehyde, formaldehyde, 1,3-butadiene, acrolein, and naphthalene.

3.1.2.1 Upstream Criteria Pollutants

3.1.2.1.1 Agricultural Sector

Introduction

In prior EPA estimates, such as the RFS1 rule, changes in agricultural emissions were

based solely on the increases in bushels of corn (and soybeans for biodiesel), and the necessary

acreage to produce those additional bushels. Given the greater pressure on farmland use likely in

the 2022 timeframe for today’s rule (15 billion gallons of corn ethanol plus up to 16 billion

gallons of cellulosic ethanol) compared to the 2012 assessment for RFS1 (6.7 or 9.6 billion

gallons of ethanol depending on scenario), additional factors have been added to the agricultural

analysis, such as likely shifts of acreage to corn from certain other crops as corn prices increase.
533

The number of acres of cropland for corn, soy, and all other principle crops were

estimated using the FASOM agriculture and forestry model, as described in Section 5.1 of this

document. We are using the change in total acres of planted cropland to estimate changes in

certain agricultural emissions, such as tillage dust, that are not directly calculated by FASOM.

Another substantial source of agricultural emissions (especially ammonia and methane) is

livestock. Changes in livestock-related emissions are estimated based on the change in head

counts of cattle, swine, and poultry predicted by FASOM.

The impacts relative to the RFS1 mandate reference case (6.7 billion gallons of ethanol)

rely only on applying ethanol volume proportions to the modeling results of the AEO reference

case (13.2 billion gallons). Due to the complex interactions involved in projections in the

agricultural modeling, we did not attempt to adjust the agricultural inputs of the AEO reference

case for the RFS1 reference case. So the fertilizer and pesticide quantities, livestock counts, and

total agricultural acres were the same for both reference cases. The agricultural modeling that

had been done for the RFS1 rule itself was much simpler and inconsistent with the new

modeling, so it would be inappropriate to use those estimates. We had planned to conduct

additional agricultural modeling specifically for the RFS1 mandate case prior to finalizing this

rule, but there was not sufficient time and resources to accomplish that after all the other updates

and sensitivities analyzed for the AEO case alone for the final rule.

3.1.2.1.1.1 VOC/NOx/CO/SOx/PM2.5

Criteria pollutants related to agricultural operations come from five major sources: farm

equipment (mainly diesel engine emissions), fertilizer production and application, pesticide

production and application, burning of crop residue, and fugitive dust from field tilling and

related activities.

Agricultural Equipment Emissions

Changes in farm equipment emissions were estimated by multiplying an average fuelbased emission factor for diesel or gasoline farm equipment by the change in farm fuel

consumption predicted by FASOM. The emission factors for each pollutant in units of grams

emitted per million BTU of fuel burned were calculated from EPA NONROAD2005 nationwide

modeling outputs for 2022 (pollutant tons emitted, gallons of fuel consumed) for each year of

interest. The diesel emissions include all agricultural diesel equipment, which are dominated by

agricultural tractors, while the gasoline emissions include only the limited number of larger

agricultural gasoline-fueled equipment, such as tractors, combines, balers, swathers, and

irrigation sets. The fuel energy contents (lower heating value) used for the unit conversions were

115,000 BTU/gallon for gasoline and 130,000 BTU/gallon for diesel. For comparison, the

corresponding 2020 emission factors from GREET are shown, where available. Most of the

differences between NONROAD and GREET are small and are likely attributable to the

difference between 2020 and 2022 values. And although the gasoline equipment emission

factors for VOC and CO from NONROAD are much greater than those used in GREET, this

does not have much impact on emission inventories due to the small number of gasoline-fueled

equipment used in agriculture relative to diesel equipment.
534

Table 3.1-10.

Agricultural Equipment Emission Factors

(grams per mmBTU of fuel burned)

Diesel Gasoline Pollutant NONROAD GREET NONROAD GREET

NOx 306 298 204 208

VOC 30.55 34.87 355.53 52.30

PM10 21.12 22.67 7.49 9.07

PM2.5 20.49 20.41 6.89 8.34

CO 130 136 10,067 204

Benzene 0.62 -- 11.90 --

Ethanol 0.00 -- 0.00 --

1,3-Butadiene 0.057 -- 1.90 --

Acetaldehyde 1.62 -- 1.63 --

Formaldehyde 3.61 -- 3.17 --

Naphthalene 0.027 -- 0.66 --

Acrolein 0.09 -- 0.14 --

SO2 0.44 -- 15.88 --

NH3 0.68 -- 1.01 --

Fertilizer and Pesticide Production

The manufacturing processes for agricultural fertilizer and pesticides generate a variety

of air pollutants. The agricultural inputs from GREET provide emission factors in grams of

pollutant per ton of nutrient for various types of fertilizers, herbicides, and insecticide, as shown

in Table 3.1-12. These air emission factors were multiplied by the changes in fertilizer and

pesticide use predicted by FASOM, as shown in Table 3.1-11, to give projected changes in

nationwide agricultural fertilizer and pesticide production emissions.

Table 3.1-11.

Changes in Agricultural Chemical Use for 2022 RFS2 Control Case Relative to AEO2007

Reference Case

Nitrogen

(average)

Phosphate

(P2O5)

Potash

(K2O)

Limestone

(CaCO3) Herbicides Pesticides

Annual Short

Tons 750,629 357,069 662,157 260,304 -750 -381

Percentage 5.73% 12.72% 20.16% 2.55% -0.38% -0.86%
535

Table 3.1-12.

Agricultural Chemical Production & Transport Air Emission Factors

(grams per ton of nutrient)

Pollutant Nitrogen

(average)

Phosphate

(P2O5)

Potash

(K2O)

Limestone

(CaCO3) Herbicides Pesticides

NOX 1,605 4,484 734 573 19,371 21,628

VOC 2,761 240 40.7 56.8 1,575 2,040

PM10 454 1,551 148 506 10,840 11,746

PM2.5 262 1,018 74.5 167 4,869 5,479

CO 2,595 790 129 186 5,417 6,872

Benzene 0.00 0.00 0.00 0.00 3.21 4.16

Ethanol 0.00 0.00 0.00 0.00 0.00 0.00

1,3-Butadiene 0.000 0.000 0.000 0.000 0.576 0.745

Acetaldehyde 0.018 0.001 0.000 0.000 0.082 0.106

Formaldehyde 20.75 1.55 0.19 0.41 18.11 23.44

Naphthalene 0.033 0.117 0.010 0.039 114.4 124.0

Acrolein 0.009 0.001 0.000 0.000 0.024 0.031

SO2 703 53,299 321 701 11,300 12,895

NH3 0.00 0.00 0.00 0.00 0.00 0.00

Until the 1990s it was reasonable to assume that all fertilizers and pesticides used on

domestic agriculture were produced within the U.S. This has been less true in recent years as

more agricultural chemicals, especially fertilizers, are being imported from countries with a

greater availability of natural gas at lower costs. For greenhouse gases the location of these

emissions is of less importance, but for criteria pollutants and toxics it is important to reduce the

estimated impacts by the percentage of production and transportation occurring outside of the

U.S. Using data from USDA698,699 the percentages applied from domestic sources are shown in

Table 3.1-13. After applying these percentages to the production and initial transportation

portions of the GREET emission factors, the unadjusted final (domestic) transportation portion

of the GREET emission factors was added back in. Since the relative emissions from production

versus transportation vary by pollutant, the net adjustments to the GREET emission factors also

vary by pollutant, as shown in the second Section of Table 3.1-13. To calculate an overall factor

for nitrogen fertilizers, the proportions from GREET were used: 70.7% ammonia, 21.1% urea,

and 8.2% ammonium nitrate. The pesticide adjustment does not vary by pollutant because

virtually all of the pesticide emissions come from actual production rather than

transportation/distribution.
536

Table 3.1-13.

Domestic Fractions of Fertilizer and Pesticide Production Applied to Crops

Nitrogen

Fertilizers

Potash Phosphate Pesticides

Domestic Fraction of

Production

50% 20% 94% 76%

Net Adjustment to Production,

Transportation & Distribution

Emission Factor from GREET

VOC 50.63% 94.85% 53.62% 76%

CO 52.47% 94.92% 54.37% 76%

NOx 73.34% 94.92% 65.55% 76%

PM10 52.64% 94.12% 24.06% 76%

PM2.5 53.67% 94.14% 28.31% 76%

SOx 60.48% 94.02% 33.66% 76%

Fertilizer and Pesticide Application

In addition to the agricultural equipment emissions mentioned above, the application of

fertilizer and pesticides (herbicides, insecticides, fungicides, etc.) to agricultural fields causes the

release of certain types of pollutants into the air. For nitrogen fertilizers the only pollutant

considered to be significant is ammonia (NH3), the estimation of which is covered in Section

3.1.3.1.1.2. Pesticide application emissions are mainly VOC and various individual organic

compounds, most notably benzene and acrolein. A discussion of the toxic pollutant emissions as

a fraction of VOC is presented in Section 3.1.3.2.2, but the resulting emission factors and

inventory impacts are shown here in Table 3.1-14. There are also potential toxicity concerns

with volatilization of the pesticide active ingredients, and this is discussed in Section 3.4 of this

document.

The basis of the pesticide application emissions for this analysis was the 2002 NEI areasource inventory. The ton per year emissions data from the NEI was used with USDA pesticide

application data for 2002 (or the nearest year for which data were collected) to generate an

overall average estimate of the pesticide application emissions per ton of pesticide applied. This

ratio of pollutant tons (for VOC, benzene, and acrolein) per ton of pesticide applied was then

multiplied by the change in total pesticide tons used (including herbicides) as projected by

FASOM and shown in Table 3.1-11 to give the projected change in nationwide agricultural

pesticide application emissions in Table 3.1-14.
537

Table 3.1-14.

Herbicide and Pesticide Application Air Emission Factors and Impacts for 2022 RFS2

Control Case Relative to AEO2007 Reference Case

Emission Factor Air Emission

Pollutant Impact

(tons per ton applied) (annual short tons)

VOC 0.543 -614

Benzene 0.142 -161

Acrolein 0.0036 -4.06

Agricultural Residue Burning Emissions

One source of air pollution related to crop farming is the burning of crop residues. This

practice is one of the methods that is used to clear fields between crop cycles so that the old crop

residue does not build up and clog or otherwise hinder the tilling of the fields in preparation for

new crop planting. This practice is mainly used for grassy crops like wheat, rye, and barley, but

in some areas it is also used for corn and other crops.

Crop residue burning produces substantial emissions of CO2, VOC, CO, NOx, as well as

ammonia and toxic pollutants such as benzene, formaldehyde, 1,3-butadiene, and acrolein.

The use of crop residue burning is quite variable from area to area and among individual

farmers, since there are alternative methods to deal with crop residue, including use of

conservation tillage methods and equipment that allows planting through the residue. In some

locations and time periods crop residue burning has been prohibited by law, due to the possible

health effects in nearby residential areas. Another aspect of uncertainty in estimating crop

burning emissions is that the NEI does not currently cover all states where crop residue burning

occurs. Despite these data limitations, the NPRM used the available data to generate a rough

overall estimate of the average crop burning emissions per acre of planted crops, and then

multiplied that emission rate to the change in total crop acres predicted by FASOM to generate

an estimated emission inventory impact.

For this final rule analysis we have reconsidered the inclusion of any crop residue

burning impact and decided not to include it. This reconsideration was driven by the facts that

(a) the crops most likely to be impacted by this rule do not tend to be ones for which residue

burning is used, and (b) even for those crops affected by this rule that might otherwise have their

residue burned, for this analysis they would much more likely have that residue harvested and

used as cellulosic feedstock in a biofuel plant. Given the uncertainty in projecting these emission

impacts, we did not want any rough assumptions made to unduly influence the emission impact

assessment.

Agricultural Dust Emissions
538

Soil and related dust particles (e.g., fertilizer, pesticide, manure) become airborne as a

result of field tillage and animal grazing/foraging, especially in drier areas of the country. Some

of this dust is in a size range that is a concern for human health and welfare. The NEI includes

estimates of these particulate emissions by county.

The agricultural dust data from the 2002 NEI was used to generate an estimate of the

average fugitive dust emissions per acre of planted crops for the crop related dust, and per head

of cattle for the dust related to cattle. This was done using 2002 nationwide crop acreage and

livestock inventory data from USDA/NASS. The calculated pollutant mass (tons of PM) per

total acre farmed was then multiplied by the change in total planted acres projected by FASOM

to give a projected change in nationwide crop related dust emissions. And the calculated PM

tons per head of cattle was multiplied by the change in cattle inventory projected by FASOM to

give a projected change in nationwide livestock related dust emissions.

The emission factors and inventory impacts of fugitive dust from crop related activities

and livestock are shown in Table 3.1-15 and 3.1-16. The ton per year impacts for the croprelated emissions are based on a modeled increase of 8.1 million farmed acres (2.65 percent) in

2022 relative to the AEO2007 reference case. The changes in fugitive dust from livestock

operations are based on the head count changes shown in Table 3.1-18.

Table 3.1-15.

2022 Crop-related Dust Emission Impacts for the 2022 RFS2 Control Case

Relative to AEO2007 Reference Case

Emission Factors Inventory

Impacts Pollutant

(Tons per thousand

acres farmed) (annual short tons)

PM10 6.807 55,182

PM2.5 1.021 8,277

Table 3.1-16.

2022 Livestock-related Dust Emission Impacts for the 2022 RFS2 Control Case

Relative to the AEO2007 Reference Case

Beef Cattle Dust Emissions Dairy Cattle Dust Emissions Pollutant (kg/head/year) (annual short tons) (kg/head/year) (annual short tons)

PM10 0.888 -139.68 0.172 -8.34

PM2.5 0.089 -14.00 0.017 -0.82

3.1.2.1.1.2 Ammonia (NH3)

The two primary sources of ammonia emissions into the air on farms are fertilizer

application and livestock waste. Fertilizer application emissions were estimated using an
539

average emission factor of 57,428.71 grams per ton of fertilizer nitrogen applied for all forms of

nitrogen, which is a weighted average of the standard EPA emission factors that are used to

generate the NEI. The weightings for each type of fertilizer come from USDA Economic

Research Service data for 2006. The individual emission factors, weightings, and resulting

average emission factor are shown in Table 3.1-17. This average emission factor was multiplied

by the nitrogen application quantities generated by the FASOM model for each scenario.

Table 3.1-17. Fertilizer Ammonia Emission Factors

Fertilizer Type SCC Emission Factor

(lbs NH3/Ton Nitrogen)

USDA 2006 all

crops Weighting

Anhydrous Ammonia 2801700001 24 15.46%

Aqua Ammonia 2801700002 24 1.61%

Nitrogen Solutions 2801700003 61 40.88%

Urea 2801700004 364 21.73%

Ammonium Nitrate 2801700005 49 3.9%

Ammonium Sulfate 2801700006 194 4.93%

Ammonium Thiosulfate 2801700007 64

Other Straight Nitrogen 2801700008 61 11.49%

Ammonium Phosphates 2801700009 97

N-P-K 2801700010 97

avg lbs/ton 126.61

avg grams/ton 57428.71

Changes in ammonia emissions from livestock waste were estimated using emission

factors (kg/head/year) multiplied by the change in animal head counts predicted by FASOM.

The ammonia emission factors and livestock head changes used in this analysis, along with

resulting ammonia inventory impacts are shown in Table 3.1-18. This analysis was limited to

these four types of livestock because they are the ones specifically modeled by FASOM.

Table 3.1-18. Livestock Ammonia Emission Impacts for 2022 RFS2 Control Case Relative

to the AEO2007 Reference Case

Livestock

Type

kg NH3 per head

per yeara

Head count change

(million head)

Percent

change

Change in NH3 emissions

(annual short tons)

Beef Cattle 9 -0.143 -0.23% -1,416

Dairy Cattle 25 -0.044 -0.65% -1,212

Swine 5 3.95 3.24% 21,711

Poultry 0.22 -73.5 -0.98% -17,798

a

Source: EPA/600/R-02-017, "Review of Emission Factors and Methodologies to Estimate Ammonia Emissions From

Animal Waste Handling," April 2002.

Although it is a minor source of ammonia compared to fertilizer and livestock emissions

described above, changes in farm equipment ammonia emissions were estimated by multiplying

an average fuel-based emission factor for diesel or gasoline farm equipment by the change in

farm fuel consumption predicted by FASOM. The ammonia emission factors in units of grams

emitted per million BTU of fuel burned were calculated from the default ammonia emission
540

factors used in the EPA NMIM model: 116 mg per gallon of gasoline burned; and 88.3 mg per

gallon of diesel fuel burned.

3.1.2.1.2 Biofuel Production

Emissions from the production of biofuels include the emissions from the production

facility itself as well as the emissions from production and transport of the biomass and any other

fuels used by the biofuel plant, such as natural gas, coal, and electricity. The biomass feedstock

production emissions are discussed above in the section on agricultural emissions. The

calculation of emissions from corn ethanol, cellulosic ethanol, and biodiesel plants, including

feedstock transport, was done using the basic methodology of the GREET model. But some

updates and enhancements were made to GREET, including updated feedstock energy

requirements and estimates of excess electricity available for sale from new cellulosic ethanol

plants, based on modeling by the National Renewable Energy Laboratory (NREL). Since

certain biofuel production processes generate co-products that could also be used in the gasoline

market, we have accounted for those by decreasing the refined gasoline volume on an equal fuel

energy basis to the co-products. This was done in two cases -- co-product naphtha from the

Fischer-Tropsch process and C3+ alcohols from the thermochemical ethanol from mixed

alcohols process.

The facility emission factors used are shown in Table 3.1-19. These have been updated

for this final rule based on new analyses of projected plant efficiency improvements, rather than

using older analyses, such as dry mill corn plant emission data from plants existing in 2005 as

was used for the NPRM and air quality modeling inventories. These new analyses, discussed in

Section 1.5.1.3 for corn ethanol, Section 1.5.3 for cellulosic ethanol and diesel, and Section 1.5.4

for biodiesel, provide projections of energy and feedstock requirements for biofuel production.

These energy requirements are then multiplied by emission factors (grams per mmBTU of

feedstock consumed) from the GREET model to yield the gram per gallon emission factors

presented here.
541

Table 3.1-19.

Biofuel Production Plant Emission Factors in 2022

(grams per gallon produced)

Biofuel Plant Type

VOC CO NOx PM10 PM2.5 SOx NH3

Corn Ethanol, Dry Mill NG 2.29 0.58 0.94 0.94 0.23 0.01 0.00

Corn Ethanol, Dry Mill NG (wet DGS) 2.27 0.37 0.60 0.91 0.20 0.00 0.00

Corn Ethanol, Dry Mill Biogas 2.29 0.62 1.00 0.94 0.23 0.01 0.00

Corn Ethanol, Dry Mill Biogas (wet DGS) 2.27 0.39 0.63 0.91 0.20 0.00 0.00

Corn Ethanol, Dry Mill Coal 2.31 2.65 3.68 3.64 1.54 3.48 0.00

Corn Ethanol, Dry Mill Coal (wet DGS) 2.28 1.68 2.34 2.62 1.03 2.21 0.00

Corn Ethanol, Dry Mill Biomass 2.42 2.55 3.65 1.28 0.36 0.14 0.00

Corn Ethanol, Dry Mill Biomass (wet

DGS) 2.35 1.62 2.32 1.12 0.28 0.09 0.00

Corn Ethanol, Wet Mill NG 2.33 1.04 1.68 1.00 0.29 0.01 0.00

Corn Ethanol, Wet Mill Coal 2.33 3.50 4.86 4.53 1.98 4.60 0.00

Cellulosic Ethanol (Enzymatic,

switchgrass or corn stover) 1.45 4.68 6.71 1.63 0.53 0.25 0.00

Cellulosic Ethanol (Enzymatic, forest

waste) 1.46 4.93 7.06 1.67 0.55 0.26 0.00

Cellulosic Ethanol (Thermochemical,

switchgrass or corn stover) 0.49 6.99 10.03 1.16 0.58 0.37 0.00

Cellulosic Ethanol (Thermochemical,

forest waste) 0.49 6.99 10.03 1.16 0.58 0.37 0.00

Biodiesel, Soybean oil 0.04 0.43 0.69 0.06 0.06 0.01 0.00

Biodiesel, Yellow grease/tallow 0.04 0.50 0.80 0.07 0.07 0.01 0.00

Biodiesel, Fuel grade corn oil 0.04 0.50 0.80 0.07 0.07 0.01 0.00

Biodiesel, Algae 0.01 0.10 0.16 0.01 0.01 0.00 0.00

Renewable Diesel*, Yellow grease 0.00042 0.00475 0.00767 0.00065 0.00065 0.00006 0.00

Cellulosic Diesel (Thermochemical,

Fischer-Tropsch forest waste) 0.91 13.39 20.22 2.39 1.20 1.80 0.00

* The renewable diesel emission factors are based only on the energy needed for hydrotreating at an existing

refinery, which is different from stand-alone facilities we project in Section 1 will be making renewable diesel (RD).

An RD plant would have more feedstock handling, pumping, etc., as well as general plant energy overhead than for

a marginal unit in a refinery.

3.1.2.1.3 Crude Oil Production/Transport/Refining

The estimate of emissions associated with production of gasoline and diesel fuel from

crude oil is based on emission factors in the GREET model. The actual calculation of the

emission inventory impacts of the decreased gasoline and diesel production is done in EPA's

spreadsheet model for upstream emission impacts.700 This model uses the decreased volumes of

the crude based fuels and the various crude production and transport emission factors from

GREET to estimate the net emissions impact, which is shown below in Section 3.2 (see the

displaced gasoline row of Table 3.2-5).

3.1.2.1.4 Finished Fuel Transport and Distribution
542

Transfer and Storage Evaporative Emissions from Gasoline, Gasoline/Ethanol Blends,

and Ethanol -- VOC emissions are produced by transfer and storage activities associated with

distribution of gasoline, gasoline/ethanol blends, and ethanol. These are referred to as Stage I

emissions.701 Stage I distribution begins at the point the fuel leaves the production facility and

ends when it is loaded into the storage tanks at dispensing facilities.

There are five types of facilities that make up this distribution chain for gasoline. Bulk

gasoline terminals are large storage facilities that receive gasoline directly from the refineries via

pipelines, barges, or tankers (or are collocated at refineries). Gasoline from the bulk terminal

storage tanks is loaded into cargo tanks (tank trucks or railcars) for distribution to smaller

intermediate storage facilities (bulk plants), or directly to gasoline dispensing facilities (retail

public service stations and private service stations). When ethanol is blended into gasoline it

usually occurs in the pipes which supply the tank trucks.

There are two types of pipeline facilities found at various intervals along gasoline

distribution pipelines: pipeline breakout stations and pipeline pumping stations. Pipeline

breakout stations receive gasoline via pipelines, store it in storage tanks, and re-inject it into

pipelines as needed to meet the demand from downstream facilities. Pipeline pumping stations

are located along the entire length of a pipeline at about 40 mile intervals. Their purpose is to

provide the extra ‘‘push’’ needed to move the product through the pipeline. They do not

normally have gasoline storage capability.

Bulk plants are intermediate storage and distribution facilities that normally receive

gasoline or gasoline/ethanol blends from bulk terminals via tank trucks or railcars. Gasoline and

gasoline/ethanol blends from bulk plants are subsequently loaded into tank trucks for transport to

local dispensing facilities.

Gasoline and gasoline/ethanol blend dispensing facilities include both retail public outlets

and private dispensing operations such as rental car agencies, fleet vehicle refueling centers, and

various government motor pool facilities. Dispensing facilities receive gasoline and

gasoline/ethanol blends via tank trucks from bulk terminals or bulk plants. Inventory estimates

for this source category only include the delivery of gasoline at dispensing facilities and does not

include the vehicle or equipment refueling activities.

Emission factors (EFs) for gasoline were based on inventory estimates from the 2002

NEI.702 We used these data to develop E0 gasoline emission factors even though the 2002

emissions included the E10 that was in the fuel pool at that time. In 2002 this was still a

relatively small proportion of gasoline consumption, so it should not substantially affect the

national E0 estimates. Since ethanol is blended with gasoline at bulk terminals to produce E10

and E85 at the point fuel is loaded into tank trucks, we assumed bulk terminal emissions were

associated with unblended gasoline. We then divided emissions into a refinery to bulk terminal

component and a bulk terminal to dispensing facility component. Total nationwide emissions for

these two components were divided by the energy content of the total volume of gasoline

distributed in 1999, to obtain the emission factor in g/mmBTU. Total volume of gasoline was

based on gasoline sales as reported by the Energy Information Administration.703 These

emission factors are provided in Table 3.1-20.
543

We also developed emission factors for Stage 1 emissions of E10 and E85 subsequent to

blending at bulk terminals. These emission factors were calculated by applying adjustment

factors to the gasoline EF. The adjustment factors for E10 and E85 were based on an algorithm

from the 1994 On-Board Refueling Vapor Recovery Rule704:

EF (g/gal) = exp[-1.2798 - 0.0049(ΔT) + 0.0203(Td) + 0.1315(RVP)] (1) 

where delta T is the difference in temperature between the fuel in the tank and the fuel being

dispensed, and Td is the temperature of the gasoline being dispensed. We assumed delta T is

zero, temperature of the fuel being dispensed averages 60 degrees over the year, and that the

RVP of conventional gasoline is 8.7 psi, 10% ethanol is 9.7, and 85% ethanol is 6.2. Using these

assumptions, the adjustment factor is +14% for E10 and -30% for E85. Emission factors in

grams per million BTU of fuel transferred are given in Table 3.1-20.

In addition to these Stage I emissions for gasoline and gasoline/ethanol blends, transport

of ethanol to bulk terminals also results in evaporative emissions of ethanol, a VOC. For the

NPRM analysis these emissions were estimated using a very simplified approach based on an

adjustment to the gasoline transport VOC emissions to account for the much lower vapor

pressure and molecular weight of ethanol versus gasoline. Using that method the NPRM

assumed an emission factor of 3.56 g/mmBTU ethanol, which greatly underestimated the ethanol

vapor and VOC losses, since it did not attempt to account for differences between ethanol and

gasoline transport modes, distances, or transfer methods in movement of the fuel from

production facility to the bulk distribution terminal.

For the air quality analysis and final rule analysis this method was replaced using data

from an Oak Ridge National Laboratory (ORNL) analysis of projected ethanol transport modes,

distances, and volumes transferred under various ethanol volume scenarios.728 The final results

of that study yielded greatly increased EFs of 26.9 - 31.7 g/mmBTU (2.04 - 2.41 g/gal)

depending on the scenario, due to the added fuel transfer losses compared to pipeline-based

transport of gasoline. The EF shown in Table 3.1-20 (28.78 g/mmBTU) corresponds to the High

Ethanol minus RFS1 reference case, and was used for calculation of VOC and ethanol vapor for

all cases in this FRM analysis. The air quality analysis used preliminary results of the ORNL

analysis, which yielded somewhat greater ethanol and VOC emission rates than used for this

FRM analysis. Further discussion of these calculations can be found in Section 3.3 of this RIA

chapter.

Significant evaporative emissions are not expected from storage and transport of

biodiesel fuel due to its low volatility.
544

Table 3.1-20.

VOC Emission Factors for Gasoline and Gasoline/Ethanol

Blend Storage and Transfer Emissions (Stage 1)

Process Blend EF(g/mmBTU)

Refinery to Bulk Terminal E0 14.94

Refinery to Bulk Terminal E100 28.78a

Bulk Terminal to Pump E0 27.79

Bulk Terminal to Pump E10 32.74

Bulk Terminal to Pump E85 25.93

a E100 ethanol vapor EF ranges from 26.9 - 31.7 depending on scenario.

EF shown corresponds to the High Ethanol minus RFS1 reference case, and

was used for calculation of all cases in this FRM analysis.

Combustion Emissions from Transport and Distribution of Fuels and Feedstocks -- Emissions

are produced by the vehicles and engines used to transport feedstocks such as crude oil, corn, and

cellulosic biomass to fuel production facilities, as well as transport/distribution of the finished

fuels from the production plants to distribution terminals and retail outlets. For example, corn

would be transported from farms and grain facilities to ethanol plants by truck and possibly rail.

The finished ethanol would be transported from there to bulk distribution terminals by truck, rail,

or barge, and distribution from terminal to retail outlet is by truck. The emission factors for the

year 2022 in Table 3.1-21 are taken from the most recent rulemaking analyses, accounting for

the mix of newer better controlled engines (including trucks meeting the standards for 2008 and

later engines705 and engines meeting the 2008 locomotive/marine diesel engine rule706), as well

as any remaining older engines subject to less stringent standards. The truck EFs are given in

terms of grams per vehicle mile traveled, while the other EFs are in grams per million BTU of

fuel burned by the engine. The ocean tanker emission factors are from the base case analysis of

the Category 3 ocean-going vessel proposed rule.734

To estimate the net emission rates for the assumed mix of transport modes for each fuel

type, these emission factors were incorporated into a modified version of GREET707,

708, since

GREET 1.7 and 1.8 retained emission factors based only on earlier regulations. Thus, the miles

traveled and quantities of fuel burned are those used by GREET for each transport mode and fuel

being transported. For the final rule air quality analysis we will have a more detailed analysis of

miles and fuel volumes transported by mode within each county.
545

Table 3.1-21. 2022 Criteria Emissions from Fuel and Feedstock Transport/Distribution

Transport Mode Year VOC CO NOx PM10 PM2.5

Class 2B HD Diesel Trucks (g/mile) 2005 0.282 1.303 3.594 0.163 0.139

2022 (2020) 0.137 0.205 0.483 0.033 0.019

Medium HD DIesel Trucks (g/mile) 2005 0.653 2.482 8.297 0.309 0.271

2022 (2020) 0.289 0.417 1.243 0.053 0.035

Locomotive (g/mmBTU of fuel burned) 2005 84.733 212.861 1620.376 51.575 50.028

2022 (2020) 34.070 203.984 815.271 19.015 18.445

Barge (avg of C1 & C2 vessels)709 2005 26.761 237.513 1276.901 47.923 46.485

(g/mmBTU of fuel burned) 2022 (2020) 15.527 188.994 676.097 22.017 21.356

Ocean Tanker (C3 vessels) 2005 79.298 180.314 2176.240 179.982 165.408

(g/mmBTU of fuel burned) 2022 (2020) 79.160 179.525 2038.314 179.645 165.273

3.1.2.2 Upstream Air Toxics

3.1.2.2.1 Upstream Air Toxics Reference Case

Air toxic emissions are associated with a variety of upstream processes. These processes

include production of agricultural pesticides and fertilizers, as well as their application, operation

of petroleum refineries, operation of ethanol and biodiesel production facilities, operation of

electrical production facilities which supply power to these facilities, and distribution of

agricultural pesticides and fertilizers, feedstocks, gasoline, gasoline/ethanol and biodiesel blends.

Although a large number of compounds which are considered air toxics could be

impacted by this rule, we focused on those which were identified as national and regional-scale

cancer and noncancer risk drivers in the 2002 NATA710 and were also likely to be significantly

impacted by this rule. These compounds include benzene, 1,3-butadiene, formaldehyde,

acetaldehyde, and acrolein. Naphthalene impacts were included for petroleum refineries, since it

is a significant emission product for those facilities. Ethanol impacts were also included in our

analyses because of health concerns (Section 3.4.5) and its role as an acetaldehyde precursor.

2002 air toxic emissions for stationary sources, other than for fires, were obtained from

the 2002 National Emissions Inventory (NEI), version 3. Future year emissions of benzene, 1,3-

butadiene, formaldehyde, acetaldehyde, and acrolein were estimated for sectors only, rather than

individual sources. These sectors included non-EGU (electric generating unit) point sources,

EGU point sources, the nonpoint storage and transfer subsector, and other nonpoint sources.

Emissions were estimated by applying the 2002 air toxics to VOC ratio to the future year VOC

emission estimates. Air toxics from fires were estimated by applying toxics-to-VOC ratios to the

VOC emissions from a fire inventory developed for air quality modeling. 2002 and future year

ethanol emissions were estimated by speciating the VOC estimates. This was done using the

Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system, version 2.3. More details

on the methods and data used to develop these inventories are found in a memo included in the

docket for this rule.711
546

Air toxic emission estimates for agricultural equipment (mainly diesel agricultural

tractors) were obtained from the EPA NMIM model, as described for criteria pollutants in

Section 3.1.2.1.1.1.

3.1.2.2.2 Upstream Air Toxics Control Cases

As described below, we developed emission factors for several air toxics using the most

recent available data. These emission factors were used with estimates of changes in fuel

volumes and associated energy outputs to estimate inventory changes associated with the RFS2

volumes. In general, emission factors are expressed as grams per million BTU (g/mmBTU) of

energy produced or distributed as part of the process. Underlying data are available in the docket

for the rule.

Agricultural Pesticides and Fertilizers – The estimation of air toxic emissions from production

and application of pesticides and fertilizers was done using toxic fractions of the corresponding

VOC emissions described in Section 3.1.3.1.1.1. Table 3.1-22 shows the toxic fractions, which

were calculated from the 2002 NEI inventories for VOC and each of the listed toxic pollutants.

All the pollutants except acrolein from pesticide application are based on nationwide inventories.

California was the only state that reported acrolein emissions associated with pesticide

application, so the 0.66% value shown in the table represents the sum of acrolein emissions

divided by the sum of VOC emissions from pesticide application for all counties in California in

2002. The fertilizer and pesticide application data come from queries of the NEI area source

inventories for SCCs like "28017*" (for fertilizer application) and SCCs like "246180*" or like

"246185*" (for pesticide application).

The production and blending data for fertilizer and pesticides come from queries of the

NEI point source data that were submitted by 40 states and Puerto Rico for the following MACT

codes:

0911 - Pesticide Active Ingredient Production

0960 - Agricultural Chemicals and Pesticides Manufacturing

1410 - Phosphate Fertilizers Production

The data for these codes was compiled for the following four categories: Fertilizer production

(F), Fertilizer mixing blending (FMB), Pesticide production (P), and Pesticide mixing blending

(PMB).
547

Table 3.1-22. Air Toxic Fractions of VOC for Fertilizers and Pesticides

Fertilizer

Production &

Blending

Pesticide

Production &

Blending

Fertilizer

Application

Pesticide

Application

1,3-Butadiene -- 0.0003653 -- --

Acetaldehyde 6.530 E-06 5.198 E-05 -- --

Acrolein 3.320 E-06 1.513 E-05 -- 0.0066

Benzene -- 0.002038 -- 0.2615

Ethanol -- -- -- --

Formaldehyde 0.007517 0.011494 -- --

Petroleum Refineries – Total nationwide emissions of air toxics for 153 U. S. petroleum

refineries in 2002 were obtained from data collected as part of a risk and technology review

(RTR) for EPA’s proposed rule, “National Emission Standards for Hazardous Air Pollutants

From Petroleum Refineries.”712 These emissions were divided by BTUs of energy produced by

those refineries in 2002 to obtain emission factors in g/mmBTU. Thus the resultant emission

factors represent 2002 technology and emission standards. Energy output estimates included all

refinery products, such as conventional and reformulated gasoline, aviation gasoline, jet fuel,

kerosene, distillate fuel oil, residual fuel oil, petrochemical feedstocks, naphthas, lubricants, and

other miscellaneous products. Energy output was estimated by multiplying volume of each

product supplied713 by its heating value in BTUs per gallon.

Resultant emission factors are provided below in Table 3.1-24, along with those for

ethanol and electricity production.

Ethanol Production Facilities – There are a number of processes at ethanol production facilities

that result in emissions of air toxics. These processes include fermentation, distillation of the

resultant mash, and drying of spent wet grain to produce animal feed. Emissions of air toxics

vary tremendously from facility to facility due to a variety of factors, and it is difficult to

determine how differences in the production processes individually impact emissions. Numerous

production facilities have commenced operation in the last few years. To develop emission

factors we used the most recent available inventory for benzene, formaldehyde, acetaldehyde,

and acrolein, from calendar year 2005. These data were obtained from two sources:

1) 2005 NEI State submittals for SCCs associated with ethanol production facilities

2) the 2005 Toxics Release Inventory (TRI)

2005 NEI data submittals were obtained from EPA’s Office of Air Quality Planning and

Standards. These data are included in the docket for the rule. Additional data for facilities not

included in these submittals were obtained from the 2005 TRI

(http://www.epa.gov/triexplorer/list-chemical-hap.htm). Where emissions data were not

available for a facility, the facility was excluded from subsequent calculations. It should be

noted that not all States submitted data for ethanol production facilities, which could potentially

introduce some bias into estimated emission rates.
548

Only a few facilities reported very low emissions of 1,3-butadiene, and the rest reported

no emissions, so emissions of this pollutant from ethanol production facilities were assumed to

be insignificant. Almost all of the data were from dry mill plants running on natural gas, so it

was not possible to develop separate emission factors for wet and dry mill plants, or those

running on coal or natural gas.

Energy output for each facility was estimated by multiplying production capacity by the

heating value for ethanol. Since data on actual production by facility were not available, all

plants were assumed to operate at capacity. Estimates of production capacity were obtained

from data collected by the Renewable Fuels Association

(http://www.ethanolrfa.org/industry/locations/). For some major ethanol producers production

capacity was not available for specific facilities.

Data for facilities where both emissions and production capacity were available were

used to estimate nationwide emission rates in g/mmBTU. Table 3.1-23 lists the number of

ethanol production facilities with emissions data for various air toxics, as well as production

capacity estimates.

Table 3.1-23.

Number of Facilities with Emission Inventory Data

by Pollutant and Production Capacity Estimates

Pollutant No. of Facilities with Emissions Data and

Production Capacity Estimates.

Benzene 30

Formaldehyde 35

Acetaldehyde 50

Acrolein 22

An emission factor for ethanol was estimated using data collected in Minnesota from 16

facilities, all of which were dry mill plants.714 Since most ethanol emissions occur during

fermentation, and new production of ethanol is likely to occur at dry mill facilities, these data are

likely to provide representative estimates of future year increases in ethanol emissions under the

control scenarios modeled. The resultant emission factors for ethanol production facilities are

provided in Table 3.1-24.

Distillers’ grains with solubles (DGS) is a co-product of dry mill corn ethanol production

that can be used as animal feed. Corn oil remaining in the DGS can be extracted and sold for

commercial uses, such as biodiesel production, at a relatively high value compared to the DGS

itself. The oil can be extracted by gravimetric methods or by extraction with n-hexane, which is

a potentially important toxic emission associated with increased ethanol production. Capital

costs for solvent extraction are higher, but so are yields.

Corn oil for food grade use is produced by a process wherein corn is separated into

component parts, prior to fermentation, with the starch heavy dehulled-degermed corn portion

fed to the ethanol plant and the corn germ fed to a hexane-based corn oil extraction facility. This
549

process is capital intensive and must be designed into the plant. We expect the food grade

extraction process to be less widespread than commercial grade processes for these reasons.

VeraSun recently submitted an application to the Iowa Department of Natural Resources

(IDNR) to add a facility for solvent extraction of corn oil to an ethanol plant in Fort Dodge

Iowa.715 In this application, Verasun proposed to control particulate matter emissions from the

process using a baghouse, and to minimize VOC emissions through good design and operating

processes. Verasun estimated that this plant, with an annual DDGS capacity of 455,000 tons of

DDGS per year, would produce 305 tons of VOCs per year, with n-hexane emissions of 295 tons

per year. PM10 emissions would be about 13 tons.

EPA used the Verasun application data to develop an estimate of potential nationwide nhexane emissions from ethanol plants nationwide. EPA estimates that about 40% of ethanol

production will have corn oil extraction by 2022; thus, we assumed that about half of this would

be from solvent extraction and 20% of dry mill plants would employ this process. It is likely a

number of plants will use gravimetric recovery, since it can be easily retrofitted to any size plant

at modest capital cost. First, we developed emission rates per ton of DDGS production. Then

we developed an estimate of DDGS produced nationwide, using industry characterization

estimates of 13.67 billion gallons of dry mill ethanol production in 2022, and 0.00334 tons

DDGS per gallon of ethanol produced by dry mills.716 Multiplying the emission rate from the

Verasun application by total production of DDGS, EPA estimates these facilities could emit

about 9,000 tons of n-hexane nationwide. However, given the very limited data on emissions

from such facilities and the nascent nature of this process at ethanol production facilities, such

estimates should be regarded as highly uncertain.

Biodiesel Production Facilities -- To estimate emission factors for biodiesel production facilities,

we identified air toxic emission data for individual facilities developed for the 2005 NEI.

Unfortunately, only toxics data for two existing biodiesel facilities could be found. These data

were used to develop toxic to VOC ratios, then applied to VOC emission factors for biodiesel

plants obtained from GREET, with modifications to add energy used in crushing soybeans.

VOC emission rates vary by feedstock. Toxic to VOC ratios, VOC emission rates, and resultant

toxic emission rates in grams per gallon are given in Table 3.1-25.

Transportation and Distribution of Gasoline, Ethanol, Gasoline/Ethanol Blends and Biodiesel --

Air toxic emissions associated with distributing fuel and fuel blends come from two sources.

The first source is evaporative, spillage and permeation emissions from storage and transfer

activities, and the second source is emissions from vehicles and pipeline pumps used to transport

the fuels. Since a pipeline system does not exist for ethanol, increased ethanol use is likely to

increase toxic emissions from vehicles used to transport it, while a corresponding decrease in

gasoline distribution would decrease any emissions related to pipeline pumping.

Storage and transfer activities result in evaporative emissions of benzene and ethanol

from gasoline, ethanol, and gasoline/ethanol blends. Evaporative emissions from biodiesel fuel

are not expected to be significant. Emissions of ethanol occur both during transport of ethanol

from production facilities to bulk terminals, and after blending, at bulk terminals. In addition,

emission factors for benzene must be estimated separately for fuel before and after blending. As
550

previously discussed, we assumed bulk terminal emissions were associated with unblended

gasoline. We then divided emissions into a refinery to bulk terminal component and a bulk

terminal to dispensing facility component. Benzene emission factors for gasoline transport from

refinery to bulk terminals were weighted by the fraction of 2002 VOC emissions for this part of

the process, whereas emission factors for E0 gasoline, E10 gasoline, and E85 were weighted by

the fraction of 2002 VOC from the bulk terminal to the pump. Benzene emission rates from

these activities also vary with the year being modeled, since phase-in of the recently finalized

Mobile Source Air Toxics Rule will substantially reduce the amount of benzene in gasoline

beginning in 2011.717 Thus, one set of emission factors were developed for 2002, and a separate

set of emission factors for the reference case in that year. The reference case also includes

impacts of the 2007 renewable fuels standard.718 Thus, the reference case already reflects

ethanol volumes mandated by RFS1.

The emission factors used for 2002 were derived from the estimated gasoline distribution

inventory for benzene in 1999, estimated for the Mobile Source Air Toxics Rule.719 Total

nationwide emissions were divided by the energy content of the total volume of gasoline

distributed in 1999, to obtain the emission factor in g/mmBTU. Total volume of gasoline was

based on gasoline sales as reported by the Energy Information Administration.720 To estimate

the energy content, sales of fuel types (conventional, Federal reformulated, California

reformulated) were multiplied by their respective heating values.

The emission factors used for the reference case in 2022 were derived from an estimated

gasoline distribution inventory for that year. This inventory estimate was calculated by linear

interpolation of 2020 and 2030 inventories from the Mobile Source Air Toxics Rule. Total

nationwide emissions were divided by the energy content of the total volume of gasoline

projected for 2022 by the Energy Information Administration.721 To estimate the energy content,

the projected gasoline volume was multiplied by the heating value for low-sulfur gasoline

(115,000 BTU/gallon).

We assumed that in order to attain the fuel benzene standard for gasoline promulgated in

the Mobile Source Air Toxics Rule, E10 would have the same fuel benzene content per gallon as

E0 . However, for E10 the E0 emission factor was adjusted to account for the lower energy

content of E10 relative to E0. For E85, the E0 emission factor was adjusted to account for 66%

lower benzene emissions per gallon, as well as the lower energy content of E85.

The emission factors for benzene are provided in Table 3.1-26.

To estimate ethanol emissions associated with the distribution of E10 and E85, ethanol to

benzene emission ratios were applied to benzene estimates. The ratios were 14.8 for E10 and

112.8 for E85. The ratio for E10 was obtained from the profile for composite evaporative

emissions from U. S. EPA’s SPECIATE database, profile 1301.722 The ratio for E85 was

obtained from analyses of evaporative emissions from three vehicles tested as part of the

Auto/Oil program in the early 1990’s.723 These emission factors are reported in Table 3.1-26.
551

Table 3.1-24. Air Toxic Emission Factors for Petroleum Refineries, Ethanol

Refineries, and Electricity Production (g/mmBTU of fuel or electricity produced)

Pollutant

Petroleum

Refinery

Ethanol

refinery

Electricity

Production

1,3-butadiene 0.0014 N. A. 0.0001

Acetaldehyde 0.0002 3.0585 0.0297

Acrolein 0.0001 0.1323 0.0115

Benzene 0.0264 0.0998 0.0443

Ethanol 0.0000 21.6858

Formaldehyde 0.0042 0.5263 0.0629

Naphthalene 0.0029

Table 3.1-25. Air Toxic Emission Factors for Biodiesel

Production Facilities (g/gallon produced)

Pollutant Toxic/VOC

Ratio

Biodiesel

Soybean Oil EF

(g/gal)

Biodiesel Yellow

Grease/tallow

(g/gal)

Renewable

Biodiesel Soybean

Oil (g/gal)

VOC 0.040 0.042 0.029

Benzene 7.4x10-7 3.0x10-8 3.1x10-8 2.1x10-8

1,3-Butadiene 0 0 0 0

Formaldehyde 3.5x10-5 1.4x10-6 1.5x10-6 1.0x10-6

Acetaldehyde 5.6x10-6 2.3x10-7 2.4x10-7 1.6x10-7

Acrolein 4.8x10-6 1.9x10-7 2.0x10-7 1.4x10-7

Ethanol 0 0 0 0

Naphthalene 6.3x10-7 2.5x10-8 2.6x10-8 1.8x10-8
552

Table 3.1-26. Air Toxic Evaporative Emission Factors for Gasoline, Ethanol,

and Blend Transport and Distribution (g/mmBTU of fuel transported)

Pollutant Process Year Fuel EF (g/mmBTU)

Benzene Refinery to

Bulk Terminal 2002 E0 0.0488

Refinery to

Bulk Terminal 2022 E0 0.0270

Bulk Terminal

to Pump 2002 E0 0.0908

Bulk Terminal

to Pump 2022 E0 0.0502

Bulk Terminal

to Pump 2022 E10 0.0519

Bulk Terminal

to Pump 2022 E85 0.0228

Ethanol Bulk Terminal

to Pump 2022 E10 1.8591

Bulk Terminal

to Pump 2022 E85 7.1432

As mentioned previously, ethanol vapor emissions during transport from the ethanol plant

to the bulk terminal are based on an adjustment to the gasoline transport VOC emissions to

account for the much lower vapor pressure and molecular weight.

There are also toxic emissions associated with combustion of fuels used in transport and

distribution of feedstocks and fuels. The emission factors for these are shown in Table 3.1-27 as

fractions of exhaust VOC, or PM10 for exhaust naphthalene. The VOC and PM10 emission

factors that these fractions are applied to are presented above in Table 3.1-21. The locomotive,

marine distillate, and residual boiler estimates come from a 2005 EPA report.724 The heavy-duty

diesel truck emission fractions come from a 2002 report documenting the toxics module of

EPA's MOBILE6.2 model,725 and the pipeline values come from the EPA AP-42 document.726
553

Table 3.1-27.

Toxic Fractions of Exhaust VOC (or fraction of PM10 for exhaust naphthalene)

(grams toxics per gram of VOC or PM10)

Mode Source

1,3-

Butadiene Acetaldehyde Acrolein Benzene Formaldehyde Naphthalene

Rail

Diesel

Locomotive 0.003246519 0.018786 0.0031238 0.002587511 0.04328653 0.0018716

Barge

Marine

Diesel –

Distillate 0.00061 0.074298 0.0035 0.020344 0.1496 0.0018716

Ocean

Tanker

Residual

Boiler 0 0.003858 0 0.000165354 0.02645669 0.0025885

Truck

HD Diesel

Trucks 0.00061 0.0288 0.0035 0.0105 0.0782 0.00128892

Pipeline

Natural Gas

Turbines 0 0.019048 0.0030476 0.005714286 0.33809524

Gasoline

Farm Equip

HD Gasoline

Trucks 0.088005387

3.2 Non-GHG Emission Impact Results

3.2.1 U.S. Total Reference Case Inventories for All Sectors (AEO 2007 only)

The reference case emission inventories used for this final rule analysis are based on

different sources depending on sector, and for most sectors they match what was used for the

proposed rule and air quality analysis.

For stationary/area sources and aircraft we used the 2002 National Emissions Inventory

(NEI), Version 3, including the NEI projections for 2020. The development of these inventories

is documented in the November 27, 2007, memo titled, “Approach for Developing 2002 and

Future Year National Emission Summaries,” from Madeleine Strum to Docket EPA-HQ-OAR2007-0491. That memo summarizes the methodologies and additional reference documents for

criteria air pollutants (CAP) and mobile source air toxics (MSATs).

For onroad mobile sources we used a special version of the MOVES model that estimates

emissions from light-duty and heavy-duty gasoline vehicles, except for motorcycles. For other

onroad vehicles including diesel vehicles and motorcycles, we relied on the MOBILE6.2 model

as run using the NMIM platform with county specific fuel properties and temperatures. Most

nonroad equipment was modeled with NONROAD2005d using NMIM, which is a version of the

NONROAD that includes the benefits of the two nonroad regulations published in 2008 (the

locomotive and marine diesel rule and the small spark-ignition and recreational marine engine

rule).

Inventories for locomotives and commercial marine vessels are not covered by the NONROAD

model, and they have been updated since the 2002 NEI was published. Thus we used the more

recent inventories published in the regulatory impact analyses of their respective recent

rulemakings. Locomotives and C1/C2 commercial marine vessel inventories come from the
554

spring 2008 final rule, and the C3 commercial marine emission inventory is from the Notice of

Proposed Rulemaking (NPRM) published in August 2009.734

Table 3.2-1 shows the total 2022 mobile and non-mobile source inventory projections

that were used as the basis for the impact percentages shown above in Table 3.2-1 through 3.2-4.

The mobile source values in this table use the inventory values of the AEO 2007 reference case.

Table 3.2-1. 2022 AEO 2007 Reference Case Emissions by Sector

VOC CO NOx PM10 PM2.5 SO2 NH3

Onroad

Gasoline 981,432 26,547,169 2,001,543 46,284 42,619 34,031 390,486

Onroad Diesel 140,854 243,820 1,307,150 62,253 37,357 4,352 11,426

Nonroad

Gasoline 1,440,414 14,924,581 269,443 56,660 52,305 1,836 1,112

Other Nonroada

270,707 1,402,948 3,353,753 230,305 209,516 1,026,510 3,034

Stationary/Area 8,740,057 11,049,239 5,773,927 3,194,610 3,047,714 7,864,681 3,839,925

Total 11,573,464 54,167,758 12,705,817 3,590,112 3,389,512 8,931,411 4,245,983

Table 3.2-1

continued Benzene Ethanol 1,3-

Butadiene

AcetaldehydeFormaldehyde Naphthalene Acrolein

Onroad

Gasoline 33,607 15,985 4,487 6,455 10,681 3,787 513

Onroad Diesel 1,749 0 958 3,857 10,589 20 513

Nonroad

Gasoline 26,193 66,150 4,935 4,033 7,245 713 436

Other Nonroada

3,815 5,294 939 9,550 22,355 24 1,021

Stationary/Area 111,337 462,566 1,847 13,118 23,846 9,404 3,412

Total 176,701 549,995 13,166 37,013 74,716 13,949 5,895

a Nonroad diesel, LPG, CNG engines and all locomotive, aircraft, and commercial marine

3.2.2 2022 RFS2 Total Non-GHG Emission Inventory Impacts

Our projected overall emission impacts for each of the analyzed RFS2 renewable fuel

scenarios are shown in Table 3.2-2 and Table 3.2-3 for 2022, showing the expected emission

changes for the U.S. relative to each of the reference cases. The percent contribution of these

impacts relative to the total U.S. inventory across all sectors is also shown, using the AEO 2007

reference case totals from Table 3.2-1. .
555

Table 3.2-2.

RFS2 Emission Impacts in 2022 Relative to the AEO2007 Reference Case

Low Ethanol

Scenario

Mid (Primary)

Ethanol Scenario

High Ethanol

Scenario

Pollutant

Annual

Short Tons

% of Total

US

Inventory

Annual

Short Tons

% of Total

US

Inventory

Annual

Short Tons

% of Total

US

Inventory

NOx 208,316 1.64% 184,820 1.45% 131,124 1.03%

HC 20,123 0.17% 24,523 0.21% 35,342 0.31%

PM10 71,779 2.00% 63,323 1.76% 44,099 1.23%

PM2.5 17,355 0.51% 14,393 0.42% 7,678 0.23%

CO -364,400 -0.67% -376,419 -0.69% -404,199 -0.75%

Benzene -979 -0.55% -1,004 -0.57% -1,056 -0.60%

Ethanol 33,749 6.14% 54,137 9.84% 102,359 18.61%

1,3-Butadiene 59 0.45% 59 0.45% 59 0.45%

Acetaldehyde 1,978 5.34% 3,108 8.40% 5,757 15.56%

Formaldehyde 113 0.15% 130 0.17% 170 0.23%

Naphthalene -4 -0.03% -4 -0.03% -4 -0.03%

Acrolein 16 0.28% 21 0.35% 31 0.53%

SO2 20,456 0.23% 5,065 0.06% -30,058 -0.34%

NH3 48,711 1.15% 48,711 1.15% 48,709 1.15%

Table 3.2-3.

RFS2 Emission Impacts in 2022 Relative to the RFS1 Mandate Reference Case

Low Ethanol

Scenario

Mid (Primary)

Ethanol Scenario

High Ethanol

Scenario

Pollutant

Annual

Short Tons

% of Total

US

Inventory

Annual

Short Tons

% of Total

US

Inventory

Annual

Short Tons

% of Total

US

Inventory

NOx 271,100 2.13% 247,604 1.95% 193,907 1.53%

HC 96,362 0.83% 100,762 0.87% 111,581 0.96%

PM10 77,469 2.16% 69,013 1.92% 49,791 1.39%

PM2.5 18,511 0.55% 15,549 0.46% 8,834 0.26%

CO -2,857,823 -5.28% -2,869,842 -5.30% -2,897,622 -5.35%

Benzene -4,240 -2.40% -4,264 -2.41% -4,316 -2.44%

Ethanol 79,736 14.50% 100,123 18.20% 148,345 26.97%

1,3-Butadiene 224 1.70% 224 1.70% 224 1.70%

Acetaldehyde 4,718 12.75% 5,848 15.80% 8,497 22.96%

Formaldehyde 338 0.45% 355 0.48% 395 0.53%

Naphthalene -1 0.00% -1 -0.01% -1 0.00%

Acrolein 18 0.31% 22 0.38% 33 0.56%

SO2 18,678 0.21% 3,286 0.04% -31,836 -0.36%

NH3 48,711 1.15% 48,711 1.15% 48,709 1.15%
556

Fuel production and distribution emission impacts of the RFS2 program were estimated

in conjunction with the development of life cycle GHG emission impacts, and the GHG emission

inventories discussed in Chapter 2. These emissions are calculated according to the breakdowns

of agriculture, feedstock transport, fuel production, and fuel distribution; the basic calculation is

a function of fuel volumes in the analysis year and the emission factors associated with each

process or subprocess. Additionally, the emission impact of displaced petroleum is estimated,

using the same domestic/import shares discussed in chapter 2.

In general the basis for this life cycle evaluation was the analysis conducted as part of the

Renewable Fuel Standard (RFS1) rulemaking, but enhanced significantly. While our approach

for the RFS1 was to rely heavily on the “Greenhouse Gases, Regulated Emissions, and Energy

Use in Transportation” (GREET) model, developed by the Department of Energy’s Argonne

National Laboratory (ANL), we are now able to take advantage of additional information and

models to significantly strengthen and expand our analysis for this rule. In particular, the

modeling of the agriculture sector was greatly expanded beyond the RFS1 rule analysis,

employing economic and agriculture models to consider factors such as land-use impact,

agricultural burning, fertilizer, pesticide use, livestock, crop allocation, and crop exports.

Other updates and enhancements to the GREET model assumptions include updated

emission factors for NOx, CO, and SO2 from new cellulosic ethanol plant modeling by the

National Renewable Energy Laboratory (NREL), and updated fuel and feedstock transport

emission factors that account for recent EPA emission standards and modeling, such as the Tier 4

diesel truck standards published in 2004 and the locomotive and commercial marine standards

finalized in 2008. Emission factors for new corn ethanol plants continue to use the values

developed for the RFS1 rule, which were based on data submitted by states for dry mill plants.

There are no new standards planned at this time that would offer any additional control of

emissions from corn or cellulosic ethanol plants. In addition, GREET does not include air toxics

or ethanol. Thus emission factors for ethanol and the following air toxics were added: benzene,

1,3-butadiene, formaldehyde, acetaldehyde, acrolein and naphthalene.

Results of these calculations relative to each of the reference cases for 2022 are shown in

Table 3.2-4 and Table 3.2-5 for the criteria pollutants, ammonia, ethanol and individual air toxic

pollutants. It should be noted that the impacts relative to the RFS1 reference case use the same

agricultural impacts as for the AEO 2007 reference case, since there was no agricultural

modeling done for the RFS1 case. Due to the complex interactions involved in projections in

agricultural modeling, it was not considered reasonable to attempt any sort of proportional

adjustments to the AEO 2007 agricultural projections to approximate the RFS1 case.

The fuel production and distribution impacts of today's rule on VOC are mainly due to

increases in emissions connected with biofuel production, countered by decreases in emissions

associated with gasoline production and distribution as ethanol displaces some of the gasoline.

Increases in PM2.5, SOx and especially NOx are driven by stationary combustion emissions

from the substantial increase in corn and cellulosic ethanol production. Biofuel plants (corn and

cellulosic) tend to have greater combustion emissions relative to petroleum refineries on a perBTU of fuel produced basis. Increases in SOx emissions are also due to increases in agricultural

chemical production and transport, while substantial PM increases are also associated with
557

fugitive dust from agricultural operations. Ammonia emissions are expected to increase

substantially due to increased ammonia from fertilizer use.

Ethanol vapor and most air toxic emissions associated with fuel production and

distribution are projected to increase. Relative to the US total reference case emissions with

RFS1 mandate ethanol volumes, the primary RFS2 control case is estimated to yield increases of

4-13 percent for acetaldehyde and ethanol vapor, driven directly by the increased ethanol

production and distribution. Formaldehyde and acrolein increases are smaller, on the order of

0.4-1 percent. There are also very small decreases in benzene, 1,3-butadiene and naphthalene

relative to the US total emissions.

Table 3.2-4. Fuel Production and Distribution Impacts for 2022 RFS2 Control Cases

Relative to the AEO 2007 Reference Case

Low Ethanol

Scenario

Mid (Primary)

Ethanol Scenario

High Ethanol

Scenario

Pollutant

Annual

Short Tons

% of Total

US

Inventory

Annual

Short Tons

% of Total

US

Inventory

Annual

Short Tons

% of Total

US

Inventory

NOx 187,666 1.48% 164,170 1.29% 110,473 0.87%

HC 16,604 0.14% 19,737 0.17% 27,547 0.24%

PM10 72,348 2.02% 63,892 1.78% 44,669 1.24%

PM2.5 17,670 0.52% 14,707 0.43% 7,993 0.24%

CO 142,191 0.26% 130,172 0.24% 102,392 0.19%

Benzene -208 -0.12% -236 -0.13% -298 -0.17%

Ethanol 20,291 3.69% 35,865 6.52% 72,815 13.24%

1,3-Butadiene 0 0.00% 0 0.00% 0 0.00%

Acetaldehyde 823 2.22% 933 2.52% 1,193 3.22%

Formaldehyde 170 0.23% 187 0.25% 227 0.30%

Naphthalene -5 -0.04% -6 -0.04% -7 -0.05%

Acrolein 33 0.56% 37 0.63% 48 0.81%

SO2 20,435 0.23% 5,044 0.06% -30,078 -0.34%

NH3 48,711 1.15% 48,711 1.15% 48,709 1.15%
558

Table 3.2-5. Fuel Production and Distribution Impacts for 2022 RFS2 Control Cases

Relative to the RFS1 Mandate Reference Case

Low Ethanol

Scenario

Mid (Primary)

Ethanol Scenario

High Ethanol

Scenario

Pollutant

Annual

Short Tons

% of Total

US

Inventory

Annual

Short Tons

% of Total

US

Inventory

Annual

Short Tons

% of Total

US

Inventory

NOx 193,161 1.52% 169,665 1.34% 115,969 0.91%

HC 73,881 0.64% 77,014 0.67% 84,825 0.73%

PM10 78,039 2.17% 69,583 1.94% 50,360 1.40%

PM2.5 18,826 0.56% 15,864 0.47% 9,149 0.27%

CO 147,677 0.27% 135,658 0.25% 107,878 0.20%

Benzene -203 -0.12% -231 -0.13% -294 -0.17%

Ethanol 53,871 9.79% 69,445 12.63% 106,395 19.34%

1,3-Butadiene -1 -0.01% -1 -0.01% -1 -0.01%

Acetaldehyde 1,507 4.07% 1,617 4.37% 1,877 5.07%

Formaldehyde 276 0.37% 293 0.39% 333 0.45%

Naphthalene -7 -0.05% -8 -0.06% -9 -0.06%

Acrolein 62 1.06% 67 1.13% 77 1.31%

SO2 18,657 0.21% 3,266 0.04% -31,857 -0.36%

NH3 48,711 1.15% 48,711 1.15% 48,709 1.15%

A breakout of these upstream emissions by where they occur in the

production/distribution chain is shown in Table 3.2-6. The displaced gasoline line of this table

refers to the impacts of decreasing the petroleum based gasoline and diesel fuel production as

some of the needed energy is replaced with ethanol and biodiesel fuels.
559

Table 3.2-6. Emission Inventory Impacts by Fuel Production/Distribution Segment for the

Primary RFS2 Control Case Relative to the AEO 2007 Reference Case (annual short tons)

VOC CO NOx PM10 PM2.5 SO2 NH3

Agriculture 2,398 11,831 6,597 56,512 9,169 22,157 48,709

Biofuel Feedstock

Transport 355 508 1,440 239 106 584 0

Biofuel Production 18,867 129,586 193,040 22,621 11,241 8,932 2

Biofuel Transport

& Distribution 18,041 3,316 7,599 323 219 962 0

Displaced gasoline -19,925 -15,069 -44,506 -15,803 -6,028 -27,591 0

Total Upstream 19,737 130,172 164,170 63,892 14,707 5,044 48,711

Table 3.2-6

continued Benzene Ethanol 1,3-

Butadiene

AcetaldehydeFormaldehyde Naphthaleneb Acrolein

Agriculture -145.8 0 1.99 13.51 47.89 0.73 -3.24

Biofuel

Feedstock

Transport

3.73 0 0.22 10.24 27.80 0.31 1.24

Biofuel

Production 28.86 6,435 0.01 906.9 154.9 0.02 39.01

Biofuel

Transport &

Distribution

-30.84 29,430 1.18 11.01 25.67 0.57 1.46

Displaced

gasoline -91.80 0 -3.84 -8.72 -69.50 -7.55 -1.27

Total Upstream -235.8 35,865 -0.44 933.0 186.8 -5.92 37.21

Tables 3.2-7 and 3.2-8 summarize the vehicle and equipment emission impacts in 2022,

including the biodiesel impacts. Table 3.2-9 shows that the biodiesel contribution to these

impacts is quite small; as noted earlier, using MOVES2010 as baseline emissions for diesels

would likely double the NOx and PM impacts relative to the NMIM-based impacts shown

While the three fuel effect scenarios were only modeled for passenger cars and trucks, these

totals reflect the net emissions from all mobile sources, including passenger cars and trucks,

heavy duty trucks, off-road sources and portable fuel containers, using the same emissions in all

three cases for the non-passenger car/truck categories. A full description of the basis of these

vehicle and equipment emission impacts is given in Section 3.1.1 of this document.

Carbon monoxide, benzene, and acrolein are projected to decrease in 2022 under today’s

rule, while NOx, HC and the other air toxics, especially ethanol and acetaldehyde, are projected

to increase due to the impacts of E10.
560

Table 3.2-7.

2022 Vehicle and Equipment Emission Impacts for the RFS2 Control Cases

Relative to the AEO 2007 Reference Case

Low Ethanol

Scenario

Mid (Primary)

Ethanol Scenario

High Ethanol

Scenario

Pollutant

Annual

Short Tons

% of Total

US

Inventory

Annual

Short Tons

% of Total

US

Inventory

Annual

Short Tons

% of Total

US

Inventory

NOx 20,650 0.16% 20,650 0.16% 20,650 0.16%

HC 3,519 0.03% 4,786 0.04% 7,795 0.07%

PM10 -569 -0.02% -569 -0.02% -569 -0.02%

PM2.5 -315 -0.01% -315 -0.01% -315 -0.01%

CO -506,591 -0.94% -506,591 -0.94% -506,591 -0.94%

Benzene -771 -0.44% -768 -0.43% -758 -0.43%

Ethanol 13,459 2.45% 18,272 3.32% 29,544 5.37%

1,3-Butadiene 59 0.45% 59 0.45% 59 0.45%

Acetaldehyde 1,155 3.12% 2,175 5.88% 4,564 12.33%

Formaldehyde -57 -0.08% -57 -0.08% -57 -0.08%

Naphthalene 2 0.01% 2 0.01% 3 0.02%

Acrolein -16 -0.28% -16 -0.28% -16 -0.28%

SO2 21 0.00% 21 0.00% 21 0.00%

NH3 0 0.00% 0 0.00% 0 0.00%
561

Table 3.2-8.

2022 Vehicle and Equipment Emission Impacts for the RFS2 Control Cases

Relative to RFS1 Mandate Reference Case

Low Ethanol

Scenario

Mid (Primary)

Ethanol Scenario

High Ethanol

Scenario

Pollutant

Annual

Short Tons

% of Total

US

Inventory

Annual

Short Tons

% of Total

US

Inventory

Annual

Short Tons

% of Total

US

Inventory

NOx 77,939 0.61% 77,939 0.61% 77,939 0.61%

HC 22,480 0.19% 23,748 0.21% 26,756 0.23%

PM10 -569 -0.02% -569 -0.02% -569 -0.02%

PM2.5 -315 -0.01% -315 -0.01% -315 -0.01%

CO -3,005,500 -5.55% -3,005,500 -5.55% -3,005,500 -5.55%

Benzene -4,036 -2.28% -4,033 -2.28% -4,022 -2.28%

Ethanol 25,864 4.70% 30,678 5.58% 41,950 7.63%

1,3-Butadiene 225 1.71% 225 1.71% 225 1.71%

Acetaldehyde 3,210 8.67% 4,231 11.43% 6,620 17.89%

Formaldehyde 62 0.08% 62 0.08% 62 0.08%

Naphthalene 7 0.05% 7 0.05% 8 0.06%

Acrolein -44 -0.75% -44 -0.75% -44 -0.75%

SO2 21 0.00% 21 0.00% 21 0.00%

NH3 0 0.00% 0 0.00% 0 0.00%
562

Table 3.2-9.

2022 Biodiesel Emission Impacts for All RFS2 Control Cases

Relative to Reference Cases

(these impacts are included in Tables 3.2-7 and 3.2-8)

Biodiesel

Impacts Pollutant

Annual Short

Tons

NOx 1,346

HC -2,422

PM10 -569

PM2.5 -315

CO -4,104

Benzene -30.08

Ethanol 0.00

1,3-Butadiene -16.48

Acetaldehyde -66.34

Formaldehyde -182.09

Naphthalene -0.38

Acrolein -8.82

SO2 0

NH3 0

Table 3.2-10 shows a breakout of the relative impacts of the RFS2 volumes on the

various types of vehicle and equipment emissions for the primary (mid-ethanol) case relative to

the AEO 2007 reference case. The gasoline vehicle exhaust emission values were generated by

MOVES, while the NMIM model was used to generate the other vehicle and equipment emission

impacts. The impacts on portable fuel container emissions were estimated using an analysis of

available data, adjusted for the ethanol and gasoline fuel volumes in this rule. The methods used

are described above in Section 3.1.1. The substantial CO reductions and NOx and ethanol

increases from light-duty vehicles and nonroad gasoline equipment are due to the effects of

increased E10 marketshare with no E0 remaining in the market. Evaporative and refueling vapor

emissions only include VOC, ethanol, benzene, and naphthalene.
563

Table 3.2-10. Vehicle and Equipment Emission Inventory Impacts by Source Type

for the Primary RFS2 Control (mid-ethanol) Case

Relative to the AEO 2007 Reference Case (annual short tons)

VOCa CO NOx PM10 PM2.5 SO2 NH3

Light-duty gasoline

vehicle exhaust -1,437 -72,872 10,034 0.0 0.0 0.0 0.0

Light-duty gasoline

vehicle evap 3,447 n/a b

n/a n/a n/a n/a n/a

Light-duty gasoline

vehicle refueling 2,015 n/a n/a n/a n/a n/a n/a

Heavy-duty gasoline

vehicle exhaust 2,168 -21,163 58 0.0 0.0 0.0 0.0

Heavy-duty gasoline

vehicle evap -750 n/a n/a n/a n/a n/a n/a

Heavy-duty gasoline

vehicle refueling 440 n/a n/a n/a n/a n/a n/a

Nonroad gasoline

equipment exhaust -6,413 -408,453 9,212 0.0 0.0 20.7 0.0

Nonroad gasoline

equipment evap 6,702 n/a n/a n/a n/a n/a n/a

Nonroad gasoline

equipment refueling 563 n/a n/a n/a n/a n/a n/a

Portable fuel

containers 1,037 n/a n/a n/a n/a n/a n/a

Onroad diesel

vehicles -2,422 -4,104 1,346 -569 -315 0.0 0.0
564

Table 3.2-10

continued Benzene Ethanol 1,3-Butadiene

AcetaldehydeFormaldehydeNaphthalene Acrolein

Light-duty gasoline

vehicle exhaust -287 8,773 21.5 2,034 73 0.00 0.65

Light-duty gasoline

vehicle evap 6.65 500 n/a n/a n/a 1.28 n/a

Light-duty gasoline

vehicle refueling 6.63 770 n/a n/a n/a 0.82 n/a

Heavy-duty

gasoline vehicle

exhaust

-47 57 0.00 19 -2.11 0.00 0.00

Heavy-duty

gasoline vehicle

evap

-1.31 315 n/a n/a n/a 0.09 n/a

Heavy-duty

gasoline vehicle

refueling

1.25 157 n/a n/a n/a 0.05 n/a

Nonroad gasoline

equipment exhaust -737 2,497 57.4 189 54 0.00 -7.95

Nonroad gasoline

equipment evap 106 4,556 n/a n/a n/a 0.00 n/a

Nonroad gasoline

equipment refueling 106 972 n/a n/a n/a 0.00 n/a

Portable fuel

containers -0.30 646 n/a n/a n/a 0.13 n/a

Onroad diesel

vehicles -30.08 0.00 -16.48 -66.34 -182.09 -0.38 -8.82

a

"VOC" values shown are actually THC for onroad gasoline exhaust and evaporative emissions.

b

n/a = Not applicable

Table 3.2-11 shows the relative impacts of various types of renewable fuels on the basis

of tons per million BTUs of renewable fuel consumed. These values include all

vehicle/equipment as well as upstream fuel production/distribution impacts.
565

Table 3.2-11. Emission Inventory Impacts by Type of Renewable Fuel

for the Primary RFS2 Control Case Relative to the AEO 2007 Reference Case

(tons per mmBTU)

VOC CO NOx PM10 PM2.5 SO2 NH3

Ethanol from

domestic corn

(except coal)

10,301 4,013 3,128 12,175 1,991 3,403 8,420

Ethanol from

domestic corn

(coal)

439 317 320 1,330 351 427 349

Ethanol from

domestic

cellulosic

10,121 29,888 32,440 13,168 2,350 -8,377 15,107

Ethanol from

imported

sugarcane

1,717 -830 -2,621 -1,116 -418 -1,916 44

Biodiesel -1,049 913 -290 4,268 632 1,580 4,171

Renewable Diesel -1,602 23 -26.3 -102 -27 -169 0.0

Cellulosic Diesel -190 95,847 131,218 34,169 9,831 10,096 21,085

Table 3.2-11

continued Benzene Ethanol 1,3-

Butadiene

AcetaldehydeFormaldehydeNaphthalene Acrolein

Ethanol from

domestic corn

(except coal)

30.1 10,870 0.097 335 67.6 -0.617 141

Ethanol from

domestic corn

(coal)

1.2 451 0.004 13.8 2.8 -0.026 5.8

Ethanol from

domestic

cellulosic

39.3 17,024 0.078 245 47.1 -1.223 237

Ethanol from

imported

sugarcane

26.4 5,220 -0.185 0.25 -3.0 -0.500 77.5

Biodiesel 10.4 0 -0.188 1.64 0.77 -0.658 62.6

Renewable Diesel 65.6 0 0.009 0.45 0.51 -0.061 0.06

Cellulosic Diesel -0.9 2,299 -0.26 337 71.0 -2.84 331
566

3.3 Emission Inventories Used in the Air Quality Modeling

3.3.1 Overview of Inventory Differences

Section 3.2 above describes our latest emission inventory impacts projected to result from

the increased use of renewable fuels as required by the RFS2 standards. However, the air quality

modeling had to be started long before these latest emission inventory impacts could be

determined. The air quality modeling presented in Section 3.4 utilized inventory impact

estimates based in large part on the analysis conducted for the NPRM, but with a few

enhancements. Below is an overview of the differences between these inventory impact

estimates. Details of the differences between these inventories are presented in Section 3.3.2 of

this RIA.

To put the differences in context, Table 3.3-1 shows the different renewable fuel volumes

considered for the three analyses. This shows that the volumes used for the NPRM analysis were

also the basis of the inventories used for the air quality modeling. The RFS1 reference case

listed here is the RFS1 mandate case. The primary (mid-ethanol) case considered for this final

rule includes much less cellulosic ethanol than in the prior analyses, but makes up for that with

diesel fuel produced from cellulosic feedstocks. The final rule case that is most comparable to

the RFS2 control case considered in the NPRM and air quality analyses is the high ethanol case

shown in the last row of the table.

Table 3.3-1.

Renewable Fuel Volumes Used in Each Analysis

(Bgal/year in 2022)

Analysis Ethanol Scenario Corn Cellulosic Imported Total Biodiesel Renewable

Diesel

Cellulosic

Diesel

RFS1 Ref 5.81 0.25 0.64 6.70 0.38 0.0 0.0

AEO Ref 12.29 0.25 0.64 13.18 0.38 0.0 0.0

NPRM

& AQ

RFS2 15.0 16.0 3.14 34.14 0.81 0.38 0.0

RFS1 Ref 7.046 0.0 0.0 7.046 0.303 0.0 0.0

AEO Ref 12.29 0.25 0.64 13.18 0.38 0.0 0.0

Low

Ethanol 15.0 0.25 2.24 17.49 1.67 0.15 9.26

MidEthanol

(Primary)

15.0 4.92 2.24 22.16 1.67 0.15 6.52

FRM

High

Ethanol 15.0 16.0 2.24 33.24 1.67 0.15 0.0

Tables 3.3-2 and 3.3-3 summarize the differences between the US total sum of the

county-level impacts used for the air quality modeling and the final rule nationwide impacts

relative to the RFS1 mandate and AEO 2007 reference cases.
567

Table 3.3-2.

Comparison of Air Quality Inventory Impacts to FRM Impacts

in 2022 Relative to the RFS1 Reference Case

Air Quality

Inventory

Impacts

FRM Mid

(Primary) Ethanol

Impacts

FRM High

Ethanol Impacts

Pollutant

Annual Short

Tons

Annual Short

Tons

%

Change

vs AQ

Annual

Short Tons

%

Change

vs AQ

NOx 365,968 247,604 -32% 193,907 -47%

HC 119,873 100,762 -16% 111,581 -7%

PM10 68,646 69,013 1% 49,791 -27%

PM2.5 18,199 15,549 -15% 8,834 -51%

CO -4,619,904 -2,869,842 38% -2,897,622 37%

Benzene -9,662 -4,264 56% -4,316 55%

Ethanol N/Ab

100,123 -- 148,345 --

1,3-Butadiene -194 224 216% 224 216%

Acetaldehyde 7,317 5,848 -20% 8,497 16%

Formaldehyde 173 355 105% 395 128%

Acrolein 79 22 -71% 33 -58%

SO2 57,380 3,286 -94% -31,836 -155%

NH3 141 48,711 34352% 48,709 34351%

a Ethanol emissions for air quality modeling were generated by application of VOC

speciation profiles in SMOKE, the emissions pre-processor for air quality modeling, so

they were not one of the air quality inventory inputs..
568

Table 3.3-3.

Comparison of Air Quality Inventory Impacts vs FRM Impacts

in 2022 Relative to the AEO 2007 Reference Case

Air Quality

Inventory

Impacts

FRM Mid

(Primary) Ethanol

Impacts

FRM High

Ethanol Impacts

Pollutant

Annual Short

Tons

Annual

Short Tons

%

Change

vs AQ

Annual

Short Tons

%

Change

vs AQ

NOx 258,357 184,820 -28% 131,124 -49%

HC 38,186 24,523 -36% 35,342 -7%

PM10 55,877 63,323 13% 44,099 -21%

PM2.5 17,277 14,393 -17% 7,678 -56%

CO -1,743,352 -376,419 78% -404,199 77%

Benzene -4,094 -1,004 75% -1,056 74%

Ethanol N/Ab

54,137 -- 102,359 --

1,3-Butadiene -291 59 120% 59 120%

Acetaldehyde 4,727 3,108 -34% 5,757 22%

Formaldehyde -127 130 202% 170 234%

Acrolein 47 21 -55% 31 -33%

SO2 15,311 5,065 -67% -30,058 -296%

NH3 210 48,711 23065% 48,709 23064%

a Ethanol emissions for air quality modeling were generated by application of VOC

speciation profiles in SMOKE, the emissions pre-processor for air quality modeling,

so they were not one of the air quality inventory inputs..

Table 3.3-4 shows the US total emission inventories used for each of the air quality

modeling cases along with the percent change from each reference case to the control case.
569

Table 3.3-4.

Air Quality Modeling Inventories and Percent Impacts in 2022

US Total

RFS1

US Total

AEO

US Total

RFS2

RFS2 vs

RFS1

RFS2 vs

AEO

Pollutant

Annual

Short Tons

Annual

Short Tons

Annual

Short Tons

Percent

Change

Percent

Change

NOx 11,415,147 11,522,759 11,781,115 3.21% 2.24%

HC 10,292,785 10,374,472 10,412,658 1.16% 0.37%

PM10 11,999,983 12,012,752 12,068,629 0.57% 0.47%

PM2.5 3,371,024 3,371,946 3,389,223 0.54% 0.51%

CO 51,631,075 48,754,523 47,011,171 -8.95% -3.58%

Benzene 226,683 221,115 217,021 -4.26% -1.85%

Ethanola

-- -- -- -- --

1,3-Butadiene 14,458 14,554 14,264 -1.34% -2.00%

Acetaldehyde 58,405 60,995 65,722 12.53% 7.75%

Formaldehyde 140,156 140,456 140,330 0.12% -0.09%

Acrolein 6,399 6,431 6,477 1.23% 0.73%

SO2 8,878,706 8,920,775 8,936,086 0.65% 0.17%

NH3 4,213,048 4,212,979 4,213,189 0.00% 0.00%

a Ethanol emissions were generated by application of VOC speciation profiles in

SMOKE, the emissions pre-processor for air quality modeling, so they were not one of

the air quality inventory inputs..

3.3.1.1 Major Differences Between Air Quality Modeling Inventory and Nationwide NPRM

and FRM Inventories

In attempting to compare the inventory used for air quality modeling with the nationwide

inventories presented in either the draft RIA of the proposed rule or this final RIA it is important

to keep in mind that (a) the air quality inventories are actual estimates of total ton per year

emissions for all sectors, whereas the emission inventory impacts presented in the RIA are only

ton per year changes (increases or decreases) for the sectors that we consider to be affected by

the rule, and (b) as described in Section 3.3.2, very different methods are used calculate the

incremental upstream changes at a national level for the RIA versus the adjusted total countylevel upstream inventories used for air quality modeling.

Differences Between NPRM and Air Quality Modeling Inventories

 The renewable fuel volumes that were considered for the NPRM and air quality modeling

were the same, as shown in Table 3.3-1, but there were substantial changes in some

portions of the emission inventories.

 The air quality modeling inventory used a greatly increased estimate of ethanol transport

VOC and ethanol vapor losses, based on preliminary results of the ORNL analysis of
570

ethanol transport modes and distances.728 This is discussed in section 3.3.2.1.1 of this

RIA.

 The method used to calculate the upstream portion of the air quality modeling inventory

differs from the method used to calculate the estimated nationwide impacts of the rule.

The main difference was for certain non-mobile source sectors where adjustment factors

were applied to existing air quality modeling inventories, rather than attempting to

add/subtract the absolute tons of impact within each county for each source (SCC). This

difference applies mainly to agriculture, crude oil production and transport, and gasoline

refining and distribution. A more detailed description of these methodological

differences is provided in section 3.3.2 of this RIA.

 The downstream portion of the air quality modeling inventory was produced by running

an updated version of the MOVES model at the state-month level and the NMIM model

at the county-month level to generate the downstream impacts (vehicle and equipment

emissions). More details are presented in Section 3.3.2.2 below.

Differences Between Air Quality Modeling and FRM Inventories

 The FRM inventory uses the final results of the ORNL analysis of ethanol transport

modes and distances rather than the preliminary results used for the air quality modeling

inventory analysis. Relative to the version used for the air quality modeling this included

slightly increased truck and water transport, slightly less rail transport, and 16 percent

less ethanol volume loaded into transport or storage tanks in the RFS2 control case (34

bgal ethanol), due to use of fewer total mode transfers.

 The FRM inventory also includes a substantial reduction of cellulosic biofuel plant

energy requirements to account for the portion of the biomass feedstock that is not

combusted for process heat.

 The FRM downstream inventory incorporates a revision of E85 effects to remove all but

ethanol and acetaldehyde emission effects, due to lack of sufficient data to justify any

effects on other pollutants.

 The FRM downstream inventory uses a hybrid approach, applying “more sensitive”

impacts for E10 on pre-Tier 2 light duty vehicles, and applying the “less sensitive” E10

effects for Tier 2 light duty cars and trucks (meaning no impact for NOx or exhaust

NMHC due to E10 for the majority of the fleet on the road in 2022).

 The FRM downstream inventory uses updated estimates of evaporative permeation

impacts of E10 based on recent studies.

3.3.2 Detailed Explanation of Inventory Differences

This section describes how the county-level emission inventories were prepared for use in

air quality modeling, and how they differ from the NPRM nationwide inventories and the final
571

rule nationwide inventories. Air quality modeling requires much more detail and in some cases a

very different method than estimation of nationwide totals. The information provided here only

addresses the first step of inventory preparation for air quality modeling. The final steps involve

processes like hourly allocation and certain types of temperature adjustments. Those steps, as

well as application of adjustments related to the affected stationary (point and non-point) source

categories, are explained in greater detail in a separate technical support document.727

3.3.2.1 Differences in Upstream Impacts between Inventories

3.3.2.1.1 Calculation of Vapor Losses During Ethanol Transport

For “upstream” emissions associated with fuel production and distribution, the largest

change from the NPRM to the air quality modeling analysis was the improved estimate of VOC

and ethanol vapor emissions during ethanol transport, made possible by a detailed analysis of

costs and transport modes conducted by Oak Ridge National Laboratory (ORNL).

728 This

change substantially increased the ethanol and VOC emissions associated with this rule.

For the NPRM analysis these emissions were estimated using a very simplified approach

based on an adjustment to the gasoline transport VOC emissions to account for the much lower

vapor pressure (approximately 3 psi at 100F for denatured ethanol versus 9 psi for gasoline) and

molecular weight (48.7 for denatured ethanol versus approximately 72 for gasoline vapor). The

net factor is 0.23 x gasoline evap VOC. Using the gasoline VOC EF of 14.94 g/mmBTU from

Table 3.1-21 yields an EF of 5.20 g/mmBTU as shown in the following calculation. However,

an oversight in the NPRM upstream impacts spreadsheet model resulted in use of an earlier

estimate of the gasoline VOC EF of 10.2137 g/mmBTU for this ethanol calculation, which meant

that the reported ethanol EF used in the NPRM was actually 3.56 g/mmBTU.

5.20 g/mmBTU of ethanol

= 0.23 x gasoline VOC per-gallon EF / ethanol energy content

= 0.23 x (14.94 g/mmBTU x 115000 BTU/gal) / 76000 BTU/gal

As mentioned in the NPRM, the main shortcoming of this methodology was that it did

not account for differences between ethanol and gasoline transport modes, distances, or transfer

methods in movement of the fuel from production facility to the bulk distribution terminal. For

the air quality modeling analysis and final rule analysis this method was replaced using data

from the ORNL analysis of projected ethanol transport modes, distances, and volumes

transferred under various ethanol volume scenarios. That newer method yielded greatly

increased ethanol vapor and VOC emissions. The air quality modeling analysis used preliminary

results of the ORNL study, which yielded average ethanol EFs of 34.09, 36.06, and 37.94

g/mmBTU for the RFS1 reference, AEO reference, and RFS2 control cases, respectively, when

averaged across all the types of tank loading.729 For air quality modeling the detailed emission

factors for each type of tank loading, shown in Table 3.3-7, were multiplied by the preliminary

ORNL ktons loaded by type of tank for each county.730,731,732

For the FRM analysis the use of the final ORNL results yielded EFs of 26.9 - 31.7

g/mmBTU of ethanol (2.04 - 2.41 g/gal) depending on scenario, due to the added fuel transfer
572

losses compared to pipeline-based transport of gasoline. The EF shown earlier in Table 3.1-21

(28.78 g/mmBTU) corresponds to the High Ethanol minus RFS1 reference case, and was used

for calculation of VOC and ethanol vapor for all cases in this FRM analysis.

Table 3.3-5 summarizes the ethanol transport mode and volume analysis conducted by

ORNL. It shows the ethanol quantities loaded into the tanks of each transport mode, which is

used to calculate ethanol vapor losses during tank filling. The first three rows show the

preliminary set of model results that was used to generate emission impacts for the air quality

analysis. The next three rows show corrected results from the final ORNL report, which was too

late to be included in the air quality modeling. These corrected results were then used as the

basis for the ethanol transport emission estimates in this final rule, shown in the final three rows.

Because the final rule ethanol volumes differ from the proposed rule and air quality analysis

volumes, especially for the primary (mid) and low ethanol cases, we have estimated transport

volumes and distances for the final rule by interpolation from the final corrected ORNL values.

Table 3.3-5. Ethanol Transport Tank Loading Volumes in 2022

Kilotons Ethanol Loaded or Transferred into Each

Modea

Source Case Bgal

Ethanol Truck Rail Water Local Truck

AQ modeled values RFS1 6.69 3,434 18,565 2,679 20,952

AQ modeled values AEO 13.18 6,005 35,555 3,860 42,915

AQ modeled values RFS2 34.14 17,012 76,053 11,959 133,907

Corrected FRM

Basis RFS1 6.69 3,131 18,565 2,816 18,431

Corrected FRM

Basis AEO 13.18 5,597 35,553 4,178 36,736

Corrected FRM

Basis RFS2 34.14 17,151 76,023 11,619 82,460

FRM Control Low 17.49 7,973 43,875 5,708 46,138

FRM Control Mid 22.16 10,547 52,892 7,366 56,326

FRM Control High 33.24 16,654 74,285 11,299 80,496

a

Includes original loading at ethanol production or import facility plus loading during transfer from

another mode.

The VOC EFs shown in Table 3.3-7 are from AP-42733 10 psi gasoline emission rates

adjusted for ethanol vapor pressure and molecular weight (net factor = 0.20 = 3 psi / 10 psi x

48.7 MW / 72 MW). In calculating the vapor losses associated with the local truck ethanol

volumes, a factor of two was applied to account for the losses during both loading of the truck

and loading of the retail underground storage tank from the truck.
573

Table 3.3-6. SCC Assignments Used for Ethanol Tank Loading

SCC SCC Description Segments of ORNL

Analysis Applied to SCC

30205031 Denatured Ethanol Storage

Working Loss All other tank loading

30205052 Ethanol Loadout to Truck All truck loading

30205053 Ethanol Loadout to Railcar All railcar loading

Table 3.3-7 Ethanol Tank Loading Vapor Emission Factors

ORNL Ethanol

Transport Category

Description Applied to

SCC

EF (tons ethanol

vapor per thousand

tons ethanol)

H_Ld_Kt Initial transport truck loading 30205052 0.243

R-H_Trf Transfer from rail to truck 30205052 0.243

W-H_Trf Transfer from barge to truck 30205052 0.243

LocTrkKt Local distribution truck

loading

30205052 0.243

R_Ld_Kt Initial Railcar Loading 30205053 0.243

H-R_Trf Transfer from truck to rail 30205053 0.243

W-R_Trf Transfer from barge to rail 30205053 0.243

W_Ld_Kt Initial barge loading 30205031 0.103

R-W_Trf Transfer from railcar to barge 30205031 0.103

H_ULd_Kt Unloading from transport

truck to terminal tank

30205031 0.222

R_ULd_Kt Unloading from rail to

terminal tank

30205031 0.222

W_ULd_Kt Unloading from barge to

terminal tank

30205031 0.222

LocTrkKt Unloading from local truck to

retail tank

30205031 0.222a

a

This local truck loading EF was also applied to loading the retail underground tank from

the truck, using the same ethanol volume.

3.3.2.1.2 Calculation of Combustion Emissions From Ethanol Transport

Table 3.3-8 summarizes the kiloton-miles transported by mode from the ORNL analysis,

which is used for combustion (vehicle exhaust) emission impacts for tanker trucks, locomotives,

and water (barge) transport.
574

Table 3.3-8. Ethanol Transport by Mode in 2022

Kiloton-miles Ethanol Transport by Mode

Source Case Bgal

Ethanol Truck Rail Water Local

Truck

AQ modeled values RFS1 6.69 228,831 18,436,891 1,565,254 138,811

AQ modeled values AEO 13.18 436,498 30,543,455 2,415,480 268,368

AQ modeled values RFS2 34.14 1,053,071 49,422,639 3,628,079 695,386

Corrected FRM

Basis RFS1 6.69 290,156 18,630,606 1,498,611 131,712

Corrected FRM

Basis AEO 13.18 491,458 30,650,028 2,538,867 253,239

Corrected FRM

Basis RFS2 34.14 1,164,335 47,822,752 3,905,640 534,322

FRM Control Low 17.49 629,822 34,181,251 2,819,916 311,038

FRM Control Mid 22.16 779,742 38,007,426 3,124,441 373,665

FRM Control High 33.24 1,135,442 47,085,374 3,846,952 522,253

3.3.2.1.2.1 Combustion Emissions from Rail Transport of Ethanol

The emission impacts of projected increases in rail transport of ethanol resulting from

this rule were calculated by multiplying locomotive emission factors by the added ton-miles of

ethanol transport. For the air quality modeling analysis and this FRM analysis we were able to

make use of the ORNL projected ton-miles of rail transport of ethanol by county for each of the

three cases of the air quality analysis, as summarized in Table 3.3-8. These ton-miles by county

were then multiplied by the g/mmBTU EFs shown in Tables 3.1-22 and 3.1-28 along with a fuel

consumption of 2.38 gallons per thousand ton-miles188 and 130,000 BTU/gallon, to determine

the additional emissions by county.

For the air quality modeling these were then added to the base case (NEI 2020 projection)

emissions of Class I locomotive emissions (SCC 2285002006) to obtain the county-specific

emissions for the RFS1 mandate reference case, AEO reference case, and RFS2 control cases.

The 2020 NEI projection values were used as the base case because they were the closest year

with data readily available, and the difference between 2020 and 2022 was not considered to be

important for this analysis. As described elsewhere, it was later discovered that an error had

been introduced during the data handling

For the FRM analysis we used the final set of projections from ORNL, which included a

3.2 percent lesser estimate of rail transport of ethanol for the RFS2 control case compared to the

values used for the air quality modeling. When put into terms of average one-way miles per trip

for the nationwide upstream impacts spreadsheet, the final ORNL values yield a rail transport

distance of 629 miles, compared to the 800 miles from GREET that was used for the NPRM

188 Per 2006 American Association of Railroads, “Railroad Facts” 2007 edition, in 2006 4,214,459 gallons of diesel

fuel were consumed transporting 4,214,459 million ton-miles of goods, which equates to 2.38 gallons per thousand

ton-miles.
575

analysis. This value was calculated by dividing the 47,822,752 kton-miles by 76,023 kilotons

loaded into rail tank cars in the RFS2 control (EISA) case.

3.3.2.1.2.2 Combustion Emissions from Water Transport of Ethanol

Air quality modeling inventories for marine vessels using Category 3 (C3) propulsion

engines (i.e., ocean-going vessels such as container ships), for calendar year 2022 were

generated for a reference case and the RFS2 control case. Since ethanol imports were assumed

to be zero under both RFS1 and AEO reference cases, the 2022 base case gridded inventory was

used for both reference cases. The 2022 base case inventory accounts for growth and the current

Tier 1 NOX controls for C3 engines and was developed using the methodology outlined in the

C3 NPRM.734

For the RFS2 control case, the port portion of the 2022 base case inventory was adjusted

to account for projected imported ethanol volumes. Gram per freight ton emission factors (EFs)

by port were developed by dividing the emissions for each port by the corresponding commodity

tonnage.735 The projected imported ethanol volumes by port were then converted to tons and

multiplied by the gram per freight ton EFs to determine the additional emissions by port due to

imported ethanol.736 These were then added to the 2022 base case port emissions to obtain the

port-specific 2022 emissions for the RFS2 control case. The 2022 gridded inventory for the

RFS2 control case air quality model run was then developed by incorporating the adjusted port

inventories.

For vessels using Category 1 (C1) and Category 2 (C2) propulsion engines (i.e., harbor

craft), calendar year 2022 emissions by county were calculated for the two reference cases and

the RFS2 control case. The starting point was calendar year 2020 C1/C2 base case emissions by

county. Given the low growth estimated for this source category and the absence of a countylevel 2022 inventory, the 2020 inventory was used for this analysis. A ton per ton-mile EF for

each pollutant was then developed. For the numerator, the national level tons values for 2020

were taken from the 2008 locomotive/marine FRM.706 For the denominator, projected ton-miles

for U.S. domestic shipping for 2020 was used.737 ORNL supplied EPA with ton-miles of barge

traffic by county for each of the three cases, as summarized in Table 3.3-8. For the air quality

modeling the ton-miles by county were then multiplied by the tons per ton-mile EFs to determine

the additional emissions by county for each case. These were then added to the base case

emissions to obtain the county-specific emissions for the two reference cases and the RFS2

control case.

For the FRM analysis we used the final set of projections from ORNL, which included a

7.6 percent greater estimate of water transport of ethanol for the RFS2 control case compared to

the values used for the air quality modeling. When put into terms of average one-way miles per

trip for the nationwide upstream impacts spreadsheet, the final ORNL values yield a barge

transport distance of 336 miles, compared to the 520 miles from GREET that was used for the

NPRM analysis. This value was calculated by dividing the 3,905,640 kton-miles by 11,619

kilotons loaded into barges in the RFS2 control case.

3.3.2.1.2.3 Combustion Emissions from Truck Transport of Ethanol
576

For the NPRM analysis we relied on the nationwide average truck transport distances

assumed in GREET for transport to distribution/blending terminals (80 miles) and for local

trucks distributing ethanol-gasoline blends from the terminal to the retail station (30 miles). For

the air quality modeling and FRM analyses we were able to use the ORNL study described

above, which supplied kton-miles of tank truck ethanol transport by county. VMT was

calculated based on an average tank truck load of 52,720 lbs of ethanol. We doubled the VMT to

account for return trips. Non-GHG emissions from heavy-duty diesel trucks were adjusted in the

affected counties in proportion to their VMT increase. Excel versions of the ORNL data files

plus calculations are available in the docket.738

3.3.2.1.3 Calculation of Biofuel Plant Emissions

For the county-level air quality modeling emission inventories we treated the corn

ethanol plants as point sources wherever possible, since most of them are either existing plants or

under construction or planned with a specific location. The choice of corn/starch ethanol plant

locations and capacities for the 2005 baseline air quality modeling run and each of the three 2022

cases is described in Section 1.8.1.1 of this RIA. The emissions attributed to each plant were

calculated using the emission rates presented in the NPRM DRIA Table 3.1-20 multiplied by the

reported or planned capacities of each plant for each of the ethanol volume scenarios.739,740,741,742

For the county-level air quality modeling emission inventories we treated the cellulosic

biofuel plants as area sources spread across the entire area of whatever county they were

considered to be located in. The choice of plant locations and capacities is described in Section

1.8.1.3 of this RIA. They were not treated as point sources because of the substantial uncertainty

about where they might actually be built, and if their emissions were treated as a point source

their human exposures and health impacts would have been highly dependent on proximity to

urban areas. Cellulosic plant emissions were only included in the RFS2 control (EISA) case,

since the production capacities of existing and planned corn ethanol plants was sufficient to meet

the RFS1 reference case and AEO reference case ethanol volumes. The emission rates used for

the cellulosic plants in the air quality modeling were presented in the NPRM DRIA Table 3.1-20.

Those emission rates were multiplied by the assumed cellulosic ethanol plant capacities ranging

from 14 - 300 million gallons per year (MGY) from forest waste, 63 - 129 MGY from corn

stover, and 91 - 149 MGY from switchgrass.743

Biodiesel plant emissions were also treated as area sources in the air quality modeling.

All three modeled cases used the same set of 35 biodiesel plants, but used different plant

"capacities" (actually just different plant operation factors applied to the same total capacity).

The choice of plant locations and capacities is described in Section 1.8.1.4 of this RIA. The

assumed capacities for the RFS1 reference case ranged from 50,000 to 34 million gallons per

year with a combined capacity of 303 MGY. The AEO reference case capacities ranged from

63,000 to 42 million gallons per year with a total capacity of 380 MGY, and the RFS2 control

(EISA) case capacities ranged from 135,000 to 90 million gallons per year with a total capacity

of 810 MGY. Projected emissions for each plant were calculated using the emission factors for

soybean oil based biodiesel plants given in DRIA Table 3.1-20.744,745,746
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Significant updates have been made to emissions from cellulosic plants, in part to reflect

the assumed shift in volumes from cellulosic ethanol to diesel between the proposed and final

rules. In addition, after the air quality modeling was done, we discovered that for cellulosic

ethanol plants the calculation of emissions had been overestimated by a factor of about two due

to failing to account for the portion of biomass that is not used for process energy. This change

decreases the estimated NOx and CO impacts on the order of 50 percent, and shifts the PM

impact from an increase to a small decrease. However, these changes are counterbalanced to

varying degrees by shifting some of the cellulosic volume from ethanol to diesel, which requires

nearly twice the biomass to produce one gallon of fuel. While the net effect of the changes in

cellulosic plant emissions is a significant decrease in NOx and CO emissions, the shift to

cellulosic diesel under the primary scenario results in a larger increase in “upstream” PM

emissions than reported in the NPRM or used in the air quality modeling analysis.

3.3.2.1.4 Calculation of Agricultural Emissions

The county-level agriculture-related emission inventories for air quality modeling were

based on the 2002 NEI, since the NEI does not include any changes in its projections to 2022.

That inventory was used for both the RFS1 and AEO reference cases. For the RFS2 control

(EISA) case that inventory was modified to account for the changes in domestic agricultural

activity predicted by the FASOM model, as described in Section 5.1 of the DRIA. Later

modifications to the FASOM modeling that were done for the final rule analysis were not

available in time for the air quality modeling. Since FASOM was only run for the AEO

reference case and the RFS2 control (EISA) case, the air quality modeling did not attempt to

account for any differences between the RFS1 and AEO reference cases.

The RFS2 control case agricultural emissions were estimated by applying adjustment

factors shown in Table 3.3-9 to the NEI inventories for the affected source categories. The

pollutants affected by these adjustments depend on the source; for example, NEI includes

livestock dust for beef and dairy cattle, but not for swine or poultry. These adjustments were

applied equally to all counties having any of the affected sources. This is one area of uncertainty

in the inventories, since there would likely be variation from one county to another depending on

how much of the predicted agricultural changes occurred in which counties. By using percent

change adjustments rather than attempting to calculate absolute ton changes in each county we

have attempted to minimize the inventory distortions that could occur if the calculated change for

a given county was out of proportion to the reference case emissions for that county. For

instance, a different approach could estimate reductions that were larger than the reference case

NEI emissions, since there was no linkage between the NEI inventories and the FASOM

modeling. The specific sources (SCCs) and affected pollutants that these adjustments were

applied to are listed in a docket reference.747
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Table 3.3-9. Adjustments to Agricultural Emissions for RFS2 control Case

Source Description FASOM Parameters Used

(change from AEO to RFS2)

Adjustment of

Air Quality

inputs

Corresponding

Changes in Final

Ag Modeling

Nitrogen Fertilizer

Application Nitrogen fertilizer use +2.42% +5.73%

Pesticide Application Pesticide + herbicide use -4.56% -0.46%

Pesticide Production &

Transport Pesticide + herbicide use -4.56% -0.46%

Livestock Waste Beef+dairy cattle, swine, poultry

head counts -0.99% -0.90%

Livestock Dust Beef+dairy cattle head counts -1.32% -0.27%

Tilling/Harvesting Dust Total acres in crop production +0.79% +2.65%

Crop Residue Burning Total acres in crop production +0.79% --

a

a Crop residue burning emissions are not included as impacts in the final rule analysis.

Updates to agricultural modeling assumptions that have been made since the proposal and

air quality modeling have had a significant impact on ammonia (NH3) emissions. Final

modeling reflects an increase in fertilizer use with the primary control case, which results in an

increase in NH3 emissions, a change from the modest decrease projected for the proposal and air

quality analyses.

3.3.2.1.5 Calculation of Petroleum Production Emissions

Petroleum production includes crude oil extraction and transport to refineries. For the

RFS2 air quality modeling these impacts were not considered large enough relative to the other

upstream impacts to attempt to model them. In our nationwide emissions analysis we assumed

that (a) 75% of the change in gasoline supply was projected to come from domestic refineries,

and (b) 33.1% of the change in crude being used by domestic refineries would be domestic

crude. Thus, using our assumption that 1.0 gallon less of gasoline equates to approximately 1.0

gallon less crude throughput, the reduction in crude extraction and transport would equal about

25% of the change in gasoline volume. Table 3.3-10 shows what the domestic crude adjustments

would have been in the air quality modeling if they had been accounted for.
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Table 3.3-10.

Domestic Crude Oil Volume Reductions Associated with RFS2 in 2022

Scenario Gasoline Volume

Reduction (Bgal)

Domestic Crude

Reduction (Bgal)

AQ Modeling RFS2 vs RFS1 18.5 4.6

RFS2 vs AEO 14.2 3.5

Final Rule: High RFS2 vs RFS1 17.3 4.3

Ethanol Case RFS2 vs AEO 13.3 3.3

Final Rule: Mid RFS2 vs RFS1 10.0 2.5

Ethanol Case RFS2 vs AEO 5.9 1.5

Final Rule: Low RFS2 vs RFS1 6.9 1.7

Ethanol Case RFS2 vs AEO 2.8 0.7

3.3.2.1.6 Calculation of Refinery Emissions (combustion and vapor)

For the air quality modeling of refinery emissions, adjustment factors were applied to

existing NEI inventory projections for all SCCs related to refineries. These adjustments were

based on ratios of crude throughput estimates from refinery modeling for each case, which varied

by PADD. Different adjustment factors were applied for the AEO reference case and for the

RFS2 control (EISA) case.748 The RFS1 reference case was assumed to be the existing NEI

projected inventory with no adjustments applied. Table 3.3-11 summarizes the adjustments that

were used.

Table 3.3-11.

Refinery Emission Adjustments for RFS2 Air Quality Modeling

Scenario PADD

1

PADD

2

PADD

3

PADD

4 & 5

PADD

5 (CA) US Total

AEO Reference 0.0% -2.5% -1.8% -0.4% -0.7% -1.5%

RFS2 (EISA) 0.0% -9.2% -6.7% -1.6% -2.5% -5.7%

Note that this method used for estimation of county level refinery emissions is not

directly comparable with the method used for nationwide impacts in the NPRM and FRM

analyses, for which we used the GREET-based upstream impacts spreadsheet model to calculate

the absolute change in tons for each stage of the upstream inventory.

3.3.2.1.7 Calculation of Gasoline Transport, Storage and Distribution emissions: (vapor)

With the displacement of some of the gasoline pool to E10 and E85 as predicted in this

analysis there would be changes in the quantity of vapor losses during the transport and

distribution of gasoline and gasoline-ethanol blends. The analysis of these impacts was

separated into two segments: refinery to bulk terminal (RBT) and bulk terminal to pump (BTP).

The reference cases analyzed would include some amount of E0 in the BTP segment, but the

ethanol volumes considered as policy options in this rule are all beyond the volume that would

require E10 blends for all gasoline-fueled engines (onroad and nonroad). Thus the transport of
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E0 gasoline would only occur between refineries and blending terminals in the policy cases, i.e.,

the RBT segment. The BTP segment would include both E10 and E85.

E0 – Refinery to Bulk Terminal (RBT)

E0 – Bulk Terminal to Pump (BTP, used for reference cases only)

E10 – Bulk Terminal to Pump (BTP)

E85 – Bulk Terminal to Pump (BTP)

For each of the above fuel type and transport stage combinations, nationwide VOC

impacts (ton deltas) (and benzene and ethanol vapor) were calculated using EPA’s upstream

impacts spreadsheet model for each control scenario (RFS2 control vs RFS1 mandate and RFS2

control vs AEO). For air quality modeling the three BTP values were combined into a total BTP

impact for each scenario. These impact values were renormalized to be ton deltas relative to the

RFS1 mandate reference case, which was treated as corresponding to the NEI. Then all the

SCCs in the NEI related to gasoline transport, storage, and distribution (TS&D) were categorized

as either RBT or BTP, and the NEI VOC emissions were summed for each category. The

nationwide VOC percent change for the AEO case relative to the RFS1 mandate reference case

for RBT was calculated as the AEO case RBT delta tons (versus RFS1) divided by the NEI RBT

tons. Similarly, the nationwide VOC percent change for the AEO case for BTP was calculated

as the AEO case BTP delta tons (versus RFS1) divided by the NEI BTP tons. The same

calculations were done for the RFS2 control case to get RBT and BTP percent changes in VOC

from the RFS1 mandate case.749

The county level air quality inventories for the AEO and RFS2 control cases were then

calculated by applying these percent changes in VOC to the corresponding sets of SCCs (point

and non-point sources) for every county. The same adjustment factors were applied to benzene,

which is reasonable for the VOC decrease in the refinery to bulk terminal segment. But in the

terminal to pump segment benzene would be expected to decrease while VOC increases, since

the VOC increaase is due to addition of ethanol to the fuel, rather than any increase in gasoline

itself.

3.3.2.2 Differences in Downstream Impacts between Inventories

3.3.2.2.1 On-Road Gasoline

In the proposal we provided two different analyses based on two different assumptions

regarding the effects of E10 and E85 versus E0 on exhaust emissions from cars and trucks.

Those were referred to as "less sensitive" and "more sensitive" cases. Based on analysis of

recent emissions test data conducted since publication of the NPRM, we are modeling a single

case. As detailed above in Section 3.2, the case modeled for the final rule is a hybrid approach,

applying “more sensitive” impacts for E10 and pre-Tier 2 light duty vehicles, and applying the

“less sensitive” E10 effects for Tier 2 light duty cars and trucks (meaning no impact for NOx or

exhaust NMHC on the majority of light duty vehicles on the road in 2022). We have also

updated our estimates of evaporative permeation impacts of E10 based on recent studies.

Finally, for the final rule inventories we are only claiming emission effects with use of E85 in

flex-fueled vehicles relative to E0 for two pollutants: ethanol and acetaldehyde, for which data
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suggests the effects are more certain. For the “more sensitive case” presented in the NPRM, and

used in the air quality modeling, we had estimated changes to additional pollutants (including

significant PM reductions) based on some very limited data. Until such time as additional data is

collected to enhance this analysis it is premature to use such assumptions.

For the air quality inventory, EPA executed a preliminary version of MOVES dated

9/9/2008 using default database MOVESDB20080828 plus several modifications to the code and

to the database. The MOVES runs produced emission factors at the state-month level for all

onroad gasoline vehicles except motorcycles. Onroad gasoline inventories were generated by

multiplying MOVES emission factors by VMT developed for the Office of Air Quality Planning

and Standards's 2002 Version 3 Modeling Platform750 and used in the recently published

Locomotive-Marine Rule.751 The MOBILE6 Model was M6203ChcOxFixNMIM, a special

version that includes cold-start VOC and the cold-start controls of the Mobile Source Air Toxics

Rule that go into effect in 2011. The NONROAD Model version was NR05d-Bond-Final, which

is the same as the publically released version NONROAD2008. Both MOBILE6 and

NONROAD were run using NMIM (version NMIM20071009) with NMIM County Database

NCD20080724. NMIM supplied emissions from the NONROAD Model and from onroad

sources not produced from MOVES. Onroad emissions generated at the state-month level from

MOVES were distributed to the county-month level using the results from MOBILE6 as run by

NMIM. For both NMIM and MOVES, temperatures and humidity were those of the air quality

modeling base year 2005, and fuels for each case were those developed for this rule. Details of

how MOVES and NMIM were configured and run are documented in a memo contained in the

docket.752

Final rule inventories were updated using the 9/28/09 version of MOVES and database,

which reflects fuel effects that are consistent with the final MOVES2010 version (baseline

emission rates were still under development at this stage, so while the 9/28/09 version is updated

from previous versions, results are not the same as final MOVES). Differences between the air

quality modeling inventories and the NPRM and FRM inventories are due to differences in

MOVES code and database versions, run granularity (national-annual vs. state-2-month vs. state12-month), and running of the “more sensitive” case for the air quality modeling. One specific

change from the NPRM version to the air quality and FRM versions was an update of

reformulated gasoline properties to reflect the lower benzene levels called for by the MSAT rule,

as described above in Section 3.1.1.1.

3.3.2.2.2 Off-Road Gasoline

For the NPRM, emissions from nonroad gasoline equipment were developed by running

the National Mobile Inventory Model (NMIM) for January and July. We limited the runs to

these months to speed the analysis while also capturing the temperature extremes that can affect

emissions. The NMIM County Database (NCD) used by NMIM to produce those inventories

was updated as part of the 2005 National Emission Inventory (NEI) process. The NCD also

included the 2005 and 2022 fuels described in Section 3.1.1.2 of the DRIA. The version of the

NONROAD Model used included the effects of the 2008 Final Rule: Control of Emissions of Air

Pollution from New Nonroad Spark-Ignition Engines, Equipment, and Vessels.753 It is also
582

capable of modeling the effects of gasoline blends containing 10 percent or less of ethanol. That

version of NONROAD was later released as NONROAD2008.

For air quality modeling and the final rule analysis, NMIM was also used, but all twelve

months were run.

For the NPRM, emissions from onroad and nonroad diesel equipment were also

developed by running NMIM, using the same NCD and version of the NONROAD Model

described above. The version of MOBILE was MOBILE6.2. Diesel fuels are less fully

characterized than gasoline, since the only property used by MOBILE and NONROAD is fuel

sulfur.

For the NPRM, toxic emissions for off-road reference cases were taken directly from

NMIM. Inventories for the control case were developed by applying ratios of the aggregate

MOVES toxic exhaust, evaporative and refueling emissions for on-road gasoline for control

versus reference case, to reference case toxic emissions for off-road from NMIM.

For the air quality modeling and FRM analysis of nonroad gasoline engine emissions we

used the same version of NMIM. Most toxic emissions for off-road equipment were taken

directly from NMIM. The one exception was ethanol, which is not estimated by NMIM, so

ethanol emissions were based on VOC speciation from light-duty gasoline vehicles. Ethanol

inventories for the control case were developed by applying ratios of the aggregate MOVES

ethanol exhaust, evaporative and refueling emissions for on-road gasoline for control versus

reference cases, to the reference case ethanol emissions for off-road equipment.

3.3.2.2.3 On-Road Diesel

For the NPRM the proposed increase in biodiesel to 0.81 billion gallons by 2022 was

modeled assuming that the emission effects of biodiesel are linear with biodiesel concentration

as demonstrated by Sze, et al,754 and that impacts can be analyzed assuming all biodiesel is

blended as B20. We applied the B20 effects discussed in Section 3.1.1.2.4 to baseline heavyduty emissions generated by NMIM, as MOVES heavy-duty diesel estimates were not available

in time for the NPRM analysis.

For the air quality and FRM analysis diesel emission inventories were generated using

the same method as the NPRM.

3.3.2.2.4 Portable Fuel Containers

The NPRM and air quality analysis used the same projected inventories for VOC,

benzene, and ethanol vapor as the FRM analysis described above in Section 3.1.1.4.4.
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3.4 Air Quality Impacts

This section presents the methodology and results of our air quality modeling to

determine the projected impact of the renewable fuel volumes required by this rule on ambient

concentrations of criteria and air toxic pollutants. The air quality modeling results presented

here reflect the impact of increased renewable fuels use required by RFS2 compared with two

different reference cases that include the use of renewable fuels: a 2022 baseline projection based

on the RFS1-mandated volume of 7.1 billion gallons of renewable fuels, and a 2022 baseline

projection based on the AEO 2007 volume of 13.6 billion gallons of renewable fuels. Thus, the

results represent the impact of an incremental increase in ethanol and other renewable fuels. We

note that the air quality modeling results presented in this final rule do not constitute the “antibacksliding” analysis required by Clean Air Act section 211(v). EPA will be analyzing air

quality impacts of increased renewable fuel use through that study and will promulgate

appropriate mitigation measures under section 211(v), separate from this final action.

Following the discussion of modeling results in Section 3.4, Sections 3.5 and 3.6 describe the

health and environmental effects associated with the criteria and air toxic pollutants that are

impacted by the required renewable fuel volumes. In addition, Section 5.4 describes the

methodology for calculating monetized benefits due to reductions in adverse health effects

associated with PM2.5 and ozone.

3.4.1 Air Quality Modeling Methodology

Air quality models use mathematical and numerical techniques to simulate the physical

and chemical processes that affect air pollutants as they disperse and react in the atmosphere.

Based on inputs of meteorological data and source information, these models are designed to

characterize primary pollutants that are emitted directly into the atmosphere and secondary

pollutants that are formed as a result of complex chemical reactions within the atmosphere.

Photochemical air quality models have become widely recognized and routinely utilized tools for

regulatory analysis by assessing the effectiveness of control strategies. These models are applied

at multiple spatial scales - local, regional, national, and global. This section provides detailed

information on the photochemical model used for our air quality analysis (the Community Multiscale Air Quality (CMAQ) model), atmospheric reactions and the role of chemical mechanisms

in modeling, and model uncertainties and limitations. Further discussion of the modeling

methodology is included in the Air Quality Modeling Technical Support Document (AQM TSD)

found in the docket for this rule. Results of the air quality modeling are presented in Section

3.4.2.

3.4.1.1 Modeling Methodology

A national-scale air quality modeling analysis was performed to estimate future year

annual PM2.5 concentrations, 24-hour PM2.5 concentrations, 8-hour ozone concentrations, air

toxics concentrations, and nitrogen and sulfur deposition levels for future years. The 2005-based

CMAQ modeling platform was used as the basis for the air quality modeling of the two future

reference cases and the RFS2 future control scenario for this final rule. This platform represents

a structured system of connected modeling-related tools and data that provide a consistent and

transparent basis for assessing the air quality response to projected changes in emissions. The
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base year of data used to construct this platform includes emissions and meteorology for 2005.

The platform was developed by the U.S. EPA’s Office of Air Quality Planning and Standards in

collaboration with the Office of Research and Development and is intended to support a variety

of regulatory and research model applications and analyses.

The CMAQ modeling system is a non-proprietary, publicly available, peer-reviewed,

state-of-the-science, three-dimensional, grid-based Eulerian air quality grid model designed to

estimate the formation and fate of oxidant precursors, primary and secondary PM concentrations,

acid deposition, and air toxics, over regional and urban spatial scales for given input sets of

meteorological conditions and emissions.755,756,757 The CMAQ model version 4.7 was most

recently peer-reviewed in February of 2009 for the U.S. EPA.189 The CMAQ model is a wellknown and well-respected tool and has been used in numerous national and international

applications.758,759,760 This 2005 multi-pollutant modeling platform used the latest publiclyreleased CMAQ version 4.7190 with a minor internal change made by the U.S. EPA CMAQ

model developers intended to speed model runtimes when only a small subset of toxics species

are of interest.

CMAQ includes many science modules that simulate the emission, production, decay,

deposition and transport of organic and inorganic gas-phase and particle-phase pollutants in the

atmosphere. We used the most recent CMAQ version (v4.7) which was officially released by

EPA’s Office of Research and Development (ORD) in December 2008, and reflects updates to

earlier versions in a number of areas to improve the underlying science. These include (1)

enhanced secondary organic aerosol (SOA) mechanism to include chemistry of isoprene,

sesquiterpene, and aged in-cloud biogenic SOA in addition to terpene; (2) improved vertical

convective mixing; (3) improved heterogeneous reaction involving nitrate formation; and (4) an

updated gas-phase chemistry mechanism, Carbon Bond 05 (CB05), with extensions to model

explicit concentrations of air toxic species as well as chlorine and mercury. This mechanism,

CB05-toxics, also computes concentrations of species that are involved in aqueous chemistry and

that are precursors to aerosols. Section 3.4.1.2.2 of this RIA discusses the chemical mechanism,

SOA formation and details about the improvements made to the SOA mechanism within this

recent release of CMAQ.

3.4.1.1.1 Model Domain and Configuration

The CMAQ modeling domain encompasses all of the lower 48 States and portions of

Canada and Mexico. The modeling domain is made up of a large continental U.S. 36 kilometer

(km) grid and two 12 km grids (an Eastern US and a Western US domain), as shown in Figure

3.4-1. The modeling domain contains 14 vertical layers with the top of the modeling domain at

about 16,200 meters, or 100 millibars (mb).

189 Report on the peer-review is still being finalized. Draft available upon request from Director S.T.Rao,

Atmospheric Modeling and Analysis Division; rao.st@epa.gov; 919-541-4541. Allen, D., Burns, D., Chock, D.,

Kumar, N., Lamb, B., Moran, M. (February 2009 Draft Version). Report on the Peer Review of the Atmospheric

Modeling and Analysis Division, NERL/ORD/EPA. U.S. EPA, Research Triangle Park, NC.

190 CMAQ version 4.7 was released on December, 2008. It is available from the Community Modeling and Analysis

System (CMAS) as well as previous peer-review reports at: http://www.cmascenter.org.
585

Figure 3.4-1. Map of the CMAQ Modeling Domain

3.4.1.1.2 Model Inputs

The key inputs to the CMAQ model include emissions from anthropogenic and biogenic

sources, meteorological data, and initial and boundary conditions. The CMAQ meteorological

input files were derived from simulations of the Pennsylvania State University/National Center

for Atmospheric Research Mesoscale Model761 for the entire year of 2005 over model domains

that are slightly larger than those shown in Figure 3.4-1. This model, commonly referred to as

MM5, is a limited-area, nonhydrostatic, terrain-following system that solves for the full set of

physical and thermodynamic equations which govern atmospheric motions.762 The meteorology

for the national 36 km grid and the two 12 km grids were developed by EPA and are described in

more detail within the AQM TSD. The meteorological outputs from MM5 were processed to

create model-ready inputs for CMAQ using the Meteorology-Chemistry Interface Processor

(MCIP) version 3.4, for example: horizontal wind components (i.e., speed and direction),

temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical

layer.763

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, the GEOS-CHEM model.764 The global

GEOS-CHEM model simulates atmospheric chemical and physical processes driven by

assimilated meteorological observations from the NASA’s Goddard Earth Observing System

(GEOS). This model was run for 2005 with a grid resolution of 2 degree x 2.5 degree (latitudelongitude) and 30 vertical layers. The predictions were used to provide one-way dynamic
586

boundary conditions at three-hour intervals and an initial concentration field for the 36 km

CMAQ simulations. The future base conditions from the 36 km coarse grid modeling were used

as the initial/boundary state for all subsequent 12 km finer grid modeling.

The emissions inputs used for the 2005 base year and each of the future year base cases

and control scenarios analyzed for this rule are summarized in Section 3.3 of this RIA.

3.4.1.1.3 CMAQ Evaluation

An operational model performance evaluation for ozone, PM2.5 and its related speciated

components (e.g., sulfate, nitrate, elemental carbon, organic carbon, etc.), nitrate and sulfate

deposition, and specific air toxics (formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and

acrolein) was conducted using 2005 state/local monitoring data in order to estimate the ability of

the CMAQ modeling system to replicate base year concentrations. Model performance statistics

were calculated for observed/predicted pairs of daily/monthly/seasonal/annual concentrations.

Statistics were generated for the following geographic groupings: domain wide, Eastern vs.

Western (divided along the 100th meridian), and each Regional Planning Organization (RPO)

region.191 The “acceptability” of model performance was judged by comparing our results to

those found in recent regional PM2.5 model applications for other, non-EPA studies.192 Overall,

the performance for the 2005 modeling platform is within the range or close to that of these other

applications. The performance of the CMAQ modeling was evaluated over a 2005 base case.

The model was able to reproduce historical concentrations of ozone and PM2.5 over the land with

low amounts of bias and error. Model predictions of annual formaldehyde, acetaldehyde and

benzene showed relatively small bias and error percentages when compared to observations. The

model yielded larger bias and error results for 1,3-butadiene and acrolein based on limited

monitoring sites. A more detailed summary of the 2005 CMAQ model performance evaluation

is available within the AQM TSD found in the docket of this rule.

3.4.1.1.4 Model Simulation Scenarios

As part of our analysis for this rulemaking, the CMAQ modeling system was used to

calculate daily and annual PM2.5 concentrations, 8-hour ozone concentrations, annual and

seasonal air toxics concentrations, and nitrogen and sulfur deposition total levels for each of the

following emissions scenarios:

- 2005 base year

- 2022 reference case projection (RFS1 Mandate; 6.7 Bgal of ethanol, 0.38 Bgal of

biodiesel. See also Table 3.3.1)

191 Regional Planning Organization regions include: Mid-Atlantic/Northeast Visibility Union (MANE-VU),

Midwest Regional Planning Organization – Lake Michigan Air Directors Consortium (MWRPO-LADCO),

Visibility Improvement State and Tribal Association of the Southeast (VISTAS), Central States Regional Air

Partnership (CENRAP), and Western Regional Air Partnership (WRAP).

192 These other modeling studies represent a wide range of modeling analyses which cover various models, model

configurations, domains, years and/or episodes, chemical mechanisms, and aerosol modules.
587

- 2022 reference case projection (AEO 2007; 13.18 Bgal of ethanol, 0.38 Bgal of

biodiesel. See also Table 3.3.1)

- 2022 control case projection (RFS2 control,; 34.14 Bgal of ethanol, 0.81 Bgal of

biodiesel, 0.38 Bgal of renewable diesel. See also Table 3.3.1)

It should be noted that the emission inventories used in the air quality and benefits

modeling were somewhat enhanced compared to what was described in the proposal, but due to

the timing of the analysis did not include some of the later enhancements and corrections of the

final emission inventories presented in this FRM. The emissions modeling TSD, found in the

docket for this rule (EPA-HQ-OAR-2005-0161), contains a detailed discussion of the emissions

inputs used in our air quality modeling. Section 3.3 of this RIA describes the changes in the

inputs and resulting emission inventories between the preliminary assumptions used for the air

quality modeling and the final regulatory scenario. These refinements, along with other

inventory issues, have implications for modeling results. These implications are discussed in

Sections 3.4.1.3 and 3.4.2.

We use the predictions from the model in a relative sense by combining the 2005 baseyear predictions with predictions from each future-year scenario and applying these modeled

ratios to ambient air quality observations to estimate daily and annual PM2.5 concentrations, and

8-hour ozone concentrations for each of the 2022 scenarios. The ambient air quality

observations are average conditions, on a site-by-site basis, for a period centered around the

model base year (i.e., 2003-2007).

The projected daily and annual PM2.5 design values were calculated using the Speciated

Modeled Attainment Test (SMAT) approach. The SMAT uses a Federal Reference Method

(FRM) mass construction methodology that results in reduced nitrates (relative to the amount

measured by routine speciation networks), higher mass associated with sulfates (reflecting water

included in FRM measurements), and a measure of organic carbonaceous mass that is derived

from the difference between measured PM2.5 and its non-carbon components. This

characterization of PM2.5 mass also reflects crustal material and other minor constituents. The

resulting characterization provides a complete mass balance. It does not have any unknown

mass that is sometimes presented as the difference between measured PM2.5 mass and the

characterized chemical components derived from routine speciation measurements. However,

the assumption that all mass difference is organic carbon has not been validated in many areas of

the U.S. The SMAT methodology uses the following PM2.5 species components: sulfates,

nitrates, ammonium, organic carbon mass, elemental carbon, crustal, water, and blank mass (a

fixed value of 0.5 μg/m3). More complete details of the SMAT procedures can be found in the

report "Procedures for Estimating Future PM2.5 Values for the CAIR Final Rule by Application

of the (Revised) Speciated Modeled Attainment Test (SMAT)".765 For this latest analysis,

several datasets and techniques were updated. These changes are fully described within the

technical support document for the Small SI Engine Rule modeling AQM TSD.766 The projected

8-hour ozone design values were calculated using the approach identified in EPA's guidance on

air quality modeling attainment demonstrations.767
588

Additionally, we conducted an analysis to compare the annual and seasonal, absolute and

percent differences between the 2022 control case and the two 2022 reference cases for nitrate

and sulfate deposition, ethanol, and five air toxics of interest (formaldehyde, acetaldehyde,

benzene, 1,3-butadiene, and acrolein). These data were not compared in a relative sense due to

the limited observational data available.

3.4.1.2 Chemical Mechanisms in Modeling

The RFS2 rule presents inventories for NOX, VOC, CO, PM2.5, SO2, NH3, ethanol and

five air toxics: benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein. Ethanol and

the five air toxics are explicit model species in the CMAQv4.7 model with carbon bond 5

(CB05) mechanisms.768 Emissions of all the pollutants included in the rule inventories, except

ethanol, were generated using the Motor Vehicle Emissions Simulator (MOVES) hydrocarbon

(HC) emissions and toxic-to-HC ratios calculated using MOBILE 6 (see Section 3.1.1.4.1 of the

draft RIA).769 Ethanol emissions for air quality modeling were based on speciation of VOC

using different ethanol profiles (E0, E10, and E85). In addition to direct emissions,

photochemical processes mechanisms are responsible for formation of some of these compounds

in the atmosphere from precursor emissions. For formaldehyde and acetaldehyde, many

photochemical processes are involved. CMAQ therefore also requires inventories for a large

number of other air toxics and precursor pollutants. Inventories for toxic pollutants not

estimated using MOVES and MOBILE6 ratios were developed by running the National Mobile

Inventory Model (NMIM). Emissions of other precursor pollutants were estimated by

application of speciation profiles to VOC.

In the CB05 mechanism, the chemistry of thousands of different VOCs in the

atmosphere are represented by a much smaller number of model species which characterize the

general behavior of a subset of chemical bond types; this condensation is necessary to allow the

use of complex photochemistry in a fully 3-D air quality model.770

Complete combustion of ethanol in fuel produces carbon dioxide (CO2) and water (H2O).

Incomplete combustion results in the production of other air pollutants, such as acetaldehyde and

other aldehydes, and the release of unburned ethanol. Ethanol is also present in evaporative

emissions. In the atmosphere, ethanol from unburned fuel and evaporative emissions can

undergo photodegradation to form aldehydes (acetaldehyde and formaldehyde) and peroxyacetyl

nitrate (PAN), and also plays a role in ground-level ozone formation. Mechanisms for these

reactions are included in CMAQ. Additionally, other aromatic hydrocarbons (AHC) and

hydrocarbons are considered because any increase in acetyl peroxy radicals due to ethanol

increases might be counterbalanced by a decrease in radicals resulting from decreases in AHC

and other hydrocarbons.

CMAQ includes 63 inorganic reactions to account for the cycling of all relevant oxidized

nitrogen species and cycling of radicals, including the termination of NO2 and formation of nitric

acid (HNO3) without PAN formation.193

NO2 + ∙OH + M → HNO3 + M k = 1.19 x 10-11

cm3molecule-1s

-1 771

193 All rate coefficients in this RIA are listed at 298 K and, if applicable, 1 bar of air.
589

The CB05 mechanism also includes more than 90 organic reactions that include alternate

pathways for the formation of acetyl peroxy radical, such as by reaction of methylglyoxal, which

is also formed from reactions of AHC. Alternate reactions of acetyl peroxy radical, such as

oxidation of NO to form NO2, which again leads to ozone formation, are also included.

Atmospheric reactions and chemical mechanisms involving several key formation pathways are

discussed in more detail in the following sections.

3.4.1.2.1 Acetaldehyde

Acetaldehyde is the main photodegradation product of ethanol, as well as other precursor

hydrocarbons. Acetaldehyde is also a product of fuel combustion. In the atmosphere,

acetaldehyde can react with the OH radical and O2 to form the acetyl peroxy radical

[CH3C(O)OO∙].194 This radical species can then further react with nitric oxide (NO), to produce

formaldehyde (HCHO), or with nitrogen dioxide (NO2), to produce PAN [CH3C(O)OONO2].

An overview of these reactions and the corresponding reaction rates are provided below. 195

CH3CHO + ∙OH → CH3C∙O + H2O k = 1.5 x 10-11

cm3molecule-1s

-1 772

CH3C∙O + O2 + M → CH3C(O)OO∙ + M

CH3C(O)OO∙ + NO → CH3C(O)O∙ + NO2 k = 2.0 x 10-11cm3molecule-1s

-1 773

CH3C(O)O∙ → ∙CH3 + CO2

∙CH3 + O2 + M → CH3OO∙ + M 

CH3OO∙ + NO → CH3O∙ + NO2

CH3O∙ + O2 → HCHO + HO2

CH3C(O)OO∙ + NO2 + M → CH3C(O)OONO2 + M k = 1.0 x 10-11cm3molecule-1s

-1 774

Acetaldehyde can also photolyze (hν), which predominantly produces ∙CH3 and HCO:

CH3CHO + hν → ∙CH3 + HCO  = 240-380 nm 775

As mentioned above, ∙CH3 is oxidized in the atmosphere to produce formaldehyde

(HCHO). Formaldehyde is also a product of hydrocarbon combustion. In the atmosphere,

formaldehyde undergoes photolysis and reaction with the OH radical, NO3 radical, and ozone,

194 Acetaldehyde is not the only source of acetyl peroxy radicals in the atmosphere. For example, dicarbonyl

compounds (methylglyoxal, biacetyl, and others) also form acetyl radicals, which can further react to form

peroxyacetyl nitrate (PAN).

195 All rate coefficients in this RIA are listed at 298 K and, if applicable, 1 bar of air.
590

and the resulting lifetimes are ~4 hours, 1.2 days, 83 days, and >4.5 years, respectively.196

Formaldehyde is removed mainly by photolysis whereas the higher aldehydes, those with two or

more carbons such as acetaldehyde, react predominantly with OH radicals. The photolysis of

formaldehyde is a source of additional radicals, and as shown above, these radicals can react

with NO2 to form PAN in the atmosphere.

HCHO + hν → H + HCO  = 240-360 nm 776

CB05 mechanisms for acetaldehyde formation warrant a detailed discussion given the

increase in vehicle and engine exhaust emissions for this pollutant and ethanol, which can form

acetaldehyde in the air. Acetaldehyde is represented explicitly in the CB05 chemical

mechanism777,778 by the ALD2 model species, which can be both formed from other VOCs and

can decay via reactions with oxidants and radicals. The reaction rates for acetaldehyde, as well

as for the inorganic reactions that produce and cycle radicals, and the representative reactions of

other VOCs have all been updated to be consistent with recommendations in the literature.779

The decay reactions of acetaldehyde are fewer in number and can be characterized well because

they are explicit representations. Acetaldehyde can photolyze in the presence of sunlight or react

with molecular oxygen (O3(P)), hydroxyl radical (OH), or nitrate radicals. Of these reactions,

both photolysis and reaction with OH are the most important reactions determining loss of

acetaldehyde. The reaction rates are based on expert recommendations,780 and the photolysis

rate is from IUPAC recommendations.

In CMAQ v4.7, the acetaldehyde that is formed from photochemical reactions is tracked

separately from that which is due to direct emission and transport of direct emissions. In CB05,

there are 25 different reactions that form acetaldehyde in molar yields ranging from 0.02 (ozone

reacting with lumped products from isoprene oxidation) to 2.0 (cross reaction of acylperoxy

radicals, CXO3). The specific parent VOCs that contribute the most to acetaldehyde

concentrations vary spatially and temporally depending on characteristics of the ambient air, but

alkenes in particular are found to play a large role. The IOLE model species, which represents

internal carbon-carbon double bonds, has high emissions and relatively high yields of

acetaldehyde. The OLE model species, representing terminal carbon double bonds, also plays a

role because it has high emissions although lower acetaldehyde yields. Production from

peroxyproprional nitrate and other peroxyacylnitrates (PANX) and aldehydes with 3 or more

carbon atoms also play an important role. Thus, the amount of acetaldehyde (and formaldehyde

as well) formed in the ambient air as well as emitted in the exhaust (the latter being accounted

for in emission inventories) is affected by changes in these precursor compounds due to the

addition of ethanol to fuels (e.g., decreases in alkenes would cause some decrease of

acetaldehyde, and to a larger extent, formaldehyde).

The reaction of ethanol (CH3CH2OH) with OH is slower than some other important

reactions but can be an important source of acetaldehyde if the emissions are large. Based on

kinetic data for molecular reactions, the only important chemical loss process for ethanol (and

other alcohols) is reaction with the hydroxyl radical (∙OH).781 This reaction produces

196 Lifetime calculated using the following: for photolysis, with overhead sun (at noontime during the summer); for

OH radical reactions, a 12-hour daytime average of 2.0 x 106 molecule cm-3; for NO3 radical reactions, a 12-hour

nighttime average of 5 x 108 molecule cm-3; and for ozone, a 24-hour average of 7 x 1011 molecule cm-3.
591

acetaldehyde (CH3CHO) with a 90% yield.782 The lifetime of ethanol in the atmosphere can be

calculated from the rate coefficient, k, and due to reaction with the OH radical, occurs on the

order of a day in polluted urban areas or several days in unpolluted areas. 197

CH3CH2OH + ∙OH → CH3C∙HOH + H2O k = 3.2 x 10-12

cm3molecule-1s

-1 783

CH3C∙HOH + O2 → CH3CHO + HO2

In CB05, reaction of one molecule of ethanol yields 0.90 molecules of acetaldehyde. It

assumes the majority of the reaction occurs through H-atom abstraction of the more weaklybonded methylene group, which reacts with oxygen to form acetaldehyde and hydroperoxy

radical (HO2), and the remainder of the reaction occurs at the –CH3 and -OH groups, creating

formaldehyde (HCHO), oxidizing NO to NO2 (represented by model species XO2) and creating

glycoaldehyde, which is represented as ALDX:

CH3CHOH + OH → HO2 + 0.90 CH3CHO + 0.05 ALDX + 0.10 HCHO + 0.10 XO2

3.4.1.2.2 Secondary Organic Aerosols (SOA)

Secondary organic aerosol (SOA) chemistry research described below has led to implementation

of new pathways for secondary organic aerosol (SOA) in CMAQ 4.7, based on recommendations

of Edney et al. and the recent work of Carlton et al.784, 785 In previous versions of the CMAQ

model, all SOA was treated as semi-volatile, whereas in CMAQ v4.7, non-volatile SOA are

simulated as well, including SOA originating from aromatic oxidation under low-NOx

conditions.

3.4.1.2.2.1 SOA Research

SOA results when products of atmospheric transformation or photooxidation of a volatile

organic compound (VOC) form or partition to the particle phase. Current research suggests SOA

contributes significantly to ambient organic aerosol (OA) concentrations, and in Southeast and

Midwest States may make up more than 50% (although the contribution varies from area to area)

of the organic fraction of PM2.5 during the summer (but less in the winter).786,787 A wide range of

laboratory studies conducted over the past twenty years show that anthropogenic aromatic

hydrocarbons and long-chained alkanes, along with biogenic isoprene, monoterpenes, and

sesquiterpenes, contribute to SOA formation.788,789,790,791,792 Anthropogenic SOA is a small

portion of all SOA; most is biogenic and varies with season. Based on these laboratory results,

SOA chemical mechanisms have been developed and integrated into air quality models such as

the CMAQ model and have been used to predict OA concentrations.793

Over the past 10 years, ambient OA concentrations have been routinely measured in the

U.S. and some of these data have been used to determine, by employing source/receptor

methods, the contributions of the major OA sources, including biomass burning and vehicular

gasoline and diesel exhaust. Since mobile sources are a significant source of VOC emissions,

currently accounting for approximately 50% of anthropogenic VOC,794 mobile sources are also

197 All rate coefficients in this RIA are listed at 298 K and, if applicable, 1 bar of air.
592

an important source of SOA.

Toluene is an important contributor to anthropogenic SOA. Other aromatic compounds

contribute as well, but the extent of their contribution has not yet been quantified. Mobile

sources are the most significant contributor to ambient toluene concentrations as shown by

analyses done for the 2002 National Air Toxics Assessment (NATA)795 and the Mobile Source

Air Toxics (MSAT) Rule.796 2002 NATA indicates that onroad and nonroad mobile sources

accounted for 70% (2.24 μg/m3) of the total average nationwide ambient concentration of

toluene (3.24μg/m3

), when the contribution of the estimated “background” is apportioned

among source sectors.

The amount of toluene in gasoline influences the amount of toluene emitted in vehicle

exhaust and evaporative emissions, although, like benzene, some toluene is formed in the

combustion process. In turn, levels of toluene and other aromatics in gasoline are potentially

influenced by the amount of ethanol blended into the fuel. Due to the high octane quality of

ethanol, it greatly reduces the need for and levels of other high-octane components such as

aromatics including toluene (which is the major aromatic compound in gasoline). Since toluene

contributes to SOA and the toluene level of gasoline is decreasing, it is important to assess the

effect of these reductions on ambient PM.

It is unlikely that ethanol would directly form SOA or affect SOA formation indirectly

through changes in the radical populations from increasing ethanol exhausts. Nevertheless,

scientists at the U.S. EPA’s Office of Research and Development’s National Exposure Research

Laboratory recently directed experiments to investigate ethanol’s SOA forming potential.797 The

experiments were conducted under conditions where peroxy radical reactions would predominate

(irradiations performed in the absence of NOx and OH produced from the photolysis of hydrogen

peroxide). This was the most likely scenario under which SOA formation could occur, since a

highly oxygenated C4 organic would be potentially made. As expected, no SOA was produced.

From these experiments, the upper limit for the aerosol yield would have been less than 0.01%

based on scanning mobility particle sizer (SMPS) data. Given the expected negative result based

on these initial smog chamber experiments, these data were not published.

In general, a review of the literature shows limited data on SOA concentrations, largely

due to the lack of analytical methods for identifying and determining the concentrations of the

highly polar organic compounds that make up SOA. The most widely applied method of

estimating total ambient SOA concentrations is the EC tracer method using ambient data which

estimates of the OC/EC ratio in primary source emissions.798,799 SOA concentrations have also

been estimated using OM (organic mass) to OC (organic carbon) ratios, which can indicate that

SOA formation has occurred, or by subtracting the source/receptor-based total primary organic

aerosol (POA) from the measured OC concentration.800 Such methods, however, may not be

quantitatively accurate and provide no information on the contribution of individual biogenic and

anthropogenic SOA sources, which is critical information needed to assess the impact of specific

sources and the associated health risk. These methods assume that OM containing additional

mass from oxidation of OC comes about largely (or solely) from SOA formation. In particular,

the contributions of anthropogenic SOA sources, including those of aromatic precursors, are

required to determine exposures and risks associated with replacing fossil fuels with biofuels.
593

Upon release into the atmosphere, numerous VOC compounds can react with free

radicals in the atmosphere to form SOA. While this has been investigated in the laboratory, there

is relatively little information available on the specific chemical composition of SOA compounds

themselves from specific VOC precursors. This absence of compositional data from the

precursors has largely prevented the identification of aromatically-derived SOA in ambient

samples which, in turn, has prevented observation-based measurements of the aromatic and other

SOA contributions to ambient PM levels.

As a first step in determining the ambient SOA concentrations, EPA has developed a

tracer-based method to estimate such concentrations.801,802 The method is based on using mass

fractions of SOA tracer compounds, measured in smog chamber-generated SOA samples, to

convert ambient concentrations of SOA tracer compounds to ambient SOA concentrations. This

method consists of irradiating the SOA precursor of interest in a smog chamber in the presence

of NOx, collecting the SOA produced on filters, and then analyzing the samples for highly polar

compounds using advanced analytical chemistry methods. Employing this method, candidate

tracers have been identified for several VOC compounds which are emitted in significant

quantities and known to produce SOA in the atmosphere. Some of these SOA-forming

compounds include toluene, a variety of monoterpenes, isoprene, and β-caryophyllene, the latter 

three of which are emitted by vegetation and are more significant sources of SOA than toluene.

Smog chamber work can also be used to investigate SOA chemical formation

mechanisms.803,804,805,806

Although these concentrations are only estimates, due to the assumption that the mass

fractions of the smog chamber SOA samples using these tracers are equal to those in the ambient

atmosphere, there are presently no other means available for estimating the SOA concentrations

originating from individual SOA precursors. Among the tracer compounds observed in ambient

PM2.5 samples are two tracer compounds that have been identified in smog chamber aromatic

SOA samples.807 To date, these aromatic tracer compounds have been identified, in the

laboratory, for toluene and m-xylene SOA. Additional work is underway by the EPA to

determine whether these tracers are also formed by benzene and other alkylbenzenes (including

o-xylene, p-xylene, 1,2,4-trimethylbenzene, and ethylbenzene).

One caveat regarding this work is that a large number of VOCs emitted into the

atmosphere, which have the potential to form SOA, have not yet been studied in this way. It is

possible that these unstudied compounds produce SOA species which are being used as tracers

for other VOCs. This means that the present work could overestimate the amount of SOA

formed in the atmosphere by the VOCs studied to date. This approach may also estimate entire

hydrocarbon classes (e.g., all methylsubstituted-monoaromatics or all monoterpenes) and not

individual precursor hydrocarbons. Thus the tracers could be broadly representative and not

indicative of individual precursors. This is still unknown. Also, anthropogenic precursors play a

role in formation of atmospheric radicals and aerosol acidity, and these factors influence SOA

formation from biogenic hydrocarbons. This anthropogenic and biogenic interaction, important

to EPA and others, needs further study. The issue of SOA formation from aromatic precursors is

an important one to which EPA and others are paying significant attention. For benzene, smog

chamber studies show that benzene forms SOA possibly through reactions with NOx. Early
594

smog chamber work suggests benzene might be relatively inert in forming SOA, although this

study may not be conclusive.808 However, more recent work shows that benzene does form SOA

in smog chambers.809,810 This new smog chamber work shows that benzene can be oxidized in

the presence of NOx to form SOA with maximum mass of SOA being 8-25% of the mass of

benzene. As mentioned above, work is needed to determine if a tracer compound can be found

for benzene SOA which might indicate how much of ambient SOA comes from benzene.

The aromatic tracer compounds and their mass fractions have also been used to estimate

monthly ambient aromatic SOA concentrations from March 2004 to February 2005 in five U.S.

Midwestern cities.811 The annual tracer-based SOA concentration estimates were 0.15, 0.18,

0.13, 0.15, and 0.19 μg carbon/m3

for Bondville, IL, East St. Louis, IL, Northbrook, IL,

Cincinnati, OH and Detroit, MI, respectively, with the highest concentrations occurring in the

summer. On average, the aromatic SOA concentrations made up 17 % of the total SOA

concentration. Thus, this work suggests that we are finding ambient PM levels on an annual

basis of about 0.15 μg/m3associated with present toluene levels in the ambient air in these

Midwest cities. Based on preliminary analysis of recent laboratory experiments, it appears the

toluene tracer could also be formed during photooxidation of some of the xylenes.812

Over the past decade a variety of modeling studies have been conducted to predict

ambient SOA levels, with most studies focusing on the contributions of biogenic monoterpenes

and anthropogenic aromatic hydrocarbons. More recently, modelers have begun to include the

contribution of the isoprene SOA to ambient OC concentrations.813 In general, the studies have

been limited to comparing the sum of the POA and SOA concentrations with ambient OC

concentrations. The general consensus in the atmospheric chemistry community appears to be

that monoterpene contributions, which are clearly significant, and the somewhat smaller

aromatic contributions, are insufficient to account for observed ambient SOA levels. 814 Part of

this gap has been filled recently by SOA predictions for isoprene. Furthermore, the identification

in ambient SOA of a tracer compound for the sesquiterpene β-caryophyllene,815 coupled with the

high sesquiterpene SOA yields measured in the laboratory,816 suggests this class of hydrocarbons

should be included in SOA chemical mechanisms. In addition, recent data on SOA formation

from aromatic hydrocarbons suggest their contributions, while much smaller than biogenic

hydrocarbons, could be larger than previously thought.817,818

3.4.1.2.3 Ozone

As mentioned above, the addition of ethanol to fuels has been shown to contribute to

PAN formation and this is one way for it to contribute therefore to ground-level ozone formation.

PAN is a reservoir and carrier of NOx and is the product of acetyl radicals reacting with NO2 in

the atmosphere. One source of PAN is the photooxidation of acetaldehyde (Section 3.4.1.2.1),

but any hydrocarbon having a methyl group has the potential for forming acetyl radicals and
595

therefore PAN.198 PAN can undergo thermal decomposition with a lifetime of approximately 1

hour at 298K or 148 days at 250K. 199

CH3C(O)OONO2 + M → CH3C(O)OO∙ + NO2 + M k = 3.3 x 10-4

s

-1 819

The reaction above shows how NO2 is released in the thermal decomposition of PAN.

NO2 can also be formed in photodegradation reactions where NO is converted to NO2 (see OH

radical reaction of acetaldehyde in Section 3.4.1.2.1). In both cases, NO2 further photolyzes to

produce ozone (O3).

NO2 + hν → NO + O(3P)  = 300-800 nm 820

O(3P) + O2 + M → O3 + M

The temperature sensitivity of PAN allows it to be stable enough at low temperatures to

be transported long distances before decomposing to release NO2. NO2 can then participate in

ozone formation in regions remote from the original NOx source.

821 A discussion of CB05

mechanisms for ozone formation can be found in Yarwood et al. (2005).822

3.4.1.3 Modeling Uncertainties and Limitations

All the results presented below must be interpreted with the understanding that there are

considerable uncertainties in inventories, atmospheric processes in CMAQ, and other aspects of

the modeling process. While it is beyond the scope of this Regulatory Impact Analysis to

include a comprehensive discussion of all limitations and uncertainties associated with air quality

modeling, the key ones which could significantly impact analyses for this rule are addressed.

3.4.1.3.1 Emission Inventory Limitations

A key limitation of the analysis is that it employed interim emission inventories, which

were enhanced compared to what was described in the proposal, but did not include some of the

later enhancements and corrections of the final emission inventories presented in this FRM

(Section 3.3). Most significantly, our modeling of the air quality impacts of the renewable fuel

volumes required by RFS2 relied upon interim inventories that assumed that ethanol will make

up 34 of the 36 billion gallon renewable fuel mandate, that approximately 20 billion gallons of

this ethanol will be in the form of E85, and that the use of E85 results in fewer emissions of

direct PM2.5 from vehicles. The emission impacts and air quality results would be different if,

instead of E85, more non-ethanol biofuels are used or mid-level ethanol blends are approved.

In fact, as explained in Chapter 1 of the RIA, our more recent analyses indicate that

ethanol and E85 volumes are likely to be significantly lower than what we assumed in the

198 Many aromatic hydrocarbons, particularly those present in high percentages in gasoline (toluene, m-, o-, pxylene, and 1,3,5-, 1,2,4-trimethylbenzene), form methylglyoxal and biacetyl, which are also strong generators of

acetyl radicals (Smith, D.F., T.E. Kleindienst, C.D. McIver (1999) Primary product distribution from the reaction of

OH with m-, p-xylene and 1,2,4- and 1,3,5-Trimethylbenzene. J. Atmos. Chem., 34: 339- 364.).

199 All rate coefficients in this RIA are listed at 298 K and, if applicable, 1 bar of air.
596

interim inventories. Furthermore, the final emission inventories do not include vehicle-related

PM reductions associated with E85 use, as discussed in Section 3.1 and 3.3 above. There are

additional, important limitations and uncertainties associated with the interim inventories that

must be kept in mind when considering the results:

 Error in PM2.5 emissions from locomotive engines

After the air quality modeling was completed, we discovered an error in the way that

PM2.5 emissions from locomotive engines were allocated to counties in the inventory.

Locomotive emissions between the two reference cases and the control case vary due

to differences in activity for this sector due to transported volumes of ethanol. To

account for these differences, adjustments were to be applied to a common base

inventory developed for a 2022 projection of the 2005 air quality modeling platform

(http://www.epa.gov/ttn/chief/emch/index.html). The result should have been

inventories which reflected county emissions given the RFS1, AEO 2007 and RFS2

fuel volumes. However, in processing the data, errors were introduced which led to

inconsistencies in the common base inventory used to develop the PM inventories for

the three modeling cases. These errors were random, resulting in PM emission

changes that were too high in some counties and too low in other counties. This error

had very little impact on national-level PM2.5 emissions. The error in locomotive

PM2.5 inventory impacts for the RFS2 control case versus the RFS1 mandate

reference case was 111 tons, out of a total PM2.5 inventory impact of about 18,000

tons. The error in the impact of the RFS2 control case versus the AEO 2007

reference case was 1377 tons, out of a total PM2.5 inventory impact of about 16,000

tons. It is important to note that the total nationwide PM2.5 inventory is projected to

be over 3.3 million tons in 2022. However, an analysis of the error indicated local

impacts in both cases were quite large, and in a number of locations, dominated PM2.5

impacts. These impacts are summarized in a memorandum to the docket.200 As a

result of the error, we do not present the modeling results for specific localized PM2.5

impacts. However, we have concluded that PM2.5 modeling results are still

informative for national-level benefits assessment, as described in Section 5.4 of the

RIA.

 Sensitivity of light-duty vehicle exhaust emissions to ethanol blends

As discussed above in Sections 3.1 and 3.3, the interim emission inventories used for

the air quality modeling analysis are the “more sensitive” case described in the

proposal. As a result, the interim inventories used for air quality modeling assume

that vehicles operating on E10 have higher NOx emissions and lower VOC, CO and

PM exhaust emissions compared to the FRM inventories.

 Cellulosic plant emissions

200 Memorandum from Rich Cook to Docket ID No. EPA-HQ-OAR-2005-0161, “Impact of an Error in the

Locomotive Particulate Matter (PM2.5) Inventory on RFS2 Modeling Results.”
597

The interim emission inventories used in air quality modeling generally assumed

higher emissions from cellulosic plants than the FRM inventories, which used revised

estimates based on updates to the fraction of biomass burned at these plants.

However, as noted in Section 3.1 and 3.3, the shift of some cellulosic volume from

ethanol to diesel results in higher PM emissions from cellulosic plants in the final rule

inventories than used in the air quality modeling inventories.

 Ethanol volume

As mentioned above, the interim emission inventories used in our air quality

modeling reflect the use of ethanol in about 34 of the mandated 36 billion gallons and

do not include any cellulosic diesel. As shown in Table VI.A-1 of the preamble, the

FRM inventories assume 22 billion gallons of ethanol in the primary case and 6.5

billion gallons of cellulosic diesel. The inventories used for air quality modeling

assume ethanol volumes are more consistent with the FRM's high-ethanol case

inventory, which reflects the use of 33 billion gallons of ethanol and no cellulosic

diesel.

 Renewable fuel transport emissions

As discussed in Section 3.3, the estimates of renewable fuel transport volumes and

distances differ between the air quality modeling and final rule inventories.

There are also some important uncertainties associated with the emissions inventories,

apart from the differences between the interim inventories and the FRM inventories. For

example, E85 exhaust and evaporative emissions data are limited, as are data on E10 exhaust and

evaporative emissions for nonroad spark ignition engines. There is also considerable uncertainty

in how increased use of ethanol will impact other fuel properties which can affect emission

inventories and air quality. There are also limited data on activity and emission rates for key

upstream sources (especially future technology corn ethanol plants and cellulosic ethanol and

diesel plants). There are uncertainties in the surrogates used to allocate emissions spatially and

temporally; this is particularly significant in projecting the location of new ethanol plants,

especially future cellulosic biofuel plants and the location of these emissions. These plants can

have large impacts on local emissions. While most increased production of corn ethanol can

reasonably be assumed to occur at existing or planned facilities, there is no way to know with

certainty where cellulosic biofuel production will occur. Future cellulosic biofuel plant siting

was based on the types of feedstocks that would be most economical, and we assumed refineries

would be located in close proximity to feedstocks, as discussed in Section 1.8 of the RIA. While

corn ethanol plants were treated as point sources, cellulosic biofuel plants were modeled as

county-wide area sources, as described in Section 1.8. Finally, there are numerous assumptions

about land use changes that impact inventories for upstream sources and consequently can

impact air quality modeling results.

3.4.1.3.2 Uncertainties in Hydrocarbon Speciation Profiles
598

Another source of uncertainty involves the hydrocarbon speciation profiles, which are

used in the air quality modeling emission pre-processor, SMOKE, to break total hydrocarbons

down into individual constituent compounds. Given the complexity of the atmospheric

chemistry, the hydrocarbon speciation has an important influence on the air quality modeling

results. For example, we found that adjusting the speciation profile for gasoline headspace

emissions changed the ambient concentration of acetaldehyde. SMOKE uses gasoline headspace

profiles for E0 and E10 from EPA’s SPECIATE database to speciate emissions from gasoline

storage, gasoline distribution, and gas cans. These are key sources of upstream emissions

affected by increased use of E10. The EPA profiles initially used in the reference case scenarios

for gasoline headspace emissions (i.e., emissions from gas cans and tanker truck distribution –

profiles 8736 and 8737 for E10 and E0) in EPA’s SPECIATE4.2 database show much greater

differences in alkene (olefin) compounds than one would expect between E0 and E10. Alkenes

react in the atmosphere to form secondary acetaldehyde, and can also form ozone. E0 has 13%

of the VOC (volatile organic compounds) as alkenes while the E10 profile has only 4% alkenes.

By contrast, the profiles for exhaust from Tier 2 vehicles (8756 and 8757 for E0 and E10

respectively) show similar levels of alkenes for E0 and E10 (about 20%).823 The evaporative

emissions profiles (profiles 8753 and 8754 in EPA’s SPECIATE4.2 database824) show lower

olefin contents of 3% and 6% respectively.

One expects the headspace from E10 blends to have similar olefin content to that from E0

blends. Available data indicate that ethanol forms an azeotrope with various hydrocarbon

compounds such as olefins.825,826 That azeotrope for olefins would result in the partial vapor

pressure of the olefins in the E10 blends being about the same or somewhat higher than in an E0

blends. The difference between the E0 and E10 profile is likely because the limited fuel samples

taken for headspace analysis were taken in different locations and time periods.

Recent measurements of speciated gasoline headspace vapors were collected by EPA’s Office of

Research and Development (ORD) to compare differences between an E0 fuel and a splashblended E10.827 The addition of 10% ethanol to the base E0 fuel only slightly decreased the

olefin content from 7.6% to 6.3% of total VOC observed in the headspace vapors. While there is

some uncertainty in representativeness of the splash-blended fuel, a follow-up analysis of

speciated headspace vapors from in-use E10 gasolines showed significant variation in olefin

composition from one fuel to another, illustrating the need for speciation profiles collected under

controlled conditions.828

Because the E0 and E10 headspace profiles initially used in the reference case scenarios

have an uncharacteristic difference in relative alkene levels, EPA reran the control case using an

adjusted E0 gasoline headspace profile.201 However, due to time constraints, EPA did not rerun

the two reference cases with the adjusted E0 profile, resulting in an inconsistency between the

control case and the reference cases. Implications of this inconsistency are discussed in Section

3.4.2. EPA believes that it is important to correct the gasoline headspace profile, although we

recognize that using an adjusted profile introduces inconsistencies between the reference and

control cases. It should be noted that this is but one example of potential weaknesses in the

emission speciation data. Profiles for a number of key sources are based on data with significant

limitations.

201 Use of the adjusted profile in the control case rerun is discussed in the emissions modeling TSD, found in the

docket for this rule (EPA-HQ-OAR-2005-0161).
599

3.4.1.3.3 Uncertainties Associated with Chemical Mechanisms

Another key source of uncertainty is the photochemical mechanisms in CMAQ 4.7.

Pollutants such as ozone, PM, acetaldehyde, formaldehyde, acrolein, and 1,3-butadiene can be

formed secondarily through atmospheric chemical processes. Since secondarily formed

pollutants can result from many different reaction pathways, there are uncertainties associated

with each pathway. Simplifications of chemistry must be made in order to handle reactions of

thousands of chemicals in the atmosphere. Mechanisms for formation of ozone, PM,

acetaldehyde and peroxyacetyl nitrate (PAN) are particularly relevant for this rule, and are

discussed in Section 3.4.1.2.

For PM, there are a number of uncertainties associated with SOA formation that should

be addressed explicitly. As mentioned in Section 3.4.1.2.2, a large number of VOCs emitted

into the atmosphere, which have the potential to form SOA, have not yet been studied in detail.

In addition, the amount of ambient SOA that comes from benzene is uncertain. Simplifications

to the SOA treatment in CMAQ have also been made in order to preserve computational

efficiency. These simplifications are described in release notes for CMAQ 4.7 on the

Community Modeling and Analysis System (CMAS) website.829

3.4.2 Air Quality Modeling Results

As described above, we performed a series of air quality modeling simulations for the

continental U.S in order to assess the impacts of the renewable fuel volumes required by RFS2.

The results presented here are based on inventory projections for RFS2 compared against the

AEO 2007 and RFS1 mandate reference cases, both of which include some usage of ethanol

fuels. These results are important for understanding the potential differences between RFS2

volumes of ethanol and AEO 2007 or RFS1 mandate reference cases; however, these results do

not constitute the “anti-backsliding” analysis required by Clean Air Act section 211(v). EPA

will be analyzing air quality impacts of increased renewable fuel use through that study and will

promulgate appropriate mitigation measures under section 211(v), separate from this final action.

Notably, the anti-backsliding exercise will be able to include inventory improvements based on

additional results from the EPAct test program which we could not include in this analysis due to

time restraints.830 The following results are based on the interim inventories detailed in Section

3.3 and subject to the uncertainties and limitation detailed in Section 3.4.1.3.

3.4.2.1 Current and Projected Ambient Levels of Pollutants

Although the purpose of this final rule is to implement the renewable fuel requirements

established by the Energy Independence and Security Act (EISA) of 2007, the renewable fuel

volumes required by this rule would also impact emissions of criteria and air toxic pollutants and

their resultant ambient concentrations. The fuels changes detailed in Section 3.1 of the RIA will

influence emissions of VOCs, PM, NOX, SOX, CO and air toxics. Air quality modeling

performed for this final rule illustrates the changes in ambient concentrations of PM2.5 and ozone

as well as changes in ambient concentrations of ethanol and the following air toxics:

acetaldehyde, acrolein, benzene, 1,3-butadiene, and formaldehyde. These changes are expected

to occur with emissions changes from the renewable fuel volumes required by RFS2. The air
600

quality modeling results also include changes in deposition of nitrogen and sulfur which are

expected to occur with emissions changes from the renewable fuel volumes required by RFS2.

This section describes current ambient levels of ozone, PM, air toxics, and nitrogen and

sulfur deposition and presents the projected ambient levels resulting from the increased use of

renewable fuels. Note that the projected results for PM are impacted by the error in the PM2.5

locomotive inventory (Section 3.4.1.3) and therefore we do not present the modeling results for

specific localized PM2.5 impacts.

3.4.2.1.1 Particulate Matter (PM2.5 and PM10)

As described in Section 3.5, PM causes adverse health effects, and the EPA has set

national ambient air quality standards (NAAQS) to protect against those health effects. In this

section we present information on current and model-projected future PM levels.

3.4.2.1.1.1 Current Levels of PM

Figures 3.4-2 and 3.4-3 show a snapshot of annual and 24-hour PM2.5 concentrations in

2007. There are two U.S. National Ambient Air Quality Standards (NAAQS) for PM2.5: an

annual standard (15 μg/m3

) and a 24-hour standard (35 μg/m3). In 2007, the highest annual

average PM2.5 concentrations were in California, Arizona, Alabama, and Pennsylvania and the

highest 24-hour PM2.5 concentrations were in California, Idaho, and Utah.
601

Figure 3.4-2. Annual Average PM2.5 Concentrations in μg/m3for 2007 (from 2008 Air

Trends Report)
602

Figure 3.4-3. 24-hour (98th percentile 24- hour concentrations) PM2.5 Concentrations in

μg/m3for 2007 (from 2008 Air Trends Report)

The most recent revisions to the PM standards were in 1997 and 2006. In 2005, the U.S.

EPA designated nonattainment areas for the 1997 PM2.5 NAAQS (70 FR 19844, April 14,

2005).202 As of January 6, 2010, approximately 88 million people live in the 39 areas that are

designated as nonattainment for the 1997 PM2.5 National Ambient Air Quality Standard

(NAAQS). These PM2.5 nonattainment areas are comprised of 208 full or partial counties.

Nonattainment areas for the 1997 PM2.5 NAAQS are pictured in Figure 3.4-4. On October 8,

2009, the EPA issued final nonattainment area designations for the 2006 24-hour PM2.5 NAAQS

(74 FR 58688, November 13, 2009). These designations include 31 areas composed of 120 full

or partial counties with a population of over 70 million. Nonattainment areas for the 2006 PM2.5

NAAQS are pictured in Figure 3.4-5. In total, there are 54 PM2.5 nonattainment areas composed

of 245 counties with a population of 101 million people.

202 A nonattainment area is defined in the Clean Air Act (CAA) as an area that is violating an ambient standard or is

contributing to a nearby area that is violating the standard.
603

Figure 3.4-4. 1997 PM2.5 Nonattainment Areas
604

Figure 3.4-5. 2006 PM2.5 Nonattainment Areas
605

As of January 6, 2010, approximately 26 million people live in the 47 areas that are

designated as nonattainment for the PM10 NAAQS. There are 40 full or partial counties that

make up the PM10 nonattainment areas. Nonattainment areas for the PM10 NAAQS are pictured

in Figure 3.4-6.

Figure 3.4-6. PM10 Nonattainment Areas

3.4.2.1.1.2 Projected Levels of PM2.5

Generally, our modeling indicates that the required renewable fuel volumes will reduce

PM2.5 concentrations in some areas of the country and increase PM2.5 concentrations in other

areas. In the following sections we describe projected PM2.5 levels in the future, with and

without the required renewable fuel volumes. Information on the air quality modeling

methodology is contained in Section 3.4.1. Additional detail can be found in the air quality

modeling technical support document (AQM TSD) in the docket for this rule.

3.4.2.1.1.2.1 Projected PM2.5 Levels without RFS2 Volumes

EPA has already adopted many mobile source emission control programs that are

expected to reduce ambient PM levels. These control programs include the New Marine
606

Compression-Ignition Engines at or Above 30 Liters per Cylinder rule,203 the Marine SparkIgnition and Small Spark-Ignition Engine rule (73 FR 59034, October 8, 2008), the Locomotive

and Marine Compression-Ignition Engine Rule (73 FR 25098, May 6, 2008), the Clean Air

Nonroad Diesel rule (69 FR 38957, June 29, 2004), the Heavy Duty Engine and Vehicle

Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan. 18, 2001)

and the Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements

(65 FR 6698, Feb. 10, 2000). As a result of these programs, the number of areas that fail to meet

the PM2.5 NAAQS in the future is expected to decrease. However, even with the implementation

of all current state and federal regulations, there are projected to be U.S. counties violating the

PM2.5 NAAQS well into the future.

Recent air quality modeling for the “Control of Emissions from New Marine

Compression-Ignition Engines at or Above 30 Liters per Cylinder” rule projects that in 2020, at

least 10 counties with a population of almost 25 million may not attain the 1997 annual PM2.5

standard of 15 μg/m3and 47 counties with a population of over 53 million may not attain the

2006 24-hour PM2.5 standard of 35 μg/m3.

204 Since the emission changes from the volumes of

renewable fuel required by RFS2 will go into effect during the period when some areas are still

working to attain the PM2.5 NAAQS, the projected emission changes will impact state and local

agencies in their effort to attain and maintain the PM2.5 standard.

3.4.2.1.1.2.2 Projected PM2.5 Levels with RFS2 Volumes

This section includes a summary of the results of our modeling of PM2.5 air quality

impacts in the future due to the required renewable fuel volumes. We compare the RFS1

mandate reference case and AEO 2007 reference case scenarios to the RFS2 control scenario.

When discussing the projected changes in PM2.5 it is important to remember that there are

uncertainties and limitations related to the air quality modeling (see Section 3.4.1.3), in large part

due to uncertainties in projecting the future types of renewable fuels, the location of their

production, and their method of use. Section 3.3 discusses the differences in the air quality

modeling inventories and the final rule inventories in more detail.

Changes in ambient PM2.5 concentrations due to required renewable fuel volumes are a

result of changes to upstream and downstream emission sources, complex chemical reactions

(direct emissions and secondary formation), transport and meteorology. As is detailed in Section

3.2, the required renewable fuel volumes impact upstream and tailpipe emissions of primary

PM2.5 and PM2.5 precursors such as NOx and VOCs. Primary PM is emitted directly into the

atmosphere and, on a mass basis, is largely carbonaceous in nature. Various studies have shown

that mobile sources are a major source of primary PM in urban areas over many portions of the

United States.831,832,833,834,835,836,837 Primary PM that is carbonaceous is also referred to as

primary organic aerosol (POA). Secondary PM is formed in the atmosphere from chemical

203 This rule was signed on December 18, 2009 but has not yet been published in the Federal Register. The signed

version of the rule is available at http://epa.gov/otaq/oceanvessels.htm).

204 US EPA (2009). Final Rule “Control of Emissions from New Marine Compression-Ignition Engines at or Above

30 Liters per Cylinder”. This rule was signed on December 18, 2009 but has not yet been published in the Federal

Register. The signed version of the rule is available at http://epa.gov/otaq/oceanvessels.htm).
607

transformations of gases. The forms of secondary PM most impacted by the renewable fuel

volumes are nitrates and organics or secondary organic aerosol (SOA).

Due to the error in the PM inventory for locomotives we only present design value

changes averaged over all 577 modeled counties, and do not present local impacts. The

modeled counties are located across the country and have monitors that allow the calculation of a

PM2.5 design value. A large majority of the modeled counties will see relatively minor annual

average PM2.5 design value changes of between -0.05 μg/m3and +0.05 μg/m3. On a populationweighted basis, the average modeled future-year annual PM2.5 design values are projected to

decrease by 0.002 μg/m3 when compared with the RFS1 mandate or AEO reference case.205 We

also looked at changes in daily PM2.5 design values. A majority of the modeled counties will see

daily PM2.5 design value changes of between -0.25 μg/m3and +0.25 μg/m3. On a populationweighted basis, the average modeled future-year daily PM2.5 design value is projected to

decrease by 0.06 μg/m3 when compared with the RFS1 mandate reference case or 0.05 μg/m3

when compared with the AEO reference case.

The changes in ambient PM2.5 described above are likely due to both increased emissions

at biofuel production plants and from biofuel transport, and reductions in SOA formation and

reduced emissions from gasoline refineries. In addition, decreases in ambient PM are predicted

because our modeling inventory assumed large volumes of E85 use and also that E85 usage

reduces PM tailpipe emissions. As mentioned previously, these direct PM emission reductions

would not occur with final rule inventory assumptions.

3.4.2.1.2 Ozone

As described in Section 3.5, ozone causes adverse health effects, and the EPA has set

national ambient air quality standards (NAAQS) to protect against those health effects. In this

section, we present information on current and model-projected future ozone levels.

3.4.2.1.2.1 Current Levels of Ozone

Figure 3.4-7 shows a snapshot of ozone concentrations in 2007. The highest ozone

concentrations were located in California, Connecticut, Georgia, Massachusetts, North Carolina,

and Pennsylvania. Fifty-seven percent of the sites were above 0.075 ppm, the level of the 2008

standard.

205 Note that the change in annual average PM2.5 for design values differs from the change in national populationweighted annual average PM2.5 discussed in Sections I and VIII of the preamble and Chapter 5 of the RIA. National

population-weighted annual average PM2.5 with respect to health impacts is based on modeling data across all

populated grid cells rather than just those counties with monitors. We find that there is a small increase in national

population-weighted annual average PM2.5 across all populated grid cells in the air quality modeling domain.
608

Figure 3.4-7. Ozone Concentrations (fourth highest daily maximum 8-hour concentration)

in ppm for 2007 (from 2008 Air Trends Report)
609

Figure 3.4-8. 1997 Ozone Nonattainment Areas

The primary and secondary national ambient air quality standards (NAAQS) for ozone

are 8-hour standards set at 0.075 ppm. The most recent revision to these standards was in 2008;

the previous 8-hour ozone standards, set in 1997, had been set at 0.08 ppm. In 2004, the U.S.

EPA designated nonattainment areas for the 1997 8-hour ozone NAAQS (69 FR 23858, April 30,

2004). As of January 6, 2010, there are 51 8-hour ozone nonattainment areas for the 1997 ozone

NAAQS composed of 266 full or partial counties with a total population of over 122 million.

Figure 3.4-8 presents the 1997 NAAQS ozone nonattainment areas. On January 6, 2010, EPA

proposed to reconsider the 2008 ozone NAAQS to ensure they are sufficiently protective of

public health and the environment. EPA intends to complete the reconsideration by August 31,

2010. If, as a result of the reconsideration, EPA determines that the 2008 ozone standards are

not supported by the scientific record and promulgates different ozone standards, the new 2010

ozone standards would replace the 2008 ozone standards and the requirement to designate areas

for the replaced 2008 standards would no longer apply. Because of the significant uncertainty

the reconsideration proposal creates regarding the continued applicability of the 2008 ozone

NAAQS, EPA has extended the deadline for designating areas for the 2008 NAAQS by 1 year.

If EPA promulgates new ozone standards in 2010, EPA intends to accelerate the designations

process for the primary standard so that the designations would be effective in August 2011.
610

Table 3.4-1 provides an estimate, based on 2005-07 air quality data, of the counties with

design values greater than the 2008 ozone NAAQS.

Table 3.4-1.

Counties with Design Values Greater Than the 2008 Ozone NAAQS

Based on 2005-2007 Air Quality Data

NUMBER OF

COUNTIES

POPULATIONa

1997 Ozone Standard: counties within the 51

areas currently designated as nonattainment (as

of 1/6/10)

266 122,343, 799

2008 Ozone Standard: additional counties that

would not meet the 2008 NAAQSb

227 41,285,262

Total 493 163,629,061

Notes:

a

Population numbers are from 2000 census data.

b Area designations for the 2008 ozone NAAQS have not yet been made. Nonattainment for the 2008 Ozone

NAAQS would be based on three years of air quality data from later years. Also, the county numbers in the table

include only the counties with monitors violating the 2008 Ozone NAAQS. The numbers in this table may be an

underestimate of the number of counties and populations that will eventually be included in areas with multiple

counties designated nonattainment.

3.4.2.1.2.2 Projected Levels of Ozone

Achieving the required renewable fuel volumes by 2022 is projected to adversely impact

ozone air quality over much of the U.S. However, ozone air quality improvements are projected

in a few highly-populated areas which currently have poor air quality. In the following sections

we describe projected ozone levels in the future resulting from the increased use of renewable

fuels. Information on the air quality modeling methodology is contained in Section 3.4.1.

Additional detail can be found in the air quality modeling technical support document (AQM

TSD) in the docket for this rule.

3.4.2.1.2.2.1 Projected Ozone Levels without RFS2 Volumes

EPA has already adopted many emission control programs that are expected to reduce

ambient ozone levels. These control programs include the New Marine Compression-Ignition

Engines at or Above 30 Liters per Cylinder rule,206 the Marine Spark-Ignition and Small SparkIgnition Engine rule (73 FR 59034, October 8, 2008), the Locomotive and Marine Rule (73 FR

25098, May 6, 2008), the Clean Air Interstate Rule (70 FR 25162, May 12, 2005), the Clean Air

Nonroad Diesel rule (69 FR 38957, June 29, 2004), and the Heavy Duty Engine and Vehicle

Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan. 18, 2001).

As a result of these programs, 8-hour ozone levels are expected to improve in the future.

206 This rule was signed on December 18, 2009 but has not yet been published in the Federal Register. The signed

version of the rule is available at http://epa.gov/otaq/oceanvessels.htm).
611

The baseline air quality modeling projects that in 2022, with all current controls in effect

but excluding the emissions changes expected to occur as a result of the required renewable fuel

volumes, at least 7 counties, with a projected population of over 22 million people, may not

attain the 1997 8-hour ozone standard of 0.08 ppm and at least 25 counties, with a projected

population of nearly 41 million people, may not attain the 2008 8-hour ozone standard of 75 ppb.

This modeling supports the conclusion that there are a number of counties across the U.S.

projected to experience ozone concentrations at or above the ozone NAAQS into the future.

Since the emission changes from the required renewable fuel volumes go into effect during the

period when some areas are still working to attain the ozone NAAQS, the projected emission

changes will impact state and local agencies in their effort to attain and maintain the ozone

standard. In the following section we discuss projected nonattainment areas and how they

compare to the areas which are projected to experience either ozone reductions or ozone

increases from the required renewable fuel volumes.

3.4.2.1.2.2.2 Projected Ozone Levels with RFS2 Volumes

This section summarizes the results of our modeling of ozone air quality impacts in the

future due to required renewable fuel volumes. Specifically, we compare the RFS1 mandate and

AEO 2007 reference case scenarios to the RFS2 control scenario.207 Our modeling indicates that

the required renewable fuel volumes will increase ozone design value concentrations in many

areas of the country and decrease ozone design value concentrations in a small number of areas.

Figures 3.4-9 and 3.4-10 present the changes in 8-hour ozone design value concentration in 2022

when the RFS2 control scenario is compared to the RFS1 mandate reference case and the AEO

2007 reference case respectively.

207 We used a different speciation profile for E10 gasoline headspace emissions in the EISA control case than was

used for the RFS1 and AEO 2007 reference cases. This inconsistency is described in Section 3.4.1.3.
612

Figure 3.4-9. Projected Change in 2022 8-hour Ozone Design Values Between the RFS2

Control Scenario and RFS1 Mandate Reference Case Scenario
613

Figure 3.4-10. Projected Change in 2022 8-hour Ozone Design Values Between the RFS2

Control Scenario and AEO 2007 Reference Case Scenario

As can be seen in Figure 3.4-9 and 3.4-10 most counties with modeled data, especially

those in the Midwest, see increases in their ozone design values. The majority of these design

value increases are less than 0.5 ppb. However, there are some counties that will see 8-hour

ozone design value increases above 0.5 ppb; these counties are mainly in the eastern U.S. The

maximum projected increase in an 8-hour ozone design value is in Morgan County, Alabama,

1.56 ppb and 1.27 ppb when compared with the RFS1 mandate and AEO 2007 reference cases

respectively. There are also some counties that are projected to see 8-hour ozone design value

decreases. The counties with ozone design value decreases greater than 0.5 ppb are in Southern

California. The maximum decrease projected in an 8-hour ozone design value is in Riverside,

CA, 0.66 ppb and 0.60 ppb when compared with the RFS1 mandate and AEO 2007 reference

cases respectively.

There are 26 counties, mainly in California, that are projected to have 8-hour ozone

design values above the 2008 NAAQS in 2022 with the required renewable fuel volumes in

place. Table 3.4-2 below presents the changes in design values for these counties when

comparing the RFS2 control scenario with the RFS1 mandate and AEO 2007 reference case

scenarios.
614

Table 3.4-2.

Change in Ozone Design Values (ppb) for Counties Projected

to be Above the 2008 Ozone NAAQS in 2022

RFS2 control -

RFS1 mandate

RFS2 control –

AEO 2007

San Bernardino County, California -0.58 -0.53

Riverside County, California -0.66 -0.60

Los Angeles County, California -0.16 -0.16

Kern County, California 0.02 -0.02

Tulare County, California 0.34 0.07

Harris County, Texas 0.12 0.05

Fresno County, California 0.11 0.08

Brazoria County, Texas 0.18 0.09

Suffolk County, New York -0.09 -0.05

East Baton Rouge County, Louisiana 0.39 0.27

Sacramento County, California 0.04 0.04

Orange County, California -0.57 -0.52

Calaveras County, California 0.15 0.14

Nevada County, California 0.07 0.06

El Dorado County, California 0.05 0.04

Harford County, Maryland 0.23 0.03

Ventura County, California -0.01 -0.03

Fairfield County, Connecticut -0.08 -0.08

Placer County, California 0.05 0.04

San Diego County, California 0.25 0.19

Merced County, California -0.10 -0.09

Westchester County, New York 0.35 0.23

Kenosha County, Wisconsin -0.11 -0.11

Philadelphia County, Pennsylvania 0.19 0.12

New Haven County, Connecticut 0.92 0.68

Table 3.4-3 shows the average change in 2022 8-hour ozone design values for: (1) all

counties with 2005 baseline design values, (2) counties with 2005 baseline design values that

exceeded the 2008 ozone standard, (3) counties with 2005 baseline design values that did not

exceed the 2008 standard, but were within 10% of it, (4) counties with 2022 design values that

exceeded the 2008 ozone standard, and (5) counties with 2022 design values that did not exceed

the standard, but were within 10% of it. Counties within 10% of the standard are intended to

reflect counties that meet the standard, but will likely benefit from help in maintaining that status

in the face of growth. Many of these statistics show an increase in ozone design values in 2022,

more often when compared with the RFS1 case, but the magnitude of the increase varies and

there are some statistics which show a decrease in 8-hour ozone design values. On a populationweighted basis, the average modeled future-year 8-hour ozone design values are projected to

increase by 0.28 ppb in 2022 when compared with the RFS1 mandate reference case and increase

by 0.16 ppb when compared with the AEO 2007 reference case. On a population-weighted basis
615

those counties that are projected to be above the 2008 ozone standard in 2022 will see decreases

of 0.14 when compared with the RFS1 mandate reference and 0.15 ppb when compared with the

AEO 2007 reference case scenario.

Table 3.4-3.

Average Change in Projected Future Year 8-hour Ozone Design Value

as a Result of the Required Renewable Fuel Volumes

AVERAGEa NUMBER

OF US

COUNTIES

2020

POPULATIONb

CHANGE IN

2022 DESIGN

VALUE (PPB)

RFS2-RFS1

CHANGE IN

2022 DESIGN

VALUE (PPB)

RFS2-AEO

2007

All 678 238,378,342 0.46 0.30

All, population-weighted 678 238,378,342 0.28 0.16

Counties whose 2005 base year is

violating the 2008 8-hour ozone

standard 389 174,967,297 0.44 0.28

Counties whose 2005 base year is

violating the 2008 8-hour ozone

standard, population-weighted 389 174,967,297 0.26 0.14

Counties whose 2005 base year is

within 10 percent of the 2008 8-hour

ozone standard 208 43,172,228 0.52 0.36

Counties whose 2005 base year is

within 10 percent of the 2008 8-hour

ozone standard, population-weighted 215 45,008,435 0.35 0.22

Counties whose 2022 RFS2 control case

is violating the 2008 8-hour ozone

standard 26 41,017,324 0.04 0.00

Counties whose 2022 RFS2 control case

is violating the 2008 8-hour ozone

standard, population-weighted 26 41,017,324 -0.14 -0.15

Counties whose 2022 RFS2 control case

is within 10% of the 2008 8-hour ozone

standard 110 61,618,519 0.34 0.22

Counties whose 2022 RFS2 control case

is within 10% of the 2008 8-hour ozone

standard, population-weighted 110 61,618,519 0.31 0.19

Notes:

a Averages are over counties with 2005 modeled design values

b Population numbers based on 2000 census data

Ground-level ozone pollution is formed by the reaction of VOCs and NOx in the

atmosphere in the presence of heat and sunlight. The science of ozone formation, transport, and

accumulation is complex.838 The projected ozone increases in some areas and decreases in other

areas which are seen in the air quality modeling for this final rule are likely a result of the
616

emissions changes due to the increased volumes of renewable fuels combined with the

photochemistry involved, the different background concentrations of VOCs and NOx in different

areas of the country, and the different meteorological conditions in different areas of the country.

When VOC levels are relatively high, relatively small amounts of NOx enable ozone to form

rapidly. Under these conditions VOC reductions have little effect on ozone and while NOx

reductions are highly effective in reducing ozone, NOx increases lead to increases in ozone.

Such conditions are called “NOx-limited.” Because the contribution of VOC emissions from

biogenic (natural) sources to local ambient ozone concentrations can be significant, even some

areas where man-made VOC emissions are relatively low can be NOx-limited. Rural areas are

usually NOx-limited, due to the relatively large amounts of biogenic VOC emissions in such

areas. The ozone increases seen in the southeastern U.S. and many of the other rural areas are

likely due to the fact that those areas are NOx-limited and this final rule is projected to increase

NOx and decrease VOCs. A recent review article looking at ethanol in gasoline indicates that

increasing usage of E10 fuels, when compared with E0 fuels, can increase NOx emissions and

thereby increase ozone concentrations (see Section 3.4.3.3).839

When NOx levels are relatively high and VOC levels relatively low, NOx forms

inorganic nitrates (i.e., particles) but relatively little ozone. Such conditions are called “VOClimited.” Under these conditions, VOC reductions are effective in reducing ozone, but NOx

reductions can actually increase local ozone under certain circumstances. In the air quality

modeling done for this final rule, the ozone decreases seen in southern California and some of

the other urban areas, like Cleveland and Miami, are likely due to the fact that those areas are

VOC-limited areas and they are projected to see decreases in VOCs and increases in NOx due to

this final rule.

As mentioned in Section 3.3, the inventories used for the air quality modeling differ from

those being presented in this final rule, and as mentioned in Section 3.4.1.3, there are

uncertainties and limitations related to the air quality modeling. When looking at the changes in

projected ozone the most important uncertainty has to do with the fact that the modeled

inventory assumes increases in NOx for vehicles using E10 fuel. These NOx increases

contribute to the ozone increases in NOx-limited areas and the ozone decreases in VOC-limited

areas.

3.4.2.1.3 Air Toxics

3.4.2.1.3.1 Current Levels of Air Toxics

The majority of Americans continue to be exposed to ambient concentrations of air toxics

at levels which have the potential to cause adverse health effects.840 The levels of air toxics to

which people are exposed vary depending on where people live and work and the kinds of

activities in which they engage, as discussed in detail in U.S. EPA’s recent Mobile Source Air

Toxics Rule.841 In order to identify and prioritize air toxics, emission source types and locations

which are of greatest potential concern, U. S. EPA conducts the National-Scale Air Toxics

Assessment (NATA). The most recent NATA was conducted for calendar year 2002, and was

released in June 2009.842 NATA for 2002 includes four steps:
617

1) Compiling a national emissions inventory of air toxics emissions from outdoor sources

2) Estimating ambient concentrations of air toxics across the United States

3) Estimating population exposures across the United States

4) Characterizing potential public health risk due to inhalation of air toxics including both

cancer and noncancer effects

Figures 3-4.11 and 3-4.12 depict estimated county-level carcinogenic risk and noncancer

respiratory hazard from the assessment. The respiratory hazard is dominated by a single

pollutant, acrolein.

According to NATA for 2002, mobile sources were responsible for 47 percent of outdoor

toxic emissions, over 50 percent of the cancer risk, and over 80 percent of the noncancer hazard.

843,208 Benzene is the largest contributor to cancer risk of all 124 pollutants quantitatively

assessed in the 2002 NATA, and mobile sources were responsible for 59 percent of benzene

emissions in 2002. Over the years, EPA has implemented a number of mobile source and fuel

controls which have resulted in VOC reductions, which also reduced benzene and other air toxic

emissions.

Figure 3-4.11. County Level Average Carcinogenic Risk, 2002 NATA.

208 NATA relies on a Guassian plume model, Assessment System for Population Exposure Nationwide (ASPEN), to

estimate toxic air pollutant concentrations. Projected air toxics concentrations presented in this rule were modeled

with CMAQ 4.7, which has only recently been updated to include air toxics.
618

Figure 3-4.12. County Level Average Noncancer Hazard Index, 2002 NATA.

3.4.2.1.3.2 Projected Levels of Air Toxics

In the following sections, we describe results of our modeling of air toxics levels in the

future with the renewable fuel volumes required by this action. Although a large number of

compounds which are considered air toxics could be impacted by increases in renewable fuel

volumes, we focused on those which were identified as national and regional-scale cancer and

noncancer risk drivers in the 2002 NATA844 and were also likely to be significantly impacted by

the renewable fuel volumes required by RFS2. These compounds include benzene, 1,3-

butadiene, formaldehyde, acetaldehyde, and acrolein. Ethanol impacts were also included in our

analyses because of health concerns (Section 3.4.5) and its role as an acetaldehyde precursor.

Information on the air quality modeling methodology is contained in Section 3.4.1.1. Additional

detail can be found in the air quality modeling technical support document (AQM TSD) in the

docket for this rule.

It should be noted that EPA has adopted many mobile source emission control programs

that are expected to reduce ambient air toxics levels. These control programs include the Heavyduty Onboard Diagnostic Rule (74 FR 8310, February 24, 2009), Small SI and Marine SI Engine

Rule (73 FR 59034, October 8, 2008), Locomotive and Commercial Marine Rule (73 FR 25098,

May 6, 2008), Mobile Source Air Toxics Rule (72 FR 8428, February 26, 2007), Clean Air

Nonroad Diesel Rule (69 FR 38957, June 29, 2004), Heavy Duty Engine and Vehicle Standards

and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan. 18, 2001) and the Tier

2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements (65 FR 6698,

Feb. 10, 2000). As a result of these programs, the ambient concentration of air toxics in the
619

future is expected to decrease. The reference case and control case scenarios include these

controls.

This section summarizes the results of our modeling of ambient air toxics impacts in the

future from the renewable fuel volumes required by RFS2. Specifically, we compare the RFS1

mandate and AEO 2007 reference scenarios to the RFS2 control scenario for 2022 (see Section

3.3 for more information on the scenarios).209 Our modeling indicates that, while there are some

localized impacts, the renewable fuel volumes required by RFS2 have relatively little impact on

national average ambient concentrations of the modeled air toxics. An exception is increased

ambient concentrations of ethanol. Because overall impacts are small, we concluded that

assessing exposure to ambient concentrations and conducting a quantitative risk assessment of

air toxic impacts was not warranted. However, we did develop population metrics, including the

population living in areas with increases or decreases in concentrations of various magnitudes.

We also estimated aggregated populations above and below reference concentrations for

noncancer effects.

Our discussion of the air quality modeling for air toxics primarily focuses on impacts of

the renewable fuel volumes required by RFS2 in reference to the RFS1 mandate for 2022; this

comparison has a greater difference in projected ethanol volumes between the reference and the

control case than a comparison using the AEO 2007 reference case. Except where specifically

discussed below, air quality modeling results of the RFS2 control case in comparison with the

AEO 2007 reference case are presented in Appendix 3.A of this RIA.

Acetaldehyde

Our air quality modeling does not show substantial overall nationwide impacts on

ambient concentrations of acetaldehyde as a result of the renewable fuel volumes required by this

rule. Annual percent changes in ambient concentrations of acetaldehyde are less than 1% for

most of the country (Figure 3.4-13). Several urban areas show decreases in ambient acetaldehyde

concentrations ranging from 1 to 10%, and some rural areas associated with new ethanol plants

show increases in ambient acetaldehyde concentrations ranging from 1 to 10% with RFS2.

Annual absolute changes in ambient concentrations of acetaldehyde are generally less than 0.1

μg/m3 (Figure 3.4-14). However, as discussed below, there are considerable limitations and

uncertainties in our assessment of impacts of the renewable fuel volumes required by this rule on

ambient concentrations of acetaldehyde.

209 We used a different speciation profile for E10 gasoline headspace emissions in the RFS2 control case than was

used for the RFS1 and AEO reference cases. This inconsistency is described in Section 3.4.1.3.
620

Figure 3.4-13. Acetaldehyde Annual Percent Change in Concentration Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022
621

Figure 3.4-14. Acetaldehyde Annual Absolute Changes in Ambient Concentrations

Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3)

As noted above, the results show that the largest increases in ambient acetaldehyde

concentrations with RFS2 volumes occur in areas associated with new ethanol plants. This

result is due to an increase in emissions of primary acetaldehyde and precursor emissions from

ethanol plants not included in the RFS1 baseline scenario. Locations for projected corn ethanol

plants emissions were based on existing or planned plants, whereas cellulosic ethanol plants were

projected based on available feedstocks. Details on how this was done are described in Section

1.8 of the RIA.845 As discussed in Section 3.4.1.3, the location of these localized increases is

limited by uncertainties in the placement of the new plants.

Significant increases in ambient acetaldehyde might be expected based on the significant

increases in primary acetaldehyde and ethanol emissions (18% and 16% for the primary case

relative to RFS1, nationally, as described in Section 3.2). However, the chemical formation of

acetaldehyde is complex; most ambient acetaldehyde is formed from secondary photochemical

reactions of numerous precursor compounds, and many photochemical mechanisms are

responsible for this process (see Section 3.4.1.2 and 3.4.1.3). As discussed in more detail in

Section 3.4.3.1, some previous U.S. monitoring studies have suggested an insignificant or small
622

impact of increased use of ethanol in fuel on ambient acetaldehyde.846,847,848 These studies

suggest that increases in direct emissions of acetaldehyde are offset by decreases in the

secondary formation of acetaldehyde. Other past studies have shown increases in ambient

acetaldehyde with increased use of ethanol in fuel, although factors such as differences in vehicle

fleet, lack of RVP control, exclusion of upstream impacts, and differences in the levels of other

compounds in the ambient air may limit the ability of these studies to inform expected impacts

on ambient air quality (Section 3.4.3.1). Given the conflicting results among past studies and the

limitations of our analysis as discussed in the following paragraphs, considerable additional work

is needed to address the impacts of the renewable fuel volumes required by this rule on ambient

concentrations of acetaldehyde.

The comparison of the RFS1 mandate reference case with the RFS2 control case for

summer and winter shows decreases in ambient acetaldehyde concentrations in urban areas

(Figures 3.4-13 and 3.4-14). Decreases are less pronounced in winter when there is less

secondary formation of acetaldehyde (Figures 3.4-15 and 3.4-16). A key reason for the decrease

in urban areas is reductions in certain acetaldehyde precursors, primarily alkenes (olefins).

These reductions are due to differences in the E0 gasoline headspace speciation profiles used for

the control case and the reference cases, as discussed in Section 3.4.1.3. Headspace profiles are

used to speciate hydrocarbon emissions from gasoline storage, gasoline distribution, and gas

cans. The differences between cases arose when EPA noticed that the headspace profiles used in

the reference case scenarios exhibited a reduction in alkene levels going from E0 to E10 that was

not consistent with what one would expect as a result of increased ethanol use. In these cases,

the E0 gasoline headspace profile has 13% of the VOC as alkenes and the E10 profile has an

alkene content of 4%.

To address this issue, EPA adjusted the E0 headspace profile based on the assumption

that the emissions have an alkene content of 4%, consistent with the percent alkene content of

the E10 headspace profile because a 13% alkene content is much higher than typically seen in

fuel surveys and one expects the headspace from E0 and E10 to be similar.849 However, due to

time constraints, we were not able to make this improvement for the reference cases. Thus,

alkene levels associated with E0 use are lower in the control case than the reference cases,

leading to a reduction in secondarily formed acetaldehyde.

To determine the potential impact of this inconsistency, EPA conducted a sensitivity

analysis of the RFS1 mandate reference case for the Eastern U.S. modeling domain.210 This

sensitivity analysis was conducted for a single month, July, and compared results with the

control case for the following two cases:

1) RFS1 case with no change in alkene levels between headspace profiles for E0 and

E10 (i.e., adjusted E0 profile)

2) RFS1 case with higher alkene levels for E0 headspace profile

210 Details of the sensitivity run are discussed in the AQ modeling TSD, found in the docket for this rule (EPA-HQOAR-2005-0161).
623

The results of the sensitivity analysis showed that acetaldehyde levels were significantly

higher for the comparison between Case 1 and the control case than for the comparison between

Case 2 and the control case. The sensitivity analysis thus confirmed that the decrease in these

acetaldehyde precursors between the reference cases and the control case E0 headspace profile is

driving the decrease in ambient concentrations of acetaldehyde in urban areas. Thus, while the

air quality modeling results presented in this RIA suggest impacts of increased renewable fuel

use on ambient acetaldehyde are not substantial and there may be decreases in urban areas, there

is considerable uncertainty associated with these results. In fact, if the reference cases were rerun

with revised E0 headspace profiles, some of the observed decreases could become increases.

Additional research is underway to address these uncertainties, e.g., measurement of

representative fuels to create better headspace speciation profiles (Section 3.4.1.3) and

improvements in other speciation profiles based on additional results from the EPAct emissions

test program.211

It should also be noted (see Section 3.3 above) that we modeled the “more sensitive”

emission inventory case similar to that presented in the NPRM which assumed that use of E10

would lead to increases in NOx emissions for later model year vehicles. Increases in NOx may

result in more acetyl peroxy radical forming PAN rather than acetaldehyde. Recent EPA testing

results, which have been included in the FRM scenarios, do not show these increases in NOx for

later model year vehicles.850 Our air quality modeling results may therefore underestimate the

impacts of the renewable fuel volumes required by RFS2 on ambient concentrations of

acetaldehyde.

211 EPAct Phase I, II, and III Testing: Comprehensive Gasoline Light-Duty Exhaust Fuel Effects Test Program to

Cover Multiple Fuel Properties. EPA Contract: EPC-07-028EPA. Southwest Research Institute, San Antonio, TX.

Phase III of the EPAct emission test program is scheduled for completion in 2010.

a b
624

Figure 3.4-15. Summer Changes in Acetaldehyde Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes

and (b) Absolute Changes (μg/m3)

Figure 3.4-16. Winter Changes in Acetaldehyde Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes

and (b) Absolute Changes (μg/m3)

Formaldehyde

Our air quality modeling results do not show substantial impacts on ambient

concentrations of formaldehyde from the renewable fuel volumes required by this rule. As shown

in Figure 3.4-17, most of the U.S. experiences a 1% or less change in ambient formaldehyde

concentrations. Decreases in ambient formaldehyde concentrations range between 1 and 5% in a

few urban areas. Increases range between 1 and 2.5% in some rural areas associated with new

ethanol plants; this result is due to increases in emissions of primary formaldehyde and

formaldehyde precursors from the new ethanol plants. As discussed above, uncertainties in the

placement of new ethanol plants limit the model’s projected location of associated emission

increases (Section 3.4.1.3). Figure 3.4-18 shows that absolute changes in ambient concentrations

of formaldehyde are generally less than 0.1 μg/m3.

a b
625

Figure 3.4-17. Formaldehyde Annual Percent Change in Concentration Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022
626

Figure 3.4-18. Formaldehyde Annual Percent Changes in Ambient Concentrations

Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3)

Ethanol

Our modeling projects that the renewable fuel volumes required by this rule will lead to

significant nationwide increases in ambient ethanol concentrations. Increases ranging between

10 to 50% are seen across most of the country (Figure 3.4-19). The largest increases (more than

100%) occur in urban areas with high amounts of onroad emissions and in rural areas associated

with new ethanol plants. Absolute increases in ambient ethanol concentrations are above 1.0 ppb

in some urban areas (Figure 3.4-20). The location of these localized increases is limited by

uncertainties in the placement of the new plants, as discussed in Section 3.4.1.3.

It should be noted here that these increases are overestimated because the speciated

profile combination used for modeling nonroad emissions was misapplied. While sensitivity

analyses suggest that the impact of this error was negligible for other pollutants, it resulted in

overestimates of ethanol impacts by more than 10% across much of the modeling domain. For a

detailed discussion, please refer to the emissions modeling TSD, found in the docket for this rule

(EPA-HQ-OAR-2005-0161).
627

Figure 3.4-19. Ethanol Annual Percent Changes Change in Concentration Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022
628

Figure 3.4-20. Ethanol Annual Absolute Changes in Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (ppb)

Benzene

Our modeling projects that the renewable fuel volumes required by this rule will lead to

small nationwide decreases in ambient benzene concentrations. As shown in Figure 3.4-21,

decreases in ambient benzene concentrations range between 1 and 10% across most of the

country and can be higher in a few urban areas. Absolute changes in ambient concentrations of

benzene show reductions up to 0.2 μg/m3 (Figure 3.4-22).
629

Figure 3.4-21. Benzene Annual Percent Change in Concentration Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022
630

Figure 3.4-22. Benzene Annual Absolute Changes in Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3)

1,3-Butadiene

The results of our air quality modeling show small increases and decreases in ambient

concentrations of 1,3-butadiene in parts of the U.S. as a result of the renewable fuel volumes

required this rule. Generally, decreases occur in some southern areas of the country and

increases occur in some northern areas and areas with high altitudes (Figure 3.4-23). Percent

changes in 1,3-butadiene concentrations are over 50% in several areas; but the changes in

absolute concentrations of ambient 1,3-butadiene are generally less than 0.005 μg/m3 (Figure

3.4-24). Annual increases in ambient concentrations of 1,3-butadiene are driven by wintertime

rather than summertime changes (Figures 3.4-25 and 3.4-26). These increases appear in rural

areas with cold winters and low ambient levels but high contributions of emissions from

snowmobiles, and a major reason for this modeled increase may be deficiencies in available

emissions test data used to estimate snowmobile 1,3-butadiene emission inventories. These data

were based on tests using only three engines, which showed significantly higher 1,3-butadiene
631

emissions with 10% ethanol. However, they may not have been representative of real-world

response of snowmobile engines to ethanol.

Figure 3.4-23. 1,3-Butadiene Annual Percent Change in Concentration Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022
632

Figure 3.4-24. 1,3-Butadiene Annual Absolute Changes in Ambient Concentrations

Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3)
633

Figure 3.4-25. Summer Changes in 1,3-Butadiene Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes

and (b) Absolute Changes (μg/m3)

Figure 3.4-26. Winter Changes in 1,3-Butadiene Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes

and (b) Absolute Changes (μg/m3)

a b

a b
634

Acrolein

Our air quality modeling shows small regional increases and decreases in ambient

concentrations of acrolein as a result of the renewable fuel volumes required by this rule. As

shown in Figure 3.4-27, decreases in acrolein concentrations occur in some eastern and southern

parts of the U.S. and increases occur in some northern areas and areas associated with new

ethanol plants. Figure 3.4-28 indicates that changes in absolute ambient concentrations of

acrolein are between ± 0.001 μg/m3 with the exception of the increases associated with new

ethanol plants. These increases can be up to and above 0.005 μg/m3 with percent changes above

50% and are due to increases in emissions of acrolein from the new plants. As discussed in

Section 3.4.1.3, uncertainties in the placement of new ethanol plants limit the model’s projected

location of associated emission increases. Ambient acrolein increases in upper Michigan,

Canada, the Northeast, and the Rocky Mountain region are driven by wintertime changes

(Figures 3.4-29 and 3.4-30), and occur in the same areas of the country that have wintertime

rather than summertime increases in ambient 1,3-butadiene. 1,3-butadiene is a precursor to

acrolein, and these increases are likely associated with the same emission inventory issues in

areas of high snowmobile usage seen for 1,3-butadiene, as described above.

Figure 3.4-27. Acrolein Annual Percent Changes Change in Concentration Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022
635

Figure 3.4-28. Acrolein Annual Absolute Changes in Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022 (μg/m3)
636

Figure 3.4-29. Summer Changes in Acrolein Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b)

Absolute Changes (μg/m3)

Figure 3.4-30. Winter Changes in Acrolein Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (a) Percent Changes and (b)

Absolute Changes (μg/m3)

a b

a b
637

Population Metrics

To assess the impact of projected changes in air quality with increased renewable fuel

use, we developed population metrics that show population experiencing increases and decreases

in annual ambient concentrations across the modeled air toxics. Figure 3.4-31 below illustrates

the number of people impacted by changes of various magnitudes in annual ambient

concentrations with the renewable fuel volumes required by RFS2 in 2022, as compared to the

RFS1 mandate reference case. For ambient concentrations of ethanol, over 98% of the

population (334,730,202 people) experiences an increase greater than or equal to 10%. For the

other modeled air toxics, more than 90% of the population (greater than 305,658,000 people)

will experience a change in ambient concentration of ±1%. For acrolein, 9.9% of the population

(33,354,866 people) will live in areas with an increase in ambient concentrations ranging from 1

to 50%; 0.13% (439,535 people) of the population experiences an increase greater than 50%.

For 1,3-butadiene, 5.9% of the population (20,171,533 people) experiences a 1 to 50% increase

in ambient concentrations, and 0.33% (1,135,806 people) of the population experiences an

increase greater than 50%. The percentage of the population living in areas with increases in

ambient concentrations of acetaldehyde and formaldehyde are as follows: 0.30% of the

population (1,007,009 people) experiences acetaldehyde increases between 1 and 10%, and

0.48% of the population (1,642,944 people) experiences formaldehyde increases between 1 and

2.5%.
638

136,287

95,491 108,636

51 28 258,420 65,570 13,969 1,615

122,378 620 46,391 11,699 9,936 6,782 5,118 269 171

4,697 43,717 65,804 5,211 3,846 3,822 7,293 935 201

46,024 165,604 43,867 4,002 80,156

2,568 85,793 177,428 45,123 27,734 811 144 52

737 4,169 214,965 2,610 117,155

EthanolAcetaldehydeFormaldehydeBenzene1,3-Butadiene

Acrolein

≤ -100 >-100 to

≤-50

>-50 to

≤-10

>-10 to

≤-5

>-5 to

≤-2.5

>-2.5 to

≤-1

>-1 to

<+1

≥+1 to

<+2.5

≥+2.5 to 

<+5

≥+5 to 

<+10

≥+10 to 

<+50

≥+50 to 

<+100

≥ +100

Percent Change in Ambient Concentration from RFS1 Mandate to RFS2

Population (in Thousands) Impacted by Changes in Annual Ambient

Concentrations of Toxic Pollutants with RFS2

.

11

.

1

.4

.

3

Figure 3.4-31. Number of People Impacted by Changes in Annual Ambient Concentrations or Toxic Pollutants by

Percent Change Brackets, RFS 1 Mandate Reference Case Compared to the RFS2 Control Case
639

The population exposed to average ambient concentrations of air toxics above and below

reference concentrations for noncancer health effects in 2022 is presented for the two reference

cases in Table 3.4-4 below. Reference concentration (RfC) values presented in this table are the

same as those used in the 2002 NATA.851 At present, no RfC exists for ethanol; EPA is

conducting an IRIS assessment for this air toxic. The RfC is an estimate, with uncertainty

spanning perhaps an order of magnitude, of an inhalation exposure to the human population

(including sensitive subgroups) that is likely to be without appreciable risks of deleterious effects

during a lifetime. Exposures to levels above the RfC do not necessarily suggest a likelihood of

adverse health effects, because many RfCs incorporate protective assumptions in the face of

uncertainty. Exposures above the RfC can best be interpreted as indicating that a potential exists

for adverse health effects. In addition, average population exposures could be lower or higher

than the modeled ambient concentrations.

Table 3.4-4 shows that population-weighted nationwide annual average concentrations

for the modeled air toxics are below the RfC values for both the RFS1 reference case and the

AEO 2007 reference case. However, the population-weighted nationwide annual average for

acrolein is very close to the RfC. Table 3.4-4 also shows the national population that is exposed

to ambient concentrations above and below the RfC for the modeled air toxics. In both reference

cases, over 94 million people are exposed to ambient concentrations above the RfC for acrolein

but the national population is exposed to ambient concentrations below the RfC for acetaldehyde,

benzene, 1,3-butadiene, and formaldehyde.

Table 3.4-4. Populations Exposed to Ambient Concentrations of Air Toxics above and

below Reference Concentrations for Noncancer Health Effects in 2022 without RFS2

Population-weighted

Concentration

(Nationwide Annual

Average in μg/m3)

National Population

above RfC (Annual

Average)

National Population below

RfC (Annual Average) CAS No.

RFS1

Mandate AEO 2007

RfC

(μg/m3)

RFS1

Mandate AEO 2007

RFS1

Mandate AEO 2007

Acetaldehyde 75070 1.618 1.613 9 0 0 339,652,451 339,652,451

Acrolein 107028 0.018 0.017 0.02 95,059,422 94,087,145 244,593,029 245,565,306

Benzene 71432 0.535 0.527 30 0 0 339,652,451 339,652,451

1,3-Butadiene 106990 0.023 0.023 2 0 0 339,652,451 339,652,451

Ethanola64175 1.039 1.112 - - - - -

Formaldehyde 50000 1.558 1.555 9.8 0 0 339,652,451 339,652,451

Table 3.4-5 shows changes in the population exposed to average ambient concentrations

of air toxics above and below reference concentrations for noncancer health effects in 2022 that

are projected to occur with increased renewable fuel use as required by RFS2. Differences in

population-weighted annual average concentrations between the RFS2 control case and the RFS1

mandate reference case are small, and ethanol is the only compound shown to increase with

RFS2 fuel volumes. Table 3.4-5 also shows that the renewable fuel volumes required by RFS2

do not result in any increases in the number of people exposed to ambient concentrations above
640

the RfC values. The results indicate there may be a reduction in the number of people exposed

to ambient concentrations of acrolein with RFS2 fuel volumes.

Table 3.4-5. Populations Exposed to Ambient Concentrations of Air Toxics above and

below Reference Concentrations for Noncancer Health Effects in 2022 with RFS2

Population-weighted

Concentration

(Annual Average in μg/m3)

National Population above RfC

(Annual Average) CAS No.

RFS2

RFS1

Mandate Diff. RFS2

RFS1

Mandate Diff.

Acetaldehyde 75070 1.590 1.618 -0.028 0 0 0

Acrolein 107028 0.017 0.018 -0.001 92,452,143 95,059,422 -2,607,279

Benzene 71432 0.520 0.535 -0.015 0 0 0

1,3-Butadiene 106990 0.022 0.023 -0.001 0 0 0

Ethanol 64175 1.521 1.039 0.482 - - -

Formaldehyde 50000 1.549 1.558 -0.009 0 0 0

3.4.2.2 Deposition of Nitrogen and Sulfur

3.4.2.2.1 Current Levels of Nitrogen and Sulfur Deposition

Over the past two decades, the EPA has undertaken numerous efforts to reduce nitrogen

and sulfur deposition across the U.S. Analyses of long-term monitoring data for the U.S. show

that deposition of both nitrogen and sulfur compounds has decreased over the last 17 years

although many areas continue to be negatively impacted by deposition. Deposition of inorganic

nitrogen and sulfur species routinely measured in the U.S. between 2004 and 2006 were as high

as 9.6 kilograms of nitrogen per hectare per year (kg N/ha/yr) and 21.3 kilograms of sulfur per

hectare per year (kg S/ha/yr). Figures 3.4-32 and 3.4-33 show that annual total deposition (the

sum of wet and dry deposition) decreased between 1989-1999 and 2004-2006 due to sulfur and

NOX controls on power plants, motor vehicles and fuels in the U.S. The data show that

reductions were more substantial for sulfur compounds than for nitrogen compounds. These

numbers are generated by the U.S. national monitoring network and they likely underestimate

nitrogen deposition because neither ammonia nor organic nitrogen is measured. In the eastern

U.S., where data are most abundant, total sulfur deposition decreased by about 36 % between

1990 and 2005, while total nitrogen deposition decreased by 19% over the same time frame.852
641

Figure 3.4-32. Total Sulfur Deposition in the Contiguous U.S., 1989-1991 and 2004 -2006
642

Figure 3.4-33. Total Nitrogen Deposition in the Contiguous U.S., 1989-1991 and 2004-2006
643

3.4.2.2.2 Projected Levels of Nitrogen and Sulfur Deposition

Our air quality modeling does not show substantial overall nationwide impacts on the

annual total sulfur and nitrogen deposition occurring across the U.S. as a result of increased

renewable fuel volumes required by this rule. Figure 3.4-34 shows that when compared to the

RFS1 mandate reference case, the RFS2 renewable fuel volumes will result in nearly the entire

eastern half of the United States seeing nitrogen deposition increases ranging from 0.5% to more

than 2%. The largest increases will occur in the states of Illinois, Michigan, Indiana, Wisconsin,

and Missouri, with large portions of each of these states seeing nitrogen deposition increases of

more than 2%. The Pacific Northwest will also experience increases in nitrogen of 0.5% to more

than 2%. Figure 3.4-35 shows that when compared to the AEO 2007 reference case, the changes

in nitrogen deposition as a result of the RFS2 renewable fuel volumes are more limited. The

eastern half of the United States will still see nitrogen deposition increases ranging from 0.5% to

more than 2%; however, the size of the area with these changes will be smaller. Increases of

more than 2% will primarily occur only in Illinois, Indiana, Michigan, and Missouri. Fewer

areas in the Pacific Northwest will have increases in nitrogen deposition when compared to the

AEO 2007 reference case. In both the RFS1 mandate and AEO 2007 reference cases the

Mountain West and Southwest will see only minimal changes in nitrogen deposition, ranging

from decreases of less than 0.5% to increases of less than 0.5%. A few areas in Minnesota and

western Kansas would experience reductions of nitrogen up to 2%.
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Figure 3.4-34. Percent Change in Annual Total Sulfur over the U.S. Modeling Domain

Between the RFS1 Mandate Reference Case and the RFS2 Control Case in 2022
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Figure 3.4-35. Percent Change in Annual Total Nitrogen over the U.S. Modeling Domain

Between the AEO Reference Case and the RFS2 Control Case in 2022

For sulfur deposition, Figure 3.4-36 shows that when compared to the RFS1 mandate

reference case, the RFS2 renewable fuel volumes will result in annual percent increases in the

Midwest ranging from 1% to more than 4%. Some rural areas in the west, likely associated with

new ethanol plants, will also have increases in sulfur deposition ranging from 1% to more than

4% as a result of the RFS2 renewable fuel volumes. Figure 3.4-37 shows that when compared to

the AEO 2007 reference case, the changes are more limited. The Midwest will still have sulfur

deposition increases ranging from 1% to more than 4%, but the size of the area with these

changes will be smaller. The Pacific Northwest has minimal areas with increases in sulfur

deposition when compared to the AEO 2007 reference case. When compared to both the RFS1

mandate and 2007 reference cases, areas along the Gulf Coast in Louisiana and Texas will

experience decreases in sulfur deposition of 2% to more than 4%. The remainder of the country

will see only minimal changes in sulfur deposition, ranging from decreases of less than 1% to

increases of less than 1%.
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Figure 3.4-36. Percent Change in Annual Total Sulfur over the U.S. Modeling Domain

Between the RFS1 Mandate Reference Case and the RFS2 Case in 2022
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Figure 3.4-37. Percent Change in Annual Total Sulfur over the U.S. Modeling Domain

Between the AEO Reference Case and the RFS2 Case in 2022

3.4.3 Ambient Monitoring and Modeling Studies of Ethanol’s Impacts on Air Quality

A number of ambient monitoring and modeling studies in the U.S. and abroad have

quantified the relative concentrations of ethanol emissions and the potential air quality impacts

of using ethanol in fuels. This section summarizes the main results of these studies and is not

meant to be a comprehensive examination of this work.

3.4.3.1 U.S. Studies

In 1986, Colorado adopted the first state-wide regulation in the country that required the

use of oxygenated fuels in winter time months to reduce carbon monoxide (CO) emissions.

From the time the regulation was first adopted, the fuels used quickly shifted from methyl

tertiary butyl ether (MTBE) and gasoline to largely ethanol-blended fuels. By the winter of

1996-1997, nearly all of the fuel was blended with ethanol at 10% by volume. The effect of

using oxygenated fuels on formaldehyde and acetaldehyde concentrations was monitored by

ambient air quality measurements from the winter of 1987-1988 (95% of fuel blended with
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MTBE) through the winter of 1996-1997 (nearly all fuel blended with ethanol). Analysis of the

data by Anderson et al. showed no clear effect on ambient concentrations of formaldehyde and

acetaldehyde through ten winters of oxygenated fuels use.853,854 Furthermore, Anderson et al.

reported that the concentrations of formaldehyde and acetaldehyde during the winter of 1995-

1996 when nearly all of the fuel was blended with ethanol were not significantly different from

those measured during the winter of 1988-1989 when 95% of the fuel was blended with MTBE.

It was hypothesized that the photochemical production and destruction of these compounds

suppressed the emissions effect. They concluded that mobile source emissions are the major

photochemical sources and sinks for both compounds.855 Anderson et al. supported this

conclusion by citing the work of Altshuller, which showed that most acetaldehyde production

comes from alkenes.856, 857, 858

Albuquerque, NM is another location that mandated the use of oxygenates in the

wintertime to reduce CO emissions. A field study was conducted in Albuquerque to determine

the atmospheric impacts of the use of ethanol fuels.859,860 Atmospheric concentrations of ozone,

NOx, CO, PAN, aldehydes, and organic acids were measured in the summer of 1993, before the

use of ethanol fuels, and in the winters of 1994 and 1995, during the use of 10% ethanol fuel

(>99%). There were no data for pre-ethanol winter conditions. Results showed increased levels

of PAN and an increase in acetaldehyde in one winter, but a decrease in the other. Seasonal

differences were not considered. The authors noted that the daytime temperatures were fairly

comparable for the summer and winter study periods so it appeared that the significantly higher

winter values, despite the much lower photochemical reactivity in winter, were primarily due to

local production of PAN. For acetaldehyde, winter values were about twice as high as the

summer values. These acetaldehyde levels anti-correlated with PAN levels, indicating a primary

source of aldehydes in the winter.

Grosjean et al (2002) conducted monitoring studies in various California cities and

measured daily maximum PAN concentrations ranging from 0.2 to 6.9 ppb.861 Peroxypropionyl

nitrate (PPN, a compound similar to PAN) concentrations were measured at lower levels and

ranged from 0.33-1.04 ppb. This study concluded that aromatics and alkene compounds are

responsible for significant PAN formation with ethanol and acetaldehyde having a minor role.

A modeling analysis using the Urban Airshed Model (UAM), was performed by the California

Air Resources Board in 1999.862 Acetaldehyde and ethanol concentrations in 2003, relative to an

MTBE baseline, were estimated to increase for 3.5% by weight ethanol-blended gasoline by 4%

and 72%, respectively. There was no significant impact on PAN formation. Benzene increased

1%, formaldehyde increased 2 to 4%, butadiene decreased about 2%, and NO2 (0 to 1%) and

peroxypropionyl nitrate (PPN) were essentially unchanged. It should be noted that the chemical

mechanism used in this modeling is a previous version of the mechanism used in the modeling

for this rule, so comparability of results are limited.

Another air quality modeling study by Jacobson et al. investigated the projected impacts

of widespread usage of E85 in Los Angeles and the US in 2020.863 Overall results showed

increases in acetaldehyde and formaldehyde and decreases in 1,3-butadiene and benzene in Los

Angeles and the U.S. Sources of acetaldehyde included direct emissions and to a larger degree

photooxidation of unburned ethanol. Results of this modeling study also showed increases in

unburned ethanol, PAN, and ozone for a future E85 scenario. The results of Jacobson et al.
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study differ from the results of our air quality modeling analysis for a number of reasons. First,

the scenario modeled in Jacobson et al. study would result in much larger volumes of ethanol in

the fuel supply than mandated under EISA (and much greater than could feasibly be produced).

This study also did not include upstream impacts from fuel distribution. As discussed elsewhere

(Section 3.4.1.3), VOC speciation data used for gasoline storage and distribution and gas cans

result in reduction of some acetaldehyde precursor emissions. Finally, the modeled scenario

includes large reductions in NOx emissions. In contrast, we modeled the “more sensitive”

emission inventory case where NOx emissions increased with greater use of E10 fuel. Increases

in NOX, may result in more acetyl peroxy radical forming PAN rather than acetaldehyde.

The U.S. monitoring studies discussed here are largely winter studies and the lack of summer

studies makes it difficult to quantify the magnitude of air quality impacts of ethanol fuel usage

over the entire year.

3.4.3.2 Brazilian Studies

The following studies investigate changes in ambient concentrations of several air

pollutants that result from the use of ethanol fuels in Brazil. These studies are not directly

relevant to the U.S. due to differences such as vehicles (including less stringent emission

standards), fuels, and climate. However, these studies do provide useful information on potential

directional changes in pollutant levels with widespread ethanol use.

Brazil is the first country in the world where a nationwide, large-scale alcohol fuel

program has been implemented. In 1997, approximately 4 million automobiles ran on neat

ethanol and approximately nine million automobiles ran on a 22% ethanol-blended gasoline

mixture.864 It should be noted that Brazilian ethanol blended gasoline does not have RVP

controls like U. S. blends.

In Salvador, Bahia, Brazil, ambient levels of formaldehyde and acetaldehyde and their

relationship with vehicular fleet composition were evaluated.865 The measured concentrations

for formaldehyde and acetaldehyde ranged from 0.20 to 88 parts per billion by volume (ppbv)

and from 0.40 to 93 ppbv, respectively. The ratio of formaldehyde to acetaldehyde revealed the

relationship of vehicular fleet composition to ambient levels. In locations where ethanol-fueled

vehicular emissions dominated, the ratio decreased, versus locations where diesel-fueled vehicles

dominated. Sampling in rural areas showed no relationship between formaldehyde and

acetaldehyde.

Acetaldehyde and formaldehyde concentrations were measured in the winter of 1999 in

Sao Paulo, Brazil.866 Ambient levels of these carbonyls were similar. Higher average mixing

ratios of acetaldehyde and formaldehyde were found in the morning (18.9 and 17.2 ppbv) than

midday (9.5 and 11.8 ppbv) and evening (7.2 and 10.2 ppbv). In the morning, direct emission

from vehicles seemed to be the main primary source, whereas at midday and evening these

compounds appeared to result mainly from photochemistry.

A survey of volatile organic compounds in areas impacted by heavy traffic, including a

tunnel, was obtained for Sao Paulo.867 Researchers found the ambient air was dominated by

ethanol (414 ppbv) with elevated methanol and 1- and 2-propanol. These levels were well above
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those measurements available for U.S. cities, particularly Los Angeles, CA. The overall data

trend also showed levels of C4-C9 n-aldehydes to be approximately 10 times higher than in Los

Angeles. They conclude that the use of alcohol-based fuels is the primary source for these

differences since alcohol comprises about 40% of the mobile fuel by volume compared to 3% in

Los Angeles. Also, the single-ring aromatic hydrocarbons (2.6 ppbv benzene, 9.0 ppbv toluene,

4.6 ppbv m,p-xylene) and the C4-C11 n-alkanes were similar or slightly elevated in concentration

compared to Los Angeles.

A study in Rio de Janeiro, Brazil some years ago measured and modeled ambient PAN

concentrations.868 The measurements were as high as 5 ppb over a 200 day period, but typically

below 1 ppb, at one site; at another site, as high as 3 ppb, but again generally below 1 ppb.

Modeling estimates were as high as 3 ppb for PAN and 1 ppb for PPN. This study concluded

that with increased use of ethanol in fuels there would be increases in ambient PAN.

More recent monitoring studies in Brazil measured daily maximum PAN concentrations ranging

from 0.19 to 6.67 ppb.869 Also, PPN was measured at lower levels of 0.06 to 0.72 ppb. During

the 41 days of these measurements, PAN levels accounted for a large fraction of the ambient

NOx. This study concluded that aromatics and alkene compounds are responsible for significant

PAN formation with ethanol and acetaldehyde having a minor role.

Speciated ambient carbonyls have also been measured in Rio de Janeiro.870 The most

abundant carbonyls were formaldehyde (9.3 ppb) and acetaldehyde (9.0 ppb). The researchers

also examined the ambient acetaldehyde to formaldehyde concentration ratio in Brazilian cities

since mid-1980 in the context of changes in Brazil’s reliance on ethanol as a vehicle fuel. They

showed that this ratio has begun to decrease in recent years due to fleet turnover and decrease in

ethanol-fueled vehicles. Ethanol-fueled vehicles are being replaced by lower-emitting newer

models that run on a gasoline-ethanol blend.

Using an empirical kinetic modeling approach (EKMA), researchers simulated ozone,

formaldehyde, and acetaldehyde concentrations for the urban downtown area of Rio de

Janeiro.871 The simulated ozone peak was in good agreement with monitoring results. Modeling

results also showed that acetaldehyde and formaldehyde concentrations were highest in early

morning, reaching a maximum which coincided with peak vehicular traffic. Additionally, they

confirmed monitoring evidence that the high acetaldehyde to formaldehyde ratios were due to

the use of alcohol-based fuels.

These studies modeled and measured ambient concentrations of several compounds that

result from the use of ethanol fuels. However, the direct impacts of ethanol fuel usage on air

quality in Brazil could not be evaluated since there were no ambient data available prior to the

use of ethanol fuel. Notably, these studies did not include ambient measurements of

acetaldehyde prior to the use of ethanol fuel, and measured concentrations were much higher

than those found in the United States. However, gasolines in Brazil lacked RVP control,

resulting in higher evaporative ethanol emissions than would be likely in the U. S., fuel ethanol

levels were much higher (neat ethanol or 22% ethanol by volume), and there were significant

differences in the vehicle fleet and meteorology. Also, vehicles in Brazil do not meet the same

stringent emissions exhaust and evaporative emission standards as vehicles do in the U.S. These
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factors would all contribute to larger acetaldehyde impacts with ethanol use than expected in the

U.S. under the control case evaluated for this rule.

3.4.3.3 Other Studies

A review was conducted on studies that looked at the environmental impacts of E10 and

E85 compared to E0.872 The review article focused on five environmental outcomes including

the impact of increased usage of E10 and/or E85 on air pollutant emissions. The review article

focuses on studies that are relevant to Australia but includes work done in the US and elsewhere

and the results are characterized as being “broad and applicable to most industrialized countries

in moderate temperate climates.” The author concludes that using E10 fuel instead of E0 fuel

provides minimal improvements in air pollutant emissions, specifically E10 causes lower tailpipe

CO and particulate emissions but higher acetaldehyde, ethanol and NOx emissions and that there

is some evidence of a connection between E10 and higher ground-level ozone concentrations.

Since this is a review article it is difficult to compare the modeling for this final rule to the

article, however some of the conclusions from the article are useful when interpreting the air

quality modeling results.

Some smog chamber studies were recently conducted at EPA873 with two fuels. These

studies were done on headspace vapors which are the hydrocarbon compounds formed by

vaporization of hydrocarbon components from gasoline stored in closed (or semi-closed)

containers such as fuel storage tanks or tanker trucks. These emissions are different from

exhaust emissions which include products of combustion. They are also somewhat different

from vehicle evaporative emissions in that the charcoal canister tends to adsorb the higher

molecular weight hydrocarbons resulting in emissions of the more volatile fuel components.

The first fuel used in these smog chamber studies was a base gasoline and the second was

this gasoline with 10% ethanol added to it (a “splash blend” without any control of fuel

volatility). The smog chamber runs were conducted for 24 hours with a target HC/NOx ratio of

10-to-1. The chamber runs with the base gasoline had a higher initial concentration of

isopentane compared to the runs with the base gasoline with 10% ethanol. The runs showed that

the photoxidation processes of ethanol results in higher levels of acetaldehyde. This

photoxidation should also increase acetone levels with the base gasoline which is also observed.

The significance of these results is that ethanol does result in increased acetaldehyde formation

in smog chamber work. It is important to be able to translate these smog chamber results to

actual atmospheric conditions by using such studies to better improve the chemical mechanisms

in air quality models to simulate what happens in the atmosphere with other emission

components (exhaust, evaporative, other emissions) in actual atmospheric condtions (with

meteorology, mixing conditions, temperature, concentrations, and mixing being what they are in

the atmosphere).

3.5 Health Effects

In this section we discuss the health and environmental effects associated with particulate

matter, ozone, NOX, SOX, carbon monoxide and air toxics. The renewable fuel requirements
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established by the Energy Independence and Security Act (EISA) of 2007 will impact emissions

of criteria and air toxic pollutants.

3.5.1 Particulate Matter

3.5.1.1 Background

Particulate matter (PM) is a generic term for a broad class of chemically and physically

diverse substances. It can be principally characterized as discrete particles that exist in the

condensed (liquid or solid) phase spanning several orders of magnitude in size. Since 1987, EPA

has delineated that subset of inhalable particles small enough to penetrate to the thoracic region

(including the tracheobronchial and alveolar regions) of the respiratory tract (referred to as

thoracic particles). Current national ambient air quality standards (NAAQS) use PM2.5 as the

indicator for fine particles (with PM2.5 referring to particles with a nominal mean aerodynamic

diameter less than or equal to 2.5 μm), and use PM10 as the indicator for purposes of regulating

the coarse fraction of PM10 (referred to as thoracic coarse particles or coarse-fraction particles;

generally including particles with a nominal mean aerodynamic diameter greater than 2.5 μm

and less than or equal to 10 μm, or PM10-2.5). Ultrafine particles are a subset of fine particles,

generally less than 100 nanometers (0.1 μm) in aerodynamic diameter.

Particles span many sizes and shapes and consist of hundreds of different chemicals.

Particles originate from sources and are also formed through atmospheric chemical reactions; the

former are often referred to as “primary” particles, and the latter as “secondary” particles. In

addition, there are also physical, non-chemical reaction mechanisms that contribute to secondary

particles. Particle pollution also varies by time of year and location and is affected by several

weather-related factors, such as temperature, clouds, humidity, and wind. A further layer of

complexity comes from a particle’s ability to shift between solid/liquid and gaseous phases,

which is influenced by concentration, meteorology, and temperature.

Fine particles are produced primarily by combustion processes and by transformations of

gaseous emissions (e.g., SOX, NOX and VOCs) in the atmosphere. The chemical and physical

properties of PM2.5 may vary greatly with time, region, meteorology and source category. Thus,

PM2.5 may include a complex mixture of different pollutants including sulfates, nitrates, organic

compounds, elemental carbon and metal compounds. These particles can remain in the

atmosphere for days to weeks and travel through the atmosphere hundreds to thousands of

kilometers.874

3.5.1.2 Health Effects of PM

This section provides a summary of the health effects associated with exposure to

ambient concentrations of PM.212 The information in this section is based on the data and

conclusions in the PM Air Quality Criteria Document (PM AQCD) and PM Staff Paper prepared

212 Personal exposure includes contributions from many different types of particles, from many sources, and in many

different environments. Total personal exposure to PM includes both ambient and nonambient components; and

both components may contribute to adverse health effects.
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by the U.S. Environmental Protection Agency (EPA).213,875,876 We also present additional recent

studies published after the cut-off date for the PM AQCD.877,214 Taken together this information

supports the conclusion that exposure to ambient concentrations of PM are associated with

adverse health effects.

3.5.1.2.1 Short-term Exposure Mortality and Morbidity Studies

As discussed in the PM AQCD, short-term exposure to PM2.5 is associated with

premature mortality from cardiopulmonary diseases,878 hospitalization and emergency

department visits for cardiopulmonary diseases,879 increased respiratory symptoms,880 decreased

lung function881 and physiological changes or biomarkers for cardiac changes.882,

883 In addition,

the PM AQCD described a limited body of new evidence from epidemiologic studies for

potential relationships between short term exposure to PM and health endpoints such as low birth

weight, preterm birth, and neonatal and infant mortality.884

Among the studies of effects associated with short-term exposure to PM2.5, several

specifically address the contribution of mobile sources to short-term PM2.5-related effects on

premature mortality. The results from these studies generally indicated that several combustionrelated fine particle source-types are likely associated with mortality, including motor vehicle

emissions as well as other sources.885 The analyses incorporate source apportionment tools into

short-term exposure studies and are briefly mentioned here. Analyses incorporating source

apportionment by factor analysis with daily time-series studies of daily death rates indicated a

relationship between mobile source PM2.5 and mortality.886,887,888,889 Another recent study in 14

U.S. cities examined the effect of PM10 exposures on daily hospital admissions for

cardiovascular disease. This study found that the effect of PM10 was significantly greater in

areas with a larger proportion of PM10 coming from motor vehicles, indicating that PM10 from

these sources may have a greater effect on the toxicity of ambient PM10 when compared with

other sources.890 These studies provide evidence that PM-related emissions, specifically from

mobile sources, are associated with adverse health effects.

213 The PM NAAQS is currently under review and the EPA is considering all available science on PM health effects,

including information which has been published since 2004, in the development of the upcoming PM Integrated

Science Assessment Document (ISA). A second draft of the PM ISA was completed in July 2009 and was

submitted for review by the Clean Air Scientific Advisory Committee (CASAC) of EPA’s Science Advisory Board.

Comments from the general public have also been requested. For more information, see

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=210586.

214 These additional studies are included in the 2006 Provisional Assessment of Recent Studies on Health Effects of

Particulate Matter Exposure. The provisional assessment did not and could not (given a very short timeframe)

undergo the extensive critical review by CASAC and the public, as did the PM AQCD. The provisional assessment

found that the “new” studies expand the scientific information and provide important insights on the relationship

between PM exposure and health effects of PM. The provisional assessment also found that “new” studies generally

strengthen the evidence that acute and chronic exposure to fine particles and acute exposure to thoracic coarse

particles are associated with health effects. Further, the provisional science assessment found that the results

reported in the studies did not dramatically diverge from previous findings, and taken in context with the findings of

the AQCD, the new information and findings did not materially change any of the broad scientific conclusions

regarding the health effects of PM exposure made in the AQCD. However, it is important to note that this

assessment was limited to screening, surveying, and preparing a provisional assessment of these studies. For

reasons outlined in Section I.C of the preamble for the final PM NAAQS rulemaking in 2006 (see 71 FR 61148-49,

October 17, 2006), EPA based its NAAQS decision on the science presented in the 2004 AQCD.
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3.5.1.2.2 Long-term Exposure Mortality and Morbidity Studies

Long-term exposure to ambient PM2.5 is associated with premature mortality from

cardiopulmonary diseases and lung cancer,891 and effects on the respiratory system such as

decreased lung function or the development of chronic respiratory disease.892 Of specific

importance, the PM AQCD also noted that the PM components of gasoline and diesel engine

exhaust represent one class of hypothesized likely important contributors to the observed

ambient PM-related increases in lung cancer incidence and mortality.893

The PM AQCD and PM Staff Paper emphasized the results of two long-term

epidemiologic studies, the Six Cities and American Cancer Society (ACS) prospective cohort

studies, based on several factors – the large air quality data set for PM in the Six Cities Study,

the fact that the study populations were similar to the general population, and the fact that these

studies have undergone extensive reanalysis.894,895,896,897898,899 These studies indicate that there

are positive associations for all-cause, cardiopulmonary, and lung cancer mortality with longterm exposure to PM2.5. One analysis of a subset of the ACS cohort data, which was published

after the PM AQCD was finalized but in time for the 2006 Provisional Assessment, found a

larger association than had previously been reported between long-term PM2.5 exposure and

mortality from all causes and cardiopulmonary diseases in the Los Angeles area using a new

exposure estimation method that accounted for variations in concentration within the city.900

As discussed in the PM AQCD, the morbidity studies that combine the features of crosssectional and cohort studies provide the best evidence for chronic exposure effects. Long-term

studies evaluating the effect of ambient PM on children’s development have shown some

evidence indicating effects of PM2.5 and/or PM10 on reduced lung function growth.901 In another

recent publication included in the 2006 Provisional Assessment, investigators in southern

California reported the results of a cross-sectional study of outdoor PM2.5 and a measure of

atherosclerosis development in the Los Angeles basin.902 The study found positive associations

between ambient residential PM2.5 and carotid intima-media thickness (CIMT), an indicator of

subclinical atherosclerosis that is an underlying factor in cardiovascular disease.

3.5.1.2.3 Roadway-Related PM Exposure and Health Studies

A recent body of studies examines traffic-related PM exposures and adverse health

effects. However, note that the near-road environment is influenced by both gasoline sparkignition (SI) and diesel vehicles, as well as re-entrained road dust and brake and tire wear. One

study was done in North Carolina looking at concentrations of PM2.5 inside highway patrol cars

and corresponding physiological changes in state troopers driving the cars. The authors report

significant elevations in markers of cardiovascular effects (i.e., inflammation, coagulation, and

cardiac rhythm) associated with concentrations of PM2.5 inside highway patrol cars on North

Carolina state highways.903 Other studies have found associations between traffic-generated

particle concentrations at residences and adverse effects, including all-cause mortality, infant

respiratory symptoms, and reduced cognitive functional development.904,905,906,907 There are

other pollutants present in the near-roadway environment, including air toxics which are

discussed in Section 3.4.5, and it is important to note that current studies do not identify a single

pollutant that is most associated with adverse health effects. Additional information on near-
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roadway health effects can be found in the recent Mobile Source Air Toxics rule (72 FR 8428,

February 26, 2007).

3.5.2 Ozone

3.5.2.1 Background

Ground-level ozone pollution is formed by the reaction of VOCs and NOX in the

atmosphere in the presence of heat and sunlight. These pollutants, often referred to as ozone

precursors, are emitted by many types of pollution sources such as highway vehicles and

nonroad engines, power plants, chemical plants, refineries, makers of consumer and commercial

products, industrial facilities, and smaller area sources.

The science of ozone formation, transport, and accumulation is complex. Ground-level

ozone is produced and destroyed in a cyclical set of chemical reactions, many of which are

sensitive to temperature and sunlight. When ambient temperatures and sunlight levels remain

high for several days and the air is relatively stagnant, ozone and its precursors can build up and

result in more ozone than typically would occur on a single high-temperature day. Ozone can be

transported hundreds of miles downwind of precursor emissions, resulting in elevated ozone

levels even in areas with low VOC or NOX emissions.

As mentioned above in Section 3.4.2.1.2, the highest levels of ozone are produced when

both VOC and NOX emissions are present in significant quantities on clear summer days.

Relatively small amounts of NOX enable ozone to form rapidly when VOC levels are relatively

high, but ozone production is quickly limited by removal of the NOX. Under these conditions

NOX reductions are highly effective in reducing ozone while VOC reductions have little effect.

Such conditions are called “NOX-limited.” Because the contribution of VOC emissions from

biogenic (natural) sources to local ambient ozone concentrations can be significant, even some

areas where man-made VOC emissions are relatively low can be NOX-limited.

Ozone concentrations in an area also can be lowered by the reaction of nitric oxide (NO)

with ozone, forming nitrogen dioxide (NO2); as the air moves downwind and the cycle continues,

the NO2 forms additional ozone. The importance of this reaction depends, in part, on the relative

concentrations of NOX, VOC, and ozone, all of which change with time and location. When

NOX levels are relatively high and VOC levels relatively low, NOX forms inorganic nitrates (i.e.,

particles) but relatively little ozone. Such conditions are called “VOC-limited”. Under these

conditions, VOC reductions are effective in reducing ozone, but NOX reductions can actually

increase local ozone under certain circumstances. Even in VOC-limited urban areas, NOX

reductions are not expected to increase ozone levels if the NOX reductions are sufficiently large.

Rural areas are usually NOX-limited, due to the relatively large amounts of biogenic VOC

emissions in such areas. Urban areas can be either VOC- or NOX-limited, or a mixture of both,

in which ozone levels exhibit moderate sensitivity to changes in either pollutant.

3.5.2.2 Health Effects of Ozone
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Exposure to ambient ozone contributes to a wide range of adverse health effects.215

These health effects are well documented and are critically assessed in the EPA ozone air quality

criteria document (ozone AQCD) and EPA staff paper.908,909 We are relying on the data and

conclusions in the ozone AQCD and staff paper, regarding the health effects associated with

ozone exposure.

Ozone-related health effects include lung function decrements, respiratory symptoms,

aggravation of asthma, increased hospital and emergency room visits, increased asthma

medication usage, and a variety of other respiratory effects. Cellular-level effects, such as

inflammation of lungs, have been documented as well. In addition, there is suggestive evidence

of a contribution of ozone to cardiovascular-related morbidity and highly suggestive evidence

that short-term ozone exposure directly or indirectly contributes to non-accidental and

cardiopulmonary-related mortality, but additional research is needed to clarify the underlying

mechanisms causing these effects. In a recent report on the estimation of ozone-related

premature mortality published by the National Research Council (NRC), a panel of experts and

reviewers concluded that short-term exposure to ambient ozone is likely to contribute to

premature deaths and that ozone-related mortality should be included in estimates of the health

benefits of reducing ozone exposure.910 People who appear to be more susceptible to effects

associated with exposure to ozone include children, asthmatics and the elderly. Those with

greater exposures to ozone, for instance due to time spent outdoors (e.g., children and outdoor

workers), are also of concern.

Based on a large number of scientific studies, EPA has identified several key health

effects associated with exposure to levels of ozone found today in many areas of the country.

Short-term (1 to 3 hours) and prolonged exposures (6 to 8 hours) to ambient ozone

concentrations have been linked to lung function decrements, respiratory symptoms, increased

hospital admissions and emergency room visits for respiratory problems.911, 912, 913, 914, 915, 916

Repeated exposure to ozone can increase susceptibility to respiratory infection and lung

inflammation and can aggravate preexisting respiratory diseases, such as asthma.917, 918, 919, 920, 921

Repeated exposure to sufficient concentrations of ozone can also cause inflammation of the lung,

impairment of lung defense mechanisms, and possibly irreversible changes in lung structure,

which over time could affect premature aging of the lungs and/or the development of chronic

respiratory illnesses, such as emphysema and chronic bronchitis.922, 923, 924, 925

Children and adults who are outdoors and active during the summer months, such as

construction workers, are among those most at risk of elevated ozone exposures.926 Children and

outdoor workers tend to have higher ozone exposure because they typically are active outside,

working, playing and exercising, during times of day and seasons (e.g., the summer) when ozone

levels are highest.927 For example, summer camp studies in the Eastern United States and

Southeastern Canada have reported statistically significant reductions in lung function in

children who are active outdoors.928, 929, 930, 931, 932, 933, 934, 935 Further, children are more at risk of

experiencing health effects from ozone exposure than adults because their respiratory systems

are still developing. These individuals (as well as people with respiratory illnesses, such as

215 Human exposure to ozone varies over time due to changes in ambient ozone concentration and because people

move between locations which have notable different ozone concentrations. Also, the amount of ozone delivered to

the lung is not only influenced by the ambient concentrations but also by the individuals breathing route and rate.
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asthma, especially asthmatic children) can experience reduced lung function and increased

respiratory symptoms, such as chest pain and cough, when exposed to relatively low ozone levels

during prolonged periods of moderate exertion.936, 937, 938, 939

3.5.3 Nitrogen Oxides and Sulfur Oxides

3.5.3.1 Background

Sulfur dioxide (SO2), a member of the sulfur oxide (SOX) family of gases, is formed from

burning fuels containing sulfur (e.g., coal or oil), extracting gasoline from oil, or extracting

metals from ore. Nitrogen dioxide (NO2) is a member of the nitrogen oxide (NOX) family of

gases. Most NO2 is formed in the air through the oxidation of nitric oxide (NO) emitted when

fuel is burned at a high temperature. SO2 andNO2 can dissolve in water vapor and further

oxidize to form sulfuric and nitric acid which react with ammonia to form sulfates and nitrates,

both of which are important components of ambient PM. The health effects of ambient PM are

discussed in Section 3.5.1.2. NOX along with non-methane hydrocarbons (NMHC) are the two

major precursors of ozone. The health effects of ozone are covered in Section 3.5.2.2.

3.5.3.2 Health Effects of Sulfur Oxides

Information on the health effects of SO2 can be found in the U.S. Environmental

Protection Agency Integrated Science Assessment for Sulfur Oxides.216 SO2 has long been

known to cause adverse respiratory health effects, particularly among individuals with asthma.

Other potentially sensitive groups include children and the elderly. During periods of elevated

ventilation, asthmatics may experience symptomatic bronchoconstriction within minutes of

exposure. Following an extensive evaluation of health evidence from epidemiologic and

laboratory studies, the EPA has concluded that there is a causal relationship between respiratory

health effects and short-term exposure to SO2. Separately, based on an evaluation of the

epidemiologic evidence of associations between short-term exposure to SO2 and mortality, the

EPA has concluded that the overall evidence is suggestive of a causal relationship between shortterm exposure to SO2 and mortality.

3.5.3.3 Health Effects of Nitrogen Oxides

Information on the health effects of NO2 can be found in the U.S. Environmental

Protection Agency Integrated Science Assessment (ISA) for Nitrogen Oxides.217 The U.S. EPA

has concluded that the findings of epidemiologic, controlled human exposure, and animal

toxicological studies provide evidence that is sufficient to infer a likely causal relationship

between respiratory effects and short-term NO2 exposure. The ISA concludes that the strongest

evidence for such a relationship comes from epidemiologic studies of respiratory effects

216 U.S. EPA. (2008). Integrated Science Assessment (ISA) for Sulfur Oxides – Health Criteria (Final Report).

EPA/600/R-08/047F. Washington, DC: U.S. Environmental Protection Agency. Retrieved on March 18, 2009 from

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843

217 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria (Final Report).

EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 from

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645.
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including symptoms, emergency department visits, and hospital admissions. The ISA also draws

two broad conclusions regarding airway responsiveness following NO2 exposure. First, the ISA

concludes that NO2 exposure may enhance the sensitivity to allergen-induced decrements in lung

function and increase the allergen-induced airway inflammatory response following 30-minute

exposures of asthmatics to NO2 concentrations as low as 0.26 ppm. In addition, small but

significant increases in non-specific airway hyperresponsiveness were reported following 1-hour

exposures of asthmatics to 0.1 ppm NO2. Second, exposure to NO2 has been found to enhance

the inherent responsiveness of the airway to subsequent nonspecific challenges in controlled

human exposure studies of asthmatic subjects. Enhanced airway responsiveness could have

important clinical implications for asthmatics since transient increases in airway responsiveness

following NO2 exposure have the potential to increase symptoms and worsen asthma control.

Together, the epidemiologic and experimental data sets form a plausible, consistent, and

coherent description of a relationship between NO2 exposures and an array of adverse health

effects that range from the onset of respiratory symptoms to hospital admission.

Although the weight of evidence supporting a causal relationship is somewhat less certain

than that associated with respiratory morbidity, NO2 has also been linked to other health

endpoints. These include all-cause (nonaccidental) mortality, hospital admissions or emergency

department visits for cardiovascular disease, and decrements in lung function growth associated

with chronic exposure.

3.5.4 Carbon Monoxide

This section summarizes the data and conclusions in the EPA Air Quality Criteria

Document for CO (CO Criteria Document), which was published in 2000, regarding the health

effects associated with CO exposure.218,940 Carbon monoxide enters the bloodstream through the

lungs and forms carboxyhemoglobin (COHb), a compound that inhibits the blood’s capacity to

carry oxygen to organs and tissues.941,942 Carbon monoxide has long been known to have

substantial adverse effects on human health, including toxic effects on blood and tissues, and

effects on organ functions. Although there are effective compensatory increases in blood flow to

the brain, at some concentrations of COHb somewhere above 20 percent, these compensations

fail to maintain sufficient oxygen delivery, and metabolism declines.943 The subsequent hypoxia

in brain tissue then produces behavioral effects, including decrements in continuous performance

and reaction time.944

Carbon monoxide has been linked to increased risk for people with heart disease, reduced

visual perception, cognitive functions and aerobic capacity, and possible fetal effects.945 Persons

with heart disease are especially sensitive to CO poisoning and may experience chest pain if they

breathe the gas while exercising.946 Infants, elderly persons, and individuals with respiratory

diseases are also particularly sensitive. Carbon monoxide can affect healthy individuals,

218 The CO NAAQS is currently under review and the EPA is considering all available science on CO health effects,

including information which has been published since 2000, in the development of the upcoming CO Integrated

Science Assessment Document (ISA). A second draft of the CO ISA was completed in September 2009 and was

submitted for review by the Clean Air Scientific Advisory Committee (CASAC) of EPA’s Science Advisory Board.

For more information, see http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=213229.
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impairing exercise capacity, visual perception, manual dexterity, learning functions, and ability

to perform complex tasks.947

Several epidemiological studies have shown a link between CO and premature morbidity

(including angina, congestive heart failure, and other cardiovascular diseases). Several studies in

the United States and Canada have also reported an association between ambient CO exposures

and frequency of cardiovascular hospital admissions, especially for congestive heart failure

(CHF). An association between ambient CO exposure and mortality has also been reported in

epidemiological studies, though not as consistently or specifically as with CHF admissions. EPA

reviewed these studies as part of the CO Criteria Document review process and noted the

possibility that the average ambient CO levels used as exposure indices in the epidemiology

studies may be surrogates for ambient air mixes impacted by combustion sources and/or other

constituent toxic components of such mixes. More research will be needed to better clarify CO’s

role.948

3.5.5 Health Effects of Air Toxics

Motor vehicle emissions contribute to ambient levels of air toxics known or suspected as

human or animal carcinogens, or that have noncancer health effects. The population experiences

an elevated risk of cancer and other noncancer health effects from exposure to air toxics.949

These compounds include, but are not limited to, benzene, 1,3-butadiene, formaldehyde,

acetaldehyde, acrolein, polycyclic organic matter (POM), and naphthalene. These compounds,

except acetaldehyde, were identified as national or regional risk drivers in the 2002 Nationalscale Air Toxics Assessment (NATA) and have significant inventory contributions from mobile

sources.

According to NATA for 2002, mobile sources were responsible for 47 percent of outdoor

toxic emissions, over 50 percent of the cancer risk, and over 80 percent of the noncancer hazard.

Benzene is the largest contributor to cancer risk of all 124 pollutants quantitatively assessed in

the 2002 NATA and mobile sources were responsible for 59 percent of benzene emissions in

2002. In 2007, EPA finalized vehicle and fuel controls that address this public health risk; it will

reduce total emissions of mobile source air toxics by 330,000 tons in 2030, including 61,000 tons

of benzene. 950

Noncancer health effects can result from chronic,219 subchronic,220 or acute221 inhalation

exposures to air toxics, and include neurological, cardiovascular, liver, kidney, and respiratory

effects as well as effects on the immune and reproductive systems. According to the 2002

NATA, nearly the entire U.S. population was exposed to an average concentration of air toxics

that has the potential for adverse noncancer respiratory health effects. This will continue to be

the case in 2030, even though toxics concentrations will be lower. Mobile sources were

219 Chronic exposure is defined in the glossary of the Integrated Risk Information (IRIS) database

(http://www.epa.gov/iris) as repeated exposure by the oral, dermal, or inhalation route for more than approximately

10% of the life span in humans (more than approximately 90 days to 2 years in typically used laboratory animal

species).

220 Defined in the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an

organism.

221 Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less.
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responsible for over 80 percent of the noncancer (respiratory) risk from outdoor air toxics in

2002. The majority of this risk was from exposure to acrolein. The confidence in the RfC for

acrolein is medium and confidence in NATA estimates of population noncancer hazard from

ambient exposure to this pollutant is low.951,952

The NATA modeling framework has a number of limitations which prevent its use as the

sole basis for setting regulatory standards. These limitations and uncertainties are discussed on

the 2002 NATA website.953 Even so, this modeling framework is very useful in identifying air

toxic pollutants and sources of greatest concern, setting regulatory priorities, and informing the

decision making process.

3.5.5.1 Benzene

The EPA’s IRIS database lists benzene as a known human carcinogen (causing leukemia)

by all routes of exposure, and concludes that exposure is associated with additional health

effects, including genetic changes in both humans and animals and increased proliferation of

bone marrow cells in mice.954,955,956 EPA states in its IRIS database that data indicate a causal

relationship between benzene exposure and acute lymphocytic leukemia and suggest a

relationship between benzene exposure and chronic non-lymphocytic leukemia and chronic

lymphocytic leukemia. The International Agency for Research on Carcinogens (IARC) has

determined that benzene is a human carcinogen and the U.S. Department of Health and Human

Services (DHHS) has characterized benzene as a known human carcinogen.957,958

A number of adverse noncancer health effects including blood disorders, such as

preleukemia and aplastic anemia, have also been associated with long-term exposure to

benzene.959,960 The most sensitive noncancer effect observed in humans, based on current data,

is the depression of the absolute lymphocyte count in blood.961,962 In addition, recent work,

including studies sponsored by the Health Effects Institute (HEI), provides evidence that

biochemical responses are occurring at lower levels of benzene exposure than previously

known.963,964,965,966 EPA’s IRIS program has not yet evaluated these new data.

3.5.5.2 1,3-Butadiene

EPA has characterized 1,3-butadiene as carcinogenic to humans by inhalation.967,968 The

IARC has determined that 1,3-butadiene is a human carcinogen and the U.S. DHHS has

characterized 1,3-butadiene as a known human carcinogen.969,970 There are numerous studies

consistently demonstrating that 1,3-butadiene is metabolized into genotoxic metabolites by

experimental animals and humans. The specific mechanisms of 1,3-butadiene-induced

carcinogenesis are unknown; however, the scientific evidence strongly suggests that the

carcinogenic effects are mediated by genotoxic metabolites. Animal data suggest that females

may be more sensitive than males for cancer effects associated with 1,3-butadiene exposure;

there are insufficient data in humans from which to draw conclusions about sensitive

subpopulations. 1,3-butadiene also causes a variety of reproductive and developmental effects in

mice; no human data on these effects are available. The most sensitive effect was ovarian

atrophy observed in a lifetime bioassay of female mice.971
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3.5.5.3 Ethanol

EPA is conducting an assessment of the cancer and noncancer effects of exposure to

ethanol, a compound which is not currently listed in EPA’s IRIS. A description of these effects

to the extent that information is available will be presented, as required by Section 1505 of

EPAct, in a Report to Congress on public health, air quality and water resource impacts of fuel

additives. We expect to release that report in 2010.

Extensive data are available regarding adverse health effects associated with the ingestion

of ethanol while data on inhalation exposure effects are sparse. As part of the IRIS assessment,

pharmacokinetic models are being evaluated as a means of extrapolating across species (animal

to human) and across exposure routes (oral to inhalation) to better characterize the health hazards

and dose-response relationships for low levels of ethanol exposure in the environment.

The IARC has classified “alcoholic beverages” as carcinogenic to humans based on

sufficient evidence that malignant tumors of the mouth, pharynx, larynx, esophagus, and liver are

causally related to the consumption of alcoholic beverages.972 The U.S. DHHS in the 11th Report

on Carcinogens also identified “alcoholic beverages” as a known human carcinogen (they have

not evaluated the cancer risks specifically from exposure to ethanol), with evidence for cancer of

the mouth, pharynx, larynx, esophagus, liver and breast.973 There are no studies reporting

carcinogenic effects from inhalation of ethanol. EPA is currently evaluating the available human

and animal cancer data to identify which cancer type(s) are the most relevant to an assessment of

risk to humans from a low-level oral and inhalation exposure to ethanol.

Noncancer health effects data are available from animal studies as well as epidemiologic

studies. The epidemiologic data are obtained from studies of alcoholic beverage consumption.

Effects include neurological impairment, developmental effects, cardiovascular effects, immune

system depression, and effects on the liver, pancreas and reproductive system.974 There is

evidence that children prenatally exposed via mothers’ ingestion of alcoholic beverages during

pregnancy are at increased risk of hyperactivity and attention deficits, impaired motor

coordination, a lack of regulation of social behavior or poor psychosocial functioning, and

deficits in cognition, mathematical ability, verbal fluency, and spatial

memory.975,976,977,978,979,980,981,982 In some people, genetic factors influencing the metabolism of

ethanol can lead to differences in internal levels of ethanol and may render some subpopulations

more susceptible to risks from the effects of ethanol.

3.5.5.4 Formaldehyde

Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on

evidence in humans and in rats, mice, hamsters, and monkeys.983 EPA is currently reviewing

recently published epidemiological data. For instance, research conducted by the National

Cancer Institute (NCI) found an increased risk of nasopharyngeal cancer and

lymphohematopoietic malignancies such as leukemia among workers exposed to

formaldehyde.984,985 In an analysis of the lymphohematopoietic cancer mortality from an

extended follow-up of these workers, NCI confirmed an association between

lymphohematopoietic cancer risk and peak exposures.986 A recent National Institute of
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Occupational Safety and Health (NIOSH) study of garment workers also found increased risk of

death due to leukemia among workers exposed to formaldehyde.987 Extended follow-up of a

cohort of British chemical workers did not find evidence of an increase in nasopharyngeal or

lymphohematopoietic cancers, but a continuing statistically significant excess in lung cancers

was reported.988

In the past 15 years there has been substantial research on the inhalation dosimetry for

formaldehyde in rodents and primates by the CIIT Centers for Health Research (formerly the

Chemical Industry Institute of Toxicology), with a focus on use of rodent data for refinement of

the quantitative cancer dose-response assessment.989,990,991 CIIT’s risk assessment of

formaldehyde incorporated mechanistic and dosimetric information on formaldehyde. However,

it should be noted that recent research published by EPA indicates that when two-stage modeling

assumptions are varied, resulting dose-response estimates can vary by several orders of

magnitude.992,993,994,995 These findings are not supportive of interpreting the CIIT model results

as providing a conservative (health protective) estimate of human risk.996 EPA research also

examined the contribution of the two-stage modeling for formaldehyde towards characterizing

the relative weights of key events in the mode-of-action of a carcinogen. For example, the

model-based inference in the published CIIT study that formaldehyde’s direct mutagenic action

is not relevant to the compound’s tumorigenicity was found not to hold under variations of

modeling assumptions.997

Based on the developments of the last decade, in 2004, the working group of the IARC

concluded that formaldehyde is carcinogenic to humans (Group 1), on the basis of sufficient

evidence in humans and sufficient evidence in experimental animals - a higher classification than

previous IARC evaluations. After reviewing the currently available epidemiological evidence,

the IARC (2006) characterized the human evidence for formaldehyde carcinogenicity as

“sufficient,” based upon the data on nasopharyngeal cancers; the epidemiologic evidence on

leukemia was characterized as “strong.”998 EPA is reviewing the recent work cited above from

the NCI and NIOSH, as well as the analysis by the CIIT Centers for Health Research and other

studies, as part of a reassessment of the human hazard and dose-response associated with

formaldehyde.

Formaldehyde exposure also causes a range of noncancer health effects, including

irritation of the eyes (burning and watering of the eyes), nose and throat. Effects from repeated

exposure in humans include respiratory tract irritation, chronic bronchitis and nasal epithelial

lesions such as metaplasia and loss of cilia. Animal studies suggest that formaldehyde may also

cause airway inflammation – including eosinophil infiltration into the airways. There are several

studies that suggest that formaldehyde may increase the risk of asthma – particularly in the

young.999,1000

3.5.5.5 Acetaldehyde

Acetaldehyde is classified in EPA’s IRIS database as a probable human carcinogen,

based on nasal tumors in rats, and is considered toxic by the inhalation, oral, and intravenous

routes.1001 Acetaldehyde is reasonably anticipated to be a human carcinogen by the U.S. DHHS

in the 11th Report on Carcinogens and is classified as possibly carcinogenic to humans (Group
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2B) by the IARC.1002,1003 EPA is currently conducting a reassessment of cancer risk from

inhalation exposure to acetaldehyde.

The primary noncancer effects of exposure to acetaldehyde vapors include irritation of

the eyes, skin, and respiratory tract.1004 In short-term (4 week) rat studies, degeneration of

olfactory epithelium was observed at various concentration levels of acetaldehyde

exposure.1005,1006 Data from these studies were used by EPA to develop an inhalation reference

concentration. Some asthmatics have been shown to be a sensitive subpopulation to decrements

in functional expiratory volume (FEV1 test) and bronchoconstriction upon acetaldehyde

inhalation.1007 The agency is currently conducting a reassessment of the health hazards from

inhalation exposure to acetaldehyde.

3.5.5.6 Acrolein

EPA determined in 2003 that the human carcinogenic potential of acrolein could not be

determined because the available data were inadequate. No information was available on the

carcinogenic effects of acrolein in humans and the animal data provided inadequate evidence of

carcinogenicity.1008 The IARC determined in 1995 that acrolein was not classifiable as to its

carcinogenicity in humans.1009

Acrolein is extremely acrid and irritating to humans when inhaled, with acute exposure

resulting in upper respiratory tract irritation, mucus hypersecretion and congestion. The intense

irritancy of this carbonyl has been demonstrated during controlled tests in human subjects, who

suffer intolerable eye and nasal mucosal sensory reactions within minutes of exposure.1010 These

data and additional studies regarding acute effects of human exposure to acrolein are

summarized in EPA’s 2003 IRIS Human Health Assessment for acrolein.1011 Evidence available

from studies in humans indicate that levels as low as 0.09 ppm (0.21 mg/m3) for five minutes

may elicit subjective complaints of eye irritation with increasing concentrations leading to more

extensive eye, nose and respiratory symptoms.1012 Lesions to the lungs and upper respiratory

tract of rats, rabbits, and hamsters have been observed after subchronic exposure to acrolein.1013

Acute exposure effects in animal studies report bronchial hyper-responsiveness.1014 In a recent

study, the acute respiratory irritant effects of exposure to 1.1 ppm acrolein were more

pronounced in mice with allergic airway disease by comparison to non-diseased mice which also

showed decreases in respiratory rate.1015 Based on these animal data and demonstration of

similar effects in humans (i.e., reduction in respiratory rate), individuals with compromised

respiratory function (e.g., emphysema, asthma) are expected to be at increased risk of developing

adverse responses to strong respiratory irritants such as acrolein.

3.5.6.7 Peroxyacetyl nitrate (PAN)

PAN has not been evaluated by EPA’s IRIS program. Information regarding the

potential carcinogenicity of PAN is limited. As noted in the EPA air quality criteria document

for ozone and related photochemical oxidants, cytogenetic studies indicate that PAN is not a

potent mutagen, clastogen (a compound that can cause breaks in chromosomes), or DNAdamaging agent in mammalian cells either in vivo or in vitro. Some studies suggest that PAN
664

may be a weak bacterial mutagen at high concentrations much higher than exist in present urban

atmospheres.1016

Effects of ground-level smog causing intense eye irritation have been attributed to

photochemical oxidants, including PAN.1017 Animal toxicological information on the inhalation

effects of the non-ozone oxidants has been limited to a few studies on PAN. Acute exposure to

levels of PAN can cause changes in lung morphology, behavioral modifications, weight loss, and

susceptibility to pulmonary infections. Human exposure studies indicate minor pulmonary

function effects at high PAN concentrations, but large inter-individual variability precludes

definitive conclusions.1018

3.5.6.8 Naphthalene

Naphthalene is found in small quantities in gasoline and diesel fuels. Naphthalene

emissions have been measured in larger quantities in both gasoline and diesel exhaust compared

with evaporative emissions from mobile sources, indicating it is primarily a product of

combustion. EPA released an external review draft of a reassessment of the inhalation

carcinogenicity of naphthalene based on a number of recent animal carcinogenicity studies.1019

The draft reassessment completed external peer review.1020 Based on external peer review

comments received, additional analyses are being undertaken. This external review draft does

not represent official agency opinion and was released solely for the purposes of external peer

review and public comment. The National Toxicology Program listed naphthalene as

"reasonably anticipated to be a human carcinogen" in 2004 on the basis of bioassays reporting

clear evidence of carcinogenicity in rats and some evidence of carcinogenicity in mice.1021

California EPA has released a new risk assessment for naphthalene, and the IARC has

reevaluated naphthalene and re-classified it as Group 2B: possibly carcinogenic to humans.1022

Naphthalene also causes a number of chronic non-cancer effects in animals, including abnormal

cell changes and growth in respiratory and nasal tissues.1023

3.5.6.9 N-Hexane

N-Hexane is associated with polyneuropathy in humans. Effects observed in rodents

include nasal lesions as well as neurotoxic effects. EPA has developed a reference concentration

of 700 μg/m3

from a study of peripheral neuropathy.1024 There is inadequate data to assess its

carcinogenic potential.

3.5.6.10 Pesticides

There are potential toxicity concerns with volatilization of pesticide active

ingredients,1025 in addition to concerns with contamination of foods and drinking water.

Furthermore, raising acreage under corn production may increase the quantity of pesticide

products in use. As the domestic corn supply grows between the years of 2005 and 2022, the

percentage of corn used for ethanol production in the US is expected to increase, though the

agricultural impacts of this shifting of crop production domestically are anticipated to be small.

Whether there is the potential for adverse human health effects from any increase in pesticide use

associated with increased corn production domestically warrants further assessment. Additional
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information on pesticides and health effects is included in Section 6.1 of this RIA.

3.5.6.11 Other Air Toxics

In addition to the compounds described above, other compounds in gaseous hydrocarbon

and PM emissions from vehicles will be affected by today’s proposed action. Mobile source air

toxic compounds that will potentially be impacted include ethylbenzene, polycyclic organic

matter, propionaldehyde, toluene, and xylene. Information regarding the health effects of these

compounds can be found in EPA’s IRIS database.1026

3.6 Environmental Effects

In this section we discuss some of the environmental effects of PM and its precursors,

such as visibility impairment, atmospheric deposition, and materials damage and soiling. We

also discuss environmental effects associated with the presence of ozone in the ambient air, such

as impacts on plants, including trees, agronomic crops and urban ornamentals.

3.6.1 Visibility Degradation

Emissions from LD vehicles contribute to poor visibility in the U.S. through their

primary PM2.5 and secondary PM2.5 precursor emissions. These airborne particles degrade

visibility by scattering and absorbing light. Good visibility increases the quality of life where

individuals live and work, and where they engage in recreational activities.

The U.S. Government places special emphasis on protecting visibility in national parks

and wilderness areas. Section 169 of the Clean Air Act requires the U.S. Government to address

existing visibility impairment and future visibility impairment in the national parks exceeding

6,000 acres, and wilderness areas exceeding 5,000 acres, which are categorized as mandatory

class I federal areas (62 FR 38680, July 18, 1997).222 Figure 3.6-1 shows the location of the 156

mandatory class I federal areas.

3.6.1.1 Visibility Monitoring

In conjunction with the U.S. National Park Service, the U.S. Forest Service, other Federal

land managers, and State organizations in the U.S., the U.S. EPA has supported visibility

monitoring in national parks and wilderness areas since 1988. The monitoring network was

originally established at 20 sites, but it has now been expanded to 110 sites that represent all but

one of the 156 mandatory class I federal areas across the country (see Figure3.6-1). This longterm visibility monitoring network is known as IMPROVE (Interagency Monitoring of Protected

Visual Environments).

IMPROVE provides direct measurement of fine particles that contribute to visibility

impairment. The IMPROVE network employs aerosol measurements at all sites, and optical and

222 These areas are defined in section 162 of the Act as those national parks exceeding 6,000 acres, wilderness areas

and memorial parks exceeding 5,000 acres, and all international parks which were in existence on August 7, 1977.
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scene measurements at some of the sites. Aerosol measurements are taken for PM10 and PM2.5

mass, and for key constituents of PM2.5, such as sulfate, nitrate, organic and elemental carbon,

soil dust, and several other elements. Measurements for specific aerosol constituents are used to

calculate "reconstructed" aerosol light extinction by multiplying the mass for each constituent by

its empirically-derived scattering and/or absorption efficiency, with adjustment for the relative

humidity. Knowledge of the main constituents of a site's light extinction "budget" is critical for

source apportionment and control strategy development. Optical measurements are used to

directly measure light extinction or its components. Such measurements are taken principally

with either a transmissometer, which measures total light extinction, or a nephelometer, which

measures particle scattering (the largest human-caused component of total extinction). Scene

characteristics are typically recorded three times daily with 35 millimeter photography and are

used to determine the quality of visibility conditions (such as effects on color and contrast)

associated with specific levels of light extinction as measured under both direct and aerosolrelated methods. Directly measured light extinction is used under the IMPROVE protocol to

cross check that the aerosol-derived light extinction levels are reasonable in establishing current

visibility conditions. Aerosol-derived light extinction is used to document spatial and temporal

trends and to determine how proposed changes in atmospheric constituents would affect future

visibility conditions.

Annual average visibility conditions (reflecting light extinction due to both anthropogenic

and non-anthropogenic sources) vary regionally across the U.S. The rural East generally has

higher levels of impairment than remote sites in the West, with the exception of urban-influenced

sites such as San Gorgonio Wilderness (CA) and Point Reyes National Seashore (CA), which

have annual average levels comparable to certain sites in the Northeast. Regional differences are

illustrated by Figures 4-39a and 4-39b in the Air Quality Criteria Document for Particulate

Matter, which show that, for Class I areas, visibility levels on the 20% haziest days in the West

are about equal to levels on the 20% best days in the East.1027

Higher visibility impairment levels in the East are due to generally higher concentrations

of anthropogenic fine particles, particularly sulfates, and higher average relative humidity levels.

In fact, sulfates account for 60-86% of the haziness in eastern sites.1028 Aerosol light extinction

due to sulfate on the 20% haziest days is significantly larger in eastern Class I areas as compared

to western areas (Figures 4-40a and 4-40b in the Air Quality Criteria Document for Particulate

Matter).1029 With the exception of remote sites in the northwestern U.S., visibility is typically

worse in the summer months. This is particularly true in the Appalachian region, where average

light extinction in the summer exceeds the annual average by 40%.1030

3.6.1.2 Addressing Visibility in the U.S.

The U.S. EPA is pursuing a two-part strategy to address visibility. First, to address the

welfare effects of PM on visibility, EPA set secondary PM2.5 standards which act in conjunction

with the establishment of a regional haze program. In setting this secondary standard, EPA has

concluded that PM2.5 causes adverse effects on visibility in various locations, depending on PM

concentrations and factors such as chemical composition and average relative humidity. Second,

section 169 of the Clean Air Act provides additional authority to address existing visibility

impairment and prevent future visibility impairment in the 156 mandatory Class I federal areas
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(62 FR 38680-81, July 18, 1997). In July 1999, the regional haze rule (64 FR 35714) was put in

place to protect the visibility in mandatory Class I federal areas. Visibility can be said to be

impaired in both PM2.5 nonattainment areas and mandatory Class I federal areas.223

Figure 3.6-1: Mandatory Class I Areas in the U.S.

3.6.2 Particulate Matter Deposition

Particulate matter contributes to adverse effects on vegetation and ecosystems, and to

soiling and materials damage. These welfare effects result predominately from exposure to

excess amounts of specific chemical species, regardless of their source or predominant form

(particle, gas or liquid). Reflecting this fact, the PM AQCD concludes that regardless of size

fractions, particles containing nitrates and sulfates have the greatest potential for widespread

environmental significance, while effects are also related to other chemical constituents found in

ambient PM, such as trace metals and organics. The following characterizations of the nature of

223 As mentioned above, the EPA recently amended the PM NAAQS, making the secondary NAAQS equal, in all

respects, to the primary standards for both PM2.5 and PM10, (71 FR 61144, Oct. 17, 2006). In February 2009, the

D.C. Circuit Court remanded the secondary standards for fine particles, based on EPA’s failure to adequately

explain why setting the secondary PM2.5 NAAQS equivalent to the primary standards provided the required

protection for public welfare including protection from visibility impairment.
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these welfare effects are based on the information contained in the PM AQCD and PM Staff

Paper.1031,1032

3.6.2.1 Deposition of Nitrogen and Sulfur

Nitrogen and sulfur interactions in the environment are highly complex. Both are

essential, and sometimes limiting, nutrients needed for growth and productivity. Excesses of

nitrogen or sulfur can lead to acidification, nutrient enrichment, and eutrophication.1033

The process of acidification affects both freshwater aquatic and terrestrial ecosystems.

Acid deposition causes acidification of sensitive surface waters. The effects of acid deposition on

aquatic systems depend largely upon the ability of the ecosystem to neutralize the additional

acid. As acidity increases, aluminum leached from soils and sediments, flows into lakes and

streams and can be toxic to both terrestrial and aquatic biota. The lower pH concentrations and

higher aluminum levels resulting from acidification make it difficult for some fish and other

aquatic organisms to survive, grow, and reproduce. Research on effects of acid deposition on

forest ecosystems has come to focus increasingly on the biogeochemical processes that affect

uptake, retention, and cycling of nutrients within these ecosystems. Decreases in available base

cations from soils are at least partly attributable to acid deposition. Base cation depletion is a

cause for concern because of the role these ions play in acid neutralization and, because calcium,

magnesium and potassium are essential nutrients for plant growth and physiology. Changes in

the relative proportions of these nutrients, especially in comparison with aluminum

concentrations, have been associated with declining forest health.

At current ambient levels, risks to vegetation from short-term exposures to dry deposited

particulate nitrate or sulfate are low. However, when found in acid or acidifying deposition, such

particles do have the potential to cause direct leaf injury. Specifically, the responses of forest

trees to acid precipitation (rain, snow) include accelerated weathering of leaf cuticular surfaces,

increased permeability of leaf surfaces to toxic materials, water, and disease agents; increased

leaching of nutrients from foliage; and altered reproductive processes—all which serve to

weaken trees so that they are more susceptible to other stresses (e.g., extreme weather, pests,

pathogens). Acid deposition with levels of acidity associated with the leaf effects described

above are currently found in some locations in the eastern U.S.1034 Even higher concentrations

of acidity can be present in occult depositions (e.g., fog, mist or clouds) which more frequently

impacts higher elevations. Thus, the risk of leaf injury occurring from acid deposition in some

areas of the eastern U.S. is high. Nitrogen deposition has also been shown to impact ecosystems

in the western U.S. A study conducted in the Columbia River Gorge National Scenic Area

(CRGNSA), located along a portion of the Oregon/Washington border, indicates that lichen

communities in the CRGNSA have shifted to a higher proportion of nitrophilous species and the

nitrogen content of lichen tissue is elevated.1035 Lichens are sensitive indicators of nitrogen

deposition effects to terrestrial ecosystems and the lichen studies in the Columbia River Gorge

clearly show that ecological effects from air pollution are occurring.

Some of the most significant detrimental effects associated with excess nitrogen

deposition are those associated with a syndrome known as nitrogen saturation. These effects

include: (1) decreased productivity, increased mortality, and/or shifts in plant community
669

composition, often leading to decreased biodiversity in many natural habitats wherever

atmospheric reactive nitrogen deposition increases significantly above background and critical

thresholds are exceeded; (2) leaching of excess nitrate and associated base cations from soils into

streams, lakes, and rivers, and mobilization of soil aluminum; and (3) fluctuation of ecosystem

processes such as nutrient and energy cycles through changes in the functioning and species

composition of beneficial soil organisms.1036

In the U.S. numerous forests now show severe symptoms of nitrogen saturation. These

forests include: the northern hardwoods and mixed conifer forests in the Adirondack and

Catskill Mountains of New York; the red spruce forests at Whitetop Mountain, Virginia, and

Great Smoky Mountains National Park, North Carolina; mixed hardwood watersheds at Fernow

Experimental Forest in West Virginia; American beech forests in Great Smoky Mountains

National Park, Tennessee; mixed conifer forests and chaparral watersheds in southern California

and the southwestern Sierra Nevada in Central California; the alpine tundra/subalpine conifer

forests of the Colorado Front Range; and red alder forests in the Cascade Mountains in

Washington.

Excess nutrient inputs into aquatic ecosystems (i.e. streams, rivers, lakes, estuaries or

oceans) either from direct atmospheric deposition, surface runoff, or leaching from nitrogen

saturated soils into ground or surface waters can contribute to conditions of severe water oxygen

depletion; eutrophication and algae blooms; altered fish distributions, catches, and physiological

states; loss of biodiversity; habitat degradation; and increases in the incidence of disease.

Atmospheric deposition of nitrogen is a significant source of total nitrogen to many

estuaries in the United States. The amount of nitrogen entering estuaries that is ultimately

attributable to atmospheric deposition is not well-defined. On an annual basis, atmospheric

nitrogen deposition may contribute significantly to the total nitrogen load, depending on the size

and location of the watershed. In addition, episodic nitrogen inputs, which may be ecologically

important, may play a more important role than indicated by the annual average concentrations.

Estuaries in the U.S. that suffer from nitrogen enrichment often experience a condition known as

eutrophication. Symptoms of eutrophication include changes in the dominant species of

phytoplankton, low levels of oxygen in the water column, fish and shellfish kills, outbreaks of

toxic alga, and other population changes which can cascade throughout the food web. In

addition, increased phytoplankton growth in the water column and on surfaces can attenuate light

causing declines in submerged aquatic vegetation, which serves as an important habitat for many

estuarine fish and shellfish species.

Severe and persistent eutrophication often directly impacts human activities. For

example, losses in the nation’s fishery resources may be directly caused by fish kills associated

with low dissolved oxygen and toxic blooms. Declines in tourism occur when low dissolved

oxygen causes noxious smells and floating mats of algal blooms create unfavorable aesthetic

conditions. Risks to human health increase when the toxins from algal blooms accumulate in

edible fish and shellfish, and when toxins become airborne, causing respiratory problems due to

inhalation. According to a NOAA report, more than half of the nation’s estuaries have moderate

to high expressions of at least one of these symptoms – an indication that eutrophication is well

developed in more than half of U.S. estuaries.1037
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3.6.2.2 Materials Damage and Soiling

The effects of the deposition of atmospheric pollution, including ambient PM, on

materials are related to both physical damage and impaired aesthetic qualities. The deposition of

PM (especially sulfates and nitrates) can physically affect materials, adding to the effects of

natural weathering processes, by potentially promoting or accelerating the corrosion of metals,

by degrading paints, and by deteriorating building materials such as concrete and limestone.

Only chemically active fine particles or hygroscopic coarse particles contribute to these physical

effects. In addition, the deposition of ambient PM can reduce the aesthetic appeal of buildings

and culturally important articles through soiling. Particles consisting primarily of carbonaceous

compounds cause soiling of commonly used building materials and culturally important items

such as statues and works of art.

3.6.3 Impacts of Ozone on Vegetation

The Air Quality Criteria Document for Ozone and related Photochemical Oxidants notes

that “ozone affects vegetation throughout the United States, impairing crops, native vegetation,

and ecosystems more than any other air pollutant”.1038 Like carbon dioxide (CO2) and other

gaseous substances, ozone enters plant tissues primarily through apertures (stomata) in leaves in

a process called “uptake.”1039 Once sufficient levels of ozone (a highly reactive substance), or its

reaction products, reaches the interior of plant cells, it can inhibit or damage essential cellular

components and functions, including enzyme activities, lipids, and cellular membranes,

disrupting the plant's osmotic (i.e., water) balance and energy utilization patterns.1040,1041 This

damage is commonly manifested as visible foliar injury, such as chlorotic or necrotic spots,

increased leaf senescence (accelerated leaf aging) and/or reduced photosynthesis. All these

effects reduce a plant’s capacity to form carbohydrates, which are the primary form of energy

used by plants.1042 If enough tissue becomes damaged from these effects, a plant’s capacity to

fix carbon to form carbohydrates, which are the primary form of energy used by plants, is

reduced,1043 while plant respiration increases. With fewer resources available, the plant

reallocates existing resources away from root growth and storage, above ground growth or yield,

and reproductive processes, toward leaf repair and maintenance, leading to reduced growth

and/or reproduction. Studies have shown that plants stressed in these ways may exhibit a general

loss of vigor, which can lead to secondary impacts that modify plants' responses to other

environmental factors. Specifically, plants may become more sensitive to other air pollutants,

more susceptible to disease, insect attack, harsh weather (e.g., drought, frost) and other

environmental stresses. Furthermore, there is evidence that ozone can interfere with the

formation of mycorrhiza, essential symbiotic fungi associated with the roots of most terrestrial

plants, by reducing the amount of carbon available for transfer from the host to the

symbiont.1044,1045

This ozone damage may or may not be accompanied by visible injury on leaves, and

likewise, visible foliar injury may or may not be a symptom of the other types of plant damage

described above. When visible injury is present, it is commonly manifested as chlorotic or

necrotic spots, and/or increased leaf senescence (accelerated leaf aging). Because ozone damage

can consist of visible injury to leaves, it can also reduce the aesthetic value of ornamental
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vegetation and trees in urban landscapes, and negatively affects scenic vistas in protected natural

areas.

Ozone can produce both acute and chronic injury in sensitive species depending on the

concentration level and the duration of the exposure. Ozone effects also tend to accumulate over

the growing season of the plant, so that even lower concentrations experienced for a longer

duration have the potential to create chronic stress on sensitive vegetation. Not all plants,

however, are equally sensitive to ozone. Much of the variation in sensitivity between individual

plants or whole species is related to the plant’s ability to regulate the extent of gas exchange via

leaf stomata (e.g., avoidance of ozone uptake through closure of stomata).1046,1047,1048 Other

resistance mechanisms may involve the intercellular production of detoxifying substances.

Several biochemical substances capable of detoxifying ozone have been reported to occur in

plants, including the antioxidants ascorbate and glutathione. After injuries have occurred, plants

may be capable of repairing the damage to a limited extent.1049

Because of the differing sensitivities among plants to ozone, ozone pollution can also

exert a selective pressure that leads to changes in plant community composition. Given the range

of plant sensitivities and the fact that numerous other environmental factors modify plant uptake

and response to ozone, it is not possible to identify threshold values above which ozone is

consistently toxic for all plants. The next few paragraphs present additional information on

ozone damage to trees, ecosystems, agronomic crops and urban ornamentals.

Ozone also has been conclusively shown to cause discernible injury to forest trees.1050,1051

In terms of forest productivity and ecosystem diversity, ozone may be the pollutant with the

greatest potential for regional-scale forest impacts. Studies have demonstrated repeatedly that

ozone concentrations commonly observed in polluted areas can have substantial impacts on plant

function.1052,1053

Because plants are at the base of the food web in many ecosystems, changes to the plant

community can affect associated organisms and ecosystems (including the suitability of habitats

that support threatened or endangered species and below ground organisms living in the root

zone). Ozone impacts at the community and ecosystem level vary widely depending upon

numerous factors, including concentration and temporal variation of tropospheric ozone, species

composition, soil properties and climatic factors.1054 In most instances, responses to chronic or

recurrent exposure in forested ecosystems are subtle and not observable for many years. These

injuries can cause stand-level forest decline in sensitive ecosystems.1055,1056,1057 It is not yet

possible to predict ecosystem responses to ozone with much certainty; however, considerable

knowledge of potential ecosystem responses has been acquired through long-term observations

in highly damaged forests in the United States.

Laboratory and field experiments have also shown reductions in yields for agronomic

crops exposed to ozone, including vegetables (e.g., lettuce) and field crops (e.g., cotton and

wheat). The most extensive field experiments, conducted under the National Crop Loss

Assessment Network (NCLAN) examined 15 species and numerous cultivars. The NCLAN

results show that “several economically important crop species are sensitive to ozone levels

typical of those found in the Unites States.”1058 In addition, economic studies have shown
672

reduced economic benefits as a result of predicted reductions in crop yields associated with

observed ozone levels.1059,1060,1061

Urban ornamentals represent an additional vegetation category likely to experience some

degree of negative effects associated with exposure to ambient ozone levels. It is estimated that

more than $20 billion (1990 dollars) are spent annually on landscaping using ornamentals, both

by private property owners/tenants and by governmental units responsible for public areas.1062

This is therefore a potentially costly environmental effect. However, in the absence of adequate

exposure-response functions and economic damage functions for the potential range of effects

relevant to these types of vegetation, no direct quantitative analysis has been conducted.

Air pollution can have noteworthy cumulative impacts on forested ecosystems by

affecting regeneration, productivity, and species composition.1063 In the U.S., ozone in the lower

atmosphere is one of the pollutants of primary concern. Ozone injury to forest plants can be

diagnosed by examination of plant leaves. Foliar injury is usually the first visible sign of injury

to plants from ozone exposure and indicates impaired physiological processes in the leaves.1064

In the U.S. this indicator is based on data from the U.S. Department of Agriculture

(USDA) Forest Service Forest Inventory and Analysis (FIA) program. As part of its Phase 3

program, formerly known as Forest Health Monitoring, FIA examines ozone injury to ozonesensitive plant species at ground monitoring sites in forest land across the country. For this

indicator, forest land does not include woodlots and urban trees. Sites are selected using a

systematic sampling grid, based on a global sampling design.1065,1066 At each site that has at least

30 individual plants of at least three ozone-sensitive species and enough open space to ensure

that sensitive plants are not protected from ozone exposure by the forest canopy, FIA looks for

damage on the foliage of ozone-sensitive forest plant species. Monitoring of ozone injury to

plants by the USDA Forest Service has expanded over the last 10 years from monitoring sites in

10 states in 1994 to nearly 1,000 monitoring sites in 41 states in 2002.

3.6.3.1 Recent Ozone Data for the U.S.

There is considerable regional variation in ozone-related visible foliar injury to sensitive

plants in the U.S. The U.S. EPA has developed an environmental indicator based on data from

the U.S. Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA)

program which examines ozone injury to ozone-sensitive plant species at ground monitoring

sites in forest land across the country (This indicator does not include woodlots and urban trees).

Sites are selected using a systematic sampling grid, based on a global sampling design.1067, 1068

Because ozone injury is cumulative over the course of the growing season, examinations are

conducted in July and August, when ozone injury is typically highest. The data underlying the

indictor in Figure 3.6-2 are based on averages of all observations collected in 2002, the latest

year for which data are publicly available at the time the study was conducted, and are broken

down by U.S. EPA Region. Ozone damage to forest plants is classified using a subjective fivecategory biosite index based on expert opinion, but designed to be equivalent from site to site.

Ranges of biosite values translate to no injury, low or moderate foliar injury (visible foliar injury

to highly sensitive or moderately sensitive plants, respectively), and high or severe foliar injury,
673

which would be expected to result in tree-level or ecosystem-level responses, respectively.1069,

1070

The highest percentages of observed high and severe foliar injury, those which are most

likely to be associated with tree or ecosystem-level responses, are primarily found in the MidAtlantic and Southeast regions. In EPA Region 3 (which comprises the States of Pennsylvania,

West Virginia, Virginia, Delaware, Maryland and Washington D.C.), 12% of ozone-sensitive

plants showed signs of high or severe foliar damage, and in Regions 2 (States of New York, New

Jersey), and 4 (States of North Carolina, South Carolina, Kentucky, Tennessee, Georgia, Florida,

Alabama, and Mississippi) the values were 10% and 7%, respectively. The sum of high and

severe ozone injury ranged from 2% to 4% in EPA Region 1 (the six New England States),

Region 7 (States of Missouri, Iowa, Nebraska and Kansas), and Region 9 (States of California,

Nevada, Hawaii and Arizona). The percentage of sites showing some ozone damage was about

45% in each of these EPA Regions.
674

Figure 3.6-2: Ozone Injury to Forest Plants in U.S. by EPA Regions, 2002ab

3.6.3.1.1 Indicator Limitations

Field and laboratory studies were reviewed to identify the forest plant species in each

region that are highly sensitive to ozone air pollution. Other forest plant species, or even

genetic variants of the same species, may not be harmed at ozone levels that cause effects on

the selected ozone-sensitive species.
675

Because species distributions vary regionally, different ozone-sensitive plant species

were examined in different parts of the country. These target species could vary with respect to

ozone sensitivity, which might account for some of the apparent differences in ozone injury

among regions of the U.S.

Ozone damage to foliage is considerably reduced under conditions of low soil moisture,

but most of the variability in the index (70%) was explained by ozone concentration.1071 Ozone

may have other adverse impacts on plants (e.g., reduced productivity) that do not show signs of

visible foliar injury.1072

Though FIA has extensive spatial coverage based on a robust sample design, not all

forested areas in the U.S. are monitored for ozone injury. Even though the biosite data have been

collected over multiple years, most biosites were not monitored over the entire period, so these

data cannot provide more than a baseline for future trends.

3.6.4 Impacts of Ozone on Forest Health

Air pollution can impact the environment and affect ecological systems, leading to

changes in the biological community (both in the diversity of species and the health and vigor of

individual species). As an example, many studies have shown that ground-level ozone reduces

the health of plants including many commercial and ecologically important forest tree species

throughout the United States.1073

When ozone is present in the air, it can enter the leaves of plants, where it can cause

significant cellular damage. Since photosynthesis occurs in cells within leaves, the ability of the

plant to produce energy by photosynthesis can be compromised if enough damage occurs to

these cells. If enough tissue becomes damaged it can reduce carbon fixation and increase plant

respiration, leading to reduced growth and/or reproduction in young and mature trees. Ozone

stress also increases the susceptibility of plants to disease, insects, fungus, and other

environmental stressors (e.g., harsh weather). Because ozone damage can consist of visible

injury to leaves, it also reduces the aesthetic value of ornamental vegetation and trees in urban

landscapes, and negatively affect scenic vistas in protected natural areas.

Assessing the impact of ground-level ozone on forests in the eastern United States

involves understanding the risks to sensitive tree species from ambient ozone concentrations and

accounting for the prevalence of those species within the forest. As a way to quantify the risks to

particular plants from ground-level ozone, scientists have developed ozone-exposure/treeresponse functions by exposing tree seedlings to different ozone levels and measuring reductions

in growth as “biomass loss.” Typically, seedlings are used because they are easy to manipulate

and measure their growth loss from ozone pollution. The mechanisms of susceptibility to ozone

within the leaves of seedlings and mature trees are identical, though the magnitude of the effect

may be higher or lower depending on the tree species. 1074

Some of the common tree species in the United States that are sensitive to ozone are

black cherry (Prunus serotina), tulip-poplar (Liriodendron tulipifera), eastern white pine (Pinus

strobus). Ozone-exposure/tree-response functions have been developed for each of these tree

species, as well as for aspen (Populus tremuliodes), and ponderosa pine (Pinus ponderosa).
676

Other common tree species, such as oak (Quercus spp.) and hickory (Carya spp.), are not nearly

as sensitive to ozone. Consequently, with knowledge of the distribution of sensitive species and

the level of ozone at particular locations, it is possible to estimate a “biomass loss” for each

species across their range.

3.6.5 Environmental Effects of Air Toxics

Fuel combustion emissions contribute to ambient levels of pollutants that contribute to

adverse effects on vegetation. PAN is a well-established phytotoxicant causing visible injury to

leaves that can appear as metallic glazing on the lower surface of leaves with some leafy

vegetables exhibiting particular sensitivity (e.g., spinach, lettuce, chard).1075,1076,1077 PAN has

been demonstrated to inhibit photosynthetic and non-photosynthetic processes in plants and

retard the growth of young navel orange trees.1078,1079 In addition to its oxidizing capability, PAN

contributes nitrogen to forests and other vegetation via uptake as well as dry and wet deposition

to surfaces. As noted above in Section 3.6.2.1, nitrogen deposition can lead to saturation of

terrestrial ecosystems and research is needed to understand the impacts of excess nitrogen

deposition experienced in some areas of the country on water quality and ecosystems.1080

Volatile organic compounds (VOCs), some of which are considered air toxics, have long

been suspected to play a role in vegetation damage.1081 In laboratory experiments, a wide range

of tolerance to VOCs has been observed.1082 Decreases in harvested seed pod weight have been

reported for the more sensitive plants, and some studies have reported effects on seed

germination, flowering and fruit ripening. Effects of individual VOCs or their role in

conjunction with other stressors (e.g., acidification, drought, temperature extremes) have not

been well studied. In a recent study of a mixture of VOCs including ethanol and toluene on

herbaceous plants, significant effects on seed production, leaf water content and photosynthetic

efficiency were reported for some plant species.1083

Research suggests an adverse impact of vehicle exhaust on plants, which has in some

cases been attributed to aromatic compounds and in other cases to nitrogen oxides.1084,1085,1086

The impacts of VOCs on plant reproduction may have long-term implications for biodiversity

and survival of native species near major roadways. Most of the studies of the impacts of VOCs

on vegetation have focused on short-term exposure and few studies have focused on long-term

effects of VOCs on vegetation and the potential for metabolites of these compounds to affect

herbivores or insects.
677

Appendix Chapter 3A: Additional Air Toxics Modeling Results

3A.1 Annual Change Ambient Concentration Maps for Air Toxics using the

AEO Reference Case

The following section presents maps of annual changes in ambient concentrations of

modeled air toxics in 2022 using the AEO 2007 reference case compared to the RFS2 control

case.

3A.1.1 Acetaldehyde

Figure 3A-1. Acetaldehyde Annual Percent Change in Concentration Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022
678

Figure 3A-2. Acetaldehyde Annual Absolute Change in Concentration Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022

3A.1.2 Formaldehyde

Figure 3A-3. Formaldehyde Annual Percent Change in Concentration Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022
679

Figure 3A-4. Formaldehyde Annual Absolute Change in Concentration Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022

3A.1.3 Ethanol

Figure 3A-5. Ethanol Annual Percent Change in Concentration Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022
680

Figure 3A-6. Ethanol Annual Absolute Change in Concentration Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022

3A.1.4 Benzene

Figure 3A-7. Benzene Annual Percent Change in Concentration Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022
681

Figure 3A-8. Benzene Annual Absolute Change in Concentration Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022

3A.1.4 1,3-Butadiene

Figure 3A-9. 1,3-Butadiene Annual Percent Change in Concentration Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022
682

Figure 3A-10. 1,3-Butadiene Annual Absolute Change in Concentration Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022

3A.1.4 Acrolein

Figure 3A-11. Acrolein Annual Percent Change in Concentration Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022
683

Figure 3A-12. Acrolein Annual Absolute Change in Concentration Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022

3A.2 Seasonal Change Ambient Concentration Maps for Air Toxics using the

RFS1 Reference Case

The following section presents maps of seasonal changes in ambient concentrations of

modeled air toxics in 2022 using the RFS1 reference case compared to the RFS2 control case.
684

3A.2.1 Acetaldehyde

Figure 3A-13. Spring Changes in Acetaldehyde Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes

and (right) Absolute Changes (μg/m3)

Figure 3A-14. Summer Changes in Acetaldehyde Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes

and (right) Absolute Changes (μg/m3)
685

Figure 3A-15. Fall Changes in Acetaldehyde Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-16. Winter Changes in Acetaldehyde Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes

and (right) Absolute Changes (μg/m3)
686

3A.2.2 Formaldehyde

Figure 3A-17. Spring Changes in Formaldehyde Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes

and (right) Absolute Changes (μg/m3)

Figure 3A-18. Summer Changes in Formaldehyde Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes

and (right) Absolute Changes (μg/m3)
687

Figure 3A-19. Fall Changes in Formaldehyde Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-20. Winter Changes in Formaldehyde Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes

and (right) Absolute Changes (μg/m3)
688

3A.2.3 Ethanol

Figure 3A-21. Spring Changes in Ethanol Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-22. Summer Changes in Ethanol Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
689

Figure 3A-23. Fall Changes in Ethanol Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-24. Winter Changes in Ethanol Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
690

3A.2.4 Benzene

Figure 3A-25. Spring Changes in Benzene Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-26. Summer Changes in Benzene Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
691

Figure 3A-27. Fall Changes in Benzene Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-28. Winter Changes in Benzene Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
692

3A.2.5 1,3-Butadiene

Figure 3A-29. Spring Changes in 1,3-Butadiene Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes

and (right) Absolute Changes (μg/m3)

Figure 3A-30. Summer Changes in 1,3-Butadiene Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes

and (right) Absolute Changes (μg/m3)
693

Figure 3A-31. Fall Changes in 1,3-Butadiene Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-32. Winter Changes in 1,3-Butadiene Ambient Concentrations Between the

RFS1 Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes

and (right) Absolute Changes (μg/m3)
694

3A.2.6 Acrolein

Figure 3A-33. Spring Changes in Acrolein Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-34. Summer Changes in Acrolein Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
695

Figure 3A-35. Fall Changes in Acrolein Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-36. Winter Changes in Acrolein Ambient Concentrations Between the RFS1

Mandate Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

3A.3 Seasonal Change Ambient Concentration Maps for Air Toxics using the

AEO Reference Case

The following section presents maps of seasonal changes in ambient concentrations of

modeled air toxics in 2022 using the AEO 2007 reference case compared to the RFS2 control

case.
696

3A.3.1 Acetaldehyde

Figure 3A-37. Spring Changes in Acetaldehyde Ambient Concentrations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-38. Summer Changes in Acetaldehyde Ambient Concentrations Between the

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
697

Figure 3A-39. Fall Changes in Acetaldehyde Ambient Concentrations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-40. Winter Changes in Acetaldehyde Ambient Concentrations Between the

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
698

3A.3.2 Formaldehyde

Figure 3A-41. Spring Changes in Formaldehyde Ambient Concentrations Between the

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-42. Summer Changes in Formaldehyde Ambient Concentrations Between the

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
699

Figure 3A-43. Fall Changes in Formaldehyde Ambient Concentrations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-44. Winter Changes in Formaldehyde Ambient Concentrations Between the

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
700

3A.3.3 Ethanol

Figure 3A-45. Spring Changes in Ethanol Ambient Concentrations Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right)

Absolute Changes (μg/m3)

Figure 3A-46. Summer Changes in Ethanol Ambient Concentrations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
701

Figure 3A-47. Fall Changes in Ethanol Ambient Concentrations Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right)

Absolute Changes (μg/m3)

Figure 3A-48. Winter Changes in Ethanol Ambient Concentrations Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right)

Absolute Changes (μg/m3)
702

3A.3.4 Benzene

Figure 3A-49. Spring Changes in Benzene Ambient Concentrations Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right)

Absolute Changes (μg/m3)

Figure 3A-50. Summer Changes in Benzene Ambient Concentrations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
703

Figure 3A-51. Fall Changes in Benzene Ambient Concentrations Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right)

Absolute Changes (μg/m3)

Figure 3A-52. Winter Changes in Benzene Ambient Concentrations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
704

3A.3.5 1,3-Butadiene

Figure 3A-53. Spring Changes in 1,3-Butadiene Ambient Concentrations Between the

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-54. Summer Changes in 1,3-Butadiene Ambient Concentrations Between the

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
705

Figure 3A-55. Fall Changes in 1,3-Butadiene Ambient Concentrations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-56. Winter Changes in 1,3-Butadiene Ambient Concentrations Between the

AEO 2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
706

3A.3.6 Acrolein

Figure 3A-57. Spring Changes in Acrolein Ambient Concentrations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)

Figure 3A-58. Summer Changes in Acrolein Ambient Concentrations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
707

Figure 3A-59. Fall Changes in Acrolein Ambient Concentrations Between the AEO 2007

Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and (right)

Absolute Changes (μg/m3)

Figure 3A-60. Winter Changes in Acrolein Ambient Concentrations Between the AEO

2007 Reference Case and the RFS2 Control Case in 2022: (left) Percent Changes and

(right) Absolute Changes (μg/m3)
708

3A.4 Air Toxics Population Metrics using the AEO Reference Case

The following section presents population metrics for the modeled air toxics in 2022 using

the AEO 2007 reference case compared to the RFS2 control case including the estimated

aggregated populations above and below reference concentrations for noncancer effects, and

population living in areas with increases or decreases in concentrations of various magnitudes.

Table 3A-1.

Populations Exposed to Ambient Concentrations of Air Toxics above and below

Reference Concentrations for Noncancer Health Effects in 2022 with RFS2

Population-weighted

Concentration

(Annual Average in μg/m3)

National Population above RfC

(Annual Average) CAS No.

RFS2

AEO

2007 Diff. RFS2

AEO

2007 Diff.

Acetaldehyde 75070 1.590 1.613 -0.023 0 0 0

Acrolein 107028 0.017 0.017 -0.0001 92,452,143 94,087,145 -1,635,002

Benzene 71432 0.520 0.527 -0.007 0 0 0

1,3-Butadiene 106990 0.022 0.023 -0.208 0 0 0

Ethanol 64175 1.521 1.112 0.409 - - -

Formaldehyde 50000 1.549 1.555 -0.006 0 0 0
709

130,779

118,204

50 7,065 28,490 302,871 1,164

81 7,978 185,190 6,952 5,037 1,934 1,606 54 39

355 25,775 105,740 82,829 1,413 1,682 1,340 2,206 99

1,732 21,317 207,436 109,167

1,196 18,599 41,589 88,035 189,607 562 59

86 1,786 12,329 267,974 54,635 2,837

Ethanol

Acetaldehyde

Formaldehyde

Benzene

1,3-Butadiene

Acrolein

≤ -100 >-100 to

≤-50

>-50 to

≤-10

>-10 to

≤-5

>-5 to

≤-2.5

>-2.5 to

≤-1

>-1 to

<+1

≥+1 to 

<+2.5

≥+2.5 to 

<+5

≥+5 to 

<+10

≥+10 to 

<+50

≥+50 to 

<+100

≥ +100

Percent Change in Ambient Concentration from AEO 2007 to RFS2

Population (in Thousands) Impacted by Changes in Annual Ambient

Concentrations of Toxic Pollutants with RFS2

.

1

.

4

.

3

.

6

.

12

.

10

Figure 3A-61. Number of People Impacted by Changes in Annual Ambient Concentrations or Toxic

Pollutants by Percent Change Brackets, AEO 2007 Reference Case Compared to the RFS2 Control Case
710

Chapter 4: Impacts on Cost of Renewable Fuels,

Gasoline, and Diesel

4.1 Renewable Fuel Production Costs

4.1.1 Ethanol Production Costs

4.1.1.1 Corn Ethanol

Corn ethanol costs for our work were estimated using a model developed by USDA that

was documented in a peer-reviewed journal paper on cost modeling of the dry-grind corn ethanol

process.1087 The USDA model considers a 40 MMgal/yr corn plant producing ethanol with a

primary co-product of distillers dried grains with solubles (DDGS). The ethanol yield used in

the model is 2.76 gallons per bushel with 2.0% gasoline denaturant. The model is based on work

done in chemical process simulation software to generate equipment sizes, stream flowrates, and

material and energy balances. These results can then be put together with feedstock, energy, and

equipment cost information in a spreadsheet format to generate a per-gallon cost estimate.

For our primary case scenario, we used corn prices of $3.60/bu in 2022 with

corresponding DDGS prices of $124.74/ton (all 2007$). These estimates are taken from

agricultural economics modeling work done for this proposal using the Forestry and Agricultural

Sector Optimization Model. Energy prices also play a significant role in the cost of ethanol

production. For this we relied on the AEO 2009 report for projections of energy costs in 2022

and intermediate years of interest. According to the AEO 2009 updated report the relevant costs

are as follows: $7.75 per MMBTU natural gas, $2.57 per MMBTU coal, $30.32 per MMBTU

gasoline (~$3.49 per gallon), and $19.31 per MMBTU electricity ($0.066 per kWh). All of these

prices are in 2007 dollars.

Using the USDA models and the feedstock and energy prices mentioned above we were

able to generate a per gallon cost of ethanol production from a dry mill plant that produces dry

DGS, uses natural gas as it’s primary fuel source, and utilized no advanced technologies. We

did, however, assume that by 2022 the combination of process improvements and more efficient

boilers and motors allow the plant described here to produce ethanol with an energy input of

28,660 BTU natural gas per gallon and 2,251 BTU of electricity per gallon. These energy use

values are described in more detail in Section 1.5.1.3. The only modification that was made to

the USDA model, other than updating the energy and feedstock costs in line with our 2022

projections, was to change the energy demand of the plant to match our projections224. The

projected cost of ethanol production from this modified USDA model was $1.63 per gallon. For

this scenario corn feedstock minus DDGS sale credit represents about 54% of the final per-gallon

cost, while utilities, facility, chemical and enzymes, and labor comprise about 22%, 13%, 7%,

and 4%, respectively. Figure 4.1-1 shows the cost breakdown for production of a gallon of

ethanol. Note that this production model does not account for the cost to ship the DDGS. Those

costs are external and are expected to increase the price of DDGS the further an end user is

located from the plant. While we do not expect this to be the average cost of ethanol production

224 An Excel spreadsheet showing a summary of the outputs of this model and the modifications that were made has

been placed in the docket (EPA-HQ-OAR-2005-0161- 2726).
711

in 2022 it serves as a baseline cost to which the cost impacts of different types of fuel used for

primary process energy, new technologies, and DGS drying will be applied below.

Figure 4.1-1.

Cost Breakdown of Natural Gas Dry Mill Corn Ethanol Production (2007$).

The price of energy can vary greatly depending on the source of the energy. We expect,

therefore, that the cost of ethanol production would also vary depending on whether the

production facility uses natural gas, coal, or biomass as its primary thermal energy source. In

order to determine the impact that different fuel sources had on the cost of ethanol production in

2022 we first had to project how much of the corn ethanol industry will use each fuel type in

2022. For these projections we relied on our own current industry characterization as well as

projections made by Steffen Mueller of the University of Illinois at Chicago. The resulting mix

of primary fuel type used in the corn ethanol industry is shown in Table 4.1-1 below.
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Table 4.1-1.

Breakdown of fuel types used in estimating production cost of corn ethanol

Fuel Type Total by Plant Type

Plant Type Biomass Coal Natural Gas Biogas All Fuels

Coal/Biomass Boiler 11% 0% - - 11%

Coal/Biomass Boiler +

CHP

10% 4% - - 14%

Natural Gas Boiler - - 49% 14% 63%

Natural Gas Boiler +

CHP

- - 12% - 12%

Total by Fuel Type 21% 4% 61% 14% 100%

To determine per-gallon cost impact of coal, biomass, and biogas as a process energy

source we relied on the modeling work done by the USDA. The USDA modeled dry mill corn

ethanol plants using both natural gas and coal as a primary energy source. Their models take

into account the differences in capital costs that result from the differences in materials handling

and boiler types necessary to use gaseous and solid fuels. We assumed that on average, coal and

biomass combustion systems would have the same capital cost due to similarities in feed, ash

handling, and emission controls; the same argument can be made for use of biogas combustion

relative to natural gas combustion (excluding the digesters or other source). Table 4.1-2 shows

the impact that different primary fuel sources have on the overall cost per gallon of corn ethanol

production. The overall impact of using different fuel types is very small, less than $0.01 per

gallon. Thus, a change in process fuel type has very little impact on the projected future cost of

corn ethanol.

Table 4.1-2.

Breakdown of cost impacts by fuel type used in estimating production cost of corn ethanol

Fuel Type Total by Plant

Type

Plant Type Biomass a Coal Natural Gas Biogas b All Fuels

Coal/Biomass Boiler +$0.009 +$0.009 - - -

Coal/Biomass Boiler +

CHP

-$0.021 -$0.021 - - -

Natural Gas Boiler - - baseline +$0.00 -

Natural Gas Boiler +

CHP

- - -$0.032 - -

Total by Fuel Type - - - - $-0.006

Table 4.1-2 shows that for our cost analysis we made the assumption that biomass firing

has the same overall cost impact on ethanol production as coal firing. One reason for this is that

our analysis of biomass feedstock costs suggests a range of $72 per ton in future years which is

comparable to the cost of coal supplied to non-electric-power industries after transportation is

included.1088 Wood and stover biomass has on average approximately 85% of the energy content

of coal on a mass basis, varying by type of biomass and coal, again suggesting that they are

comparable on an energy per mass basis.1089 Nevertheless, we still project that biomass will

displace some coal in the future. If biomass transportation and storage costs are small it is
713

plausible that some ethanol producers near biomass sources (such as the Midwest and Southeast)

may have a cost incentive to transition from coal to biomass for process heat. In addition,

ethanol plant owners may want to improve their greenhouse gas performance to increase

capacity.

Similarly, for our cost analysis we made the assumption that biogas combustion for

process heat would have the same cost impact on ethanol production as natural gas combustion.

Use of biogas is somewhat different from biomass in that it would require some capital

investment for on-site anaerobic digesters and related feedstock and gas handling equipment.

However, we anticipate the digester feedstock itself would have very low or no cost, thus it is

reasonable to assume that the ongoing operating costs besides capital would be considerably less

than purchasing natural gas. As with biomass combustion, most plants utilizing biogas would

take advantage of situations such as co-location with feedlots or MSW facilities where suitable

biomass resources are available.

Another factor that we expect to have a significant impact on the cost of ethanol

production in 2022 is the development and adoption of new technologies. There are several new

technologies currently available or under development that reduce the energy requirements of

ethanol production facilities. These include more efficient boilers, motors and turbines, raw

starch hydrolysis, corn fractionation, corn oil extraction, and ethanol membrane dehydration.

These technologies, and their impact on the projected average energy usage of an ethanol plant in

2022, are discussed in section 1.5.1.3. In addition to reducing cost by decreasing an ethanol

plant’s energy demand, two of these technologies, corn fractionation and corn oil extraction,

produce new co-product streams that also have an impact on the cost of ethanol production. We

have adjusted the USDA cost model to reflect the impact that reduced energy usage of ethanol

plants in 2022 and new co-products have on the average cost of ethanol production225. The

impact of these technologies is outlined in Tables 4.1-3 and 4.1-4 below.

Table 4.1-3.

New Technology Impacts on Corn Ethanol Cost

Capital Cost

(40MMGY)

Capital Charge

(per gallon)

Operating

cost change

New coproduct profitAdditional

Profit

More Efficient

Boilers, etc.

None (included

in baseline)

N/A N/A N/A $0.00

Raw Starch

Hydrolysis

$0 $0.00 -$0.066 N/A $0.066

Corn

Fractionation

$14,000,000 $0.016 -$0.003 $0.106 $0.093

Corn Oil

Extraction

$5,100,000 $0.019 -$0.037 $0.060 $0.079

Membrane

Separation

$1,500,000 $0.006 -$0.070 N/A $0.064

225 As an example, the spreadsheet with adjusted values for corn oil extraction has been placed in the docket (EPAHQ-OAR-2005-0161-2727).
714

Table 4.1-4.

Breakdown of cost impacts by technology for estimating production cost of corn ethanol

Technology Percent of Plants

Adopting Technology

Cost Impact (Change

from Baseline)

Weighted

Cost Impact

More Efficient

Boilers/Motors/Turbines

100% Included in Baseline $0.00

Raw Starch Hydrolysis 22% -$0.066 -$0.015

Corn Fractionation 20% -$0.093 -$0.019

Corn Oil Extraction 70% -$0.079 -$0.055

Membrane Separation 5% -$0.064 -$0.003

Total N/A N/A -$0.092

Whether or not the distillers grains and solubles (DGS) are dried also has an impact on the

cost of ethanol production. Drying the DGS is an energy intensive process and results in a

significant increase energy usages as well as cost. The advantages of dry DGS are reduced

transportation costs and a product that is less susceptible to spoilage, and can therefore be sold to a

much wider market. If the DGS can be sold wet, the cost of ethanol production can be reduced by

$0.083 per gallon. A 2007 survey of ethanol producers indicated that 37% of DGS were being sold

wet. We anticipate that this percentage of wet DGS will remain constant in 2022. The net cost

impact of selling 37% of the DGS wet is an average cost reduction of $0.031 per gallon.

The effect of plant scaling on production cost can be estimated by applying an

engineering scaling factor to all plant equipment. In past rulemakings involving modifications to

refineries we have used a material scaling factor of 0.65. This factor is applied as an exponent to

the ratio of the new size to the original size, the result of which is then multiplied by the original

capital cost. The fact that this figure is less than 1.0 reflects the per-unit or per-gallon savings

that is often realized when processes are scaled up. However, there is information suggesting

that a general factor may be considerably higher for ethanol plants. A factor of 0.84 was put

forth in a recent publication on dry mill ethanol production.1090 Using this larger factor, we find

that the change in per-gallon production cost due to economies of scale is very small over the

range of typical plant sizes, on the order of $0.02 between 40 and 100 MMgal/yr. Thus, in

computing production costs for this rulemaking we chose to ignore effects of any changes

average plant size. A recent study has also indicated that the co-location of corn and cellulosic

ethanol plants may result in reductions of the cost of production for both the corn and cellulosic

ethanol1091. We have not been able to incorporate these findings into our evaluation due to time

constraints; however we do not expect that they would have a large impact on the overall cost of

corn ethanol production.

In order to generate a cost estimate for the production of corn ethanol in the year 2022 the

cost impact of each of these factors, primary fuel type, advanced technologies, and DGS drying,

were applied to the baseline cost produced by the USDA model. When each of these cost

reduction have been applied to the baseline cost, the result is a projected cost of production for

corn ethanol of $1.50 per gallon in 2022. As with the energy and input costs, this production

cost is also in 2007 dollars. We believe this number is an accurate projection of the cost of
715

ethanol in 2022 based on the best available information. For a summary of the cost analysis,

including the baseline cost and all the adjustments, see Table 4.1-5 below.

Table 4.1-5.

Average Ethanol Cost of Production in 2022

Baseline Cost of Production (Natural Gas,

no new technologies, 100% dry DGS)

$1.627/gal

Fuel Type Cost Impact -$0.006/gal

New Technology Cost Impact -$0.092/gal

DGS Drying Cost Impact -$0.031/gal

Average Cost of Ethanol Production (2022) $1.499/gal

4.1.1.2 Cellulosic Ethanol

4.1.1.2.1 Feedstock Costs

In order to allow for an accurate estimate of the cost of production for cellulosic biofuels

in 2022 we must first determine the cost of the cellulosic feedstocks. We relied on the Forest

and Agriculture Sector Optimization Model (FASOM) to project the roadside cost of agricultural

residues, energy crops, and wood residues for 2022. For more details on the FASOM model see

Chapter 5 of the RIA. FASOM does not model MSW costs. We therefore relied on

conversations with companies who intend to use MSW as a renewable fuel feedstock, as well as

our own analysis of the necessary steps required to acquire appropriate feedstock streams from

MSW to help inform our feedstock cost estimates. In order to validate the reasonableness of the

FASOM cost estimates we also conducted an internal assessment of the potential agricultural

residue, energy crop, wood residue, and MSW feedstock systems. The description of this

analysis is discussed in Section 4.1.1.2.2.

To each of these roadside costs we added the cost of transportation and secondary storage

where appropriate using a tool we developed for this purpose. The framework of this tool is

discussed in Section 1.3.3 and a more detailed discussion of the assumptions and equations used

in this tool can be found below. Table 4.1-6 shows a summary of the individual roadside costs

for each of the feedstock sources, as well as average costs for storage and transport and grinding

of the materials. A weighted average cost, based on the percentage of the overall feedstock

supply each of the categories is expected to represent according to the FASOM model, is also

given.
716

Table 4.1-6.

Summary of Cellulosic Feedstock Costs

Ag Residue Switchgrass Forest Residue MSW

36% of Total

Feedstock

49% of total

Feedstock

1% of Total

Feedstock

15% of Total

Feedstock

Mowing,

Raking, Baling,

Hauling,

Nutrients and

Farmer

Payment

$34.49/ton

Land Rent,

Mowing,

Raking, Baling,

Hauling,

Nutrients and

Farmer

Payment

$40.85/ton

Harvesting,

Hauling to

Forest Edge,

$20.79/ton

Sorting,

Contaminant

Removal,

Tipping Fees

Avoided

$15/ton

Hauling to Secondary Storage, Secondary Storage, Hauling to Plant

$21.53/ton (average)

Grinding

$11/ton

Total

$67.42/ton

Crop Residue Costs

The FASOM agricultural econometric model described in Chapter 5 estimated the

roadside price for corn stover, which we used to be representative of all agricultural residues.

The FASOM model accounted for harvesting, shredding, raking, baling and hauling the corn

stover to the farm edge, and replenishing the soil with nutrients. It predicts a roadside price of

$34.49 per dry ton in 2022 for corn residue. In order to validate FASOM’s cost estimate we also

performed an analysis of a possible agricultural residue harvest system. We based our analysis

on a Purdue University study of the logistics of corn stover storage and transportation. Our

analysis, described in Section 4.1.1.2.2, predicts a roadside cost for corn stover ranging from

$44.97 to $46.20 per dry ton, depending on the size of the farms from which the stover is

harvested. The FASOM cost is approximately $10 per ton lower than the price calculated in our

analysis. While this is a significant price difference it is in the same vicinity as the FASOM

estimate, and it is not unreasonable to expect that advances in technology and changes in

harvesting practice for corn stover and other agricultural residues will significantly reduce the

cost for these feedstocks. For all of the biofuel production cost analysis work, the FASOM price

was used as the projected price of agricultural residue. To this roadside cost was added the cost

of transportation and secondary storage as calculated by the tool discussed in Section 1.3. A

detailed description of our analysis of a likely agricultural residue harvest system using existing

machinery can be found below.
717

Energy Crops

The FASOM model predicts a roadside cost for switchgrass and similar energy crops of

$40.85 per dry ton in 2022. While this costs is higher than the cost of agricultural residues the

delivered cost of energy crops is expected to be slightly lower because of the lower

transportation and secondary storage costs associated with the higher production density of

energy crops. The $40.85 per dry ton roadside cost has been used in our cost analysis of biofuel

production, with appropriate transportation and storage costs added using the tool described in

Section 1.3. See Table 4.1-6 for a summary of the expected delivered costs of various

feedstocks.

Wood Residue

The FASOM model estimates costs for two different types of wood residues, hardwood

logging residue ($21.16 per dry ton) and softwood logging residue ($18.37 per dry ton). We

decided to use $19.77 per dry ton, an average of these two prices, as the roadside cost of wood

residue for our analysis of biofuel production costs. Despite the low roadside cost of wood

residues, FASOM predicts that few biofuels production facilities (less than 1%) will use wood

residues. We believe that the reason for this is that the high transportation costs for wood residue

in the FASOM model cause the delivered cost of wood residue to be relatively high. The

FASOM model allowed biofuel producers to use only one type of feedstock (wood residue,

agricultural residue, energy crops, or MSW). Therefore, in order for a facility to use wood

residues, the entire feedstock supply must come from wood residues. Logging operations are

less likely to be concentrated in a small area than other potential feedstock supply systems such

as agricultural residues or energy crops. The result is that in order to supply a 100 million gallon

per year biofuel production plant (the size specified in the FASOM model) with sufficient

feedstock from wood residues the residues must be transported long distances. In most cases

these high transportation costs outweighed the low roadside costs despite the fact that no

secondary storage will be required for wood residues. If this assumption were relaxed and the

FASOM model allowed biofuel production facilities to use locally available wood residues in

combination with other feedstocks such as agricultural residues, energy crops, or MSW, we

believe that much more wood residues would be selected for biofuel production. At this point,

however, it is not clear whether biofuel production facilities would require uniform feedstock or

be able to process a diverse feedstock stream. This uncertainty was one of the factors in our

decision to use the FASOM estimates for our cost analysis work.

Municipal Solid Waste

Unlike the other three sources of cellulosic feedstock, the FASOM model does not

predict a roadside cost for MSW. We therefore relied on our own analysis, together with input

from the Office of Solid Waste and conversations with companies who intend to use MSW as a

feedstock for producing biofuels. One of the biggest costs associated with using MSW as a

biofuel feedstock is the cost to sort the waste material. Some materials, such as metals and

contaminated materials, must be removed so that they do not interfere with the biofuel

production process. Other materials, such as paper or plastics, may also be separated due to their

value as recovered materials or in order to increase the renewability content of the resulting fuel.
718

The Office of Solid Waste has estimated that sorting costs will likely be $20 - $30 per ton. In

addition to sorting costs, the biofuel producer would also have to pay for the transportation and

disposal of unusable material. These costs may be relatively high due to the nature of this

contaminated material. Offsetting these costs would be tipping fees received by the biofuel

producer, which we estimate would be in the $30 per ton range.

In addition to our own analyses presented in Section 4.1.1.2.2, we also contacted companies

that intend to use MSW as a feedstock for biofuel production. In confidential conversations

these companies indicated that they believed that MSW would be available, at least initially, at

close to zero net cost, after accounting for the receipt of tipping fees and the sale of recoverable

materials. One company told us that whether the MSW was sorted or unsorted was not expected

to make much of a difference from a cost perspective, as they expected that the higher tipping

fees received for unsorted MSW and the money received from the sale of the recovered materials

would pay for the cost of sorting. We believe that while these costs may be accurate, they are

likely only representative of the cheapest and most readily available sources of MSW. It is likely

that as more biofuel producers seek to use MSW as a feedstock, the cost of this feedstock source

will increase. Competition from waste to energy companies may be another driver for the cost

increase of MSW. Taking all this into account, we have conservatively estimated that the

average cost of MSW will be $15 per dry ton in 2022. While this is an admittedly conservative

estimate, MSW remains the cheapest source of feedstock for the production of biofuels. More

details of our cost assessment for MSW can be found below.

4.1.1.2.2 EPA Internal Assessment of Potential Roadside Cellulosic Feedstock Costs

While the FASOM model provides an estimate for the roadside costs of agricultural

residue, energy crops, and wood residue, we were also interested in performing our own internal

assessment of the potential roadside cost of these feedstocks. This assessment served as a second

estimate for the costs of these feedstocks, and allowed us to verify the costs generated by

FASOM. They also allowed us to have a better understanding of the costs of each of the steps in

the harvest process. In each of these cases we found that our own assessments matched

reasonably well with the roadside costs reported by FASOM. Our internal assessment of the

roadside costs of crop residue, energy crops, wood residue, and MSW is shown in detail in the

following sections.

Crop Residue

We could have used any of the crop residues as an example feedstock in the following

discussion, since similar logistics apply to all of them. We chose to use corn stover, e.g., the

stalks, leaves, and cobs that remain following grain harvest, since it is likely to represent a

significant portion of cellulosic feedstocks in the future. Since there is no equipment specifically

designed to harvest corn, the system we describe below uses combines, mowers, rakes, balers,

and bale haulers already in use for harvesting hay or straw. Differences in stalk or stem diameter

and density, bale density, moisture content, machine field speeds and efficiencies are a few

things that make it relatively more difficult to harvest stover than hay or straw. One of the main

concerns is that the density of the large stover bales, whether round or rectangular, can be as
719

little as one-half that of similar dimension hay bales, which usually translates into higher

transportation costs.1092

Most biomass feedstocks must be harvested, stored, and transported to a processing

facility before they can be converted into ethanol. At present, there are no commercial sized

cellulosic ethanol plants in the U.S. Likewise, there are no commercially proven, fullyintegrated feedstock supply systems dedicated to providing any of the crop residues or other

feedstocks to ethanol facilities of any size. We emphasize ‘integrated feedstock supply systems’

because logistically the delivery of a feedstock to a processing facility will require the planning,

executing, and controlling of several different, closely integrated operations, e.g., feedstock

harvesting, gathering, storing, and moving by road and rail. Apart from the large numbers and

wide variety of equipment, these operations will require professional and technical support

services and personnel such as office space, staff, and office equipment such as computers and

printers. Also, engineers, light- and heavy-duty equipment operators, vehicle maintenance

personnel and repair and storage facilities for tractors, rakes, balers, loaders, and trucks and

trailers, as well as transportation infrastructure planning and management.

Ordinarily, to determine the operational sufficiency and efficiency of such a system, we

would ‘analyze’ it. We would first break it down into its component parts or essential features

and then study them, e.g., how much they cost, and how and/or whether they operate efficiently

within the ‘system.’ However, no such system currently exists. Therefore, we ‘synthesized’ a

feedstock supply system in order to analyze it. We used a Purdue University, School of

Industrial Engineering simulation study of corn stover logistics from satellite storage to an

ethanol plant, to set up our feedstock harvesting and gathering operation. Purdue’s notional

cellulosic ethanol plant was to be constructed next to an actual existing corn grain plant in

northern Indiana. They used discrete event simulation software and GIS tools to study the

transportation logistics associated with supplying the conversion facility directly from satellite

storage. They identified 785,200 available acres out of 848,453 potential acres, on 2,052 actual

farms, of 200-, 400-, or 800-acres, in 12 northern Indiana counties within 50-miles of the

production facility (they disregarded fields or farms of less than 200-acres). We reproduced their

original table as Table. 4.1-7.
720

Table 4.1-7.

Feedstock Availability at Various Distances From South Bend, IN

Average Farm Size

Average 200 400 800

Distance County ActualAcres

Cumulative

Acres Acres

Available

Acres

Cumulative

Acres

bales/

day

bales/day

/farm set

12 St Joseph 69 69 74 51 35 63 63 4,956 4,956

25 Elkhart 150 219 90 47 23 55 118 4,328 10,257

Marshall 110 52 41 76 194 5,928

30 La Porte 113 332 113 73 70 108 302 8,453 8,453

35 Starke 60 392 34 39 41 55 357 4,328 4,328

40 Kosciusk

o

93 485 108 66 43 82 439 6,461 6,461

45 Lagrange 132 617 53 47 20 45 485 3,560 9,049

Fulton 86 66 43 70 555 5,489

50 Porter 225 842 76 59 34 66 621 5,175 18,224

Noble 115 47 21 59 679 4,595

Pulaski 39 72 64 108 787 8,453

842 998 619 435 787 61,726 61,728

We initially assumed that the stover had been harvested (square bales), gathered, fieldtransported, and stored at seven-satellite storage areas located near the corn fields.1093 However,

upon further study, we determined the counties that Purdue combined into each of the farm sets,

weren’t anywhere near each other. In reality, it would have been far too costly to gather all the

bales from the Porter, Noble, and Pulaski counties into one site, because these three counties are

actually separated by other counties. Rather than try to construct seven satellite storage sites, we

constructed a site at the center of each county. This was done in order to estimate the cost to

collect the bales from all the fields in each county. We determined that the distance from the

center of each of the Porter, Noble, and Pulaski counties, as well as the other two so-called

farm-sets, to the ethanol plant was about equal, so regardless of whether we treat them as single

sites, the transport costs for the bales to the ethanol plant will be the same. We ‘synthesized’ the

feedstock system to harvest, gather, field transport, and store stover bales at the 11-notional

satellite storage units rather than the seven farm-set units used in the Purdue transportation model

(our study was not done in conjunction with the Purdue study; rather we used their

information/data as the basis from which to synthesize our notional operation). The format we

chose to analyze was to shred, rake, square-bale, gather, field-side, and then load and haul the

bales to satellite storage; then, as needed, haul the bales to the processing plant.

Rather than guess at how such a system should look and function, we carefully studied

several similar systems that were put forward by various agricultural and biological experts.1094,

1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102 We used the American Society of Agricultural and Biological

Engineers (ASABE), 2007 Standards, Engineering Practices, and Data as the primary source for

our equipment capital and operating cost estimates. It has a machinery management section,

ASE EP496.3, FEB2006, devoted to providing helpful information in making management

decisions involving machine power requirements, capacities, cost, selection, and replacement, as

well section ASAE D497.5 FEB2006, with data which includes representative values of farm

machinery operating parameters, to aid managers, planners, and designers in estimating the

performance of field machines. These data are intended for use with ASAE EP496 (some data

are also presented in equation form for easier use with computers, etc.).1103 We used these

sections along with other examples by other experts to estimate the machinery capital and
721

operating costs for our analysis.1104, 1105, 1106, 1107, 1108 We were able to get some machinery purchase

prices from vendors whose identities are confidential. We reduced the equipment listed price by

10% to determine the purchase price, a standard industry estimating practice. Otherwise, most of

the data used to calculate machinery costs were generated with equations and appropriate data

from the ASABE 2007 Standards. We used the equipment, list and purchase prices, along with

their power and size estimates, with the suggested data and equations, mentioned previously, to

calculate the lifetime hours and years, annual use, field efficiency, salvage value, fuel and oil use

and cost (we obtained vendor quotes for oil cost), capital charge, repairs, insurance, housing,

taxes, and labor. We compared our data, where appropriate, with the USDA 2006 Price

Summary, published July 2007.1109 We also compared our results with those generated by the

experts we listed earlier in this paragraph.

The Purdue study was based on supplying a 100-million gallon per year ethanol

production facility, which they assumed would convert the stover-to-ethanol at 72-gallons per

ton; they assumed that 2-dry tons corn stover could be harvested per acre, as did we. We used

National Agricultural Statistical Service (NASS) data to determine the actual corn grain yield in

2005 (the data year for the Purdue study) for the counties studied in the simulation. We

determined how much corn each county produced and from that how much stover was produced,

2,455,000 tons or 3.12 dry tons per acre, with an assumed harvest index (HI) of 1:1 (see Table.

4.1.1.2.2.)1110 HI is based on the assumption that, for a single corn plant, half of the above

ground dry matter is made up of stover and the other half is made up of grain. This is a fairly

common assumption, although more than one group of researchers has found that this 1:1 ratio

may not be the most accurate under some conditions. When considering above ground dry

matter before and after full grain physiological maturation, they found that a stover to grain ratio

of 0.8 to 1 may be more realistic especially when grain moisture is between 18 and 31 percent.1111
722

Table 4.1-8.

NASS Indiana Data and Purdue Data Comparison

USDA-NASS – Counties in State of Indiana - 2005 Purdue Model Year – 2005

Planted Harvested Grain

Yield

Grain

Production Model AvailableAcres

Prorated

Grain

Production

Wet Tons

Stover

Dry Tons

Stover

County acres x

103

acres

x 103

bu/

acre

bu x 106 acres x

103

acres x

103 bu x 106

x 103x 103

St Joseph 71 70 147 10.2 69 65 9.5 266 225

Elkhart 60 53 142 7.6 150 52 7.4 206 174

Marshall 94 89 150 13.4 0 87 13.1 366 309

La Porte 117 112 137 15.3 113 104 14.2 398 336

Starke 61 60 137 8.2 60 56 7.7 214 181

Kosciusko 102 100 149 14.9 93 93 13.8 386 326

Lagrange 55 51 113 5.8 132 45 5.0 141 119

Fulton 90 89 159 14.1 0 78 12.3 345 291

Porter 68 67 137 9.2 225 61 8.4 234 198

Noble 65 62 142 8.8 0 57 8.0 225 190

Pulaski 103 101 152 15.3 0 92 13.9 390 330

855 842 787 3,172 2,455

However, a professor of agricultural engineering at the University of Wisconsin found

that several researchers, going back to 1973 reported a grain mass fraction of 45% to 55% of

total corn crop DM yield. On average, the variations seem to confirm the common rule of thumb

of one unit mass of stover for a unit mass of grain. However, differences among harvesting

methods, stages of maturity, and harvest dates can no doubt lead to much of the variation the

researchers found in this estimate. His research indicates that the ratio of grain dry mass to total

mass increased from about 38% in late August to about 59% in mid-October, during a recent

harvest. Therefore, the stover to total ratio declined from 62% to 41%. During the typical

harvest period in the Upper Midwest when grain moisture is between 20% and 30%, the ratio of

stover to total dry mass was less than 45% and averaged 43%. These results are similar to those

found by others.1112 Mainly, because we have no information upon which to base a reason to use

something different, we chose to use the 1:1 ratio for corn stover to corn grain. We also assumed

56-lbs per wet bushel (15.5% moisture) and 47.3-lbs per dry bushel, for the corn grain, to make

our stover yield calculations.1113 Table 4.1-9 summarizes the general operating parameters for

our study.
723

Table 4.1-9. Operating Parameters

EtOH Operating Year 350-days/yr

On Stream Factor 0.96

Hours per year 8,400-hr/yr

EtOH Production Rate 100,000,000-gal/yr

EtOH Yield 72-gal/dry ton

Feedstock Required

per year 1,389,000-dry tons/yr

per day 3,970-dry tons/day

Expected Dry Matter Loss 11.8%

Feedstock Harvested 1,574,000-dry tons/yr

Feedstock Yield 2-dry tons/acre

Harvest Period

Days, 50-days

Hours per Day 16-hr/day

Harvest Hours 800-hr

Format: Shred/Rake,

Bale - Lg. Sq. - 3'x4'x8'

Field Side - Self-Propelled

Wagon

Satellite Storage –

Pole Barn on Concrete

Transport to EtOH Facility –

Truck & Trailer

At 72 gallons per dry ton, the processing plant would require 1,389,000 dry tons of stover

per year. However, we believe storage and transportation losses can be significant and should be

taken into account. If, as stated in the report, they harvested 2-tons per acre, they actually

harvested 1,574,000 tons of stover, or 64% of the 2,455,000 tons of available dry stover. The

quantity of stover used versus the quantity harvested represents an 11.8% loss.226 Thus, we

assumed that with an 11% loss, we would need to harvest 4,500- dry tons of stover per day,

which by the time it reaches the plant will actually equal 3,970-dry tons – the amount required

per day at the ethanol plant. During a 350-day production year, 1.574-million tons of stover

would have be stored in about 3.5-million, 900-lb bales (Purdue study bale weight), at the

various satellite storage areas. For this study, we assumed all the loss took place between the

satellite storage areas and the ethanol plant, rather than guess what the losses would be at various

points within the harvest/transport scheme.

In the following analysis, we did not account for the extra time or equipment that would

be necessary for inevitable break-downs. Nor was time and equipment factored in for driving

between fields and for weather delays. Stover suppliers face several, in some cases, difficult

problems. At best, the actual harvest period is nearly always too short; winter weather can

suddenly set in, which in some cases may completely stop a potential stover or straw harvest.

Once the grain is harvested and the stalks are mowed, stover usually must be left in the field for

three or four days to dry to below 20% moisture before it’s baled, otherwise spoilage or rot as

well as spontaneous combustion are possible. If it rains, additional time is required for drying

and muddy roads and fields can be badly damaged and the field-soil compacted by the increased

226 We indicated in a previous section that there may be as little as 25% to 50% stover actually available; however,

since we didn’t have the computer software and database Purdue used and therefore couldn’t rerun the simulation,

we chose to use the data we had.
724

heavy-weight harvest and transport equipment traffic. One expert commented that, “If there’s a

rainy harvest, you might as well forget about it. Also, the longer the wet material is left in the

field, there’s more of a chance for microbes to eat away at the hemicellulose.”1114 Delayed baling

also raises the chances of dry matter loss. The stover needs to field-dry, so the stover harvest

can’t actually begin for at least a few days after the grain harvest starts. But, once it begins it can

continue until either it’s finished or until winter weather stops it.

For reasons that weren’t explained in the report, the researchers at Purdue chose 50-days

for the harvest period, which at their 16-hr per day schedule, provided a total of 800-hrs to

complete the harvest and store the stover. In this harvest format (800-hrs), most of the

machinery will be stored for the balance of the year. Crop harvest schedules in the Midwest and

upper-Midwest are determined by the length of the growing season, the time of year when the

crops are mature enough to harvest, and the time when winter weather sets in. Under ordinary

conditions, farmers use their harvesting machinery during just a few weeks each year. During

the past few years, as machinery costs have risen, many farmers have turned to custom

harvesters, that move into an area and harvest several farms. A farmer must always weigh the

differences in the custom rates and what it would cost him to own the equipment and complete

the harvest himself, but then store most of the harvesting machinery for the rest of the year. In

the South, winters are milder and it’s possible to harvest some crops all year long. In such cases,

feedstocks could conceivably be harvested and shipped to a conversion facility on an ‘as needed’

basis; storage costs could be saved and machinery would be used all year long. However, an

important factor, when it comes to harvest machinery, is the usable-life of the equipment. The

more hours used each year, the more often the machine will need to be replaced. A machine

lasts only so many hours, whether it’s used 800-hrs per year or 8,000-hours per year. We

obviously could have arbitrarily chosen some longer period, but in order to maintain at least

some consistency with the Purdue study, we chose to use the 50-day schedule for our study.

This short period means we must harvest and store a full year’s inventory within a few weeks.

Mow, Shred, Rake: Modern corn combines strip most of the leaves from a corn stalk, but

leave up to about half of the stalk standing when they cut it off just below the bottom ear. In the

combine, the corn grain is stripped from the cob, and the top part of the stalk, the leaves, and the

cobs are subsequently discharged out the spreader at the rear of the machine. According to a

group of researchers, at the time of grain harvest, of the total stover dry mass, 16% resides in the

cob, 7% in the husk, 16% in the leaves, and 60% in the stalk fractions. Of the stalk dry mass,

roughly 45% is found in the bottom one-quarter and 80% in bottom one-half of the stalk. If

stover yield is to be maximized, harvesting systems must be developed that allow the bottom half

of the stalk to be fully harvested.1115 We summarized the costs to shred and rake in Table 4.1-10
725

Table 4.1-10. Corn Stover Shredding & Raking Operation

Tractor –

245-hp

FlailShredder -30’Tractor

75-hp MFWD

Wheeled

V-Rake - 20 ft

Equipment Factors

Purchase price $ 144,502 28,733 59,383 3,660

Useful life yrs 11.3 3.1 11.3 15

Discounted Salvage value $ 11,736 0.00 7,861

Annual use hr 800 800 800 800

Fixed Costs $/hr

Depreciation and interest 25.47 14.51 10.06 1.87

Taxes Insurance Housing (THI) $/hr 3.97 0.79 1.63 0.10

Total Fixed Costs $/hr 29.44 15.30 11.69 1.97

Variable Costs

Repairs and maintenance $/hr 19.62 14.05 8.06 1.63

Fuel consumption227 gal/hr 9.6 8 5.6

Fuel and lubrication $/hr 23.82 19.87 13.81 4.27

Operating Interest 1.70 1.21 0.82 0.21

Labor $/hr 15.91 15.91 15.91 4.00

Total Variable Cost $/hr 61.05 51.04 38.60 10.11

Total Costs

Total $/hr 90.49 66.34 50.29 12.08

Equipment capacity MT/hr 32.7 13.27

Total $/ton 4.80 4.70

It will likely be necessary to flail-cut or mow the standing-stalks, and then rake and bale

the windrows. We estimated that it cost about $4.80 per ton of stover for shredding and about

$4.70 per ton for raking.

Bale: As previously discussed, large square bales will likely be the bale-format for this

system, although large round bales could be used. There are currently more round balers than

square balers in use, mainly because large square balers are more expensive. However,

gathering, stacking, and transporting large, round bales is much less efficient. It is difficult to

stack round bales more than about three high, since they tend to deform rather badly, during even

short storage periods; square bale stacks can be stacked up to five or six bales high, which

translates into a more efficient use of storage area as well as more stable stacks that are far less

prone to deformation over extended storage periods. Although large round bales tend to weather

better out in the open, for the reasons just stated as well as those given in the Purdue report, we

used large square bales in this analysis. Table 4.1-11 summarizes the cost of the baling

operation, which we estimate to be $10.87 per ton.1116, 1117

227 We have received comment that it may be inappropriate to assign a fuel usage to both the 245-hp tractor and the

flail shredder. We were unable to determine whether this was the case. The impact of not including such fuel would

be a decrease in the price per ton cost of $0.61.
726

Table 4.1-11. Corn Stover Baling Operation

Tractor -

275-hp

Lg. Sq.

Baler - 3' x 4' x 8'

Equipment Factors

Purchase price $ 147,102 110,723

Useful life yrs 11.3 3.8

Discounted Salvage value $ 7,553 32,455

Annual use hr 800 800

Fixed Costs $/hr

Depreciation and interest 25.93 36.74

Taxes Insurance Housing (THI) $/hr 4.05 3.04

Total Fixed Costs $/hr 29.98 39.78

Variable Costs

Repairs and maintenance $/hr 19.98 45.11

Fuel consumption228 gal/hr 10 8

Fuel and lubrication $/hr 24.84 19.87

Operating Interest 1.71 2.38

Labor $/hr 15.91 10.66

Total Variable Cost $/hr 62.44 78.02

Total Costs

Total $/hr 92.42 117.80

Equipment capacity DMT/hr 19.4

Total $/ton 10.87

Bale Pick-Up & Field Side: It is important to remove the stover bales from off the fields.

Few farmers will tolerate bales left for long periods on their fields, especially if there is a chance

spring planting will be negatively affected. Nor do we expect farmers will allow random piles of

bales left at field edges, for retrieval over the winter and spring months. Aside from the

likelihood that trucks and other equipment would get stuck in muddy roads and fields, thus

slowing down deliveries and running up operating costs, farmers would have little tolerance for

torn-up roads and fields. This may not be a big problem, if the farmer/grower intends to plow a

field in the spring. However, it could be highly problematic for a farmer/grower who “no till”

farms and would be forced to repair ruts and holes in fields and roads before spring planting.

Apart from this, dry matter losses from bales, left out in the open on dirt, can be as high as 10%

to 20%. At harvest time, the bales, regardless of format, must be picked up and hauled either to

a satellite storage site for intermediate storage or hauled directly to the processing plant.

Several variables must be taken into account for bale pickup and the field-side haul

operation that could easily affect the cost. Because the exact location on a field where a bale

lands as it falls from a baler varies according to stover yield and harvest efficiency, there is no

easy or accurate method for predicting the exact location of each bale on the field, either relative

to each other or to the field edges or entry. The distance between bales and the potential

variability in the area, shape, and relative dimensions of each field add to the difficulty of

estimating bale pickup costs. If it was possible to somehow tag each bale with GPS coordinates

as it fell to the ground, theoretically the coordinates could be used in some type of ‘bale

retrieval’ program to optimize the time and pickup distance traveled.1118, 1119

228 We have received comment that it may be inappropriate to assign a fuel usage to both the 275-hp tractor and the

baler. We were unable to determine whether this was the case. The impact of not including such fuel would be a

decrease in the price per ton cost of $1.06.
727

For this study, we used the theoretical stover density on the field, the speed and width of

the harvester to estimate the distance between bales. We used a spreadsheet with these data to

position the bales in a variety of patterns on a notional rectangular 100-acre field. We devised

three or four drive-patterns in which the bales could be retrieved, by using simple visual

inspection. We calculated the time to pick up the bales using each pattern and the average speed

of the self-propelled bale-wagon. There were a few variables for which we couldn’t adjust our

numbers because we simply had no way of knowing their effect. For example, if the field was

furrowed, it seemed that the less time spent driving across the furrows, at, as we assumed a

slower speed than could be traveled along the furrows, the more efficient would be the pick up.

Table 4.1-12 summarizes the information we used for our calculations.

Table 4.1-12. Bale Pickup and Field-Side

Ft. Between Each Bale 490

Pickup One 10-Bale Load - Ft/Load 4,901

Bales/Acre 4.4

Bales/Load 10

Tons/Load 4.5

Loader Speed - mph 7.5

Field Size - Acres 200 400 800

Number of Loads per Field Size 89 178 356

Bales/Field Size - Total 889 1,778 3,556

Miles Traveled per Load 1.74 2.05 2.51

Tons/Hour 26 22 18

Cost per Ton – Pickup & Field-Side $2.82 $3.31 $ 4.05

In any case, using our basic assumptions, the time it took to retrieve a 10-bale load didn’t

vary significantly for any of our plots. Table 4.1-13 presents the operating data for the bale

wagon.
728

Table 4.1-13. Self-Propelled Bale-Wagon

Bale Wagon

Equipment Factors

Purchase price $ 153,716

Useful life yrs 18.1

Discounted Salvage value 6,013

Annual use hr 800

Fixed Costs $/hr

Depreciation and Interest $/hr 20.70

Taxes Insurance Housing (THI) $/hr 4.23

Total Fixed Costs $/hr 24.93

Variable Costs

Repairs and Maintenance $/hr 12.96

Fuel Consumption gal/hr 7

Fuel and Lubrication $/hr 17.39

Operating Interest 1.21

Labor $/hr 15.91

Total Variable Cost $/hr 47.47

Total Costs $/hr 72.40

Total $/hr 72.40

Equipment capacity DMT/hr 23

Total $/ton 3.15

Since the fields were no smaller than 200-acres, we piled the bales at one of the corners,

which we assumed as the field-entry. We calculated the cost to haul bales for each of the 200-,

400-, and 800-acre fields. We assumed the 20-ft rake made 148-passes across the 200-acre field;

209-passes across the 400-ft field; and 295-passes across the 800-ft field. We used the number

of windrows the rake left to determine the number of passes the baler would make. The baler

dropped a bale every 490-ft; to collect a 10-bale load the loader would need to travel 4,900-ft.

We estimated that the average distance every bale would need to travel to the corner of the field,

e.g., the field entry, would be the distance from the field center to the corner (field-entry); we

assumed the fields were square. The loader would need to travel that distance and then return

empty, for the next load. Therefore, each loader would travel 4,900-ft to pickup the load, and

then an additional 4,174-ft for the 200-acre field; 5,903-ft for the 400-acre field; and 8,348-ft for

the 800-acre field to haul to the field edge (corner) and return. We chose to pick up 10-bales per

load with a self-propelled bale wagon with an average speed of about 10-mph. We assumed the

bales would be picked up, transported, and dumped at the field-edge at a cost of $2.82-per ton for

the 200-acre fields; $3.31-per ton for the 400-acre fields; and $4.05-per ton for the 800-acre

fields. We weighted the DM hauled for each field size by the total tons recovered from each

size, to arrive at 23-weighted dry tons/hr hauled for $3.15-per ton

Haul to Satellite Storage: Theoretically, we could store all the bales at the ethanol plant.

If so, we would need to move 3,149,000-bales or about 100-loads per hour for 16 hours each day

during the 50-day harvest. It would require 50-stacks, each, eight-bales wide by 5-bales high, by

1,577-bales (12,615-ft.) long, with 51 x 20-ft aprons and isles, between each stack and along

two-sides of the entire area, plus a 20-ft apron, across the entire front and rear. The area would

total ~33-million square feet or 1.19-square miles.
729

We stored the bales at satellite facilities near the center of each county. As previously

discussed, the Purdue study established ‘farm-sets,’ but did not describe how they were

configured and how bales were to be hauled to each storage site, nor did the study specify

exactly where each storage site was. Rather, they only estimated the distance between each

storage area and the ethanol plant. The following table summarizes the cost factors used to

estimate the costs to haul the bales to satellite storage.

Table 4.1-14.

Haul - Field Side to Satellite Storage

High-Speed

Tractor

Bale

Wagon

2-

Telescopic

Handlers

Equipment Factors

Purchase price $ 133,865 23,851 130,106

Useful life yrs 15 15 12.5

Discounted Salvage Value $ 10,782 4,803 6,490

Annual use hr 800 800 800

Fixed Costs $/hr

Depreciation and Interest $/hr 19.62 3.34 21.48

Taxes Insurance Housing(THI) $/hr 3.68 0.66 3.58

Total Fixed Costs $/hr 23.30 4.00 25.06

Variable Costs

Repairs and Maintenance $/hr 13.63 1.94 15.90

Fuel Consumption gal/hr 6 0 2

Fuel and Lubrication $/hr 14.90 0 9.94

Operating Interest $/hr 1.13 0.09 1.02

Labor $/hr 15.91 7.95 31.82

Total Variable Cost $/hr 45.57 9.98 58.68

Total Costs $/hr 68.87 13.98 83.74

Total $/hr 166.59

Equipment capacity DMT/hr 9.3

Total $/ton 17.91

We assumed that a telescopic loader would load the bales at the field-edges onto 20-bale,

2-axle, 30-ft long wagons, each pulled by a high-speed tractor to the storage area located at the

center of each county, where they were unloaded by another telescopic loader and the bales

stacked for temporary storage. Several variables make the cost of this operation difficult to

estimate. A cursory inspection of the general outline/shape of many of the counties in the study

reveals that they are by no means square. However, to make our calculations manageable, we

assumed they were in order to determine the average distance each bale would need to be

transported to its respective storage area in each county. We estimated that the average distance

any load would travel from any position in the counties, e.g., from the furthest to the nearest,

would be equal to one-half the distance from the corner of the county to its center. We used the

published area of each county, from which we determined the distance from one-corner to the

center; that distance equaled the trip to the storage area and the return. We multiplied each by a

30% winding factor (rather than a straight-line drive, this accounts for turns and other

meanderings).1120 We estimated the operation would cost about $17.91 per ton.
730

Satellite Storage: We assumed each storage unit would consist of a concrete slab with

open sides and pole-supported tin roof. Smooth paved surfaces are safer and make work easier.

Gravel and dirt do not stick to the bottoms of the bales. If winter (wet, muddy) access is

necessary, this cost should be included in the overall costs for storage; particularly from the

highway to the stack. Beyond the need to keep the area around the stack accessible and clean,

there could be problems with local authorities if trucks leaving the property carry significant

quantities of mud onto a public highway. Ordinarily, in an agricultural area a certain amount of

mud is expected to be left on highways during wet weather. However, at the truck and trailer

volumes we’re anticipating, the amount being tracked onto highways and possibly through

municipalities, would increase rapidly. We used 1% of construction costs for upkeep, and 2% of

the construction cost for the storage unit to cover the cost of access. These are incurred costs

within the overall maintenance of stored stacks of biomass. These costs are essentially

insignificant in the overall storage costs, are subject to great fluctuations due to weather and

equipment availability, and, therefore, were rolled up into a percentage of the overall storage

costs of stacked bales. The following table summarizes our storage area construction cost factors

and costs.

Table 4.1-15.

Satellite Storage Construction & Maintenance Costs – For Each of 11-Areas

Land Rent ($/acre/yr) 100

Land Preparation ($/acre) 30,000

Construction ($/sq ft) 3.75

Upkeep – 1% of Construction

($/ton) 0.91

Access – 2% of Construction ($/ton) 1.81

Depreciation Period (yrs) 12

Number

of Bales

Bale

Storage

Area (sq ft)

Bale

Storage

Area (acres)

Tons

Stover

per site

Supply

days

Total

Storage

Cost

St Joseph 280,889 2,943,716 67.6 126,400 28.1 $1,123,463

Elkhart 245,333 2,571,093 59.0 110,400 24.5 $981,252

Marshall 336,000 3,521,280 80.8 151,200 33.6 $1,343,889

La Porte 479,111 5,021,084 115.3 215,600 47.9 $1,916,286

Starke 245,333 2,571,093 59.0 110,400 24.5 $981,252

Kosciusko 366,222 3,838,009 88.1 164,800 36.6 $1,464,768

Lagrange 201,778 2,114,631 48.6 90,800 20.2 $807,044

Fulton 311,111 3,260,444 74.8 140,000 31.1 $1,244,342

Porter 293,333 3,074,133 70.6 132,000 29.3 $1,173,236

Noble 260,444 2,729,458 62.7 117,200 26.1 $1,041,692

Pulaski 479,111 5,021,084 115.3 215,600 47.9 $1,916,286

Total 1,574,400 350 $13,993,510

Total Cost $8.89/ton

With well-paved surfaces, equipment can be maneuvered regardless of weather, and

surfaces can be sloped to enhance drainage. We also assumed the bales would be stored in

multiple stacks, 8-bales wide and 5-bales high, and long enough to accommodate the number of

bales we expect; there would 20-ft aprons along the outside of the stacks and 20-ft isles between

stacks for stacking, stack management, and for general and fire safety.1121 One researcher

determined that the economics of scale, in the current situation, did not really apply. The cost for
731

secondary storage would therefore be approximately equal on a per ton basis regardless of the

size of the biofuel production facility. Based on the numbers above, we estimated that storage

would cost about $8.89 per ton.1122 We used this as the cost per ton for the satellite storage as

well as for storage at the plant.

We used 11-telehandlers at the storage areas. (However, once the harvest is complete the

telehandlers being used for loading and unloading bale wagons during the field-side to storage

area operation could possibly be pressed into transport – load, unload service (see Table 4.1-15).

If so, the cost to use the telehandlers could be reduced from $3.28 to $3.08 per ton.) Plus 11-

telehandlers at the plant to load and unload trucks and trailers that deliver the stover from

satellite storage to the plant; extra telehandler time at the plant will be used to move feedstock as

needed. The following table summarizes the cost associated with the transportation. The

following table summarizes the cost associated with the transportation.

Table 4.1-16. Haul From Satellite Storage to Plant

Class 8

Truck

53-ft Flatbed

Trailer

22-Telescopic

Handlers

Equipment Factors

Purchase price $ 103,839 42,173 130,106

Useful life yrs 20 22 13

Discounted Salvage Value $ 4,025 931 6,490

Annual use hr 5,600 5,600 800

Fixed Costs $/hr

Depreciation and Interest $/hr 1.91 0.74 236.28

Taxes Insurance Housing(THI) $/hr 0.41 0.71 39.38

Total Fixed Costs $/hr 2.31 1.45 275.66

Variable Costs

Repairs and Maintenance $/hr 1.13 3.16 174.90

Fuel Consumption gal/hr 9 0 2

Fuel and Lubrication $/hr 33.95 0 109.34

Operating Interest $/hr 0.37 0.14 11.22

Labor $/hr 17.46 350.02

Total Variable Cost $/hr 52.91 2.31 $645.48

Total Costs $/hr 55.22 3.76 921.14

Total $/hr 58.97 921.14

Equipment capacity DMT/hr 4.5 281

Total, each $/ton 13.10 3.28

Total $/ton 16.38

To transport the bales to the ethanol plant, we calculated the ton-weighted average triptime to be 4.09 hr. We plan to ship 4,497-tons of stover to the plant on each of the 350-operating

days. At 17.5-tons per load, we anticipate there will be about 256-loads per day. Using the 4.09-

ton weighted trip time, we estimated that it would require 63-trucks and trailers to haul 4 x 17.5-

ton per day. The cost of transportation plus loading and unloading is estimated to be $16.38 per

dry ton. The following table summarizes all the costs to harvest, bale, field-side, haul to satellite

storage, store, and haul to the plant.
732

Table 4.1-17.

Ag Residue Cost Summary

Farm-Set Size, acres 200 400 800 Total Tons

Tons per Farm-Set, t 756,4

39 476,402 341,559 1,574,400

Farmer/Grower $/t 10.00 10.00 10.00

Nutrient Replace $/t 11.81 11.81 11.81

Shred $/t 4.80 4.80 4.80

Rake $/t 4.70 4.70 4.70

Bale $/t 10.84 10.84 10.84

Haul – Edge $/t 2.82 3.31 4.05

Total Farm Edge Cost $/t 44.97 45.46 46.20

Haul – SS $/t 17.91 17.91 17.91

Storage $/t 8.89 8.89 8.89

Haul to Ethanol Plant $/t 16.38 16.38 16.38

Field to Plant – Total $/t 43.18 43.18 43.18

Per Farm- Set –

Total $/t 88.15 88.64 89.38

Avg. Total Cost $/t 88.71

We anticipate that by 2022 the industry will improve the efficiency of feedstock

delivery and reduce the cost of feedstocks. The current harvest-system is usually referred to as a

multipass system: the corn grain is first combined, and then the stover is shredded, raked, baled,

and the bales hauled to the field side. Each field-pass adds to the final cost and further compacts

the soil; soil compaction is especially critical if the soil is prone to compaction or in no-till

situations.1123 Because the combine-spreader drops the stover on to the ground, not only are

fewer cobs collected, but dirt, dirt clods, and other debris, including metal, are inevitably

gathered up with the stover by the baler.1124, 1125 Thus, extra effort and money must be expended

to remove the debris before processing can begin, apart from the fact that dry matter is also lost

during this operation. In their 2002 study report, NREL included a wash table to remove dirt and

grit and had magnets to remove tramp iron, e.g., wire, etc. from the stover.1126

According to a few sources, which for reasons of confidentiality, we can’t quote, there

appears to be active interest in restructuring the system we just described to move the

preprocessing (feedstock preparation) forward in the chain, away from the ethanol plant, and

closer to the fields. Including the issues highlighted in the previous paragraphs, a major concern

has to do with the use of standard hay and forage equipment, for which the overall collection

efficiency of stover (ratio of stover collected to the total above-ground stover excluding grain)

using flail choppers, rakes, and balers was less than 30%.1127 Until now, most research has been

based on a multi-pass system similar to the one we synthesized.1128,1129 In addition, the timeliness

for collection (weather concerns) and moisture content issues are major problems associated with

a multi-pass corn stover harvest.1130

The restructuring efforts also include exploring other methods to more efficiently gather

the stover that avoids the need to pick it up from the ground, e.g., gather or catch it before it hits
733

the ground.1131 In one early case, a baler was hitched directly onto a combine, to capture the

combine effluent and square-bale it. The problem was that there was a strict need to limit

moisture to under 20% if bales are to be stored, plus the extra equipment slowed the grain

harvest.1132 Ideally, the stover harvest system should be capable of harvesting stover at any level

of moisture even while the grain is being harvested. All the cobs would be collected, the stover

wouldn’t touch the ground and a controlled amount of residue would be left to meet any

conservation requirements (we believe finding a way to leave the correct amount of residue

behind will be difficult, and should be a top priority).

A modification of the system we previously described would be to use a mobile tub

grinder that could be towed from one satellite storage area to the next. A telehandler would feed

the grinder to directly fill trucks for transport to the production facility. The ‘walking floor,’

rear-dump, or belly-dump trailers would unload the ground-up stover into silos or tanks at the

facility. These silos or tanks could be sized to provide as much feed surge capacity as the facility

required to maintain continuous operation.1133

Again, ideally, the corn stover harvest should be reduced to a single-pass operation

during which the amount of residue left on the field will be less a function of harvest efficiency

and more a function of the farmer/grower and the harvesting company being able to determine

how much residue must be left to maintain soil health. In reality, most of the equipment doesn’t

yet exist that could perform some of the operations we will describe. Nevertheless, we believe

this reflects some of the forward thinking that is currently taking place. For example, a combine

designed specifically for the job must still be constructed. A single-pass harvester would cut the

whole stalk a few inches above the soil, leaving some stalk anchored to the ground. It would pull

the entire plant, e.g., stalks, leaves, and cobs with grain into the combine, where they are mixed

into a single, clean, grain and stover stream. It would then blow the entire stream into tractorpulled grain-carts that run along-side the harvester. It is important to be able to change full carts

for empties without stopping the harvester. As a cart is filled, it is pulled from beneath the

discharge tube, as an empty cart is pulled under it. The full cart is hauled to the field side, where

the harvested material is unloaded directly into bulk ‘walking-floor’ semi trailers, for transport to

a co-op or depot type elevator/facility. After the biomass stream is unloaded, equipment at the

elevator/depot separates the stover from the grain, following which the stover is chopped, dried,

and sent to tanks or silos for intermediate storage. Currently, there are no simple methods for

drying wet corn stover, other than to let it field-dry. However, if the single-pass harvest is to

become a reality, the stover will need to be dried or else stored in much the same way silage is

stored.1134 At harvest, corn grain has a moisture content of 25%, while at the same time, the

stover ordinarily ranges from 35% to well over 50% moisture. There have been studies to

artificially dry corn stover as well as other biomass types; there will likely be changes to the

reported results of these and other studies, but, then we expect advancements and certainly

changes in several parts of the feedstock supply system.1135, 1136, 1137 Given that these changes

take place, the stover, would have flowability characteristics similar to small cereal grains, and

could be moved by standard grain loading and unloading systems into large corrugated steel bins

(silos) for intermediate storage. In this harvest format, the stover is handled by only two

machines before it reaches the roadside and never hits the ground. Dry matter losses should be

significantly reduced.1138
734

Harvesting, storing, and transporting a denser feedstock should offer significant

savings.1139 Using this and other anticipated improvements, it appears possible that in the out

years, e.g., by 2022, corn stover and other residues could be commoditized, much as is the case

with grain, and then purchased by a processor on an as-needed basis.

However, commoditization offers its own set of issues, among which are both tangible

and non-tangible infrastructures. Although tangible infrastructure with regard to ethanol

distribution is discussed in greater detail in Section 4.2, we believe the following comments fit

within the context of our preceding information. The impact of both feedstock and finished

ethanol on rural road, highway and railroad infrastructure is likely to be even greater than the

current and anticipated impact of corn based ethanol. Raw cellulosic feedstocks have lower

levels of concentrated fermentable carbohydrates and therefore require a greater mass of

feedstock to produce an equivalent level of ethanol. Thus, public and private transportation

infrastructure must move a greater volume of feedstock per gallon of ethanol produced. The

magnitude of the impact will depend on the field density of feedstocks near the plant and

whether feedstock densification will make it possible to ship more dense carbohydrate product to

the cellulosic ethanol plant.

Intangible infrastructure is essentially absent for crop residue type cellulosic feedstocks.

Intangible infrastructure includes such things as uniform grade and quality standards, market

price discovery mechanisms, collateral warehouse receipts, regulatory structure and other

marketing institutions. Grain market institutions have been developed and fine-tuned over the

past century which provide corn ethanol plants a decided benefit. Daily price information, as

well as a wealth of crop condition, and supply and demand information from a variety of public

and private sources is available on corn grain. Well known institutions such Uniform Grade and

Quality Standards, FGIS, Grain Warehouse Regulations, Collateral Warehouse Receipts, Trade

Associations, Non-Recourse Government Commodity Loans, and a set of futures markets that

efficiently price grain over time and space are all readily available. This infrastructure is already

in place, tested and readily accessible to corn grain ethanol producers. Although not highly

visible and frequently taken for granted, it plays a critical role in efficient feedstock pricing, risk

management, trading and financing. The cellulosic marketing infrastructure required for similar

efficient commercial transactions will need to be established from top to bottom.

Pricing infrastructure is one of the most pressing needs. Large daily volumes of corn and

other grains are traded on well established exchanges with a great deal of confidence on the part

of buyers and sellers that the other party will perform. Initially, it could be difficult, at best, to

develop these infrastructure benefits for crop residues such as corn stover. Cellulosic feedstocks

will be starting from a relatively small production base with no pricing institutions in place.

There are no existing grades and quality standards to underpin transactions over distance and

time. Nor are there any trade rules or established patterns for prompt and efficient settlement of

trade disputes between buyers and sellers. The absence of these factors does not mean that they

won’t develop, but there could be a stressful transition period.

Also, there is no regulatory infrastructure to protect producers who wish to hold

inventory after harvest in a public warehouse or handlers warehouse. This kind of infrastructure

serves an important role in underpinning warehouse receipts and producer financing by creating
735

a higher and more reliable collateral value for inventory. Nor are there equivalents to the U.S.

grain grades and quality standards or Federal Grain Inspection Service. While there are other

ways these functions can be provided some type of commodity grades and standards will be

necessary to permit trading. Another possibility would be to have the production of cellulosic

feedstocks and the production of ethanol vertically integrated in some fashion so that the

responsibility for quality is internalized.1140

Energy Crops

Energy crops such as switchgrass and miscanthus would be harvested, baled, stored and

transported very similar to crop residues. Because energy crops are not currently grown on a

commercial scale, and are therefore not harvested, it is difficult to model the costs of harvesting

these crops. Given the process for harvesting is likely to resemble the corn stover harvesting

system described in our internal analysis of agricultural residue costs, we believe the costs would

also be similar. Despite these similarities, there are several key differences that will cause the

price of energy crops to differ from that of corn stover and other agricultural residues.

One of the main advantages of growing energy crops is the low nutrient inputs, and

therefore low nutrient replacement costs, that are required as compared to traditional crops.

Energy crops are also expected to produce higher yields per acre than the harvesting of

agricultural residues. This higher density is likely to increase the number of tons of feedstock

than can be harvested per hour, while at the same time decreasing the transportation distance

between the farms where the feedstock is produced and the biofuel production facility. Both of

these factors would further decrease the cost of energy crop production, as labor and

transportation costs would be lower and less secondary storage facilities would be required.

Not all of the traits of energy crops would indicate lower costs of production, however.

Energy crops will also incur a land rent cost. In the scenario described in our internal analysis

for corn stover no land rent cost was charged to the production of corn stover. This is because

the stover is assumed to be a secondary crop, and therefore all the land rent charges are assumed

to be included in the cost of grain production. Because energy crops will likely be grown as

primary crops there will be some land rent cost associated with their production. How big this

cost is will vary greatly, depending on where and on what type of land the energy crops are

grown. If energy crops are grown on marginal land that is unsuitable for traditional crops, as has

been suggested, this cost may be low. In any case, it will be an increase when compared with

agricultural residues, which do not have any associated land rent costs229.

Wood Residue

As we did with agricultural residues, we also examined the costs associated with wood

residue harvesting to validate the cost generated with FASOM. Harvest and transport costs for

woody biomass in its different forms vary due to tract size, tree species, volumes removed,

distance to the wood-using/storage facility, terrain, road condition, and other many other

considerations. There is a significant variation in these factors within the United States, so

timber harvest and delivery systems must be designed to meet constraints at the local level.

229 Land rent charges are included in the FASOM estimate of the roadside cost of energy crops.
736

Harvesting costs also depend on the type of equipment used, season in which the operation

occurs, along with a host of other factors. Much of the forest residue is already being harvested

by logging operations, or is available from milling operations. However, the smaller branches

and smaller trees proposed to be used for biofuel production are not collected for their lumber so

they are normally left behind. Thus, this forest residue would simply have to be collected and

transported out of the forest, although it would still have to be chipped before transport to the

biofuel plant.

In general, most operators in the near future will chip at roadside in the forest, blowing

the chips directly into a chip van. When the van is full it will be hauled to an end user's facility

and a new van will be moved into position at the chipper. The process might change in the

future as baling systems become economically feasible or as roll-off containers are proven as a

way to handle logging slash. At present, most of the chipping for biomass production is done in

connection with fuel-reduction treatments. This could change if the price of raw biomass

increases to a point where it becomes feasible to recover logging residues associated with normal

commercial operations. The major problem associated with collecting logging residues and

biomass from small trees is handling the material in the forest before it gets to the chipper.

Balers and roll-off containers offer some promise to reduce this cost. Whether the material is

collected from a fuel-reduction treatment or a commercial logging operation, chips from residues

will be dirty and will require screening or some type of filtration at the end-user's facility.230

Results from a study in South Georgia show that under the right conditions, a small

chipper can be added to a larger operation to obtain additional chip production without adversely

impacting roundwood production, and chips can be produced from limbs and tops of harvested

trees at costs ranging from $11 per ton and up. Harvesting understory (the layer formed by

grasses, shrubs, and small trees under the canopy of larger trees and plants) for use in making

fuel chips is about $1 per ton more expensive.

Per ton costs decrease as the volume chipped increases per acre. Some estimates suggest

that if no more than 10 loads of roundwood are produced before a load of chips is made, that

chipper-modified system could break even. Cost projections suggest that removing only limbs

and tops may be marginal in terms of cost since one load of chips is produced for about every 15

loads of roundwood.

The U.S. Forest Service provided us a cost curve for different categories of forest residue,

including logging residue, other removals (i.e., clearing trees for new building construction),

timberland trimmings (forest fire prevention strategy) and mill residues.1141 The data was

provided to us on a county-by-county basis. The national forest lands are omitted from

consideration, and the urban forest residue is not considered here, but in the section discussing

MSW. The information was also provided at different price points. The quantities of forest

residue are summarized by source type in Tables 4.1-18, 4.1-19 and 4.1-20. To avoid presenting

230 Personal Communication, Eini C. Lowell, Research Scientist, USDA Forest Service
737

a huge amount of data, we aggregated the county data by state, and we are presenting the data at

specific price points: $30/dry ton, $45/dry ton and $70/dry ton.
738

Table 4.1-18.

Volume of Wood Residue Available for Producing Biofuel

Biomass Available at $30/ton (dry tons)

Logging

Residue

Other

Removals

Timberland

Thinnings

Unused Mill

Residue

Total

Quantity

Alabama 1,202,541 253,620 433,519 7,117 1,896,798

Arizona 8,849 22,436 33,085 1,351 65,721

Arkansas 851,772 385,492 369,083 12,889 1,619,236

California 334,870 0 871,351 65,088 1,271,309

Colorado 9,203 7 0 2,302 11,511

Connecticut 4,195 15,339 10,465 3,949 33,949

Delaware 15,051 12,109 4,918 0 32,077

Florida 535,215 257,704 240,947 2,202 1,036,067

Georgia 1,556,954 496,631 553,627 45,138 2,652,350

Idaho 126,573 0 41,548 6,006 174,126

Illinois 139,101 117,589 115,431 18,523 390,644

Indiana 281,242 52,087 198,112 10,627 542,068

Iowa 56,049 27,580 48,991 159 132,780

Kansas 7,329 44,202 9,676 8,720 69,928

Kentucky 513,989 332,179 344,948 55,196 1,246,311

Louisiana 1,317,139 440,293 300,924 30,075 2,088,431

Maine 1,206,438 470 80,314 42,483 1,329,705

Maryland 90,722 415 40,994 17,067 149,197

Massachusetts 35,461 31,043 13,801 0 80,305

Michigan 379,463 122,476 327,640 13,763 843,343

Minnesota 348,807 331,492 132,712 26,878 839,889

Mississippi 1,548,534 355,071 425,344 95,138 2,424,088

Missouri 387,434 265,146 342,077 79,787 1,074,443

Montana 131,335 0 66,592 9,136 207,063

Nebraska 10,572 9,386 11,707 4,971 36,637

Nevada 15 53 0 0 67

New Hampshire 157,321 174 47,802 7,019 212,316

New Jersey 2,959 39 2,288 1,437 6,723

New Mexico 11,929 1,279 25,898 4,902 44,008

New York 367,003 54,671 163,336 27,390 612,400

North Carolina 1,013,165 629,632 560,814 12,811 2,216,422

North Dakota 1,453 7,601 3,822 265 13,141

Ohio 185,398 9,053 83,676 22,600 300,726

Oklahoma 173,869 98,794 53,043 495 326,200

Oregon 760,276 31 527,702 16,316 1,304,326

Pennsylvania 543,663 699 224,978 170,972 940,312

Rhode Island 884 22,860 2,800 389 26,934

South Carolina 714,551 348,289 301,850 1,051 1,365,741

South Dakota 6,972 14,436 2,993 2,294 26,695

Tennessee 316,706 244,920 423,906 187,583 1,173,115

Texas 616,777 218,464 185,718 3,021 1,023,979

Utah 2,973 7 9,909 4,437 17,325

Vermont 104,876 18,652 48,395 0 171,923

Virginia 741,673 406,800 436,870 39,366 1,624,709

Washington 641,144 22 925,479 21,446 1,588,091

West Virginia 488,356 24,714 161,653 118,779 793,502

Wisconsin 568,800 491,132 260,293 60,410 1,380,636

Wyoming 11,343 0 14,050 34,014 59,407

Total 18,530,943 6,165,088 9,485,083 1,295,560 35,476,674
739

Table 4.1-19.

Tons of Wood Residue Available for Producing Biofuel

Biomass Available at $45/ton (dry tons)

Logging

Residue

Other

Removals

Timberland

Thinnings

Unused Mill

Residue

Total

Quantity

Alabama 1,202,541 253,620 506,045 7,117 1,969,324

Arizona 13,566 21,210 34,967 1,351 71,094

Arkansas 851,772 385,492 429,414 12,889 1,679,567

California 583,478 0 949,468 65,088 1,598,034

Colorado 10,056 11 30,619 2,302 42,988

Connecticut 4,301 16,095 10,465 3,949 34,810

Delaware 17,932 14,145 6,700 0 38,777

Florida 535,215 257,704 266,597 2,202 1,061,718

Georgia 1,556,954 496,631 644,295 45,138 2,743,018

Idaho 216,303 0 52,594 6,006 274,902

Illinois 139,153 117,589 115,431 18,523 390,696

Indiana 281,464 52,087 221,845 10,627 566,023

Iowa 56,050 27,607 49,551 159 133,367

Kansas 7,329 44,202 9,676 8,720 69,928

Kentucky 513,989 332,179 407,371 55,196 1,308,735

Louisiana 1,317,139 440,293 330,512 30,075 2,118,019

Maine 1,280,511 495 102,442 42,483 1,425,931

Maryland 94,579 421 40,994 17,067 153,060

Massachusetts 39,127 33,191 13,801 0 86,119

Michigan 391,732 128,600 410,302 13,763 944,398

Minnesota 358,518 341,894 159,990 26,878 887,280

Mississippi 1,548,534 355,071 467,935 95,138 2,466,679

Missouri 387,434 265,146 466,082 79,787 1,198,448

Montana 215,597 0 70,775 9,136 295,507

Nebraska 10,710 9,434 11,707 4,971 36,822

Nevada 22 71 0 0 93

New Hampshire 165,519 197 57,566 7,019 230,301

New Jersey 3,184 40 2,423 1,437 7,084

New Mexico 17,239 1,287 26,862 4,902 50,291

New York 384,457 56,552 189,696 27,390 658,094

North Carolina 1,013,165 629,632 668,420 12,811 2,324,028

North Dakota 1,454 7,601 3,822 265 13,142

Ohio 186,022 9,069 88,572 22,600 306,263

Oklahoma 173,869 98,794 62,700 495 335,858

Oregon 1,341,835 34 574,948 16,316 1,933,133

Pennsylvania 1,341,835 34 574,948 170,972 2,087,789

Rhode Island 957 25,039 2,800 389 29,185

South Carolina 714,551 348,289 352,018 1,051 1,415,909

South Dakota 11,872 15,581 3,253 2,294 32,999

Tennessee 316,706 244,920 507,698 187,583 1,256,906

Texas 616,777 218,464 219,187 3,021 1,057,448

Utah 3,758 0 10,786 4,437 18,980

Vermont 108,542 19,182 53,836 0 181,560

Virginia 741,673 406,800 524,372 39,366 1,712,212

Washington 1,067,587 23 981,839 21,446 2,070,895

West Virginia 488,356 24,714 241,184 118,779 873,033

Wisconsin 576,938 499,302 327,027 60,410 1,463,677

Wyoming 18,163 0 18,202 34,014 70,380

Total 20,928,463 6,198,742 11,301,737 1,295,560 39,724,502
740

Table 4.1-20.

Tons of Wood Residue Available for Producing Biofuels

Biomass available at $70/ton (dry tons)

Logging

Residue

Other

Removals

Timberland

Thinnings

Unused Mill

Residue

Total

Quantity

Alabama 1,202,541 253,620 581,654 7,117 2,044,933

Arizona 13,566 24,510 38,678 1,351 78,105

Arkansas 851,772 385,492 492,094 12,889 1,742,247

California 583,478 0 1,000,615 65,088 1,649,181

Colorado 10,056 11 30,619 2,302 42,988

Connecticut 4,301 16,095 10,465 3,949 34,810

Delaware 17,932 14,145 6,700 0 38,777

Florida 535,215 257,704 332,353 2,202 1,127,474

Georgia 1,556,954 496,631 776,911 45,138 2,875,634

Idaho 216,303 0 61,926 6,006 284,235

Illinois 139,153 117,589 115,431 18,523 390,696

Indiana 281,464 52,087 221,845 10,627 566,023

Iowa 56,050 27,607 49,551 159 133,367

Kansas 7,329 44,202 9,676 8,720 69,928

Kentucky 513,989 332,179 463,904 55,196 1,365,268

Louisiana 1,317,139 440,293 375,052 30,075 2,162,559

Maine 1,280,511 495 166,117 42,483 1,489,605

Maryland 94,579 421 40,994 17,067 153,060

Massachusetts 39,127 33,191 13,801 0 86,119

Michigan 391,732 128,600 533,107 13,763 1,067,203

Minnesota 358,518 341,894 200,599 26,878 927,889

Mississippi 1,548,534 355,071 516,598 95,138 2,515,342

Missouri 387,434 265,146 643,929 79,787 1,376,295

Montana 215,597 0 83,023 9,136 307,755

Nebraska 10,710 9,434 11,707 4,971 36,822

Nevada 22 71 0 0 93

New Hampshire 165,519 197 58,098 7,019 230,833

New Jersey 3,184 40 2,423 1,437 7,084

New Mexico 17,239 1,287 32,187 4,902 55,616

New York 384,457 56,552 192,851 27,390 661,249

North Carolina 1,013,165 629,632 800,455 12,811 2,456,063

North Dakota 1,454 7,601 3,822 265 13,142

Ohio 186,022 9,069 88,572 22,600 306,263

Oklahoma 173,869 98,794 81,634 495 354,792

Oregon 1,251,094 34 566,594 16,316 1,834,037

Pennsylvania 546,418 707 340,497 170,972 1,058,594

Rhode Island 957 25,039 2,800 389 29,185

South Carolina 714,551 348,289 395,555 1,051 1,459,446

South Dakota 11,872 15,581 4,129 2,294 33,875

Tennessee 316,706 244,920 516,550 187,583 1,265,759

Texas 616,777 218,464 253,670 3,021 1,091,931

Utah 3,758 7 14,717 4,437 22,918

Vermont 108,542 19,182 71,105 0 198,829

Virginia 741,673 406,800 630,366 39,366 1,818,206

Washington 1,067,587 23 1,029,985 21,446 2,119,041

West Virginia 488,356 24,714 287,639 118,779 919,489

Wisconsin 576,938 499,302 420,775 60,410 1,557,425

Wyoming 18,163 0 21,598 34,014 73,775

Total 20,042,304 6,202,722 12,593,373 1,295,560 40,133,959
741

The relatively flat supply curve indicates that small changes in the demand for wood

residues could have a significant impact on the cost of these residues. This makes predicting an

appropriate cost for these residues a difficult task. The FASOM model estimates that 1.67

million dry tons of wood residues will be used in cellulosic biofuel production in 2022. This

number is far less than the 35.5 million dry tons of wood residue that the US Forestry service

estimates will be available at $30 a ton. This would suggest that the low costs estimated by

FASOM ($19.77 per dry ton) are reasonable.

Delivery of woody biomass from the harvesting site to a conversion facility, like delivery

of more conventional forest products, accounts for a significant portion of the delivered cost. In

fact, transportation of wood fiber (including hauling within the forest) accounts for about 25 to

50 percent of the total delivered costs and highly depends on fuel prices, haul distance, material

moisture content, and vehicle capacity and utilization. Also, beyond a certain distance,

transportation becomes the limiting factor and the costs become directly proportional to haul

distance. We believe Class 8 over-the-road hauling will be used to transport the wood residues

from the roadside to the biofuel production facilities. The cost for the transportation of wood

residues was determined using the transportation and secondary storage tool presented in Section

1.3 and in further detail below. Because wood residues can be harvested throughout the year and

delivered to the biofuel production plant on an as needed basis no secondary storage costs were

included.

Municipal Solid Waste

Million of tons of municipal solid waste (MSW) continue to be disposed of in landfills

across the country, despite recent large gains in waste reduction and diversion. The biomass

fraction of this total stream represents a potentially significant resource for renewable energy

(including electricity and biofuels). Because this waste material is already being generated,

collected and transported (it would solely need to be transported to a different location), its use is

likely to be less expensive than other cellulosic feedstocks. One important difficulty facing those

who plan to use MSW fractions for fuel production is that in many places, even today, MSW is a

mixture of all types of wastes, including biomaterials such as animal fats and grease, tin, iron,

aluminum, and other metals, painted woods, plastics, and glass. Many of these materials can’t be

used in biochemical and thermochemical ethanol production, and, in fact, would inflate the

transportation costs, impede the operations at the biofuel plant and leave an expensive waste

stream for biofuel producers.

Thus, accessing sorted MSW would likely be a requirement for firms planning on using

MSW for producing cellulosic biofuels. In a confidential conversation, a potential producer who

plans to use MSW to produce ethanol indicated that their plant plans are based on the obtaining

cellulosic biowaste which has already been sorted at the waste source (e.g., at the curbside,

where the refuse hauler picks up waste already sorted by the generating home-owner or

business). For example, in a tract of homes, one refuse truck would pick up glass, plastic, and

perhaps other types of waste destined for a specific disposal depot, whereas a different truck

would follow to pick up wood, paper, and other cellulosic materials to be hauled to a depot that

supplies an ethanol plant. However, only a small fraction of the MSW generated today is sorted

at the curbside.
742

Another alternative would be to sort the waste either at a sorting facility, or at the landfill,

prior to dumping. There are two prominent options here. The first is that there is no sorting at

the waste creation site, the home or business, and thus a single waste stream must be sorted at the

facility. This operation would likely be done by hand or by automated equipment at the facility.

To do so by hand is very labor intensive and somewhat slower than using an automated system.

In most cases the ‘by-hand’ system produces a slightly cleaner stream, but the high cost of labor

usually makes the automated system more cost-effective. Perhaps the best approach for low cost

and a clean stream is the combination of hand sorting with automated sorting.

The third option is a combination of the two which requires that there is at least some

sorting at the home or business which helps to prevent contamination of the waste material, but

then the final sorting occurs downstream at a sorting site, or at the landfill.

We have little data and few estimates for the cost to sort MSW. One estimate from our

Office of Solid Waste for a combination of mechanically and manually sorting of a single waste

stream downstream of where the waste is generated puts the cost in the $20 to $30 per ton range.

There is a risk, though, that the waste stream could still be contaminated and this would increase

the cost of both transporting and using this material at the biofuel plant due to the toxic ash

produced which would require disposal at a toxic waste facility. If a less contaminated stream is

desired it would probably require sorting at the generation site – the home or business - which

would likely be more costly since many more people in society would then have to be involved

and special trucks would need to be used. Also, widespread participation is difficult when a

change in human behavior is required as some may not be so willing to participate. Offering

incentives could help to speed the transition to curbside recycling (i.e., charging fee for

nonsorted waste, or paying a small amount for sorted tree trimmings and construction and

demolition waste). Assuming that curbside sorting is involved, at least in a minor way, total

sorting costs might be in the $30 to $40 per ton range.

These sorting costs would be offset by the cost savings for not disposing of the waste

material as well as the value of the recovered materials. Most landfills charge tipping fees, the

cost to dump a load of waste, a societal cost that would be avoided. In the United States, the

national average nominal tipping fee increased fourfold from 1985 to 2000. The real tipping fee

almost doubled, up from a national average (in 1997 dollars) of about $12 per ton in 1985 to just

over $30 in 2000. Equally important, it is apparent that the tipping fee is much higher in densely

populated regions. Statewide averages also varied widely, from $8 a ton in New Mexico to $75

in New Jersey. Tipping fees ranged from $21 to 98 per ton in 2006 for MSW and $18/ton to

$120/ton for construction and demolition waste. It is likely that the tipping fees are highest for

waste contaminated by toxic materials that require the disposal at Resource Conservation and

Recovery Act (RCRA) certified toxic waste sites as opposed to a composting site. However, this

same contaminated material would not be desirable to biofuel producers. Presuming that only

the noncontaminated cellulosic waste (yard and food wastes, building construction and

demolition waste and some paper) is collected as feedstocks for biofuel plants, the handling and

tipping fees are likely much lower, in the $30 per ton range.231

231 A much more thorough analysis of tipping fees by waste type is planned for the final rulemaking analysis.
743

The avoidance of tipping fees, however, is a complex issue since landfills are generally

not owned by municipalities anymore. Both large and small municipalities recognized their

inability to handle the new and complex solid waste regulations at a reasonable cost. Only 38

out of the 100 largest cities own their own landfills. To deal with the solid waste, large private

companies built massive amounts of landfill capacity. The economic incentive is for private

landfill operators to fill their landfills with garbage as early as possible to pay off their capital

investment (landfill site) quickly. Also, the longer the landfill is operating the greater is its

exposure to liability due to leakages and leaching. Furthermore, landfills can more costeffectively manage the waste as the scale of the landfill is enlarged. As a result, there are fewer

landfills and landfill owners, and an expansion of market share by large private waste

management firms, thus decreasing the leverage a biofuel producer may have.232 Hence, MSWbiofuel plants could be opposed by landfill operators. This may also be true in the case of a

waste-to-energy (WTE) facility, which burns as much garbage as possible to produce electricity.

For sustainable operation, a certain amount of daily waste supply should be guaranteed. A

MSW-biofuel plant may therefore be seen as an unwelcome competition to both landfill owners

and WTE facilities. This competition may increase the cost of cellulosic biomass to the biofuel

producers.

Once the cellulosic biomass has been sorted from the rest of MSW, it would have to be

transported to the biofuels plant. Transportation is different for MSW biomass compared to

forest and crop residues. Forest and crop residues are collected from forests and farms, which

are both rural sites, and transported to the biofuel plant which likely is located at a rural site. The

trucks which transport the forest and crop residues are large over-the-road trucks which can

average moderate speeds because of the lower amount of traffic that they experience.

Conversely, MSW is collected from throughout urban areas and would have to be transported

through those urban areas to the plant site. If the cellulosic biomass is being collected at

curbside, it would likely be collected in more conventional refuse trucks. If the biofuel plant is

nearby, then the refuse trucks could transport the cellulosic biomass directly to the plant. In this

case, the refuse trucks would simply be delivering the MSW to the biofuel producer instead of a

landfill or waste sorting facility, and therefore would not result in any additional cost to the

biofuel producer. If, however, the plant is located far away from where the waste is collected,

then the refuse trucks would probably to be offloaded to more conventional over-the-road trucks

with sizable trailers to make transport more cost-effective. This would likely be an additional

cost charged to the biofuel producer, as the MSW is now being transported farther to be used as a

biofuel feedstock instead of disposed of at a local landfill. Because the roadside cost of MSW is

significantly lower than the other feedstock sources it may still be cheaper to import MSW from

some distance away rather than use an alternative, locally available feedstock.

Cellulosic biomass sourced from MSW is generated year-round. If a steady enough

stream of this material is available, then secondary storage would not be necessary, thus avoiding

the need to install secondary storage. We assumed that no secondary storage costs would be

incurred for MSW-sourced cellulosic biomass. If the MSW is sourced from within the same

county as the biofuel plant we have assumed that there is no cost to the biofuel producer for the

232 Osamu Sakamoto, The Financial Feasibility Analysis of Municipal Solid Waste to Ethanol Conversion, Michigan

State University, Plan B Master Research Paper in partial fulfillment of the requirement for the degree of Master of

Science, Department of Agricultural Economics, 2004
744

transportation of the feedstock. If, however, the feedstock comes from a county other than the

one in which the biofuel plant is located we assume that the biofuel producer must pay to have

the MSW transported in large over-the-road trucks. These assumptions are used for the

transportation and secondary storage tool discussed in Section 1.3 and in detail in the following

section.

4.1.1.2.3 Transportation and Secondary Storage

For many of the feedstocks such as corn or soy oil, the feedstock costs include the cost

for transportation and any storage costs. However, for cellulosic feedstocks, the FASOM model

only estimates the feedstock costs at the “farm-side” or “forest side,” therefore, it was necessary

to estimate the transportation and secondary storage costs for these feedstocks. These

transportation and secondary storage cost estimates were ultimately used in the Cellulosic

Feedstock Transportation and Storage Cost Tool described in Section 1.3. Each feedstock

involves a different set of assumptions for transportation and whether or not secondary storage

would be necessary.

Agricultural Residue and Energy Crops

For agricultural residue and energy crops (assumed to be switchgrass), we assume that for

most of these feedstocks, that the feedstocks will be transported to secondary storage, stored and

then transported from secondary storage to the plant. Since a portion of this feedstock may be

harvested close to the plant, we assumed that some of the feedstock would be transported directly

to the plant.

For transportation to secondary storage, we used the cost information in Table 4.1-14.

The transportation to secondary storage involves loading a trailer with 12 tons of baled cellulosic

material, and pulling the trailer to the secondary storage site using a high speed farm tractor. At

the secondary storage site, the bales must be offloaded for storage. The loading and unloading of

the trailer involves the use of a tele-handler. Total time for loading and unloading is estimated to

require 40 minutes of time, or 20 minutes at each site.

For estimating costs, we subdivided the transportation from farm to secondary storage

into two different operations. One operation is the loading and offloading of the cellulosic

feedstock bales which require the use of the farm tractor, trailer and one tele-handler per

tractor/trailer. To estimate these costs we summed the total per-hour costs of these three pieces

of equipment (assuming one telehandler instead of two), minus the farm tractor fuel and lube

costs since the farm tractor is parked, which sums to $110 per hour. Assuming that these loading

and unloading operations require 40 minutes of time for 12 tons of cellulosic feedstock, we

derive a cost of $6.14 per ton.

The second operation for the transportation costs to secondary storage involves the actual

hauling of the agricultural residue and energy crop cellulosic feedstocks from the farm to the

secondary storage site. For this operation, the farm tractor pulls the trailer containing the bales

of cellulosic feedstock. We again use the total costs of the farm tractor and trailer, but include

the fuel and lube costs since the farm tractor is using fuel pulling the trailer; this totals to $83 per
745

hour. We assume that the farm tractor will average 22 miles per hour hauling the 12 tons of

cellulosic feedstock to secondary storage. This results in a cost of $0.64 per ton-mile which

accounts for the roundtrip from the farm to the secondary storage facility. Therefore, the total

transportation costs per ton of feedstock is $6.14 + $0.64 x D where “D” is the one way distance

from farm to secondary storage. Whenever we estimated the distance traveled from the farm to

secondary storage, we assumed that the distance would be 1.3 times the most direct route

between the two. This assumption makes sense because roads in farming regions of the country

are less numerous than in urban areas, and they often require traveling in circuitous routes to

reach the desired site. For example to reach a location north and west of a starting location, it

may be necessary to travel due north and then due west on two different roads until the desired

site is reached. Also, rural roads can be windy which also adds to the travelled distance

The secondary storage costs are accounted for in Table 4.1-15. However, an inherent

assumption of that analysis is that the per-ton storage costs are the same $8.89 per ton regardless

of the size of the storage facility, which is inconsistent with how construction costs are incurred.

To optimize the secondary storage costs with the transportation costs, we scaled the secondary

storage costs such that small secondary storage facilities would incur a higher per ton cost than a

larger facility. The scaling factor we used is 0.8.233 The base size for the secondary storage

facility is 25,800 tons of cellulosic feedstock which is the size of secondary storage site which

matched the costs for another secondary storage cost estimate. The costs scale up and down

from the $8.89 per ton value indicated in Table 4.1-15 depending on whether the facilities are

smaller or larger facilities. For example, a secondary storage facility which stores 9900 tons of

cellulosic feedstock is estimated to cost $10.7 per ton of feedstock stored, while a facility which

stores 132,000 tons of cellulosic feedstock is estimated to cost $6.40 per ton of feedstock stored.

Scaling the secondary storage costs was important for conducting an optimization

analysis with respect to transportation and secondary storage costs. If secondary storage costs

did not vary based on size, then the most efficient means for storing the cellulosic feedstock

would be in very small secondary storage facilities located at the farm, which would essentially

eliminate transportation costs to the secondary site. However, we feel that such an assumption

would not be realistic considering how the construction costs are incurred.

In conducting our optimization analysis, we assumed that denser agricultural residue or

energy crop densities would help to lower the transportation and secondary storage costs

compared to less dense cellulosic feedstocks. This is logical because the higher the density, the

shorter the distance that cellulosic feedstock would have transported to secondary storage

facilities and the less numerous and larger the secondary storage facilities could be for the same

amount of feedstock. The optimization analysis we conducted considered three different

densities for the cellulosic material and these were 1.8, 5.7 and 15 tons per acre. We assumed

that 25% of the acres of the land are planted in the area. We then assessed an array of distances

from farm to secondary storage sites and secondary storage site capacities consistent with the

233 The capital cost is estimated using an exponential equation. The equation is as follows: (Sb/Sa)e

xCa=Cb, where

Sa is the size of reference sized secondary storage site which can hold 25,800 tons of cellulosic feedstock, Sb is the

size of the unit for which the cost is desired, e is the exponent, Ca is the cost of the reference secondary storage site,

and Cb is the desired cost for the different sized unit. The exponential value “e” used in this equation is 0.8 for

secondary storage sites (equation from Peters and Timmerhaus, 1991)
746

distances. For example, for the 5.7 tons per acre crop density case, one situation we analyzed

assumed that the cellulosic feedstock would be transported 7.5 miles to 10 different secondary

storage sites each storing 107,000 tons of cellulosic feedstock per site. This example estimated

an average transportation to secondary storage and secondary storage cost of $17.63 per ton.

Another example for the 5.7 tons per acre crop density case assumed that there would be 60

secondary storage sites each storing 17,900 tons of cellulosic feedstock which would require

that, on average, the cellulosic feedstock would need to be transported 3.1 miles to the secondary

storage site. This example costs about the same, which is $17.60 per ton of feedstock. However,

our optimization analysis estimates that the optimum situation for the 5.7 tons per acre crop

density case is 23 secondary storage sites each storing 46,800 tons of cellulosic feedstock. The

average transportation distance for this optimum case is 5.0 miles and the average cost for both

the transportation to the secondary storage site and the secondary storage cost is $17.20 per ton

of cellulosic feedstock.

When conducting this analysis, we realized that for a portion of the feedstock grown

closest to the plant, that it would make more sense to build the secondary storage site at or

adjacent to the plant to avoid extensive transportation costs moving the feedstock for storage

when the plant was nearby. The assumption we made is that for all feedstock grown within the

radius equal to the optimal transportation distance to secondary storage, the feedstock would be

transported directly to the plant. For example, for the 5.7 tons per acre case, the average

transportation distance to secondary storage is 5.0 miles when the costs are optimized. To avoid

excessive transportation costs, we assumed that all the feedstock grown within 5.0 miles of the

plant would be transported directly to the plant or to storage adjacent to the plant. This

assumption for the optimized 5.7 tons per acre case resulted in 14% of the cellulosic feedstock

being transported directly to the plant. As the density of the feedstock increases, this assumption

resulted in a larger amount of cellulosic feedstock being transported directly to the plant.

Table 4.1-21 summarizes the optimal number and size of secondary storage sites and

average transportation distances for transporting the cellulosic feedstock from the farm to the

secondary storage sites, and the costs for the optimal situation for each cellulosic feedstock

density case. Table 4.1-21 also summarizes our estimate of the percentage of cellulosic

feedstock being transported directly to the plant.
747

Table 4.1-21

Optimal Number of Secondary Storage Sites and

Optimal Transportation Distances to those Sites

Celluosic Feedstock Density (tons/acre) 1.8 5.7 15

Average Transportation Distance (miles) 5.66 4.96 4.36

Number of Secondary Storage Sites 60 23 11

Amount of Cellulosic Feedstock per Secondary

Storage Site (tons)

19,400 46,800 88,000

Cost for Storage and Transportation to Secondary

Storage Site ($/ton)

19.17 17.20 15.88

Percentage of Cellulosic Transported directly to

the Plant

7 14 23

Average Cost including Storage and

Transportation to Secondary Storage Site ($/ton)

17.83 14.81 12.30

Our transportation and secondary storage optimization analysis shows that as crop

density decreases, there is a corresponding increase in the secondary storage costs and the

transportation costs for transporting the cellulosic feedstock from the farm to secondary storage.

Low crop density requires an increased number of smaller secondary storage sites, however

smaller secondary storage sites are associated with higher per-ton costs. Another advantage of

higher density feedstocks is it becomes more likely that more of the cellulosic feedstocks will be

taken directly to the plant as opposed to being stored at secondary storage sites, which further

helps to reduce the feedstock costs.

For integrating the secondary storage costs and the transportation costs into our model

described in Section 1.3, we needed to represent these costs in a form that could be used by the

model. Since our model contains crop density information for each crop, we conducted a linear

regression of the cellulosic feedstock density values against the average secondary storage cost

and transportation to secondary storage costs (bottom row of Table 4.1-21). This regression

resulted in the following equation: SSTC = -0.473 x DY +18.90 where “SSTC” equals the

secondary storage cost and transportation cost from the farm to secondary storage and “DY”

equals the cellulosic feedstock density in the farm field in tons per acre.

Once the cellulosic feedstock is at a secondary storage facility, it must be transported

from secondary storage to the plant. This additional transportation step will incur an additional

cost. To estimate this transportation cost we used the cost information in Table 4.1-16.

To facilitate these calculations we once again subdivided the cost estimate into two

different operations. One operation is the loading and offloading of the cellulosic feedstock

bales. For the loading operation, we assumed the use of the truck, flatbed trailer and two telehandlers. We assumed that two tele-handlers would be used to optimize the time required for

loading of 24 tons of cellulosic feedstock. To estimate these costs we summed the total per-hour

costs of these three pieces of equipment, minus the truck fuel and lube costs because the truck is

parked (using one-eleventh of the costs for 22 tele-handlers), which sums to $109 per hour. We

assume that the actual loading operation would require about 30 minutes. However, the overthe-road truck would likely require that a tarp be secured over the bales to as a safety measure for
748

hauling the bales over principal roadways, so we increased the loading time to an hour. For

offloading the feedstock, we assumed that the plant would have its own offloading equipment, so

we only included the total cost minus fuel and lube for the truck and trailer, which is $25.03 per

ton. However, because the truck would also have to be weighed for determining the mass of

feedstock being delivered, we increased the total offloading time including weighing of the

cellulosic feedstock to 1 1⁄2 hours. Totaling the loading and offloading costs for 24 tons of

cellulosic feedstock, we derive a cost of $6.90 per ton.

The second operation is the actual hauling of the cellulosic feedstock from secondary

storage to the plant. For this operation, the truck pulls the flatbed trailer containing the bales of

cellulosic feedstock. We used the total costs of the truck and flatbed trailer, but include the fuel

and lube costs since the truck is pulling the trailer; this totals to $69 per hour. We assume that

the truck will average 30 miles per hour hauling the 24 tons of cellulosic feedstock to the plant.

Although the over-the-road is capable of much higher speeds compared to the farm tractor which

hauled the feedstock to the secondary storage, we assumed that the truck’s speed would be

limited by driving on dirt roads and smaller county roads and by needing to drive through

smaller towns located in rural areas. This results in a cost of $0.17 per ton-mile which accounts

for the roundtrip from the secondary storage facility to the plant. Therefore, the total

transportation costs per ton of feedstock can be summarized into the following equation: TC =

$6.90 + $0.17 x D where “TC” is transportation cost in dollars per ton and “D” is the one-way

distance in miles.

We needed to estimate an average distance traveled for the cellulosic feedstock when it is

being transported from secondary storage to the plant. The average distance can be estimated by

knowing the size of the area from which the cellulosic feedstock is being harvested, such as the

entire county. The average distance from each point within the area to the centerpoint of the area

(the cellulosic biofuel plant is assumed to be located in the center of the country) is estimated to

be 70 percent of the total radius or distance to the outer edge of the area. Another way to

understand the 70 percent value, assuming that the area is a circle, is that half the area of the

circle is within the area marked by 70% of the circle’s total radius while the other half of the

radius falls within the last 30% of the circle’s radius. This same relationship holds true for a

square as well. Therefore, knowing the area of the region from which the cellulosic feedstock is

being harvested for processing by a particular plant, we assumed that the average transportation

distance is 70% of the total average distance from the centerpoint to the outer edge of the area.

Forest Residue and Municipal Solid Waste

By the nature of how they are produced, MSW and forest residue were assumed to not

need secondary storage. MSW is created throughout the year and can be processed and then

transported directly to the plant as it is produced. This is true for forest residue as well. Forest

material is “stored on the stump” until it is needed. Since the primary uses of forest material is

for pulp and paper production and wood products for the building industry, and these uses

demand product year-round, the forest residue from these operations are assumed to be made

available on the year-round basis as well. Thus for these categories of cellulosic feedstocks, only

transportation directly to the plant was assumed.
749

Another factor that we considered is that MSW already incurs a transportation cost for

transporting the MSW to a landfill. Thus, when MSW is being transported to a cellulosic biofuel

plant, instead of additional transportation cost being incurred, the MSW would simply be

rerouted to a cellulosic biofuel plant and no new transportation cost would be incurred. This

assumes that the MSW has been partially or perhaps completely presorted such that the MSW

feedstock would simply be rerouted to the cellulosic biofuel plant. However, if sorting still

needs to occur, it very well would occur at the landfill and additional transportation costs would

be incurred if the cellulosic biofuel plant is not located at the landfill. If the MSW is being

transported to a plant located away from the landfill, for example, if the plant were to be located

in an adjacent county, then additional transportation costs would be incurred.

For estimating the transportation costs for MSW and forest residue we assumed that the

transportation cost methodology for transporting cellulosic feedstock from secondary storage to

the cellulosic biofuel plant would apply in this case as well. Thus, the transportation costs per

ton of feedstock for forest residue and for intercounty shipments of MSW would be TC = $6.90

+ $0.17 x D where “TC” is transportation cost in dollars per ton and “D” is the one-way distance

in miles.

4.1.1.2.4 Cellulosic Ethanol Production Costs

Two different technologies served as the basis for estimating the costs for converting

cellulose into ethanol. One technology relies on the biochemical conversion of the cellulose to

ethanol. The second technology converts the cellulose to a syngas and then reacts the syngas to

mixed alcohols over a catalyst.

Biochemical Conversion of Cellulose to Ethanol

We contracted with the National Renewable Energy Laboratory (NREL) to estimate the

cost to convert corn stover into ethanol for the years 2010, 2015, and 2022. It is of particular

importance for the following discussion, to note the following: NREL used the same feedstock

mass (772,168 dry tons of corn stover) in all three cases.

For the three cases, NREL assumed the feedstock, ‘as-needed,’ was hauled to the plant by

trucks and trailers from satellite storage, already shredded to the appropriate size for processing,

and free of dirt, iron, and other contaminates; in other words – process ready. The transport

vehicles were unloaded into surge tanks, large enough to hold feedstock for three days of

operation. The pretreatment and hydrolysis reactors are charged from these feed surge tanks.

The following is background information for our discussion of both operating and capital

costs, some of which is also included in our brief discussion of the process flow description and

capital equipment charges. The first step was to develop a set of process flow diagrams that set

the arrangement of the equipment. Based on the desired production volume, these diagrams,

were used within an ASPEN Plus4© model to develop complete mass and energy balance. The

model consists of 164 unit operation blocks, 457 streams (247 material and 210 heat or work), 63

components, and 82 control blocks.
750

The overall model is thermodynamically rigorous and uses physical properties for the

feedstock and process chemicals included in the ASPEN software as well as property data

developed at NREL. The individual unit models are also thermodynamically consistent and can

be either rigorous (for example, the simulation of the distillation) or simple. The reactors could

be modeled with kinetic expressions, but because of the level of development of the

experimental data, they were modeled as experimentally determined conversions of specific

reactions. This type of model still satisfies the rigorous mass and energy balance. Other unit

operations, such as liquid-solid separations, are typically modeled with fixed solids removal and

liquid retention (in the solids stream) data from vendor tests.234 Using the process flow diagrams

and the mass and energy balance information, NREL estimated stream flows and conditions,

along with the estimated quantities of raw materials and other process chemicals.

The following table presents NREL’s summary of each of the three year’s total project

investment. For each year’s total project investment, NREL provided capital charge, which

includes income tax, depreciation, and average return on investment, the cost of raw materials,

waste handling charges, and by-product credits.

234 A. Aden, M. Ruth, K. Ibsen, J. Jechura, K. Neeves, J. Sheehan, and B. Wallace

National Renewable Energy Laboratory (NREL); L. Montague, A. Slayton, and J. Lukas Harris Group, Seattle,

Washington, Ethanol Process Design and Economics Utilizing Co-Current Dilute Acid Prehydrolysis and Enzymatic

Hydrolysis for Corn Stover; June 2002; NREL is a U.S. Department of Energy Laboratory Operated by Midwest

Research Institute • Battelle • Bechtel; Contract No. DE-AC36-99-GO10337
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Table 4.1-22. Summary of NREL’s Capital Charges and Operating Costs

Year Technology 2010 2015 2022

Plant Size

MMgal/yr 56 69 71

Capital Cost $MM

(TPI) 232 220 199

$MM/yr ¢/gal $MM/yr ¢/gal $MM/yr ¢/gal

Capital Charge

10% after tax ROI 42 75 39 56 35 50

Fixed Costs 9 16 9 12 8 12

Feedstock Cost 84 46 51 35 50 35

Other Raw Matl.

Costs 17 30 4 5 16 16

Enzyme Cost 18 32 7 10 5 8

Enzyme Nutrients 8 14 2 3 2 2

Electricity -6 -10 -7 -9 -12 -16

Waste Disposal 1 2 3 4 1 1

Total Costs 173 205 108 116 105 108

The quantities of all raw material, generated electricity, and produced wastes were

determined using the ASPEN mass and energy balance model. These costs include: Feedstock –

corn stover, CSL – purchased corn steep liquor (a nutrient); Cellulase – purchased cellulase

enzymes; Other Raw Materials – sulfuric acid, diammonium phosphate, make-up water, boiler

feed water chemicals, cooling water chemicals. Waste Disposal – waste water chemicals, waste

water polymers, ash disposal, gypsum disposal. Electricity – marketing and distribution of

surplus electricity to the grid for credit.

We note that the percent change in total project investment from year to year is not

insignificant and reflects improvements in mechanical process efficiencies among other general

improvements in the process technology, including the automatic distributed process control

system, all of which are off-set to some extent by increases in the real cost of the technology

improvements, as well as those of constructions materials. We discuss capital costs following

this discussion of operating costs.

The most notable reductions in NREL’s operating costs are in the price per dry ton of the

corn stover feedstock and in the cost of cellulase enzyme. NREL anticipates significant

improvement in the efficiency of these enzymes, especially those that saccharify glucan to
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glucose and xylose oligomers235 generated during hydrolysis. They also expect improvement in

the yeasts that ferment xylose. According to the 2007 NREL – State of Technology report, they

anticipate that as a first step, the relationship between corn stover hydrolysate conditioning and

fermentation will be better defined and understood. Commercial cellulase preparations will

continue to be analyzed for baseline performance (specific activity), and due to increased

research efforts, cellulase function will be better understood, which should lead to efficiency

improvements. Integrated testing of whole slurry and recycle options will also be conducted

resulting in potential improvements in that area. Last, the efficacy of advanced enzyme

preparations (including oligomerases and/or hemicellulases) will continue to be tested in

conjunction with alternative pretreatment technologies. NREL expects that the cost of

pretreatment will deminish, hydrolysis time will decrease, and the sugar (xylose and glucose)

yields will increase.236

As the process costs decline over time, the feedstock costs become a larger fraction of the

overall costs. We also note that in the following table that the cost of the feedstock makes up

50% of the total cost in 2010; 67% in 2015; and 68% in 2022. The reduction in feedstock cost,

from $60 per dry ton in 2010, to $45.90 per dry ton in both 2015 and 2022 also has a significant

effect on operating costs. In addition, NREL did not include payments to the farmers/growers

nor for soil nutrients (fertilizer, etc.) that were removed with the harvested corn stover. The cost

of the cellulase enzyme is the next highest contributor, with percent reductions contributed to

total cost that reduced from ~19% in 2010, to 13% in 2015 and 10% in 2022. It should be

obvious that any reductions in these costs have significant effects on the total operating cost.

The majority of research going forward will be focused on these two items, although some work

will be done to reduce the cost of the others. Table 4.1-23 summarizes NREL’s operating costs

for a biochemical cellulosic ethanol plant.

235 Xylan polymer chains, with considerably fewer residue numbers in the chain than were in the original xylan

polymer; they were broken off the polymer as these short chains rather than as single molecule sugars.

236 Andy Aden, National Renewable Energy Laboratory, Golden, Colorado, Biochemical Production of Ethanol

from Corn Stover: 2007 State of Technology Model, Technical Report NREL/TP-510-43205, Task No. BB07.2410;

May 2008
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Table 4.1-23.

Percent of the total operating cost for each actual operating cost item

2010 2015 2022

¢/gal % of

Total ¢/gal % of Total ¢/gal % of Total

Feedstock 84 50 51 67 50 68

Biomass to

Boiler 0.0 0.0 0.0 0.0 0.0 0.0

CSLa

14 8 3 4 2 3

Cellulase 32 19 9 13 8 10

Other Raw

Matl. Costs 31 18 5 7 16 22

Waste Disposal 2 1 4 5 2 2

Electricity -10 -6 -9 -12 -16 -22

Fixed Costs 16 10 12 16 12 16

168 76 72

aCorn steep liquor – provides nutrients for the enzymes.

The following table includes our adjustments to NREL’s variable or operating cost data

for the three years studied. We note that the two main differences between NREL’s and our

estimates are in the feedstock costs and in the way we calculate capital charges. We adjusted

NREL’s capital charges which were calculated using a 10% after tax return on investment, to

reflect a 7 percent before tax rate of return, which is the capital cost basis for our cost analyses.

We also adjusted the NREL feedstock costs to those that we estimated in Table 4.1-6, which was

$67.42 per dry ton. This significant difference between their and our feedstock cost estimates is

due to our including payments to farmers/growers plus covering the cost to replace nutrients

(fertilizer, etc.) removed at the time the stover was harvested. According to a personal

communication, NREL used unpublished data from the Idaho National Laboratory that indicate

feedstock costs will be significantly reduced between 2010 and 2015.
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Table 4.1-24.

Adjusted Capital Charges and Operating Costs

Year

Technology 2010 2015 2022

Plant Size

MMgal/yr 56 69 71

Capital Cost $MM 232 220 199

$MM/yr ¢/gal $MM/yr ¢/gal $MM/yr ¢/gal

Capital Cost 7%

ROI before taxes 25 46 24 35 22 31

Fixed Costs 9 16 9 12 8 12

Feedstock Cost 52 94 55 75 52 73

Other Raw Matl.

Costs 17 30 4 5 16 16

Enzyme Cost 18 32 7 10 5 8

Enzyme Nutrients 8 14 2 3 2 2

Electricity -6 -10 -7 -9 -12 -16

Waste Disposal 1 2 3 4 1 1

Total Costs 124 224 94 135 90 127

The changes in the minimum ethanol selling prices for the three years studied are

partially due to the changes in necessary capital investments. In order to determine capital costs,

NREL developed specifications for pieces of equipment that fall within different areas of a

biochemical plant. A biochemical plant is divided up into 8 different areas (Area 200 through

Area 900). For each equipment specification, they developed individual purchased equipment

and installation costs. Vendors supplied installation costs where possible; in other cases

installation factors were used. Equipment costs were obtained from vendor quotations when

possible, especially for uncommon equipment such as pretreatment reactors. These costs reflect

the base case for which the equipment was designed. If process changes were made and the

equipment size changed, the equipment is not generally re-costed, in detail. Rather, the cost was

adjusted by scaling using the following exponential scaling expression, [New Cost = Original

Cost x (New Size/Original Size)^exp]. They also scaled the size of equipment that was known to

change linearly with a change in inlet flow. The scaling exponents (exp) were obtained from

vendor quotes, or from a standard reference, such as Garrett.237

237 Garrett, D.E., Chemical Engineering Economics, Van Nostrand Reinhold, New York, 1989
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Installation costs were taken primarily from Delta-T, a process consultant’s experience.

Once the scaled, installed equipment costs (total installed capital costs) were determined, they

applied overhead and contingency factors to determine a total plant investment cost. That cost,

along with the plant operating expenses (generally developed from the ASPEN model) was used

in a discounted cash flow analysis to determine the cost of ethanol production, using a set

discount rate. NREL use a discount rate of 10%, whereas we used 7%, a factor generally used in

our financial calculations. For this analysis, the minimum ethanol selling price was the primary

value used to compare cases.

The total project investment was briefly discussed previously in our summary discussion

of operating costs; we used NREL’s total project investment for our estimates. The following

summarizes the capital expenditures that account for that capital investment.

Area 200: Pretreatment and Hydrolysis. The equipment in this area consists of an

assortment of pipe, pumps, tanks, tank-agitators, tank-mixers, coolers, 3-pneumapress filters, as

well as three separate process trains, each of which includes a presteamer, a blow tank, and a

reactor. The presteamer uses low-pressure steam to heat the feedstock to about 212 oF. It

discharges the hot, saturated mix into a blow tank that serves as a seal between the presteamer

and the hydrolysis reactor. The mix is charged to the reactor and dilute sulfuric acid is added;

the reactor operates at 191 psia and 547 oF. Most of the hemicellulose, e.g., primarily xylose,

mannose, arabinose, and galactose are converted into sugars. Glucan in the hemicellulose and a

small portion of the glucan in the cellulose are converted to glucose. These conditions also

solubilize some of the lignin in the feedstock and ‘expose’ the cellulose for subsequent

enzymatic hydrolysis, in a downstream section. In addition, acetic acid is liberated from the

hemicellulose hydrolysis. Degradation products of pentose sugars (primarily furfural) and hexose

sugars (primarily hydroxymethyl furfural (HMF)) are also formed.

Following the pretreatment reactor, the hydrolyzate liquid and solids are flash cooled,

which vaporizes a large amount of water, a portion of the acetic acid, and much of the furfural

and HMF, which can be toxic to downstream fermentation microorganisms.

In addition to the flash removal of aldehydes, the solids are washed and filter-pressed to

remove the liquid portion of the hydrolyzate, which contains sulfuric acid. The liquid is then

neutralized to pH 10 with ammonia and held until the gypsum precipitates and is filtered out.

The hydrolyzate, which contains the hydrolyzed xylose sugars and some glucose sugars, is

mixed back with dilution water and the filter cake, which contains the unhydrolyzed cellulose

and is sent to saccharification and co-fermentation (Area 300)

An important issue on an industrial scale is accurate pH control. By pH 11, as much as

30% of the glucose may be lost to HMF and other side reactions. Several factors increase the

probability of overshooting pH endpoints during neutralization. The natural buffering capacity

of hydrolyzates causes neutralization reactions to be slow. Plus, measurements using pH

membrane probes are affected by temperature and the presence of dissolved organic compounds

(sugars and lignin).
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Since we are handling the same mass of feedstock in each of the modeled years, we don’t

expect the cost of the equipment for pretreatment and hydrolysis will change much over the

2010-2022 time period. The equipment costs for Area 200 – Pretreatment & Hydrolysis are

2010: $23-million; 2015: $22.7-million; and 2022: $18.9-million. Neutralization and

Conditioning costs were separated out from the Pretreatment & Hydrolysis costs, even though

the NREL’s design report includes both cost centers in Area 200. Neutralization and

Conditioning costs are as follows: 2010: $8.4-million; 2015: $9.4-million; and 2022: $7.7-

million. The combined cost for Area 200 is as follows: 2010: $31.4-million; 2015: $32.1-

million; and 2022: $26.6-million. In total, this area contributed about 23.5% to the total installed

capital cost of the project in 2010; about 25.3% in 2015; and 23.2% in 2022

Area 300: Saccharification and Co-Fermentation. The equipment in this area consists of

pumps, tanks, tank-agitators, coolers, and heaters. Two different operations take place in this

process area — the saccharification of the cellulose to glucose using cellulase enzymes, and the

fermentation to ethanol of that glucose plus the xylose and glucose sugars from the dilute acid

pretreatment of hemicellulose from Area 200.

Glucan from the cellulose undergoes hydrolysis or saccharification, at about 149 oF, prior

to fermentation. This slightly higher temperature increases enzyme activity and reduces the time

and amount of enzyme required for saccharification. Saccharification or cellulase enzymes,

purchased from an enzyme manufacturer, and the diluted, detoxified hydrolyzate are

continuously added to a train of five 1-million gallon saccharification vessels; residence time is

estimated to be 36-hours.

Cellulase enzyme is actually a ‘cocktail’ of enzymes, comprised of: (1) endoglucanases,

which attack randomly along the cellulose fiber to reduce polymer size rapidly; (2)

exoglucanases, which attack the ends of cellulose fibers, allowing it to hydrolyze highly

crystalline cellulose; and (3) β-glucosidase, which hydrolyzes cellobiose to glucose. Several 

bacteria and fungi naturally produce these enzymes, including bacteria in ruminant and termite

guts and white rot fungus. The most common organism used to produce cellulase industrially is

Trichoderma reesei. Genencor International and Novozymes Biotech are developing more cost

effective cellulase enzymes. DOE is funding this important work, which should improve the

economic viability of biomass conversion.

The recombinant Z. mobilis bacterium is used as the biocatalyst to ferment glucoses and

xyloses to ethanol. Several research institutions are genetically engineering strains, such as Z.

mobilis, to treat additional sugars and identifying other naturally occurring organisms that

metabolize hemicellulosic sugars.

The Z. mobilis must be ‘grown’ in increasingly higher volume stages. Initially, a small

amount of saccharified slurry and nutrients are combined in a very small vessel with a seed

inoculum, that’s been grown in the laboratory. This initial seed batch is used as the inoculum for

the next larger size seed batch, and so on. This series of batch scale-ups continues until the last

batch is large enough to support the actual production fermentation.
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Finally, the seed inoculum, nutrients (corn steep liquor) & (diammonium phosphate – a

source of nitrogen for the yeast), and saccharified slurry are cooled to about 106 oF and added to

a train of five 1-million gallon continuous fermentors. At this point, the process actually

becomes a simultaneous saccharification and co-fermentation (SSCF) process. Even though the

temperature in the fermentation tanks has been reduced to account for the ethanologen’s

intolerance to heat, the enzymes do continue to hydrolyze cellulose, albeit at a slightly reduced

rate. The main byproduct, produced during fermentation is carbon dioxide (CO2), which is

removed in a later process stage. The ethanol broth called ‘beer’ is collected in a storage tank,

called a beer well, before it’s pumped to distillation.

NREL anticipates significant capital savings for saccharification and co-fermentation

between 2010 and 2015, with fewer between 2015 and 2022. We note that this area contributed

15.4% to the total installed capital cost in 2010, but only 8.8% in 2015 and 8.8% in 2022. The

equipment costs for Area 300 – Saccharification and Fermentation are 2010: $20.5-million;

2015: $11.2-million; and 2022: $10.1-million.

Area 400 – In earlier studies, NREL included plans to produce enzymes in Area 400. For

the current studies, Area 400 has been removed and enzymes will be purchased and grown on

site under licensing agreements with enzyme suppliers.

Area 500 – Product, Solids, and Water Recovery (Distillation, Dehydration, Evaporation,

and Solid-Liquid Separation). The equipment in this area includes distillation and rectification

columns, pumps, condensers and coolers, pumps, pipe, filter-presses, and evaporators.

Beer, from the beer well in Area 300, is preheated and fed to a distillation column. The

column overhead containing all the CO2 and about 0.2% of the ethanol and a small quantity of

water is sent to a scrubber, which recovers and recycles about 99% of the vented ethanol. In the

tower bottoms, about 90% of the water has been removed and it contains approximately 0.7% of

the total volume of ethanol fed to the tower. Over 99% of the total ethanol fed to the tower is

removed as a 39.4% w/w mixture with water vapor through a side draw and fed directly to a

rectification column for further ethanol enrichment. We discuss the distillation column bottoms

in the evaporation and solid-liquid separation section of this area.

The rectification tower operating conditions are set to produce an overhead 92.5% w/w

ethanol/water saturated vapor mixture. The tower bottoms are a 0.05% w/w ethanol/water

mixture. In fact, only 0.1% of the total ethanol from the fermentation area is lost to the bottoms.

The rectification column overhead is superheated and fed to one of two adsorption

columns in a molecular sieve adsorption unit. The two columns operate alternately; while one

bed is operated to remove water from the ethanol, the other is regenerated by passing a very

small slipstream of pure ethanol vapor back through the loaded bed that strips the water off the

adsorbent, while the column is under a vacuum. The mixture is condensed and returned to the

rectification column feed stream. The adsorption column removes 95% of the water and a small

quantity of ethanol. The 99.5% pure ethanol vapor is condensed, cooled, and pumped to storage.
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Evaporation, and Solid-Liquid Separation: The beer column bottoms, with about 5.8%

insoluble solids, are fed to the first effect evaporator, where 24% of the water in the feed is

evaporated. The evaporator bottom slurry, cooled from ~243 oF to ~189 oF, is sent to a filterpress, from which the filtrate is returned to the second evaporator effect; the filter cake is not

washed. In the second effect evaporator, 44% of the feed water is evaporated. The third effect

evaporates 76% of the remaining water. The final vapor is condensed and fed to a condensate

drum. Of the total feed to the evaporation/separation system, 10.5% remains as syrup, 11.5% is

removed as a wet cake in the pressure filter, 17% is recycled back to the process as recycle

water, and 61% is evaporated. The syrup from the third evaporator bottoms is 60% water, e.g.,

the maximum dissolved solids level that can be achieved without rapid fouling of the evaporator;

the flow of very low-pressure steam to the evaporator is set to achieve this level. This syrup is

mixed with the cake from the filter-press and sent to the combustor for disposal. Air from the

filter-press is used for combustion air.

The equipment costs for Area 500 – Product, Solids, and Water Recovery (Distillation,

Dehydration, Evaporation, and Solid-Liquid Separation) are for 2010: $23.4-million; 2015:

$26.1-million; and 2022: $23.3-million. This area’s contribution to the total installed capital

cost in 2010 is 17.5%; in 2015, 20.6%; and in 2022, 20.3%. We believe that some of the

increase from 2010 to 2015 has to do with the increased liquid flow due to the conversion

improvements; larger pipe, pumps, tanks, etc may be necessary to handle the increased flow.

The changes from 2015 to 2022 are not that significant, as reflected by the percent contribution

of the area to total installed equipment cost.

Area 600 – Wastewater Treatment: The equipment in Area 600 consists mainly of

aerobic and anaerobic digesters, digester agitators, tanks (basins), a biogas emergency flare,

coolers, and pumps. The main purpose of the wastewater treatment section is to reduce the plant

makeup water requirement by recovering, treating and recycling as much process water as

possible. The feed to the wastewater treatment section consists of: condensed pretreatment flash

vapor, condensate from the hydrolyzate filter-press vent, boiler blowdown, cooling tower

blowdown, clean-in-place waste, and the non-recycled evaporator condensate. Rain and snow

run-off, equipment washing, and other non-process wastewater are assumed to flow to the

municipal wastewater treatment system. The stream is screened to remove large waste particles

that are sent to a landfill; any remaining organic matter is anaerobically and aerobically digested.

Anaerobic digestion produces a methane rich (75%-methane, 25%-carbon dioxide) biogas that’s

fed to the combustor. Aerobic digestion produces relatively clean water that’s recycled back to

the process and sludge that’s burned in the combustor.

NREL didn’t expect much change over the 2010 to 2022 time period. In 2010 the

installed capital cost for this area was $3.4-million; in 2015, $3.7-million; and in 2022 it was 3.1-

million. As important as this area is to the entire operation its contribution to the total project

installed capital cost is relatively minor. In 2010 the contribution was 2.5%; in 2015, 2.9%; and

in 2022 it was 2.7%.

Area 700: Bulk Storage of Chemicals. This section of the plant stors chemicals in bulk

for the process and for finished, fuel-grade ethanol. The feedstock feed surge tanks we discussed

just prior to the discussion of Area 200 are not included in this area. Process chemicals stored in
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this area include: corn steep liquor (a nutrient), sulfuric acid, cellulase enzyme, gasoline (used as

a denaturant finish the fuel grade ethanol), and water for fire suppression.

There are approximately five-days of SS316-stainless steel238 tank sulfuric acid storage.

Corn steep liquor (CSL), a nutrient for fermentation seed growth and ethanol production, also

has about five-days of SS304-stainless steel239 storage; NREL expects the plant will require

about three-25,000 gallon rail cars of CSL every three-days. There are seven-days of storage for

diammonium phosphate (DAP), delivered as pellets via rail car. Appropriate quantities of CSL

and DAP are mixed in a day-tank and used in Area 300 for fermentation seed production and

ethanol production. A producer that supplies cellulase enzymes is expected to set up an enzyme

production operation either on site or on a nearby location. Liquid enzyme storage is set for

four-days in SS304 stainless steel tanks. The carbon steel fire-fighting water storage tanks

provide about four-hours of operating time; the firewater pump delivers 2,500 gpm. Other pumps

are sized per process requirements

There are seven-days of ethanol product storage in two 600,000 gallon carbon steel tanks.

Five percent gasoline (v/v), a denaturant, is added to the ethanol as it’s loaded for shipment to

customers. The pumps in this section are generally sized to load a 10,000 gallon truck and trailer

in about 15 min. to 20 min. maximum filling time. They can also be used to fill process day

tanks.

The installed capital costs for bulk storage are, for 2010, $3.8-million; for 2015, $2.4-

million; and for 2022, $2.4-million. The contribution to total project installed capital costs are,

for 2010, 2.8%; for 2015, 1.9%; and for 2022, 2.1%.

Area 800: Combustor, Boiler, and Turbogenerator. The purpose of the combustor,

boiler, and turbogenerator is to burn various by-product or waste streams to produce steam and to

generate electricity. All of the feedstock lignin and some of the cellulose and hemicellulose are

not hydrolyzed in Area 300.

As previously discussed, a high soluble, solids syrup is generated in Area 600 and

anaerobic and aerobic digestion of the remaining wastewater produced biogas and a small

quantity of biomass sludge which are burned to generate steam and produce electricity. This

contributes to over-all plant energy self-sufficiency, reduces solid waste disposal costs, and

generates additional revenue through sales of excess electricity. Because of heightened interest

in using biomass, pulping wastes, and sewage sludge in place of fossil fuels, new methods are

being developed to handle higher moisture feeds. Traditional methods include blending the wet

feed with dry material or adding auxiliary fuel to maintain the combustion temperature. When

the dry solids from the filter-press cake are combined with the high soluble, solids syrup, it helps

238 SS316 is an improved version of SS304, with the addition of molybdenum and a slightly higher nickel content.

The resultant composition of 316 gives it much increased corrosion resistance in many aggressive environments.

The molybdenum makes the steel more resistant to pitting and crevice corrosion in chloride-contaminated media,

sea water and acid vapors.

239 SS304-stainless steel is the most versatile and the most widely used of all stainless steels. Its chemical

composition, mechanical properties, weldability and corrosion/oxidation resistance provide the best all-round

performance stainless steel at relatively low cost.
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ensure a stable combustion bed temperature and improved boiler efficiency. In these studies

NREL used a circulating fluidized bed combustor that is suitable for varying feeds and feed

characteristics; however, this flexibility makes the unit more expensive than a grate or pile

combustor. A Lower Heating Value (LHV) of 2,000-2,500 BTU/lb is considered the minimum

for maintaining combustion.240 The combined feed to the combustor has a LHV of 4,179 Btu/lb.

Thus, the total higher-heating value energy of the combined feed streams to the combustor is 706

MMBtu/hr. The solids contribute 59% of this energy and the syrup contributes 37%. A

baghouse removes particulates from the combustion flue gas after it preheats the incoming

combustion air and before it’s discharged through the stack.

The boiler feed water (BFW) system includes a softener for makeup and condensate

water, a deaerator to remove air and other non-condensables, surge tanks and pumps. The

amount of water pretreatment necessary depends on the incoming water quality, metallurgy of

the boiler, and the ratio of makeup to condensate in the feed water. Pretreatment chemicals for

pH control, scale removal, and oxygen removal are added. Treated well water used for makeup

and condensate are softened, deaerated, preheated and mixed to provide BFW that’s converted to

steam that’s superheated to 950 oF at 1,265 psia at the rate of 407,420 lb/hr. Support equipment

includes BFW pumps, deaerator, automatic water pretreatment chemical injection, and

condensate gathered from the various heat exchangers in the process. Boiler efficiency, the

percentage of the feed heat converted to steam heat, is estimated to be 68%. Boiler blowdown is

3% of steam production. The turbine efficiency was estimated to be 85%.

The turbogenerator consisting of a multistage turbine with extraction ports, a generator,

and condenser is used to generate electricity. After high pressure steam drives the multistage

turbines, it is extracted at three different conditions for injection into the pretreatment reactor and

heat exchange in distillation and evaporation. Twenty-eight percent of the steam is extracted

from the turbine at 191 psia and 514°F, 60% at 65 psia and 327°F, and 3% at 25 psia and 239°F

for process needs, as described. The remaining steam (9%) is condensed at 1.5 psia with cooling

water and returned to the BFW system. For this design, a total of 30.4 megawatts (MW) of

power is generated from the system. The process uses 11.7 MW, leaving 18.7 MW that is sold to

the grid.

The installed capital cost for Area 800 are, for 2010, $45.5-million; for 2015, $46-

million; and for 2022, $43.3-million. This area’s contribution to the total installed capital cost is,

for 2010, 34.1%; for 2015, 36.2%; and for 2022, 37.7%.

Area 900: Utilities. All utilities, except steam and electricity, necessary for the

production of ethanol are accounted for in this area. The utilities provided include cooling water,

chilled water, plant and instrument air, process water, and the clean-in-place (CIP) system. No

chilled water is used in the plant; the required process temperatures can be achieved by cooling

water year-round.

240 Steam and Electricity Generation Options For the Biomass-To-Ethanol Process, NREL Subcontract ACO-8-

18019-01, Reaction Engineering International, Salt Lake City, UT, March 16, 1998.

http://www.ott.doe.gov/biofuels/process_engineering.html
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The plant and instrument air systems provide compressed air for air-driven equipment,

instrument operation, for clean up, and the filter-press units in the post-distillation dewatering.

The process water system mixes fresh well water with treated wastewater and provides water at a

constant pressure to the facility. Water is provided to seed production, boiler feed water, cooling

tower make-up, the CIP system, and the scrubber. It is also mixed with recycle water for dilution

before saccharification. Process water is also used throughout the facility for cleaning on an asneeded basis. The CIP system provides a solution that can be heated and includes cleaning and

sterilization chemicals to saccharification and co-fermentation, seed vessels, and the distillation

system.

The installed capital costs for the utilities area are, for 2010, $5.6-million; for 2015, $5.5-

million; and for 2022, $6.1-million. This area’s contribution to the total project installed

equipment costs are, for 2010, 4.2%; for 2015, 4.3%; and for 2022, 5.3%.

Table 4.1-25 summarizes the total projected capital costs for a biochemical cellulosic

ethanol plant for the years 2010, 2015 and 2022.
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Table 4.1-25.

Projected Capital Costs for a Biochemical Cellulosic Ethanol Plant

($million/yr)

2010 2015 2022

Total Capital

Investment

133.5 127 114.8

Added Costs* 98.2 93.1 83.8

Total Project

Investment

231.7 220.1 198.6

*Added costs include the following:

Warehouse: This is estimated to be 1.5% of total installed cost

Site Development: This includes fencing, curbing, parking, lot, roads, well drainage, rail system,

soil borings, and general paving. This factor allows for minimum site development assuming a

clear site, with no unusual problems such as right-of-way, difficult land clearing, or unusual

environmental problems, usually calculated as 9% of the installed cost of process equipment.

Prorateable Costs: This includes fringe benefits, burdens, and insurance of the construction

contractor, usually calculated as 10% of total installed cost.

Field Expenses: This includes consumables, small tool equip. rental, field services, temporary

construction facilities, and field construction supervision, usually calculated as 10% of total

installed cost.

Home Office and Const.: This includes engineering plus incidentals, purchasing, and

construction, usually calculated as 25% of total installed cost.

Project Contingency: These costs are small because of the detail included in the process design

usually calculated as 3% of total installed cost.

Other Costs: This includes start-up and commissioning costs; land, rights-of-way, permits,

surveys, and fees; piling, soil compaction/dewatering, unusual foundations; sales, use, and other

taxes; freight, insurance in transit and import duties on equipment, piping, steel, instrumentation,

etc.; overtime pay during construction; field insurance; project team; transportation equipment,

bulk shipping containers, plant vehicles, etc.; escalation or inflation of costs over time; interest

on construction loan. These other costs are usually calculated as 10% of total capital investment.

The costs for cellulosic ethanol produced biochemically could be lower if not all the

water would have to be removed from the ethanol. Separating the water from ethanol is costly

because water forms an azeotrope with ethanol. Removing the last of the water above the

azeotrope requires additional capital and operating costs. Some research conducted with hydrous

ethanol as well as practical experience in Brazil suggests that by not removing the last few

percent of water from ethanol, the ethanol production costs would be lower and the water

contained in ethanol might not cause driveability, not cause corrosion problems and not lower

the fuel economy. A lot more research needs to be conducted before hydrous ethanol would be

proven as a viable and safe motor vehicle fuel in existing U.S. vehicles.
763

Thermochemical Conversion of Cellulose to Ethanol

Thermochemical conversion is another reaction pathway which exists for converting

cellulose to ethanol. Thermochemical technology is based on the heat and pressure-based

gasification or pyrolysis of nearly any biomass feedstock, including those we’ve highlighted as

likely biochemical feedstocks. The syngas could then be converted into mixed alcohols,

hydrocarbon fuels, chemicals, and power. To produce ethanol, the syngas is passed over a

catalyst which converts the syngas to mixed alcohols – mainly methanol. The methanol can be

reacted further to ethanol.

NREL has authored a thermochemical report which already provided a cost estimate.1142

However, this report only hypothesized how a thermochemical ethanol plant could achieve

production costs at a very low cost of $1 per gallon. However, to obtain a more detailed cost

assessment that may be achievable within the timeframe of our program, EPA contracted NREL

to assess the costs for a thermochemical technology which produces mixed alcohols for years

2010, 2015 and 2022.1143 Table 4.1-26 summarizes the cost information provided by NREL.

Table 4.1-26

Summary of Mixed Cellulosic Alcohol Production Costs by NREL

(2007 dollars, 10% after tax ROI)

2010 2015 2022

Annual Ethanol Production 48.8 56.7 61.9

Annual Total Alcohol Production 57.5 66.6 72.7

Ethanol Yield 63.2 73.4 80.1

Total Alcohol Yield 74.5 86.2 94.1

Capital Costs

Feed Handling and Drying 25.2 25.2 25.2

Gasification 14.0 14.0 14.0

Tar Reforming and Quench 53.4 38.6 41.6

Acid Gas and Sulfur Removal 20.4 14.6 15.8

Alcohol Synthesis Compression 35.4 18.5 17.3

Other Synthesis Costs 6.1 4.7 5.1

Alcohol Separation and Purification 6.8 7.5 7.8

Steam System and Power Generation 19.2 19.7 18.2

Cooling Water and other Utilities 4.2 4.3 3.9

Total Installed Equipment Cost 184.7 147.1 148.9

Added Cost Factors 72.1 57.5 58.1

Total Project Investment 256.8 204.6 207.0

Operating and

Amortized Capital Costs

$MM/yr $/gal

Ethanol

$MM/yr $/gal

Ethanol

$MM/yr $/gal

Ethanol

Feedstock 46.4 0.95 35.4 0.63 35.6 0.58

Catalysts 7.6 0.16 0.2 0.003 0.2 0.003

Olivine 0.5 0.01 0.5 0.01 0.5 0.01

Other Raw Materials 0.4 0.02 0.4 0.02 0.3 0.02

Waste Disposal 0.3 0.01 0.3 0.01 0.3 0.01

Electricity -1.8 -0.04 0.0 0.00 0.0 0.00

Fixed Costs 14.8 0.30 12.7 0.23 12.8 0.21

Co-Product Credits -10.4 -0.21 -11.8 -0.21 -12.9 -0.21

Capital Depreciation, Income Tax and

Return on Investment

46.3 0.95 37.1 0.66 37.5 0.61

Total 104.1 2.15 74.8 1.33 74.3 1.21
764

In its mixed cellulosic alcohol report, NREL did not assess the technology’s costs on the

same basis that EPA is using. NREL used a feedstock cost of $46.1/dry ton which is lower than

our estimate of $67.4/dry ton. Also, NREL amortized the capital costs based on a 10 percent

after tax return on investment (ROI) compared to our 7% before tax ROI. Thus, we adjusted the

NREL cellulosic mixed alcohol costs to reflect our feedstock costs and capital cost amortization

assumptions. Table 4.1-27 contains a summary of the mixed cellulosic alcohol costs based on

our feedstock and capital amortization cost assumptions.

Table 4.1-27

Summary of Adjusted Mixed Cellulosic Alcohol Production Costs by NREL

(2007 dollars, 7% before tax ROI)

2010 2015 2022

Operating and

Amortized Capital Costs

$MM/yr $/gal

Ethanol

$MM/yr $/gal

Ethanol

$MM/yr $/gal

Ethanol

Feedstock 67.8 1.39 51.8 0.92 52.1 0.85

Catalysts 7.6 0.16 0.2 0.003 0.2 0.003

Olivine 0.5 0.01 0.5 0.01 0.5 0.01

Other Raw Materials 0.4 0.02 0.4 0.02 0.3 0.02

Waste Disposal 0.3 0.01 0.3 0.01 0.3 0.01

Electricity -1.8 -0.04 0.0 0.00 0.0 0.00

Fixed Costs 14.8 0.30 12.7 0.23 12.8 0.21

Co-Product Credits -10.4 -0.21 -11.8 -0.21 -12.9 -0.21

Capital Depreciation, Income Tax and

Return on Investment

28.3 0.58 22.8 0.40 22.8 0.37

Total 107.5 2.22 76.8 1.38 76.0 1.26

4.1.1.3 Imported Sugarcane Ethanol Costs

Our analysis of imported ethanol costs began with a literature search of recent estimates

for production costs for sugar cane ethanol in Brazil. Since the liberalization of ethanol prices in

Brazil, few cost estimation studies have been made and most of the cost analyses refer to the

same study.1144 This study was carried out by the Brazilian Ministry of Science and Technology

(MC&T), based on 1990 data, and referred to a production cost of $0.87/gallon. Table 4.1-28

gives a breakdown of costs based on this data.
765

Table 4.1-28. Sugarcane Ethanol Production Costs in Brazil, circa 1990

Average cost

(US$ per gallon)

Operating costs

Labor

Maintenance

Chemicals

Energy

Other

Interest payments on working capital

Feedstock (cane)

$0.64

$0.02

$0.02

$0.01

$0.01

$0.02

$0.08

$0.48

Fixed costs

Capital at 12% depreciation rate

Other

$0.23

$0.19

$0.04

Total $0.87

Since then, there have been significant variations in exchange rates, costs of sugarcane

and oil products, etc. For example, earlier estimates may underestimate crude and natural gas

costs which influence the cost of feedstock as well as energy costs at the plant. Another possible

difference in production cost estimates is whether or not the estimates are referring to hydrous or

anhydrous ethanol. Costs for anhydrous ethanol (for blending with gasoline) are typically

several cents per gallon higher than hydrous ethanol (for use in dedicated ethanol vehicles in

Brazil).1145 It is not entirely clear from the majority of studies whether reported costs are for

hydrous or anhydrous ethanol. Yet another difference could be the slate of products the plant is

producing, for example, future plants may be dedicated ethanol facilities while others involve the

production of both sugar and ethanol in the same facility. Due to economies of scale, production

costs are also typically smaller per gallon for larger facilities. Table 4.1-29 summarizes the

various estimates reported by others. Production costs range from as low as $0.57 per gallon of

ethanol to as high as $1.48 per gallon of ethanol.
766

Table 4.1-29. Other Sugarcane Ethanol Production Cost Estimates

Reference Cost (US$ per gallon)

AgraFNP. 2007. Sugar and Ethanol in Brazil: A

Study of the Brazilian Sugar Cane, Sugar and

Ethanol Industries.

$0.80-$1.07 per gallon (in 2006 $’s depending on

region in Brazil), avg. is $0.78 per gallon for cane

production cost and $0.13 per gallon for industrial

costs

IEA. 2004. Biofuels for Transport: An

International Perspective.

$0.87 per gallon (in 1990 $’s) references MC&T

study; also reports recent production cost estimates

for hydrous ethanol as low as $0.57 per gallon (at

the prevailing exchange rate in Jan. 2004)

USDA. 2006. The Economic Feasibility of

Ethanol Production from Sugar in the United

States.

Avg. is $0.81 per gallon

Von Lampe, Martin. OECD. 2006. Working

Party on Agricultural Policies and Markets:

Agricultural Market Impacts of Future Growth in

the Production of Biofuels.

$0.83 per gallon

Brazil Institute. April 2007. The Global

Dynamics of Biofuels: Potential Supply and

Demand for Ethanol. Issue No. 3.

$0.83 per gallon.

ESMAP. October 2005. Potential for Biofuels

for Transport in Developing Countries.

$.87-$1.10 per gallon

OECD, March 2008. ITF Round Tables No. 138.

Biofuels: Linking Support to Performance.

Avg. is $1.40 per gallon

Bain, R. December 2007. World Biofuels

Assessment Worldwide Biomass Potential:

Technology Characterizations. NREL/MP-510-

42467.

$1.04-$1.48 per gallon depending on size of plant,

i.e. 100 MGY-4.6 MGY; Sugarcane feedstock

costs $0.68 per gallon, Variable operating costs

$0.27 per gallon, Fixed costs $0.02-0.13 per gallon,

and Capital costs $0.07-0.40 per gallon

Macedo, I.C. and L.A.H. Nogueira. 2005.

“Biocombusíveis”. Cadernos NAE, No. 2.

Núcleo de Assuntos Estratégicos da Presidência

da República, Brasilia; As sited in OECD, op.

sit.

$0.79 per gallon in the Center-South Brazil

Kojima, M. and T. Johnson. 2006. “Potential for

Biofuels for Transport in Developing Countries”.

ESMAP Knowledge Exchange Series, No. 4.; As

sited in OECD, op sit.

$0.87-$1.09 per gallon

Smeets, E. 2008. The Sustainability of Brazilian

Ethanol-An Assessment of the Possibilities of

Certified Production. Biomass and Bioenergy

$1.18 assuming exchange rate of $1.20= 1 Euro

Van den Wall Bake, J.D., et. al. 2009.

Explaining the experience curve: Cost reductions

of Brazilian ethanol from sugarcane

$1.29 per gallon presently; Estimates for 2020

range from $0.76-$0.98 per gallon, Sugarcane costs

$0.35-$0.46 per gallon, rest from industrial costs241

241 Costs were given in $/TC and $/m3

, conversions were used to translate to per gallon numbers.
767

The study by OECD (2008) entitled “Biofuels: Linking Support to Performance”, appears

to provide the most recent and detailed set of assumptions and production costs. As such, our

estimate of sugarcane production costs primarily relies on the assumptions made for the study,

which are shown in Table 4.1-30. The estimate assumes an ethanol-dedicated mill and is based

off an internal rate of return of 12%, a debt/equity ratio of 50% with an 8% interest rate and a

selling of surplus power at $57 per MWh.

Table 4.1-30. Cost of Production in a Standard Ethanol Project in Brazil

Sugarcane Productivity 71.5 t/ha

Sugarcane Consumption 2 million tons/year

Harvesting days 167

Ethanol productivity 85 liters/ton (22.5 gal/ton)

Ethanol Production 170 million liters/year (45 MGY)

Surplus power produced 40 kWh/ton sugarcane

Investment cost in mill USD 97 million

Investment cost for sugarcane production USD 36 million

O & M (Operating & Maintenance) costs $0.26/gal

Variable sugarcane production costs $0.64/gal

Capital costs $0.49/gal

Total production costs $1.40/gal

The estimate above is based on the costs of producing ethanol in Brazil on average,

today. However, we are interested in how the costs of producing ethanol will change by the year

2022. Although various cost estimates exist, analysis of the cost trends over time shows that the

cost of producing ethanol in Brazil has been steadily declining due to efficiency improvements in

cane production and ethanol conversion processes. Between 1980 and 1998 (total span of 19

years) ethanol cost declined by approximately 30.8%.1146 This change in the cost of production

over time in Brazil is known as the ethanol cost “Learning Curve”. See Figure 4.1-2.

Figure 4.1-2. Ethanol Cost “Learning Curve”

The change in ethanol costs will depend on the likely productivity gains and

technological innovations that can be made in the future. As the majority of learning has already

occurred, it is likely that the decline in ethanol costs will be less drastic in the future as the
768

production process and cane practices have matured. Industrial efficiency gains are already at

about 85% and are expected to increase to 90% in 2015.1147 Most of the productivity growth is

expected to come from sugarcane production, where yields are expected to grow from the current

70 tons/ha, to 96 tons/ha in 2025.1148 Sugarcane quality is also expected to improve, with

sucrose content growing from 14.5% to 17.3% in 2025. 1149 All productivity gains together could

allow the increase in the production of ethanol from 6,000 liters/ha (at 85 liters/ton sugarcane) to

10,400 liters/ha (at 109 liters/ton sugarcane) in 2025. 1150

Assuming that ethanol productivity increases to 100 liters/ton by 2015 and 109 liters/ton

by 2025, variable sugarcane ethanol production costs are be expected to decrease to

approximately $0.51/gal from $0.64/gal since less feedstock is needed to produce the same

volume of ethanol using the estimates from Table 4.1-30, above. Table 4.1-31 shows the

calculated decrease for the years 2005-2025. We assumed a linear decrease between data points

for 2005, 2015, and 2025. Adding operating ($0.26/gal) and capital costs ($0.49/gal) from Table

4.1-27, to a sugarcane cost of $0.51/gal, total production costs are $1.26/gal in 2022.

Table 4.1-31.

Estimated Decrease in Sugarcane Production Cost by 2022

Due to Increases in Ethanol Productivity

Sugarcane

Production Cost

$/gal liters/ton gal/ton

2005 0.64 85 22.46

2006 0.63 86.5 22.85

2007 0.62 88 23.25

2008 0.61 89.5 23.65

2009 0.60 91 24.04

2010 0.59 92.5 24.44

2011 0.58 94 24.83

2012 0.57 95.5 25.23

2013 0.56 97 25.63

2014 0.55 98.5 26.02

2015 0.54 100 26.42

2016 0.54 100.9 26.66

2017 0.53 101.8 26.90

2018 0.53 102.7 27.13

2019 0.53 103.6 27.37

2020 0.52 104.5 27.61

2021 0.52 105.4 27.85

2022 0.51 106.3 28.08

2023 0.51 107.2 28.32

2024 0.50 108.1 28.56

2025 0.50 109 28.80

Brazil sugarcane producers are also expected to move from burned cane manual

harvesting to mechanical harvesting. See Figure 4.1-3.1151 As a result, large amounts of straw

are expected to be available. Costs of mechanical harvesting are lower compared to manually
769

harvesting, therefore, we would expect costs for sugarcane to decline as greater sugarcane

producers move to mechanical harvesting. However, diesel use increases with mechanical

harvesting and with diesel fuel prices expected to increase in the future, costs may be higher than

expected. Therefore, we have not assumed any changes to harvesting costs due to the switchover

from manual harvesting to mechanical harvesting.

Figure 4.1-3. Phase-out Schedule for Trash Burning Practices

As more straw is expected to be collected at future sugarcane ethanol facilities, there is

greater potential for production of excess electricity. The production cost estimates in the OECD

study assumes an excess of 40 kWh per ton sugarcane, however, future sugarcane plants are

expected to produce 135 kWh per ton sugarcane assuming the use of higher efficiency

condensing-extraction steam turbine (CEST) systems and use of 40% of available straw.1152

Assuming excess electricity is sold for $57 per MWh, the production of an additional 95 kWh

per ton would be equivalent to a credit of $0.22 per gallon ethanol produced. We have included

this potential additional credit from greater use of bagasse and straw in our estimates at this time,

calculated as a decrease in operating costs from $0.26 per gallon to $0.04 per gallon.

It is also important to note that ethanol production costs can increase if the costs of

compliance with various sustainability criteria are taken into account. For instance, using

organic or green cane production, adopting higher wages, etc. could increase production costs for

sugarcane ethanol.1153 Such sustainability criteria could also be applicable to other feedstocks,

for example, those used in corn- or soy-based biofuel production. If these measures are adopted

in the future, production costs will be higher than we have projected.
770

In addition to production costs, there are also logistical and port costs. We used the

report from AgraFNP to estimate such costs since it was the only resource that included both

logistical and port costs. The total average logistical and port cost for sugarcane ethanol is

$0.20/gal and $0.09/gal, respectively, as shown in Table 4.1-32.

Table 4.1-32.

Imported Ethanol Cost at Port in Brazil (2007 $’s)

Logistical Costs Port Cost

Region US ($/gal) US ($/gal)

NE Sao Paulo 0.150 0.097

W Sao Paulo 0.210 0.097

SE Sao Paulo 0.103 0.097

S Sao Paulo 0.175 0.097

N Parana 0.238 0.097

S Goias 0.337 0.097

E Mato Grosso do sul 0.331 0.097

Triangulo mineiro 0.207 0.097

NE Cost 0.027 0.060

Sao Francisco Valley 0.193 0.060

Average 0.197 0.089

Total fuel costs must also include the cost to ship ethanol from Brazil to the U.S. The

average cost from 2006-2008 was estimated to be approximately $0.17 per gallon of ethanol.1154

Costs were estimated as the difference between the unit value cost of insurance and freight (CIF)

and the unit value customs price. The average cost to ship ethanol from Caribbean countries

(e.g. El Salvador, Jamaica, etc.) to the U.S. from 2006-2008 was approximately $0.13 per gallon

of ethanol. Although this may seem to be an advantage for Caribbean countries, it should be

noted that there would be some additional cost for shipping ethanol from Brazil to the Caribbean

country. Therefore, we assume all costs for shipping ethanol to be $0.17 per gallon regardless of

the country importing ethanol to the U.S.

The total imported ethanol fuel costs (at U.S. ports) over the time period of 2010 to 2022

are shown in Table 4.1-33. In 2022, the total sugarcane ethanol cost estimate prior to tariffs and

taxes is $1.50/gallon. Direct Brazilian imports are also subject to an additional $0.54 per gallon

tariff, whereas those imports arriving in the U.S. from Caribbean Basin Initiative (CBI) countries

are exempt from the tariff. In addition, all imports are given an ad valorem tax of 2.5% for

undenatured ethanol and a 1.9% tax for denatured ethanol. We assumed an ad valorem tax of

2.5% for all ethanol. Thus, including tariffs and ad valorem taxes, the average cost of imported

ethanol is shown in Table 4.1-34 in the “Brazil Direct w/ Tax & Tariff” and “CBI w/ Tax”

columns for the years 2010-2022.
771

Table 4.1-33. Average Imported Ethanol Costs Prior to Tariff and Taxes

Sugarcane

Production

Cost ($/gal)

Operating

Cost

($/gal)

Capital

Cost

($/gal)

Logistical

Cost

($/gal)

Port

Cost

($/gal)

Transport Cost

from Port to US

($/gal)

Total

Cost

($/gal)

2010 0.59 0.04 0.49 0.20 0.09 0.17 1.58

2011 0.58 0.04 0.49 0.20 0.09 0.17 1.57

2012 0.57 0.04 0.49 0.20 0.09 0.17 1.56

2013 0.56 0.04 0.49 0.20 0.09 0.17 1.55

2014 0.55 0.04 0.49 0.20 0.09 0.17 1.54

2015 0.54 0.04 0.49 0.20 0.09 0.17 1.53

2016 0.54 0.04 0.49 0.20 0.09 0.17 1.53

2017 0.53 0.04 0.49 0.20 0.09 0.17 1.52

2018 0.53 0.04 0.49 0.20 0.09 0.17 1.52

2019 0.53 0.04 0.49 0.20 0.09 0.17 1.52

2020 0.52 0.04 0.49 0.20 0.09 0.17 1.51

2021 0.52 0.04 0.49 0.20 0.09 0.17 1.51

2022 0.51 0.04 0.49 0.20 0.09 0.17 1.50

Table 4.1-34. Average Imported Ethanol Costs

Brazil

Direct

($/gal)

Brazil Direct w/

Tax & Tariff ($/gal)

CBI

($/gal)

CBI w/ Tax

($/gal)

2010 1.58 2.16 1.58 1.62

2011 1.57 2.15 1.57 1.61

2012 1.56 2.14 1.56 1.60

2013 1.55 2.13 1.55 1.59

2014 1.54 2.12 1.54 1.58

2015 1.53 2.11 1.53 1.57

2016 1.53 2.11 1.53 1.57

2017 1.52 2.10 1.52 1.56

2018 1.52 2.10 1.52 1.56

2019 1.52 2.10 1.52 1.56

2020 1.51 2.09 1.51 1.55

2021 1.51 2.09 1.51 1.55

2022 1.50 2.08 1.50 1.54
772

4.1.2 Biodiesel Production Costs

Virgin vegetable oils, fats, waste oils and greases costs

The feedstocks that we project to make up the largest share of biodiesel are virgin

vegetable oil (primarily soy oil) and non-food-grade corn oil generated as a co-product of dry

mill ethanol production. These feedstock streams were included in the agricultural commodity

modeling done for this rulemaking using the FASOM model. This work is described in detail in

Section 2.5 of this RIA. Table 4.1-35 summarizes the volumes and costs of these feedstocks.

Rendered fats and other waste greases are expected to make up a smaller, but still

important, source of biodiesel feedstock. These were not explicitly modeled by FASOM;

242therefore their value was estimated to be 70% that of soy oil, based on historical trends.

Table 4.1-35. Summary of biodiesel feedstock use and cost

for primary control case in 2022 (2007$).

Fuel / Feedstock Feedstock Price

($/lb)

Projected use in 2022

(MMgal)

Soy oil 0.33 a660

Corn oil from dry mill ethanol production 0.17 a680

Yellow grease or other rendered fats 0.23 b230

Algae oil or other advanced feedstock 0.58 c100

Algae-derived oils costs

Algae oil cost projections are based on Aspen modeling completed by NREL and are

reported in 2007 dollars. Detailed cost information is provided in their report submitted to the

docket.1155 The results are summarized below in Figure 4.1-4. Two production pathways were

assumed, open pond (OP) and photobioreactor (PBR) systems. For each production pathway a

base case, aggressive case, and maximum development case were evaluated based on

assumptions on key variables e.g., yield, lipid content on algae, etc. The oil production cost for

the open pond case ranged from $11.25/gal in the base case to $3.99/gal for the max case. The

oil production cost for the PBR case ranged from $19.49/gal in the base case to $6.62/gal in the

max case.

242 Data available from various sources suggests that tallow and yellow grease prices have been closer to half the

value of crude soy oil, but we have chosen to assume 70% as this is what USDA/ARS had assumed during some

initial cost modeling they had done for us. Also, given that rendered fat volumes will be more limited than

vegetable feedstocks, we might expect their prices to rise against the alternative (and still more expensive) vegetable

feedstocks in a climate of higher biofuel production.
773

Figure 4.1-4.

Cost to Produce 10 MMgal/yr oil at growth rate scenarios developed by NREL for open

pond (op) and photobioreactor (PBR) production. TAG =

triacylglyceride

Since algal biofuel technology is still in a relatively early stage of development, there is a

higher degree of uncertainty associated with potential performance and cost relative to more

established technologies. It is important to note that the “max” case merely means the maximum

algae growth and oil content applied to the specific configuration and associated assumptions

analyzed and does not imply that these are the absolute lowest costs that can ever be achieved as

technology develops. For the algal technology systems analyzed, the report indicated that the

areas with the highest economic impact include the optimum amount of nutrients required, the

CO2 delivery cost, the flocculant requirement for harvesting, and the material costs for the PBR

production system. The economic modeling assumptions and results from NREL for

microalgae-derived oil correspond well with other studies which report the cost of production for

algae oil from $1 to >$40/gal.1156

NREL also investigated the uncertainty in key assumptions and the associated potential

cost impact of such assumptions in a sensitivity analysis. Figure 4.1-5 shows that for open

ponds, the amount of nutrients required has the highest impact on production cost of the

variables evaluated. Figure 4.1-6 shows that the single largest cost item in the PBR system is the

cost of the tubes themselves.
774

Figure 4.1-5. Open Pond Sensitivity Analysis

Figure 4.1-6: PBR Sensitivity Analysis

For this rulemaking, we made the simplifying assumption of using the production cost of

the open pond aggressive case of $5.11/gal or $0.68/lb for this feedstock to estimate costs of

algae-derived fuel. Given the uncertainties in estimating costs for algae as well as the need for a

single-value estimate for algal oil for cost analyses purposes, we chose the open pond aggressive

case which appears to represent a somewhat middle value as well as a more reasonably

competitive feedstock with alternatives, such as soy oil.
775

Biodiesel production costs

Biodiesel production costs for this rule were estimated using two variations of a model

generated by USDA for a 10 million gallon-per-year transesterification biodiesel plant. One

version uses degummed soy oil as feedstock, and a second version includes acid pre-treatment

steps required to utilize feedstocks such as rendered fat and yellow grease, which have higher

free fatty acid content. USDA used the SuperPro Designer chemical process simulation software

to build up a process model with estimates of heat and material flowrates and equipment sizing.

Outputs from this model were then combined in a spreadsheet with capital, energy, labor, and

feedstock costs to generate a final estimate of production cost. Additional details on the model

are given in a 2006 technical publication in the peer-reviewed scientific journal Bioresource

Technology.1157 At 10 million gallons per year, the modeled plant size is between the mean and

median plant sizes (16 million and 6 million gal/yr, respectively) as given in our industry

characterization. Therefore, the model cost estimate is believed to be sufficiently accurate for

our analyses and no further work was done to determine the effect of scale on production cost.

This model is periodically updated by USDA to reflect technology upgrades, changes to

cost of capital, etc. Such an update was made to the model just before its outputs were used in

the analyses presented here. We also made modifications to the capital cost to be consistent with

typical cost amortization schemes used for regulatory programs. The capital charge estimate was

derived as shown in Table 4.1-36. Installed capital cost was $11.9 million; adding 3% annual

maintenance charge, we arrive at a final capital charge of 14% annually. Energy prices were

taken from AEO 2009: natural gas at $7.75/MMBtu and electricity at $0.066/kWh for 2022 in

2007 dollars.
776

Table 4.1-36. Economic Factors Used in Deriving the Capital Cost Amortization Factor

Amortization

Scheme

Depreciation

Life

Economic

and Project

Life

Federal and

State Tax

Rate

Return on

Investment

(ROI)

Annual

Maintenance

Charge

Resulting

Capital

Amortization

Factor

Societal Cost 10 Years 15 Years 0% 7% 3% 0.14

The value of the glycerin co-product has been depressed and volatile in recent years due

to a large increase in production in biodiesel facilities. This has been balanced at times by new

uses coming online as feedstocks for traditionally petrochemical-based products as well as

increased demand in personal care and other consumer products as the standard of living

increases in many parts of the world. Some facilities are even experimenting with using it as a

supplemental fuel along with biomass or other materials. We can expect that new uses for

glycerin will continue to be found as long as it is plentiful and cheap. For a simple and

conservative projection of its value in the future, we have assumed an energy-equivalent value

based on residual heating oil. Using a heating value of 7,979 Btu/lb and a heating oil value of

$18.90/MMBtu in 2022, we arrive at $0.15/lb as a co-product value. Table 4.1-37 shows the

overall process material balance output by the model.

Table 4.1-37. Material Balance and Values for Biodiesel Production Model in 2022

Stream By weight Estimated value (2007$)

Soy oil input 100 $0.33/lb

Methanol input 11 $0.16/lb

Biodiesel output (main product) 100 -

Glycerin output (co-product) 12 $0.15/lb

Table 4.1-38 shows the production cost allocation for the soy oil-to-biodiesel facility as

modeled in the 2022 primary control case. Production cost for biodiesel is primarily a function of

feedstock price, with other process inputs, facility, labor, and energy comprising much smaller

fractions.

Table 4.1-38. Production cost allocation for soy biodiesel derived from this analysis for the

primary control case in 2022

Cost Category Share of Per-Gallon Cost

Soy Oil 85%

Other Materials a6%

Capital & Facility 6%

Labor 2%

Utilities 2%

a

Includes acids, bases, methanol, catalyst
777

4.1.3 Renewable Diesel Production Costs

The renewable diesel process converts rendered fats (or plant oils) into diesel fuel using

thermal depolymerization, which is similar to refinery hydrotreating used to remove sulfur. The

process uses hydrogen and catalyst to remove oxygen from the triglyceride molecules in the

feedstocks oils via a decarboxylation and hydro-oxygenation reaction, yielding some light

petroleum products and water as byproducts. The reactions also saturate the olefin bonds in the

feedstock oils, converting them to paraffins, and may also isomerize some paraffins. Depending

on process operating conditions, the yield to diesel-range material is typically between 90-99%

volume, with the rest being naphtha and light fuel gases (primarily propane).

As described in the industry characterization discussion (see RIA Section 1.5.4), we have

chosen to focus on stand-alone renewable diesel production, as we believe this will be the

primary pathway given tax incentives and the definition of the biomass-based diesel fuel

category. We assume a total project cost of $150MM for a standalone facility based on materials

made publically available by Syntroleum Corp. related to their Geismar, LA, project.1158

Our operating cost and yield estimates were derived from material presented by UOP and

Eni at a 2007 industry conference, which describes producing renewable diesel in a grass roots

standalone production process inside a refinery.1159 In addition to feedstock and facility costs,

another significant cost input is hydrogen. For hydrogen operating costs, we used the UOP

analysis and guidance from Conoco Philips to derive our estimate to make renewable diesel. 1160

The UOP paper presents a range of 1000 to 2000 standard cubic feed (scf) per barrel for

converting the various feedstock to renewable diesel. Based on characteristics of rendered fats,

we estimated a hydrogen demand of 1,590 SCF/bbl of feedstock processed.

Accounting for this quantity of hydrogen, along with a value taken from our refinery

modeling work, we derived a figure of 6.9 cents/gallon of diesel product to cover utilities, labor,

and other costs. Finally, total cost per gallon was estimated at $2.42 for the 2022 primary control

case (2007 dollars). Table 4.1-39 gives more details for the process assumed in this analysis.

Co-product values were also taken from refinery modeling work done for this rulemaking. Table

4.1-40 shows the cost allocation we arrived at for renewable diesel production.

Table 4.1-39.

Parameters used in renewable diesel production cost estimates.

Stream By volume Estimated value (2007$)

Fat input 100 gal $0.23/lb

Hydrogen input 505 scf $0.0044/scf

Renewable diesel output (main product) 93.5 gal -

Naphtha output (co-product) 5 gal $0.55/lb

Light fuel gas output (co-product) 9 gal $0.13/lb
778

Table 4.1-40.

Production Cost Allocation for Renewable Diesel for Primary Control Case in 2022

Cost Category Contribution to Cost

Feedstock 78%

Capital & Facility 11%

Hydrogen 7%

Other variable costs 3%

4.1.4. Biodiesel and Renewable Diesel Cost Summary

Table 4.1-41 summarizes the feedstock prices and fuel production cost for biodiesel.

Table 4.1-42 gives the same information for renewable diesel. Combined with information from

Table 4.1-35, a weighted average production cost could be estimated (our overall economic

impacts take into account this information).

Table 4.1-41

Summary of Costs for Biodiesel for the Primary Control Case in 2022

Fuel / Feedstock Feedstock Price

($/lb)

Fuel Production Cost

($/gal)

Biodiesel / soy oil 0.33 a2.73

Biodiesel / corn oil extraction at ethanol

plants

0.17 a1.90

Biodiesel / yellow grease or other rendered

fats

0.23 b2.43

Biodiesel / algae or other advanced virgin oil

feedstock

0.58 c4.52 d

a Taken from outputs of FASOM model.

b Derived from outputs of FASOM model, assuming 70% value of soy oil.

c Derived from figures in a Technical Memo by Ryan Davis of NREL entitled “Techno-economic analysis of

microalgae-derived biofuel production” (available in docket).

d This production cost assumes this advanced feedstock has very low free fatty acid content.

Table 4.1-42

Summary of Cost for Renewable Diesel for the Primary Control Case in 2022

Fuel / Feedstock Feedstock Price

($/lb)

Fuel Production Cost

($/gal)

Renewable diesel / yellow grease or other

rendered fats

0.23 a2.42

a Derived from outputs of FASOM model, assuming 70% value of soy oil.
779

4.1.5 BTL Diesel Production Costs

Biofuels-to-Liquids (BTL) processes, which are also thermochemical processes, convert

biomass to liquid fuels via a syngas route. If cellulose is converted to syngas, rather than

converting the syngas to mixed alcohols, a Fischer Tropsch reactor can be added to convert the

syngas to diesel fuel and naphtha. The primary product produced by this process is diesel fuel.

This technology is commonly termed biomass-to-liquids (BTL) because of its similarity to gasto-liquids and coal-to-liquids technology. Diesel fuel’s higher energy density per gallon than

ethanol and even biodiesel provides it an inherent advantage over these other fuels. In addition,

BTL diesel fuel can be more easily distributed from production to retail outlets and used by

motor vehicles. The diesel fuel produced by the Fischer Tropsch process tends to be comprised

of paraffins which provide a much higher cetane number than petroleum diesel fuel, with a

downside of poorer cloud point which reduces its widespread use in cold temperatures.

There are many steps involved in a BTL process which makes this a capital-intensive

process. The first step, like all the cellulosic processes, requires that the feedstocks be processed

to be dried and ground to a fine size. The second step is the syngas step, which

thermochemically reacts the biomass to carbon monoxide and hydrogen. Since carbon monoxide

production exceeds the stoichiometric ideal fraction of the mixture, a water shift reaction must be

carried out to increase the relative balance of hydrogen. The syngas products must then be

cleaned to facilitate the following Fischer-Tropsch (FT) reaction. The Fischer-Tropsch reaction

reacts the syngas to a range of hydrocarbon compounds – a type of synthetic crude oil. This

hydrocarbon mixture is then hydrocracked to maximize the production of high cetane diesel fuel,

although some low octane naphtha and small amounts of wax are also produced. The many steps

of the BTL process contribute to its high capital cost.

Although there were several studies available which provided costs estimates for BTL

diesel fuel, they did not provide sufficient detail to understand all the cost elements of BTL

diesel fuel and naphtha. EPA therefore contracted with NREL to estimate the production costs

for BTL diesel fuel and naphtha. Like the other technologies, we asked for cost estimates for the

same years assessed above for cellulosic ethanol which was for 2010, 2015 and 2022, however,

NREL did not believe that the costs would change that much over this time span. So NREL only

provided the costs for 2022, advising us that the costs would only be slightly less for earlier

years, and most of that difference would because of the poorer economies of scale if the initial

plants are sized smaller. Table 4.1-43 summarizes the cost information provide by NREL to

EPA for a year 2022 cellulosic BTL plant.
780

Table 4.1-43

Year 2022 Production Costs for Thermochemcal (BTL) Cellulosic

Fischer Tropsch Diesel Fuel Provided by NREL

(2007 dollars and 10% after tax rate of return)

Plant Size MMgal/yr 33.2 Diesel Fuel

49.4 All Liquid

Capital Cost $MM 346

Capital Cost 10% ROI after taxes ($MM/yr) 61.7

Fixed Costs ($MM/yr) 18.3

Feedstock Cost ($MM/yr) 39.1

Coproduct Credit ($MM/yr)a-53.5

Other raw material Costs ($MM/yr) 0.9

Waste Disposal and Catalyst Costs ($MM/yr) 1.1

Total Costs ($MM/yr) 79

Total Costs (cents/gallon of diesel fuel) 206

a Based on a naphtha coproduct value of 327 cents per gallon.

NREL estimated that diesel fuel made by a 33 million gallon per year FT plant in 2022

could be produced at $2.06 per gallon estimated (in year 2007 dollars).1161 Three adjustments

however are needed to make the NREL production cost compatible with the rest of our analysis:

1) increase the feedstock costs, 2) reduce the capital charge costs and 3) adjust the co-product

prices for naphtha and wax.

For capital charges, the NREL costs were based on amortizing capital assuming a 10

percent rate of return after taxes, using an annual capital charge factor of 0.178. The report’s

estimate for capital costs was $346 million for the plant, resulting in annual capital cost of $61.6

million or $1.85 per gallon of diesel fuel produced. We adjusted the capital cost by amortizing

the capital cost assuming a 7 percent rate of return before taxes, using an annual capital charge

factor of 0.11 which resulted in yearly cost of $38 million or $1.14 per gallon of diesel fuel

produced.

In the NREL study, the total operating cost due to feedstock is $1.17 per gallon, using

wood at $50.7 per dry ton. We adjusted the feedstock cost to $67.4 per dry ton, see subsection

4.1.1.2.1, which increased the feedstock costs to $1.56 per gallon of diesel fuel.

In the NREL analysis, the co-products produced have a credit value of $1.60 per gallon

of diesel fuel, assuming a price for naphtha of $3.27 per gallon and wax at $0.49 per pound. The

price of naphtha was set by NREL at 40 cents per gallon below the price of gasoline to account

for its low octane value. The naphtha produced by the BTL process is also largely comprised of

paraffins, however, as a gasoline blendstock it is poor because its octane could potentially be as

low as 50. This material could be processed by refinery isomerization units raising its octane to

perhaps 70 octane, but it cannot be processed by refinery reformers since it does not contain the

napthenic compounds that are necessary for octane improvement by those units. Because of the

large amount of octane rich ethanol which is expected to be made available from both corn and

cellulose, it could be that BTL naphtha could be blended along with the ethanol into the gasoline

pool. Rather than prejudge how this naphtha may be utilized in the future, for our cost analysis
781

we simply assigned it a coproduct credit. So we set the BTL naphtha cost to be 83% as much of

the cost of BTL diesel fuel based on its relative energy density. This results in a naphtha price of

$1.98 per gallon. We adjusted the price of wax, by ratio-ing the NREL price by the change in

price for naphtha. The price adjustment for naphtha and wax, results in a co-product value of

$0.97 per gallon of FT diesel produced. The result is a diesel fuel production cost of $2.37 per

gallon from the FT process.

Table 4.1-44 summarizes NREL’s estimated and projected production costs for a

thermochemical Fischer Tropsch biochemical cellulosic ethanol plant technology for their

projected year 2022 technology in 2007 dollars reflecting a 7 percent before tax rate of return on

investment. The costs are based on a cellulosic feedstock cost of $67.4 per dry ton.

Table 4.1-44

Year 2022 Production Costs for Thermochemcal (BTL) Cellulosic

Fischer Tropsch Diesel Fuel Provided by NREL

(2007 dollars and 7% before tax rate of return)

Plant Size MMgal/yr 33.2 Diesel Fuel

49.4 All Liquid

Capital Cost $MM 346

Capital Cost 7% ROI before taxes ($MM/yr) 38

Fixed Costs ($MM/yr) 18

Feedstock Cost ($MM/yr) 52

Coproduct Credit ($MM/yr)a-32

Other raw matl. Costs ($MM/yr) 1.5

Waste Disposal and Catalyst Costs ($MM/yr) 1.5

Total Costs ($MM/yr) 79

Total Costs (cents/gallon of diesel fuel) 237

a Based on a naphtha coproduct value of 198 cents per gallon.

Initially, the estimated cost of $2.37 per gallon seems high relative to the projected cost

for a year 2022 biochemical cellulosic ethanol plant, which is 126 cents per gallon of ethanol

(see subsection 4.1.1.2.4). However, ethanol provides about half the energy content on a per

gallon basis as Fischer Tropsch diesel fuel. So, if we double the biochemical cellulosic ethanol

costs to $2.52 to be consistent with the energy per diesel fuel-equivalent gallon, the estimated

costs are very consistent between the two. The cellulosic biofuel tax subsidy currently favors the

biochemical ethanol plant, though, because it is a per-gallon subsidy regardless of the energy

content, and it therefore offsets twice as much cost as the BTL plant producing diesel fuel.

However, the cellulosic diesel fuel may still be more valuable in the marketplace than cellulosic

ethanol. In 2008 and for much of 2009 diesel fuel was priced higher than gasoline, and if this

trend continues in the future, it may provide a better market for selling the BTL diesel fuel than

for selling biochemical ethanol into the E85 market.

It was necessary to estimate cellulosic diesel fuel costs for previous years for the year-byyear cost analysis (see Section 4.4). However, NREL did not provide costs for previous years,

although NREL did say that the primary difference in costs for the previous years would be
782

economies of scale impacts for the capital costs due to smaller sized plants. Thus, to derive a

cost for 2010, we estimated that the cellulosic diesel fuel plants that would be installed in 2010

would be half the size of the plant estimated for 2022. While the total capital costs decrease, the

capital costs increase relative to the volume of cellulosic diesel fuel produced. We increased the

naphtha credit from 198 cents per gallon to 216 cents per gallon to maintain an 83% cost

percentage relative to the cellulosic diesel fuel. For this smaller plant size, we estimate the cost

for cellulosic diesel fuel to be 258 cents per gallon.

Other Cellulosic Diesel Fuel Costs

For our volumes analysis, we assumed early on for our final rule analysis that there

would likely be one or more other cellulosic diesel fuel technologies, other than BTL, producing

cellulosic diesel fuel. However, we were either not able to obtain cost information from them, or

we were uncertain enough about their future that we felt that we should not base the cost of the

program on them. For example, Cello Energy has already built a cellulosic diesel fuel facility in

Alabama here in the US with projected costs of about one dollar per gallon of diesel fuel.

However, the facility has had difficulty operating as designed. As a result, perhaps very

conservatively, we assumed that the other cellulosic diesel fuel costs would be the same as the

BTL diesel fuel costs, and used the 237 cents per gallon cost for BTL diesel fuel for the entire

cost for cellulosic diesel fuel.

4.2 Renewable Fuel Distribution Costs

Our analysis of the costs associated with distributing the volumes of biofuels that we

project will be used under RFS2 focuses on: 1) the capital cost of making the necessary

upgrades to the fuel distribution infrastructure system directly related to handling these fuels, and

2) the ongoing additional freight costs associated with shipping renewable fuels to the point

where they are blended with petroleum-based fuels. Our analysis considers distribution costs

within the U.S. only. The costs associated with bringing ethanol imports to the U.S. are

considered in the context of the cost of the imports themselves. We chose to evaluate the

distribution costs for cellulosic distillate and renewable diesel together because the same

considerations apply to their handling in the fuel distribution system and because the projected

volume of renewable diesel fuel is relatively small. The following sections outline our estimates

of the distribution costs for the additional volumes of ethanol, cellulosic distillate fuel/renewable

diesel fuel, and biodiesel that we project will be used to meet the RFS2 standards. There will be

ancillary costs associated with upgrading the basic rail, marine, and road transportation nets to

handle the increase in freight volume due to the RFS2. We have not sought to quantify these

ancillary costs because 1) the growth in freight traffic that is attributable to RFS2 represents a

minimal fraction of the total anticipated increase in freight tonnage (approximately 0.4% by

2022, see Section 1.6.3 of this RIA), and 2) we do not believe there is an adequate way to

estimate such non-direct costs.

The biofuels used in response to the RFS2 standards will displace petroleum-based fuels

that would otherwise be used. Thus, it would be appropriate to subtract the distribution costs for

the displaced petroleum-based fuels from the distribution costs attributed to the biofuels that
783

replace these petroleum-based fuels. However, we chose not to do so as it is difficult to project

exactly what changes would result in cost savings. As a result, our analysis should provide a

conservatively high estimate of biofuel distribution costs given the uncertainties in our analysis.

A discussion of the changes that will be needed in the biofuels distribution system to

accommodate the increased volumes of biofuels that we project will be used to meet the RFS2

standards is contained in Section 1.6 of this RIA. In this Section, we further detail the nature of

these projected changes and estimate the associated costs. Distribution capital costs associated

with the additional volume of ethanol, cellulosic distillate fuel/renewable diesel, and biodiesel

that we project will be used by 2022 to meet the RFS2 standards under the primary mid-ethanol

scenario relative to the AEO 2007 reference case totals $8.4 billion, of which 68% is attributed to

ethanol, 17% to cellulosic distillate/renewable diesel fuel, and 14% to biodiesel.

4.2.1 Ethanol Distribution Costs

As discussed in the following sections, we estimate that the total capital costs in the U.S

to support distribution of the additional volume of ethanol that will be used to meet the RFS2

standards under the primary mid-ethanol scenario will be $5.5 billion by 2022 relative to the

AEO 2007 reference case. When amortized, this translates to 7 cents per gallon of additional

ethanol used to meet the RFS2 standards. Amortization of capital costs was done over 15 years

at a 7% annual cost of capital except in the case of the cost of tank trucks where a 10 year

amortization schedule was used. These costs were calculated relative to the AEO 2007 baseline

which projects that 13.2 BGY of ethanol would be used in 2022 absent the RFS2 standards.

Under the mid-ethanol primary scenario, we project that 22.2 BGY of ethanol will be used by

2022. Ethanol freight costs are estimated to be 13 cents per gallon on a national average basis.

Thus, we estimate that total ethanol distribution costs will be 20 cents per gallon of ethanol that

we project will be used to meet the RFS2 standards.243

The ethanol distribution capital and freight costs for all the control scenarios relative to

the 2 reference cases that we evaluated in this FRM is summarized in Table 4.2-1. The itemized

ethanol capital costs are presented in Table 4.2-2 relative to the AEO 2007 reference case, and in

Table 4.2-3 relative to the RFS1 reference case. These costs do not include the potential costs to

supply butane to terminals for E85 blending, which are presented in Table 4.2-4. The way in

which we estimated these costs is detailed in the following sections.

243 As noted previously, we chose not to subtract the distribution costs for the petroleum-based fuels that would be

displaced by the use of biofuels from our estimated biofuel distribution costs. We believe that the freight costs to

ship petroleum-based fuels to the terminal are approximately 4 cents per gallon. If we were to subtract these costs

from the estimated ethanol distribution costs under the mid-ethanol scenario relative to the AEO 2007 reference

case, the result would be 16 cents per gallon.
784

Table 4.2-1.

Summary of Estimated Ethanol Distribution Capital and Freight Costs for the RFS2

Control Scenarios Relative to the Reference Cases

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

RFS1

Reference

AEO

Reference

RFS1

Reference

AEO

Reference

RFS1

Reference

AEO

Reference

Billion $

Capital

5.5 3.0 7.9 5.5 11.9 9.9

Capital

Costs (cpg)

6 8 6 7 5 6

Freight

Costs

(cpg)

13 13 13 13 12 12

Total

Distribution

Costs

(cpg)

19 21 19 20 17 18

Table 4.2-2.

Summary of Estimated Ethanol Distribution Capital Costs

for the RFS2 Control Scenarios Relative to the AEO 2007 Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Rail Cars 365 760 1,699

Barges 22 45 102

Tank Trucks 42 87 194

Storage Tanks at Petroleum Terminals 355 739 1,568

Blending and other Miscellaneous

Equipment at Petroleum Terminals

345 411 503

Unit Train Receipt Facilities 238 434 748

Manifest Rail Receipt Facilities 7 12 21

Marine Receipt Facilities for Inta-U.S.

Transport

76 100 144

Import Receipt Facilities 49 53 63

E85 Retail Facilities 1,526 2,863 4,893

Total (Million $) 3,025 5,505 9,935

Total (cpg) 8 7 6
785

Table 4.2-3.

Summary of Estimated Ethanol Distribution Capital Costs

for the RFS2 Control Scenarios Relative to the RFS1 Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Rail Cars 884 1,279 2,218

Barges 53 77 133

Tank Trucks 107 154 268

Storage Tanks at Petroleum Terminals 859 1,243 2,073

Blending and other Miscellaneous

Equipment at Petroleum Terminals

1,006 1,064 1,144

Unit Train Receipt Facilities 444 586 838

Manifest Rail Receipt Facilities 15 20 28

Marine Receipt Facilities for Inta-U.S.

Transport

98 130 186

Import Receipt Facilities 49 53 63

E85 Retail Facilities 1,957 3,293 4,973

Total (Million $) 5,471 7,898 11,922

Total (cpg) 6 6 5

Table 4.2-4.

Potential Costs to Provide Butane to Terminals for E85 Blending

for the RFS2 Control Scenarios

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Butane Blending, Storage, Receipt and

Other Miscellaneous Terminal Costs

235 536 1,249

Tank Trucks 325 830 837

Rail Cars 32 81 89

Total Capital Costs 592 942 2,175

Freight Cost (Annual cost in 2022) 16 24 40

Total Butane Distribution Costs

Relative to the RFS1 Reference Case

(cpg ethanol)

1 1 1

Total Butane Distribution Costs

Relative to the AEO Reference Case

(cpg ethanol)

2 2 2
786

4.2.1.1 Capital Costs to Upgrade the Ethanol Distribution System

4.2.1.1.1 Petroleum Terminal Ethanol Distribution Capital Costs

The terminal facility modifications needed to support the use of the volume of ethanol

that we project will be used to meet the RFS2 standards are discussed in Section 1.6.7. A

summary of the costs associated with these modifications is detailed in Tables 4.2-5 and 4.2-6.

Table 4.2-5.

Ethanol Associated Petroleum Terminal Costs

for the RFS2 Control Scenarios Relative to the AEO Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Costs 858 1,150 1,913

Total Storage Tank Costs 355 739 1,568

New Storage Tank Construction Costs 291 606 1,287

Cost to Retrofit Existing Storage Tanks 64 133 282

Costs to Prepare Terminals to Handle

Ethanol for the First Time

167 167 167

Tank Truck Unloading Facilities 277 186 123

Ethanol Blending and Miscellaneous

Ethanol Handling Costs

59 57 55

Table 4.2-6.

Ethanol Associated Petroleum Terminal Costs

for the RFS2 Control Scenarios Relative to the RFS1 Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Costs 2,003 2,307 3,078

Total Storage Tank Costs 859 1,243 2,073

New Storage Tank Construction Costs 705 1,020 1,701

Cost to Retrofit Existing Storage Tanks 154 223 372

Costs to Prepare Terminals to Handle

Ethanol for the First Time

594 594 594

Tank Truck Unloading Facilities 366 286 231

Ethanol Blending and Ancillary

Ethanol Handling Costs

184 183 180

The above cost estimates are bases on the following. Input from terminal operators

indicates that the primary modifications needed to prepare a terminal to handle ethanol for the
787

first time are associated with the modification of vapor recovery equipment to handle ethanolcontaining gasoline at a cost of $1,000,000 per terminal. We added another $20,000 to account

for related ancillary costs. Input from terminal operators indicates that the cost of ethanol

blending equipment is $300 thousand for E10-capable equipment, $310 thousand for E85-

capable equipment, and $10,000 to upgrade E10-capable equipment to handle E85. Input from

companies that are familiar with the installation of ethanol truck unloading equipment at

terminals indicates that the cost averages $500 thousand per facility. Input from terminal

operators indicates that the cost of new ethanol storage tank construction is $40 per barrel of

capacity, and that the cost of retrofitting existing gasoline storage tanks for ethanol service is $5

per barrel of capacity for the size of tanks that are likely to be used.

4.2.1.1.2 Capital Cost of Unit Train Receipt Facilities for Ethanol

Our estimation of the number of unit train receipt facilities that will be need to support

the transport of the volumes of ethanol and cellulosic distillate fuel that we project will be used

to meet the RFS2 standards is discussed in Section 1.6.4 of this RIA. Input from industry

indicates that the cost of unit train receipt facility capable of handling 229 million gallons of

biofuels per year is $10 million and the cost of a facility capable of handling 613 million gallons

is $25 million. We interpolated between these two estimates to derive a cost estimate for each

unit train receipt facility that we projected will be constructed based on its anticipated annual

throughput volume. The total cost of unit train receipt facilities was divided between ethanol and

cellulosic distillate fuel/renewable diesel fuel in proportion to the fraction of the total ethanol +

cellulosic distillate fuel/renewable diesel fuel volume under the respective control scenario.

Our projections of the total cost of unit train receipt facilities and the portion that we

attributed to the volume of ethanol that we project will be used to meet the RFS2 standards is

presented in Tables 4.2.7 and 4.2.8.

Table 4.2-7.

Cost of Unit Train Facilities to Facilitate the Transport of Ethanol

for the RFS2 Control Scenarios Relative to the AEO Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Cost of Unit Train Facilities

Needed for the Transport of Ethanol

and Cellulosic Distillate

Fuel/Renewable Diesel Fuel

748 748 748

Cost of Unit Train Facilities

Attributed to Ethanol Transport

238 434 748
788

Table 4.2-8.

Cost of Unit Train Facilities to Facilitate the Transport of Ethanol

for the RFS2 Control Scenarios Relative to the RFS1 Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Cost of Unit Train Facilities

Needed for the Transport of Ethanol

and Cellulosic Distillate

Fuel/Renewable Diesel Fuel

838 838 838

Cost of Unit Train Facilities

Attributed to Ethanol Transport

444 586 838

4.2.1.1.3 Capital Cost of Manifest Rail Receipt Facilities for Ethanol

Our estimation of the number of manifest rail receipt facilities that will be needed to

support the transport of the volumes of ethanol and cellulosic distillate fuel that we project will

be used to meet the RFS2 standards is discussed in Section 1.6.4 of this RIA. The cost of these

facilities was divided between ethanol and cellulosic distillate fuel/renewable diesel fuel in

proportion to the fraction of the total ethanol + cellulosic distillate fuel/renewable diesel fuel

volume. Based on input from companies familiar with the installation of manifest rail receipt

facilities, we estimate that the total cost per facility will be $500 thousand.

Our projections of the total cost of the manifest rail receipt facilities and the portion that

we attributed to the volume of ethanol that we project will be used to meet the RFS2 standards is

presented in Tables 4.2.9 and 4.2.10.

Table 4.2-9.

Cost of Manifest Rail Receipt Facilities

for the RFS2 Control Scenarios Relative to the AEO Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Cost of Manifest Rail Receipt

Facilities Needed for the Transport

of Ethanol and Cellulosic Distillate

Fuel/Renewable Diesel Fuel

21 21 21

Cost of Manifest Rail Receipt

Facilities Attributed to Ethanol

Transport

14 9 21
789

Table 4.2-10.

Cost of Manifest Rail Receipt Facilities

for the RFS2 Control Scenarios Relative to the RFS1 Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Cost of Manifest Rail Receipt

Facilities Needed for the Transport

of Ethanol and Cellulosic Distillate

Fuel/Renewable Diesel Fuel

28 28 28

Cost of Manifest Rail Receipt

Facilities Attributed to Ethanol

Transport

13 8 28

4.2.1.1.4 Ethanol Import Facility Capital Costs

Our estimation of the number of marine facilities that will be needed to support the

receipt of the volume of imported ethanol that we project will be used to meet the RFS2

standards is discussed in Section 1.6.5 of this RIA. We estimate that a total of 30 port facilities

will receive imported ethanol by 2022. Of these ports, 14 will need to accommodate ethanol

receipts for the first time under the low-ethanol scenario, 15 under the mid-ethanol scenario, and

18 under the high ethanol scenario.

We believe that all such port facilities also serve as petroleum terminals. Thus, the cost

of additional ethanol storage, ethanol blending equipment, and other miscellaneous equipment

related to handling ethanol from the standpoint of terminal operations at such facilities is

accounted for in the context of the costs at petroleum terminals (see Section 4.2.1.1.1 of this

RIA). However, there will be additional costs at the port facilities which had not received

ethanol in the past. Input from industry indicates that offloading large marine transport

containers of ethanol requires significantly upgraded vapory recovery equipment. Based on this

input, we estimated the cost of making the needed upgrades to vapor recovery equipment at 2.5

million dollars per facility. We further estimated miscellaneous costs associated with delivery of

ethanol into storage tanks from marine vessels at 1 million dollars per facility. This is meant to

include new piping, pumps, various other fittings, and a contingency cost. The actual cost could

be significantly lower. Thus, we estimate that the total cost to prepare for delivery of ethanol at a

port that had not received ethanol before at 3.5 million dollars per facility.

Based on the above, we estimate that the ethanol import port costs due to the importation

of the additional volume of ethanol used to meet the RFS2 standards will be $49 million under

the low-ethanol scenario, $52 million under the mid-ethanol scenario, and $63 million under the

high-ethanol scenario. We assumed the same costs relative to both the RFS1 and AEO reference

cases because we believe that the use of the incremental volume of ethanol imports attributed to

meeting the RFS2 standards will take place after the RFS1 and AEO ethanol use volumes are

met.
790

4.2.1.1.5 Capital Costs of Barge Receipt Facilities for Intra-U.S Ethanol Transport

Our estimation of the number of barge receipt facilities for intra-U.S. biofuel shipment

that will be need to support the transport of the volumes of ethanol and cellulosic distillate fuel

that we project will be used to meet the RFS2 standards is discussed in Section 1.6.5 of this RIA.

We estimate that 41 additional barge receipt facilities will receive shipments of ethanol and

cellulosic distillate fuel/renewable diesel fuel relative to the AEO reference case and 53 relative

to the RFS1 reference case. We used the same $3.5 million per facility cost that we estimated for

the ethanol import facilities (see Section 4.2.1.1.4). The cost of these facilities was divided

between ethanol and cellulosic distillate fuel/renewable diesel fuel in proportion to the fraction

of the total ethanol + cellulosic distillate fuel/renewable diesel fuel volume.

Our projections of the total cost of the barge receipt facilities and the portion that we

attributed to the volume of ethanol that we project will be used to meet the RFS2 standards is

presented in Tables 4.2.11 and 4.2.12.

Table 4.2-11.

Cost of Barge Receipt Facilities

for the RFS2 Control Scenarios Relative to the AEO Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Cost of Barge Receipt

Facilities Needed for the Transport

of Ethanol and Cellulosic Distillate

Fuel/Renewable Diesel Fuel

143 143 143

Cost of Barge Receipt Facilities

Attributed to Ethanol Transport

76 100 143

Table 4.2-12.

Cost of Barge Receipt Facilities

for the RFS2 Control Scenarios Relative to the RFS1 Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Cost of Barge Receipt

Facilities Needed for the Transport

of Ethanol and Cellulosic Distillate

Fuel/Renewable Diesel Fuel

185 185 185

Cost of Barge Receipt Facilities

Attributed to Ethanol Transport

98 129 185
791

4.2.1.1.6 Ethanol Rail Car Capital Costs

Our estimation of the number of rail cars needed to transport the additional volume of

ethanol that we project will be used to meet the RFS2 standards is discussed in Section 1.6.4 of

this RIA. Based on input from rail car manufactures, we estimate that the cost of a new 30,000

gallon rail car suitable for ethanol service is $90 thousand. The cost of the additional ethanol rail

cars needed under the 3 control scenarios relative to the 2 reference cases is presented in Tables

4.2-13 and 4.2-14.

Table 4.2-13.

Cost of Additional Ethanol Rail Cars

for the RFS2 Control Scenarios Relative to the AEO Reference Case

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Number of Additional Rail Cars 4,050 8,450 18,870

Rail Car Cost ($Million) $365 $760 $1,699

Table 4.2-14.

Cost of Additional Ethanol Rail Cars

for the RFS2 Control Scenarios Relative to the RFS1 Reference Case

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Number of Additional Rail Cars 9,820 14,210 24,639

Rail Car Cost ($Million) $884 $1,279 $2,218

4.2.1.1.7 Ethanol Barge Capital Costs

Our estimation of the number of barges needed for intra-U.S. transport of the additional

volume of ethanol that we project will be used to meet the RFS2 standards is discussed in

Section 1.6.5 of this RIA. Based on input from fuel barge manufactures, we estimate that the

cost of a new 10,000 barrel barge suitable for ethanol service is $1.4 million. The cost of the

additional ethanol barges needed under the 3 control scenarios relative to the 2 reference cases is

presented in Tables 4.2-15 and 4.2-16.
792

Table 4.2-15.

Cost of Additional Ethanol Barges

for the RFS2 Control Scenarios Relative to the AEO Reference Case

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Number of Additional Barges 16 32 73

Cost of Barges ($Million) $22 $45 $101

Table 4.2-16.

Cost of Additional Ethanol Barges

for the RFS2 Control Scenarios Relative to the RFS1 Reference Case

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Number of Additional Barges 38 55 95

Cost of Barges ($Million) $53 $76 $133

4.2.1.1.8 Ethanol Tank Truck Capital Costs

Our estimation of the number of tank trucks needed to transport the additional volume of

ethanol that we project will be used to meet the RFS2 standards is discussed in Section 1.6.6 of

this RIA. Based on input from ethanol tank truck manufactures, we estimate that the cost of a

new 8,000 gallon tank truck suitable for ethanol service is $180 thousand. The cost of the

additional ethanol tank trucks needed under the 3 control scenarios relative to the 2 reference

cases is presented in Tables 4.2-17 and 4.2-18.

Table 4.2-17.

Cost of Additional Ethanol Tank Trucks

for the RFS2 Control Scenarios Relative to the AEO Reference Case

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Number of Additional Tank Trucks 230 480 1,080

Cost of Tank Trucks ($Million) $42 $87 $194

Table 4.2-18.

Cost of Additional Ethanol Tank Trucks

for the RFS2 Control Scenarios Relative to the RFS1 Reference Case

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Number of Additional Tank Trucks 590 860 1,490

Cost of Tank Trucks ($Million) $107 $154 $268
793

4.2.1.1.9 E85 Retail Facility Costs

Our estimates of the number of additional E85 retail facilities and the number of E85

refueling positions needed at such facilities to enable the use of the volumes of E85 that we

project will be used to meet the RFS2 standards under the 3 control scenarios is contained in

Section 1.6.9 of this RIA. Our estimates of the additional E85 refueling infrastructure that will

be needed relative to the 2 reference cases are presented in Table 4.2-19 and 4.2-20.

Table 4.2.19.

Additional E85 Retail Facilities Needed by 2022 to Support the Projected Increase in E85

use under the RFS2 Control Scenarios Relative to the RFS1 Reference Case

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

New E85 Installation with 1 Dispenser 14,967 10,923 0

New E85 Installation with 2 Dispensers 0 12,133 0

New E85 Installation with 3 Dispensers 0 0 27,099

Addition of 2 Dispensers to Retail

Facility that had 1 Dispenser

0 0 1,210

Table 4.2.20.

Additional E85 Retail Facilities Needed by 2022 to Support the Projected Increase in E85

use under the RFS2 Control Scenarios Relative to the AEO Reference Case

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

New E85 Installation with 1 Dispenser 11,677 7,633 0

New E85 Installation with 2 Dispensers 0 12,133 0

New E85 Installation with 3 Dispensers 0 0 23,809

Addition of 2 Dispensers to Retail

Facility that had 1 Dispenser

0 0 4,500

The following estimates regarding the cost of E85 compatible retail equipment are based

on input from gasoline retailers and other parties with experience in the requirements and costs

associated with installing E85 retail equipment. The total cost of installing a two nozzle E85

dispenser is estimated at $23,000. This is composed of $17,000 for the dispenser itself, $750 for

hanging hardware, $950 for refueling island hardware, $3,000 for installation, and a $1,300

contingency cost. Hanging hardware costs are composed of $310 for 2 nozzles, $135 for 2

breakaway connections, $135 for 2 swivel connections, and $170 for 2 hoses. Refueling island

hardware costs are composed of $450 for the dispenser island, $250 for an island sump pump,

and $250 for bumper posts. Installation costs are composed of $1,500 for concrete removal and

replacement, and $1,500 for wiring and piping.

The cost of automatic tank level gauging equipment is estimated at $6,500. It is

estimated that 65% of retailers will install automatic tank gauging (ATG) equipment and the

remainder will rely on manual means of determining the amount of fuel remaining in their
794

underground storage tank. Thus, the average cost per facility will be $4,225 for ATG equipment.

We estimate the cost of installing a canopy addition to provide cover for an additional dispenser

at $15,000. We estimated that only 10% of facilities will need to install additional canopy

coverage in order to accommodate the new E85 retail dispenser. Thus, the average canopy cost

per facility is estimated at $1,500. The cost of installing a new 15,000 underground E85 storage

tank is estimated at $102,000. The cost of connecting the tank to the dispenser(s) is included in

this cost along with other miscellaneous storage tank related costs. In the NPRM, we estimated

that an 8,000 gallon storage tank would be used at E85 retail facilities. We increased the size to

15,000 based on industry comments that the added storage volume will be needed to keep pace

with fuel throughput without necessitating an overly-frequent fuel delivery schedule. The use of

a 15,000 gallon storage tank will also allow the delivery of a full 8,000 gallon tank truck at a

single retail facility. Input from fuel retailers indicates that there typically is at least 15,000

gallons of gasoline storage at current retail facilities. Based on the above, the cost of an E85

installation with one dispenser is estimated at $131 thousand, the cost of for a new E85

installation with 2 dispensers is estimated at $154 thousand, and the cost of a new E85

installation with 3 dispensers is estimated at $177 thousand. The cost of upgrading an existing

E85 facility with a single dispenser to add 2 additional dispensers is estimated at $130 thousand.

Our E85 retail facility cost estimates are presented in Table 4.2-21. These estimates are

based on the above E85 equipment cost estimates and the estimated facility requirements

detailed in Tables 4.2-19 and 4.2-20.

Table 4.2-21.

Cost of the Additional E85 Retail Facilities Needed by 2022

to Support the Projected Increase in E85 use under the RFS2 Control Scenarios

E85 Capital Costs ($Billion)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Relative to the AEO 2007 Reference

Case

1.526 2.863 4.893

Relative to the RFS1 Reference Case 1.956 3.293 4.973

4.2.1.1.10 Potential Costs of Supplying Special Blendstocks at Petroleum Terminals

for E85

As discussed in Section 1.6.8 of this RIA, special blendstocks may need to be supplied to

terminals to facilitate the manufacture of E85 which meets ASTM International minimum

volatility specifications. To evaluate the potential impacts to the fuel distribution system if this

is the case, we assumed that butane would be used as the special blendstock and that it would be

blended into gasoline before being blended with denatured ethanol to produce E85. As such, we

estimated the potential costs associated with automated inline butane blending systems, butane

storage tanks, tank trucks, railcars, transloading facilities, and other facility changes needed for

butane blending into E85. These costs are based upon discussions with industry representatives.
795

We assume that butane would be transported by tank truck and/or railcar from petroleum

refineries to E85-producing petroleum terminals and stored until blended into a final product. Of

the 1,063 terminals identified in our analysis, two-thirds (709) are assumed to blend E85. All of

these terminals are assumed to require butane blending equipment. Our cost estimates assumed

that twenty-five percent (177) of these terminals will receive butane via 31,500 gallon railcar and

the remaining seventy-five percent (532) will receive butane via 8,200 gallon tank truck. Of the

177 terminals that receive butane via railcar, fifty-percent are assumed to directly off-load butane

to tank storage for eventual blending into E85. The other fifty-percent of the terminals which

received butane via railcar are assumed to transload the butane from railcars to tank trucks for

final delivery to terminals which store butane for eventual blending into E85.

The blending of butane into E85 requires petroleum terminals to have on-site butane

blending equipment. In developing our cost estimates, we assume that each terminal which

blends E85 uses an automated, in-line butane blending system and two 60,000 gallon butane

storage tanks. The cost of an in-line butane blending systems is assumed to be $1.5 million per

unit. The cost each 60,000 gallon butane storage tank is assumed to be $150,000. Transloading

equipment is assumed to cost $500,000 per unit.

Transport cost estimates were based upon the ORNL transport analysis discussed in

Section 1.6.3. In that analysis, the cost of freight rail transport was assumed to average $0.12 per

ton-mile. The cost of truck transport was assumed to average $0.14 per ton-mile. Average

round trip distance is assumed to be 1,200 miles for railcars and 300 miles for tank trucks.

Travel speed estimates are truck 35 mph and railcar 10 mph with roundtrip times being

approximately 8 hours for trucks and 5 days for railcars. Each tank truck is assumed to cost

$150,000 and each railcar is assumed to cost $135,000.

Estimates of the number of tank trucks and railcars required to deliver butane appears by

low, medium, and high volume cases in Table 4.2-22 and a summary of cost estimates for the

three volume cases appears in Table 4.2-23.

Table 4.2-22.

Estimated Number of Tank Trucks and Rail Cars Needed for Shipment of Butane under

the RFS2 Control Scenarios

Number of Tank Trucks and Rail Cars Needed to

Transport Butane

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Tank Truck (8,200 gallons) 2,165 3,280 5,530

Railcar (31,500 gallons) 236 358 602
796

Table 4.2-23.

Summary of Potential Costs to Provide Butane for E85 Blending

under the RFS2 Control Scenarios

4.2.1.2 Ethanol Freight Costs

Our estimates of ethanol freight costs are based on a study conducted by Oakridge

National Laboratories (ORNL).1162 The ORNL analysis contains detailed projections of which

transportation modes and combination of modes (e.g. unit train to barge) are best suited for

delivery of ethanol to specific markets considering ethanol source and end use locations, the

current configuration and projected evolution of the distribution system, and cost considerations

for the different transportation modes. The NPRM analysis assumed that all biofuel volumes

other than biodiesel would be ethanol. For this FRM, we analyzed three scenarios under which

varying volumes of cellulosic distillate fuel take the place of ethanol production volumes to meet

the RFS2 standards. However, due to the timing of the various analyses for the FRM, the NPRM

projections of the location of ethanol production facilities and end use areas contained in the

NPRM had to be used as the inputs into the ORNL analysis.244 Therefore, our use of the ORNL

analysis to evaluate the freight costs for the final rule assumes that cellulosic distillate production

plants will take the place of some of the ethanol production plants projected in the NPRM. It

further assumes that cellulosic distillate fuel use will coincide with the ethanol end-use areas

projected in the NPRM and that both fuels will be transported by the same means.

We estimated the freight costs under the FRM control scenarios by totaling the cellulosic

distillate/renewable diesel fuel and ethanol volume under each scenario and interpolating

between the freight costs projected by ORNL for 2 NPRM ethanol volume scenarios. This

approach provides for the economy of scale in biofuel freight costs with increased volume.

Based on this approach, we estimate that ethanol freight costs will be 12 cents per gallon (cpg)

under the high-ethanol scenario and 13 cpg under the low-ethanol and mid-ethanol scenarios.

Our use of the ORNL freight cost estimates to derive the freight FRM freight cost estimates is

illustrated in Table 4.2-24.

244 The ORNL final report contains maps of projected ethanol production locations and end use areas.

Freight Costs Capital Costs

Low-Ethanol Scenario $16 million $357 million

Mid-Ethanol Scenario $24 million $911 million

High-Ethanol Scenario $40 million $927 million
797

Table 4.2-24.

Interpolation of FRM Ethanol and Cellulosic Distillate Fuel/Renewable Diesel Fuel Freight

Costs from ORNL Freight Cost Estimates

for NPRM Ethanol Volume Scenarios

Volume of Ethanol, Cellulosic

Distillate Fuel, and Renewable

Diesel Fuel (BGal/yr in 2022)

Freight Cost

(cpg)

NPRM Control Scenario 34.14 12.2

(ORNL estimate)

FRM High-Ethanol Scenario 33.24 12

(Interpolation)

FRM Mid-Ethanol Scenario 28.68 13

(Interpolation)

FRM Low Ethanol Scenario 26.75 13

(Interpolation)

AEO 2007 Reference Case 13.18 15.3

(ORNL estimate)

4.2.2 Cellulosic Distillate Fuel Distribution Costs

As discussed in the following sections, we estimate that the total capital costs in the U.S

to support distribution of the additional volume of cellulosic distillate fuel/renewable diesel fuel

that will be used in to meet the RFS2 standards under the primary mid-ethanol scenario will be

1,392 billion dollars by 2022 relative to the AEO 2007 reference case. When amortized, this

translates to 2 cents per gallon of additional cellulosic distillate fuel/renewable fuel attributed to

the RFS2 standards. Amortization of capital costs was done over 15 years at a 7% annual cost of

capital except in the case of the cost of tank trucks where a 10 year amortization schedule was

used. Under the mid-ethanol primary scenario, we project that 6.7 BGY of cellulosic distillate

fuel/renewable diesel fuel will be used by 2022. Cellulosic distillate fuel/renewable diesel fuel

freight costs are estimated to be 13 cents per gallon on a national average basis. Thus, we

estimate that total cellulosic distillate fuel/renewable diesel fuel distribution costs will be 15

cents per gallon of cellulosic distillate fuel/renewable diesel fuel that we project will be used to

meet the RFS2 standards.245

The cellulosic distillate fuel/renewable diesel fuel distribution capital and freight costs for

all the control scenarios relative to the 2 reference cases that we evaluated in this FRM is

summarized in Table 4.2-25. The itemized cellulosic distillate fuel/renewable diesel fuel capital

costs are presented in Table 4.2-26 relative to the AEO 2007 reference case, and in Table 4.2-27

relative to the RFS1 reference case. The way in which we estimated these costs is detailed in the

following sections. As discussed in the following sections, some biofuel infrastructure assets

245 As noted previously, we chose not to subtract the distribution costs for the petroleum-based fuels that will be

displaced by the use of biofuels from our estimated biofuel distribution costs. We believe that the freight costs to

ship petroleum-based fuels to the terminal are approximately 4 cents per gallon. If we were to subtract these costs

from the estimated cellulosic distillate fuel/renewable diesel fuel distribution costs under the mid-ethanol scenario

relative to the AEO 2007 reference case, the result would be 11 cents per gallon.
798

such as unit train receipt facilities are used to distribute both cellulosic distillate fuel/renewable

diesel fuel and ethanol. We attributed a fraction of the capital costs for such facilities to either

cellulosic distillate fuel/renewable diesel fuel or ethanol in proportion to the fraction of the total

additional volume of these fuels that we project will be used to meet the RFS2 standards relative

to the reference case. This approach results in a slight difference in the capital costs under the 2

reference case despite the fact that the incremental volume of cellulosic distillate/renewable

diesel fuel used to meet the RFS2 standards is the same under both reference cases.

Table 4.2-25.

Summary of Estimated Cellulosic Distillate Fuel/Renewable Diesel Fuel

Distribution Capital and Freight Costs

under the RFS2 Control Scenarios Relative to the Reference Cases

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol Scenario

RFS1

Reference

AEO

Reference

RFS1

Reference

AEO

Reference

RFS1

Reference

AEO

Reference

Billion $

Capital

1,999 2,036 1,375 1,392 NA NA

Capital

Costs (cpg)

2 2 2 2 NA NA

Freight

Costs

(cpg)

13 13 13 13 NA NA

Total

Distribution

Costs

(cpg)

16 16 15 15 NA NA
799

Table 4.2-26.

Summary of Estimated Cellulosic Distillate Fuel/Renewable Diesel Fuel Distribution

Capital Costs under the RFS2 Control Scenarios

Relative to the AEO 2007 Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Rail Cars 784 552 NA

Barges 47 33 NA

Tank Trucks 90 63 NA

Storage Tanks at Petroleum Terminals 218 154 NA

Blending and other Miscellaneous

Equipment at Petroleum Terminals

304 223 NA

Unit Train Receipt Facilities 511 315 NA

Manifest Rail Receipt Facilities 15 9 NA

Marine Receipt Facilities for Inta-U.S.

Transport

67 43 NA

Total (Million $) 2,036 1,392 NA

Total (cpg) 2 2 NA

Table 4.2-27.

Summary of Estimated Cellulosic Distillate Fuel/Renewable Diesel Fuel Distribution

Capital Costs under the RFS1 Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Rail Cars 784 552 NA

Barges 47 33 NA

Tank Trucks 95 67 NA

Storage Tanks at Petroleum Terminals 218 154 NA

Blending and other Miscellaneous

Equipment at Petroleum Terminals

361 252 NA

Unit Train Receipt Facilities 394 253 NA

Manifest Rail Receipt Facilities 13 8 NA

Marine Receipt Facilities for Inta-U.S.

Transport

87 56 NA

Total (Million $) 1,999 1,375 NA

Total (cpg) 2 2 NA

4.2.2.1 Cellulosic Distillate Fuel Distribution Capital Costs

4.2.2.1.2 Petroleum Terminal Cellulosic Distillate Fuel/Renewable Diesel Fuel

Distribution Capital Costs
800

The terminal facility modifications needed to support the use of the volume of cellulosic

diesel fuel/renewable diesel fuel that we project will be used to meet the RFS2 standards are

discussed in Section 1.6.7. A summary of the costs associated with these modifications is

detailed in Tables 4.2-28 and 4.2-29. The estimated costs

vary depending on the reference case considered because of the way we attributed the cost of

tank truck unloading facilities (which are used to handle both ethanol and cellulosic distillate

fuel/renewable diesel fuel) to either ethanol or cellulosic distillate fuel/renewable diesel fuel.

The cost of such facilities was divided between ethanol and cellulosic distillate fuel/renewable

diesel fuel in proportion to the fraction of the total ethanol + cellulosic distillate fuel/renewable

diesel fuel volume.

Table 4.2-28.

Cellulosic Distillate Fuel/Renewable Diesel Fuel Associated Petroleum Terminal Costs

under the RFS2 Control Scenarios Relative to the AEO Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Costs 522 377 NA

New Storage Tank Construction Costs 218 154 NA

Tank Truck Unloading Facilities 65 55 NA

Blending and Miscellaneous Fuel

Handling Costs

239 168 NA

Table 4.2-29.

Cellulosic Distillate Fuel/Renewable Diesel Fuel Associated Petroleum Terminal Costs

under the RFS2 Control Scenarios Relative to the RFS1 Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Costs 579 406 NA

New Storage Tank Construction Costs 218 154 NA

Tank Truck Unloading Facilities 122 84 NA

Blending and Miscellaneous Fuel

Handling Costs

239 168 NA

The above cost estimates are based on the following. Input from terminal operators

indicates that the cost of ethanol blending equipment is $310 thousand for E85-capable

equipment. Input from companies that are familiar with the installation of ethanol truck

unloading equipment at terminals indicates that the cost averages $500 thousand per facility.

Input from terminal operators indicates that the cost of new diesel fuel storage tank construction

is $35 per barrel for the size of tanks that are likely to be used. We used the above estimates
801

regarding the costs of installing similar equipment for cellulosic distillate fuel/renewable diesel

fuels.

4.2.2.1.3 Capital Cost of Unit Train Receipt Facilities for Cellulosic Distillate

Fuel/Renewable Diesel fuel

Our estimation of the number of unit train receipt facilities that will be needed to support

the transport of the volumes of ethanol and cellulosic distillate fuel/renewable diesel fuel that we

project will be used to meet the RFS2 standards is discussed in Section 1.6.4 of this RIA. The

cost of these facilities was divided between ethanol and cellulosic distillate fuel/renewable diesel

fuel in proportion to the fraction of the total ethanol plus cellulosic distillate fuel/renewable

diesel fuel volume. See Section 4.2.1.1.2 for additional discussion regarding the derivation of

the total cost of unit train receipt facilities.

Our projections of the total cost of unit train receipt facilities and the portion that we

attributed to the volume of cellulosic distillate fuel/renewable diesel fuel that we project will be

used to meet the RFS2 standards is presented in Tables 4.2-30 and 4.2-31.

Table 4.2-30.

Cost of Unit Train Facilities to Facilitate the Transport of Cellulosic Distillate

Fuel/Renewable Diesel Fuel under the RFS2 Control Scenarios

Relative to the AEO Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Cost of Unit Train Facilities

Needed for the Transport of Ethanol

and Cellulosic Distillate

Fuel/Renewable Diesel Fuel

748 748 748

Cost of Unit Train Facilities

Attributed to Cellulosic Distillate

Fuel/Renewable Diesel Fuel

Transport

394 253 NA
802

Table 4.2-31.

Cost of Unit Train Facilities to Facilitate the Transport of Cellulosic Distillate

Fuel/Renewable Diesel Fuel under the RFS2 Control Scenarios

Relative to the RFS1 Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Cost of Unit Train Facilities

Needed for the Transport of Ethanol

and Cellulosic Distillate

Fuel/Renewable Diesel Fuel

838 838 838

Cost of Unit Train Facilities

Attributed to Cellulosic Distillate

Fuel/Renewable Diesel Fuel

Transport

511 315 NA

4.2.2.1.4 Capital Cost of Manifest Rail Receipt Facilities for Cellulosic Distillate

Fuel/Renewable Diesel Fuel

Our estimation of the number of manifest rail receipt facilities that will be needed to

support the transport of the volumes of ethanol and cellulosic distillate fuel that we project will

be used to meet the RFS2 standards is discussed in Section 1.6.4 of this RIA. The cost of these

facilities was divided between ethanol and cellulosic distillate fuel/renewable diesel fuel in

proportion to the fraction of the total ethanol plus cellulosic distillate fuel/renewable diesel fuel

volume. See Section 4.2.1.1.2 for additional discussion regarding the derivation of the total cost

of manifest rail receipt facilities.

Our projections of the total cost of the manifest rail receipt facilities and the portion that

we attributed to the volume of cellulosic distillate fuel/renewable diesel fuel that we project will

be used to meet the RFS2 standards is presented in Tables 4.2-32 and 4.2-33.
803

Table 4.2-32.

Cost of Manifest Rail Receipt Facilities under the RFS2 Control Scenarios

Relative to the AEO Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Cost of Manifest Rail Receipt

Facilities Needed for the Transport

of Ethanol and Cellulosic Distillate

Fuel/Renewable Diesel Fuel

21 21 21

Cost of Unit Train Facilities

Attributed to Cellulosic Distillate

Fuel/Renewable Diesel Fuel

Transport

14 9 NA

Table 4.2-33.

Cost of Manifest Rail Receipt Facilities under the RFS2 Control Scenarios

Relative to the RFS1 Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Cost of Manifest Rail Receipt

Facilities Needed for the Transport

of Ethanol and Cellulosic Distillate

Fuel/Renewable Diesel Fuel

28 28 28

Cost of Unit Train Facilities

Attributed to Cellulosic Distillate

Fuel/Renewable Diesel Fuel

Transport

13 8 NA

4.2.2.1.5 Barge Receipt Facility Costs for Cellulosic Distillate Fuel/Renewable

Diesel Fuel

Our estimation of the number of barge receipt facilities for intra-U.S. biofuel shipments

that will be needed to support the transport of the volumes of ethanol and cellulosic distillate

fuel/renewable diesel fuel that we project will be used to meet the RFS2 standards is discussed in

Section 1.6.5 of this RIA. The cost of these facilities was divided between ethanol and cellulosic

distillate fuel/renewable diesel fuel in proportion to the fraction of the total ethanol plus

cellulosic distillate fuel/renewable diesel fuel volume.

Our projections of the total cost of the barge receipt facilities and the portion that we

attributed to the volume of cellulosic distillate fuel/renewable diesel fuel that we project will be

used to meet the RFS2 standards is presented in Tables 4.2-34 and 4.2-35. See Section 4.2.1.1.5

of this RIA for additional discussion of the derivation of these estimates.
804

Table 4.2-34.

Cost of Barge Receipt Facilities under the RFS2 Control Scenarios

Relative to the AEO Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Cost of Barge Receipt

Facilities Needed for the Transport

of Ethanol and Cellulosic Distillate

Fuel/Renewable Diesel Fuel

143 143 143

Cost of Barge Receipt Facilities

Attributed to Cellulosic Distillate

Fuel/Renewable Diesel Fuel

Transport

87 56 NA
805

Table 4.2-35.

Cost of Barge Receipt Facilities under the RFS2 Control Scenarios

Relative to the RFS1 Reference Case

Capital Cost (Million $)

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Total Cost of Barge Receipt

Facilities Needed for the Transport

of Ethanol and Cellulosic Distillate

Fuel/Renewable Diesel Fuel

185 185 185

Cost of Barge Receipt Facilities

Attributed to Cellulosic Distillate

Fuel/Renewable Diesel Fuel

Transport

67 46 NA

4.2.2.1.6 Cellulosic Distillate Fuel/Renewable Diesel Fuel Rail Car Capital Costs

Our estimation of the number of rail cars needed to transport the additional volume of

cellulosic distillate fuel/renewable diesel fuel that we project will be used to meet the RFS2

standards is discussed in Section 1.6.4 of this RIA. Based on input from rail car manufactures,

we estimate that the cost of a new 30,000 gallon rail car suitable for ethanol service is $90

thousand. We used this estimate as the cost of a rail car suitable for cellulosic distillate

fuel/renewable diesel fuel service. This may tend to overstate the cost of such rail cars given that

ethanol rail cars need to be constructed of ethanol tolerant materials. The cost of the additional

cellulosic distillate fuel/renewable diesel fuel rail cars needed under the 3 control scenarios is

presented in Table 4.2-36. Our estimate of the additional number of cellulosic distillate

fuel/renewable diesel fuel rail cars needed to support meeting the RFS2 standards is the same

relative to both reference cases.

Table 4.2-36.

Cost of Additional Cellulosic Distillate Fuel/Renewable Diesel Fuel Rail Cars

under the RFS2 Control Scenarios

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Number of Additional Rail Cars 8,710 6,130 NA

Rail Car Cost ($Million) $784 $552 NA
806

4.2.2.1.7 Cellulosic Distillate Fuel/Renewable Diesel Fuel Barge Capital Costs

Our estimation of the number of barges needed for intra-U.S. transport of the additional

volume of cellulosic distillate fuel/renewable diesel fuel that we project will be used to meet the

RFS2 standards is discussed in Section 1.6.5 of this RIA. Based on input from fuel barge

manufactures, we estimate that the cost of a new 10,000 barrel barge suitable for ethanol service

is $1.4 million. We used this estimate as the cost of a barge suitable for cellulosic distillate

fuel/renewable diesel fuel service. This may tend to overstate the cost of such barges given that

ethanol barges need to be constructed of ethanol tolerant materials. The cost of the additional

cellulosic distillate fuel/renewable diesel fuel barges needed under the 3 control scenarios is

presented in Table 4.2-37. Our estimate of the additional number of cellulosic distillate

fuel/renewable diesel fuel barges needed to support meeting the RFS2 standards is the same

relative to both reference cases.

Table 4.2-37.

Cost of Additional Cellulosic Distillate Fuel/Renewable Diesel Fuel Barges

under the RFS2 Control Scenarios

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Number of Additional Barges 33 24 NA

Cost of Barges ($Million) $47 $33 NA

4.2.2.1.8 Cellulosic Distillate Fuel/Renewable Diesel Fuel Tank Truck Capital

Costs

Our estimation of the number of tank trucks needed to transport the additional volume of

cellulosic distillate fuel/renewable diesel fuel that we project will be used to meet the RFS2

standards is discussed in Section 1.6.6 of this RIA. Based on input from ethanol tank truck

manufactures, we estimate that the cost of a new 8,000 gallon tank truck suitable for ethanol

service is $180 thousand. We used this estimate as the cost of a tank truck suitable for cellulosic

distillate fuel/renewable diesel fuel service. This may tend to overstate the cost of such tank

trucks given that ethanol tank trucks need to be constructed of ethanol tolerant materials. The

cost of the additional cellulosic distillate fuel/renewable diesel fuel tank trucks needed under the

3 control scenarios is presented in Table 4.2-38. Our estimate of the additional number of

cellulosic distillate fuel/renewable diesel fuel tank trucks needed to support meeting the RFS2

standards is the same relative to both reference cases.
807

Table 4.2-38.

Cost of Additional Cellulosic Distillate Fuel/Renewable Diesel Fuel Tank Trucks under the

RFS2 Control Scenarios

Low-Ethanol

Scenario

Mid-Ethanol

Scenario

High-Ethanol

Scenario

Number of Additional Tank Trucks 500 350 NA

Cost of Tank Trucks ($Million) $90 $63 NA

4.2.2.2 Cellulosic Distillate Fuel Freight Costs

We used a study conducted by Oakridge National Laboratories (ORNL) to estimate

ethanol and cellulosic distillate fuel/renewable diesel fuel freight costs. Refer to Section 4.2.1.2

of this RIA for a discussion of how these costs were derived. We estimate that cellulosic

distillate fuel/renewable diesel fuel freight costs will be 13 cents per gallon under both the lowethanol and mid-ethanol scenarios.

4.2.3 Biodiesel Distribution Costs

As discussed in the following sections, we estimate that the total capital costs in the U.S

to support distribution of the additional volume of biodiesel that we project will be used to meet

the RFS2 standards will be 1,141 billion dollars relative to the AEO 2007 reference case and

1,212 billion dollars relative to the RFS1 reference case.246 When amortized, this translates to 10

cents per gallon of additional biodiesel volume that we project will be used to meet the RFS2

standards relative to both reference cases. Amortization of capital costs was done over 15 years

at a 7% annual cost of capital except in the case of the cost of tank trucks where a 10 year

amortization schedule was used. We project that 1.67 BGY of biodiesel will be used by 2022 to

meet the RFS2 standard volumes. Under the AEO reference case 380 BG/yr of biodiesel will be

used by 2022. Under the RFS1 reference case, 300 BG/yr of biodiesel will be used by 2022.

Thus, the additional amount of biodiesel that will be used by 2022 to meet the RFS2 standard

volumes is 1,290 BG/yr relative to the AEO reference case and 1,370 BG/yr relative to the RFS1

reference case. Biodiesel freight costs are estimated to be 10 cents per gallon on a national

average basis. Thus, we estimate that biodiesel distribution costs will be 20 cents per gallon of

biodiesel that we project will be used to meet the RFS2 standards.247

The biodiesel distribution capital and freight costs relative to the 2 reference cases that

we evaluated in this FRM are summarized in Table 4.2-39. The itemized biodiesel capital costs

are presented in Table 4.2-40. The way in which we estimated these costs is detailed in the

following sections.

246 Biodiesel distribution costs do not vary under the three control scenarios evaluated in this final rule.

247 As noted previously, we chose not to subtract the distribution costs for the petroleum-based fuels that will be

displaced by the use of biofuels from our estimated biofuel distribution costs. We believe that the freight costs to

ship petroleum-based fuels to the terminal are approximately 4 cents per gallon. If we were to subtract these costs

from the estimated biodiesel distribution, the result would be 16 cents per gallon.
808

Table 4.2-39.

Summary of Estimated Biodiesel Capital and Freight Costs

for the RFS2 Control Scenario Relative to the Reference Cases

RFS1

Reference

AEO

Reference

Billion $

Capital

1,212 1,141

Capital

Costs (cpg)

10 10

Freight

Costs

(cpg)

10 10

Total

Distribution

Costs

(cpg)

20 20

Table 4.2-40.

Summary of Estimated Biodiesel Distribution Capital Costs

for the RFS2 Control Scenario Relative to the Reference Cases

Capital Costs (Million$)

RFS1

Reference

AEO

Reference

Rail Cars 111 105

Barges 53 50

Tank Trucks 25 24

Storage Tanks at Petroleum Terminals 411 387

Blending and other Miscellaneous

Equipment at Petroleum Terminals

612 576

Total (Million $) 1,212 1,141

Total (cpg) 10 10
809

4.2.3.1 Capital Costs to Upgrade the Biodiesel Distribution System

4.2.3.1.1 Petroleum Terminal Biodiesel Distribution Capital Costs

The terminal facility modifications needed to support the use of the volume of biodiesel

that we project will be used to meet the RFS2 standards are discussed in Section 1.6.7. Total

capital costs at terminals by 2022 are estimated at $963 million relative to the AEO reference

case and $1,023 million relative to the RFS1 reference case.

We estimate that a total of 5.5 million barrels of new biodiesel storage will be needed at

petroleum terminals to facilitate meeting the projected RFS2 biodiesel volume relative to the

AEO reference case, and 5.9 million barrels relative to the RFS1 reference case. We assumed

that all of the additional biodiesel storage will be satisfied by new construction. Based on

information from industry, we estimate that the cost of constructing new biodiesel storage tanks

would be 70 dollars per barrel of capacity. This is considerably higher than the 40 per barrel cost

we estimated for construction of new ethanol tanks for two reasons. Biodiesel tanks need to be

heated/insulated in colder climes and they tend to be of considerably smaller size compared to

ethanol tanks. Both of these factors contribute significantly to the cost per barrel of constructing

a new storage tank. We estimate that the total cost at petroleum terminals of new biodiesel

storage tanks would be $387 million dollars relative to the AEO reference case and $411 million

relative to the RFS1 reference case.

We projected that 600 additional petroleum terminals will need to install biodiesel

blending equipment by 2022 to facilitate meeting the RFS2 biodiesel volume relative to the AEO

reference case and 637 relative to the RFS1 reference case. Based on input from industry, we

estimated that the cost of biodiesel blending equipment will be 400 thousand dollars per

terminal. The cost of additional piping is estimated at 60,000 per terminal. Ancillary costs

associated with receiving/blending/storing biodiesel are estimated at 500 thousand dollars per

terminal.248 Based on the above, the cost of additional biodiesel blending and other

miscellaneous biodiesel handling equipment at terminals is estimated at $576 million relative to

the AEO reference case, and $612 million relative to the RFS1 reference case. Estimated

equipment costs for handling biodiesel are higher than those for similar equipment designed to

handle ethanol due to the need for insulated/heated equipment in colder climes.

4.2.3.1.2 Biodiesel Rail Car Capital Costs

As discussed in Section 1.64 of this RIA, we estimate that an additional 1,060 rail cars

will be needed by 2022 to facilitate transport of the volume of biodiesel that we project will be

used to meet the RFS2 standards relative to the AEO reference case, and 1,120 relative to the

RFS1 reference case. Based on input from industry, we estimate that the cost of a new biodiesel

tank car of 25,600 gallon capacity is $99,000. The estimated cost for a biodiesel rail car is 10%

higher than that of an ethanol rail car to accommodate the need for insulated/heated tanks in

colder climes. Thus, we estimate that the cost of the biodiesel rail tanks cars needed by 2022 to

facilitate transport of the volume of biodiesel that we project will be used to meet the RFS2

248 This includes the installation of biodiesel truck receipt facilities, quality control testing equipment, and other

ancillary equipment at terminals.
810

standards would be $105 million relative to the AEO reference case, and $111 million relative to

the RFS1 reference case.

4.2.3.1.3 Biodiesel Barge Capital Costs

As discussed in Section 1.65 of this RIA, we estimate that an additional 32 barges will be

needed by 2022 to facilitate transport of the volume of biodiesel that we project will be used to

meet the RFS2 standards relative to the AEO reference case, and 34 relative to the RFS1

reference case. Based on input from industry, we estimate that the cost of a new biodiesel barge

of 10,000 bbl capacity is $1.54 million. The estimated cost for a biodiesel barge is 10% higher

than that of an ethanol rail car to accommodate the need for insulated/heated storage

compartments in colder climes. Thus, we estimate that the cost of the biodiesel barges needed by

2022 to facilitate transport of the volume of biodiesel that we project will be used to meet the

RFS2 standards would be $49 million relative to the AEO reference case, and $52 million

relative to the RFS1 reference case.

4.2.3.1.4 Biodiesel Tank Truck Capital Costs

As discussed in Section 1.66 of this RIA, we estimate that an additional 120 tank trucks

will be needed by 2022 to facilitate transport of the volume of biodiesel that we project will be

used to meet the RFS2 standards relative to the AEO reference case, and 130 relative to the

RFS1 reference case. Based on input from industry, we estimate that the cost of a new biodiesel

tank truck of 8,000 gallon capacity is $198,000. This is based on an 110,000 dollar cost for the

tractor and an 88,000 thousand dollar cost for the tank trailer. This estimate is 25% higher than

the cost of a tank trailer designed to transport ethanol due to the need for an insulated/heated tank

in colder climes. Based on the above, we estimate that the cost of the biodiesel tank trucks

needed by 2022 to facilitate transport of the volume of biodiesel that we project will be used to

meet the RFS2 standards would be $23 million relative to the AEO reference case, and $25

million relative to the RFS1 reference case.

4.2.3.2 Biodiesel Freight Costs

Our analysis of biodiesel freight costs for this FRM draws upon the analysis conducted

for the NPRM. The NPRM analysis was based on a total biodiesel production volume of 810

million gallons by 2022 and was conducted relative to the AEO reference case under which 380

million gallons of biodiesel would be used. For the FRM, we are assuming that 1,670 million

gallons of biodiesel would be used by 2022. The biodiesel capital cost analysis was conducted

relative both the AEO and RFS1 reference cases. Under the RFS1 reference case 300 million

gallons of biodiesel would be used by 2022 compared to the 380 million gallons under the AEO

reference case. We believe that the difference between to the two reference cases is sufficiently

small (80 million gallons per year by 2022) so that a single analysis of biodiesel freight costs

conducted relative to the AEO reference case provides a reasonable estimate relative to both

reference case. Hence, we used the results of our biodiesel freight cost analysis under the AEO

reference case for the RFS1 reference case as well.
811

Our estimation of biodiesel freight costs for the NPRM was based on our evaluation of

where biodiesel would be produced and the potential biodiesel demand centers. Our projections

of where biodiesel would be produced and used under the NPRM analysis is contained in Section

1.8.2 of this RIA. Our projections of where biodiesel will be produced under the final rule

(FRM) analysis are contained in Section 1.5.4 of this RIA. The NPRM estimate of where

biodiesel would be used was used in our final rule analysis. Due to time constraints, we used a

modified NPRM biodiesel freight cost analysis (which assumes the same production and demand

centers used in the NPRM) to estimate biodiesel freight costs for this final rule. This analysis is

described below. Our comparison of projected biodiesel production centers under the NPRM

and FRM analyses relative to the projected demand centers indicates that the biodiesel

transportation modes and distances are substantially similar. As a result, the freight cost

estimates should be similar.

The distribution of biodiesel from production plants to petroleum terminals where it

would be blended with diesel fuel is discussed in Section 1.6.2 of this RIA. Tank truck was the

assumed method of shipment for distances of less than 300 miles. Where distances are longer

than 300 miles, shipment by manifest rail was assumed to be the preferred option other than in

cases on the East coast where there were apparent barge routes from production to demand

centers. Biodiesel that could not be consumed in the state where it was produced to meet State

level biodiesel mandates, demand for biodiesel use in heating oil, or other projected biodiesel use

in diesel fuel was assumed to be shipped to market by manifest rail.249 A 1,000 mile shipping

distance was selected to ensure that all biodiesel not used to satisfy a state mandate, otherwise

used in state, or used for bio-heat could find a market.

Our estimates of the freight costs for shipping biodiesel by tank truck are based on the

ethanol tank truck freight costs that we developed for the RFS1 final rule. These ethanol

transport costs were increased by 10% to account for the increased cost associated with

preventing fuel gelling during cold conditions. The cost of shipping biodiesel by truck when the

trip (or multiple trips) could be completed in a day was estimated to range from 7 to 8 cents per

gallon. Some long truck transports were assumed to be necessary (up to 300 miles), where a

round trip could not be completed in a single day. In such cases, the need for an overnight

layover was assumed to add 120 dollars to shipping costs, resulting in an estimated 9.5 cents per

gallon freight cost.

Our estimate of the cost of shipping biodiesel by manifest rail cars is based on publicly

available biodiesel freight tariff information from BNSF railway from February 2008.1163

Specific tariff information was not available for source/destinations needed for our analysis. A

minimum cost of 9 cents per gallon was assumed to accommodate loading, unloading, and rail

car lease costs. Based on the BNSF tariff information, we estimated that every 100 miles of

additional shipment by manifest rail car beyond 600 miles adds 1.4 cents per gallon to shipping

cost. Thus, for the assumed 1,000 mile shipping distance for biodiesel used to meet

miscellaneous demand (i.e. not used to meet state mandates or for bioheat) the cost to ship by

manifest rail car was estimated at 15 cents per gallon. Barge shipping costs were assumed to be

comparable to the cost of shipping by manifest rail. This will tend to overstate barge shipping

249 Biodiesel is projected to be blended into most heating oil used in the Northeast by 2022. The blended product is

commonly referred to as bioheat.
812

costs, since we understand that barge freight costs tend to be significantly less than rail freight

costs. However, given the small fraction of biodiesel is projected to me moved by barge, this

will have only a minimal effect on our overall estimation of biodiesel freight costs. Shipping

distances were estimated based on a review of biodiesel production plant locations, demand

centers, and the rail/barge transportation net.250

Considering the location of biodiesel plants and biodiesel demand centers, 86% of

biodiesel was projected to be shipped by truck, 13% was estimated to be shipped by manifest rail

car, and 1% was estimated to be shipped by barge. We project that approximately 44% of the

biodiesel production volume in 2022 would be used in the state where it was produced to meet

state mandates, satisfy the demand for bioheat, or to meet other in-state miscellaneous demand.

The average cost of shipping this volume by tank truck is estimated to average 8 cents per gallon.

Approximately 3% of biodiesel production volume is estimated to be shipped out-of-state by

manifest rail car to meet miscellaneous biodiesel demand at an average freight cost of 16 cents

per gallon. Approximately 54% of biodiesel production is projected to be shipped out of state to

satisfy state mandates or bioheat demand which could not be satisfied with in-state production.

We assigned portions of the production volumes from states that had already satisfied this

demand to meet this demand in other states based on minimizing overall shipping distances (and

costs).

A freight cost estimate was derived based on the fraction of the volumes that would be

shipped by each mode and the freight cost for each mode used given the shipping distance. On

average the cost of shipping biodiesel from out-of-state to satisfy state biodiesel mandates or the

demand for bioheat is estimated at 10 cents per gallon. By weighting the biodiesel volumes used

to satisfy the three demand categories by the respective freight cost to ship that volume we

arrived at a national average biodiesel freight cost estimate of 10 cents per gallon. Biodiesel

freight costs are summarized in Table 4.2-41.

Table 4.2-41.

Estimated Biodiesel Freight Costs for the RFS2 Control Case

Biodiesel Demand Category

Fraction of

Biodiesel Production

Freight Cost

(cpg)

Shipped In-State to Satisfy In-State Demand 43% 8

Shipped Out-of-State to Satisfy State Mandates

and Demand for Bioheat

54% 11

Shipped Out-of-State to Satisfy

Miscellaneous Demand

3% 16

Total (National Average) 100% 10

250 See Section 1.8.2 of this RIA.
813

4.2.4 Potential Fuel Retail Costs to Facilitate the Use of Mid-Level Ethanol

Blends

As discussed in Section 1.6.10 of this RIA, our preliminary projections regarding the

potential costs to the fuel distribution system are based on the premise that the facility changes

needed would be limited to retail facilities. There may be additional costs upstream of retail

facilities if separate gasoline blendstocks are needed to blend E10 and E15.

Testing is still underway regarding what changes might be needed to retail fuel storage

and dispensing equipment originally designed to handle E10 to ensure its compatibility for an

E15 blend. Thus there is considerable uncertainty regarding the potential costs. Ideally, E15

could be dispensed and stored in existing retail equipment with no physical modifications.

However, it seems most prudent to assume that the potential changes might range from the

replacement of hanging hardware (hoses attached to the dispenser and the nozzles), to the

replacement of dispensers with E85 compatible equipment, and/or to the modification to

underground piping which connects the dispenser to the underground storage tanks. There may

also be the need to replace the underground storage tanks themselves at some facilities. Some

newer facilities might need to make fewer changes, while older facilities may require more

extensive modifications.

We evaluated 3 cost scenarios regarding the potential modifications needed to ensure the

compatibility of current retail fuel storage and dispensing facilities to handle E15. Under the

first scenario, each retail facility would need to replace only the hanging hardware on the pumps

which dispense gasoline. The National Association of Convenience Stores (NACS) estimates

that there are 3.9 gasoline refueling dispensers on average at retail facilities.1164 The National

Petroleum News (NPN) estimates that there were 161,768 retail facilities in the U.S. in 2008.1165

Thus, we estimate that there are approximately 631,000 gasoline dispensers at retail facilities.

Information from fuel retailers indicates that the cost of hanging hardware that is compatible

with E85 is $750 for the 2 hoses and nozzles needed for a single dispenser. We choose to use the

cost for E85-compatible hardware because there is no information on whether there may be

lower costs for E15-compatible equipment.251 We assumed a $25 installation cost per dispenser.

Assuming that the hanging hardware on all gasoline dispensers would need to replaced, the

resulting cost would be $127 million. However, a significant fraction of this cost might be

deferred to the extent that hanging hardware could be replaced on a timetable that is consistent

with the normal maintenance schedule as the number of E15 facilities ramps up. Information

from fuel retailers indicates that hanging hardware is typically replaced every 3 to 5 years.

Under the second scenario, the wetted components inside fuel dispensers would need to

be replaced in addition to the hanging hardware.252 Information from fuel dispenser

manufactures indicates that the cost of the wetted fuel dispenser components is approximately

$10,000. We assumed a $1,000 installation cost per dispenser. Fuel dispenser equipment

installers indicated that the replacement of the wetted components in a fuel dispenser is not

251 Underwriters Laboratories has separate certification for E10, E25, and E85 retail dispensing equipment. No

equipment has currently been certified for E25 or E85 use. However, there is considerable experience regarding the

equipment suitable for E85 service.

252 The wetted components refers to the components inside the fuel dispenser which come into contact with the fuel.
814

standard practice, and there may be logistical and administrative difficulties that would need to

be overcome. The cost would be $6.940 billion, if assume that the wetted components in all fuel

dispensers would need to replaced. Adding the cost of replacing the wetted dispenser

components to the cost of replacing the hanging hardware, results in a total cost estimate of

$7.067 billion.

Under the third scenario, a fraction of retail facilities would need to break concrete to

modify underground components such as the piping between dispensers and storage tanks and

the joints between piping and other underground components. This is the most speculative

scenario given that the costs of modifying underground components could vary greatly

depending on the extent of the modifications that might be needed. We assumed an average cost

of $25,000 per facility to make the changes needed to underground facilities. We believe that

this is a low-end estimate given that the cost of modifications to underground retail fuel storage

facilities can escalate quickly. We assumed that 50% of all retail facilities would need to make

such changes for a total cost of $2.022 billion. Adding in the cost of replacing the wetted

dispenser components and cost of replacing the hanging hardware, results in a total cost estimate

of $9.089 billion.

These cost estimates could be altered significantly as a result of the findings of the test

programs currently underway regarding the compatibility of existing fuel retail equipment with

E15. As discussed in Section 1.6.10 of this RIA, there may be difficulties in identifying what

changes are needed at some retail facilities due to a lack of records on the type of equipment

currently installed. This may limit the ability to make such retail facilities E15 compatible.

4.3 Reduced U.S. Refining Demand

As renewable and alternative fuel use increases, the volume of petroleum-based products,

such has gasoline and diesel fuel produced by U.S. refineries, would decrease. This reduction in

finished refinery petroleum products results in reduced refinery industry costs. The reduced

costs would essentially be the volume of fuel displaced multiplied by the cost for producing the

fuel. There is also a reduction in capital costs as investment in new refinery capacity is displaced

by investments in renewable and alternative fuels capacity.

Although we conducted refinery modeling for estimating the cost of blending ethanol

(see Section 4.4), we did not rely on the refinery model results for estimating the volume of

displaced petroleum as other economic factors also come into play. Instead we conducted an

energy balance around the increased use of renewable fuels, estimating the energy-equivalent

volume of gasoline or diesel fuel displaced. This allowed us to more easily apply our best

estimates for how much of the petroleum would displace imports of finished products versus

crude oil for our energy security analysis which is discussed in Section 5.2 of the RIA.

As part of this petroleum displacement analysis, we accounted for the change in

petroleum demanded by upstream processes related to additional production of the renewable

fuels as well as reduced production of petroleum fuels. For example, growing corn used for

ethanol production requires the use of diesel fuel in tractors, which reduces the volume of
815

petroleum displaced by the ethanol. Similarly, the refining of crude oil uses by-product

hydrocarbons for heating within the refinery, therefore the overall effect of reduced gasoline and

diesel fuel consumption is actually greater because of the additional upstream effect. We used

the lifecycle petroleum demand estimates provided for in the GREET model to account for the

upstream consumption of petroleum for each of the renewable and alternative fuels, as well as

for gasoline and diesel fuel. Although there may be some renewable fuel used for upstream

energy, we assumed that this entire volume is petroleum because the volume of renewable and

alternative fuels is fixed by the RFS2 standard.

We assumed that a portion of the gasoline displaced by ethanol would have been

produced from domestic refineries causing reduced demand from US refineries, while the rest of

the additional ethanol displaces imported gasoline or gasoline blendstocks which does not affect

domestic refining sector costs. To estimate the portion of new ethanol which displaces US

refinery production we relied on some Markal refinery modeling conducted for us by DOE. The

Markal refinery model models all the refinery sectors of the world and thus can do a fair job

estimating how renewable fuels would impact imports of finished gasoline and gasoline

blendstocks. The Markal refinery model estimated that 2/3rds of a reduction in petroleum

gasoline demand would be met by a reduction in imported gasoline or gasoline blendstocks,

while the other 1/3rd would be met by reduced refining production by the US refining sector. In

the case of biodiesel and renewable diesel, all of it is presumed to offset domestic diesel fuel

production. For ethanol, biodiesel and renewable diesel, the amount of petroleum fuel displaced

is estimated based on the relative energy contents of the renewable fuels to the fuels which they

are displacing. The savings due to lower imported gasoline and diesel fuel is accounted for in

the energy security analysis contained in Section 5.2.

For estimating the U.S. refinery industry cost reductions, we multiplied the estimated

volume of domestic gasoline and diesel fuel displaced by the projected wholesale price for each

of these fuels in 2022, which are $3.42 per gallon for gasoline, and $3.83 per gallon for diesel

fuel (see Section 4.4). For the volume of petroleum displaced upstream, we valued it using the

wholesale diesel fuel price. Table 4.3-1 shows the net volumetric impact on the petroleum

portion of gasoline and diesel fuel demand, as well as the reduced refining industry costs for

2022.
816

Table 4.3-1

Changes in U.S. Refinery Industry Volumes and Costs for the RFS2 Program in 2022

Relative to the AEO 2007 Reference Case

(2007 dollars)

Low Ethanol

Case

Primary Case

(mid-ethanol case)

High Ethanol Case

Bil

Gals

Bil $ Bil

Gals

Bil $ Bil

Gals

Bil $

Upstream Petroleum 0.34 1.3 0.34 1.3 0.33 1.3

End Use Gasoline -0.9 -3.1 -2.0 -6.8 -4.4 -15.0

Diesel

Fuel

-10.1 -38.7 -7.5 -28.7 -1.3 -5.0

Total -10.7 -40.5 -9.2 -34.2 -5.4 -18.7

For the primary control case, this analysis estimates that the RFS2 program would reduce

the gasoline and diesel fuel production volume of US refineries by 9.2 billion gallons in 2022,

which would reduce their production costs by $34 billion dollars. Accounting for all the

petroleum displaced (domestic and foreign), the RFS2 program is estimated to reduce gasoline

and diesel fuel demand by 13.6 billion gallons.

4.4 Overall Costs to Gasoline and Diesel Fuel

The previous sections of this chapter have presented estimates of the cost of producing

and distributing ethanol, biodiesel and renewable and cellulosic diesel fuel. In this section, we

summarize the overall cost of the RFS2 program by assessing the costs of using more renewable

fuels in the transportation fuel supply. The analysis was conducted in two steps. One step

involved running a refinery model for estimating the costs of using more ethanol and blending it

into gasoline. We used the Haverly Linear Programming (LP) model to conduct the refinery

analysis. This model is widely used by the refining industry, consultants, engineering firms and

government agencies to analyze refinery economics, refinery operations, fuel quality changes,

refinery capital investments, environmental changes and demand changes. This Haverly model

uses Jacobs’s Refining Process Technology Database to represent refining operations.

While the change in volumetric demand for petroleum-based diesel fuel was modeled

with the Haverly refinery model, the modeling of the cost of blending and using cellulosic and

renewable diesel fuel and biodiesel on the overall cost of diesel fuel was estimated as a second

step post-refinery modelling. Assessing the costs of blending of renewable diesel fuels postrefinery modeling inherently assumes that the renewable diesel fuels will be drop in

replacements for petroleum-based diesel fuel. However, if the higher cetane values for cellulosic

and renewable diesel fuel and biodiesel can be taken advantage of by refiners in blending up

diesel fuel, then our cost estimates are likely to be conservative. We provide a more detailed

description about how we estimated the costs of using cellulosic and renewable diesel fuel and

biodiesel at the end of subsection 4.4.1.

4.4.1 Description of Cases Modeled and Methodology
817

The fuels cost analysis was set up to analyze the volumes required by the RFS 2 as

described in Section 1.2. Because of the uncertainty in how cellulosic biofuel industry will

develop over time, we assessed three different renewable fuels scenarios each of which totaled

36 billion gallons based on the energy equivalency of ethanol. The three cases represent a high

quantity of cellulosic ethanol and a low amount of cellulosic diesel fuel, a low amount of

cellulosic ethanol and a high amount of cellulosic diesel, and a midpoint between these two cases

which served as our primary control case. We also considered a small amount of biodiesel and

renewable diesel as required under EISA.

The refinery modeling analysis analyzed the extent to which ethanol will be used in

conventional gasoline and reformulated gasoline by region and the resulting effects on gasoline

composition. The refining industry was modeled based on five aggregate complex refining

regions, representing Petroleum Administration for Defense Districts (PADD) 1, 2, 3, 4 & 5

together minus California, and California separately. All of the PADDS were modeled

simultaneously together in the LP model which allowed the refinery model to most efficiently

rebalance the regional gasoline production volumes in response to the addition of the renewable

fuels. We conducted the refinery modeling analysis assuming that crude oil would be priced at

about $51 per barrel and product prices were set based on this crude oil price. We adjusted the

costs to reflect a crude oil price of $116 per barrel which is the reference crude oil price

estimated by the Energy Information Administration (EIA) for its 2009 Annual Energy Outlook

(AEO).1166

The fuels cost analysis was conducted in three distinct steps which involved a base case,

two reference cases and the three control cases.

4.4.1.1 Base Case

The first step involved the establishment of a 2004 base case which calibrated the

refinery model against 2004 volumes, gasoline quality, and refinery capital in place. We chose

2004 because the following year, 2005, as well as the beginning of 2006, were affected by

hurricanes and would not be representative of a typical year, and 2007 and 2008 were years of

extreme crude oil price volatility which skews the price relationships between crude oil and

gasoline and diesel fuel. Refinery unit capacities from the Oil and Gas Journal and Energy

Information Administration (EIA), as well as refinery feedstock and product volumes from EIA

data were entered into the refinery model. The refinery model was then run and the resulting

gasoline quality compared and calibrated to actual gasoline quality data information from EPA’s

Reformulated Gasoline data base.

4.4.1.2 Reference Cases

The reference cases are benchmark cases that serve as references to the control cases.

Thus, the only difference between the control cases and the reference cases is that the control

cases model the RFS2 volumes of renewable fuels versus lesser volumes in the reference cases.

We established two reference cases. One represented the volumes of renewable fuels assumed

for the RFS1 program. The second reference case represented the growth of renewable fuels by
818

EIA’s 2007 Annual Energy Outlook (AEO). The volumes for both these cases are summarized

in Section 1.2 above. Because the RFS2 fuel standard becomes fully implemented in 2022, we

established our reference cases using the projected volumes of finished products in 2022. These

projected volumes were based on the energy demand for gasoline and diesel fuel from the EIA’s

AEO 2009. The projected volumes were used for establishing total finished product production

which then led to refinery production levels for each PADD.

The refinery modeling was conducted using a projection of crude oil and product prices

in 2022 that Jacobs made for the proposed rulemaking. The average price of crude oil was

projected to be about $51 per barrel, although crude oil prices varied by PADD. Jacobs based

the prices for refined products based on the historical price spreads of fuels between the PADDs,

using information from EIA’s 2004 price information tables, Platts, and AEO 2006. For the

reference case as well as for the control cases, we assumed the same crude oil and product prices

when conducting the refinery modeling work. The crude oil prices and summertime and

wintertime prices for gasoline, diesel fuel and jet fuel used in the refinery modeling are

summarized in Table 4.4-1.

Table 4.4-1. Crude Oil and Finished Product Prices used in Refinery Modeling

PADD 1 PADD 2 PADD 3 PADD 4/5

CA excl.

California

Crude Oil

($/bbl)

Year-round

Average

53.6 53.0 50.6 51.8 50.2

Reformulated Summer 175.2 173.5 170.8 - 186.7

Gasoline

(c/gal)

Winter 161.4 158.7 155.3 - 173.7

Conventional Summer 167.6 165.1 161.4 173.6 -

Gasoline

(c/gal)

Winter 154.0 151.8 148.5 160.4 -

Diesel Fuel Summer 156.6 157.6 154.6 166.6 164.6

(c/gal) Winter 162.6 163.6 160.6 172.6 170.6

Jet Fuel Summer 158.6 158.6 156.6 164.6 164.6

(c/gal) Winter 156.6 156.6 154.6 162.6 162.6

However, AEO 2009 projected crude oil prices to be $116 bbl in 2022, which is much

higher than those that Jacobs had estimated. After completing the refinery modeling, we

adjusted the costs of using the renewable fuels post-refinery modeling using a methodology

described below in Subsection 4.4.1.6.

We also modeled the implementation of several environmental programs that will have

required changes in fuel quality by 2022, including the 30 ppm average gasoline sulfur standard,

the 15 ppm cap standards on highway and nonroad diesel fuel and the Mobile Source Air Toxics

(MSAT) benzene standard. Although there may still be a small amount high sulfur diesel fuel,

we assumed that all distillate fuel would be ultra low sulfur in compliance with the 15 ppm cap

standard required by the highway and nonroad diesel fuel sulfur standards. We modeled the

implementation of the 2005 Energy bill, which by rescinding the RFG oxygenate standard,
819

resulted in the discontinued use of MTBE, and a large increase in the amount of ethanol blended

into reformulated gasoline.1167 By using the AEO 2009 energy demand we also included the

EISA Corporate Average Fuel Economy (CAFE) standards that were mandated in EISA and

modeled in AEO 2009.1168 The assumed EISA vehicle CAFE standard as projected by EIA in

AEO phases-in less aggressively than the EPA and NHTSA proposed greenhouse gas and CAFE

standards. However, because both programs would be fully phased in by 2022, their net effects

in 2022 would not be that different. So basing our refinery modeling analysis on the EISA

CAFE standard versus the EPA/NHTSA proposed standard would not make a significant

difference in our costs.

4.4.1.3 Control Cases

The third step in the refinery modeling analysis was to model the control cases. As stated

above, we modeled three different control cases to capture the impacts of a range of different

levels of cellulosic ethanol versus cellulosic diesel fuel. All three cases model increased

volumes of corn ethanol, biodiesel and renewable diesel fuel. Tables 1.2-1 through 1.2-3 in

Section 1.2 of the RIA summarize the volumes modeled for the control cases. For the additional

ethanol blended into the gasoline pool, a substantial portion of the additional ethanol was

blended as E85. The cost and other implications of the control cases are compared to the

reference cases to assess the costs of the program. We ran these multiple cases to understand

how costs change based on different levels of cellulosic ethanol versus cellulosic diesel fuel.

The gasoline and diesel fuel product energy output for each control case modeled was

maintained the same as that for the reference cases which modeled the energy demand of the

AEO 2009. Thus, as the volumes of lower energy dense renewable fuels increased, the total

volume of combined renewable and conventional fuels was increased to maintain the same level

of energy demand. Maintaining constant energy output assumes that vehicle miles traveled

would remain the same between the various cases despite any change in gasoline and diesel fuel

prices caused by the use of renewable fuels. In reality the increased use of renewable fuels may

result in changes to fuel prices to consumers, either directly as estimated in this section, or

indirectly by affecting world oil prices as discussed in Chapter 5, and changes in fuel prices

would be expected to affect demand. However, our analysis was conducted in parallel without

the ability to input the results of the other analysis. Furthermore, it is difficult to predict the

impacts on fuel prices to consumers, especially in light of the federal tax subsidies which we

attempted to account for in our analysis, and the many and diverse state tax subsidies which we

did not attempt to account for. Maintaining constant fuel product energy output captures the

capital cost differences between the cases. Table 4.4-3 below summarizes the volumes of

gasoline and diesel fuel used for the two reference cases, the primary control case and the two

other control cases. The gasoline volumes include the volumes of renewable fuels, while the

diesel fuel volumes do not since diesel fuel costs were estimated post refinery modelling.
820

Table 4.4-2.

Volumes of Gasoline and Diesel Fuel Used in Refinery Modeling

(Billion Gallons/yr)

RFS 1

Reference

Case

AEO 2007

Reference

Case

Low

Ethanol

Control

Case

Mid-Ethanol

Control

Case

Primary

Case

High Ethanol

Control Case

Gasoline

Volume

(Gasoline and

Ethanol)

136.3 138.2 139.6 141.1 144.7

Diesel Fuel

Volume

(Diesel Only)

70.7 70.6 60.7 63.3 69.4

All the other fuel standards and ASTM fuel quality constraints modeled in the reference

case described above are assumed to apply to the control case as well. The reference and control

cases where modeled assuming that ethanol conventional gasoline blends are entitled to the

current 1.0 psi RVP waiver during the summer (i.e., for all 9.0 RVP and many low RVP control

programs) so as to correctly assess the use of butanes in summertime conventional gasoline.

Reformulated gasoline (RFG), however, must meet the same volatility standard with or without

ethanol, so the addition of ethanol into RFG forced the refinery model to remove the last of the

butane and some pentanes to rebalance RVP. Wintertime conventional and reformulated

gasoline are normally blended up to either the RVP or vapor/liquid ASTM limits so the addition

of ethanol into the wintertime gasoline pools resulted in the removal of light hydrocarbon

compounds – normally butane. The crude oil and product prices for the control cases were the

same as the reference cases. The capital investments made for the reference cases are not

assumed to be sunk when the refinery model is assessing the economics for capital investments

for the various control cases. Thus, the refinery model is free to optimize the capital investments

made for each control case incremental to the base case. The control cases are run with capital

costs evaluated at a 15 percent rate of return on investment (ROI) after taxes, but are then

adjusted post-modeling to a 7 percent ROI before taxes.

4.4.1.4 Ethanol Blending and Prices

A special procedure was set up in the refinery model to capture the costs of blending

ethanol. Because ethanol is primarily produced in the Midwest, but distributed to the final

terminals where it is blended with the gasoline (or gasoline blendstock for blending with

ethanol), hypothetical terminals were set up in each PADD within the refinery model which

would receive the shipped ethanol as well as the gasoline blendstock for blending with ethanol

(also referred to as conventional blendstock for oxygenate blending (CBOB) for conventional

gasoline and reformulated blendstock for oxygenate blending (RBOB) for RFG and for

California RFG (CARBOB). The gasoline blendstock either comes from the same PADD where

the terminal is located, or transferred from a different PADD. This refinery modeling technique

helps to more correctly estimate the distribution costs for both the ethanol and the gasoline. The
821

refinery model assessed ethanol’s use in each PADD based on its price relative to CG and RFG,

which is based on its production cost and distribution costs, and its blending economics. For the

base case we assumed that ethanol would be splash blended into gasoline. But for both the

reference and control cases modeled in the year 2022 we expect that most, if not all, of the

ethanol will be octane match-blended for blending up E10.

The price of ethanol used in the reference case and the primary control cases were based

on the 2004 yearly average price spread between regular conventional gasoline sold into the spot

market in Houston and ethanol sold on the spot market on Chicago Board of Trade (CBOT).

This price spread was 12.3 cents per gallon lower than gasoline in the summer, and 7.7 cents per

gallon lower than gasoline in the winter. This worked out to an average ethanol price of 146.8

c/gal in the summer, and 139.4 in the summer. To derive ethanol prices for all the PADDs, the

Midwest ethanol production price was then adjusted for transportation costs to deliver ethanol

from the Midwest to end use terminals (see Section 4.2 for additional details about the

distribution costs). This assumes that the Midwest ethanol market will continue to set the price

for ethanol – a reasonable assumption considering the significant amount of corn and other

biomass available in the Midwest. The sales prices assigned to ethanol are summarized in Table

4.4-4.

Table 4.4-3. Ethanol Prices used in Refinery Modeling (c/gal)

PADD 1 PADD 2 PADD 3 PADD

4/5 CA

excl.

California

Summer 166.2 158.9 164.3 170.4 170.2

Winter 158.8 151.5 156.9 163.0 162.8

After the refinery modeling was completed, the ethanol prices and the costs for each case

were adjusted to reflect the ethanol production and distribution costs described above in Sections

4.1 and 4.2. The ethanol production cost is the volume-weighted average for ethanol sourced

from corn, cellulose and imports. The ethanol production and distribution cost and cellulosic and

renewable diesel and biodiesel costs for 2022 are shown in Table 4.4-4
822

Table 4.4-4.

Average 2022 Production and Distribution Costs

for Ethanol, Biodiesel and Cellulosic and Renewable Diesel Fuels

Production

Cost (c/gal)

Low Ethanol Case Primary Case Mid

Ethanol Case

High Ethanol Case

Distribution

Cost

Total

Cost

Distribution

Cost

Total

Cost

Distribution

Cost

Total

Cost

Corn Ethanol 150 23.2 173.2 21.6 171.6 18.8 168.8

Cellulosic

Ethanol

127 23.2 150.2 21.6 148.6 18.8 145.8

Imported

Ethanol

150 23.2 173.2 21.6 171.6 18.8 168.8

Biodiesel from

virgin oils

273 20.0 293 20.0 293 20.0 293

Biodiesel from

waste oil

243 20.0 263 20.0 263 20.0 263

Biodiesel from

corn oil

190 20.0 210 20.0 210 20.0 210

Biodiesel from

algae oil

452 20.0 472 20.0 472 20.0 472

Renewable

Diesel Fuel

from waste oil

242 15.7 257.7 15.4 257.4 14 256

Cellulosic

diesel from

BTL

237 15.7 252.7 15.4 252.4 14 251

The ethanol production and distribution costs summarized in Table 4.4-4 are different in

value compared to the ethanol prices used in the refinery modeling summarized above in Table

4.4-3. To capture the costs of the RFS2 program renewable fuel volumes, we adjusted the initial

costs of the refinery modeling cost analysis using the ethanol production and distribution costs.

This cost adjustment was made by multiplying the difference in ethanol cost or price between

Tables 4.4-4 and 4.4-3 by the difference in ethanol volume modeled between the control case

and the reference case.

We also estimated the costs of the RFS2 program renewable fuel volumes taking into

account the consumption subsidies for corn ethanol, cellulosic ethanol, biodiesel and renewable

diesel fuel. While these subsidies conceal large portions of the costs of renewable fuels, their

economic effects deserve to be understood.

4.4.1.5 E85 Blending and Prices

Today E85 is blended at 85 percent by volume in the summer and at 70 percent by

volume in the winter. Ethanol must be blended at less than 85 percent in the winter because of

ethanol’s low blending Reid Vapor Pressure (RVP). Unlike when ethanol is blended at 10

percent and causes a large vapor pressure increase, when ethanol is blended at 85 percent it

blends much closer to its very low neat blending RVP of 2.2. The denaturant provides a small
823

RVP increase to the neat ethanol, however, the amount of denaturant which may be blended into

neat ethanol was recently limited to 2 percent. The lower denaturant volume limits the RVP

increase that the denaturant will have on neat ethanol.

When ethanol is blended with gasoline at the terminal to make E85, the available

gasoline blendstock must be used. Today this blendstock is either a conventional blendstock for

oxygenate blending (CBOB) or reformulated blendstock for oxygenate blending (RBOB) used

for blending E10 to the local gasoline specifications. For example, reformulated gasoline (RFG)

must comply with the hydrocarbon standard of the RFG program and therefore RFG tends to

have an RVP of 6.8, and the RBOB that is blended with ethanol has an RVP of about 5.8. When

this 5.8 RVP RBOB is blended with ethanol to make E85, its final RVP is estimated to be 4.72.

However, the RVP minimum specified in the E85 ASTM standard shows the summertime lower

limit of E85 is 5.5, thus, blending RBOB with 85 percent ethanol would not comply with the

ASTM lower RVP standard. Table 4.4-7 summarizes ethanol’s blending RVP, gasoline’s RVP

and the final blend RVP for summertime RFG and CG gasoline, and compares the blends RVP

values to that of the E85 ASTM RVP standards.1169

Table 4.4-5. Comparison of E85 RVP levels to the ASTM RVP Standard

Summer E85 Winter E70

Gasoline

RVPa

CG 10.0 RFG & low

RVP 6.8

ASTM Std CG/RFG

14

ASTM Std

Gasoline

Blendstock

RVP

9.0 5.8 13

Ethanol

Blending

RVP

4.37 4.37 - 6.1

Blend

RVP

5.20 4.72 5.5

minimum

5.85 9.5

minimum

a

Summertime CG is allowed a 1 psi waiver for blending with ethanol, however, RFG and some

low RVP areas and wintertime CG/RFG do not normally receive 1 psi waivers.

Table 4.4-7 shows that summertime RFG and CG and wintertime gasoline cannot meet

the ASTM RVP minimum standards based on blending ethanol with the locally available

gasoline blendstock for blending up E10. For this reason, we ran the refinery model assuming

that E85 will be blended differently in the future than it is today if it is to be used in large

volumes. We assumed in the refinery modeling that all E85 will be blended at 85 percent

ethanol year-round by being blended with some butanes or pentanes (whichever is available from

the nearest refineries) to bring E85 up to the maximum ASTM RVP standard for E85, in addition

to the CBOB or RBOB being supplied to the local area. The maximum ASTM E85 RVP

standard is 8.5 in the summertime and 12.0 in the wintertime.

E85 is expected to be priced much lower in the marketplace than E10 and even less

relative to gasoline (E0) because of E85’s lower energy density. E85 contains about 83,500

BTUs per gallon compared to E10 which contains about 111,300 BTUs per gallon. Thus, when

consumers consider refueling their vehicle using E85, they will bypass using it unless it is priced
824

at parity with gasoline on an energy basis. Parity pricing means that E85 would have to be

priced about 25 percent lower than E10. Assuming that E85 is priced 25 percent lower than E10

at retail to account for the energy content differences, the pricing disparity between ethanol and

gasoline is even greater at the terminal. Table 4.4-6 summarizes the pricing of E85 at retail and

at the terminal where ethanol is usually blended with gasoline blendstock to create the E85.

Retail markup averages about 10 cents per gallon.1170 Federal and state taxes average 46 cents

per gallon (although this varies significantly by state), and transportation from the terminal to

retail averages 3 cents per gallon.1171 Thus, if E10 gasoline is priced at 163 cents per gallon at

the terminal,253 it would be priced at 222 cents per gallon at retail when the costs for transporting

the fuel to the retail market and taxes and retail markup are added on. Based on E85’s 25 percent

lower energy density, E85 would have to be priced at 167 cents per gallon at retail to reflect its

lower energy density. Using the same terminal to retail costs/taxes, E85 would be priced at 108

cents per gallon at the terminal. All this is shown in Table 4.4-6. The bottom row of the table

shows what ethanol (E100 plus denaturant) would have to be priced at for terminals to breakeven

using ethanol in E85 (this assumes that E85’s gasoline blendstock is priced the same as E10 at

the terminal). Thus, unlike with E10 where the lower energy content of ethanol is largely

transparent to the consumer, based on ethanol’s energy content alone, ethanol used in E85 would

have to be discounted significantly compared to gasoline for refiners to find it cost-effective to

use.

Table 4.4-6. E85 Pricing at Retail and at Terminals (cents per gallon)

Price at

Retail

Retail

Markup

Average

Federal and

State Taxes

Transportation

terminal to

retail

Terminal

Price

Gasoline E10 222 10 46 3 163

E85 167 10 46 3 108

Ethanol

Breakeven

Price

- - - - 97

In addition to this effect of energy equivalency, Section 1.7 above outlines the difficulty

of using all this E85 because of the relatively low number of fuel flexible vehicles (FFVs) that

will be available to consume the fuel. The relatively low number of FFVs means that the

refueling rate of these vehicles will have to be very high. In the year 2022, we estimate that

FFVs will have to refuel 74 percent of the time to use the volume of ethanol required by the

RFS2 standard.254 This means that E85 may have to be priced significantly lower than gasoline

for FFV owners to choose to fuel on it instead of gasoline. In addition, it is unlikely that every

service station would make the capital investments to make E85 available for sale. In Section

4.2, we estimate that, at most, one out of very four service stations will carry E85. Thus, E85

may have to be priced even lower than its fuel economy-adjusted price to entice FFV owners to

refuel at a station carrying E85. To estimate the marginal lower price at which FFV owners

253 The terminal price of 163 c/gal is the wholesale gasoline priced used in the refinery modeling analysis.

254 The FFV refueling percentage was estimated at 74% for the proposed rulemaking. Because the refinery

modeling analysis was started before the FFV refueling percentage was reassessed for the final rulemaking, we

continued to base our analysis on the 74% refueling figure as well as other proposed rulemaking assumptions.

Section 1.7.4 contains the updated FFV refueling percentages that we estimated for the final rulemaking.
825

would refuel at this high rate, we referenced an analysis based on a willingness to pay survey

conducted by David Greene of the Oak Ridge National Laboratory.1172 The summary of this

analysis is depicted in the Figure 4.4-1.

Figure 4.4-1

Alternative Fuel Market Share as a Function of Fuel Availability and

Price Advantage (David L. Greene, 1997, Figure 6)

Refueling frequency given

“reasonable access”

Percent of stations offering E85 in areas of the

country with “reasonable access”, i.e., 25%

X $0.21/gal Price Advantage (1997$)

Alternative Fuel Market Share as a Function of Fuel Availability and

Price Advantage (David L. Greene, 1997, Figure 6)

Refueling frequency given

“reasonable access”

Percent of stations offering E85 in areas of the

country with “reasonable access”, i.e., 25%

X $0.21/gal Price Advantage (1997$)

Based on our estimates that E85 would have to be purchased 74 percent of the time and

that one out of every four service stations would carry E85, then Figure 4.4-1 estimates that E85

would have to be priced 21 cents per gallon lower than gasoline to match this availability and

refueling scenario in addition to the adjustment for energy content. This cost estimate is based in

1997 dollars. Adjusting this cost estimate to 2006 dollars increases this estimate to 26 cents per

gallon in addition to the adjustment for energy content.

There is one more factor which we believe could affect the price of E85. FFV owners

who refuel on E85 will drive fewer miles before having to refuel compared to operating their

vehicle on gasoline. The FFV drivers will therefore spend more time refueling their vehicle. As

described above, FFV owners will drive 25 percent fewer miles per gallon and thus, will have to

spend 25 percent more time refueling. We estimate that each refueling event requires 6 minutes

of time, and that a person’s time is worth an average of 30 dollars per hour.1173,1174,1175,1176

Finally we assumed that a typical refueling volume for a refueling event is 15 gallons. Using

these assumptions, the increased refueling frequency is costing the average FFV owner 5 cents

per gallon more to use E85. To account for this additional cost, E85 would have to be priced 5

cents per gallon lower to make refueling FFVs a breakeven proposition. For our refinery

modeling work, we reduced the refiner purchase price of E85 used in our refinery modeling

analysis by this additional 5 cents per gallon.
826

Table 4.4-7 summarizes the E85 refinery purchase prices at terminals by PADD used in

our refinery modeling work. These prices represent the total of the energy content, the price

adjustment for reduced fuel availability and the cost for increased refueling events. The E85

prices should be compared to regular grade gasoline because the FFVs generally only require

regular grade gasoline when operated on gasoline.

Table 4.4-7. Wholesale E85 Prices used within the Refinery Model

E85 used in Conventional

Gasoline Areas (c/gal)

E85 used in Reformulated

Gasoline Areas (c/gal)

PADD 1 Summer 76 81

Winter 67 72

PADD 2 Summer 75 80

Winter 66 71

PADD 3 Summer 74 79

Winter 65 70

PADDs 4/5 Summer 84 80

Winter 75 81

CA Summer - 86

Winter - 77

While we used these E85 prices within the refinery model, they don’t necessarily

represent the societal costs for using E85. The pricing to reflect reduced fuel availability, in

particular, contains a significant amount of transfer payments from the refining industry to

consumers and other entities, and these transfer payments do not represent the true cost for using

E85.255

For estimating the program costs for using E85 shown in Tables 4.4-12 and 4.4-13 (and

subsequent tables for related cases), we adjusted E85 price back up to 5 cents per gallon less than

the gasoline price for each case (the additional time spent refueling is a true cost). We then used

the relative energy density of the E85 to that of gasoline as reported by the refinery model (see

energy content values in Table 4.4-19 and other similar tables) to account for the energy density

costs for using E85. We preferred the energy content price cost made by the refinery model to

our preliminary E85 energy content price adjustments because the refinery model accounts for

other changes in gasoline energy content made when accommodating the blending of ethanol .

We are not assuming that the price adjustment that we made account for lower E85 availability

accounts for any additional social cost. If the FFV driver would have to drive out of his way

from time-to-time to find the E85, then there would be some extra cost associated with the lower

availability. But most of the time FFV drivers would likely learn where to find E85 along the

routes that they normally frequent, thus, no additional effort and cost would be incurred for

refueling on E85. Thus, we assume that the lower E85 price to account for reduced E85

availability is purely a transfer payment from the refiner to the FFV owner. Despite assessing

the costs on a different basis, estimating E85 prices for the refinery modeling is important so that

the refinery model can correctly choose which gasoline type and which part of the country to

255 The possibility for this potentially large transfer payment associated with using ethanol in E85 would encourage

obligated parties to pursue the development of nonethanol renewable fuels.
827

blend the ethanol in the refinery model.

The assumption used here and throughout this rulemaking is that ethanol’s fuel economy

is directly proportional to its energy density and its concentration in the fuel. Since the

volumetric energy content of ethanol is approximately 33 percent less than conventional

gasoline, we assumed this loss in fuel economy proportional to its concentration in the fuel.

Some studies have suggested, however, that ethanol’s decrease in fuel economy may be less than

its relative decrease in volumetric energy content of the fuel. In other words, there is less of a

fuel consumption decrease than what the lower energy density of ethanol would suggest.

However, the results may more be a function of how the testing was conducted than the true

effect of ethanol on fuel economy. We therefore intend to investigate this issue more as more

data becomes available.

As discussed above, we needed to adjust the estimated program costs from costs based on

$51/barrel crude oil, the crude oil price at which the refinery model was run, to $116/bbl which

is the crude oil costs that served as the basis for our cost analysis. To make these adjustments we

estimated the wholesale gasoline and diesel fuel prices (which are the surrogate for the gasoline

and diesel fuel production costs) at the adjusted crude oil price and compared these adjusted

wholesale gasoline and diesel fuel adjusted prices to the baseline wholesale gasoline and diesel

fuel prices. The baseline wholesale gasoline and diesel fuel prices, based on an average $51/bbl

crude oil price, are summarized in Table 4.4-1. To adjust these wholesale prices, we estimated

how the price of crude oil will affect them. We conducted a regression between the annual

average spot price of price of Western Texas Intermediate crude oil and the annual average retail

gasoline and diesel fuel prices for the years 2002 through 2008.1177,1178 This regression is

reflected in Table 4.4-8 as Gasoline Retail Price = Crude Oil Price multiplied times 2.65 plus

79.0, or Gasoline Retail Price = Crude Oil Price x “X” + “Y.” The slope “X” and intercept “Y”

for this equation are summarized in Table 4.4-8. The X and Y factors for diesel fuel are also

summarized in Table 4.4-8 as well. However, we needed to estimate the wholesale prices

instead of the retail prices, so we adjusted the equations to estimate the wholesale price using the

Jacob’s wholesale prices as the calibrating values. The regression, including the adjustment

values to derive the wholesale prices equations are summarized in Table 4.4-8.

Table 4.4-8.

Equations Used for Estimating Wholesale Average Gasoline and Diesel Fuel Prices*

Equation for Retail Prices Equation for Wholesale Prices

X Y X Adjusted Y

Gasoline 2.65 79.0 2.65 +27.0

Diesel Fuel 3.38 44.8 3.38 -11.7

* The equation is used by multiplying the crude oil price ($/bbl) times the X and then adding Y to that

product resulting in a gasoline or diesel fuel cost expressed in cents per gallon.

The equations were used to estimate the average wholesale gasoline and diesel fuel

prices. These average wholesale gasoline and diesel fuel prices are summarized in Table 4.4-9.
828

Table 4.4-9

Average Gasoline and Diesel Fuel Wholesale Prices by Crude Oil Price

Crude Oil Price Gasoline Diesel Fuel

$/bbl c/gal c/gal

51 163 160

116 342 383

Table 4.4-9 shows the nationwide average costs, but our cost analysis was conducted on a

PADD basis, thus, it was necessary to estimate revised gasoline and diesel fuel wholesale prices

in each PADD. This was accomplished by generating a ratio of the average wholesale gasoline

and diesel fuel prices at the higher crude oil price relative to the average gasoline and diesel fuel

wholesale prices at the lower crude oil price, and applying this ratio to the gasoline price in each

PADD. It is important to point out one aspect of the gasoline and diesel fuel pricing changes

captured by our crude oil/gasoline and diesel fuel price relationship price model. Prior to 2005,

diesel fuel was priced about the same as gasoline (+/- 5 cents per gallon on a yearly average). In

2005 and 2006, when crude oil was priced higher, diesel fuel was priced 13 cents per gallon

higher than gasoline. In 2007 gasoline and diesel fuel were priced about the same, but then in

2008, diesel fuel was priced much higher than gasoline. Thus, the equation picks up this

relatively higher diesel fuel price at the higher crude oil prices in 2005, 2006 and 2008 and

projects a greater relative higher price for diesel fuel at our high crude oil price of $116/bbl. A

higher relative diesel fuel price at higher crude oil prices in the future may be appropriate for a

couple reasons. The first reason is that from January to mid-October 2008, when crude oil prices

were very high, diesel fuel averaged 51 cents per gallon higher than gasoline. While we cannot

say for certain that this association would always hold true at higher crude oil prices, we do have

a possible explanation for a possible relationship here. Higher crude oil prices are likely to affect

gasoline demand more than diesel fuel as more of the trips made by gasoline powered light-duty

vehicles are discretionary. For example, people may readily change their vacations plans at

higher crude oil prices, while diesel fuel used to power trucks that bring food to markets would

be expected to continue. Thus, as crude oil prices increase, gasoline consumption is likely more

elastic resulting in greater reductions in gasoline demand compared to diesel fuel. We therefore

believe that higher crude oil prices will tend towards relatively higher diesel fuel prices

compared to gasoline.

For other reasons, diesel fuel prices may trend higher in the future relative to gasoline

prices. Because EISA required that corporate average fuel economy (CAFE) standards be

increased for light duty motor vehicles, over time light duty vehicles, which are almost

exclusively gasoline powered, will become more fuel efficient. This will cause gasoline demand

to decrease, while diesel demand is projected to continue to increase with GDP to transport

goods and services. A second reason why refinery gasoline production will decrease is that most

of the renewable fuel volume being produced to comply with the RFS will displace gasoline.

This will contribute to the over supply of gasoline and the relative undersupply of diesel fuel,

thus causing gasoline prices to be soft relative to diesel fuel prices.
829

Another adjustment we made to the costs directly estimated by the LP refinery cost

model was to add additional cost for distributing gasoline from the refinery to the terminal. The

refinery cost model assigned a low distribution costs to gasoline for moving the gasoline from

the refinery to the terminal. We estimate that this distribution cost should be about 4 cents per

gallon, but the refinery model only assigned 2.5 cents per gallon for this. Thus, we credited

ethanol 1.5 cents per gallon for each gasoline-equivalent gallon of ethanol blended into each

PADD’s gasoline, since this roughly corresponded to the volume of gasoline displaced by the

ethanol.

The diesel fuel costs are estimated based on two calculations conducted post-refinery

modeling. The first calculation estimates costs based on the difference in the renewable fuels

production costs compared to the production costs for petroleum-based diesel fuel multiplied

times the increased volume of renewable fuels in the control case compared to the reference case.

For example, producing and distributing cellulosic diesel fuel for the primary control case is

estimated to cost 252 cents per gallon (see Table 4.4-4). The cost of producing diesel fuel is

estimated to be 383 cents per gallon when crude oil is priced at $116 per barrel (see Table 4.4-9),

which is 131 cents per gallon higher than the cellulosic diesel fuel costs. From the volume tables

in Section 1.2, the incremental volume of cellulosic diesel fuel is 6.52 billion gallons for the

primary reference relative to the AEO 2007 reference case. Therefore, the net production cost

for cellulosic diesel fuel is -131 cents per gallon times 6.52 billion gallons for a cost of -8540

million dollars in 2022.

The second calculation for estimating the cellulosic diesel fuel costs is an estimate of

the fuel economy cost. This is calculated by multiplying the percent loss in fuel economy for

cellulosic diesel fuel compared to petroleum-based diesel fuel times the production cost for

petroleum-based diesel fuel. Continuing with the cellulosic diesel fuel example, we assume that

cellulosic diesel fuel is from the biomass-to-liquids (BTL) process and contains 123,000 BTUs

per gallon versus petroleum diesel fuel which is estimated to contain 130,000 BTUs per gallon.

Thus, cellulosic diesel fuel contains about 95% of the energy content of petroleum-based diesel

fuel. The 5% shortfall in energy content is multiplied times the production cost of petroleumbased diesel fuel, which is 383 cents per gallon, and multiplied times 6.52 billion gallons - the

volume of cellulosic diesel fuel. This results in a cost of 1250 million dollars in 2022.

The total cost for cellulosic diesel fuel is the sum of the production cost and the fuel

economy cost which is -7290 million dollars in 2022. The total annual cost is converted to a

per-gallon cost by dividing the total annual cost by the total volume of petroleum-based and

renewable diesel fuel.

4.4.1.6 Other Adjustments to the Costs

The assumed volumes of E85 in our control cases will require increased numbers of

flexible fuel vehicles (FFVs) be available to use the fuel. In Section 1.7 above, we estimate the

number of FFVs that would be produced to enable the consumption of the volume of E85 that we

project would have to be consumed for each control case. As the number of FFVs increases it

results in higher costs due to the production of FFVs. In the following tables, we resummarize
830

the number of FFVs that we projected would be available for the AEO 2007 reference case and

each control case that we described above in Section 1.7. However, to estimate the cost impacts,

we attribute a cost for every FFV produced. For the low ethanol case, we assume that each FFV

would cost $100 per vehicle, which is an estimate for producing an FFV today. For the higher

ethanol cases, as FFV production volumes increase, because of economies of scale, we expect

that the per-vehicle costs would decrease. Furthermore, overhead costs associated with

producing FFVs would be amortized over a larger number of vehicles further lowering the pervehicle costs. For the primary case we project that FFV costs would decrease to $75 per vehicle.

For the high ethanol case we assume that FFV costs would decrease to $50 per vehicle. This

range in costs is consistent with estimates in literature.1179 Tables 4.4-10 and 4.4-11 provide

estimates of the FFV costs above the business-as-usual FFV costs in the AEO 2007 and RFS 1

reference cases.

Table 4.4-10

Numbers of FFVs and Total FFV Costs for the Control Cases

Relative to the AEO 2007 Reference Case

AEO 2007

Reference

Case

Number of

FFVs

(millions)

Number of

FFVs

(millions)

Incremental

FFV Cost

(millon dollars)

($100/FFV)

Number of

FFVs

(millions)

Incremental

FFV Cost

(millon dollars)

($75/FFV)

Number of

FFVs

(millions)

Incremental

FFV Cost

(millon dollars)

($50/FFV)

2010 1.67 1.25 -$41.7 1.85 $13.4 3.62 $97.4

2011 1.75 1.60 -$14.8 2.66 $68.6 5.44 $184.6

2012 1.77 1.90 $13.6 3.52 $131.6 7.39 $281.2

2013 1.80 2.25 $45.6 3.74 $145.9 9.42 $381.1

2014 1.83 2.52 $69.7 3.88 $154.1 11.40 $478.8

2015 1.82 2.69 $87.6 3.96 $160.5 13.29 $573.4

2016 1.82 2.76 $94.4 3.97 $161.3 13.32 $575.3

2017 1.83 2.80 $97.8 4.00 $163.3 13.44 $580.8

2018 1.83 2.93 $109.4 4.04 $165.6 13.57 $586.9

2019 1.86 2.83 $97.0 4.08 $167.2 13.71 $592.8

2020 1.90 2.77 $87.1 4.12 $166.3 13.82 $596.2

2021 1.91 2.67 $75.6 4.10 $163.9 13.76 $592.4

2022 1.91 2.61 $69.4 4.10 $163.7 13.75 $591.9

Totals 23.7 31.6 $791 48.0 $1,826 145.9 $6,113

Low Ethanol Control Case High Ethanol Control Case Primary Control Case

(mid ethanol case)

Year

Table 4.4-10 summarizes our estimated costs for the increased numbers of FFVs that

would be produced to use the projected increased volume of E85. For the primary control case

we estimate that increased FFV production would cost $1.8 billion. For the low and high ethanol

control cases we estimate that increased FFV production would cost $0.8 and $6.1 billion,

respectively.
831

Table 4.4-11

Numbers of FFVs and Total FFV Costs for the Control Cases

Relative to the RFS 1 Reference Case

RFS 1

Reference

Case

Number

of FFVs

(millions)

Number

of FFVs

(millions)

Incremental

FFV Cost

(millon dollars)

($100/FFV)

Number

of FFVs

(millions)

Incremental

FFV Cost

(millon dollars)

($75/FFV)

Number

of FFVs

(millions)

Incremental

FFV Cost

(millon dollars)

($50/FFV)

2010 0.98 1.25 $27.0 1.85 $64.9 3.62 $131.7

2011 1.08 1.60 $51.5 2.66 $118.3 5.44 $217.8

2012 1.16 1.90 $74.1 3.52 $177.1 7.39 $311.5

2013 1.23 2.25 $101.7 3.74 $188.0 9.42 $409.2

2014 1.28 2.52 $124.2 3.88 $195.1 11.40 $506.1

2015 1.31 2.69 $138.7 3.96 $198.9 13.29 $599.0

2016 1.31 2.76 $145.2 3.97 $199.4 13.32 $600.7

2017 1.32 2.80 $148.3 4.00 $201.2 13.44 $606.0

2018 1.33 2.93 $159.5 4.04 $203.2 13.57 $612.0

2019 1.35 2.83 $147.8 4.08 $205.2 13.71 $618.2

2020 1.36 2.77 $141.2 4.12 $206.9 13.82 $623.2

2021 1.35 2.67 $131.7 4.10 $206.0 13.76 $620.5

2022 1.35 2.61 $125.6 4.10 $205.8 13.75 $620.0

Totals 16.4 31.6 $1,516 48.0 $2,370 145.9 $6,476

High Ethanol Control Case Low Ethanol Control

Case

Primary Control Case

(mid ethanol case)

Year

Table 4.4-11 summarizes our estimated costs for the increased numbers of FFVs that

would be produced to use the projected increased volume of E85. For the primary control case

we estimate that increased FFV production would cost $2.4 billion. For the low and high ethanol

control cases, we estimate that increased FFV production would cost $1.5 and $6.5 billion,

respectively.

4.4.2 Refinery Modeling Results

In this subsection, we summarize the results of the three control cases that we modeled

and compare them to the two different reference cases.

Table 4.4-12 summarizes the costs for the primary control case relative to the AEO 2007

reference case excluding federal ethanol, biodiesel and renewable diesel tax subsidies. By

excluding the federal ethanol, biodiesel and renewable diesel fuel consumption subsidies, we

avoid the transfer payments caused by these subsidies that would hide a portion of the program’s

costs. The costs are reported by different cost components and adjustments described above, as

well as aggregated to show the total annual and per-gallon costs. The costs are reported

separately for gasoline and diesel fuel. The estimate of gasoline costs are based on the refinery

model and reflect the changes in gasoline that are estimated to occur by the refinery model

accommodating the expanded use of ethanol. The refinery model variable operating costs

include the labor, utility and other operating costs and are a direct output from the refinery

model. These costs reflect ethanol’s and E85’s prices used in the refinery model and reflect
832

crude oil priced at about $51 per barrel. The reduced refinery capital costs are shown in the table

amortized annually and over the gasoline pool (the nonamortized refinery and biofuel capital

costs are summarized in Table 4.4-18). The fixed costs shown in the table include the

maintenance and insurance costs and are calculated to be 3 percent of the reduced capital costs.

Next in the table we show the adjustment to remove the costs associated with low E85 prices,

except for the 5 cent per gallon refueling cost, and then we show the costs for using lower energy

density E10 gasoline and E85. The energy density costs changes rely on the fractional change in

energy density shown in Table 4.4-19, multiplied by the wholesale price of gasoline. The cost

adjustment is shown for basing the gasoline costs on $116 per barrel crude oil price versus the

$51 per barrel price that was the basis for the refinery model runs. At $116 per barrel crude oil

costs, ethanol’s production and distribution costs are lower than the wholesale cost of gasoline

which results in the cost savings. Finally for the gasoline costs, the table summarizes our

estimated costs for producing an appropriate number of fuel flexible vehicles (FFVs) for using

E85. With respect to the cellulosic and renewable diesel fuel and biodiesel costs, the production

costs and for the fuel economy effects are both presented in Table 4.4-12.

Table 4.4-12.

Primary Control Case Costs without Tax Subsidies

Relative to the AEO 2007 Reference Case

(2007 dollars, 7% ROI before taxes)

Gasoline Diesel Fuel

Refinery Model Variable Operating Cost $MM/yr 10,998 -

Amortized Refinery Capital Costs $MM/yr -997 -

Fixed Operating Costs $MM/yr -275 -

Added Gasoline Transportation Cost $MM/yr -88 -

Removal of E85 Pricing Effect $MM/yr -8,338 -

Crude Oil Cost $51 to $116/bbl $MM/yr -15,302 -

Lower Energy Density $MM/yr 8,745 1,722

Adjustment from Ethanol Price to Cost $MM/yr 83 -

FFV Costs $MM/yr 1,826

Renewable Diesel Cost vs Petroleum Diesel $MM/yr - -10,268

Total Costs $MM/yr -3,349 -8,546

Refinery Model Variable Operating Cost c/gal 7.79 -

Amortized Refinery Capital Costs c/gal -0.71 -

Fixed Operating Costs c/gal -0.19 -

Added Gasoline Transportation Cost c/gal -0.06 -

Removal of E85 Pricing Effect c/gal -5.91 -

Crude Oil Cost $51 to $116/bbl c/gal -10.84 -

Lower Energy Density c/gal 6.20 2.42

Adjustment from Ethanol Price to Cost c/gal 0.06 -

FFV Costs c/gal 1.29

Renewable Diesel Cost vs Petroleum Diesel c/gal - -14.41

Total Costs c/gal -2.37 -11.99
833

Our analysis shows that when considering all the costs associated with the expanded use

of ethanol for the primary control case relative to the AEO 2007 reference case that the cost of

gasoline will decrease by $3.3 billion in the year 2022. Expressed as per-gallon costs, these fuel

changes will save the U.S. 2.4 cents per gallon of gasoline. The addition of biodiesel, renewable

and cellulosic diesel fuel is estimated to reduce the cost of diesel fuel by $8.5 billion in the year

2022, or save 12.0 cents per gallon.

Table 4.4-13 expresses the total and per-gallon gasoline costs for the primary control case

with the federal ethanol, biodiesel and renewable diesel subsidies included. The federal tax

subsidy is 45 cents per gallon for each gallon of ethanol blended into gasoline and 101 cents per

gallon for each gallon of cellulosic biofuel. Imported ethanol is also assumed to receive the 45

cents per gallon ethanol subsidy, although we assume that a greater volume of imported ethanol

would be used than that which can flow through the Caribbean Basin, tariff free. Thus the 51

cents per gallon tariff would apply to that incremental volume of imported ethanol above the

allowable Caribbean Basin initiative volume. We estimate that imported ethanol would earn 23

cents per gallon net subsidy. The biodiesel subsidy is 100 cents per gallon, and the renewable

diesel fuel subsidy is 50 cents per gallon. The cost adjustment is estimated by multiplying the

subsidy times the volume of new ethanol, biodiesel and renewable diesel estimated to be used.

Table 4.4-13.

Primary Control Case Costs Reflecting Tax Subsidies

Relative to the AEO 2007 Reference Case

(2007 dollars, 7% ROI before taxes)

Gasoline Diesel Fuel

Total Costs $MM/yr -3,349 -8,546

Federal Subsidies $MM/yr -6,313 -7,944

Revised Total Cost $MM/yr -9663 -16,490

Total Costs c/gal -2.37 -11.99

Federal Subsidies c/gal -4.47 -11.15

Total Costs c/gal -6.85 -23.14

The cost including subsidies would represent gasoline and diesel fuel’s apparent cost as

reflected to the fuel industry as a whole because the federal tax subsidies tends to transfer a

portion of the actual costs to consumers through non-fuel taxes. Our analysis estimates that

relative to the AEO 2007 reference case, the primary control case would cause a 6.9 cent per

gallon decrease in the apparent cost of producing gasoline, and a 23.1 cent decrease in the

apparent cost of producing diesel fuel. These costs would also represent the apparent cost to

consumers “at the pump” if the full tax credit were passed along to the consumers. However, it

is possible that only a portion of the tax subsidy will be passed along to the consumer

(historically, this has been the case). Thus, the price impact at the pump may be somewhere

between the values in Tables 4.4-12 and 4.4-13. However, consumers would also pay the full tax

subsidy through higher taxes in addition to the values in Tables 4.4-13.

Table 4.4-14 summarizes the volumetric inputs to refineries in each PADD for the

primary control case and provides the incremental difference relative to the AEO 2007 reference

case. Because of the increased use of biofuels, petroleum inputs would be expected to decrease,

and this is confirmed.
834

Table 4.4-14.

Summary of the Total and Incremental Volumetric Refinery Inputs by PADD

for the Primary Control Case Relative to the AEO 2007 Reference Case

(barrels/day)

Control Difference Control Difference Control Difference Control Difference Control Difference

Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case

PADD Crude 1,246,981 -281,399 3,117,973 -340,516 7,056,501 -194,917 1,460,255 -73,349 1,865,224 -24,171

GTL Naphtha 0 0 0 0 0 0 0 0 0 0

GTL Diesel 0 0 0 0 0 0 0 0 0 0

VGO HS 0 0 0 0 0 -43,785 0 0 0 0

VGO LS 0 0 38,063 0 0 0 0 0 0 0

HS AR (A960) 0 0 0 0 0 0 0 0 0 0

LS AR (Alg) 290,427 60,773 0 0 732,497 26,283 0 0 0 0

Normal Butane 26,527 4,042 65,106 13,451 111,678 25,512 40,206 490 39,573 0

Isobutane 9,412 8,868 17,236 -6,556 25 25 19,001 12,735 0 0

Other 0 0 0 0 0 0 0 0 0 0

MTBE 0 0 0 0 0 0 0 0 0 0

Ethanol - E10 300,677 27,590 188,968 -8,932 155,813 -19,904 69,817 -573 145,656 2,998

Ethanol - E20 0 0 0 0 0 0 0 0 0 0

Ethanol - E85 243,490 243,490 111,243 111,243 212,446 212,446 17,420 17,420 0 0

Reformer Feed 0 0 0 0 0 0 0 0 0 0

Methanol 0 0 0 0 0 0 0 0 0 0

Natural Gas (FOE) 68,419 -12,688 135,263 -9,379 490,908 -13,379 87,228 -6,669 154,101 -1,567

Hydrogen (MSCF) 0 0 0 0 0 0 0 0 0 0

Pentanes Plus 0 0 32,124 0 52,055 0 18,580 0 0 0

Import CBOB 10% 148,371 142,085 0 0 0 0 0 0 0 0

Import CBOB 20% 0 0 0 0 0 0 0 0 0 0

Import RBOB 10% 65,943 -79,650 0 0 0 0 0 0 0 0

Import RBOB 20% 0 0 0 0 0 0 0 0 0 0

Import Alkylate 19,134 16,901 0 0 0 0 0 0 0 0

Import Raffinate 38,375 -15,151 0 0 0 0 0 0 45,808 0

Import Reformate 7,080 -1,749 0 0 0 0 0 0 0 0

Import FCC Naphtha 0 0 0 0 17,503 17,503 0 0 0 0

Import Lt Naphtha 0 0 0 0 0 0 0 0 584 0

Import Hvy Naph 0 0 0 0 41,644 0 0 0 0 0

Transfer Lt Naphtha 23,342 19,010 0 0 0 0 0 0 23,053 4,669

Transfer Reformate 17,226 569 0 0 0 0 0 0 0 0

Transfer Alkylate 59,431 -569 9,795 9,795 0 0 0 0 60,000 0

Transfer FCC Naphtha 0 0 20,822 0 0 0 0 0 0 0

Transfer Raffinate 0 0 784 -10,853 0 0 0 0 60,000 12,556

Transfer RBOB 10% 242,605 0 37,596 37,596 0 0 0 0 0 0

Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0

Transfer CBOB 10% 1,355,660 219,387 81,092 27,037 0 0 9,099 2,747 0 0

Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0

Isooctane 3,278 3,178 100 0 100 0 100 0 13,287 -1,975

Isooctene 17,071 16,971 100 0 100 0 100 0 2,461 1,861

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA

Table 4.4-14 shows that inputs of crude oil decreases substantially in most of the PADDs.

In all the PADDs the input of crude oil to refineries decreases, which is expected since

renewable fuels will supplant the need for petroleum feedstocks. Imports of gasoline

blendstocks into PADD 1 also decreases. Butane inputs increase due to its blending into E85.

Table 4.4-15 below summarizes the refinery output volumes and changes in refinery

output volumes relative to the reference case by PADD.
835

Table 4.4-15.

Summary of Total and Incremental Refinery Outputs by PADD

for the Primary Control Case Relative to the AEO 2007 Reference Case

(barrels/day)

Control Difference Control Difference Control Difference Control Difference Control Difference

Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case

Propane 30,305 -1,816 50,863 -6,424 100,460 -17,092 21,022 -1,481 52,834 1,242

Propylene 18,685 0 42,525 0 245,407 0 2,041 0 11,774 0

Normal Butane 0 -4,967 1,698 733 23,031 -10,632 0 0 0 0

Isobutane 0 0 0 0 0 -411 0 0 39,318 -2,172

PC Naphtha 15,830 0 40,290 0 432,937 0 0 0 0 0

PC Gasoil 0 0 502,059 -58,642 157,500 0 0 0 0 0

CG Reg 0 0 0 0 0 0 0 0 0 0

CG Prem 0 0 0 0 0 -182,001 0 0 0 0

CG E10 Reg 1,654,618 397,669 1,277,077 -100,639 850,572 -224,891 577,355 -8,008 96,902 1,992

CG E10 Prem 214,377 144,044 267,934 5,512 209,149 4,299 113,837 2,340 18,457 379

RFG E10 Reg 885,097 -271,021 273,652 5,624 405,579 19,731 0 0 1,114,370 22,903

RFG E10 Prem 222,613 2,400 52,124 1,071 77,253 3,758 0 0 212,261 4,363

CG E20 Reg 0 0 0 0 0 0 0 0 0 0

RFG E20 Reg 0 0 0 0 0 0 0 0 0 0

E85 to CG 0 0 129,577 129,577 247,459 247,459 20,291 20,291 0 0

E85 to RFG 283,619 283,619 0 0 0 0 0 0 0 0

Transfer RBOB 10% 0 0 0 0 280,201 37,596 0 0 0 0

Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0

Transfer CBOB 10% 0 0 0 0 1,445,851 249,171 0 0 0 0

Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0

Jet/Kero A (450ppm) 70,000 0 143,275 3,194 936,227 0 274,537 0 229,653 0

X-Fer Diesel Rundown to Storage 0 0 0 0 0 0 0 0 0 0

HSD Gr 76 (0.2%) 0 0 0 0 0 0 0 0 0 0

LSD Gr 74 (.05%) 0 0 0 0 0 0 0 0 0 0

ULSD (15 ppm) 505,645 -128,972 562,879 -109,292 2,011,680 -124,623 470,858 -62,441 0 0

CARB Diesel 0 0 0 0 0 0 0 0 326,650 -37,699

X-Fer C5's to Storage 0 0 0 0 0 0 0 0 0 0

1% Residual Fuel 0 0 0 0 0 0 0 0 0 0

Residual Fuel 50,000 -41,643 57,026 -7,562 262,834 0 126,642 4,352 49,880 0

Slurry 31,083 3,675 76,076 -10,192 108,692 -5,256 18,513 5,031 28,829 -178

Asphalt & Wax 91,682 0 198,329 -9,357 157,500 0 5,250 0 41,774 0

Gasoil 0 0 4,895 0 0 0 0 0 9,814 0

Lubes 18,706 0 17,313 0 157,500 0 0 0 20,149 0

Benzene 11,003 0 11,003 0 51,347 0 0 0 0 0

Toluene 0 0 0 0 34,910 0 0 0 0 0

Xylenes 0 0 0 0 7,777 0 0 0 0 0

Cumene 0 0 0 0 0 0 0 0 0 0

Other 0 0 0 0 0 0 0 0 0 0

Cyclohexane 0 0 0 0 0 0 0 0 0 0

Transfer Raffinate 0 0 0 0 60,784 1,703 0 0 0 0

Transfer Alkylate 0 0 0 0 129,226 9,226 0 0 0 0

Transfer Reformate 0 0 0 0 17,226 569 0 0 0 0

Transfer FCC naphtha 0 0 0 0 20,822 0 0 0 0 0

Transfer Lt Naphtha 0 0 0 0 23,342 19,010 0 0 23,053 4,669

Transfer Blendstock 0 0 0 0 0 0 0 0 0 0

Sulfur (STons) 1,008 -146 3,676 -438 11,971 -427 1,925 -162 3,504 -42

Coke (STon) 2,913 -1,037 10,524 -2,203 47,524 -2,276 7,111 -1,368 17,012 -282

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA

What stands out in Table 4.4-15 is that E85 volumes increase while 10 percent ethanol

gasoline blends decrease in response to the increased ethanol blended into the gasoline pool.

Similarly, ultra low sulfur diesel fuel output decreases in response to the new volume of

cellulosic and renewable diesel fuel and biodiesel.

Table 4.4-16 summarizes the change in refinery unit capacities by PADD comparing the

primary control case to the AEO 2007 reference case.
836

Table 4.4-16.

Change in Refinery Unit Capacities by PADD

for the Primary Control Case Relative to the AEO 2007 Reference Case

(thousand barrels/day)

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA California US Total

Crude Tower 0 -414 0 0 0 -414

Vacuum Tower 0 -185 0 0 0 -185

Sats Gas Plant -12 0 -2 0 8 -6

Unsats Gas Plant 0 0 0 0 0 0

FCC DeC5 Tower 0 0 0 0 0 0

FCC 0 0 0 0 0 0

FCC Splitter 4 -6 0 0 0 -2

Hydrocracker -16 0 0 -21 0 -37

H-Oil Unit 0 0 0 0 0 0

Delayed Coker 0 0 0 -24 0 -24

Visbreaker 0 0 0 0 0 0

Thermal Naphtha Splitter -1 -6 0 -1 0 -8

CRU Reformer 0 0 0 0 0 0

SRU Reformer 0 0 0 0 -5 -5

BTX Reformer 0 0 -10 0 0 -10

C4 Isomerization 0 0 0 0 1 1

C5/C6 Isomerization 21 0 0 0 0 21

HF Alkylation 0 0 0 0 0 0

H2SO4 Alkylation -14 0 0 13 0 -2

Dimersol 0 0 0 0 0 0

Cat Poly 6 0 0 0 0 6

Isooctane 0 0 0 0 0 0

DHT - Total -132 0 -75 -49 0 -256

DHT 2nd RCT - Total -117 -116 -146 -51 -4 -435

DHT Arom Saturation 0 0 0 0 0 0

NHT - Total Fd 0 0 0 0 0 0

CGH - Generic -16 -23 -6 -2 0 -48

CGH - Olefin Sat'n 0 0 0 0 0 0

FCCU Fd HDT 55 0 0 0 0 55

LSR Splitter 0 0 0 0 0 0

LSR Bz Saturator 0 0 0 0 0 0

Reformate Saturator -14 -4 1 0 0 -16

Reformate Splitter -41 -12 4 0 0 -49

SDA 0 0 0 0 0 0

MTBE 0 0 0 0 0 0

TAME 0 0 0 0 0 0

Hydrogen Plant - Total MSCF -88 -72 -84 -71 0 -316

Lube Unit 0 0 0 0 0 0

Sulfur Plant -95 0 0 -204 -69 -368

Merox Jet 0 0 0 0 0 0

Merox Diesel 0 0 0 0 0 0

BTX Reformer - Tower feed 0 0 0 0 0 0

BTX Reformer - Extract feed 0 0 0 0 0 0

Most of the capacity throughput changes are negative, reflecting the decreased processing

of crude oil and vacuum gas oil and decreased downstream refining units as projected by the

refinery model. Another important decrease in refinery unit throughput is the distillate
837

hydrotreater and hydrocracker units which reflect the displacement of petroleum distillate by

cellulosic and renewable diesel and biodiesel.

These changes in refinery unit throughputs are associated with changes in capital

investments. Table 4.4-17 summarizes the projected change in capital investments between the

primary control case and the AEO 2007 reference case.
838

Table 4.4-17.

Change in Refinery Unit Investments by PADD

for the Primary Control Case Relative to the AEO 2007 Reference Case

(million dollars/year)

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA California US Total

Crude Tower 0 -960 0 0 0 -960

Vacuum Tower 0 -572 0 0 0 -572

Sats Gas Plant -67 0 -4 0 53 -18

Unsats Gas Plant -2 0 0 0 0 -2

FCC DeC5 Tower 0 0 0 0 0 0

FCC 0 0 0 0 0 0

FCC Splitter 2 -7 0 0 0 -5

Hydrocracker -584 0 0 -641 0 -1,225

H-Oil Unit 0 0 0 0 0 0

Delayed Coker 0 0 0 -534 0 -534

Visbreaker 0 0 0 0 0 0

Thermal Naphtha Splitter -3 -7 0 -2 0 -12

CRU Reformer 0 0 0 0 0 0

SRU Reformer 0 0 0 0 -30 -30

BTX Reformer 0 0 -63 0 0 -63

C4 Isomerization 0 0 0 0 6 6

C5/C6 Isomerization 187 0 0 0 0 187

HF Alkylation 0 0 0 0 0 0

H2SO4 Alkylation -246 0 0 246 0 1

Dimersol 0 0 0 0 0 0

Cat Poly 48 0 0 0 0 48

Isooctane 0 0 0 0 0 0

DHT - Total -1,364 0 -496 -531 0 -2,391

DHT 2nd RCT - Total -905 -771 -694 -308 -21 -2,699

DHT Arom Saturation 0 0 0 0 0 0

NHT - Total Fd 0 0 0 0 0 0

CGH - Generic -75 -131 -60 -7 0 -274

CGH - Olefin Sat'n 0 0 0 0 0 0

FCCU Fd HDT 695 0 0 0 0 695

LSR Splitter 0 0 0 0 0 0

LSR Bz Saturator 0 0 0 0 0 0

Reformate Saturator -92 -19 5 0 0 -105

Reformate Splitter -53 -16 2 0 0 -68

SDA 0 0 0 0 0 0

MTBE 0 0 0 0 0 0

TAME 0 0 0 0 0 0

Hydrogen Plant - Total MSCF -352 -221 -227 -237 0 -1,038

Lube Unit 0 0 0 0 0 0

Sulfur Plant -3 0 0 -2 -2 -6

Merox Jet 0 0 0 0 0 0

Merox Diesel 0 0 0 0 0 0

BTX Reformer - Tower feed 0 0 -1 0 0 -1

BTX Reformer - Extract feed 0 0 0 0 0 0

Total -2,814 -2,704 -1,539 -2,016 6 -9,067

Table 4.4-17 shows that incremental to the AEO 2007 reference case, refiners are

expected to reduce their capital investments by $9.1 billion under the primary control case. The

reduction in capital investments occurs in PADDs 1 through 4 and PADD 5 outside of

California. Table 4.4-17 essentially expresses the change in refinery capacity input shown in
839

Table 4.4-16, but expresses the changes in terms of dollars instead of thousands of barrels per

day.

The capital cost decrease in refineries is countered by the capital costs incurred to build

new renewable fuels plants and to put into place the distribution system that the new renewable

fuels require. The increased use of renewable and alternative fuels would require capital

investments in corn and cellulosic ethanol plants, and renewable diesel fuel plants. In addition to

producing the fuels, storage and distribution facilities along the whole distribution chain,

including at retail, will have to be constructed for these new fuels. In Table 4.4-18, we list the

total incremental capital investments that we project would be made for the primary control case

incremental to the AEO 2007 reference case. All these capital costs are represented in the

summary of costs in Table 4.4-12 either in the per-gallon biofuel production costs, or the pergallon distribution costs.

Table 4.4-18.

Projected Total U.S. Capital Investments for the Primary Control Case

Relative to the AEO 2007 Reference Case

(billion dollars)

Cost Type Plant Type Capital

Investments

Corn Ethanol 3.9

Cellulosic Ethanol 14.3

Cellulosic Diesel a68.0

Production

Costs

Renewable Diesel and

Algae

1.1

All Ethanol 8.2

Cellulosic and

Renewable Diesel Fuel

1.4

Distribution

Costs

Biodiesel 1.2

FFV Costs 1.8

Refining -9.4

Total Capital Investments 90.5

a The cellulosic diesel fuel capital costs are based on biomass-to-liquids (BTL)

technology which is a very capital intensive technology. If other cellulosic biofuel

technologies are used which are less capital intensive than BTL technologies, these

capital costs would be lower.

Table 4.4-18 shows that the total U.S. incremental capital investments to achieve the

RFS2 volumes under the primary control case in 2022 is $90.5 billion. One contributing reason

why the capital investments made for renewable fuels technologies is so much more than the

decrease in refining industry capital investments is that a part of the decrease in petroleum

gasoline supply was from reduced imports. In addition, renewable fuels technologies are more

capital intensive per gallon of fuel produced than incremental increases in gasoline and diesel

fuel production at refineries.
840

Table 4.4-19 summarizes the gasoline volume and qualities by different gasoline types

for the primary control case, and also, for comparison, lists the same for the AEO 2007 reference

case.

Table 4.4-19.

Ethanol and Gasoline Volume, Quality and Energy Density by Gasoline Type at the PADD

Terminal for the Primary Control Case Relative to the AEO 2007 Reference Case

Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control

RFG

Total ('000 BPD) 1,376,331 1,107,710 319,080 325,776 459,343 482,832 0 0 1,299,365 1,326,631 3,454,120 3,242,949

Ethanol ('000 BPD) 139,023 111,890 32,230 32,907 46,397 48,771 0 0 131,244 134,003 348,895 327,571

RVP (psi) 10.8 10.8 10.6 10.6 9.7 9.8 0.0 0.0 9.5 9.5 10.1 10.1

Sulfur (ppm) 24.4 24.6 20.0 20.5 23.2 23.4 0.0 0.0 8.8 8.5 18.0 17.4

Density 258.9 258.9 258.2 258.3 259.5 259.4 0.0 0.0 258.5 258.7 258.8 258.8

Octane (R+M/2) 88.1 88.3 88.0 88.0 88.0 88.0 0.0 0.0 87.6 87.6 87.9 87.9

Aromatics (vol%) 19.9 19.9 19.9 19.9 19.6 19.7 0.0 0.0 22.2 22.1 20.7 20.8

Benzene (vol%) 0.57 0.57 0.57 0.57 0.56 0.56 0.00 0.00 0.53 0.54 0.55 0.56

Olefins (vol%) 13.6 14.7 9.4 9.3 11.5 11.1 0.0 0.0 5.7 5.7 10.0 9.9

Oxygen (wt%) 3.7 3.7 3.7 3.7 3.7 3.7 0.0 0.0 3.7 3.7 3.7 3.7

E200 (vol%) 55.6 55.1 58.3 57.4 53.3 52.7 0.0 0.0 58.2 58.2 56.5 56.2

E300 (vol%) 93.9 95.3 93.9 93.8 93.9 93.4 0.0 0.0 86.2 86.2 91.0 91.2

Energy (MMBtu/Bbl) 4.947 4.961 4.924 4.935 4.981 4.990 0.000 0.000 4.994 5.002 4.967 4.979

CG

Total ('000 BPD) 1,830,582 1,868,996 1,640,138 1,545,011 1,280,314 1,059,721 696,861 691,193 112,988 115,359 5,560,884 5,280,279

Ethanol ('000 BPD) 134,064 188,787 165,671 156,062 129,320 107,043 70,390 69,817 11,413 11,652 510,857 533,362

RVP (psi) 11.4 11.8 11.6 11.5 10.7 10.4 11.4 11.4 10.6 10.6 11.3 11.3

Sulfur (ppm) 22.9 24.5 23.6 23.3 23.1 23.1 28.0 28.0 26.6 26.3 23.9 24.4

Density 258.9 259.5 259.1 259.1 260.5 260.1 258.1 257.9 262.7 264.1 259.3 259.4

Octane (R+M/2) 87.8 87.8 88.0 88.0 88.0 88.2 86.9 86.9 89.3 89.5 87.8 87.9

Aromatics (vol%) 23.1 22.4 22.5 22.5 22.4 21.8 15.9 15.6 26.5 28.3 21.9 21.6

Benzene (vol%) 0.53 0.53 0.53 0.52 0.51 0.51 1.05 1.01 0.59 0.61 0.59 0.59

Olefins (vol%) 13.2 14.0 11.0 11.0 13.1 12.3 8.5 7.6 17.8 18.4 12.0 12.0

Oxygen (wt%) 2.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.4 3.7

E200 (vol%) 52.5 53.1 58.8 58.0 53.0 52.5 63.0 62.7 58.0 56.4 55.9 55.8

E300 (vol%) 93.9 95.3 93.9 83.8 93.9 79.5 93.9 90.6 86.2 86.2 93.7 88.0

Energy (MMBtu/Bbl) 4.995 4.980 4.925 4.937 4.988 5.005 0.000 4.912 4.942 4.964 4.961 4.963

E85

Total ('000 BPD) 0 283,619 0 129,577 0 247,459 0 20,291 0 0 0 680,947

Ethanol ('000 BPD) 0 243,490 0 111,243 0 212,446 0 17,420 0 0 0 584,599

RVP (psi) 0.0 11.1 0.0 12.2 0.0 12.1 0.0 11.9 0.0 0.0 0.0 11.7

Sulfur (ppm) 0.0 9.5 0.0 8.8 0.0 8.8 0.0 8.8 0.0 0.0 0.0 9.1

Density 0.0 267.7 0.0 266.3 0.0 266.2 0.0 266.1 0.0 0.0 0.0 266.8

Octane (R+M/2) 0.0 107.9 0.0 108.0 0.0 108.0 0.0 107.9 0.0 0.0 0.0 107.9

Aromatics (vol%) 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3

Benzene (vol%) 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01

Olefins (vol%) 0.0 2.4 0.0 3.0 0.0 1.8 0.0 0.0 0.0 0.0 0.0 2.2

Oxygen (wt%) 0.0 30.6 0.0 30.8 0.0 30.8 0.0 30.8 0.0 0.0 0.0 30.7

E200 (vol%) 0.0 128.1 0.0 130.1 0.0 129.8 0.0 129.3 0.0 0.0 0.0 129.1

E300 (vol%) 0.0 96.1 0.0 96.6 0.0 96.6 0.0 96.6 0.0 0.0 0.0 96.4

Energy (MMBtu/Bbl) 0.000 3.628 0.000 3.599 0.000 3.597 0.000 3.594 0.000 0.000 0.000 3.610

All Mogas

Total BPD 3,206,913 3,260,324 1,959,219 2,000,364 1,739,657 1,790,012 696,861 711,484 1,412,353 1,441,990 9,015,003 9,204,175

Ethanol BPD 273086.9 544,167 197900.9 300,212 175717.0 368,259 70390.0 87,238 142657.3 145,656 859752.1 1,445,532

RVP 11.1 11.4 11.4 11.4 10.4 10.5 11.4 11.4 9.6 9.6 10.8 10.9

Sulfur ppm 24 23 23 22 23 21 28 27 10 10 22 21

Density 258.9 260 259.0 259 260.2 261 258.1 258 258.9 259 259.1 260

R+M/2 87.9 89.7 88.0 89.3 88.0 90.9 86.9 87.5 87.8 87.8 87.8 89.4

Aromatics 21.7 19.7 22.1 20.6 21.7 18.2 15.9 15.2 22.5 22.6 21.5 19.7

Benzene 0.55 0.50 0.53 0.50 0.53 0.45 1.05 0.98 0.54 0.55 0.58 0.53

Olefins 13.4 13.2 10.7 10.2 12.7 10.6 8.5 7.4 6.7 6.7 11.2 10.6

Oxygen (wt%) 3.1 6.1 3.7 5.5 3.7 7.5 3.7 4.5 3.7 3.7 3.5 5.8

E200 53.8 60.3 58.7 62.6 53.1 63.2 63.0 64.6 58.2 58.1 56.1 61.4

E300 93.9 95.4 93.9 86.3 93.9 85.6 93.9 90.8 86.2 86.2 92.7 89.7

Energy (MMBtu/Bbl) 4.974 4.856 4.925 4.850 4.986 4.806 4.913 4.874 4.990 4.999 4.963 4.869

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA US

Several very important differences are evident when comparing the U.S. gasoline

(mogas) qualities of the primary control case to the AEO 2007 reference case in Table 4.4-19.

First, the energy content of the control case gasoline is lower than that of the reference case.
841

Other obvious changes include decreases in aromatics, benzene, olefins and sulfur. Another

important change is the increase in the gasoline pool octane. Since the reference case gasoline

pool was compliant in octane, the increased octane of the control case represents octane

giveaway. After further review, it is evident that virtually all these changes are caused by the

blending of E85 which significantly dilutes these properties in the gasoline blendstock in E85.

Year-by-Year Costs

To understand entire costs of the increase in renewable fuel use mandated by EISA, their

impacts on the U.S. economy, and to compare those costs to the benefits, we estimated the yearby-year costs from 2010, the first year of the RFS2 program, to 2030. We first estimated

renewable fuels volumes for each renewable fuels type based on the RFS2 volume standards and

based on our projections of which renewable fuels would be used to comply with the standard

(see Section 1.2). These volumes represent the increment between the AEO 2007 reference case

and the primary control case. Based on AEO 2009, we also estimated the overall gasoline and

diesel fuel volumes. Table 4.4-20 below summarizes the projected year-by-year incremental

renewable fuel, and total gasoline and diesel fuel volumes.

Table 4.4-20.

Summary of Year-by-Year Volumes

for the Primary Control Case Relative to the AEO 2007 Reference Case

(Billion Gallons/year)

Year

Diesel

Fuel

Volume

Cellulosic

Diesel

Fuel

Renewable

Diesel Fuel

Biodiesel

(all types)

Gasoline

Volume

Corn

Ethanol

Imported

Ethanol

Cellulosic

Ethanol

2010 62.93 0.04 0.04 0.51 158.65 0.75 0.00 -0.09

2011 63.98 0.10 0.08 0.56 162.54 1.38 0.00 -0.11

2012 65.47 0.20 0.08 0.71 162.15 2.01 0.00 -0.10

2013 66.42 0.41 0.08 0.87 161.03 2.49 0.00 0.06

2014 66.50 0.71 0.15 1.01 159.48 3.08 0.00 0.29

2015 66.65 1.22 0.15 1.13 157.14 3.69 0.00 0.67

2016 67.21 1.73 0.15 1.06 154.50 3.84 0.12 1.06

2017 67.85 2.24 0.15 1.09 152.44 3.70 0.54 1.44

2018 68.57 2.85 0.15 1.13 149.84 3.51 0.97 1.90

2019 69.23 3.46 0.15 1.16 148.08 3.31 1.38 2.36

2020 69.98 4.28 0.15 1.20 145.78 3.17 1.29 2.98

2021 70.59 5.50 0.15 1.24 144.14 2.93 1.19 3.90

2022 71.25 6.52 0.15 1.28 141.10 2.71 1.60 4.67

2023 72.16 6.52 0.15 1.28 141.18 2.71 1.60 4.67

2024 73.17 6.52 0.15 1.28 141.80 2.71 1.60 4.67

2025 74.21 6.52 0.15 1.28 141.66 2.71 1.60 4.67

2026 75.16 6.52 0.15 1.28 141.70 2.71 1.60 4.67

2027 76.07 6.52 0.15 1.28 139.74 2.71 1.60 4.67

2028 76.99 6.52 0.15 1.28 139.65 2.71 1.60 4.67

2029 77.87 6.52 0.15 1.28 138.14 2.71 1.60 4.67

2030 78.97 6.52 0.15 1.28 138.59 2.71 1.60 4.67
842

Also for our year-by-year cost analysis, we needed year-by-year estimates of the

production and distribution cost for each renewable fuel type. The feedstock costs were

available for the years 2012, 2017 and 2022 from FASOM for corn ethanol and soy oil, used for

producing biodiesel, with crude oil priced at $116 per barrel. We entered those feedstock costs

into the respective cost models and interpolated and extrapolated the production costs for the

years provided to develop production costs for the in-between years. For cellulosic ethanol, we

assumed the same feedstock costs for all years, but we adjusted the production costs based on

our adjusted NREL production costs, interpolating between the years for which we have specific

cost data. Imported ethanol costs by year are from Section 4.1 above. Tables 4.4-21 summarize

the renewable fuels production costs by year along with the projected crude oil, gasoline and

diesel fuel wholesale costs.

Table 4.4-21.

Renewable Fuel Production Costs Used in the Year-by-Year Analysis

for the Primary Control Case Relative to the AEO 2007 Reference Case

Year

Crude Oil

Price

($/bbl)

Wholesale

Diesel Fuel

Cost

(c/gal)

Cellulosic

Diesel

Fuel Cost

(c/gal)

Renewable

Diesel Fuel

Cost

(c/gal)

Biodiesel

Fuel Cost

(c/gal)

Wholesale

Gasoline

Cost

(c/gal)

Corn

Ethanol

Cost

(c/gal)

Imported

Ethanol

Cost

(c/gal)

Cellulosic

Ethanol

Cost

(c/gal)

2010 49.0 154 258 236 261 157 160 158 220

2011 62.0 198 256 236 264 191 160 157 203

2012 72.1 232 255 236 227 218 160 156 186

2013 81.0 262 253 238 227 241 159 155 168

2014 88.6 288 251 240 225 262 158 154 151

2015 96.8 315 249 243 226 283 158 153 134

2016 101.9 333 248 245 228 297 157 153 133

2017 106.2 347 246 247 232 308 156 152 132

2018 110.5 362 244 246 234 320 155 152 131

2019 112.8 369 242 245 235 326 154 152 130

2020 114.5 375 241 244 238 330 152 151 129

2021 115.8 380 239 243 241 334 151 151 128

2022 116.5 383 237 242 244 335 150 150 127

2023 117.7 386 237 242 244 339 150 150 127

2024 118.3 388 237 242 244 340 150 149 127

2025 116.1 380 237 242 244 334 150 149 127

2026 117.5 385 237 242 244 338 150 149 127

2027 119.2 391 237 242 244 343 150 149 127

2028 121.2 398 237 242 244 348 150 149 127

2029 121.9 400 237 242 244 350 150 149 127

2030 124.4 409 237 242 244 356 150 149 127

Based on the volumes and renewable fuels production and distribution costs, we

estimated the net cost for the increased volumes of renewable fuels in years other than 2022 and

summarized them in Table 4.4-22. We started with the year 2022 costs as our basis. We then

adjusted those costs using the volume and price relationship between ethanol and gasoline to

estimate the costs in other years. We also calculated the total dollar amount of the subsidies

based on the volumes of renewable fuels and the subsidy that applies to each renewable fuel, and

what the subsidized cost would be when the subsidies are applied.
843

Table 4.4-22.

Year-by-Year Annual Average and Per-Gallon Costs for Gasoline

for the Primary Control Case Relative to the AEO 20007 Reference Case

Total

Annual

Cost

($MM/yr)

PerGallon

Cost

(c/gal)

Subsidy

($MM/yr)

Subsidized

Per-Gallon

Cost

(c/gal)

Total

Annual

Cost

($MM/yr)

PerGallon

Cost

(c/gal)

Subsidy

($MM/yr)

Subsidized

Per-Gallon

Cost

(c/gal)

2010 456 0.3 242 0.1 815 1.3 602 0.3

2011 703 0.4 511 0.1 719 1.1 693 0.0

2012 835 0.5 808 0.0 377 0.6 940 -0.9

2013 831 0.5 1181 -0.2 152 0.2 1312 -1.7

2014 758 0.5 1678 -0.6 -251 -0.4 1771 -3.0

2015 198 0.1 2339 -1.4 -963 -1.4 2405 -5.1

2016 -229 -0.1 2839 -2.0 -1601 -2.4 2797 -6.5

2017 -1110 -0.7 3275 -2.9 -2351 -3.5 3343 -8.4

2018 -2076 -1.4 3726 -3.9 -3400 -5.0 3992 -10.8

2019 -2825 -1.9 4150 -4.7 -4333 -6.3 4640 -13.0

2020 -2922 -2.0 4721 -5.2 -5479 -7.8 5504 -15.7

2021 -2936 -2.0 5554 -5.9 -7099 -10.1 6779 -19.7

2022 -3349 -2.4 6308 -6.8 -8546 -12.0 7849 -23.0

2023 -3545 -2.5 6308 -7.0 -8787 -12.2 7849 -23.1

2024 -3640 -2.6 6308 -7.0 -8933 -12.2 7849 -22.9

2025 -3285 -2.3 6308 -6.8 -8369 -11.3 7849 -21.9

2026 -3512 -2.5 6308 -6.9 -8728 -11.6 7849 -22.1

2027 -3769 -2.7 6308 -7.2 -9157 -12.0 7849 -22.4

2028 -4097 -2.9 6308 -7.5 -9679 -12.6 7849 -22.8

2029 -4199 -3.0 6308 -7.6 -9857 -12.7 7849 -22.7

2030 -4589 -3.3 6308 -7.9 -10473 -13.3 7849 -23.2

Gasoline Costs Diesel Fuel Costs

Year

The projected costs in Table 4.4-22 show that in the initial years of 2010 to 2013 for

diesel fuel and 2010 to 2015 for gasoline, the per-gallon costs are positive reflecting the

generally higher projected production costs for renewable fuels and the lower crude oil prices.

After those initial years, the program would accrue a cost savings assuming that the crude oil

prices projected by EIA hold true.

4.4.2.1.2 Primary (Mid-Ethanol) Control Case Incremental to the RFS 1 Reference Case

We also assessed the gasoline and diesel fuel costs and other impacts of the primary

control case relative to the RFS1 reference case. The costs contained in Table 4.4-23 are

reported by different cost components as well as aggregated total and per-gallon costs.
844

Table 4.4-23.

Primary Control Case Costs without Tax Subsidies

Relative to the RFS 1 Reference Case

(2007 dollars, 7% ROI before taxes)

Gasoline Diesel Fuel

Refinery Model Variable Operating Cost $MM/yr 11,252 -

Amortized Refinery Capital Costs $MM/yr -1,031 -

Fixed Operating Costs $MM/yr -284 -

Added Gasoline Transportation Cost $MM/yr -148 -

Removal of E85 Pricing Effect $MM/yr -8,338 -

Crude Oil Cost $51 to $116/bbl $MM/yr -25,884 -

Lower Energy Density $MM/yr 15,928 1,750

Adjustment from Ethanol Price to Cost $MM/yr 507 -

FFV Costs $MM/yr 2,370

Renewable Diesel Cost vs Petroleum Diesel $MM/yr - -10,382

Total Costs $MM/yr -5,628 -8,632

Refinery Model Variable Operating Cost c/gal 7.97 -

Amortized Refinery Capital Costs c/gal -0.73 -

Fixed Operating Costs c/gal -0.20 -

Added Gasoline Transportation Cost c/gal -0.10 -

Removal of E85 Pricing Effect c/gal -5.91 -

Crude Oil Cost $51 to $116/bbl c/gal -18.34 -

Lower Energy Density c/gal 11.29 2.45

Adjustment from Ethanol Price to Cost c/gal 0.36 -

FFV Costs c/gal 1.68

Renewable Diesel Cost vs Petroleum Diesel c/gal - -14.56

Total Costs c/gal -3.99 -12.10

Our analysis shows that when considering all the costs associated with the expanded use

of ethanol for the primary control case relative to the RFS 1 reference case that the cost of

gasoline will decrease by $5.6 billion in the year 2022. Expressed as per-gallon costs, these fuel

changes will reduce the cost of producing gasoline in the U.S. by 4.0 cents per gallon. The

addition of biodiesel, renewable and cellulosic diesel fuel is estimated to reduce the cost of diesel

fuel by $8.6 billion in the year 2022, or save 12.1 cents per gallon.

Table 4.4-24.

Primary Control Case Costs Reflecting Tax Subsidies

Relative to the RFS 1 Reference Case

(2007 dollars, 7% ROI before taxes)

Gasoline Diesel Fuel

Total Costs $MM/yr -5,628 -8,632

Federal Subsidies $MM/yr -9,075 -8,026

Revised Total Cost $MM/yr -14,703 -16,659

Total Costs c/gal -3.99 -12.10

Federal Subsidies c/gal -6.43 -11.25

Total Costs c/gal -10.42 -23.35

Our analysis of the primary control case costs relative to the RFS 1 reference case
845

reflecting the federal tax subsidies would cause a 10.4 cent per gallon decrease in the apparent

cost of producing gasoline, and a 23.4 cent decrease in the apparent cost of producing diesel fuel.

While this could represent the cost of the renewable fuel use to consumers at retail, it is possible

that only a portion of the tax subsidy will be passed along to the consumer. Thus, the price

impact at the pump may be somewhere between the values in Tables 4.4-23 and 4.4-24.

However, consumers would also pay the full tax subsidy through higher taxes which would

offset the cost savings caused by the subsidies.

Table 4.4-25 summarizes the volumetric inputs to refineries in each PADD for this

control case and shows the relative changes of the primary control case compared to the RFS 1

reference case. Because of the increased use of biofuels, petroleum inputs would be expected to

decrease, and this is confirmed.

Table 4.4-25.

Summary of the Total and Incremental Volumetric Refinery Inputs by PADD for the

Primary Control Case Relative to the RFS 1 Reference Case

(barrels/day)

Control Difference Control Difference Control Difference Control Difference Control Difference

Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case

PADD Crude 1,347,342 -151,714 3,058,241 -475,740 7,117,305 -241,263 1,534,859 -4,119 1,886,357 -1,136

GTL Naphtha 0 0 0 0 0 0 0 0 0 0

GTL Diesel 0 0 0 0 0 0 0 0 0 0

VGO HS 0 0 0 0 0 -33,772 0 0 0 0

VGO LS 0 0 7,920 -23,914 0 0 0 0 0 0

HS AR (A960) 0 0 0 0 0 0 0 0 0 0

LS AR (Alg) 279,819 39,889 0 0 738,057 21,829 0 0 0 0

Normal Butane 30,814 8,329 78,293 19,185 69,363 -14,040 25,526 -13,917 39,573 0

Isobutane 12,582 12,582 16,476 -8,390 8,234 8,234 1,178 -22,245 0 0

Other 0 0 0 0 0 0 0 0 0 0

MTBE 0 0 0 0 0 0 0 0 0 0

Ethanol - E10 270,313 130,437 168,062 63,684 142,590 88,811 66,626 40,273 143,845 8,610

Ethanol - E20 0 0 0 0 0 0 0 0 0 0

Ethanol - E85 575,048 575,048 332,715 332,715 361,999 361,999 60,129 60,129 46,969 46,969

Reformer Feed 0 0 0 0 0 0 0 0 0 0

Methanol 0 0 0 0 0 0 0 0 0 0

Natural Gas (FOE) 74,115 -6,201 136,509 -5,559 493,553 6,876 86,015 -3,625 154,032 -2,444

Hydrogen (MSCF) 0 0 0 0 0 0 0 0 0 0

Pentanes Plus 0 0 58 -32,066 52,055 0 0 -17,467 0 0

Import CBOB 10% 0 0 0 0 0 0 0 0 0 0

Import CBOB 20% 0 0 0 0 0 0 0 0 0 0

Import RBOB 10% 0 -200,000 0 0 0 0 0 0 0 0

Import RBOB 20% 0 0 0 0 0 0 0 0 0 0

Import Alkylate 45,167 45,167 0 0 0 0 0 0 0 0

Import Raffinate 3,442 -61,147 0 0 0 0 0 0 45,808 0

Import Reformate 0 0 0 0 0 0 0 0 0 0

Import FCC Naphtha 15,980 15,980 0 0 11,943 11,943 18,580 18,580 0 0

Import Lt Naphtha 0 0 17,575 17,575 0 0 0 0 584 0

Import Hvy Naph 0 0 0 0 41,644 0 0 0 0 0

Transfer Lt Naphtha 0 -23,342 0 -20,822 0 0 0 0 11,387 -9,842

Transfer Reformate 16,658 0 0 -14,074 0 0 0 0 0 0

Transfer Alkylate 60,000 17,381 0 0 0 0 0 0 60,000 16,933

Transfer FCC Naphtha 0 0 20,822 20,822 0 0 0 0 0 0

Transfer Raffinate 0 0 11,353 -5,888 0 0 0 0 60,000 11,077

Transfer RBOB 10% 242,605 0 0 0 0 0 0 0 0 0

Transfer RBOB 20% 0 0 8,720 8,720 0 0 0 0 0 0

Transfer CBOB 10% 1,398,409 1,381,833 80,262 77,306 0 0 6,493 -19,539 0 0

Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0

Isooctane 100 0 100 0 100 0 100 0 16,462 2,029

Isooctene 100 0 100 0 100 0 100 0 600 0

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA

The changes in 4.4-25 are similar to those in 4.4-14 except because the new volume of

ethanol is larger, some of the differences are larger.

Table 4.4-26 below summarizes the refinery output volumes and changes in refinery

output volumes for the primary control case relative to the RFS 1 reference case by PADD.
846

Table 4.4-26.

Summary of Total and Incremental Refinery Outputs by PADD

for the Primary Control Case Relative to the RFS 1 Reference Case

(barrels/day)

Control Difference Control Difference Control Difference Control Difference Control Difference

Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case

Propane 34,143 1,539 50,843 -12,263 103,144 -32,064 23,369 -1,541 52,782 1,461

Propylene 18,685 0 42,525 0 245,407 0 2,041 0 11,774 0

Normal Butane 0 -7,832 0 -2,370 0 -45,506 0 0 0 0

Isobutane 0 0 0 0 0 -345 0 0 40,012 5,009

PC Naphtha 15,830 0 40,290 0 432,937 0 0 0 0 0

PC Gasoil 0 0 455,618 -108,658 157,500 0 0 0 0 0

CG Reg 0 -118,066 0 -852,131 0 -2,020,447 0 -374,129 0 -53,481

CG Prem 0 0 0 0 0 -416,926 0 -51,984 0 0

CG E10 Reg 1,535,198 1,516,962 1,172,450 704,964 872,274 872,274 542,856 325,693 99,374 59,284

CG E10 Prem 306,669 306,669 274,765 23,409 214,485 134,887 116,742 72,990 18,928 1,105

RFG E10 Reg 605,554 -542,443 163,150 -101,096 246,042 -134,362 0 0 1,088,091 12,026

RFG E10 Prem 228,678 10,012 53,454 3,122 78,844 6,386 0 0 217,676 12,712

CG E20 Reg 0 0 0 0 0 0 0 0 0 0

RFG E20 Reg 0 0 0 0 0 0 0 0 0 0

E85 to CG 74,814 74,814 270,064 270,064 253,773 253,773 70,039 70,039 0 0

E85 to RFG 595,006 595,006 117,484 117,484 167,888 167,888 0 0 54,710 54,710

Transfer RBOB 10% 0 0 0 0 251,325 8,720 0 0 0 0

Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0

Transfer CBOB 10% 0 0 0 0 1,485,164 1,439,600 0 0 0 0

Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0

Jet/Kero A (450ppm) 70,000 0 138,523 -3,108 936,227 0 274,537 0 229,653 0

X-Fer Diesel Rundown to Storage 0 0 0 0 0 0 0 0 0 0

HSD Gr 76 (0.2%) 0 0 0 0 0 0 0 0 0 0

LSD Gr 74 (.05%) 0 0 0 0 0 0 0 0 0 0

ULSD (15 ppm) 587,042 -39,165 643,331 -30,227 2,150,901 2,423 523,035 -11,120 0 0

CARB Diesel 0 0 0 0 0 0 0 0 358,152 -6,714

X-Fer C5's to Storage 0 0 0 0 0 0 0 0 0 0

1% Residual Fuel 0 0 0 0 0 0 0 0 0 0

Residual Fuel 68,283 -21,481 67,826 8 262,834 0 138,312 22,862 45,680 -4,199

Slurry 30,026 3,876 67,363 -19,645 112,554 -4,041 12,295 -4,210 29,893 1,191

Asphalt & Wax 86,013 -5,669 198,329 -11,671 157,500 0 5,250 0 41,774 0

Gasoil 0 0 4,895 0 0 0 0 0 9,814 0

Lubes 18,706 0 17,313 0 157,500 0 0 0 20,149 0

Benzene 11,003 0 11,003 0 51,347 0 0 0 0 0

Toluene 0 0 0 0 34,910 0 0 0 0 0

Xylenes 0 0 0 0 7,777 0 0 0 0 0

Cumene 0 0 0 0 0 0 0 0 0 0

Other 0 0 0 0 0 0 0 0 0 0

Cyclohexane 0 0 0 0 0 0 0 0 0 0

Transfer Raffinate 0 0 0 0 71,353 5,190 0 0 0 0

Transfer Alkylate 0 0 0 0 120,000 34,314 0 0 0 0

Transfer Reformate 0 0 0 0 16,658 16,658 0 0 0 0

Transfer FCC naphtha 0 0 0 0 20,822 20,822 0 0 0 0

Transfer Lt Naphtha 0 0 0 0 0 -23,342 0 0 11,387 -9,842

Transfer Blendstock 0 0 0 0 0 0 0 0 0 0

Sulfur (STons) 1,076 -74 3,571 -630 12,042 -533 2,038 -59 3,559 -10

Coke (STon) 3,495 -227 10,154 -3,068 48,646 -2,278 7,899 -790 17,358 86

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA

Table 4.4-27 summarizes the change in refinery unit capacities by PADD comparing the

primary control case to the RFS 1 reference case.
847

Table 4.4-27.

Change in Refinery Unit Capacities by PADD

for the Primary Control Case Relative to the RFS 1 Reference Case

(thousand barrels/day)

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA California US Total

Crude Tower 0 -439 0 0 0 -439

Vacuum Tower 0 -196 0 -2 0 -198

Sats Gas Plant 3 0 -30 0 9 -18

Unsats Gas Plant -2 0 0 0 0 -2

FCC DeC5 Tower 0 0 0 0 0 0

FCC 0 0 0 -8 0 -8

FCC Splitter 2 -6 0 0 0 -5

Hydrocracker -18 0 0 -14 0 -33

H-Oil Unit 0 0 0 0 0 0

Delayed Coker 0 0 0 -28 0 -28

Visbreaker 0 0 0 0 0 0

Thermal Naphtha Splitter -1 -6 0 -1 0 -8

CRU Reformer 0 0 0 0 0 0

SRU Reformer 0 0 0 0 -1 -1

BTX Reformer 0 -10 -18 0 0 -27

C4 Isomerization 0 0 0 0 1 1

C5/C6 Isomerization 26 0 0 0 0 26

HF Alkylation 0 0 0 0 0 0

H2SO4 Alkylation 1 0 0 -5 0 -4

Dimersol 0 0 0 0 0 0

Cat Poly 0 0 0 0 0 0

Isooctane 0 0 0 0 0 0

DHT - Total -132 0 -103 -50 -4 -290

DHT 2nd RCT - Total -117 -153 -174 -52 -4 -501

DHT Arom Saturation 0 0 0 0 0 0

NHT - Total Fd 0 0 0 -4 0 -4

CGH - Generic -12 -19 -19 -6 0 -57

CGH - Olefin Sat'n 0 0 0 0 0 0

FCCU Fd HDT 37 0 0 0 0 37

LSR Splitter 0 41 38 0 0 80

LSR Bz Saturator 0 0 0 0 0 0

Reformate Saturator -4 -4 1 0 0 -7

Reformate Splitter -12 -12 3 0 0 -21

SDA 0 0 0 0 0 0

MTBE 0 0 0 0 0 0

TAME 0 0 0 0 0 0

Hydrogen Plant - Total MSCF -72 11 109 -47 0 1

Lube Unit 0 0 0 0 0 0

Sulfur Plant -128 0 0 -204 -71 -402

Merox Jet 0 0 0 0 0 0

Merox Diesel 0 0 0 0 0 0

BTX Reformer - Tower feed 0 -1 -1 0 0 -2

BTX Reformer - Extract feed -1 0 0 0 0 -1

These changes in refinery unit throughputs are associated with changes in capital

investments. Table 4.4-28 summarizes the projected change in capital investments between the

primary control case and the RFS 1 reference case.
848

Table 4.4-28.

Change in Refinery Unit Investments by PADD

for the Primary Control Case Relative to the RFS 1 Reference Case

(million dollars/year)

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA California US Total

Crude Tower 0 -997 0 0 0 -997

Vacuum Tower 0 -591 0 0 0 -591

Sats Gas Plant 12 0 -101 0 55 -34

Unsats Gas Plant -10 0 0 0 0 -10

FCC DeC5 Tower 0 0 0 0 0 0

FCC 0 0 0 -193 0 -193

FCC Splitter 1 -7 0 0 0 -6

Hydrocracker -631 0 0 -505 0 -1,137

H-Oil Unit 0 0 0 0 0 0

Delayed Coker 0 0 0 -592 0 -592

Visbreaker 0 0 0 0 0 0

Thermal Naphtha Splitter -3 -7 0 -3 0 -13

CRU Reformer 0 0 0 0 0 0

SRU Reformer 0 0 0 0 -4 -4

BTX Reformer 0 -93 -231 0 0 -324

C4 Isomerization 0 0 0 0 8 8

C5/C6 Isomerization 227 0 0 0 0 227

HF Alkylation 0 0 0 0 0 0

H2SO4 Alkylation 16 0 0 -70 0 -54

Dimersol 0 0 0 0 0 0

Cat Poly -1 0 0 0 0 -1

Isooctane 0 0 0 0 0 0

DHT - Total -1,367 0 -702 -539 -96 -2,703

DHT 2nd RCT - Total -904 -998 -840 -312 -21 -3,076

DHT Arom Saturation 0 0 0 0 0 0

NHT - Total Fd 0 0 0 -46 0 -46

CGH - Generic -58 -63 -92 -25 0 -238

CGH - Olefin Sat'n 0 0 0 0 0 0

FCCU Fd HDT 555 0 0 0 0 555

LSR Splitter 0 29 18 0 0 47

LSR Bz Saturator 0 0 0 0 0 0

Reformate Saturator -46 -19 4 0 0 -60

Reformate Splitter -20 -16 2 0 0 -34

SDA 0 0 0 0 0 0

MTBE 0 0 0 0 0 0

TAME 0 0 0 0 0 0

Hydrogen Plant - Total MSCF -255 69 274 -171 0 -84

Lube Unit 0 0 0 0 0 0

Sulfur Plant -3 0 0 -2 -2 -7

Merox Jet 0 0 0 0 0 0

Merox Diesel 0 0 0 0 0 0

BTX Reformer - Tower feed 0 -2 -1 0 0 -3

BTX Reformer - Extract feed -3 0 0 0 0 -3

Total -2,491 -2,694 -1,669 -2,459 -59 -9,373

Table 4.4-28 shows that incremental to the RFS 1 reference case, refiners under the

primary control case are expected to reduce their capital investments by $9.4 billion compared to
849

business as usual. The reduction in capital investments occurs in PADDs 1 through 4 and PADD

5 outside of California. Table 4.4-17 essentially expresses the change in refinery capacity input

shown in Table 4.4-16, but expresses the changes in terms of dollars instead of thousands of

barrels per day.

Table 4.4-29 summarizes the gasoline volume and qualities by different gasoline types

for the primary control case, and also, for comparison, lists the same for the RFS 1 reference

case.
850

Table 4.4-29.

Ethanol and Gasoline Volume, Quality and Energy Density by Gasoline Type at the PADD

Terminal for the Primary Control Case Relative to the RFS 1 Reference Case

Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control

RFG

Total ('000 BPD) 1,366,663 1,107,710 314,578 325,776 452,862 482,832 0 0 1,281,031 1,326,631 3,415,134 3,242,949

Ethanol ('000 BPD) 138,034 111,890 31,773 32,907 45,739 48,771 0 0 129,386 134,003 344,932 327,571

RVP (psi) 10.7 10.8 10.3 10.6 9.6 9.8 0.0 0.0 9.5 9.5 10.1 10.1

Sulfur (ppm) 23.8 24.6 20.1 20.5 23.5 23.4 0.0 0.0 8.7 8.5 17.8 17.4

Density 258.5 258.9 256.2 258.3 259.1 259.4 0.0 0.0 258.5 258.7 258.4 258.8

Octane (R+M/2) 88.1 88.3 88.0 88.0 88.0 88.0 0.0 0.0 87.6 87.6 87.9 87.9

Aromatics (vol%) 19.9 19.9 18.2 19.9 19.5 19.7 0.0 0.0 22.2 22.1 20.5 20.8

Benzene (vol%) 0.57 0.57 0.57 0.57 0.57 0.56 0.00 0.00 0.54 0.54 0.56 0.56

Olefins (vol%) 13.1 14.7 8.0 9.3 9.3 11.1 0.0 0.0 5.7 5.7 9.3 9.9

Oxygen (wt%) 3.7 3.7 3.8 3.7 3.7 3.7 0.0 0.0 3.7 3.7 3.7 3.7

E200 (vol%) 55.7 55.1 62.5 57.4 54.5 52.7 0.0 0.0 58.5 58.2 57.2 56.2

E300 (vol%) 93.9 95.3 91.5 93.8 93.9 93.4 0.0 0.0 86.2 86.2 90.8 91.2

Energy (MMBtu/Bbl) 4.963 4.961 4.864 4.935 5.027 4.990 0.000 0.000 4.988 5.002 4.971 4.979

CG

Total ('000 BPD) 1,841,024 1,868,996 1,570,973 1,545,011 1,262,249 1,059,721 687,028 691,193 111,394 115,359 5,472,668 5,280,279

Ethanol ('000 BPD) 1,842 188,787 72,606 156,062 8,040 107,043 26,352 69,817 5,849 11,652 114,689 533,362

RVP (psi) 10.5 11.8 11.8 11.5 10.7 10.4 11.3 11.4 10.8 10.6 11.0 11.3

Sulfur (ppm) 27.8 24.5 25.3 23.3 27.3 23.1 24.6 28.0 26.4 26.3 26.6 24.4

Density 259.3 259.5 258.8 259.1 259.4 260.1 256.8 257.9 260.8 264.1 258.9 259.4

Octane (R+M/2) 88.0 87.8 88.0 88.0 88.0 88.2 86.8 86.9 88.0 89.5 87.8 87.9

Aromatics (vol%) 28.7 22.4 26.3 22.5 28.4 21.8 19.3 15.6 26.8 28.3 26.7 21.6

Benzene (vol%) 0.65 0.53 0.64 0.52 0.65 0.51 0.80 1.01 0.62 0.61 0.67 0.59

Olefins (vol%) 15.2 14.0 11.2 11.0 16.0 12.3 8.0 7.6 18.6 18.4 13.4 12.0

Oxygen (wt%) 0.0 3.7 1.7 3.7 0.2 3.7 1.4 3.7 1.9 3.7 0.8 3.7

E200 (vol%) 45.2 53.1 52.9 58.0 45.9 52.5 58.9 62.7 54.5 56.4 49.5 55.8

E300 (vol%) 93.9 95.3 91.5 83.8 93.9 79.5 93.9 90.6 86.2 86.2 93.0 88.0

Energy (MMBtu/Bbl) 5.133 4.980 5.007 4.937 5.127 5.005 5.004 4.912 4.998 4.964 5.077 4.963

E85

Total ('000 BPD) 0 283,619 0 129,577 0 247,459 0 20,291 0 0 0 680,947

Ethanol ('000 BPD) 0 243,490 0 111,243 0 212,446 0 17,420 0 0 0 584,599

RVP (psi) 0.0 11.1 0.0 12.2 0.0 12.1 0.0 11.9 0.0 0.0 0.0 11.7

Sulfur (ppm) 0.0 9.5 0.0 8.8 0.0 8.8 0.0 8.8 0.0 0.0 0.0 9.1

Density 0.0 267.7 0.0 266.3 0.0 266.2 0.0 266.1 0.0 0.0 0.0 266.8

Octane (R+M/2) 0.0 107.9 0.0 108.0 0.0 108.0 0.0 107.9 0.0 0.0 0.0 107.9

Aromatics (vol%) 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3

Benzene (vol%) 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01

Olefins (vol%) 0.0 2.4 0.0 3.0 0.0 1.8 0.0 0.0 0.0 0.0 0.0 2.2

Oxygen (wt%) 0.0 30.6 0.0 30.8 0.0 30.8 0.0 30.8 0.0 0.0 0.0 30.7

E200 (vol%) 0.0 128.1 0.0 130.1 0.0 129.8 0.0 129.3 0.0 0.0 0.0 129.1

E300 (vol%) 0.0 96.1 0.0 96.6 0.0 96.6 0.0 96.6 0.0 0.0 0.0 96.4

Energy (MMBtu/Bbl) 0.000 3.628 0.000 3.599 0.000 3.597 0.000 3.594 0.000 0.000 0.000 3.610

All Mogas

Total BPD 3,207,687 3,260,324 1,885,551 2,000,364 1,715,111 1,790,012 687,028 711,484 1,392,425 1,441,990 8,887,802 9,204,175

Ethanol BPD 139875.7 544,167 104378.5 300,212 53779.6 368,259 26352.4 87,238 135235.5 145,656 459621.7 1,445,532

RVP 10.6 11.4 11.5 11.4 10.4 10.5 11.3 11.4 9.6 9.6 10.6 10.9

Sulfur ppm 26.1 23.2 24.5 21.9 26.3 21.2 24.6 27.4 10.1 9.9 23.2 20.8

Density 258.9 260.0 258.4 259.5 259.3 260.8 256.8 258.1 258.7 259.2 258.7 259.8

R+M/2 88.0 89.7 88.0 89.3 88.0 90.9 86.8 87.5 87.7 87.8 87.8 89.4

Aromatics 24.9 19.7 24.9 20.6 26.0 18.2 19.3 15.2 22.6 22.6 24.3 19.7

Benzene 0.62 0.50 0.63 0.50 0.63 0.45 0.80 0.98 0.55 0.55 0.63 0.53

Olefins 14.3 13.2 10.7 10.2 14.2 10.6 8.0 7.4 6.7 6.7 11.8 10.6

Oxygen (wt%) 1.6 6.1 2.0 5.5 1.2 7.5 1.4 4.5 3.6 3.7 1.9 5.8

E200 49.7 60.3 54.5 62.6 48.1 63.2 58.9 64.6 58.1 58.1 52.5 61.4

E300 93.9 95.4 91.5 86.3 93.9 85.6 93.9 90.8 86.2 86.2 92.2 89.7

Energy (MMBtu/Bbl) 5.061 4.856 4.983 4.850 5.101 4.806 5.004 4.874 4.988 4.999 5.036 4.869

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA US

4.4.2.2 Low Ethanol Control Case

This section contains the tables which summarize the costs and other impacts of the low

ethanol control case relative to the AEO 2007 and RFS 1 reference cases.

4.4.2.2.1 Low Ethanol Control Case Incremental to the AEO 2007 Reference Case
851

This subsection summarizes the costs and other impacts of the low ethanol control case

relative to the AEO 2007 reference case.

Table 4.4-30

Low Ethanol Control Case Costs without Tax Subsidies

Relative to the AEO 2007 Reference Case

(2007 dollars, 7% ROI before taxes)

Gasoline Diesel Fuel

Refinery Model Variable Operating Cost $MM/yr 6,066 -

Amortized Refinery Capital Costs $MM/yr -1,138 -

Fixed Operating Costs $MM/yr -314 -

Added Gasoline Transportation Cost $MM/yr -42 -

Removal of E85 Pricing Effect $MM/yr -3,549 -

Crude Oil Cost $51 to $116/bbl $MM/yr -7,232 -

Lower Energy Density $MM/yr 4,098 2,254

Adjustment from Ethanol Price to Cost $MM/yr 648 -

FFV Costs $MM/yr 791

Renewable Diesel Cost vs Petroleum Diesel $MM/yr - -13,962

Total Costs $MM/yr -672 -11,707

Refinery Model Variable Operating Cost c/gal 4.35 -

Amortized Refinery Capital Costs c/gal -0.82 -

Fixed Operating Costs c/gal -0.22 -

Added Gasoline Transportation Cost c/gal -0.03 -

Removal of E85 Pricing Effect c/gal -2.54 -

Crude Oil Cost $51 to $116/bbl c/gal -5.18 -

Lower Energy Density c/gal 2.94 3.16

Adjustment from Ethanol Price to Cost c/gal 0.46 -

FFV Costs c/gal 0.57

Renewable Diesel Cost vs Petroleum Diesel c/gal - -19.56

Total Costs c/gal -0.48 -16.40

Table 4.4-31.

Low Ethanol Control Case Costs Reflecting Tax Subsidies

Relative to the AEO 2007 Reference Case

(2007 dollars, 7% ROI before taxes)

Gasoline Diesel Fuel

Total Costs $MM/yr -672 -11,707

Federal Subsidies $MM/yr -1,597 -10,712

Revised Total Cost $MM/yr -2,269 -22,419

Total Costs c/gal -0.48 -16.40

Federal Subsidies c/gal -1.14 -15.00

Total Costs c/gal -1.63 -31.40
852

Table 4.4-32.

Summary of the Total and Incremental Volumetric Refinery Inputs by PADD

for the Low Ethanol Control Case Relative to the AEO 2007 Reference Case

(barrels/day)

Control Difference Control Difference Control Difference Control Difference Control Difference

Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case

PADD Crude 1,279,888 -248,492 3,227,551 -230,937 6,947,777 -303,641 1,418,700 -114,904 1,842,788 -46,607

GTL Naphtha 0 0 0 0 0 0 0 0 0 0

GTL Diesel 0 0 0 0 0 0 0 0 0 0

VGO HS 0 0 0 0 11,581 -32,204 0 0 0 0

VGO LS 0 0 38,063 0 0 0 0 0 0 0

HS AR (A960) 0 0 0 0 0 0 0 0 0 0

LS AR (Alg) 296,982 67,329 0 0 738,419 32,205 0 0 0 0

Normal Butane 22,485 0 55,234 3,579 111,678 25,512 39,858 142 39,573 0

Isobutane 9,412 8,868 15,826 -7,966 617 617 20,399 14,133 0 0

Other 0 0 0 0 0 0 0 0 0 0

MTBE 0 0 0 0 0 0 0 0 0 0

Ethanol - E10 319,378 46,291 200,104 2,203 157,892 -17,825 68,870 -1,520 144,107 1,450

Ethanol - E20 0 0 0 0 0 0 0 0 0 0

Ethanol - E85 25,821 25,821 27,311 27,311 178,439 178,439 18,976 18,976 0 0

Reformer Feed 0 0 0 0 0 0 0 0 0 0

Methanol 0 0 0 0 0 0 0 0 0 0

Natural Gas (FOE) 71,943 -9,163 135,126 -9,516 486,804 -17,483 84,312 -9,585 150,902 -4,765

Hydrogen (MSCF) 0 0 0 0 0 0 0 0 0 0

Pentanes Plus 0 0 32,124 0 52,055 0 18,580 0 0 0

Import CBOB 10% 241,570 235,283 0 0 0 0 0 0 0 0

Import CBOB 20% 0 0 0 0 0 0 0 0 0 0

Import RBOB 10% 107,364 -38,229 0 0 0 0 0 0 0 0

Import RBOB 20% 0 0 0 0 0 0 0 0 0 0

Import Alkylate 19,290 17,057 0 0 0 0 0 0 0 0

Import Raffinate 45,299 -8,228 0 0 0 0 0 0 45,808 0

Import Reformate 0 -8,829 0 0 0 0 0 0 0 0

Import FCC Naphtha 0 0 0 0 0 0 0 0 0 0

Import Lt Naphtha 0 0 0 0 0 0 0 0 584 0

Import Hvy Naph 7,291 7,291 0 0 41,644 0 0 0 0 0

Transfer Lt Naphtha 39,298 34,965 0 0 0 0 0 0 22,233 3,849

Transfer Reformate 9,504 -7,154 0 0 0 0 0 0 0 0

Transfer Alkylate 48,790 -11,210 1,609 1,609 0 0 0 0 60,000 0

Transfer FCC Naphtha 2,408 2,408 20,822 0 0 0 0 0 0 0

Transfer Raffinate 0 0 1,066 -10,571 0 0 0 0 59,963 12,519

Transfer RBOB 10% 242,605 0 37,596 37,596 0 0 0 0 0 0

Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0

Transfer CBOB 10% 1,289,975 153,702 114,969 60,914 0 0 9,099 2,747 0 0

Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0

Isooctane 6,030 5,930 100 0 100 0 100 0 13,445 -1,817

Isooctene 24,113 24,013 100 0 100 0 100 0 804 204

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA
853

Table 4.4-33.

Summary of Total and Incremental Refinery Outputs by PADD

for the Low Ethanol Control Case Relative to the AEO 2007 Control Case

(barrels/day)

Control Difference Control Difference Control Difference Control Difference Control Difference

Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case

Propane 30,661 -1,460 54,531 -2,756 101,580 -15,973 20,156 -2,347 52,426 834

Propylene 18,685 0 42,525 0 245,407 0 2,041 0 11,774 0

Normal Butane 6,350 1,383 2,614 1,650 22,377 -11,285 0 0 0 0

Isobutane 0 0 0 0 0 -411 0 0 41,656 167

PC Naphtha 15,830 0 40,290 0 432,937 0 0 0 0 0

PC Gasoil 0 0 535,896 -24,805 157,500 0 0 0 0 0

CG Reg 0 0 0 0 0 0 0 0 0 0

CG Prem 0 0 0 0 0 -182,001 0 0 0 0

CG E10 Reg 1,684,887 427,937 1,388,234 10,518 878,511 -196,953 569,190 -16,174 95,872 961

CG E10 Prem 168,009 97,675 270,485 8,063 206,925 2,075 112,627 1,129 18,261 183

RFG E10 Reg 1,094,702 -61,416 270,743 2,715 401,267 15,419 0 0 1,102,523 11,057

RFG E10 Prem 214,244 -5,969 51,570 517 76,432 2,937 0 0 210,004 2,106

CG E20 Reg 0 0 0 0 0 0 0 0 0 0

RFG E20 Reg 0 0 0 0 0 0 0 0 0 0

E85 to CG 0 0 31,813 31,813 207,848 207,848 22,103 22,103 0 0

E85 to RFG 30,077 30,077 0 0 0 0 0 0 0 0

Transfer RBOB 10% 0 0 0 0 280,201 37,596 0 0 0 0

Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0

Transfer CBOB 10% 0 0 0 0 1,414,043 217,363 0 0 0 0

Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0

Jet/Kero A (450ppm) 70,000 0 143,275 3,194 936,227 0 274,537 0 229,653 0

X-Fer Diesel Rundown to Storage 0 0 0 0 0 0 0 0 0 0

HSD Gr 76 (0.2%) 0 0 0 0 0 0 0 0 0 0

LSD Gr 74 (.05%) 0 0 0 0 0 0 0 0 0 0

ULSD (15 ppm) 487,798 -146,819 552,466 -119,705 1,937,875 -198,428 448,555 -84,744 0 0

CARB Diesel 0 0 0 0 0 0 0 0 313,223 -51,127

X-Fer C5's to Storage 0 0 0 0 0 0 0 0 0 0

1% Residual Fuel 0 0 0 0 0 0 0 0 0 0

Residual Fuel 50,000 -41,643 56,903 -7,686 262,834 0 115,134 -7,157 49,880 0

Slurry 32,981 5,572 76,116 -10,151 106,272 -7,675 20,000 6,518 28,956 -51

Asphalt & Wax 91,682 0 210,000 2,314 157,500 0 5,250 0 41,774 0

Gasoil 0 0 4,895 0 0 0 0 0 9,814 0

Lubes 18,706 0 17,313 0 157,500 0 0 0 20,149 0

Benzene 11,003 0 11,003 0 51,347 0 0 0 0 0

Toluene 0 0 0 0 34,910 0 0 0 0 0

Xylenes 0 0 0 0 7,777 0 0 0 0 0

Cumene 0 0 0 0 0 0 0 0 0 0

Other 0 0 0 0 0 0 0 0 0 0

Cyclohexane 0 0 0 0 0 0 0 0 0 0

Transfer Raffinate 0 0 0 0 61,029 1,948 0 0 0 0

Transfer Alkylate 0 0 0 0 110,399 -9,601 0 0 0 0

Transfer Reformate 0 0 0 0 9,504 -7,154 0 0 0 0

Transfer FCC naphtha 0 0 0 0 34,440 13,618 0 0 0 0

Transfer Lt Naphtha 0 0 0 0 28,088 23,755 0 0 22,233 3,849

Transfer Blendstock 0 0 0 0 0 0 0 0 0 0

Sulfur (STons) 1,034 -120 3,768 -345 11,741 -657 1,880 -207 3,459 -86

Coke (STon) 3,201 -749 10,908 -1,818 46,210 -3,591 7,111 -1,368 16,750 -544

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA
854

Table 4.4-34.

Change in Refinery Unit Capacities by PADD

for the Low Ethanol Control Case Relative to the AEO 2007 Control Case

(thousand barrels/day)

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA California US Total

Crude Tower 0 -175 0 0 0 -175

Vacuum Tower 0 -78 0 0 0 -78

Sats Gas Plant -12 0 -13 0 6 -18

Unsats Gas Plant 1 0 0 0 0 1

FCC DeC5 Tower 0 0 0 0 0 0

FCC 0 0 0 0 0 0

FCC Splitter 4 -6 0 0 0 -2

Hydrocracker -16 0 0 -33 0 -49

H-Oil Unit 0 0 0 0 0 0

Delayed Coker 0 0 0 -24 0 -24

Visbreaker 0 0 0 0 0 0

Thermal Naphtha Splitter -1 -4 0 -1 0 -6

CRU Reformer 0 0 0 0 0 0

SRU Reformer 0 0 0 0 -9 -9

BTX Reformer 0 7 -6 0 0 1

C4 Isomerization 0 0 0 0 1 1

C5/C6 Isomerization 21 0 0 0 0 21

HF Alkylation 0 0 0 0 0 0

H2SO4 Alkylation -14 0 0 15 0 0

Dimersol 0 0 0 0 0 0

Cat Poly 6 0 0 0 0 6

Isooctane 0 0 0 0 0 0

DHT - Total -136 0 -213 -61 0 -410

DHT 2nd RCT - Total -127 -150 -220 -63 -10 -571

DHT Arom Saturation 0 0 0 0 0 0

NHT - Total Fd 0 0 0 0 0 0

CGH - Generic 24 -10 -17 -3 0 -6

CGH - Olefin Sat'n 0 0 0 0 0 0

FCCU Fd HDT 57 0 0 0 0 57

LSR Splitter 0 0 0 0 0 0

LSR Bz Saturator 0 0 0 0 0 0

Reformate Saturator -14 -9 1 0 0 -22

Reformate Splitter -43 -27 3 0 0 -66

SDA 0 0 0 0 0 0

MTBE 0 0 0 0 0 0

TAME 0 0 0 0 0 0

Hydrogen Plant - Total MSCF -79 -52 -149 -101 0 -381

Lube Unit 0 0 0 0 0 0

Sulfur Plant -95 0 0 -264 -73 -432

Merox Jet 0 0 0 0 0 0

Merox Diesel 0 0 0 0 0 0

BTX Reformer - Tower feed 0 0 0 0 0 0

BTX Reformer - Extract feed 0 0 0 0 0 0
855

Table 4.4-35.

Change in Refinery Unit Investments by PADD

for the Low Ethanol Control Case Relative to the AEO 2007 Control Case

(million dollars/year)

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA California US Total

Crude Tower 0 -372 0 0 0 -372

Vacuum Tower 0 -297 0 0 0 -297

Sats Gas Plant -65 0 -24 0 48 -41

Unsats Gas Plant 6 0 0 0 0 6

FCC DeC5 Tower 0 0 0 0 0 0

FCC 0 0 0 0 0 0

FCC Splitter 2 -7 0 0 0 -5

Hydrocracker -584 0 0 -891 0 -1,475

H-Oil Unit 0 0 0 0 0 0

Delayed Coker 0 0 0 -534 0 -534

Visbreaker 0 0 0 0 0 0

Thermal Naphtha Splitter -3 -3 0 -2 0 -9

CRU Reformer 0 0 0 0 0 0

SRU Reformer 0 0 0 0 -66 -66

BTX Reformer 0 70 -38 0 0 32

C4 Isomerization 0 0 0 0 5 5

C5/C6 Isomerization 184 0 0 0 0 184

HF Alkylation 0 0 0 0 0 0

H2SO4 Alkylation -245 0 0 274 0 29

Dimersol 0 0 0 0 0 0

Cat Poly 48 0 0 0 0 48

Isooctane 0 0 0 0 0 0

DHT - Total -1,394 0 -1,511 -606 0 -3,510

DHT 2nd RCT - Total -949 -985 -1,103 -443 -57 -3,536

DHT Arom Saturation 0 0 0 0 0 0

NHT - Total Fd 0 0 0 0 0 0

CGH - Generic 157 -88 -87 -12 0 -29

CGH - Olefin Sat'n 0 0 0 0 0 0

FCCU Fd HDT 709 0 0 0 0 709

LSR Splitter 0 0 0 0 0 0

LSR Bz Saturator 0 0 0 0 0 0

Reformate Saturator -94 -60 5 0 0 -149

Reformate Splitter -55 -36 2 0 0 -88

SDA 0 0 0 0 0 0

MTBE 0 0 0 0 0 0

TAME 0 0 0 0 0 0

Hydrogen Plant - Total MSCF -324 -172 -385 -363 0 -1,244

Lube Unit 0 0 0 0 0 0

Sulfur Plant -3 0 0 -2 -2 -7

Merox Jet 0 0 0 0 0 0

Merox Diesel 0 0 0 0 0 0

BTX Reformer - Tower feed 0 1 0 0 0 1

BTX Reformer - Extract feed 0 0 0 0 0 0

Total -2,609 -1,948 -3,141 -2,578 -72 -10,349
856

Table 4.4-36.

Projected Total U.S. Capital Investments

for the Low Ethanol Control Case Relative to the AEO 2007 Reference Case

(billion dollars)

Cost Type Plant Type Capital

Investments

Corn Ethanol 3.9

Cellulosic Ethanol 0

Cellulosic Diesel a96.5

Production

Costs

Renewable Diesel and

Algae

1.1

All Ethanol 5.6

Cellulosic and

Renewable Diesel Fuel

2.0

Distribution

Costs

Biodiesel 1.2

FFV Costs 0.8

Refining -10.3

Total Capital Investments 110.0
857

Table 4.4-37.

Ethanol and Gasoline Volume, Quality and Energy Density by Gasoline Type at the PADD

Terminal for the Low Ethanol Control Case Relative to the AEO 2007 Control Case

Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control

RFG

Total ('000 BPD) 1,376,331 1,308,946 319,080 322,313 459,343 477,699 0 0 1,299,365 1,312,528 3,454,120 3,421,486

Ethanol ('000 BPD) 139,023 132,217 32,230 32,557 46,397 48,252 0 0 131,244 132,579 348,895 345,605

RVP (psi) 10.8 10.7 10.6 10.6 9.7 9.7 0.0 0.0 9.5 9.5 10.1 10.1

Sulfur (ppm) 24.4 24.5 20.0 20.7 23.2 24.1 0.0 0.0 8.8 8.8 18.0 18.0

Density 258.9 258.6 258.2 258.2 259.5 258.5 0.0 0.0 258.5 258.7 258.8 258.6

Octane (R+M/2) 88.1 88.1 88.0 88.0 88.0 88.0 0.0 0.0 87.6 87.6 87.9 87.9

Aromatics (vol%) 19.9 19.8 19.9 19.9 19.6 18.5 0.0 0.0 22.2 22.1 20.7 20.5

Benzene (vol%) 0.57 0.56 0.57 0.57 0.56 0.56 0.00 0.00 0.53 0.54 0.55 0.55

Olefins (vol%) 13.6 14.8 9.4 9.1 11.5 11.2 0.0 0.0 5.7 5.7 10.0 10.2

Oxygen (wt%) 3.7 3.7 3.7 3.7 3.7 3.7 0.0 0.0 3.7 3.7 3.7 3.7

E200 (vol%) 55.6 54.8 58.3 57.6 53.3 53.7 0.0 0.0 58.2 58.3 56.5 56.3

E300 (vol%) 93.9 82.3 93.9 91.1 93.9 93.9 0.0 0.0 86.2 86.2 91.0 86.2

Energy (MMBtu/Bbl) 4.947 4.953 4.924 4.939 4.981 4.973 0.000 0.000 4.994 5.000 4.967 4.972

CG

Total ('000 BPD) 1,830,582 1,852,896 1,640,138 1,658,720 1,280,314 1,085,436 696,861 681,817 112,988 114,133 5,560,884 5,393,001

Ethanol ('000 BPD) 134,064 187,161 165,671 167,547 129,320 109,640 70,390 68,870 11,413 11,529 510,857 544,748

RVP (psi) 11.4 11.8 11.6 11.5 10.7 10.5 11.4 11.4 10.6 10.6 11.3 11.4

Sulfur (ppm) 22.9 24.2 23.6 23.6 23.1 23.0 28.0 28.0 26.6 26.0 23.9 24.3

Density 258.9 258.9 259.1 258.9 260.5 259.6 258.1 257.6 262.7 263.4 259.3 259.0

Octane (R+M/2) 87.8 87.7 88.0 88.0 88.0 88.1 86.9 86.9 89.3 89.4 87.8 87.8

Aromatics (vol%) 23.1 22.4 22.5 22.5 22.4 21.8 15.9 15.2 26.5 27.6 21.9 21.5

Benzene (vol%) 0.53 0.52 0.53 0.53 0.51 0.51 1.05 1.03 0.59 0.63 0.59 0.59

Olefins (vol%) 13.2 13.9 11.0 10.8 13.1 11.9 8.5 7.6 17.8 17.5 12.0 11.8

Oxygen (wt%) 2.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.4 3.7

E200 (vol%) 52.5 52.9 58.8 58.5 53.0 52.5 63.0 63.1 58.0 57.2 55.9 55.9

E300 (vol%) 93.9 79.3 93.9 84.0 93.9 79.3 93.9 91.0 86.2 86.2 93.7 82.4

Energy (MMBtu/Bbl) 4.995 4.966 4.925 4.939 4.988 4.990 4.907 4.942 4.946 4.961 4.955

E85

Total ('000 BPD) 0 30,077 0 31,813 0 207,848 0 22,103 0 0 0 291,840

Ethanol ('000 BPD) 0 25,821 0 27,311 0 178,439 0 18,976 0 0 0 250,548

RVP (psi) 0.0 12.9 0.0 12.2 0.0 12.1 0.0 11.9 0.0 0.0 0.0 12.1

Sulfur (ppm) 0.0 8.9 0.0 8.8 0.0 8.8 0.0 8.8 0.0 0.0 0.0 8.8

Density 0.0 266.0 0.0 266.3 0.0 266.2 0.0 266.1 0.0 0.0 0.0 266.2

Octane (R+M/2) 0.0 108.1 0.0 108.0 0.0 107.9 0.0 107.9 0.0 0.0 0.0 108.0

Aromatics (vol%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Benzene (vol%) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Olefins (vol%) 0.0 3.0 0.0 3.0 0.0 1.3 0.0 0.0 0.0 0.0 0.0 1.5

Oxygen (wt%) 0.0 30.8 0.0 30.8 0.0 30.8 0.0 30.8 0.0 0.0 0.0 30.8

E200 (vol%) 0.0 130.1 0.0 130.1 0.0 129.7 0.0 129.3 0.0 0.0 0.0 129.7

E300 (vol%) 0.0 96.6 0.0 96.6 0.0 96.6 0.0 96.6 0.0 0.0 0.0 96.6

Energy (MMBtu/Bbl) 0.000 3.591 0.000 3.599 0.000 3.596 0.000 3.594 0.000 0.000 0.000 3.596

All Mogas

Total BPD 3,206,913 3,191,919 1,959,219 2,012,845 1,739,657 1,770,983 696,861 703,921 1,412,353 1,426,660 9,015,003 9,106,327

Ethanol BPD 273,087 345,199 197,901 227,416 175,717 336,332 70,390 87,846 142,657 144,107 859,752 1,140,900

RVP 11.1 11.4 11.4 11.4 10.4 10.5 11.4 11.4 9.6 9.6 10.8 10.9

Sulfur ppm 24 24 23 23 23 22 28 27 10 10 22 21

Density 259 259 259 259 260 260 258 258 259 259 259 259

R+M/2 87.9 88.0 88.0 88.3 88.0 90.4 86.9 87.6 87.8 87.8 87.8 88.5

Aromatics 21.7 21.1 22.1 21.7 21.7 18.3 15.9 14.8 22.5 22.6 21.5 20.4

Benzene 0.55 0.53 0.53 0.52 0.53 0.46 1.05 1.00 0.54 0.55 0.58 0.56

Olefins 13.4 14.1 10.7 10.4 12.7 10.5 8.5 7.4 6.7 6.6 11.2 10.9

Oxygen (wt%) 3.1 4.0 3.7 4.2 3.7 7.0 3.7 4.6 3.7 3.7 3.5 4.6

E200 53.8 54.4 58.7 59.5 53.1 61.9 63.0 65.2 58.2 58.2 56.1 58.4

E300 93.9 80.7 93.9 85.3 93.9 85.3 93.9 91.2 86.2 86.2 92.7 84.3

Energy (MMBtu/Bbl) 4.974 4.948 4.925 4.918 4.986 4.822 4.913 4.866 4.990 4.995 4.963 4.918

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA US

4.4.2.2.2 Low Ethanol Control Case Incremental to the RFS 1 Reference Case

This subsection summarizes the gasoline and diesel fuel costs and other impacts of the

low ethanol control case relative to the RFS1 reference case.
858

Table 4.4-38.

Low Ethanol Control Case Costs without Tax Subsidies

Relative to the RFS 1 Reference Case

(2007 dollars, 7% ROI before taxes)

Gasoline Diesel Fuel

Refinery Model Variable Operating Cost $MM/yr 6,289 -

Amortized Refinery Capital Costs $MM/yr -1,172 -

Fixed Operating Costs $MM/yr -323 -

Added Gasoline Transportation Cost $MM/yr -102 -

Removal of E85 Pricing Effect $MM/yr -3,549 -

Crude Oil Cost $51 to $116/bbl $MM/yr -17,805 -

Lower Energy Density $MM/yr 10,917 2,283

Adjustment from Ethanol Price to Cost $MM/yr 1,108 -

FFV Costs $MM/yr 1,516

Renewable Diesel Cost vs Petroleum Diesel $MM/yr - -14,071

Total Costs $MM/yr -3,121 -11,788

Refinery Model Variable Operating Cost c/gal 4.50 -

Amortized Refinery Capital Costs c/gal -0.84 -

Fixed Operating Costs c/gal -0.23 -

Added Gasoline Transportation Cost c/gal -0.07 -

Removal of E85 Pricing Effect c/gal -2.54 -

Crude Oil Cost $51 to $116/bbl c/gal -12.75 -

Lower Energy Density c/gal 7.82 3.19

Adjustment from Ethanol Price to Cost c/gal 0.79 -

FFV Costs c/gal 1.09

Renewable Diesel Cost vs Petroleum Diesel c/gal - -19.69

Total Costs c/gal -2.24 -16.49

Table 4.4-39.

Low Ethanol Control Case Costs Reflecting Tax Subsidies

Relative to the RFS 1 Reference Case

(2007 dollars, 7% ROI before taxes)

Gasoline Diesel Fuel

Total Costs $MM/yr -3,121 -11,788

Federal Subsidies $MM/yr -4,358 -10,794

Revised Total Cost $MM/yr -7479 -22,582

Total Costs c/gal -2.24 -16.49

Federal Subsidies c/gal -3.12 -15.10

Total Costs c/gal -5.36 -31.60
859

Table 4.4-40.

Summary of the Total and Incremental Volumetric Refinery Inputs by PADD

(barrels/day)

Control Difference Control Difference Control Difference Control Difference Control Difference

Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case

PADD Crude 1,279,888 -219,169 3,227,551 -306,429 6,947,777 -410,791 1,418,700 -120,278 1,842,788 -44,705

GTL Naphtha 0 0 0 0 0 0 0 0 0 0

GTL Diesel 0 0 0 0 0 0 0 0 0 0

VGO HS 0 0 0 0 11,581 -22,191 0 0 0 0

VGO LS 0 0 38,063 6,229 0 0 0 0 0 0

HS AR (A960) 0 0 0 0 0 0 0 0 0 0

LS AR (Alg) 296,982 57,053 0 0 738,419 22,191 0 0 0 0

Normal Butane 22,485 0 55,234 -3,874 111,678 28,274 39,858 415 39,573 0

Isobutane 9,412 9,412 15,826 -9,041 617 617 20,399 -3,024 0 0

Other 0 0 0 0 0 0 0 0 0 0

MTBE 0 0 0 0 0 0 0 0 0 0

Ethanol - E10 319,378 179,502 200,104 95,726 157,892 104,113 68,870 42,518 144,107 8,872

Ethanol - E20 0 0 0 0 0 0 0 0 0 0

Ethanol - E85 25,821 25,821 27,311 27,311 178,439 178,439 18,976 18,976 0 0

Reformer Feed 0 0 0 0 0 0 0 0 0 0

Methanol 0 0 0 0 0 0 0 0 0 0

Natural Gas (FOE) 71,943 -8,373 135,126 -6,941 486,804 126 84,312 -5,329 150,902 -5,574

Hydrogen (MSCF) 0 0 0 0 0 0 0 0 0 0

Pentanes Plus 0 0 32,124 0 52,055 0 18,580 1,113 0 0

Import CBOB 10% 241,570 241,570 0 0 0 0 0 0 0 0

Import CBOB 20% 0 0 0 0 0 0 0 0 0 0

Import RBOB 10% 107,364 -92,636 0 0 0 0 0 0 0 0

Import RBOB 20% 0 0 0 0 0 0 0 0 0 0

Import Alkylate 19,290 19,290 0 0 0 0 0 0 0 0

Import Raffinate 45,299 -19,291 0 0 0 0 0 0 45,808 0

Import Reformate 0 0 0 0 0 0 0 0 0 0

Import FCC Naphtha 0 0 0 0 0 0 0 0 0 0

Import Lt Naphtha 0 0 0 0 0 0 0 0 584 0

Import Hvy Naph 7,291 7,291 0 0 41,644 0 0 0 0 0

Transfer Lt Naphtha 39,298 15,956 0 -20,822 0 0 0 0 22,233 1,004

Transfer Reformate 9,504 -7,154 0 -14,074 0 0 0 0 0 0

Transfer Alkylate 48,790 6,171 1,609 1,609 0 0 0 0 60,000 16,933

Transfer FCC Naphtha 2,408 2,408 20,822 20,822 0 0 0 0 0 0

Transfer Raffinate 0 0 1,066 -16,175 0 0 0 0 59,963 11,040

Transfer RBOB 10% 242,605 0 37,596 37,596 0 0 0 0 0 0

Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0

Transfer CBOB 10% 1,289,975 1,273,399 114,969 112,012 0 0 9,099 -16,933 0 0

Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0

Isooctane 6,030 5,930 100 0 100 0 100 0 13,445 -988

Isooctene 24,113 24,013 100 0 100 0 100 0 804 204

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA
860

Table 4.4-41.

Summary of Total and Incremental Refinery Outputs by PADD

for the Low Ethanol Control Case Relative to the RFS 1 Control Case

(barrels/day)

Control Difference Control Difference Control Difference Control Difference Control Difference

Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case

Propane 30,661 -1,943 54,531 -8,575 101,580 -33,628 20,156 -4,754 52,426 1,106

Propylene 18,685 0 42,525 0 245,407 0 2,041 0 11,774 0

Normal Butane 6,350 -1,482 2,614 244 22,377 -23,129 0 0 0 0

Isobutane 0 0 0 0 0 -345 0 0 41,656 6,653

PC Naphtha 15,830 0 40,290 0 432,937 0 0 0 0 0

PC Gasoil 0 0 535,896 -28,380 157,500 0 0 0 0 0

CG Reg 0 -118,066 0 -852,131 0 -2,020,447 0 -374,129 0 -53,481

CG Prem 0 0 0 0 0 -416,926 0 -51,984 0 0

CG E10 Reg 1,684,887 1,666,651 1,388,234 920,748 878,511 878,511 569,190 352,027 95,872 55,782

CG E10 Prem 168,009 168,009 270,485 19,129 206,925 127,328 112,627 68,875 18,261 438

RFG E10 Reg 1,094,702 -53,295 270,743 6,497 401,267 20,863 0 0 1,102,523 26,457

RFG E10 Prem 214,244 -4,422 51,570 1,238 76,432 3,974 0 0 210,004 5,040

CG E20 Reg 0 0 0 0 0 0 0 0 0 0

RFG E20 Reg 0 0 0 0 0 0 0 0 0 0

E85 to CG 0 0 31,813 31,813 207,848 207,848 22,103 22,103 0 0

E85 to RFG 30,077 30,077 0 0 0 0 0 0 0 0

Transfer RBOB 10% 0 0 0 0 280,201 37,596 0 0 0 0

Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0

Transfer CBOB 10% 0 0 0 0 1,414,043 1,368,478 0 0 0 0

Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0

Jet/Kero A (450ppm) 70,000 0 143,275 1,643 936,227 0 274,537 0 229,653 0

X-Fer Diesel Rundown to Storage 0 0 0 0 0 0 0 0 0 0

HSD Gr 76 (0.2%) 0 0 0 0 0 0 0 0 0 0

LSD Gr 74 (.05%) 0 0 0 0 0 0 0 0 0 0

ULSD (15 ppm) 487,798 -138,408 552,466 -121,092 1,937,875 -210,603 448,555 -85,599 0 0

CARB Diesel 0 0 0 0 0 0 0 0 313,223 -51,643

X-Fer C5's to Storage 0 0 0 0 0 0 0 0 0 0

1% Residual Fuel 0 0 0 0 0 0 0 0 0 0

Residual Fuel 50,000 -39,764 56,903 -10,915 262,834 0 115,134 -316 49,880 0

Slurry 32,981 6,831 76,116 -10,892 106,272 -10,322 20,000 3,494 28,956 254

Asphalt & Wax 91,682 0 210,000 0 157,500 0 5,250 0 41,774 0

Gasoil 0 0 4,895 0 0 0 0 0 9,814 0

Lubes 18,706 0 17,313 0 157,500 0 0 0 20,149 0

Benzene 11,003 0 11,003 0 51,347 0 0 0 0 0

Toluene 0 0 0 0 34,910 0 0 0 0 0

Xylenes 0 0 0 0 7,777 0 0 0 0 0

Cumene 0 0 0 0 0 0 0 0 0 0

Other 0 0 0 0 0 0 0 0 0 0

Cyclohexane 0 0 0 0 0 0 0 0 0 0

Transfer Raffinate 0 0 0 0 61,029 -5,135 0 0 0 0

Transfer Alkylate 0 0 0 0 110,399 24,713 0 0 0 0

Transfer Reformate 0 0 0 0 9,504 9,504 0 0 0 0

Transfer FCC naphtha 0 0 0 0 34,440 34,440 0 0 0 0

Transfer Lt Naphtha 0 0 0 0 28,088 4,746 0 0 22,233 1,004

Transfer Blendstock 0 0 0 0 0 0 0 0 0 0

Sulfur (STons) 1,034 -115 3,768 -433 11,741 -834 1,880 -216 3,459 -110

Coke (STon) 3,201 -520 10,908 -2,313 46,210 -4,714 7,111 -1,577 16,750 -523

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA
861

Table 4.4-42.

Change in Refinery Unit Capacities by PADD

for the Low Ethanol Control Case Relative to the RFS 1 Control Case

(thousand barrels/day)

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA California US Total

Crude Tower 0 -200 0 0 0 -200

Vacuum Tower 0 -89 0 -2 0 -92

Sats Gas Plant 4 0 -40 0 7 -29

Unsats Gas Plant 0 0 0 0 0 0

FCC DeC5 Tower 0 0 0 0 0 0

FCC 0 0 0 -8 0 -8

FCC Splitter 2 -6 0 0 0 -5

Hydrocracker -18 0 0 -26 0 -45

H-Oil Unit 0 0 0 0 0 0

Delayed Coker 0 0 0 -28 0 -28

Visbreaker 0 0 0 0 0 0

Thermal Naphtha Splitter -1 -4 0 -1 0 -6

CRU Reformer 0 0 0 0 0 0

SRU Reformer 0 0 0 0 -5 -5

BTX Reformer 0 -3 -14 0 0 -16

C4 Isomerization 0 0 0 0 1 1

C5/C6 Isomerization 25 0 0 0 0 25

HF Alkylation 0 0 0 0 0 0

H2SO4 Alkylation 1 0 0 -3 0 -2

Dimersol 0 0 0 0 0 0

Cat Poly 0 0 0 0 0 0

Isooctane 0 0 0 0 0 0

DHT - Total -137 0 -241 -62 -4 -444

DHT 2nd RCT - Total -126 -187 -248 -64 -10 -637

DHT Arom Saturation 0 0 0 0 0 0

NHT - Total Fd 0 0 0 -4 0 -4

CGH - Generic 28 -6 -30 -7 0 -15

CGH - Olefin Sat'n 0 0 0 0 0 0

FCCU Fd HDT 39 0 0 0 0 39

LSR Splitter 0 41 38 0 0 80

LSR Bz Saturator 0 0 0 0 0 0

Reformate Saturator -4 -9 1 0 0 -13

Reformate Splitter -13 -27 3 0 0 -38

SDA 0 0 0 0 0 0

MTBE 0 0 0 0 0 0

TAME 0 0 0 0 0 0

Hydrogen Plant - Total MSCF -62 31 44 -77 0 -64

Lube Unit 0 0 0 0 0 0

Sulfur Plant -128 0 0 -264 -75 -467

Merox Jet 0 0 0 0 0 0

Merox Diesel 0 0 0 0 0 0

BTX Reformer - Tower feed 0 -1 0 0 0 -1

BTX Reformer - Extract feed -1 0 0 0 0 -1
862

Table 4.4-43.

Change in Refinery Unit Investments by PADD

for the Low Ethanol Control Case Relative to the RFS 1 Control Case

(million dollars/year)

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA California US Total

Crude Tower 0 -408 0 0 0 -408

Vacuum Tower 0 -317 0 0 0 -317

Sats Gas Plant 14 0 -120 0 50 -57

Unsats Gas Plant -1 0 0 0 0 -1

FCC DeC5 Tower 0 0 0 0 0 0

FCC 0 0 0 -193 0 -193

FCC Splitter 1 -7 0 0 0 -6

Hydrocracker -631 0 0 -755 0 -1,386

H-Oil Unit 0 0 0 0 0 0

Delayed Coker 0 0 0 -592 0 -592

Visbreaker 0 0 0 0 0 0

Thermal Naphtha Splitter -3 -3 0 -3 0 -10

CRU Reformer 0 0 0 0 0 0

SRU Reformer 0 0 0 0 -40 -40

BTX Reformer 0 -22 -206 0 0 -229

C4 Isomerization 0 0 0 0 7 7

C5/C6 Isomerization 224 0 0 0 0 224

HF Alkylation 0 0 0 0 0 0

H2SO4 Alkylation 17 0 0 -42 0 -25

Dimersol 0 0 0 0 0 0

Cat Poly -1 0 0 0 0 -1

Isooctane 0 0 0 0 0 0

DHT - Total -1,397 0 -1,716 -614 -96 -3,822

DHT 2nd RCT - Total -948 -1,212 -1,249 -447 -57 -3,913

DHT Arom Saturation 0 0 0 0 0 0

NHT - Total Fd 0 0 0 -46 0 -46

CGH - Generic 175 -20 -119 -29 0 7

CGH - Olefin Sat'n 0 0 0 0 0 0

FCCU Fd HDT 569 0 0 0 0 569

LSR Splitter 0 29 18 0 0 47

LSR Bz Saturator 0 0 0 0 0 0

Reformate Saturator -48 -60 4 0 0 -104

Reformate Splitter -21 -36 2 0 0 -55

SDA 0 0 0 0 0 0

MTBE 0 0 0 0 0 0

TAME 0 0 0 0 0 0

Hydrogen Plant - Total MSCF -227 118 117 -298 0 -290

Lube Unit 0 0 0 0 0 0

Sulfur Plant -3 0 0 -2 -2 -7

Merox Jet 0 0 0 0 0 0

Merox Diesel 0 0 0 0 0 0

BTX Reformer - Tower feed 0 -1 -1 0 0 -2

BTX Reformer - Extract feed -3 0 0 0 0 -3

Total -2,285 -1,938 -3,272 -3,021 -138 -10,654
863

Table 4.4-44.

Ethanol and Gasoline Volume, Quality and Energy Density by Gasoline Type at the PADD

Terminal for the Low Ethanol Control Case Relative to the RFS 1 Control Case

Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control

RFG

Total ('000 BPD) 1,366,663 1,308,946 314,578 322,313 452,862 477,699 0 0 1,281,031 1,312,528 3,415,134 3,421,486

Ethanol ('000 BPD) 138,034 132,217 31,773 32,557 45,739 48,252 0 0 129,386 132,579 344,932 345,605

RVP (psi) 10.7 10.7 10.3 10.6 9.6 9.7 0.0 0.0 9.5 9.5 10.1 10.1

Sulfur (ppm) 23.8 24.5 20.1 20.7 23.5 24.1 0.0 0.0 8.7 8.8 17.8 18.0

Density 258.5 258.6 256.2 258.2 259.1 258.5 0.0 0.0 258.5 258.7 258.4 258.6

Octane (R+M/2) 88.1 88.1 88.0 88.0 88.0 88.0 0.0 0.0 87.6 87.6 87.9 87.9

Aromatics (vol%) 19.9 19.8 18.2 19.9 19.5 18.5 0.0 0.0 22.2 22.1 20.5 20.5

Benzene (vol%) 0.57 0.56 0.57 0.57 0.57 0.56 0.00 0.00 0.54 0.54 0.56 0.55

Olefins (vol%) 13.1 14.8 8.0 9.1 9.3 11.2 0.0 0.0 5.7 5.7 9.3 10.2

Oxygen (wt%) 3.7 3.7 3.8 3.7 3.7 3.7 0.0 0.0 3.7 3.7 3.7 3.7

E200 (vol%) 55.7 54.8 62.5 57.6 54.5 53.7 0.0 0.0 58.5 58.3 57.2 56.3

E300 (vol%) 93.9 82.3 91.5 91.1 93.9 93.9 0.0 0.0 86.2 86.2 90.8 86.2

Energy (MMBtu/Bbl) 4.963 4.953 4.864 4.939 5.027 4.973 0.000 0.000 4.988 5.000 4.971 4.972

CG

Total ('000 BPD) 1,841,024 1,852,896 1,570,973 1,658,720 1,262,249 1,085,436 687,028 681,817 111,394 114,133 5,472,668 5,393,001

Ethanol ('000 BPD) 1,842 187,161 72,606 167,547 8,040 109,640 26,352 68,870 5,849 11,529 114,689 544,748

RVP (psi) 10.5 11.8 11.8 11.5 10.7 10.5 11.3 11.4 10.8 10.6 11.0 11.4

Sulfur (ppm) 27.8 24.2 25.3 23.6 27.3 23.0 24.6 28.0 26.4 26.0 26.6 24.3

Density 259.3 258.9 258.8 258.9 259.4 259.6 256.8 257.6 260.8 263.4 258.9 259.0

Octane (R+M/2) 88.0 87.7 88.0 88.0 88.0 88.1 86.8 86.9 88.0 89.4 87.8 87.8

Aromatics (vol%) 28.7 22.4 26.3 22.5 28.4 21.8 19.3 15.2 26.8 27.6 26.7 21.5

Benzene (vol%) 0.65 0.52 0.64 0.53 0.65 0.51 0.80 1.03 0.62 0.63 0.67 0.59

Olefins (vol%) 15.2 13.9 11.2 10.8 16.0 11.9 8.0 7.6 18.6 17.5 13.4 11.8

Oxygen (wt%) 0.0 3.7 1.7 3.7 0.2 3.7 1.4 3.7 1.9 3.7 0.8 3.7

E200 (vol%) 45.2 52.9 52.9 58.5 45.9 52.5 58.9 63.1 54.5 57.2 49.5 55.9

E300 (vol%) 93.9 79.3 91.5 84.0 93.9 79.3 93.9 91.0 86.2 86.2 93.0 82.4

Energy (MMBtu/Bbl) 5.133 4.966 5.007 4.939 5.127 4.990 5.004 4.907 4.998 4.946 5.077 4.955

E85

Total ('000 BPD) 0 30,077 0 31,813 0 207,848 0 22,103 0 0 0 291,840

Ethanol ('000 BPD) 0 25,821 0 27,311 0 178,439 0 18,976 0 0 0 250,548

RVP (psi) 0.0 12.9 0.0 12.2 0.0 12.1 0.0 11.9 0.0 0.0 0.0 12.1

Sulfur (ppm) 0.0 8.9 0.0 8.8 0.0 8.8 0.0 8.8 0.0 0.0 0.0 8.8

Density 0.0 266.0 0.0 266.3 0.0 266.2 0.0 266.1 0.0 0.0 0.0 266.2

Octane (R+M/2) 0.0 108.1 0.0 108.0 0.0 107.9 0.0 107.9 0.0 0.0 0.0 108.0

Aromatics (vol%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Benzene (vol%) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Olefins (vol%) 0.0 3.0 0.0 3.0 0.0 1.3 0.0 0.0 0.0 0.0 0.0 1.5

Oxygen (wt%) 0.0 30.8 0.0 30.8 0.0 30.8 0.0 30.8 0.0 0.0 0.0 30.8

E200 (vol%) 0.0 130.1 0.0 130.1 0.0 129.7 0.0 129.3 0.0 0.0 0.0 129.7

E300 (vol%) 0.0 96.6 0.0 96.6 0.0 96.6 0.0 96.6 0.0 0.0 0.0 96.6

Energy (MMBtu/Bbl) 0.000 3.591 0.000 3.599 0.000 3.596 0.000 3.594 0.000 0.000 0.000 3.596

All Mogas

Total BPD 3,207,687 3,191,919 1,885,551 2,012,845 1,715,111 1,770,983 687,028 703,921 1,392,425 1,426,660 8,887,802 9,106,327

Ethanol BPD 139,876 345,199 104,378 227,416 53,780 336,332 26,352 87,846 135,236 144,107 459,622 1,140,900

RVP 10.6 11.4 11.5 11.4 10.4 10.5 11.3 11.4 9.6 9.6 10.6 10.9

Sulfur ppm 26.1 24.2 24.5 22.9 26.3 21.6 24.6 27.4 10.1 10.2 23.2 21.4

Density 258.9 258.9 258.4 258.9 259.3 260.1 256.8 257.9 258.7 259.1 258.7 259.1

R+M/2 88.0 88.0 88.0 88.3 88.0 90.4 86.8 87.6 87.7 87.8 87.8 88.5

Aromatics 24.9 21.1 24.9 21.7 26.0 18.3 19.3 14.8 22.6 22.6 24.3 20.4

Benzene 0.62 0.53 0.63 0.52 0.63 0.46 0.80 1.00 0.55 0.55 0.63 0.56

Olefins 14.3 14.1 10.7 10.4 14.2 10.5 8.0 7.4 6.7 6.6 11.8 10.9

Oxygen (wt%) 1.6 4.0 2.0 4.2 1.2 7.0 1.4 4.6 3.6 3.7 1.9 4.6

E200 49.7 54.4 54.5 59.5 48.1 61.9 58.9 65.2 58.1 58.2 52.5 58.4

E300 93.9 80.7 91.5 85.3 93.9 85.3 93.9 91.2 86.2 86.2 92.2 84.3

Energy (MMBtu/Bbl) 5.061 4.948 4.983 4.918 5.101 4.822 5.004 4.866 4.988 4.995 5.036 4.918

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA US

4.4.2.3 High Ethanol Control Case

This section summarizes the costs and other impacts of the high ethanol control case

relative to the AEO 2007 and RFS 1 reference cases. This case assumes that the cellulosic

biofuel standard would be met solely through the production and use of cellulosic ethanol.
864

4.4.2.3.1 High Ethanol Control Case Relative to the AEO 2007 Reference Case

This subsection summarizes the gasoline and diesel fuel costs and other impacts of the

high ethanol control case relative to the AEO 2007 reference case.

Table 4.4-45.

High Ethanol Control Case Costs without Tax Subsidies

Relative to the AEO 2007 Reference Case

(2007 dollars, 7% ROI before taxes)

Gasoline Diesel Fuel

Refinery Model Variable Operating Cost $MM/yr 23,616 -

Amortized Refinery Capital Costs $MM/yr -426 -

Fixed Operating Costs $MM/yr -117 -

Added Gasoline Transportation Cost $MM/yr -197 -

Removal of E85 Pricing Effect $MM/yr -19,854 -

Crude Oil Cost $51 to $116/bbl $MM/yr -34,958 -

Lower Energy Density $MM/yr 21,728 473

Adjustment from Ethanol Price to Cost $MM/yr -1,866 -

FFV Costs $MM/yr 6,113 -

Renewable Diesel Cost vs Petroleum Diesel $MM/yr - -1,744

Total Costs $MM/yr -5,961 -1,271

Refinery Model Variable Operating Cost c/gal 16.32 -

Amortized Refinery Capital Costs c/gal -0.29 -

Fixed Operating Costs c/gal -0.08 -

Added Gasoline Transportation Cost c/gal -0.14 -

Removal of E85 Pricing Effect c/gal -13.72 -

Crude Oil Cost $51 to $116/bbl c/gal -24.16 -

Lower Energy Density c/gal 15.02 0.67

Adjustment from Ethanol Price to Cost c/gal -1.29 -

FFV Costs c/gal 4.22 -

Renewable Diesel Cost vs Petroleum Diesel c/gal - -2.46

Total Costs c/gal -4.12 -1.79

Table 4.4-46.

High Ethanol Control Case Costs Reflecting Tax Subsidies

Relative to the AEO 2007 Reference Case

(2007 dollars, 7% ROI before taxes)

Gasoline Diesel Fuel

Total Costs $MM/yr -5,961 -1,271

Federal Subsidies $MM/yr -17,504 -1,359

Revised Total Cost $MM/yr -23,465 -2,630

Total Costs c/gal -4.12 -1.79

Federal Subsidies c/gal -12.10 -1.92

Total Costs c/gal -16.22 -3.71
865

Table 4.4-47.

Summary of the Total and Incremental Volumetric Refinery Inputs by PADD for the High

Ethanol Control Case Relative to the AEO 2007 Reference Case

(barrels/day)

Control Difference Control Difference Control Difference Control Difference Control Difference

Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case

PADD Crude 1,347,342 -181,037 3,058,241 -400,248 7,117,305 -134,113 1,534,859 1,256 1,886,357 -3,037

GTL Naphtha 0 0 0 0 0 0 0 0 0 0

GTL Diesel 0 0 0 0 0 0 0 0 0 0

VGO HS 0 0 0 0 0 -43,785 0 0 0 0

VGO LS 0 0 7,920 -30,143 0 0 0 0 0 0

HS AR (A960) 0 0 0 0 0 0 0 0 0 0

LS AR (Alg) 279,819 50,165 0 0 738,057 31,843 0 0 0 0

Normal Butane 30,814 8,329 78,293 26,638 69,363 -16,802 25,526 -14,190 39,573 0

Isobutane 12,582 12,038 16,476 -7,315 8,234 8,234 1,178 -5,088 0 0

Other 0 0 0 0 0 0 0 0 0 0

MTBE 0 0 0 0 0 0 0 0 0 0

Ethanol - E10 270,313 -2,774 168,062 -29,838 142,590 -33,127 66,626 -3,764 143,845 1,188

Ethanol - E20 0 0 0 0 0 0 0 0 0 0

Ethanol - E85 575,048 575,048 332,715 332,715 361,999 361,999 60,129 60,129 46,969 46,969

Reformer Feed 0 0 0 0 0 0 0 0 0 0

Methanol 0 0 0 0 0 0 0 0 0 0

Natural Gas (FOE) 74,115 -6,992 136,509 -8,134 493,553 -10,734 86,015 -7,881 154,032 -1,636

Hydrogen (MSCF) 0 0 0 0 0 0 0 0 0 0

Pentanes Plus 0 0 58 -32,066 52,055 0 0 -18,580 0 0

Import CBOB 10% 0 -6,286 0 0 0 0 0 0 0 0

Import CBOB 20% 0 0 0 0 0 0 0 0 0 0

Import RBOB 10% 0 -145,593 0 0 0 0 0 0 0 0

Import RBOB 20% 0 0 0 0 0 0 0 0 0 0

Import Alkylate 45,167 42,933 0 0 0 0 0 0 0 0

Import Raffinate 3,442 -50,084 0 0 0 0 0 0 45,808 0

Import Reformate 0 -8,829 0 0 0 0 0 0 0 0

Import FCC Naphtha 15,980 15,980 0 0 11,943 11,943 18,580 18,580 0 0

Import Lt Naphtha 0 0 17,575 17,575 0 0 0 0 584 0

Import Hvy Naph 0 0 0 0 41,644 0 0 0 0 0

Transfer Lt Naphtha 0 -4,333 0 0 0 0 0 0 11,387 -6,997

Transfer Reformate 16,658 0 0 0 0 0 0 0 0 0

Transfer Alkylate 60,000 0 0 0 0 0 0 0 60,000 0

Transfer FCC Naphtha 0 0 20,822 0 0 0 0 0 0 0

Transfer Raffinate 0 0 11,353 -284 0 0 0 0 60,000 12,556

Transfer RBOB 10% 242,605 0 0 0 0 0 0 0 0 0

Transfer RBOB 20% 0 0 8,720 8,720 0 0 0 0 0 0

Transfer CBOB 10% 1,398,409 262,136 80,262 26,208 0 0 6,493 142 0 0

Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0

Isooctane 100 0 100 0 100 0 100 0 16,462 1,200

Isooctene 100 0 100 0 100 0 100 0 600 0

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA
866

Table 4.4-48.

Summary of Total and Incremental Refinery Outputs by PADD

for the High Ethanol Control Case Relative to the AEO 2007 Reference Case

(barrels/day)

Control Difference Control Difference Control Difference Control Difference Control Difference

Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case

Propane 34,143 2,023 50,843 -6,445 103,144 -14,409 23,369 866 52,782 1,190

Propylene 18,685 0 42,525 0 245,407 0 2,041 0 11,774 0

Normal Butane 0 -4,967 0 -965 0 -33,663 0 0 0 0

Isobutane 0 0 0 0 0 -411 0 0 40,012 -1,478

PC Naphtha 15,830 0 40,290 0 432,937 0 0 0 0 0

PC Gasoil 0 0 455,618 -105,083 157,500 0 0 0 0 0

CG Reg 0 0 0 0 0 0 0 0 0 0

CG Prem 0 0 0 0 0 -182,001 0 0 0 0

CG E10 Reg 1,535,198 278,248 1,172,450 -205,266 872,274 -203,190 542,856 -42,507 99,374 4,464

CG E10 Prem 306,669 236,335 274,765 12,343 214,485 9,635 116,742 5,244 18,928 850

RFG E10 Reg 605,554 -550,564 163,150 -104,878 246,042 -139,806 0 0 1,088,091 -3,375

RFG E10 Prem 228,678 8,465 53,454 2,401 78,844 5,349 0 0 217,676 9,778

CG E20 Reg 0 0 0 0 0 0 0 0 0 0

RFG E20 Reg 0 0 0 0 0 0 0 0 0 0

E85 to CG 74,814 74,814 270,064 270,064 253,773 253,773 70,039 70,039 0 0

E85 to RFG 595,006 595,006 117,484 117,484 167,888 167,888 0 0 54,710 54,710

Transfer RBOB 10% 0 0 0 0 251,325 8,720 0 0 0 0

Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0

Transfer CBOB 10% 0 0 0 0 1,485,164 288,485 0 0 0 0

Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0

Jet/Kero A (450ppm) 70,000 0 138,523 -1,557 936,227 0 274,537 0 229,653 0

X-Fer Diesel Rundown to Storage0 0 0 0 0 0 0 0 0 0

HSD Gr 76 (0.2%) 0 0 0 0 0 0 0 0 0 0

LSD Gr 74 (.05%) 0 0 0 0 0 0 0 0 0 0

ULSD (15 ppm) 587,042 -47,575 643,331 -28,840 2,150,901 14,598 523,035 -10,264 0 0

CARB Diesel 0 0 0 0 0 0 0 0 358,152 -6,197

X-Fer C5's to Storage 0 0 0 0 0 0 0 0 0 0

1% Residual Fuel 0 0 0 0 0 0 0 0 0 0

Residual Fuel 68,283 -23,360 67,826 3,237 262,834 0 138,312 16,021 45,680 -4,199

Slurry 30,026 2,617 67,363 -18,904 112,554 -1,394 12,295 -1,187 29,893 886

Asphalt & Wax 86,013 -5,669 198,329 -9,357 157,500 0 5,250 0 41,774 0

Gasoil 0 0 4,895 0 0 0 0 0 9,814 0

Lubes 18,706 0 17,313 0 157,500 0 0 0 20,149 0

Benzene 11,003 0 11,003 0 51,347 0 0 0 0 0

Toluene 0 0 0 0 34,910 0 0 0 0 0

Xylenes 0 0 0 0 7,777 0 0 0 0 0

Cumene 0 0 0 0 0 0 0 0 0 0

Other 0 0 0 0 0 0 0 0 0 0

Cyclohexane 0 0 0 0 0 0 0 0 0 0

Transfer Raffinate 0 0 0 0 71,353 12,273 0 0 0 0

Transfer Alkylate 0 0 0 0 120,000 0 0 0 0 0

Transfer Reformate 0 0 0 0 16,658 0 0 0 0 0

Transfer FCC naphtha 0 0 0 0 20,822 0 0 0 0 0

Transfer Lt Naphtha 0 0 0 0 0 -4,333 0 0 11,387 -6,997

Transfer Blendstock 0 0 0 0 0 0 0 0 0 0

Sulfur (STons) 1,076 -78 3,571 -542 12,042 -356 2,038 -49 3,559 14

Coke (STon) 3,495 -455 10,154 -2,572 48,646 -1,154 7,899 -580 17,358 64

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA
867

Table 4.4-49.

Change in Refinery Unit Capacities by PADD

for the High Ethanol Control Case Relative to the AEO 2007 Reference Case

(thousand barrels/day)

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA California US Total

Crude Tower 0 -459 0 0 0 -459

Vacuum Tower 0 -205 0 8 0 -197

Sats Gas Plant -21 0 -45 0 10 -56

Unsats Gas Plant 1 0 0 0 0 1

FCC DeC5 Tower 0 0 0 0 0 0

FCC 0 0 0 0 0 0

FCC Splitter 1 -6 0 0 0 -4

Hydrocracker 14 0 0 -6 0 7

H-Oil Unit 0 0 0 0 0 0

Delayed Coker 0 0 0 -1 0 -1

Visbreaker 0 0 0 0 0 0

Thermal Naphtha Splitter 0 -6 0 0 0 -6

CRU Reformer 0 0 0 0 0 0

SRU Reformer 0 0 0 0 9 9

BTX Reformer 0 7 -9 0 0 -1

C4 Isomerization 0 0 0 0 3 3

C5/C6 Isomerization 16 0 0 0 0 16

HF Alkylation 0 0 0 0 0 0

H2SO4 Alkylation -27 0 0 0 0 -27

Dimersol 0 0 0 0 0 0

Cat Poly 1 0 0 0 0 1

Isooctane 0 0 0 0 0 0

DHT - Total -40 0 -19 -10 0 -69

DHT 2nd RCT - Total -38 -52 -20 -10 0 -120

DHT Arom Saturation 0 0 0 0 0 0

NHT - Total Fd 0 0 0 2 0 2

CGH - Generic 13 -30 -6 1 0 -23

CGH - Olefin Sat'n 0 0 0 0 0 0

FCCU Fd HDT 24 0 0 0 0 24

LSR Splitter 0 -25 0 0 0 -25

LSR Bz Saturator 0 0 0 0 0 0

Reformate Saturator -18 0 -2 0 0 -20

Reformate Splitter -54 0 -6 0 0 -59

SDA 0 0 0 0 0 0

MTBE 0 0 0 0 0 0

TAME 0 0 0 0 0 0

Hydrogen Plant - Total MSCF -2 -43 -80 -31 0 -156

Lube Unit 0 0 0 0 0 0

Sulfur Plant -24 0 0 -30 -22 -76

Merox Jet 0 0 0 0 0 0

Merox Diesel 0 0 0 0 0 0

BTX Reformer - Tower feed 0 0 0 0 0 0

BTX Reformer - Extract feed 0 0 0 0 0 0
868

Table 4.4-50.

Change in Refinery Unit Investments by PADD

for the High Ethanol Control Case Relative to the AEO 2007 Reference Case

(million dollars/year)

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA California US Total

Crude Tower 0 -1,121 0 0 0 -1,121

Vacuum Tower 0 -676 0 70 0 -606

Sats Gas Plant -107 0 -129 0 60 -176

Unsats Gas Plant 8 0 0 0 0 8

FCC DeC5 Tower 0 0 0 0 0 0

FCC 0 0 0 0 0 0

FCC Splitter 1 -7 0 0 0 -6

Hydrocracker 305 0 0 -129 0 176

H-Oil Unit 0 0 0 0 0 0

Delayed Coker 0 0 0 -22 0 -22

Visbreaker 0 0 0 0 0 0

Thermal Naphtha Splitter 0 -7 0 0 0 -7

CRU Reformer 0 0 0 0 0 0

SRU Reformer 0 0 0 0 45 45

BTX Reformer 0 70 -55 0 0 16

C4 Isomerization 0 0 0 0 20 20

C5/C6 Isomerization 156 0 0 0 0 156

HF Alkylation 0 0 0 0 0 0

H2SO4 Alkylation -470 0 0 0 0 -470

Dimersol 0 0 0 0 0 0

Cat Poly 8 0 0 0 0 8

Isooctane 0 0 0 0 0 0

DHT - Total -390 0 -82 -174 0 -646

DHT 2nd RCT - Total -355 -361 -64 -43 0 -822

DHT Arom Saturation 0 0 0 0 0 0

NHT - Total Fd 0 0 0 31 0 31

CGH - Generic 109 -157 -59 4 0 -102

CGH - Olefin Sat'n 0 0 0 0 0 0

FCCU Fd HDT 319 0 0 0 0 319

LSR Splitter 0 -17 0 0 0 -17

LSR Bz Saturator 0 0 0 0 0 0

Reformate Saturator -110 0 -9 0 0 -119

Reformate Splitter -64 0 -4 0 0 -68

SDA 0 0 0 0 0 0

MTBE 0 0 0 0 0 0

TAME 0 0 0 0 0 0

Hydrogen Plant - Total MSCF -7 -148 -186 -129 0 -469

Lube Unit 0 0 0 0 0 0

Sulfur Plant 0 0 0 0 0 -1

Merox Jet 0 0 0 0 0 0

Merox Diesel 0 0 0 0 0 0

BTX Reformer - Tower feed 0 1 0 0 0 1

BTX Reformer - Extract feed 0 0 0 0 0 0

Total -597 -2,422 -587 -391 124 -3,874
869

Table 4.4-51.

Projected Total U.S. Capital Investments

for the High Ethanol Control Case Relative to the AEO 2007 Reference Case

(billion dollars)

Cost Type Plant Type Capital

Investments

Corn Ethanol 3.9

Cellulosic Ethanol 48.3

Cellulosic Diesel a0

Production

Costs

Renewable Diesel and

Algae

1.1

All Ethanol 11.9

Cellulosic and

Renewable Diesel Fuel

-

Distribution

Costs

Biodiesel 1.2

FFV Costs 6.1

Refining -4.1

Total Capital Investments 68.4
870

Table 4.4-52.

Ethanol and Gasoline Volume, Quality and Energy Density by Gasoline Type at the PADD

Terminal for the High Ethanol Control Case Relative to the AEO 2007 Reference Case

Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control

RFG

Total ('000 BPD) 1,376,331 834,232 319,080 216,604 459,343 324,886 0 0 1,299,365 1,305,768 3,454,120 2,681,490

Ethanol ('000 BPD) 139,023 84,266 32,230 21,879 46,397 32,817 0 0 131,244 131,896 348,895 270,858

RVP (psi) 10.8 10.3 10.6 12.5 9.7 11.3 0.0 0.0 9.5 9.4 10.1 10.1

Sulfur (ppm) 24.4 23.4 20.0 24.5 23.2 22.4 0.0 0.0 8.8 8.9 18.0 16.3

Density 258.9 259.4 258.2 257.1 259.5 258.7 0.0 0.0 258.5 258.9 258.8 258.9

Octane (R+M/2) 88.1 88.6 88.0 88.5 88.0 88.5 0.0 0.0 87.6 87.7 87.9 88.1

Aromatics (vol%) 19.9 20.0 19.9 20.0 19.6 19.8 0.0 0.0 22.2 22.2 20.7 21.1

Benzene (vol%) 0.57 0.58 0.57 0.57 0.56 0.54 0.00 0.00 0.53 0.55 0.55 0.56

Olefins (vol%) 13.6 14.0 9.4 11.4 11.5 13.7 0.0 0.0 5.7 5.7 10.0 9.7

Oxygen (wt%) 3.7 3.7 3.7 3.8 3.7 3.7 0.0 0.0 3.7 3.7 3.7 3.7

E200 (vol%) 55.6 56.7 58.3 60.7 53.3 54.2 0.0 0.0 58.2 58.2 56.5 57.4

E300 (vol%) 93.9 95.1 93.9 95.1 93.9 95.1 0.0 0.0 86.2 86.2 91.0 90.7

Energy (MMBtu/Bbl) 4.947 4.956 4.924 4.895 4.981 4.951 0.000 0.000 4.994 4.997 4.967 4.970

CG

Total ('000 BPD) 1,830,582 1,841,867 1,640,138 1,447,215 1,280,314 1,086,759 696,861 659,598 112,988 118,302 5,560,884 5,153,740

Ethanol ('000 BPD) 134,064 186,047 165,671 146,183 129,320 109,774 70,390 66,626 11,413 11,950 510,857 520,580

RVP (psi) 11.4 11.7 11.6 11.4 10.7 10.4 11.4 11.2 10.6 10.6 11.3 11.3

Sulfur (ppm) 22.9 24.1 23.6 24.7 23.1 22.6 28.0 28.0 26.6 25.6 23.9 24.5

Density 258.9 260.0 259.1 259.3 260.5 260.8 258.1 259.1 262.7 262.5 259.3 259.9

Octane (R+M/2) 87.8 88.0 88.0 88.1 88.0 88.2 86.9 87.0 89.3 89.2 87.8 88.0

Aromatics (vol%) 23.1 22.5 22.5 22.5 22.4 22.4 15.9 17.7 26.5 26.7 21.9 22.0

Benzene (vol%) 0.53 0.53 0.53 0.53 0.51 0.51 1.05 1.03 0.59 0.57 0.59 0.59

Olefins (vol%) 13.2 13.9 11.0 10.9 13.1 12.6 8.5 9.5 17.8 16.1 12.0 12.3

Oxygen (wt%) 2.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.4 3.7

E200 (vol%) 52.5 53.5 58.8 58.9 53.0 51.4 63.0 63.5 58.0 58.5 55.9 56.0

E300 (vol%) 93.9 80.4 93.9 95.0 93.9 79.2 93.9 95.8 86.2 86.2 93.7 86.3

Energy (MMBtu/Bbl) 4.995 4.974 4.925 4.938 4.988 4.998 0.000 4.930 4.942 4.929 4.961 4.962

E85

Total ('000 BPD) 0 669,821 0 387,549 0 421,660 0 70,039 0 54,710 0 1,603,779

Ethanol ('000 BPD) 0 575,048 0 332,715 0 361,999 0 60,129 0 46,969 0 1,376,860

RVP (psi) 0.0 9.9 0.0 10.3 0.0 10.7 0.0 11.9 0.0 12.0 0.0 10.4

Sulfur (ppm) 0.0 10.1 0.0 9.7 0.0 9.6 0.0 9.0 0.0 8.8 0.0 9.8

Density 0.0 268.9 0.0 268.1 0.0 267.8 0.0 266.6 0.0 266.2 0.0 268.2

Octane (R+M/2) 0.0 107.7 0.0 107.7 0.0 107.8 0.0 107.9 0.0 107.9 0.0 107.7

Aromatics (vol%) 0.0 1.2 0.0 0.8 0.0 0.7 0.0 0.1 0.0 0.0 0.0 0.9

Benzene (vol%) 0.00 0.03 0.00 0.02 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.03

Olefins (vol%) 0.0 2.4 0.0 2.0 0.0 2.1 0.0 2.8 0.0 1.2 0.0 2.2

Oxygen (wt%) 0.0 30.5 0.0 25.7 0.0 30.6 0.0 28.6 0.0 30.8 0.0 29.3

E200 (vol%) 0.0 126.8 0.0 127.7 0.0 128.0 0.0 129.8 0.0 129.6 0.0 127.6

E300 (vol%) 0.0 95.6 0.0 95.9 0.0 96.0 0.0 96.6 0.0 96.6 0.0 95.8

Energy (MMBtu/Bbl) 0.000 3.656 0.000 3.642 0.000 3.633 0.000 3.605 0.000 3.596 0.000 3.642

All Mogas

Total BPD 3,206,913 3,345,919 1,959,219 2,051,367 1,739,657 1,833,305 696,861 729,637 1,412,353 1,478,780 9,015,003 9,439,008

Ethanol BPD 273,087 845,361 197,901 500,777 175,717 504,590 70,390 126,755 142,657 190,815 859,752 2,168,297

RVP 11.1 11.0 11.4 11.3 10.4 10.6 11.4 11.3 9.6 9.6 10.8 10.8

Sulfur ppm 24 21 23 22 23 19 28 26 10 10 22 20

Density 259 262 259 261 260 262 258 260 259 259 259 261

R+M/2 87.9 92.1 88.0 91.9 88.0 92.7 86.9 89.0 87.8 88.5 87.8 91.4

Aromatics 21.7 17.6 22.1 18.1 21.7 16.9 15.9 16.0 22.5 21.8 21.5 18.1

Benzene 0.55 0.44 0.53 0.44 0.53 0.40 1.05 0.93 0.54 0.53 0.58 0.49

Olefins 13.4 11.6 10.7 9.3 12.7 10.3 8.5 8.9 6.7 6.4 11.2 9.8

Oxygen (wt%) 3.1 9.2 3.7 8.0 3.7 10.0 3.7 6.2 3.7 4.7 3.5 8.2

E200 53.8 69.0 58.7 72.1 53.1 69.5 63.0 69.9 58.2 60.9 56.1 68.6

E300 93.9 87.1 93.9 95.1 93.9 85.9 93.9 95.9 86.2 86.6 92.7 89.2

Energy (MMBtu/Bbl) 4.974 4.706 4.925 4.689 4.986 4.676 4.913 4.803 4.990 4.939 4.963 4.740

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA US

4.4.2.3.2 High Ethanol Control Case Relative to the RFS 1 Reference Case

This subsection summarizes the gasoline and diesel fuel costs and other impacts of the

high ethanol control case relative to the RFS1 reference case.
871

Table 4.4-53.

High Ethanol Control Case Costs without Tax Subsidies

Relative to the RFS 1 Reference Case

(2007 dollars, 7% ROI before taxes)

Gasoline Diesel Fuel

Refinery Model Variable Operating Cost $MM/yr 23,894 -

Amortized Refinery Capital Costs $MM/yr -460 -

Fixed Operating Costs $MM/yr -127 -

Added Gasoline Transportation Cost $MM/yr -257 -

Removal of E85 Pricing Effect $MM/yr -19,854 -

Crude Oil Cost $51 to $116/bbl $MM/yr -45,548 -

Lower Energy Density $MM/yr 29,609 502

Adjustment from Ethanol Price to Cost $MM/yr -1,524 -

FFV Costs $MM/yr 6,476

Renewable Diesel Cost vs Petroleum Diesel $MM/yr - -1,851

Total Costs $MM/yr -7,790 -1,350

Refinery Model Variable Operating Cost c/gal 16.51 -

Amortized Refinery Capital Costs c/gal -0.32 -

Fixed Operating Costs c/gal -0.09 -

Added Gasoline Transportation Cost c/gal -0.18 -

Removal of E85 Pricing Effect c/gal -13.72 -

Crude Oil Cost $51 to $116/bbl c/gal -31.48 -

Lower Energy Density c/gal 20.46 0.71

Adjustment from Ethanol Price to Cost c/gal -1.05 -

FFV Costs c/gal 4.48

Renewable Diesel Cost vs Petroleum Diesel c/gal - -2.61

Total Costs c/gal -5.38 -1.90

Table 4.4-54.

High Ethanol Control Case Costs Reflecting Tax Subsidies

Relative to the RFS 1 Control Case

(2007 dollars, 7% ROI before taxes)

Gasoline Diesel Fuel

Total Costs $MM/yr -7,790 -1,350

Federal Subsidies $MM/yr -20,266 -1,441

Revised Total Cost $MM/yr -28,055 -2,791

Total Costs c/gal -5.38 -1.90

Federal Subsidies c/gal -14.01 -2.03

Total Costs c/gal -19.39 -3.94
872

Table 4.4-55.

Summary of the Total and Incremental Volumetric Refinery Inputs by PADD

for the High Ethanol Control Case Relative to the RFS 1 Reference Case

(barrels/day)

Control Difference Control Difference Control Difference Control Difference Control Difference

Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case

PADD Crude 1,347,342 -151,714 3,058,241 -475,740 7,117,305 -241,263 1,534,859 -4,119 1,886,357 -1,136

GTL Naphtha 0 0 0 0 0 0 0 0 0 0

GTL Diesel 0 0 0 0 0 0 0 0 0 0

VGO HS 0 0 0 0 0 -33,772 0 0 0 0

VGO LS 0 0 7,920 -23,914 0 0 0 0 0 0

HS AR (A960) 0 0 0 0 0 0 0 0 0 0

LS AR (Alg) 279,819 39,889 0 0 738,057 21,829 0 0 0 0

Normal Butane 30,814 8,329 78,293 19,185 69,363 -14,040 25,526 -13,917 39,573 0

Isobutane 12,582 12,582 16,476 -8,390 8,234 8,234 1,178 -22,245 0 0

Other 0 0 0 0 0 0 0 0 0 0

MTBE 0 0 0 0 0 0 0 0 0 0

Ethanol - E10 270,313 130,437 168,062 63,684 142,590 88,811 66,626 40,273 143,845 8,610

Ethanol - E20 0 0 0 0 0 0 0 0 0 0

Ethanol - E85 575,048 575,048 332,715 332,715 361,999 361,999 60,129 60,129 46,969 46,969

Reformer Feed 0 0 0 0 0 0 0 0 0 0

Methanol 0 0 0 0 0 0 0 0 0 0

Natural Gas (FOE) 74,115 -6,201 136,509 -5,559 493,553 6,876 86,015 -3,625 154,032 -2,444

Hydrogen (MSCF) 0 0 0 0 0 0 0 0 0 0

Pentanes Plus 0 0 58 -32,066 52,055 0 0 -17,467 0 0

Import CBOB 10% 0 0 0 0 0 0 0 0 0 0

Import CBOB 20% 0 0 0 0 0 0 0 0 0 0

Import RBOB 10% 0 -200,000 0 0 0 0 0 0 0 0

Import RBOB 20% 0 0 0 0 0 0 0 0 0 0

Import Alkylate 45,167 45,167 0 0 0 0 0 0 0 0

Import Raffinate 3,442 -61,147 0 0 0 0 0 0 45,808 0

Import Reformate 0 0 0 0 0 0 0 0 0 0

Import FCC Naphtha 15,980 15,980 0 0 11,943 11,943 18,580 18,580 0 0

Import Lt Naphtha 0 0 17,575 17,575 0 0 0 0 584 0

Import Hvy Naph 0 0 0 0 41,644 0 0 0 0 0

Transfer Lt Naphtha 0 -23,342 0 -20,822 0 0 0 0 11,387 -9,842

Transfer Reformate 16,658 0 0 -14,074 0 0 0 0 0 0

Transfer Alkylate 60,000 17,381 0 0 0 0 0 0 60,000 16,933

Transfer FCC Naphtha 0 0 20,822 20,822 0 0 0 0 0 0

Transfer Raffinate 0 0 11,353 -5,888 0 0 0 0 60,000 11,077

Transfer RBOB 10% 242,605 0 0 0 0 0 0 0 0 0

Transfer RBOB 20% 0 0 8,720 8,720 0 0 0 0 0 0

Transfer CBOB 10% 1,398,409 1,381,833 80,262 77,306 0 0 6,493 -19,539 0 0

Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0

Isooctane 100 0 100 0 100 0 100 0 16,462 2,029

Isooctene 100 0 100 0 100 0 100 0 600 0

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA
873

Table 4.4-56.

Summary of Total and Incremental Refinery Outputs by PADD

for the High Ethanol Control Case Relative to the RFS 1 Reference Case

(barrels/day)

Control Difference Control Difference Control Difference Control Difference Control Difference

Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case Case from Ref Case

Propane 34,143 1,539 50,843 -12,263 103,144 -32,064 23,369 -1,541 52,782 1,461

Propylene 18,685 0 42,525 0 245,407 0 2,041 0 11,774 0

Normal Butane 0 -7,832 0 -2,370 0 -45,506 0 0 0 0

Isobutane 0 0 0 0 0 -345 0 0 40,012 5,009

PC Naphtha 15,830 0 40,290 0 432,937 0 0 0 0 0

PC Gasoil 0 0 455,618 -108,658 157,500 0 0 0 0 0

CG Reg 0 -118,066 0 -852,131 0 -2,020,447 0 -374,129 0 -53,481

CG Prem 0 0 0 0 0 -416,926 0 -51,984 0 0

CG E10 Reg 1,535,198 1,516,962 1,172,450 704,964 872,274 872,274 542,856 325,693 99,374 59,284

CG E10 Prem 306,669 306,669 274,765 23,409 214,485 134,887 116,742 72,990 18,928 1,105

RFG E10 Reg 605,554 -542,443 163,150 -101,096 246,042 -134,362 0 0 1,088,091 12,026

RFG E10 Prem 228,678 10,012 53,454 3,122 78,844 6,386 0 0 217,676 12,712

CG E20 Reg 0 0 0 0 0 0 0 0 0 0

RFG E20 Reg 0 0 0 0 0 0 0 0 0 0

E85 to CG 74,814 74,814 270,064 270,064 253,773 253,773 70,039 70,039 0 0

E85 to RFG 595,006 595,006 117,484 117,484 167,888 167,888 0 0 54,710 54,710

Transfer RBOB 10% 0 0 0 0 251,325 8,720 0 0 0 0

Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0

Transfer CBOB 10% 0 0 0 0 1,485,164 1,439,600 0 0 0 0

Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0

Jet/Kero A (450ppm) 70,000 0 138,523 -3,108 936,227 0 274,537 0 229,653 0

X-Fer Diesel Rundown to Storage 0 0 0 0 0 0 0 0 0 0

HSD Gr 76 (0.2%) 0 0 0 0 0 0 0 0 0 0

LSD Gr 74 (.05%) 0 0 0 0 0 0 0 0 0 0

ULSD (15 ppm) 587,042 -39,165 643,331 -30,227 2,150,901 2,423 523,035 -11,120 0 0

CARB Diesel 0 0 0 0 0 0 0 0 358,152 -6,714

X-Fer C5's to Storage 0 0 0 0 0 0 0 0 0 0

1% Residual Fuel 0 0 0 0 0 0 0 0 0 0

Residual Fuel 68,283 -21,481 67,826 8 262,834 0 138,312 22,862 45,680 -4,199

Slurry 30,026 3,876 67,363 -19,645 112,554 -4,041 12,295 -4,210 29,893 1,191

Asphalt & Wax 86,013 -5,669 198,329 -11,671 157,500 0 5,250 0 41,774 0

Gasoil 0 0 4,895 0 0 0 0 0 9,814 0

Lubes 18,706 0 17,313 0 157,500 0 0 0 20,149 0

Benzene 11,003 0 11,003 0 51,347 0 0 0 0 0

Toluene 0 0 0 0 34,910 0 0 0 0 0

Xylenes 0 0 0 0 7,777 0 0 0 0 0

Cumene 0 0 0 0 0 0 0 0 0 0

Other 0 0 0 0 0 0 0 0 0 0

Cyclohexane 0 0 0 0 0 0 0 0 0 0

Transfer Raffinate 0 0 0 0 71,353 5,190 0 0 0 0

Transfer Alkylate 0 0 0 0 120,000 34,314 0 0 0 0

Transfer Reformate 0 0 0 0 16,658 16,658 0 0 0 0

Transfer FCC naphtha 0 0 0 0 20,822 20,822 0 0 0 0

Transfer Lt Naphtha 0 0 0 0 0 -23,342 0 0 11,387 -9,842

Transfer Blendstock 0 0 0 0 0 0 0 0 0 0

Sulfur (STons) 1,076 -74 3,571 -630 12,042 -533 2,038 -59 3,559 -10

Coke (STon) 3,495 -227 10,154 -3,068 48,646 -2,278 7,899 -790 17,358 86

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA
874

Table 4.4-57.

Change in Refinery Unit Capacities by PADD

for the High Ethanol Control Case Relative to the RFS 1 Reference Case

(thousand barrels/day)

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA California US Total

Crude Tower 0 -439 0 0 0 -439

Vacuum Tower 0 -196 0 -2 0 -198

Sats Gas Plant 3 0 -30 0 9 -18

Unsats Gas Plant -2 0 0 0 0 -2

FCC DeC5 Tower 0 0 0 0 0 0

FCC 0 0 0 -8 0 -8

FCC Splitter 2 -6 0 0 0 -5

Hydrocracker -18 0 0 -14 0 -33

H-Oil Unit 0 0 0 0 0 0

Delayed Coker 0 0 0 -28 0 -28

Visbreaker 0 0 0 0 0 0

Thermal Naphtha Splitter -1 -6 0 -1 0 -8

CRU Reformer 0 0 0 0 0 0

SRU Reformer 0 0 0 0 -1 -1

BTX Reformer 0 -10 -18 0 0 -27

C4 Isomerization 0 0 0 0 1 1

C5/C6 Isomerization 26 0 0 0 0 26

HF Alkylation 0 0 0 0 0 0

H2SO4 Alkylation 1 0 0 -5 0 -4

Dimersol 0 0 0 0 0 0

Cat Poly 0 0 0 0 0 0

Isooctane 0 0 0 0 0 0

DHT - Total -132 0 -103 -50 -4 -290

DHT 2nd RCT - Total -117 -153 -174 -52 -4 -501

DHT Arom Saturation 0 0 0 0 0 0

NHT - Total Fd 0 0 0 -4 0 -4

CGH - Generic -12 -19 -19 -6 0 -57

CGH - Olefin Sat'n 0 0 0 0 0 0

FCCU Fd HDT 37 0 0 0 0 37

LSR Splitter 0 41 38 0 0 80

LSR Bz Saturator 0 0 0 0 0 0

Reformate Saturator -4 -4 1 0 0 -7

Reformate Splitter -12 -12 3 0 0 -21

SDA 0 0 0 0 0 0

MTBE 0 0 0 0 0 0

TAME 0 0 0 0 0 0

Hydrogen Plant - Total MSCF -72 11 109 -47 0 1

Lube Unit 0 0 0 0 0 0

Sulfur Plant -128 0 0 -204 -71 -402

Merox Jet 0 0 0 0 0 0

Merox Diesel 0 0 0 0 0 0

BTX Reformer - Tower feed 0 -1 -1 0 0 -2

BTX Reformer - Extract feed -1 0 0 0 0 -1
875

Table 4.4-58.

Change in Refinery Unit Investments by PADD

for the High Ethanol Control Case Relative to the RFS 1 Reference Case

(million dollars/year)

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA California US Total

Crude Tower 0 -997 0 0 0 -997

Vacuum Tower 0 -591 0 0 0 -591

Sats Gas Plant 12 0 -101 0 55 -34

Unsats Gas Plant -10 0 0 0 0 -10

FCC DeC5 Tower 0 0 0 0 0 0

FCC 0 0 0 -193 0 -193

FCC Splitter 1 -7 0 0 0 -6

Hydrocracker -631 0 0 -505 0 -1,137

H-Oil Unit 0 0 0 0 0 0

Delayed Coker 0 0 0 -592 0 -592

Visbreaker 0 0 0 0 0 0

Thermal Naphtha Splitter -3 -7 0 -3 0 -13

CRU Reformer 0 0 0 0 0 0

SRU Reformer 0 0 0 0 -4 -4

BTX Reformer 0 -93 -231 0 0 -324

C4 Isomerization 0 0 0 0 8 8

C5/C6 Isomerization 227 0 0 0 0 227

HF Alkylation 0 0 0 0 0 0

H2SO4 Alkylation 16 0 0 -70 0 -54

Dimersol 0 0 0 0 0 0

Cat Poly -1 0 0 0 0 -1

Isooctane 0 0 0 0 0 0

DHT - Total -1,367 0 -702 -539 -96 -2,703

DHT 2nd RCT - Total -904 -998 -840 -312 -21 -3,076

DHT Arom Saturation 0 0 0 0 0 0

NHT - Total Fd 0 0 0 -46 0 -46

CGH - Generic -58 -63 -92 -25 0 -238

CGH - Olefin Sat'n 0 0 0 0 0 0

FCCU Fd HDT 555 0 0 0 0 555

LSR Splitter 0 29 18 0 0 47

LSR Bz Saturator 0 0 0 0 0 0

Reformate Saturator -46 -19 4 0 0 -60

Reformate Splitter -20 -16 2 0 0 -34

SDA 0 0 0 0 0 0

MTBE 0 0 0 0 0 0

TAME 0 0 0 0 0 0

Hydrogen Plant - Total MSCF -255 69 274 -171 0 -84

Lube Unit 0 0 0 0 0 0

Sulfur Plant -3 0 0 -2 -2 -7

Merox Jet 0 0 0 0 0 0

Merox Diesel 0 0 0 0 0 0

BTX Reformer - Tower feed 0 -2 -1 0 0 -3

BTX Reformer - Extract feed -3 0 0 0 0 -3

Total -2,491 -2,694 -1,669 -2,459 -59 -9,373
876

Table 4.4-59.

Ethanol and Gasoline Volume, Quality and Energy Density by Gasoline Type at the PADD

Terminal for the High Ethanol Control Case Relative to the RFS 1 Reference Case

Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control

RFG

Total ('000 BPD) 1,366,663 834,232 314,578 216,604 452,862 324,886 0 0 1,281,031 1,305,768 3,415,134 2,681,490

Ethanol ('000 BPD) 138,034 84,266 31,773 21,879 45,739 32,817 0 0 129,386 131,896 344,932 270,858

RVP (psi) 10.7 10.3 10.3 12.5 9.6 11.3 0.0 0.0 9.5 9.4 10.1 10.1

Sulfur (ppm) 23.8 23.4 20.1 24.5 23.5 22.4 0.0 0.0 8.7 8.9 17.8 16.3

Density 258.5 259.4 256.2 257.1 259.1 258.7 0.0 0.0 258.5 258.9 258.4 258.9

Octane (R+M/2) 88.1 88.6 88.0 88.5 88.0 88.5 0.0 0.0 87.6 87.7 87.9 88.1

Aromatics (vol%) 19.9 20.0 18.2 20.0 19.5 19.8 0.0 0.0 22.2 22.2 20.5 21.1

Benzene (vol%) 0.57 0.58 0.57 0.57 0.57 0.54 0.00 0.00 0.54 0.55 0.56 0.56

Olefins (vol%) 13.1 14.0 8.0 11.4 9.3 13.7 0.0 0.0 5.7 5.7 9.3 9.7

Oxygen (wt%) 3.7 3.7 3.8 3.8 3.7 3.7 0.0 0.0 3.7 3.7 3.7 3.7

E200 (vol%) 55.7 56.7 62.5 60.7 54.5 54.2 0.0 0.0 58.5 58.2 57.2 57.4

E300 (vol%) 93.9 95.1 91.5 95.1 93.9 95.1 0.0 0.0 86.2 86.2 90.8 90.7

Energy (MMBtu/Bbl) 4.963 4.956 4.864 4.895 5.027 4.951 0.000 0.000 4.988 4.997 4.971 4.970

CG

Total ('000 BPD) 1,841,024 1,841,867 1,570,973 1,447,215 1,262,249 1,086,759 687,028 659,598 111,394 118,302 5,472,668 5,153,740

Ethanol ('000 BPD) 1,842 186,047 72,606 146,183 8,040 109,774 26,352 66,626 5,849 11,950 114,689 520,580

RVP (psi) 10.5 11.7 11.8 11.4 10.7 10.4 11.3 11.2 10.8 10.6 11.0 11.3

Sulfur (ppm) 27.8 24.1 25.3 24.7 27.3 22.6 24.6 28.0 26.4 25.6 26.6 24.5

Density 259.3 260.0 258.8 259.3 259.4 260.8 256.8 259.1 260.8 262.5 258.9 259.9

Octane (R+M/2) 88.0 88.0 88.0 88.1 88.0 88.2 86.8 87.0 88.0 89.2 87.8 88.0

Aromatics (vol%) 28.7 22.5 26.3 22.5 28.4 22.4 19.3 17.7 26.8 26.7 26.7 22.0

Benzene (vol%) 0.65 0.53 0.64 0.53 0.65 0.51 0.80 1.03 0.62 0.57 0.67 0.59

Olefins (vol%) 15.2 13.9 11.2 10.9 16.0 12.6 8.0 9.5 18.6 16.1 13.4 12.3

Oxygen (wt%) 0.0 3.7 1.7 3.7 0.2 3.7 1.4 3.7 1.9 3.7 0.8 3.7

E200 (vol%) 45.2 53.5 52.9 58.9 45.9 51.4 58.9 63.5 54.5 58.5 49.5 56.0

E300 (vol%) 93.9 80.4 91.5 95.0 93.9 79.2 93.9 95.8 86.2 86.2 93.0 86.3

Energy (MMBtu/Bbl) 5.133 4.974 5.007 4.938 5.127 4.998 5.004 4.930 4.998 4.929 5.077 4.962

E85

Total ('000 BPD) 0 669,821 0 387,549 0 421,660 0 70,039 0 54,710 0 1,603,779

Ethanol ('000 BPD) 0 575,048 0 332,715 0 361,999 0 60,129 0 46,969 0 1,376,860

RVP (psi) 0.0 9.9 0.0 10.3 0.0 10.7 0.0 11.9 0.0 12.0 0.0 10.4

Sulfur (ppm) 0.0 10.1 0.0 9.7 0.0 9.6 0.0 9.0 0.0 8.8 0.0 9.8

Density 0.0 268.9 0.0 268.1 0.0 267.8 0.0 266.6 0.0 266.2 0.0 268.2

Octane (R+M/2) 0.0 107.7 0.0 107.7 0.0 107.8 0.0 107.9 0.0 107.9 0.0 107.7

Aromatics (vol%) 0.0 1.2 0.0 0.8 0.0 0.7 0.0 0.1 0.0 0.0 0.0 0.9

Benzene (vol%) 0.00 0.03 0.00 0.02 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.03

Olefins (vol%) 0.0 2.4 0.0 2.0 0.0 2.1 0.0 2.8 0.0 1.2 0.0 2.2

Oxygen (wt%) 0.0 30.5 0.0 25.7 0.0 30.6 0.0 28.6 0.0 30.8 0.0 29.3

E200 (vol%) 0.0 126.8 0.0 127.7 0.0 128.0 0.0 129.8 0.0 129.6 0.0 127.6

E300 (vol%) 0.0 95.6 0.0 95.9 0.0 96.0 0.0 96.6 0.0 96.6 0.0 95.8

Energy (MMBtu/Bbl) 0.000 3.656 0.000 3.642 0.000 3.633 0.000 3.605 0.000 3.596 0.000 3.642

All Mogas

Total BPD 3,207,687 3,345,919 1,885,551 2,051,367 1,715,111 1,833,305 687,028 729,637 1,392,425 1,478,780 8,887,802 9,439,008

Ethanol BPD 139,876 845,361 104,378 500,777 53,780 504,590 26,352 126,755 135,236 190,815 459,622 2,168,297

RVP 10.6 11.0 11.5 11.3 10.4 10.6 11.3 11.3 9.6 9.6 10.6 10.8

Sulfur ppm 26.1 21.0 24.5 21.8 26.3 19.5 24.6 26.1 10.1 10.3 23.2 19.6

Density 258.9 261.7 258.4 260.8 259.3 262.1 256.8 259.8 258.7 259.4 258.7 261.1

R+M/2 88.0 92.1 88.0 91.9 88.0 92.7 86.8 89.0 87.7 88.5 87.8 91.4

Aromatics 24.9 17.6 24.9 18.1 26.0 16.9 19.3 16.0 22.6 21.8 24.3 18.1

Benzene 0.62 0.44 0.63 0.44 0.63 0.40 0.80 0.93 0.55 0.53 0.63 0.49

Olefins 14.3 11.6 10.7 9.3 14.2 10.3 8.0 8.9 6.7 6.4 11.8 9.8

Oxygen (wt%) 1.6 9.2 2.0 8.0 1.2 10.0 1.4 6.2 3.6 4.7 1.9 8.2

E200 49.7 69.0 54.5 72.1 48.1 69.5 58.9 69.9 58.1 60.9 52.5 68.6

E300 93.9 87.1 91.5 95.1 93.9 85.9 93.9 95.9 86.2 86.6 92.2 89.2

Energy (MMBtu/Bbl) 5.061 4.706 4.983 4.689 5.101 4.676 5.004 4.803 4.988 4.939 5.036 4.740

PADD 1 PADD 2 PADD 3 PADD 4/5 ex CA CA US
877

Chapter 5: Economic Impacts and Benefits

5.1 Agricultural Impacts

5.1.1 Models Utilized

EPA used a suite of tools to model the potential domestic and international impacts of the

RFS2 renewable fuel volumes on the agricultural sector. The Forest and Agricultural Sector

Optimization Model (FASOM), developed by Professor Bruce McCarl of Texas A&M

University and others, provides detailed information on domestic agricultural and greenhouse gas

impacts of renewable fuels. The Food and Agricultural Policy Research Institute (FAPRI) at

Iowa State University and the University of Missouri-Columbia maintains a number of

econometric models that are capable of providing detailed information on impacts on

international agricultural markets from the wider use of renewable fuels in the U.S. EPA worked

directly with the Center for Agriculture and Rural Development (CARD) at Iowa State

University to implement the FAPRI model to analyze the impacts of the RFS2 on the global

agriculture sector. Thus, this model will henceforth be referred to as the FAPRI-CARD model.

FASOM is a long-term economic model of the U.S. forest and agricultural sectors that

maximizes the net present value of the sum of producer and consumer surplus across the two

sectors over time subject to market, technology, and other constraints. Using a number of inputs,

the agricultural component of FASOM determines the equilibrium combination of crops,

livestock, and processed agricultural products that would be produced in the U.S. for each model

solution period. In each model simulation, crops and livestock compete for price sensitive inputs

such as land and labor at the regional level. The cost of these and other inputs are used to

determine the price and level of production of primary commodities (e.g., field crops, livestock,

and biofuel products). FASOM also estimates prices using costs associated with the processing

of primary commodities into secondary products (e.g., converting livestock to meat and dairy

products, crushing soybeans to soybean meal and oil). FASOM does not capture short-term

fluctuations (i.e., month-to-month, annual) in prices and production, however, as it is designed to

identify long-term trends.

The FASOM model also contains a forestry component, which details forest acres across

the U.S., as well as production of forestry products. Running the forestry and agriculture

components of the model simultaneously shows the interaction between these two sectors as they

compete for land, as well as the effect on products and prices in each respective sector. In total,

FASOM includes a representation of seven major land use categories, including cropland,

cropland pasture, forestland, forest pasture, rangeland, developed land, and acres enrolled in the

Conservation Reserve Program (CRP). More information on these land categories can be found

below in Chapter 5.1.2.

FASOM uses supply and demand curves for the 11 major U.S. domestic regions,256

which are calibrated to historic price and production data. FASOM also includes detailed supply

256 U.S. regions consist of the Pacific Northwest (West and East), Pacific Southwest, Rocky Mountains, Great

Plains, Southwest, South Central, Corn Belt, Lake States, Southeast, and the Northeast.
878

and demand data for corn, wheat, soybeans, rice and sorghum across 37 foreign regions.257

FASOM contains transportation costs to all regions and then uses all of this information to solve

for the level of U.S. exports where prices are then equated in all markets.

We chose to use FASOM to model the full potential impacts on the domestic agricultural

and forestry sectors given higher renewable fuel volumes, in part because FASOM also provides

detailed greenhouse gas information resulting from these changes (see Chapter 2 of this RIA for

more information). FASOM does not model agricultural sector changes internationally,

however. Therefore, we are working with the FAPRI-CARD modeling system to better

understand international agricultural impacts. Additional details on the FASOM model are

included in the docket.258

The FAPRI-CARD models are a system of econometric models covering many

agricultural commodities. These models capture the biological, technical, and economic

relationships among key variables within a particular commodity and across commodities. They

are based on historical data analysis, current academic research, and a reliance on accepted

economic, agronomic, and biological relationships in agricultural production and markets. The

international modeling system includes international grains, oilseeds complex, biofuel (ethanol

and biodiesel), sugar, cotton, dairy, and livestock models. In general, for each commodity

sector, the equilibrium economic relationship that supply equals demand is solved by

determining a market-clearing price for the commodity. In countries where domestic prices are

not solved endogenously, these prices are modeled as a function of the world price using a price

transmission equation. Since econometric models for each sector are linked, changes in one

commodity sector will impact other sectors. Elasticity values for supply and demand responses

are based on econometric analysis and on consensus estimates. Additional details on the FAPRICARD models are included in the docket.259

Agricultural and trade policies for each commodity in a country are included in the

models to the extent that they affect the supply and demand decisions of the economic agents.

These policies include taxes on exports and imports, tariffs, tariff rate quotas, export subsidies,

intervention prices, and set-aside rates. The FAPRI-CARD models assume that existing

agricultural and trade policy variables will remain unchanged in the outlook period.

We recognize that there are inherent challenges in reconciling the results from two

different models, however using two models provides a more complete and robust analysis than

either model would be able to provide alone. As described in Chapter 5.1.3, we have attempted

to align as many of the key assumptions as possible to get a consistent set of modeling results.

However, there are structural differences in the models that account for some of the differences

257 FASOM Foreign Regions include: the European Economic Community, North Central Europe, Southwest

Europe, Eastern Europe, Adriatic, Eastern Mediterranean, Former Soviet Union, North Africa, East Africa, West

Africa, South Africa, Red Sea, Iran, India, Taiwan, Japan, South Korea, North Korea, China, Bangladesh, Indonesia,

Myanmar, Pakistan, Philippines, Thailand, Vietnam, West Asia, Southeast Asia, Australia, Caribbean, Eastern

Mexico, Eastern South America, Western South America, Argentina, Brazil, Canada, Other.

258 Beach, Robert; McCarl, Bruce, U.S. Agricultural and Forestry Impacts of the Energy Independence and Security

Act: FASOM Results and Model Description, RTI International, January, 2010.

259 Technical Report: An Analysis of EPA Renewable Fuel Scenarios with the FAPRI-CARD International Models,

CARD Staff, December, 2009.
879

in the model results. For example, since FASOM is a long-term dynamic optimization model,

short-term spikes are smoothed out over the five year reporting period. In comparison, the

FAPRI-CARD model captures annual fluctuations that may include short-term supply and

demand responses. In addition, some of the discrepancies may be attributed to different

underlying assumptions pertaining to elasticities of supply and demand for different

commodities. These differences, in turn, affect projections of imports and exports, acreage

shifting, and total consumption and production of various commodities. Some of the differences

in results are described in more detail in the following sections.

5.1.2 Model Modifications Since the RFS2 Proposal

Since the analysis for the RFS2 proposal was completed, a number of updates have been

made to the FASOM and FAPRI-CARD models to reflect comments received and the

availability of new data. The major changes to the agricultural modeling framework include

adding price-induced yields, updating cellulosic yields, updating distillers grains replacement

rates of corn and soybean meal in animal feed, adding corn oil from extraction as a biodiesel fuel

pathway, adding additional land categories in the FASOM model, and adding a detailed Brazil

module to the FAPRI-CARD modeling system.

5.1.2.1 Price-Induced Yields

The FAPRI-CARD model includes elasticity factors for yields to respond to changes in

prices over time both in the U.S. and internationally. As the price of corn increase, farmers, seed

producers, and others involved in crop production have an additional incentive to improve yields.

The price induced yield phenomenon is partially offset by the reduced yields that result from

expanding on to new crop acres, which is often referred to as extensification. However, the

price-induced yield impact is projected to be larger than the extensification effect. For example,

in 2022 the price of corn increases by $0.10 (3.3 percent) in the U.S. In response, the average

corn yield in 2022 increases by 0.4 bushels per acre (0.4 percent). In another example, in 2022,

world corn prices increase by $0.12 per bushel (3.1 percent). As a result, corn yields in China

increased from 101.9 bushels per acre in the Reference Case to 102.3 bushels per acre in the

Control Case in 2022, a 0.3 percent improvement. Additional details on the methodology behind

the estimation of price-induced yields can be found in the FAPRI-CARD Technical Report in the

docket.260 Additional international results can be found below in this chapter.

5.1.2.2 Cellulosic Yields

Based on new research conducted by the National Renewable Energy Laboratory

(NREL), we have updated the rates of conversion for cellulosic feedstocks into ethanol in the

FASOM model.261 As a result of these changes, the gallons per ton yields for switchgrass and

several other feedstocks increased from the values used in the RFS2 proposal, while the yields

for corn residue and several other feedstocks decreased slightly from the values used in the

proposal. For additional detail, please see chapters 1 and 2. In addition, we also updated our

260 Technical Report: An Analysis of EPA Renewable Fuel Scenarios with the FAPRI-CARD International Models,

CARD Staff, December, 2009.

261 Tao, Aden, Technoeconomic Modeling to Support the EPA Notice of Proposed Rulemaking (NOPR), Nov. 2008.
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switchgrass production yields based on new work conducted by the Pacific Northwest National

Laboratory (PNNL).262 In the analysis for the RFS2 proposal, national average switchgrass

yields were 6.3 wet tons per acre in the Control Case in 2022. For the final rulemaking analysis,

national average switchgrass yields are 7.8 wet tons per acre in the Control Case in 2022. For

more information on switchgrass yields, please refer to the FASOM technical documentation.263

5.1.2.3 Distillers Grains Replacement Rates

One of the byproducts of the dry mill ethanol processes is the creation of distillers grains

with solubles (DGS). This byproduct is a common source of animal feed, and can be used to

feed beef cattle, dairy cows, swine, and poultry. When DGS are used in feed, they can replace

other sources of feed that would otherwise be used, such as corn and soybean meal. Based on

research conducted by Argonne National Laboratory,264 one pound of DGS can be used to

replace 1.196 pounds of total corn and soybean meal for various beef cattle and dairy cows due

to the ability of this livestock to take advantage of the higher nutritional content of DGS per

pound compared to corn and soybean meal. Current livestock production practices use 1 pound

of DGS to replace 1 pound of a combination of corn and soybean meal. For our analysis, the

replacement rates for corn and soybean meal increase steadily over time from a 1:1 replacement

rate, to the maximum technological replacement rate of 1:1.196 in 2015 for beef cattle and dairy

cows. A replacement rate of 1:1 is used for swine and poultry throughout the time period

analyzed. Based on work by Shurson,265 DGS produced in combination with the corn oil

fractionation/extraction processes has different nutritional characteristics than traditional DGS

containing higher levels of oil. According to this research, fractionated/extracted DGS replaces a

slightly higher proportion of soybean meal rather than corn compared to traditional DGS when

used for swine and poultry feed (although the total displacement rate for both types of DGS is

1:1). We have therefore used these modified replacement rates of corn and soybean meal for

fractionated/extracted DGS fed to swine and poultry. The Shurson paper does not include

changes in replacement rates of corn and soybean meal for beef cattle and dairy cows, so

replacement rates are assumed to be the same for traditional DGS and fractionated/extracted

DGS. Maximum inclusion rates for DGS in feed are 40 to 50 percent for beef, 27 to 30 percent

for dairy, and 21 to 25 percent for swine and poultry.

5.1.2.4 Corn Oil Extraction as Biodiesel Pathway

For the RFS2 analysis, both FASOM and FAPRI-CARD explicitly model corn oil

withdrawn from the extraction as a source for biodiesel. Based on engineering research (see

Chapter 1.4) regarding expected technological adoption, it is estimated that 70 percent of dry

mill ethanol plants will withdraw corn oil via extraction from distillers grains, resulting in corn

262 Thomson, A.M., R.C. Izarrualde, T.O. West, D.J. Parrish, D.D. Tyler, and J.R. Williams. 2009. Simulating

Potential Switchgrass Production in the United States. PNNL-19072. College Park, MD: Pacific Northwest

National Laboratory.

263 Beach, Robert; McCarl, Bruce, U.S. Agricultural and Forestry Impacts of the Energy Independence and Security

Act: FASOM Results and Model Description, RTI International, January, 2010.

264 Salil Arora, May Wu, and Michael Wang, “Update of Distillers Grains Displacement Ratios for Corn Ethanol

Life-Cycle Analysis,” September 2008. See http://www.transportation.anl.gov/pdfs/AF/527.pdf

265 Shurson, The Value of High-Protein Distillers Coproducts in Swine Feeds, Distillers Grains Quarterly, First

Quarter 2006.
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oil that is non-food grade and can only be used as a biodiesel source; 20 percent will withdraw

corn oil via fractionation (prior to the creation of distillers grains), resulting in corn oil that is

food-grade; and 10 percent will do neither extraction or fractionation.

5.1.2.5 Detailed Land Use Categories

Since the proposal, the FASOM model has been updated to include several additional

land use categories covering the majority of the U.S. land base. These categories are based on

the USDA National Agriculture Statistics Service (NASS) data. These land classifications

enable the FASOM model to explicitly link the interaction between livestock, pasture land,

cropland, and forest land. For each of these categories, FASOM accounts for how much is

actively used in production, and how much idled, in a particular time period. A brief description

of these categories is described below. Additional detail on the land categories are included in

the technical report on the FASOM model included in the docket.266

 Cropland is actively managed cropland, used for both traditional crops (e.g., corn and

soybeans) and dedicated energy crops (e.g., switchgrass).

 Cropland pasture is managed pasture land used for livestock production, but which can

also be converted to cropland production.

 Forestland contains a number of sub-categories, tracking the number of acres of private

forestland existing at the starting point of the model that remain in standing forests (i.e.,

have not yet been harvested), the number of acres harvested, the number of harvested

acres that are reforested, and the area converted from other land uses (afforested). Public

forestland area is not explicitly tracked because it is assumed to remain constant over

time, although exogenous estimates of forest products production from these lands are

included in the model.

 Forest pasture is unmanaged pasture land with varying amounts of tree cover that can be

used for livestock production. A portion of this land may be used for timber harvest.

 Rangeland is unmanaged land that can be used for livestock grazing production. While

the amount of rangeland idled or used for production may vary, it is assumed that

rangeland may not be used for any other purpose than for animal grazing due to its low

productivity. In addition, much of the rangeland in the U.S. is publicly owned.

 Developed (urban) land is assumed to have an inherently higher value than land used for

any other use. Thus, the rate of urbanization is assumed to be exogenous based on

projections of population and income growth and does not change between the cases

analyzed.

 Conservation Reserve Program (CRP) refers to land that is voluntarily taken out of

crop production and placed in the USDA CRP. Land in the CRP is generally marginal

cropland retired from production and converted to vegetative cover, such as grass, trees,

or woody vegetation to conserve soil, improve water quality, enhance wildlife habitat, or

produce other environmental benefits.

266 Beach, Robert; McCarl, Bruce, U.S. Agricultural and Forestry Impacts of the Energy Independence and Security

Act: FASOM Results and Model Description, RTI International, January, 2010.
882

5.1.2.6 Brazil Module

In the FAPRI-CARD modeling system, all non-U.S. countries are analyzed at the

national level, with the exception of Brazil. Due to the importance of Brazil in determining the

international impacts of increased biofuel demand, including the increase in U.S. demand for

imported ethanol, the FAPRI-CARD model was updated to include additional agricultural detail

in Brazil. The FAPRI-CARD model now includes an integrated Brazil module that provides

additional detail on agricultural land use in Brazil for six geographic regions: the Amazon

Biome, Northeast (NE), North-Northeast Cerrados (North-NE Cerrados), Central-West Cerrados,

Southeast (SE), and the South. The Brazil module explicitly models the competition between

cropland and pastureland used for livestock production in each region. In addition, the Brazil

module allows for region-specific agriculture practices such as double cropping and livestock

intensification in response to higher commodity prices. This level of detail allows for a more

refined analysis of land use change and economic impacts in Brazil than a national-level

analysis. For more detail on the Brazil module and its development, please refer to the FAPRICARD model technical report in the docket.267

267 Technical Report: An Analysis of EPA Renewable Fuel Scenarios with the FAPRI-CARD International Models,

CARD Staff, December, 2009
883

Figure 5.1-1.

Map of Brazil by Geographic Region in FAPRI-CARD

5.1.3 Key Modeling Assumptions

To analyze the U.S. and international agriculture sectors impact of the RFS2 renewable

fuel volumes, a number of key assumptions and input parameters were standardized in the

FASOM and FAPRI-CARD models. These assumptions were developed with the input of other

government agencies, such as USDA and DOE. As shown in Table 5.1-1, key assumptions

include corn and soybean yields,268 corn ethanol dry and wet mill plant processing energy use,

corn ethanol yields, corn ethanol by-product use, estimated corn stover yields, domestic energy

prices,269 and others. For other estimates of input parameters, we relied on external expertise,

such as the Greenhouse gases, Regulated Emissions and Energy use in Transportation (GREET)

268 USDA Agricultural Projections to 2018 (OCE-2009-1), February, 2008.

269 Energy prices in all cases are based on the April Release of the 2009 Annual Energy Outlook, published by the

Energy Information Administration (EIA) within DOE. Prices used include Gasoline, Diesel, E85, Coal, and

Electricity. See: http://www.eia.doe.gov/oiaf/servicerpt/stimulus/index.html
884

model;270 the Assessment System for Population Exposure Nationwide (ASPEN) model;271 and

the Agriculture Resource Management Survey (ARMS).272 Additional details on the

assumptions included in FASOM273 and FAPRI-CARD274 are included in the docket.

270 The GREET model is run by Argonne National Laboratory at the Department of Energy. GREET can simulate

more than 100 fuel production pathways and more than 80 vehicle/fuel systems.

271 ASPEN is a computer simulation model used to estimate toxic air pollutant concentrations is called the

Assessment System for Population Exposure Nationwide. This model is based on the EPA’s Industrial Source

Complex Long Term model (ISCLT) which simulates the behavior of the pollutants after they are emitted into the

atmosphere. ASPEN uses estimates of toxic air pollutant emissions and meteorological data from National Weather

Service Stations to estimate air toxics concentrations nationwide.

272 ARMS is sponsored by the Economic Research Service (ERS) and National Agricultural Statistics Service

(NASS) at USDA, and provides observations of field-level farm practices, the economics of the farm business, and

the characteristics of the American farm household.

273 Beach, Robert; McCarl, Bruce, U.S. Agricultural and Forestry Impacts of the Energy Independence and Security

Act: FASOM Results and Model Description, RTI International, January, 2010.

274 Technical Report: An Analysis of EPA Renewable Fuel Scenarios with the FAPRI-CARD International Models,

CARD Staff, December, 2009.
885

Table 5.1-1.

Agriculture Model Assumptions

Assumption Notes

Feedstock Production

Prices for Gasoline, Diesel, E85, Coal, and Electricity

used in all volume cases AEO 2009, April Release

U.S. national average corn yields are approximately

170 bu/acre in 2017 and 180 bu/acre in 2022 (a 1.6%

annual increase over the baseline year)

U.S. national average soybean yields are

approximately 50 bu/acre in 2022 (a 0.4% annual

increase)

Consistent with USDA projections

(http://www.ers.usda.gov/publications/oce081/)

International corn yields increasing over time, for

example:

Argentina ~134 bu/acre in 2022 (a 1.2% annual

increase)

Brazil ~72 bu/acre in 2022 (a 1.7% annual increase)

International soybean yields increasing over time, for

example:

Argentina ~46 bu/acre in 2022 (a 0.9% annual

increase)

Brazil ~46 bu/acre in 2022 (a 0.9% annual increase)

Price-Induced Yields, for example:

World Price of Corn increases by 3.1% from the

Reference Case to the Control Case in 2022;

Corn Yields in China increases by 0.3% from the

Reference Case to the Control Case in 2022

FAPRI-CARD Models

High Yield sensitivity runs for corn and soybeans:

For example, U.S. High Yields in 2022:

Corn: approximately 232 bu/acre (28% higher than

Base Yield)

Soybeans: approximately 61 bu/acre (31% higher

than Base Yield)

Similar yield increases for top producers of corn

(China, Mexico, EU, Argentina, Brazil) and soybeans

(China, Argentina, Brazil)

FASOM and FAPRI-CARD Models

Represented as increases in technological rates

of progress, no additional inputs required

Corn residue removal rates of 50% are allowed for no

till practices; 35% removal rate allowed for reduced till

practices (no removal from conventional till)

Derived from Graham et. al., Agronomy Journal,

99:1–11 (2007). “Current and Potential U.S. Corn

Stover Supplies.” and Perlack, R. D., L. L.

Wright, A. F. Turhollow, R. L. Graham, B. J.

Stokes, and D. C. Erbach. 2005. Biomass as

Feedstock for a Bioenergy and Bioproducts

Industry: the Technical Feasibility of a Billion-ton

Annual Supply. Report prepared for the U.S.

Department of Energy and the U.S. Department

of Agriculture.
886

Updated Conversion Rates (gallons per dry ton) for all

cellulosic ethanol feedstock sources.

For example:

Proposal Analysis:

Corn Resiude: 94.2 gallons/dry ton

Switchgrass: 78.9 gallons/dry ton

Final Rule Analysis:

Corn Resiude: 92.3 gallons/dry ton

Switchgrass: 92.3 gallons/dry ton

Based on NREL Research

Tao, Aden, Technoeconomic Modeling to

Support the EPA Notice of Proposed Rulemaking

(NOPR), Nov. 2008.

Switchgrass Yields by Region in the U.S. Based on preliminary PNNL Research

Non-Food Grade (NFG) Corn Oil modeled in FASOM

and FAPRI-CARD as a biodiesel feedstock.

NFG Corn Oil is a byproduct of the extraction and

fractionation processes of dry mill ethanol plants.

Based on engineering cost projections and

expected rates of technology adoption. See

Chapter 1 of the RIA.

By 2022: 70% of dry mill ethanol plants will

conduct extraction, 20% will conduct

fractionation, and 10% will do neither

The Conservation Reserve Program (CRP) has a

maximum limit of 32 million acres enrolled in the

program at any given time

2008 Farm Bill

USDA baseline assumptions

Fertilizer Use

U.S. nitrogen application rate for corn is

approximately 136 lbs/acre in the corn belt in 2022

U.S. phosphorous application rate for corn is

approximately 28 lbs/acre in the corn belt in 2022

Based on ARMS data, adjusted for differences in

regions and irrigation practices

For U.S. assume higher yields require no increase in

fertilizer use

Based on USDA baseline assumptions

This holds for all farming rotations (e.g., corn /

soybean and corn / corn) and land types (e.g.,

prime and marginal land); see below for stover

removal impacts

Nitrogen nutrient replacement application = 7 lbs/ton

corn residue removed

Phosphorous nutrient replacement application = 3.6

lbs/ton corn residue removed

These numbers come from the Argonne National

Lab Report, Fuel Cycle Assessment of Selected

Bioethanol Production Pathways in the United

States. (November 7, 2006). (Used and cited by

GREET)

Processing

1 bushel of corn produces 17 lbs of dried distillers

grains (dry tons).

1 pound of DGS substitutes 1.196 pounds of corn and

soybean meal feed for beef cattle, dairy cows by 2015

1 pound of DGS substitutes 1 pound of corn and

soybean meal feed for swine and poultry, with

adjustments for fractionated/extracted DGS

www.ethanol.org

Argonne National Laboratory: Update of Distillers

Grains Displacement Ratios for Corn Ethanol

Life-Cycle Analysis,” September 2008

Shurson: The Value of High-Protein Distillers

Coproducts in Swine Feeds, Distillers Grains

Quarterly, First Quarter 2006.

Projected crop yields, both domestically and internationally, are an important factor in the

analysis of increased renewable fuel volumes. The U.S. yields presented in Table 5.1-1 are

based on the USDA projections through 2018 (the last year of the USDA baseline projections

report) and then extrapolated out to 2022. The U.S. yields in this table represent the national

weighted average yields, with yields varying across regions. Regional yields are based on
887

historical averages for the region. Although the initial crop yields vary by region, the regional

yield for each crop is increased at the same crop-specific annual percentage rate. For instance,

FASOM assumes the rate of increase for corn yields are 1.6 percent per year. The rates of

increase are assumed to be the same in both the AEO 2007 Reference Case and the EISA Control

Case.

The international crop yields included in FAPRI-CARD are different for each country

and for each crop. The FAPRI-CARD model bases each country’s crop yield on historical trends

and projects this technical rate of progress into the future. As described in the previous section,

the FAPRI-CARD model also incorporates yield responses to changes in price and for expansion

onto marginal lands. Examples of how yields vary by region and crop are included in Table 5.1-

1.

For the lifecycle analysis, sensitivity runs were conducted in both FASOM and FAPRICARD to observe the effects of higher yields for both corn and for soybeans. The assumption

behind these high-yield runs is that the technological rate of progress over time is higher for corn

and soybeans than compared to our “base yield” modeling efforts used for the rulemaking

impacts. This increase in the technological rate of progress begins in 2012 in the FASOM

model, and in 2010 in the FAPRI-CARD model (the next future time period in each respective

model). By 2012, Corn yields in the U.S. are 7.1 percent higher in the High Yield Control Case

than in the Base Yield Control Case. By 2017, it is 18 percent higher, and by 2022 it is 30

percent higher. Similarly, soybean yields in the U.S. are 7.8 percent higher in 2012, 20 percent

higher in 2017, and 31 percent higher in the High Yield Control Case than in the Base Yield

Control Case. In the FAPRI-CARD model, similar increases in technological rates of progress

for corn were applied to other top corn producers (China, Mexico, the EU, Argentina, and

Brazil), and likewise for other top soybean producers (China, Argentina, and Brazil). Results

from these high yield sensitivity runs can be found in Chapter 2 of the RIA. Table 5.1-2 lists the

yields for the top producers of corn and soybeans in the Control Case in 2022, both for the

“base” results and for the “high-yield” results. For overall impacts of the “high-yield” sensitivity

model runs, please refer to Chapter 2.
888

Table 5.1-2.

Corn and Soybean Yields of Top Producers in the Control Case in 2022

with “Base Yield” and “High-Yield” Sensitivity Runs

(bushels per acre)

Corn

Country Base Yield High Yield % Difference

U.S. 181.2 231.6 27.9%

China 102.3 129.9 27.0%

Mexico 55.8 70.9 27.0%

EU 110.5 140.4 27.0%

Argentina 133.9 170.1 27.0%

Brazil * 72.2 81.8 13.3%

Soybeans

Country Base Yield High Yield % Difference

U.S. 46.0 60.5 31.4%

China 29.8 38.9 30.4%

Argentina 46.1 60.1 30.4%

Brazil * 46.3 57.8 25.0%

* Note: Yields in individual regions in Brazil are 27% higher than in the Base yield, similar to other countries. However, since some

regions are more productive than others, the regional distribution of soybean production is different between the “Base” and “HighYield” model runs. This results in the overall yields of corn and soybeans in Brazil in the “High Yield” sensitivity run to not be the

same percentage higher than the “Base” model run compared to other countries.

For cellulosic biofuels from corn residues, the current assumptions in FASOM for residue

removal rates are based on the Graham et. al. paper275 and the Perlack et. al. study.276 This

approach uses a maximum percent removal of residues on acres based on tillage practices.277

Although we requested comment on whether a better metric is the minimum amount of mass that

must remain on an acre of land to prevent runoff and maintain soil carbon levels, we did not

receive sufficient data to incorporate this approach into our modeling framework.278

FASOM assumes fertilizer application rates do not increase over time in proportion to the

increase in yields (i.e., delinks fertilizer application rates and crop yield changes through time).

The principal reason for this is USDA data that shows fertilizer application rates per acre

remaining relatively steady for the past 30 years, during which time corn yields have increased

approximately 70 percent.279 However, when residues are removed from the field, some of the

nutrients that are contained in the residue must be replaced through additional fertilizer use. For

the analysis, we assumed that 7 additional pounds of nitrogen and 3.6 pounds of phosphorous

must be applied per ton of corn stover residue removed. 280

275 See also http://www.cpnrd.org/Harvesting%20Stover.pdf

276 Available at http://feedstockreview.ornl.gov/pdf/billion_ton_vision.pdf.

277 Many site specific factors associated with the sustainable removal of residue (e.g., crop type, soil type, soil

fertility, slope, and climate) affect which geographic regions are suitable for crop residue removal. Detailed

modeling of these factors was beyond the scope of this analysis.

278 Wilhelm et. al Corn Stover to Sustain Soil Organic Carbon Further Constrains Biomass Supply, Ag Journal

(2007)

279 Data from the National Agriculture Statistics Service (NASS) at USDA: http://www.nass.usda.gov/

280 Wu, M., M. Wang, and H. Huo. Fuel-Cycle Assessment of Selected Bioethanol Production Pathways in the

United States. ANL/ESD/06-7. November 2006.
889

Lastly, there is a limit to how many acres of cropland can be placed in the Conservation

Reserve Program (CRP).281 CRP is run by the Natural Resources Conservation Service at USDA.

This program is designed to maintain Federal, State, and tribal environmental laws by making

payments to farmers equivalent to the income otherwise earned from developing the land

enrolled in the program. In the 2008 Farm Bill, the number of acres enrolled in the CRP was

given a maximum limit of 32 million acres. Based on input from USDA, we assumed that

USDA will increase payments to maintain 32 million acres in CRP through 2022.

5.1.4 Volumes

For the agricultural sector analysis, we modeled the AEO2007 Reference Case and the

Control Case (i.e., EISA mandated) volumes described in Chapter 1.2. Where possible, we

modeled the same volumes in both FASOM and FAPRI-CARD. However, some of the

projected future sources of renewable fuels are not explicitly included in both models. For

example, since FASOM is a domestic agriculture and forestry model, it cannot explicitly model

U.S. biofuel imports and their impacts on worldwide trade and land use as the FAPRI-CARD

model does. In addition, the FAPRI-CARD model does not currently model cellulosic

renewable fuel feedstock production. Therefore, the cellulosic renewable fuel analysis relies on

results from the FASOM model.282 Neither of the two models used for this analysis — FASOM

or FAPRI-CARD — include biofuel produced from domestic municipal solid waste (MSW).

Thus, for the RFS2 analysis, this biofuel was modeled outside of the agriculture sector. For

more information on how MSW was modeled elsewhere in the RFS2 analysis, please see

Chapters 1 and 2. We estimate that approximately 2.3 Bgal of cellulosic renewable fuels will be

produced from municipal solid waste in 2022.

All the results presented in the following section are relative to the AEO 2007 Reference

Case renewable fuel volumes, which include 12.3 Bgal of grain-based ethanol, 0.1 Bgal of

biodiesel from soybean oil, 0.3 Bgal of biodiesel from waste oils and greases, 0.3 Bgal of

cellulosic ethanol, and 0.6 Bgal of imported ethanol in 2022. The domestic figures are provided

by FASOM and FAPRI-CARD, and all of the international numbers are provided by FAPRICARD. For a more detailed set of results of the agricultural sector impacts of the RFS2 volumes,

see the analytical reports submitted by the FASOM and FAPRI-CARD modeling groups in the

docket of this rule.

For ethanol, we assumed 15 billion gallons (Bgal) of corn ethanol would be produced for

use as transportation fuel in the U.S. by 2022 in the Control Case in both FASOM and FAPRICARD. FASOM modeled an increase of 13.5 Bgal of cellulosic renewable fuel from the

Reference Case in 2022.283 To satisfy the cellulosic renewable fuel requirements, the FASOM

model was allowed to choose how much cellulosic renewable fuel was produced from different

feedstocks, taking account the various harvesting and processing costs and the income the

281 See: http://www.nrcs.usda.gov/programs/CRP/

282 The FAPRI-CARD model was used to estimate the indirect land use effects of additional switchgrass acres on

other crops in the U.S., as shown by the FASOM model, and the resultant impacts on trade and land use worldwide.

Please refer to Chapter 2 of the RIA for more information.

283 FASOM does not include renewable diesel or biomass to liquids as potential cellulosic pathways, therefore all

cellulosic volumes were assumed to be ethanol.
890

agriculture and forestry sectors derive from each feedstock. FASOM projects that 7.9 Bgal of

cellulosic renewable fuel will be produced from switchgrass in 2022, 4.9 Bgal from corn residue,

0.4 Bgal from sugarcane bagasse, and 0.1 billion gallons from forestry logging and milling

residues. FAPRI-CARD modeled an increase of 1.6 Bgal from ethanol imported to the U.S. over

the AEO2007 Reference Case level in 2022.

For biodiesel, both FASOM and FAPRI-CARD modeled an increase of 0.5 Bgal of

biodiesel produced from soybean oil above the AEO2007 Reference Case in 2022. FASOM and

FAPRI-CARD also modeled an increase of 0.1 Bgal of biodiesel produced from various animal

fats, waste oils, and greases to 0.4 Bgal in the Control Case in 2022. Similarly, FASOM and

FAPRI-CARD modeled an increase of 0.6 Bgal of biodiesel from non-food grade corn oil. This

non-food grade corn oil used for biodiesel production is a byproduct of dry mill ethanol plants

that undertake the extraction processes.

Table 5.1-3.

Biofuel Volumes Modeled in 2022

(Billions of Gallons)

Biofuel AEO2007

Reference Case

Control Case Change

Corn Ethanol 12.3 15.0 2.7

Switchgrass Cellulosic Ethanol 0 7.9 7.9

Corn Residue Cellulosic Ethanol 0 4.9 4.9

Sugarcane Bagasse Cellulosic Ethanol 0.2 0.6 0.4

Forest Residue Cellulosic Ethanol 0 0.1 0.1

Imported Ethanol 0.6 2.2 1.6

Total Ethanol (FASOM) 12.5 28.7 16.2

Total Ethanol (FAPRI-CARD) 13.2 17.5 4.3

Soybean Oil Biodiesel 0.1 0.6 0.5

Corn Oil NFG Biodiesel 0.0 0.6 0.6

Biodiesel from Other Fats, Oils, Greases 0.3 0.4 0.1

Total Biodiesel 0.4 1.7 1.3

5.1.5 Domestic Agricultural Impacts

For this economic analysis, the FASOM model is utilized for all domestic agriculture

impacts. Although the FAPRI-CARD models do not provide the same amount of detail on GHG

emissions for the domestic agriculture impacts as the FASOM model, FAPRI-CARD does

estimate some of the same outputs, such as national crop acres, prices, and exports. In this

section, we present both the FASOM and FAPRI-CARD results to demonstrate the range of

potential agricultural impacts. Presenting both sets of results allows for a useful comparison

between the two models, reinforces the accuracy of our domestic analysis, and ensures

consistency when analyzing the impacts of the RFS2 fuel volume requirements on the domestic

and international agriculture markets.
891

5.1.5.1 Commodity Prices

To meet the RFS2 renewable fuel volumes, there are a number of price effects on the

agricultural commodities. For instance, FASOM estimates that the Control Case renewable fuel

volumes result in an increase in the U.S. corn price of $0.27 per bushel (8.2 percent) above the

Reference Case price in 2022. By 2022, FASOM projects that U.S. soybean prices increase by

$1.02 per bushel (10.3 percent) above the Reference Case price. FASOM also projects the price

of soybean oil increases by $183 per ton (37.9 percent) over the 2022 Reference Case price. In

2022, FAPRI-CARD projects that the price of corn increases by $0.10 per bushel (3.3 percent),

the price of soybeans increases by $0.07 per bushel (0.9 percent), and the price of soybean oil

increases by $12.35 (1.6 percent) relative to the AEO2007 Reference Case.

FASOM projects that the price of switchgrass increases by $20.12 per wet ton as a result

of the Control Case renewable fuel volumes in 2022. Similarly, the price of corn residue

increases by $29.48 per wet ton in 2022, relative to the AEO2007 reference case. FASOM also

projects that the Control Case price of sugarcane bagasse increases by $23.27 per wet ton in

2022. By 2022, FASOM projects that hardwood and softwood logging residues are used to

produce cellulosic ethanol, and their prices are $23.22 per wet ton and $18.37 per wet ton,

respectively, in 2022 in the Control Case. These prices do not include the storage, handling, or

delivery costs. Since the FAPRI-CARD models do not explicitly model cellulosic ethanol

production from agriculture residues or dedicated energy crops, comparable price impacts are not

available. Additional details on the changes in commodity prices are included in Table 5.1.4.

The prices for byproducts of renewable fuel production are also affected by the increased

demand for renewable fuels required by the RFS2 rule. Soybean meal, while not exclusively

used for animal feed, is an important element of the feed market. In 2022, FASOM projects that

the price of soybean meal decreases by $0.48 per ton (-0.1 percent) relative to the AEO2007

Reference Case. In 2022, FASOM projects the price of fractionated/extracted DGS increases by

$7.69 per ton (6.5 percent) relative to the AEO2007 Reference Case. FASOM projects that the

price of traditional DGS, produced by corn ethanol plants that do not conduct fractionation or

extraction of corn oil, increases by $7.94 per ton (6.8 percent) relative to the AEO2007

Reference Case in 2022. In FAPRI-CARD, the price of soybean meal increases by $1.05 (0.5

percent), and the price of DGS increases by $3.52 per ton (3.9 percent) in 2022 relative to the

AEO2007 Reference Case.
892

Table 5.1-4.

U.S. Commodity Prices in 2022

(2007$ per Unit)

Biofuel Feedstocks

Commodity (Unit) FASOM FAPRI-CARD

AEO 2007

Reference

Case

Control

Case Change %Change

AEO 2007

Reference

Case

Control

Case Change %Change

Corn (bushel) $3.32 $3.60 $0.27 8.2% $2.96 $3.06 $0.10 3.3%

Soybeans (bushel) $9.85 $10.87 $1.02 10.3% $8.12 $8.19 $0.07 0.9%

Soybean Oil (ton) $483.10 $666.42 $183.32 38% $782.13 $794.48 $12.35 1.6%

Switchgrass (wet ton) $20.73 $40.85 $20.12 97% N/A N/A N/A N/A

Corn Residue (wet

ton) $5.01 $34.49 $29.48 588% N/A N/A N/A N/A

Bagasse (wet ton) $6.43 $29.70 $23.27 362% N/A N/A N/A N/A

Hardwood Logging

Residue (wet ton) $5.37 $23.22 $17.85 332% N/A N/A N/A N/A

Softwood Logging

Residue (wet ton) $9.37 $18.37 $8.99 96% N/A N/A N/A N/A

Byproducts

Commodity (Unit) FASOM FAPRI-CARD

AEO 2007

Reference

Case

Control

Case Change %

Change

AEO 2007

Reference

Case

Control

Case Change %

Change

Soybean Meal (ton) $402.11 $401.63 -$0.48 -0.1% $206.81 $207.87 $1.05 0.5%

DGS Traditional (ton) $116.75 $124.69 $7.94 6.8% N/A N/A N/A N/A

DGS

Fractionated/Extracted

(ton)

$118.88 $126.57 $7.69 6.5% N/A N/A N/A N/A

DGS (Overall, FAPRICARD) N/A N/A N/A N/A $90.60 $94.12 $3.52 3.9%

5.1.5.2 Commodity Use Changes

The increased demand for renewable fuels also affects the use of these feedstocks in other

markets. This section will review the use of these commodities for biofuels, their levels of

exports, and their use in the animal feed market. In 2022, FASOM projects an additional 1

billion bushels of corn will be used for corn ethanol production (22 percent) relative to the

Reference Case. FASOM also projects that in 2022, an additional 98 million wet tons of

switchgrass, 60 million wet tons of corn residue, 6.1 million tons of sugarcane bagasse, and 1.7

million tons of forestry residues will be used to produce cellulosic ethanol, relative to the

AEO2007 Reference Case.

FASOM estimates that an additional 2 million tons of soybean oil is used to produce

soybean biodiesel in 2022, relative to the AEO2007 Reference Case. In addition, FASOM

projects that an additional 17.5 million tons of non-food grade corn oil from the extraction

process will be used for biodiesel production in 2022.
893

The increase in renewable fuel volumes required by the RFS2 also impacts U.S. exports.

For instance, FASOM estimates that the amount of corn exported from the U.S. decreases by 188

million bushels (-8.2 percent) in 2022, relative to the AEO2007 Reference Case. This change

represents a decrease of $57 million (-0.8 percent) in the total value of corn exports in the

FASOM model in 2022. In FAPRI-CARD, U.S. corn exports decrease by 407 million bushels (-

15.7 percent) in 2022. This change translates into a decrease in the total value of corn exports of

$991 million (-12.9 percent) in 2022.

Similarly, as more soybean oil is used for biodiesel production, the amount of soybeans

and soybean oil exported from the U.S. can be expected to be affected. In FASOM, soybean

exports decrease by 135 million bushels (-13.6 percent) in 2022 relative to the AEO2007

Reference Case. This change represents a decrease of $453 million (-4.6 percent) in the total

value of U.S. soybean exports in 2022. In FAPRI-CARD, soybean exports decrease by 32

million bushels (-3 percent), relative to the AEO2007 Reference Case. This change represents a

decrease in the total value of U.S. soybean exports of $185 million (-2.1 percent) in 2022. The

FASOM model projects that U.S. soybean oil exports decrease by 1.2 million tons (-51 percent)

in 2022 relative to the AEO2007 Reference Case. In the FAPRI-CARD model, soybean oil

exports decrease by 0.3 million tons (-6.2 percent) in 2022 relative to the AEO2007 Reference

Case.

Table 5.1-5.

U.S. Exports in 2022

Exports (millions of units)

Commodity FASOM FAPRI-CARD

(Unit) AEO 2007

Reference

Case

Control

Case Change %

Change

AEO 2007

Reference

Case

Control

Case Change %

Change

Corn (bushel) 2,281 2,093 -188 -8.2% 2,589 2,182 -407 -15.7%

Soybeans

(bushel) 993 858 -135 -13.6% 1,073 1,041 -32 -3.0%

Soybean Oil (ton) 2.3 1.1 -1.2 -51.2% 4.8 4.5 -0.3 -6.2%

Total Value of Exports (millions of 2007$)

Commodity FASOM FAPRI-CARD

AEO 2007

Reference

Case

Control

Case Change %

Change

AEO 2007

Reference

Case

Control

Case Change %

Change

Corn $7,585 $7,527 -$57 -0.8% $7,669 $6,679 -$991 -12.9%

Soybeans $9,780 $9,327 -$453 -4.6% $8,709 $8,524 -$185 -2.1%

Higher U.S. demand for renewable fuels leads to an increase in the price of corn and

causes a decrease in the use of corn for U.S. livestock feed. Substitutes are available for feed

corn and this market is price sensitive. The total amount of corn used for feed in FASOM

decreases by 3.3 million tons (-2.5 percent) in 2022 relative to the AEO2007 Reference Case.

Several ethanol processing byproducts can be used to replace a portion of the corn used as feed,

depending on the type of animal. DGS are a byproduct of the dry milling process, whereas

gluten meal and gluten feed are byproducts of wet milling corn ethanol production. FASOM

estimates that total DGS used in feed increases by 5.2 million tons (15.2 percent), gluten meal
894

used in animal feed decreases by 0.1 million tons (-4.5 percent), and gluten feed use increases by

0.3 million tons (6.4 percent) in 2022 relative to the AEO2007 Reference Case. As DGS are

used more in the feed market in the Control Case than in the Reference Case, corn and soybean

meal used in the feed market is replaced. Thus, soybean meal used in feed decreases by 0.04

million tons (-0.4 percent) in 2022 to 10.5 million tons in the Control Case. Overall, the total

ethanol byproducts used in feed (DGS, gluten meal, and gluten feed) increase by 5.4 million tons

(13.2 percent) in 2022 relative to the AEO2007 Reference Case.

Table 5.1-6.

Animal Feed Sources in 2022

(millions of tons)

Feed Source FASOM FAPRI-CARD

AEO 2007

Reference

Case

Control

Case Change %

Change

AEO 2007

Reference

Case

Control

Case Change %

Change

Corn 134.4 131.1 -3.3 -2.5% 158.8 152.3 -6.5 -4.1%

Soybean Meal 10.53 10.49 -0.04 -0.4% 39.4 38.7 -0.7 -1.7%

DGS Total 34.1 39.3 5.2 15.2% 33.9 39.1 5.2 15.2%

Gluten Meal 2.2 2.1 -0.1 -4.5% 1.0 1.1 0.1 7.5%

Gluten Feed 4.5 4.8 0.3 6.4% 7.6 7.9 0.3 4.1%

Total Ethanol

Byproducts 40.7 46.1 5.4 13.2% 42.5 48.1 5.5 13.0%

5.1.5.3 Changes in Crop Acres

In order to meet the Control Case volumes of renewable fuels, FASOM estimates an

increase of 3.6 million acres (4.6 percent) for harvested corn acres in 2022.284 Most of the new

corn acres come from a reduction in existing crop acres, such as rice, wheat, barley, rye and hay.

FASOM projects that rice acres decrease by 788 thousand acres (-20.6 percent), wheat acres

decrease by 2.9 million acres (-4.9 percent), and hay acres decrease by 752 thousand acres (-1.5

percent) in 2022 relative to the AEO2007 Reference Case. See Table 5.1-7 for additional

changes in crop acres in the FASOM and FAPRI-CARD models.

Although the RFS2 Control Case includes more soybean biodiesel than the AEO2007

Reference Case, competing demands for land results in a decrease in U.S. harvested soybean

acres. According to the FASOM model, harvested soybean acres decrease by approximately 1.4

million acres (-2.1 percent) in 2022 relative to the Control Case. As described in the previous

section, most of the additional soybeans needed for increased biodiesel production are diverted

from exports. FAPRI-CARD also projects that the increased demand for biodiesel from soybean

oil results chiefly in a reduction in soybean oil exports, rather than an increase in acres harvested.

FAPRI-CARD projects that harvested soybean acres decrease by 0.9 million acres (-1.1 percent)

in 2022 relative to the AEO2007 Reference Case.

284 FASOM estimates that total planted corn acres increase to 89.4 million acres in the Control Case from the

Reference Case level of 84.6 million acres in 2017. Total planted acres increases to 87.1 million acres in the Control

Case from the Reference Case level of 83.5 million acres in 2022.
895

As the demand for cellulosic renewable fuels increases, FASOM projects that most of the

cellulosic biofuels will be derived from switchgrass. In 2022, switchgrass acres increase by 12.5

million acres, relative to the AEO2007 Reference Case. The remainder of the cellulosic biofuel

is produced from corn residue, forestry residues, and sugarcane bagasse. The FAPRI-CARD

models do not explicitly model the production of cellulosic renewable fuel, nor does it explicitly

model the feedstocks for cellulosic renewable fuel. Table 5.1-7 and Figure 5.1-2 shows the

change in acres for all crops in the U.S. in 2022.

Table 5.1-7.

U.S. Crop Acres in 2022

(millions of acres)

Crop FASOM FAPRI-CARD

AEO 2007

Reference

Case

Control

Case Change %

Change

AEO 2007

Reference

Case

Control

Case Change %

Change

Barley 9.7 8.5 -1.2 -12.4 2.96 3.0 0.04 1.2%

Corn 77.9 81.5 3.6 4.6% 79.1 80.9 1.8 2.2%

Cotton 11.3 11.1 -0.2 -1.7% 7.7 7.7 0.0 0.1%

Wheat 59.0 56.0 -2.9 -4.9% 48.3 48.1 -0.2 -0.5%

Hay 50.7 50.0 -0.8 -1.5% 60.8 60.8 0.0 -0.1%

Oats 5.5 5.4 -0.2 -3.2% 1.1 1.1 0.0 -1.0%

Rice 3.8 3.0 -0.8 -20.6% 2.6 2.6 0.0 0.0%

Sorghum 8.7 8.5 -0.2 -1.8% 5.8 5.8 0.0 0.3%

Soybeans 68.1 66.6 -1.4 -2.1% 78.3 77.4 -0.9 -1.1%

Sugarbeet 1.3 1.2 -0.1 -7.8% 1.2 1.2 0.0 -0.3%

Sugarcane 0.7 0.9 0.1 19.8% 0.8 0.8 0.0 0.2%

Switchgrass 0.1 12.6 12.5 20,261% 0.0 0.0 0.0 N/A

Other (FASOM)* 9.5 9.1 -0.4 -4.4% N/A N/A N/A N/A

Other (FAPRICARD)** N/A N/A N/A N/A 4.7 4.7 0.0 0.1%

Total 306.3 314.4 8.1 2.6% 288.7 289.4 0.7 0.2%
896

Figure 5.1-2.

Estimated Change in U.S. Crop Acres

Relative to the AEO 2007 Reference Case in 2022

-4

-2

0

2

4

6

8

10

12

14

Barley

Corn

CottonWheat

Hay

Oats

Rice

Sorghum

Soybeans

Sugarbeet

Sugarcane

SwitchGrass

Other (FASOM)

Other (FAPRI)

Total

Millions of Acres

FASOM

FAPRI

As switchgrass and corn residue are the largest feedstocks of cellulosic renewable fuel, it

is important to know which regions in the U.S. these feedstocks are the most competitive. Corn

residue removal takes place chiefly in the Corn Belt region of the U.S. Based on the residue

removal rates outlined in Table 5.1-1, corn acres with residue removal in the Control Case takes

place mostly in Iowa (9 million acres) and Illinois (7.4 million acres). Switchgrass production,

on the other hand, takes place mostly in the Southwest region of the U.S. This includes 2.7

million acres in Kansas, 1.7 million acres each in Missouri and Texas, as well as 1.3 million

acres in Oklahoma. To see the top ten producing states for corn residue and switchgrass, please

refer to Figures 5.1-3 and 5.1-4 below.
897

Figure 5.1-3.

Top Ten Producing States of Corn Acres with Residue Removal in 2022

FASOM Control Case

0

1

2

3

4

5

6

7

8

9

10

Iowa

Illinois

SouthDakota

Nebraska

NorthDakota

Missouri

Michigan

Indiana

Kansas

Ohio

Millions of Acres
898

Figure 5.1-4.

Top Ten Producing States of Switchgrass in 2022

FASOM Control Case

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Kansas

Missouri

Texas

OklahomaArkansas

Ohio

Kentucky

NorthCarolina

Tennessee

Louisiana

Wisconsin

Florida

Millions of Acres

5.1.5.4 Land Use Change

Changes in these land categories are summarized in Table 5.1-8 below. In 2022,

FASOM projects that total cropland increases by 3.1 million acres (1.0 percent) relative to the

Reference Case. The increase in cropland is derived primarily from a combination of decreased

cropland pasture acres, and a decrease in forest acres. FAPRI-CARD does not explicitly model

U.S. forest or pasture acres.
899

Table 5.1-8.

Change in U.S. Major Land Use Categories in 2022

Relative to the Reference Case

(millions of acres)

Land Category AEO 2007

Reference Case

Control Case Change %

Change

Cropland 311.7 314.8 3.1 1.0%

Used for Production 306.3 314.4 8.1 2.6%

Idled 5.4 0.4 -5.0 -93.0%

Cropland Pasture 32.0 30.1 -1.9 -5.8%

Used for Production 23.1 25.0 1.8 8.0%

Idled 8.9 5.2 -3.7 -41.7%

Forest Pasture 148.4 149.5 1.1 0.7%

Used for Production 113.1 114.4 1.3 1.1%

Idled 35.3 35.1 -0.2 -0.5%

Forestland 344.5 343.3 -1.2 -0.3%

Rangeland 578.8 578.8 0 0%

Used for Production 522.6 516.8 -5.8 -1.1%

Idled 56.3 62.0 5.7 10.2%

CRP 32.0 32.0 0.0 0%

Developed Land 35.0 35.0 0.0 0%

5.1.5.5 Fertilizer Use

As crop acres increase to meet the additional demand for corn and other crops for biofuel

production, fertilizer use increases as a result. In addition, the harvesting of corn stover and

other crop residues used to make cellulosic renewable fuel removes nutrients from the soil and

requires greater fertilizer application. In 2022, FASOM estimates that nitrogen fertilizer use in

the U.S. agricultural sector will increase by 1.5 billion pounds (5.7%) relative to the AEO2007

Reference Case levels. FASOM also projects that phosphorous fertilizer use will increase by

714 million pounds (12.7%) relative to the Reference Case level in 2022. The FAPRI-CARD

model does not provide estimates for fertilizer use.

Table 5.1-9.

Change in U.S. Fertilizer Use

Relative to the Reference Case

(millions of pounds)

Fertilizer AEO 2007

Reference Case

Control Case Change %Change

Nitrogen 26,209 27,710 1,501 5.7%

Phosphorous 5,614 6,328 714 12.7%

5.1.5.6 Impact on U.S. Farm Income
900

The increase in renewable fuel production provides a significant increase in net farm

income to the U.S. agricultural sector. FASOM predicts that net U.S. farm income will increase

by $13 billion dollars in 2022 (36 percent).

5.1.5.7 Impact on U.S. Food Prices

Higher corn and soybean prices also result in higher meat prices, although the increased

production of coproducts that can be used as animal feed (e.g., DGS) that accompanies expanded

biofuels production tends to limit price effects. For example, in 2022, the average price for all

meat production in the FASOM model increases by 0.1 percent. In FAPRI-CARD, the price of

beef increases by $0.37 per hundredweight (0.4 percent) in 2022 to $95.84 per hundredweight in

the Control Case, and the price of pork increases by $0.77 per hundredweight (1.6 percent) in

2022 to $49.19 per hundredweight in the Control Case.

Due to higher commodity prices, FASOM estimates that U.S. food costs285 would

increase by roughly $10 per person per year by 2022, relative to the Reference Case.286 Total

effective farm gate food costs would increase by $3.6 billion (0.2 percent) in 2022.287 To put

these changes in perspective, average U.S. per capita food expenditures in 2007 were $3,778 or

approximately 10 percent of personal disposable income. The total amount spent on food in the

U.S. in 2007 was $1.14 trillion dollars.288

5.1.6 International Impacts

The FAPRI-CARD models are utilized to assess the international impacts on trade, land

use, and food consumption as a result of the RFS2 renewable fuel volume requirement in the

U.S. In the FAPRI-CARD models, links between the U.S. and international models are made

through commodity prices and net trade equations. In general, for each commodity sector, the

economic relationship that quantity supplied equals quantity demanded is achieved through a

market-clearing price for the commodity. In non-U.S. countries, domestic prices are modeled as

a function of the world price using a price transmission equation. Since econometric models for

each sector can be linked, changes in one commodity sector will impact the other sectors.

The model for each commodity considers a number of specific countries/regions, and

then includes a rest-of-the-world aggregate to close the model. The models specify behavioral

equations for production, use, stocks, and trade between countries/regions. The models solve for

representative world prices by equating excess supply and demand across countries. Using price

285 FASOM does not calculate changes in price to the consumer directly. The proxy for aggregate food price change

is an indexed value of all food prices at the farm gate. It should be noted, however, that according to USDA,

approximately 80% of consumer food expenditures are a result of handling after it leaves the farm (e.g., processing,

packaging, storage, marketing, and distribution). These costs consist of a complex set of variables, and do not

necessarily change in proportion to an increase in farm gate costs. In fact, these intermediate steps can absorb price

increases to some extent, suggesting that only a portion of farm gate price changes are typically reflected at the retail

level. See http://www.ers.usda.gov/publications/foodreview/septdec00/FRsept00e.pdf.

286 These estimates are based on U.S. Census population projections of 331 million people in 2017 and 348 million

people in 2022. See http://www.census.gov/population/www/projections/summarytables.html

287 Farm Gate food prices refer to the prices that farmers are paid for their commodities.

288 See www.ers.usda.gov/Briefing/CPIFoodAndExpenditures/Data/table15.htm.
901

transmission equations, the domestic price for each country is linked with the representative

world price through exchange rates. It is through changes in world prices that change in

worldwide commodity production and trade is determined.

5.1.6.1 Global Commodity Price Changes

As demand for renewable fuels in the U.S. increases, the FAPRI-CARD model projects

that U.S. and world commodity prices will generally increase. FAPRI-CARD projects that the

world price of corn increases by $0.12/bu (3.1 percent) relative to the AEO2007 Reference Case

in 2022. Similarly, FAPRI-CARD projects that world soybean prices increase by $0.08/bu (0.8

percent) and the world soybean oil price increases by $13.22 per ton (1.5 percent) in 2022

relative to the AEO2007 Reference Case.

Since increased biofuel demand in the U.S. also impacts the livestock market, in terms of

land use (i.e., pasture) and the feed market, we expect prices to change as well. The world price

for beef, which is based on U.S. prices, increases $7.34 per ton (0.4 percent) in 2022 to $1,917

per ton in the Control Case.

Table 5.1-10.

Global Commodity Price

Changes from RFS2 in 2022

(2007$ per unit)

Commodity (Unit) AEO 2007

Reference Case

Control Case Change % Change

Corn (bushel) $3.76 $3.88 $0.12 3.1%

Soybeans (bushel) $9.55 $9.63 $0.08 0.8%

Soybean Oil (ton) $854.45 $867.67 $13.22 1.5%

Beef (ton) $1,909.42 $1,916.76 $7.34 0.4%

5.1.6.2 World Renewable Fuels Trade

As the U.S. increases its demand for renewable fuels, world trade markets for renewable

fuels are also likely to be impacted. As described in Section 1.2, we estimate that in 2022, the

U.S. will increase net imports of ethanol by 1.6 billion gallons (248%) relative to the AEO2007

Reference Case. In response, FAPRI-CARD projects that Brazil will increase net exports by 1.5

billion gallons (37.8 percent) in 2022 relative to the Reference Case. However, since the U.S.

demand for ethanol imports exceeds the increase in Brazilian net exports, FAPRI-CARD projects

that other countries will reduce their net imports of ethanol. In 2022, FAPRI-CARD projects

that China decreases net imports of ethanol by 7.9 million gallons (-5.3 percent), the European

Union decreases net imports by 16.8 million gallons (-2.3 percent), India decreases net imports

by 15.2 million gallons (-7.4 percent), Japan decreases net imports by 3.1 million gallons (-0.5

percent), and South Korea decreases net imports by 1.6 million gallons (-0.4 percent) relative to

the AEO2007 Reference Case. The rest of the world decreases net imports of ethanol by 0.7

million gallons (-0.2 percent) in 2022 relative to the Reference Case.
902

Figure 5.1-5.

Ethanol Net Exports by Country in 2022

-3

-2

-1

0

1

2

3

4

5

6

Brazil China Canada EU India Japan South Korea US ROW

Billions of Gallons

Reference Case

Control Case

Since the world price of soybean oil increases (1.5 percent), whereas the world price of

biodiesel decreases (-1.3 percent) due to the RFS2 renewable fuel volume requirements, it

becomes relatively more profitable to increase net exports of soybean oil for major producers.

This results in less soybean oil being used in the production of biodiesel and therefore a decrease

in biodiesel net exports in some countries. Argentina decreases their biodiesel net exports by 1.3

million gallons (-0.8 percent) to 152.4 million gallons, Brazil decreases their biodiesel net

exports by 6.2 million gallons (-7.4 percent) to 77.4 million gallons in 2022. In response the EU

increases their net exports of biodiesel by 8.6 million gallons (29.6 percent) to 37.8 million

gallons in the Control Case. Additionally, Japan reduces its net imports of biodiesel by 0.5

million gallons (-5.9 percent).
903

Figure 5.1-6.

Biodiesel Net Exports by Country in 2022

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

Argentina Brazil Indonesia Malaysia US EU Japan ROW

Billions of Gallons

Reference Case

Control Case

5.1.6.3 International Crop Acre Changes

Changes to the global commodity trade markets and world commodity prices result in

changes in international land use. The FAPRI-CARD model provides international change in

crop acres as a result of the RFS2 renewable fuel volumes. Internationally, Brazil has the largest

increase in crop acres in 2022, followed by a subset of nations in Africa, Japan, the EU, Mexico,

and Indonesia. As the U.S. increases its net imports of ethanol by 1.5 billion gallons in 2022, the

major supplier of this increase in ethanol is Brazil which produces ethanol from sugarcane. The

FAPRI-CARD model estimates that Brazil crop acres increase by 2.2 million acres (1.6 percent)

relative to the AEO2007 Reference Case. The major crop contributing to this increase is

sugarcane, which increases by 1.2 million acres (4.4 percent) in 2022. “Africa, Other” increases

total crop acres by 0.20 million acres (0.25 percent), the large majority of which is corn, which

increases by 0.19 million acres (0.32 percent). Japan increases its total crop acres by 0.14

million acres (3.3 percent), solely due to a 0.14 million acre increase in rice acres. The EU

increases its crop acres as well, by 0.12 million acres (0.1 percent). This is a result of a 0.06

million acre increase in wheat (0.1 percent), a 0.04 million acre increase in barley (0.1 percent),

and a 0.02 million acre increase in corn (0.1 percent). Mexico increases its crop acres by 0.11

million acres in 2022 (0.4 percent), which is primarily due to a 0.14 million acre increase in corn

(0.6 percent) and small decreases in other crops. Indonesia increases its total crop acres by 0.11
904

million acres in 2022 (0.2 percent), including a 0.09 million acre increase in corn (0.9 percent)

and a 0.02 million acre increase in rice (0.1 percent).

Figure 5.1-7.

Change in World Crop Acres from

the RFS2 Rule by Country in 2022

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Brazil US Africa,

Other

Japan EU Mexico Indonesia Rest of

World

World

Millions of Acres

In response to the increased U.S. demand for imported ethanol, which FAPRI-CARD

estimates will be satisfied by increases in Brazil exports of sugarcane ethanol, sugarcane acres

increase in various regions in Brazil. For instance, FAPRI-CARD projects 2022 sugarcane acres

will increase by 0.01 million acres (2.7 percent) in the Amazon Biome region, 0.13 million acres

(3.8 percent) in the Central-West Cerrados region, 0.15 million acres (3.6 percent) in the

Northeast Coast region, 0.02 million acres (2.7 percent) in the North-Northeast Cerrados region,

0.05 million acres (2.5 percent) in the South region, and 0.87 million acres (5.0 percent) in the

Southeast region, relative to the AEO2007 Reference Case.

Area for other crops in Brazil, including corn and soybeans, are affected not only by the

increase in sugarcane crop acres, but also by the changes in world price for each commodity.

Overall, total crop area in Brazil increases by 2.2 million acres (1.6 percent) in 2022 relative to

the AEO2007 Reference Case. This change is includes an increase of 0.2 million acres (1.8

percent) in the Amazon Biome, an increase of 0.6 million acres (1.9 percent) in the Central-West

Cerrados, an increase of 0.2 million acres (2.1 percent) in the Northeast Coast, an increase of 0.2

million acres (1.6 percent) in the North-Northeast Cerrados, an increase of 0.2 million acres (0.4
905

percent) in the South, and an increase of 0.7 million acres (2.6 percent) in the Southeast, relative

to the AEO2007 Reference Case in 2022.

Table 5.1-11.

Change in Brazil Sugarcane Acres and Total

Crop Area from the RFS2 Rule by Region in 2022

(millions of acres)

Region Sugarcane Total Crops

AEO 2007

Reference

Case

Control

Case

Change %

Change

AEO 2007

Reference

Case

Control

Case

Change %

Change

Amazon Biome 0.45 0.46 0.01 2.7% 12.0 12.2 0.2 1.8%

Central-West

Cerrados 3.38 3.51 0.13 3.8% 31.8 32.4 0.6 1.9%

Northeast Coast 4.07 4.21 0.15 3.6% 11.1 11.3 0.2 2.1%

North-Northeast

Cerrados 0.64 0.66 0.02 2.7% 14.6 14.8 0.2 1.6%

South 2.02 2.07 0.05 2.5% 39.9 40.1 0.2 0.4%

Southeast 17.47 18.34 0.87 5.0% 26.3 27.0 0.7 2.6%

Brazil, Total 28.04 29.27 1.23 4.4% 135.7 137.9 2.2 1.6%

5.1.6.4 World Food Markets

The increase in renewable fuel volumes associated with the RFS2 will also impact world

food consumption patterns.289 Since major agricultural commodity prices increase globally,

FAPRI-CARD projects that world consumption of food decreases by 2.5 million metric tons (-

0.1 percent) in 2022, relative to the AEO2007 Reference Case. This change is includes a

decrease of consumption of dairy food products of 0.1 million metric tons (-0.03 percent), a

decrease of 0.1 million metric tons (-0.05 percent) of livestock, a decrease of 0.3 million metric

tons (-0.15 percent) of sugar, a decrease of 0.4 million metric tons (-0.12 percent) of grains, a

decrease of 1.7 million metric tons (-4.5 percent) of vegetable oils, and an increase of 0.1 million

metric tons (0.03 percent) of rice, relative to the AEO2007 Reference Case in 2022. Wheat

consumption levels do not change between the Reference Case and the Control Case. While

FAPRI-CARD provides estimates of changes in world food consumption, estimating effects on

global nutrition is beyond the scope of this analysis.

289 The food commodities included in the FAPRI model include corn, wheat, sorghum, barley, soybeans, sugar,

peanuts, oils, beef, pork, poultry, and dairy products.
906

Table 5.1-12.

Change in World Food Consumption Relative to the Reference Case

(millions of metric tons)

Category AEO 2007

Reference

Case

Control

Case

Change % Change

Dairy 288.2 288.1 -0.1 -0.03%

Livestock 301.4 301.3 -0.1 -0.05%

Sugar 206.3 206.0 -0.3 -0.15%

Wheat 605.9 605.9 0.0 0.00%

Grains 314.5 314.1 -0.4 -0.12%

Vegetable Oils 37.1 35.4 -1.7 -4.5%

Rice 500.6 500.7 0.1 0.03%

Total Food 2,258.4 2,256.0 -2.5 -0.1%

5.2 Petroleum, Renewable Fuels and Energy Security Impacts

Increasing usage of renewable fuels helps to reduce U.S. petroleum imports. A reduction

of U.S. petroleum consumption and imports reduces both financial and strategic risks associated

with a potential disruption in supply or a spike in cost of a particular energy source. This

reduction in risks is a measure of improved U.S. energy security. In this section, we detail an

updated methodology for estimating the energy security benefits of reduced U.S. oil imports

which explicitly includes renewable fuels. Based upon this updated approach, we estimate the

monetary value of the energy security benefits associated with the increased usage of renewable

fuels in the U.S. required by the RFS2 rule.

5.2.1 Implications of Reduced Petroleum Use on U.S. Imports

In 2008, U.S. petroleum import expenditures represented 21 percent of total U.S. imports

of all goods and services.290 In 2008, the U.S. imported 66 percent of the petroleum it consumed,

and the transportation sector accounted for 70 percent of total U.S. petroleum consumption. This

compares to approximately 37 percent of petroleum from imports and 55 percent consumption of

petroleum in the transportation sector in 1975.291 It is clear that petroleum imports have a

significant impact on the U.S. economy. Requiring the wider use of renewable fuels in the U.S.

is expected to lower U.S. petroleum imports.

For this rule, EPA estimated the reductions in U.S. petroleum imports using a modified

version of the National Energy Modeling System (EPA-NEMS). EPA-NEMS is an energyeconomy modeling system of U.S. energy markets through the 2030 time period. EPA-NEMS

projects U.S. production, imports, conversion, consumption, and prices of energy subject to

290 Source: U.S. Bureau of Economic Analysis, U.S. International Transactions Accounts Data, as shown on June 24,

2009.

291 Source: U.S. Department of Energy, Annual Energy Review 2008, Report No. DOE/EIA-0384(2008), Tables 5.1

and 5.13c, June 26, 2009.
907

assumptions on world energy markets, resource availability and costs, behavioral and

technological choice criteria, cost and performance characteristics of energy technologies, and

demographics. For this analysis, the 2009 NEMS model was modified to use the 2007 (preEISA) Annual Energy Outlook (AEO) levels of renewable fuels in the Reference Case. These

results were compared to our Control Case, which assumes the renewable fuel volumes required

by EISA will be met by 2022. Details on how the EPA-NEMS model was adjusted to

incorporate these volumes are included in the docket.292 The reduction in U.S. oil imports

projected by EPA-NEMS is roughly 0.9 million barrels per day (a 9.5 per cent reduction in

2022). It is estimated that U.S. oil production in 2022 declines by much less, just 0.01 million

barrels per day.

Using the EPA-NEMS model, we also calculated the change in expenditures in both U.S.

petroleum and renewable fuel imports with the RFS2 rule and compared these with the U.S.

trade position measured as U.S. net exports of all goods and services economy-wide. Changes in

fuel expenditures were estimated by multiplying the changes in petroleum and renewable fuel net

imports by the respective imported petroleum prices and wholesale ethanol price forecasts. In

Table 5.2.1-1, the net expenditures in reduced petroleum imports and increased renewable fuel

imports are compared to the total value of U.S. net exports of goods and services of the whole

economy for 2022 as estimated by the EPA-NEMS model. We project that avoided expenditures

on imported crude oil and petroleum products from the 2022 RFS2 volumes of renewable fuels

would be roughly $41.5 billion. Taking into consideration imports of renewable fuels, the total

avoided expenditures on imported transportation fuels are projected to be $37.2 billion in the

RFS2 control case.

292 See OnLocation, Inc. RFS2 Modeling Analysis Documentation, dated January 12, 2010.
908

Table 5.2.1-1.

Selected U.S. Exports and Imports in 2022

(billions of 2007$)

Category

AEO

2007

Reference

Case

RFS2

Control

Case

Change Percent

Change

Total U.S. Exports of Goods and

Services 3,838 3831 7 +0.22%

Total U.S. Imports of Goods and

Services 3,840 3833 7 +0.23%

Total U.S. Net Imports of Goods

and Services 2 2 0 0%

Expenditures on Net Petroleum

Imports 456 414 -41.5 -9.1%293

Expenditures on Imported Ethanol 1 5.3 +4.3 +419%

Total Expenditures on

Transportation Fuel Imports 457 420 -37.2 -8.1%

5.2.2 Background on U.S. Energy Security

U.S. energy security is broadly defined as protecting the U.S. economy against

circumstances that threaten significant short- and long-term increases in energy costs. Most

discussion of U.S. energy security revolves around the topic of the economic costs of U.S.

dependence on oil imports, although energy security is also a function of the stability of overall

fuel supply and the flexibility of demand. An important part of the problem stems from U.S.

reliance on imported oil, and the global oil market is strongly influenced by potentially

unfriendly and unstable sources. In addition, oil exporters have the ability to raise the price of

oil by exerting monopoly power through the formation of a cartel, the Organization of Petroleum

Exporting Countries (OPEC). Finally, these factors contribute to the vulnerability of the U.S.

economy to episodic oil supply shocks and price spikes. In 2008, U.S. imports of crude oil were

roughly $391 billion (2007$, see Figure 5.2.2-1).

293 Note: the 9.1 per cent reduction included in this table is a change in the monetary value of the oil reductions,

whereas the 9.5 per cent reduction in oil imports cited in the previous paragraph refers to the volumetric change in

imports.
909

Figure 5.2.2-1. U.S. Expenditures on Crude Oil

U.S. Expenditures on Crude Oil

0

100

200

300

400

500

600

700

1970 1975 1980 1985 1990 1995 2000 2005

Year

(2007$ Billions)

Domestic

Imported

Source: Annual Energy Reviews and AEO 2009.

By requiring the wider use of renewable fuels, the RFS2 rule promotes diversification of

transportation fuels in the U.S. and helps to improve the U.S.’s energy security. For the RFS2

proposal, an “oil import premium” approach was utilized to identify those energy securityrelated impacts which are not reflected in the market price of oil, and which are expected to

change in response to an incremental change in the level of U.S. oil imports. For this analysis,

the “oil import premium” approach was extended to explicitly consider the energy security

implications of the expansion of renewable fuels required by the RFS2 rule.

5.2.3 Methodology Used to Estimate U.S. Energy Security Benefits

In order to understand the energy security implications of reducing U.S. oil imports, EPA

has worked with Oak Ridge National Laboratory (ORNL), which has developed approaches for

evaluating the social costs and energy security implications of oil use. In a recent study entitled

"The Energy Security Benefits of Reduced Oil Use, 2006-2015," completed in 2007 for the final

RFS1 rulemaking, ORNL updated and applied the method used in the 1997 report "Oil Imports:

An Assessment of Benefits and Costs", by Leiby, Jones, Curlee and Lee.294,295 The updated 2007

294 Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits

and Costs, ORNL-6851, Oak Ridge National Laboratory, November, 1997.
910

report was included as part of the record in the final RFS1 rulemaking, and revisions were made

based on external comment and peer review. 296,297

Significant factors that drive energy security costs have been changing over the last

decade, including: projected world oil prices, current and anticipated levels of OPEC production,

U.S. oil import levels, the estimated responsiveness of regional oil supplies and demands to

price, and the likelihood of oil supply disruptions. For this analysis, oil prices and supply and

demand energy balances from the EIA's AEO 2009 Reference Case were used. In many

instances, the recent market trends and projections suggest reasons for greater concern about oil

security costs, compared to the prior decade: higher oil prices; growing U.S. import levels; and a

larger value-share of oil in GDP. To the extent that the U.S. economy has become more

resilient, and less sensitive to oil shocks, or that improved macroeconomic policies have reduced

the impact of oil shocks, there may be influences countervailing to the oil market trends. This

possibility is considered in the security estimates, but recent macroeconomic disturbances

indicate that greater future macroeconomic stability cannot be assured. The degree to which

sharply higher oil prices contributed to, or exacerbated, the recent global recession has not yet

been resolved.298

In order to understand the energy security implications of this rule, EPA used the Oil

Security Metrics Model299,300,301 (OSMM), developed and maintained by Oak Ridge National

Laboratory. The OSMM estimates the U.S. energy security benefits from increased availability

and use of renewable transportation fuels. The OSMM took as inputs the renewable fuel volumes

295 The 1997 ORNL paper was cited and its results used in DOT/NHTSA’s rules establishing CAFE standards for

2008 through 2011 model year light trucks. See DOT/NHTSA, Final Regulatory Impacts Analysis: Corporate

Average Fuel Economy and CAFE Reform MY 2008-2011, March 2006.

296Leiby, Paul N. “Estimating the Energy Security Benefits of Reduced U.S. Oil Imports: Final Report”,

ORNL/TM-2007/028, Oak Ridge National Laboratory, March, 2008.

297 Updating the ORNL methodology to incorporate the comments from the Peer Reviewers, based on AEO2007,

ORNL estimated that the total energy security benefits associated with a reduction of imported oil is $12.38/barrel,

with a range of $6.88 - $18.52/barrel of imported oil reduced ($2006). When the same methods and assumptions are

applied to the AOE2009 Reference outlook, comparable estimates for 2025 are $19.21/barrel, with a range of $10.8

to $29.6/barrel.

298 See Hamilton, J. D. 2009, “Causes and Consequences of the Oil Shock of 2007-08”, Brookings Papers on

Economic Activity, 2009, or the congressional testimony of Yergin, D. "The Long Aftershock: Oil: Oil and Energy

Security After the Price Collapse" Testimony to U.S. Congress, Hearings, Joint Economic Committee, Oil and the

Economy: The Impact of Rising Global Demand on the U.S. Recovery. May 20, 2009.

299 The OSMM methods are consistent with the recommended methodologies of the National Resource Council’s

(NRC’s) (2005) Committee on Prospective Benefits of DOE’s Energy Efficiency and Fossil Energy R&D Programs.

The OSMM defines and implements a method that makes use of the NRC’s typology of prospective benefits and

methodological framework, satisfies the NRC’s criteria for prospective benefits evaluation, and permits

measurement of prospective energy security benefits for policies and technologies related to oil. It has been used to

estimate the prospective oil security benefits of Department of Energy’s Energy Efficiency and Renewable Energy

R&D programs, and is also applicable to other strategies and policies aimed at changing the level and composition

of U.S. petroleum demand. To evaluate the RFS2, the OSMM was modified to include supplies and demand of

biofuels as well as petroleum.

300 Greene D.L. and P.N. Leiby, 2006. The Oil Security Metrics Model: A Tool for Evaluating the Prospective Oil

Security Benefits of DOE's Energy Efficiency and Renewable Energy R&D Programs, ORNL/TM-2006/505, Oak

Ridge National Laboratory (ORNL), 2006.

301 Leiby, P.N., Energy Security Impacts of Renewable Fuel Use Under the RFS2 Rule – Methodology, Oak Ridge

National Laboratory, January 19, 2010.
911

that are required under EISA as well at the renewable fuel costs estimated in Chapter 4.4 of this

RIA. In addition, it assumed EPA’s projections of flexible fueled vehicles and use of E85. In

conducting this analysis, ORNL considered the full economic cost of importing petroleum into

the U.S. The full economic cost of importing petroleum into the U.S. is defined for this analysis

to include two components in addition to the purchase price of petroleum itself. These are: (1)

the higher costs for oil imports resulting from the effect of U.S. import demand on the world oil

price and OPEC market power (i.e., the "import demand" or "monopsony" costs); and (2) the risk

of reductions in U.S. economic output and disruption of the U.S. economy caused by sudden

disruptions in the supply of imported oil to the U.S. (i.e., “macroeconomic disruption/adjustment

costs”). Analogously, this analysis for the RFS2 rule also considers the economic costs of

importing renewable fuels to meet the RFS2 rule requirements, and the estimated

disruption/adjustment costs to the economy of renewable fuel price volatility due to renewable

fuel supply disruptions (e.g., droughts and floods, etc.).

This energy security analysis extends the prior “oil import premium” analysis by

considering risk-shifting that might occur as the U.S. reduces its dependency on petroleum by

increasing its use of renewable fuels. The analysis accounts for the energy security implications

associated with renewable fuels, such as possible supply disruptions of ethanol made from corn

or ethanol derived from cellulosic feedstocks such as switchgrass. The use of OSMM broadens

our energy security analysis to incorporate estimates of overall motor fuel supply and demand

flexibility and reliability, and the impacts of possible agricultural sector market disruptions. For

example, the use of renewable fuels can modestly alter short and long run demand elasticities

(i.e., flexibility) in the motor fuel market, with implications for robustness of the fuel system in

the face of diverse supply shocks.

As in the “oil import premium” analysis for the RFS2 proposal, U.S. military costs are

excluded from the analysis performed by ORNL because their attribution to particular missions

or activities is difficult. Most military forces serve a broad range of security and foreign policy

objectives. Attempts to attribute some share of U.S. military costs to oil imports are further

challenged by the need to estimate how those costs might vary with incremental variations in

U.S. oil consumption and imports. Similarly, the Strategic Petroleum Reserve (SPR) size and

policy is assumed unchanged by the RFS2 rule.

5.2.4 Effect of Oil Use on Long-Run Oil Price and U.S. Import Costs

The first component of the full economic costs of oil use in the U.S. follows from the

effect of U.S. import demand on the world oil price over the long-run. Because the U.S. is a

sufficiently large purchaser of foreign oil supplies, its purchases can affect the world oil price.

This monopsony power means that increases in U.S. petroleum demand can cause the world

price of crude oil to rise, and conversely, that reductions in U.S. petroleum demand can reduce

the world price of crude oil. Thus, one benefit of decreasing U.S. oil purchases, due to the

increased availability and use of other transportation fuels, is the potential decrease in the crude

oil price paid for all crude oil purchased.

The demand or monopsony effect can be readily illustrated with an example. If the U.S.

imports 10 million barrels per day at a world oil price of $50 per barrel, its total daily bill for oil
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imports is $500 million. If a decrease in U.S. imports to 9 million barrels per day causes the

world oil price to drop to $49 per barrel, the daily U.S. oil import bill drops to $441 million (9

million barrels times $49 per barrel). While the world oil price only declines $1, the resulting

decrease in oil purchase payments of $59 million per day ($500 million minus $441 million) is

equivalent to an incremental benefit of $59 per barrel of oil imports reduced, or $10 more than

the newly-decreased world price of $49 per barrel. This additional $10 per barrel “import cost

premium” or “monopsony” benefit represents the incremental external benefit to U.S. society as

a whole for avoided import costs beyond the price paid for oil purchases. This additional benefit

arises only to the extent that the reduction in U.S. oil imports affects the world oil price.

A similar rationale can be applied to estimate the monopsony disbenefits of the increased

use of renewable fuels from the RFS2 rule. In the same way, but working in the opposite

direction of the oil market, increased use of renewable fuels in the U.S. is expected to increase

demand for domestic and imported renewable fuels, and to increase the world price of renewable

fuels. This results in higher total costs of U.S. renewable fuel imports. While the total cost of

renewable fuel imports under a policy like RFS2 rule will include the cost of the additional

imports, the monopsony cost portion is the added amount paid for the imports that would have

occurred without the RFS2 rule renewable fuel volumes. Thus, to look at the total monopsony

impacts of the RFS2 rule renewable fuel volumes, two separate impacts need to be assessed.

First, U.S. oil import reductions result in paying lower prices for all barrels of U.S. imported oil,

providing monopsony benefits. Second, increased use of renewable fuels results in higher prices

of U.S. imported renewable fuels, yielding monopsony disbenefits for renewable fuels. The total

monopsony benefit is the combined sum of these separate market impacts.

Table 5.2.4.1 shows the RFS2 Reference Case levels of U.S. oil and renewable fuel

imports as well as the average change in oil prices and renewable fuel prices projected due to the

RFS2 volumes in 2022. The Reference Case renewable fuel imports are relatively modest

compared to oil (roughly 0.015 billions of barrels of renewable fuel are imported versus 3.283

billion barrels of oil). Projected U.S. renewable fuel imports in 2022 are ethanol, principally

made from sugar cane harvested in Brazil. In 2022, the estimated change in ethanol price due to

the RFS2 renewable fuel volumes is $0.61/barrel, and the estimated reduction in the world oil

price is $1.05/barrel. The monopsony effect is the change in costs of the quantities of fuel

imported without the RFS2 renewable fuel volumes (i.e., the Reference Case fuel volumes).

Since the change in the renewable fuel price applies to a much smaller quantity of renewable fuel

imports than U.S. oil imports, the monopsony disbenefit per barrel of increased renewable fuel

use is much smaller, only $0.02/barrel, compared to the oil monopsony benefit, $7.88/barrel.

Thus, including the impact of expanded renewable fuel use on renewable fuel imports and price

yields a slightly lower estimate of the total monopsony benefits.
913

Table 5.2.4-1.

Determinates of Monopsony Benefits

of the RFS2 Renewable Fuel Volumes

vs. AEO2007 Reference Case

Fuel Reference Case

Import Quantity

(billions of barrels

in 2022)

Change in Price

($ per barrel in

2022)

Monopsony

Benefit

($ billion in

2022)

Monopsony

Benefit

($/barrel of

renewable fuel)

in 2022)

Renewable Fuels 0.015 0.61 -0.009 -0.02

Oil 3.282 -1.06 3.476 7.88

Total 7.86

This analysis of the import cost and monopsony effect is based on the net import levels of

petroleum as projected by the EPA-NEMS, and is not sensitive to the mix of crude and product

imports. It is possible that in the future, while the U.S. will import most of its crude oil and some

petroleum products, it may be a net exporter of others, e.g. diesel fuel.302 However, oil security

concerns stem from the total consumption and net import of all petroleum fuels, whose prices are

all directly dependent on the volatile (and non-competitive) world crude oil market. For this

analysis, the key issue is not the trade balance for particular petroleum products, but the net level

of U.S. consumption and import of all petroleum, both crude and products. Reducing domestic

gasoline or diesel fuel with renewable fuels use can reduce net imports of all petroleum, and net

import costs, even if the U.S. remains a net exporter of some petroleum products. Consider the

case of U.S. diesel fuel. Replacing U.S. diesel fuel consumption with renewable biodiesel, whose

root supply volatility is largely independent of that of petroleum, can reduce the volatility of

productive inputs to the macroeconomy, regardless of the trade balance in diesel fuel.

5.2.5 Macroeconomic Dislocation Costs Associated with Oil and Renewable Fuel Price

Variability

Fluctuations in oil and renewable fuel prices are estimated to cause macroeconomic

losses due to dislocations and adjustment costs. Macroeconomic losses during price shocks

reflect both aggregate output losses and so called “allocative” losses. The former are a reduction

in the level of output that the U.S. economy can produce fully using its available resources; and

the latter stem from temporary dislocation and underutilization of available resources due to the

shock, such as labor unemployment and idle plant capacity. The aggregate output effect, a

reduction in “potential” economic output, will last so long as the price is elevated. It depends on

the extent and duration of any disruption in the world supply of oil, since these factors determine

302 While at the time of the implementation of this rule, the U.S. exports some diesel fuel, it is not clear that this

situation will long persist. Under EIA AEO2009, the U.S. is a net importer of refined products (as well as crude)

throughout the forecast horizon. In particular, over the RFS2 horizon (2010-2022) diesel fuel consumption is

expected to grow at 1.4 per cent per year (Table 11) while gasoline demand will decline at 1.0 per cent per year.

Thus, under this outlook, crude refinery runs needed to meet gasoline supply will decline, while U.S. demand for the

distillate/diesel cuts will grow.
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the magnitude of the resulting increase in prices for petroleum products, as well as whether and

how rapidly these prices return to their pre-disruption levels.

In addition to the aggregate contraction, there are “allocative” or “adjustment” costs

associated with dislocated energy markets. Because supply disruptions and resulting price

increases occur suddenly, empirical evidence shows they impose additional costs on businesses

and households which must seek to adjust their use of petroleum and other productive factors

more rapidly than if the same price increase had occurred gradually. Opportunities for short run

adjustments of energy use and other productive factors of the economy are limited and costly.

Dislocational effects include the unemployment of workers and other resources during the time

needed for their intersectoral or interregional reallocation, and pauses in capital investment due

to uncertainty. These adjustments temporarily reduce the level of economic output that can be

achieved even below the “potential” output level that would ultimately be reached once the

economy’s adaptation to higher petroleum prices was complete. The additional costs imposed on

businesses and households for making these adjustments reflect their limited ability to adjust

prices, output levels, and their use of energy, labor and other inputs quickly and smoothly in

response to rapid changes in prices for petroleum products.

In the prior “oil import premium” analysis undertaken for the RFS2 proposal, oil price

shocks were estimated to have macroeconomic losses based on a single fixed elasticity of Gross

Domestic Product (GDP) with respect to oil price. For this final RFS2 rule analysis in the

OSMM, it is recognized that the dislocation portion of disruption costs depends not only on the

magnitude of the price change, but on the changing importance of both oil and renewable fuels in

the economy, as well as the degree to which the price movement is novel and disturbing.303

Thus, when a shock causes fuel prices to jump up and stay up, initially the dislocation is larger,

and over time the economy adjusts to higher prices and the macroeconomic dislocation

dissipates. To account for this, OSMM tracks the evolution of an “adjusted” oil price and

renewable fuel price, which is based on a lagged partial adjustment process, essentially yielding

a weighted average of past prices with geometrically declining weights. This weighted-lag

adjusted price is constructed to represent the average price level to which the economy has

already had time to adjust.304 It is deviations from this level that are dislocational and costly.

For both oil price and renewable fuel price fluctuations, the macroeconomic dislocation cost is

calculated by applying a GDP loss elasticity to the ratio of the current price to the adjusted price.

Furthermore, the applied GDP elasticity varies with the value share of expenditure on the fuel,

both for oil and renewable fuels, in the economy. The estimated GDP losses from renewable

303 This attention to the degree to which the observed price is unusual or “novel” follows the work of Lee et al. (Lee,

K.; Ni, S. & Ratti, R. “Oil shocks and the Macroeconomy: The Role of Price Variability” Energy Journal, 1995, 16,

39-56, and Hamilton’s NOPI formulation (e.g. Hamilton, J. “What is an Oil Shock?”, Journal of Econometrics,

Elsevier, 2003, 113, 363-398). Several works of Brown, Huntington, and Gately, for example, also consider the role

of an “adjusted price” in the determination of supply and demand responses.

304 The adjusted price is the geometric distributed (i.e., Koyck) lag average of all prior prices, to represent that the

economy only partially adjusts to changing prices each year. The annual adjustment rate of this price, which

corresponds to the assumed annual accommodation of new prices by the macroeconomy, is taken as 33 per cent.

This is consistent with empirical evidence that the dislocational impact of energy price shocks extends more than

one year, but is mostly complete after three years.
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fuel price fluctuations are based on a GDP Adjustment Cost305 elasticity with respect to oil

prices, but rescaled in accordance with the ratio of renewable fuel expenditures to total oil

expenditures.

One feature of the OSMM is its explicit treatment of renewable fuel volatility. Renewable

fuel supply and hence, the price of renewable fuels, is also subject to disturbances, and the

resulting production cost volatility is anticipated to impose some costs on the economy. While

E85 and gasoline prices are often strongly correlated at the level of individual retail stations, this

correlation reflects primarily the process of market substitution, and the expected phenomenon

that ordinarily end-use prices for close substitutes will equilibrate and track one another. Further

upstream (i.e., closer to the terminal and production plant gate), this price correlation diminishes.

The historical prices of agricultural products that are likely to be used as feedstocks for

renewable fuels are somewhat volatile, but almost completely uncorrelated with oil prices. For

example, consider monthly price changes between crude oil and key agricultural crops that

would be used as feedstocks for renewable fuels—sugar, corn, switchgrass, and softwood

lumber—during the time period from January, 1990 to December, 2008 (Table 5.2.5-1 below). In

the case considered here, wheat prices are used as a surrogate for switchgrass prices, since both

crops are likely to be grown in similar agricultural areas in the U.S. (i.e., the Southwest region of

the U.S.) and subject to similar weather patterns. These agricultural commodities have relatively

low correlations with crude oil; 3 percent, 5 percent, -1 percent and 1 percent, for sugar, wheat,

corn and softwood lumber, respectively.

Table 5.2.5-1. Cross Correlations of Monthly Price Changes of Crude Oil

and Selected Key Renewable Fuel Feedstocks, January, 1990 to December, 2008

Crude Oil Sugar Wheat Corn Softwood

Crude Oil 100% 3% 5% -1% 1%

Sugar 100% 20% 10% 2%

Wheat 100% 46% 23%

Corn 100% -10%

Softwood 100%

Source: Leiby 2009, based on IMF/IFS database, Commodity Prices & Indices, Monthly, 1970 to December 2008.

From the standpoint of quantifying the macroeconomic/disruption of the increased use of

renewable fuels, two factors are important. The first factor is an estimate of the variability in the

supply of renewable fuels. The second factor is the change in renewable fuel production costs

that stems from fundamental supply volatility at the feedstock level. This analysis represents

renewable fuel supply volatility and risk based on historical variations in annual crop yields.

Crop yields vary substantially from year to year based on growing conditions, including droughts

305 GDP Adjustment Costs from biofuel price fluctuations are based on applying the adjustment cost elasticity to the

ratio of the current year price to the adjusted price. See Huntington (2005, “The Economic Consequences of Higher

Crude Oil Prices,” Final Report EMF SR 9, Energy Modeling Forum, Stanford University, October, p. 43) notes

“Economic theory suggests strongly that, in the absence of major threshold effects, the direct response of the GDP

and price levels to oil price changes should be proportional to oil’s value share in total output.” In the OSMM, for

both oil and biofuels, the adjustment cost elasticity varies from year to year according to the expenditure share in

GDP. For biofuels it is given by the 1983 reference value for oil times the ratio of current biofuel expenditure share

to 1983 oil expenditure share.
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and floods, and a variety of other factors.306 The supply risk of lower volumes of feedstock

production due to a host of factors is assumed to be independent of oil prices.

Data on renewable fuel feedstock yield volatilities are presented in Table 5.2.5-2 below.

Resulting estimates of renewable fuel cost volatility, based upon standard deviations from

historical trends in renewable fuel feedstock yields, vary from 2.4 percent to 8.7 percent, with

ethanol derived from sugar cane estimated to have the lowest volatility, and ethanol derived from

corn estimated to have the highest volatility. These estimates of feedstock yield volatility are

then used to estimate variations in feedstock cost307 and implied variations in renewable fuel

production costs at the plant gate.308 As one would expect, renewable fuel cost volatility

increases with the volatility of feedstock supply, but decreases in cases where feedstocks

comprise a lower percentage of total production cost (as is the case with cellulosic renewable

fuel). By way of comparison, the historical volatility of world oil prices over the last twenty-five

years is 28 percent, considerably higher than the estimated volatility of renewable fuels.309

Table 5.2.5-2.

Selected Key Renewable Fuel Feedstocks Annual Yield Volatility

Renewable Fuel

Feedstock

Corn Soybeans Wheat Sugar Cane

Historical Yield

Volatility

8.69% 6.81% 13.07% 2.37%

Based on Years 1960-2008 1960-2008 1960-2008 1997-2008

Source: OSSM Supporting Data, Leiby/ORNL 2009. Volatility is measured as the standard deviation of annual

percentage deviation from historical trend yields. Yield data and volatility are from Bruce Babcock, CARD, Iowa

State, November, 2009.

The introduction of renewable fuels affects macroeconomic disruption costs from oil by

reducing the oil-intensity of the economy, and by changing oil price movements slightly (since

the addition of renewable fuels slightly alters the elasticity of demand for motor fuels). In

addition, the introduction of renewable fuels affects the macroeconomic disruption costs by

adding separate disruption costs associated with the independent volatility of renewable fuels

supply. The magnitude of GDP dislocation losses for a given oil price change is calculated

based on a summary parameter, “GDP elasticity.” That elasticity is adjusted from historical (i.e.,

306 In applying historical yield and feedstock price variations to projected outcomes, it was recognized that two

offsetting factors may cause future biofuel feedstock supply risk to differ from the past. These factors may offset

each other: future drought risk may increase with climate change; yet some crops are also becoming increasingly

drought resistant.

307 Data on yield volatility were obtained from Bruce Babcock of CARD, Iowa State. Variations in yield were

converted to estimated variations in feedstock cost based on elasticities from Thompson, W., Meyer, S. & Westhoff,

P. “How Does Petroleum Price and Corn Yield Volatility Affect Ethanol Markets With and Without an Ethanol Use

Mandate?” Energy Policy, Elsevier, 2009, 37, Pages 745-749.

308 Biofuel production cost economics used in this analysis are based upon estimates from Tao L. and A. Aden

2009, "The Economics of Current and Future Biofuels," In Vitro Cell. Dev.Biol. - Plant, 45:199-217.

309 Volatility is calculated as the standard deviation of annual percentage price changes in the real price of imported

crude oil, 1984-2009, U.S. EIA data. This result is robust over the choice of the starting year for the volatility

calculation: 2000-2009: 30.0 percent; 29.2 percent; 1990-2009: 26.4 percent; 1980-2009: 27.2 percent; and 1970-

2009: 29.5 percent.
917

early 1980’s) levels based on the oil expenditure share in GDP. In the Reference Case, from

2010 to 2022, oil expenditures as a cost share of the U.S. economy vary from 2.5 percent in 2010

to peak at 4.7 percent in 2017, while declining thereafter to 3.8 percent by 2020. By way of

comparison, in 1983, the oil cost share was 4.6 percent (for which the assumed 1983

macroeconomic adjustment elasticity is -0.041). Under the RFS2 control case, renewable fuel

cost shares in the economy are quite small, growing from under 0.1 percent to about 0.2 percent,

an order of magnitude lower than the oil cost shares.

The reduction in GDP adjustment losses due to oil shocks is a result of both the reduction

in oil price and the slight decrease in oil share (about 0.2%). As mentioned above, the estimated

volatility of crude oil price is greater than renewable fuel production costs under the RFS2

control case. This is understandable for two reasons: historical crude prices are more volatile

than agricultural commodity prices that are projected to be the renewable fuel feedstocks; and

renewable fuel production costs are less volatile than feedstock costs. The combination of these

effects—lower costs shares of oil because of the introduction of higher levels of renewable fuels

and less volatility in renewable fuel costs compared to oil—leads to the offsetting gains and

losses in terms of macroeconomic dislocation costs, with the avoided losses from crude shocks

being greater than the added losses from renewable fuel shocks. Estimates of the avoided

macroeconomic dislocation benefits, in dollars per barrel of renewable fuel, are displayed in

Table 5.2.5-3 below.

Table 5.2.5-3.

Avoided Macroeconomic Dislocations Benefits for the RFS2 Control Case

vs. the AEO2007 Reference Case ($ per barrel of renewable fuel)

Reduction in Dislocation Cost from Oil Shocks $7.08

Reduction in Dislocation Cost from Renewable Fuel

Shocks

-$0.52

Avoided Macroeconomic Dislocation Benefits $6.56

This approach has implications for estimated GDP losses due to shock and price

fluctuations. If the use of renewable fuels reduces the expenditure share of oil in the U.S.

economy, then the sensitivity of the economy to oil shocks is also reduced, and estimated GDP

adjustment losses from oil shocks are lower. These effects are all relatively modest, since the

changes in the renewable fuel cost share are fairly modest. However, the overall importance of

oil in the economy and the greater estimated volatility of oil prices lead to a significant gain per

barrel of oil use avoided. The applicable GDP elasticity for renewable fuel price shocks is much

smaller than that for oil, in proportion to the expenditure share of renewable fuels. It does, of

course, grow over time with increased use renewable fuels.

Although retail renewable fuel prices will move closely with petroleum prices, and to a

lesser extent the renewable fuel plant-gate market prices and feedstock prices, there is still a

macroeconomic benefit from replacing oil with renewable fuels. This is true for three reasons.

First, the nature of the oil price increase and renewable fuel price increases are quite distinct.

When the price of oil increases from an oil supply shock, the resulting increase in renewable
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fuels has a very different origin–it is due to substitution toward renewable fuels, and thus reflects

a demand response rather than a supply shock. While price change is commonly used as the

summary measure of the shock, the underlying quantity changes cannot be forgotten as the root

cause of the economic loss. Unlike oil, the quantity of renewable fuel supplied during an oil

shock will increase, or at least remain little changed. Second, the short-run increase in the price

of renewable fuel is no different from the induced price increase in all other substitutes for oil:

energy or non-energy. Adding a new, independent supply source like renewable fuel is

equivalent to adding any other alternative fuel, or even to adding a conservation alternative. The

price of all of these alternatives will rise to some extent with oil prices, but this substitution

effect is part of the solution to the disruption rather than part of the problem. Through

substitution, renewable fuels can also dampen the oil price shock, a beneficial effect that is only

partially represented in this analysis. Third, to the extent that renewable fuel prices rise with

little change in production, the payments will be largely to domestic producers, as windfall gains

rather than losses.

5.2.6 Estimates of per Barrel Energy Security Benefits

Table 5.2.6-1 below summarizes ORNL’s estimate of the energy security benefits

associated with the RFS2 renewable fuel volumes, including the components of the energy

security benefit.

Table 5.2.6-1.

Energy Security Benefits from the RFS2

Control Case vs. the AEO 2007 Reference Case

(2007$/barrel of renewable fuel)

Effect Updated ORNL Study

Monopsony (best estimate)

(range)

$7.86

($5.37-$10.71)

Macroeconomic Disruption (best estimate)

(range)

$6.56

($0.94-$12.23)

Total (best estimate)

(range)

$14.42

($6.31-$22.95)

The literature on the energy security for the last two decades has routinely combined the

monopsony and the macroeconomic disruption components when calculating the total value of

the energy security premium. However, in the context of using a global value for the Social Cost

of Carbon (SCC) the question arises: how should the energy security premium be used when

some benefits from the rule, such as the benefits of reducing greenhouse gas emissions, are

calculated using a global value? Monopsony benefits represent avoided payments by the U.S. to

oil producers in foreign countries that result from a decrease in the world oil price as the U.S.

decreases its consumption of imported oil (net of increased imported renewable fuel payments by

the U.S.) Although there is clearly a benefit to the U.S. when considered from the domestic

perspective, the decrease in price due to decreased demand in the U.S. also represents a loss to

other countries. Given the redistributive nature of this effect, do the negative effects on other

countries “net out” the positive impacts to the U.S.? If this is the case, then, the monopsony

portion of the energy security premium should be excluded from the net benefits calculation for
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the rule. Based on this reasoning, EPA's estimates of net benefits for this rule exclude the

portion of energy security benefits stemming from the U.S. exercising its monopsony power in

oil markets. Thus, EPA only includes the macroeconomic disruption/adjustment cost portion of

the energy security premium.

However, even when the global value for greenhouse gas reduction benefits is used, an

argument can be made that the monopsony benefits should be included in net benefits calculation

for this rule. Maintaining the earth’s climate is a global public good and as such requires that a

global cooperative perspective be taken on the benefits of GHG mitigation by all nations,

including the U.S. Given that a cooperative global approach is required to address the climate

change issue, each country (and market participant) should face the global SCC. In other words,

using the global SCC does not transform the calculation from a domestic (i.e., U.S.) to a global

one. Instead, the global SCC represents the domestic value that the U.S. should utilize to

contribute cooperatively to a global solution of the climate change problem.

Energy security, on the other hand, is broadly defined as protecting the U.S. economy

against circumstances that threaten significant short- and long-term increases in energy costs.

Energy security is inherently a domestic benefit. However, the use of the domestic monopsony

benefit is not necessarily in conflict with the use of the global SCC, because the global SCC

represents the benefits against which the costs associated with our (i.e., the U.S.’s) domestic

mitigation efforts should be judged. In addition, the U.S. values both maintaining the earth’s

climate and providing for its own energy security. If this reasoning holds, the two benefits—the

global benefits of reducing greenhouse gas emissions and the full energy security premium,

including the monopsony benefits—should be counted in the net benefits estimates of the rule.

In the final analysis, the Agency determined that the first argument is more compelling and

therefore has determined that using only the macroeconomic disruption component of the energy

security benefit is the appropriate metric for this rule.

5.2.7 Total Energy Security Benefits from RFS2 Rule

The energy security benefits of increasing the total renewable fuel volumes from the

AEO 2007 Reference Case volumes of 13.56 billion gallons to the Primary Control Case

volumes of 30.5 billion gallons are shown in Table 5.2.7-1. Total annual energy security

benefits are estimated by multiplying the change in renewable fuel volumes (16.94 billion

gallons or 403 million barrels) and the macroeconomic disruption/adjustment portion of the

energy security premium ($6.56/barrel of renewable fuels).

Table 5.2.7-1.

Total Energy Security Benefits from

the RFS2 Control Case vs. the AEO 2007 Reference Case

(billions of 2007$)

Year 2022

Benefits $2.6

5.3. Benefits of Reducing GHG Emissions
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5.3.1 Introduction

As discussed in Chapter 2, the increased volumes of renewable fuels mandated by the

RFS2 standards are projected to reduce greenhouse gas emissions (GHG). This section presents

estimates of the economic benefits that could be monetized for the reductions in GHG emissions

projected due to the RFS2 renewable fuel volumes. The total benefit estimates were calculated

by multiplying a marginal dollar value (i.e., cost per ton) of carbon emissions, also referred to as

“social cost of carbon” (SCC), by the anticipated level of emissions reductions in tons.

The SCC values underlying the benefits estimates for this rule represent U.S.

government-wide interim values for SCC. As discussed below, federal agencies will use these

interim values to assess some of the economic benefits of GHG reductions while an interagency

workgroup develops SCC values for use in the long-term. The interim values should not be

viewed as an expectation about the results of the longer-term process. Although these values

were not used in the NPRM, some commenters raised issues with these values and the

methodology used to develop them in response to their publication elsewhere. Many of these

issues are being examined by the interagency workgroup.

The rest of this section provides the basis for the interim SCC values, and the estimates of

the total climate-related benefits of the RFS2 renewable fuel volumes that follow from these

interim values. As discussed below, the interim dollar estimates of the SCC represent a partial

accounting of climate change impacts.

In addition to the partial quantitative account presented in this section, a qualitative

appraisal of climate-related impacts that are not fully captured in these values is published in

other recent climate change analyses. For example, EPA’s final Endangerment and Cause or

Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act and the

accompanying Technical Support Document (TSD) presents a summary of impacts and risks of

climate change projected in the absence of actions to mitigate GHG emissions.310 The TSD

synthesizes major findings from the best available scientific assessments of the scientific

literature that have gone through rigorous and transparent peer review, including the major

assessment reports of both the Intergovernmental Panel on Climate Change (IPCC) and the U.S.

Climate Change Science Program (CCSP).

5.3.2 Derivation of Interim Social Cost of Carbon Values

The “social cost of carbon” (SCC) is intended to be a monetary measure of the

incremental damage resulting from carbon dioxide (CO2) emissions, including (but not limited

to) net agricultural productivity loss, human health effects, property damages from sea level rise,

and changes in ecosystem services. Any effort to quantify and to monetize the consequences

associated with climate change will raise serious questions of science, economics, and ethics. But

with full regard for the limits of both quantification and monetization of impacts, the SCC can be

used to provide an estimate of the social benefits of reductions in GHG emissions.

310 See Federal Register /Vol.74, No.2398/Wednesday, December 16, 2009/Rules and Regulations at

http://frwebgate4.access.gpo.gov/cgi-bin/PDFgate.cgi?WAISdocID=969788398047+0+2+0&WAISaction=retrieve
921

For at least three reasons, any particular figure will be contestable. First, scientific and

economic knowledge about the impacts of climate change continues to grow. With new and

better information about relevant questions, including the cost, burdens, and possibility of

adaptation, current estimates will inevitably change over time. Second, some of the likely and

potential damages from climate change—for example, the loss of endangered species—are

generally not included in current SCC estimates. These omissions may turn out to be significant

in the sense that they may mean that the best current estimates are too low. As noted by the IPCC

Fourth Assessment Report, “It is very likely that globally aggregated figures underestimate the

damage costs because they cannot include many non-quantifiable impacts.” Third, when

economic efficiency criteria, under specific assumptions, are juxtaposed with ethical

considerations, the outcome may be controversial. These ethical considerations, including those

involving the treatment of future generations, should and will also play a role in judgments about

the SCC (see in particular the discussion of the discount rate, below).

To date, SCC estimates presented in recent regulatory documents have varied within and

among agencies, including DOT, DOE, and EPA. For example, a regulation proposed by DOT in

2008 assumed a value of $7 per metric ton CO2

311 (2006$) for 2011 emission reductions (with a

range of $0-14 for sensitivity analysis). One of the regulations proposed by DOE in 2009 used a

range of $0-$20 (2007$). Both of these ranges were designed to reflect the value of damages to

the United States resulting from carbon emissions, or the “domestic” SCC. In the final MY2011

CAFE EIS, DOT used both a domestic SCC value of $2/t-CO2 and a global SCC value of $33/tCO2 (with sensitivity analysis at $80/t-CO2) (in 2006 dollars for 2007 emissions), increasing at

2.4% per year thereafter. The final MY2011 CAFE rule also presented a range from $2 to $80/tCO2.

In the May 2009 RFS2 Proposal leading to today’s final rule, EPA identified preliminary

SCC estimates that spanned three orders of magnitude. EPA’s May 2009 proposal also presented

preliminary global SCC estimates developed from a survey analysis of the peer reviewed

literature (i.e., meta analysis). The global mean values from the meta analysis were $68 and

$40/t-CO2 for discount rates of 2% and 3% respectively (in 2006 real dollars for 2007

emissions).312

Since publication of the May 2009 RFS2 proposal, a federal interagency working group

has established a methodology for selecting a range of interim SCC estimates for use in

regulatory analyses. Today’s final rule presents the methodology and the resulting interim set of

SCC estimates, which reflect the Administration’s current understanding of the relevant

literature. Recent federal regulatory documents have also presented the interim SCC estimates,

including a proposal to limit vehicle greenhouse gas emissions that requests public comment on

the estimates and underlying methodology.313

311 For the purposes of this discussion, we present all values of the SCC as the cost per metric ton of CO2 emissions.

Some discussions of the SCC in the literature use an alternative presentation of a dollar per metric ton of carbon.

The standard adjustment factor is 3.67, which means, for example, that a SCC of $10 per ton of CO2 would be

equivalent to a cost of $36.70 for a ton of carbon emitted. Unless otherwise indicated, a “ton” refers to a metric ton.

312 74 FR 25094 (May 26, 2009).

313 Federal Register 40 CFR Parts 86 and 600, September 28, 2009 “Proposed Rulemaking To Establish Light-Duty

Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; Proposed Rule”
922

It should be emphasized that the analysis here is preliminary. These interim estimates are

being used for the short-term while an interagency group develops a more comprehensive

characterization of the distribution of SCC values for future economic and regulatory analyses.

The interim values should not be viewed as an expectation about the results of the longer-term

process.

This process will allow the workgroup to explore questions raised in the May 2009 RFS2

Proposal as they are relevant to the development of SCC values for use in the long-term. The

workgroup may evaluate factors not currently captured in today’s estimates due to time

constraints, such as the quantification of additional impact categories where possible and an

uncertainty analysis. The Administration will seek comment on all of the scientific, economic,

and ethical issues before establishing improved estimates for use in future rulemakings.

The outcomes of the Administration’s process to develop interim values are judgments in

favor of a) global rather than domestic values, b) an annual growth rate of 3%, and c) interim

global SCC estimates for 2007 (in 2007 dollars) of $56, $34, $20, $10, and $5 per metric ton of

CO2. The interim set of values is based on the following judgments.

5.3.2.1 Global and Domestic Measures

Because of the distinctive nature of the climate change problem, we present both a global

SCC and a fraction of that value that represents impacts that may occur within the borders of the

U.S. alone, or a “domestic” SCC, but fix our attention on the global measure. This approach

represents a departure from past practices, which relied, for the most part, on domestic measures.

As a matter of law, both global and domestic values are permissible; the relevant statutory

provisions are ambiguous and allow selection of either measure.314

Under OMB guidance, analysis from the domestic perspective is required, while analysis

from the international perspective is optional. The domestic decisions of one nation are not

typically based on a judgment about the effects of those decisions on other nations. But the

climate change problem is highly unusual in the sense that it involves (a) a global public good in

which (b) the emissions of one nation may inflict significant damages on other nations and (c)

the United States is actively engaged in promoting an international agreement to reduce

worldwide emissions.

In these circumstances, we believe the global measure is preferred. Use of a global

measure reflects the reality of the problem and is consistent with the continuing efforts of the

United States to ensure that emissions reductions occur in many nations.

Domestic SCC values are also presented. The development of a domestic SCC is greatly

complicated by the relatively few region- or country-specific estimates of the SCC in the

literature. One potential source of estimates comes from EPA’s ANPR Benefits TSD, using the

314 It is true that federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the

United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give

extraterritorial effect to federal law and hence does not intrude on such interests.
923

FUND model. The resulting estimates suggest that the ratio of domestic to global benefits varies

with key parameter assumptions. With a 3% discount rate, for example, the U.S. benefit is about

6 percent of the global benefit of GHG reductions for the “central” (mean) FUND results, while,

for the corresponding “high” estimates associated with higher climate sensitivity and lower

global economic growth, the U.S. benefit is less than 4 percent of the global benefit. With a 2

percent discount rate, the U.S. share is about 2-5 percent of the global estimate.

Based on this available evidence, an interim domestic SCC value equal to 6 percent of the

global damages is proposed. It is recognized that the 6 percent figure is approximate and highly

speculative and alternative approaches will be explored before establishing improved values for

future rulemakings. However, it should be noted that it is difficult to properly apportion global

benefits to different regions, because not all the damages citizens of one country would be

willing to pay to avoid will occur only within their own borders. For example, impacts outside

U.S. border can have significant welfare implications for U.S. populations (e.g. tourism, disaster

relief) and if not included, these omissions will lead to an underestimation of the “domestic”

SCC.

5.3.2.2 Filtering existing analyses

There are numerous SCC estimates in the existing literature, and it is reasonable to make

use of those estimates in order to produce a figure for current use. A starting point is provided by

the meta-analysis in Richard Tol, 2008.315 With that starting point, the Administration proposes

to “filter” existing SCC estimates by using those that (1) are derived from peer-reviewed studies;

(2) do not weight the monetized damages to one country more than those in other countries; (3)

use a “business as usual” climate scenario; and (4) are based on the most recent published

version of each of the three major integrated assessment models (IAMs): FUND, PAGE, and

DICE.

Proposal (1) is based on the view that those studies that have been subject to peer review

are more likely to be reliable than those that have not. Proposal (2) avoids treating the citizens of

one nation (or different citizens within the US) differently on the basis of income considerations,

which some may find controversial and in any event would complicate that analysis. Further it is

consistent with the potential compensation tests of Kaldor (1939) and Hicks (1940), which form

the conceptual foundations of benefit-cost analysis and use unweighted sums of willingness to

pay. Finally, this is the approach used in rulemakings across a variety of settings and

consequently keeps USG policy consistent across contexts.

Proposal (3) stems from the judgment that as a general rule, the proper way to assess a

policy decision is by comparing the implementation of the policy against a counterfactual state

where the policy is not implemented. In addition, our expectation is that most policies to be

evaluated using these interim SCC estimates will constitute small enough changes to the larger

economy to safely assume that the marginal benefits of emissions reductions will not change

between the baseline and policy scenarios.

315 Richard Tol, The Social Cost of Carbon: Trends, Outliers, and Catastrophes, Economics: The Open-Access,

Open-Assessment E-Journal, Vol. 2, 2008-25. http://www.economics-ejournal.org/economics/journalarticles/2008-

25 (2008).
924

Proposal (4) is based on four complementary judgments. First, the FUND, PAGE, and

DICE models now stand as the most comprehensive and reliable efforts to measure the economic

damages from climate change. Second, the latest versions of the three IAMs are likely to reflect

the most recent evidence and learning, and hence they are presumed to be superior to those that

preceded them. However, it is acknowledged that the most recently published results do not

necessarily repeat prior modeling exercises with an updated model, so valuable information may

be lost, for instance, estimates of the SCC using specific climate sensitivities or economic

scenarios. In addition, although some older model versions were used to produce estimates

between 1996 and 2001, there have been no significant modeling paradigm changes since 1996.

Third, any effort to choose among them, or to reject one in favor of the others, would be

difficult to defend at the present time. In the absence of a clear reason to choose among them, it

is reasonable to base the SCC on all of them. Fourth, in light of the uncertainties associated with

the SCC, a range of values is more representative and the additional information offered by

different models is important.

5.3.2.3 Use a Model-weighted Average of the Estimates at Each Discount Rate

At this time, a strong reason to prefer any of the three major IAMs (FUND, PAGE, and

DICE) over the others has not been identified. Accordingly, to address the concern that certain

models not be given unequal weight relative to the other models, the estimates are based on an

equal weighting of the means of the estimates from each of the models. Among estimates that

remain after applying the filter, we begin by taking the average of all estimates within a model.

The estimated SCC is then calculated as the average of the three model-specific averages. This

approach is used to ensure that models with a greater number of published results do not exert

unequal weight on the interim SCC estimates. However, note that the resulting set of SCC

estimates does not provide information about variability among or within models except in so far

as they have different discounting assumptions. In the future interagency process to generate a

more comprehensive distribution of SCC, we expect to exercise the available SCC models in a

systematic manner such that the resulting distributions of SCC values may incorporate a wider

range of uncertainties including discount rates, growth rates, climate sensitivities, and other

important parameters. This may lead to changes in the span of SCC estimates that are relevant

for policy analyses.

5.3.2.4 Apply a 3 Percent Annual Growth Rate to the Chosen SCC Values

SCC is expected to increase over time, because future emissions are expected to produce

larger incremental damages as physical and economic systems become more stressed as the

magnitude of climate change increases. Indeed, an implied growth rate in the SCC can be

produced by most of the models that estimate economic damages caused by increased GHG

emissions in future years. But neither the rate itself nor the information necessary to derive its

implied value is commonly reported. In light of the limited amount of debate thus far about the

appropriate growth rate of the SCC, applying a rate of 3 percent per year seems appropriate at

this stage. This value is consistent with the range recommended by IPCC (2007) and close to the

latest published estimate (Hope 2008).
925

5.3.2.5 Discount Rates

For estimation of the benefits associated with the mitigation of climate change, one of the

most complex issues involves the appropriate discount rate. OMB’s current guidance offers a

detailed discussion of the relevant issues and calls for discount rates of 3 percent and 7 percent. It

also permits a sensitivity analysis with low rates (1 – 3 percent) for intergenerational problems:

“If your rule will have important intergenerational benefits or costs you might consider a further

sensitivity analysis using a lower but positive discount rate in addition to calculating net benefits

using discount rates of 3 and 7 percent.”316

The choice of a discount rate, especially over long periods of time, raises highly

contested and exceedingly difficult questions of science, economics, philosophy, and law. See,

e.g., William Nordhaus, The Challenge of Global Warming (2008); Nicholas Stern, The

Economics of Climate Change (2008); Discounting and Intergenerational Equity (Paul Portney

and John Weyant eds. 1999). It is not clear that future generations would be willing to trade

environmental quality for consumption at the same rate as the current generations. Under

imaginable assumptions, decisions based on cost-benefit analysis with high discount rates might

harm future generations – at least if investments are not made for the benefit of those

generations. See Robert Lind, Analysis for Intergenerational Discounting, id. at 173, 176-177. It

is also possible that the use of low discount rates for particular projects might itself harm future

generations, by diverting resources from private or public sector investments with higher rates of

return for future generations. In the context of climate change, questions of intergenerational

equity are especially important.

Because of the substantial length of time in which CO2 and other GHG emissions reside

in the atmosphere, choosing a discount rate which is higher than the actual discount rate could

result in irreversible changes in CO2 concentrations, and possibly irreversible climate changes

(unless substantial reductions in short-lived climate forcing emissions are achieved). Even if

these changes are reversible, delaying mitigation efforts could result in substantially higher costs

of stabilizing CO2 concentrations. On the other hand, using too low a discount rate in benefitcost analysis may recommend some potentially economically unwarranted investments in

mitigation. In many cases these investments could be discontinued with little long term economic

disruptions. However, it is also possible that the use of low discount rates for particular projects

might itself harm future generations, by ensuring that resources are not used in a way that would

greatly benefit them.

Reasonable arguments support the use of a 3 percent discount rate. First, that rate is

among the two figures suggested by OMB guidance, and hence it fits with existing national

policy. Second, it is standard to base the discount rate on the compensation that people receive

for delaying consumption, and the 3 percent is close to the risk-free rate of return, proxied by the

return on long term inflation-adjusted U.S. Treasury Bonds, as of this writing. Although these

rates are currently closer to 2.5 percent, the use of 3 percent provides an adjustment for the

liquidity premium that is reflected in these bonds’ returns. However, this approach does not

adjust for the significantly longer time horizon associated with climate change impacts. It also

316 See OMB Circular A-4, pp. 35-36, citing Portney and Weyant, eds. (1999), Discounting and Intergenerational

Equity, Resources for the Future, Washington, DC.
926

could be argued that the appropriate interest rate should be lower than 3 percent if the benefits of

climate mitigation policies tend to be higher than expected in time periods when the returns to

investments in rest of the economy are lower than normal.

At the same time, others would argue that a 5 percent discount rate can be supported. The

argument relies on several assumptions. First, this rate can be justified by reference to the level

of compensation for delaying consumption, because it fits with market behavior with respect to

individuals’ willingness to trade-off consumption across periods as measured by the estimated

post-tax average real returns to risky private investments (e.g., the S&P 500). In the climate

setting, the 5 percent discount rate may be preferable to the riskless rate because the benefits to

mitigation are not known with certainty. In principal, the correct discount rate would reflect the

variance in payoff from climate mitigation policy and the correlation between the payoffs of the

policy and the broader economy.317

Second, 5 percent, and not 3 percent, is roughly consistent with estimates implied by

inputs to the theoretically derived Ramsey equation presented below, which specifies the optimal

time path for consumption. That equation specifies the optimal discount rate as the sum of two

components. The first term (the product of the elasticity of the marginal utility of consumption

and the growth rate of consumption) reflects the fact that consumption in the future is likely to be

higher than consumption today, so diminishing marginal utility implies that the same monetary

damage will cause a smaller reduction of utility in the future. Standard estimates of this term

from the economics literature are in the range of 3 percent-5 percent.318 The second component

reflects the possibility that a lower weight should be placed on utility in the future, to account for

social impatience or extinction risk, which is specified by a pure rate of time preference (PRTP).

A common estimate of the PRTP is 2 percent, though some observers believe that a principle of

intergenerational equity suggests that the PRTP should be close to zero. It follows that discount

rate of 5 percent is near the middle of the range of values that are able to be derived from the

Ramsey equation.319

It is recognized that the arguments above – for use of market behavior and the Ramsey

equation – face objections in the context of climate change, and of course there are alternative

317 Specifically, if the benefits of the policy are highly correlated with the returns from the broader economy, then

the market rate should be used to discount the benefits. If the benefits are uncorrelated with the broader economy the

long term government bond rate should be applied. Furthermore, if the benefits are negatively correlated with the

broader economy, a rate less than that on long term government bonds should be used (Lind, 1982 pp. 89-90).

318 For example, see: Arrow KJ, Cline WR, Maler K–G, Munasinghe M, Squitieri R, Stiglitz JE. 1996. Intertemporal

equity, discounting, and economic efficiency. Chapter 4 in Economic and Social Dimensions of Climate Change:

Contribution of Working Group III to the Second Assessment Report, Summary for Policy Makers. Cambridge:

Cambridge University Press; Dasgupta P. 2008. Discounting climate change. Journal of Risk and Uncertainty

37:141–169; Hoel M, Sterner T. 2007. Discounting and relative prices. Climatic Change 84:265–280; Nordhaus

WD. 2008. A Question of Balance: Weighing the Options on Global Warming Policies. New Haven, CT: Yale

University Press; Stern N. 2008. The economics of climate change. The American Economic Review 98(2):1–37..

319 Sterner and Persson (2008) note that a consistent treatment of the marginal utility of consumption would require

that if higher discount rates are justified by the diminishing marginal utility of consumption, e.g., a dollar of

damages is worth less to future generations because they have greater income, then so-called equity weights should

be used to account for the higher value that countries with lower income would place on a dollar of damages relative

to the U.S. This is a consistent and logical outcome of application of the Ramsey framework. Because the

distribution of climate change related damages is expected to be skewed towards developing nations with lower

incomes, this can have significant implications for estimates of total global SCC if the Ramsey framework is used to

derive discount rates.
927

approaches. In light of climate change, it is possible that consumption in the future will not be

higher than consumption today, and if so, the Ramsey equation will suggest a lower figure. The

historical evidence is consistent with rising consumption over time. 320

Some critics note that using observed interest rates for inter-generational decisions

imposes current preferences on future generations, which some economists say may not be

appropriate. For generational equity, they argue that the discount rate should be below market

rates to correct for market distortions and inefficiencies in intergenerational transfers of wealth

(which are presumed to compensate future generations for damage), and to treat generations

equitably based on ethical principles (see Broome 2008).321

Additionally, some analyses attempt to deal with uncertainty with respect to interest rates

over time. We explore below how this might be done.322

5.3.2.6 Interim Social Cost of Carbon Estimates

The application of the methodology outlined above yields interim estimates of the SCC

that are reported in Table 5.3.2.6-1. These estimates are reported separately using 3 percent and 5

percent discount rates. The cells are empty in rows 10 and 11, because these studies did not

report estimates of the SCC at a 3 percent discount rate. The model-weighted means are reported

in the final or summary row; they are $34 per t-CO2 at a 3% discount rate and $5 per t-CO2 with

a 5 percent discount rate.

320However, because climate change impacts may be outside the bounds of historical evidence, predictions about

future growth in consumption based on past experience may be inaccurate.

321 See Arrow, K.J., W.R. Cline, K-G Maler, M. Munasinghe, R. Squiteri, J.E.Stiglitz, 1996. "Intertemporal equity,

discounting and economic efficiency," in Climate Change 1995: Economic and Social Dimensions of Climate

Change, Contribution of Working Group III to the Second Assessment Report of the Intergovernmental Panel on

Climate Change. See also Weitzman, M.L., 1999, in Portney P.R. and Weyant J.P. (eds.), Discounting and

Intergenerational Equity, Resources for the Future, Washington, D.C.

322 Richard Newell and William Pizer, Discounting the distant future: how much do uncertain rates increase

valuations? J. Environ. Econ. Manage. 46 (2003) 52-71.
928

Table 5.3.2.6-1.

Global Social Cost of Carbon (SCC) Estimates ($/t-CO2 in 2007 (2007$)), Based on 3

Percent and 5 Percent Discount Rates *

Model Study Climate Scenario 3% 5%

1 FUND Anthoff et al. 2009 FUND default 6 -1

2 FUND Anthoff et al. 2009 SRES A1b 1 -1

3 FUND Anthoff et al. 2009 SRES A2 9 -1

4 FUND Link and Tol 2004 No THC 12 3

5 FUND Link and Tol 2004 THC continues 12 2

6 FUND Guo et al. 2006 Constant PRTP 5 -1

7 FUND Guo et al. 2006 Gollier discount 1 14 0

8 FUND Guo et al. 2006 Gollier discount 2 7 -1

FUND Mean 8.47 0

9 PAGE Wahba & Hope 2006 A2-scen 59 7

10 PAGE Hope 2006 7

11 DICE Nordhaus 2008 8

Summary Model-weighted Mean 34 5

*The sample includes all peer reviewed, non-equity-weighted estimates included in Tol (2008), Nordhaus (2008),

Hope (2008), and Anthoff et al. (2009), that are based on the most recent published version of FUND, PAGE, or

DICE and use business-as-usual climate scenarios.323324 All values are based on the best available information from

the underlying studies about the base year and year dollars, rather than the Tol (2008) assumption that all estimates

included in his review are 1995 values in 1995$. All values were updated to 2007 using a 3% annual growth rate in

the SCC, and adjusted for inflation using GDP deflator.

In today’s rule, benefits of reducing GHG emissions have been estimated using global

SCC values of $34 and $5 as these represent the estimates associated with the 3 percent and 5

percent discount rates, respectively.325 The 3 percent and 5 percent estimates have independent

323 Most of the estimates in Table 1 rely on climate scenarios developed by the Intergovernmental Panel on Climate

Change (IPCC). The IPCC published a new set of scenarios in 2000 for use in the Third Assessment Report (Special

Report on Emissions Scenarios - SRES). The SRES scenarios define four narrative storylines: A1, A2, B1 and B2,

describing the relationships between the forces driving greenhouse gas and aerosol emissions and their evolution

during the 21st century for large world regions and globally. Each storyline represents different demographic, social,

economic, technological, and environmental developments that diverge in increasingly irreversible ways. The

storylines are summarized in Nakicenovic et al., 2000 (see also http://sedac.ciesin.columbia.edu/ddc/sres/ ).

Although they were intended to represent BAU scenarios, at this point in time the B1 and B2 storylines are widely

viewed as representing policy cases rather than business-as-usual projections, estimates derived from these scenarios

to be less appropriate for use in benefit-cost analysis. They are therefore excluded.

324 Guo et al. (2006) report estimates based on two Gollier discounting schemes. The Gollier discounting assumes

complex specifications about individual utility functions and risk preferences. After various conditions are satisfied,

declining social discount rates emerge. Gollier Discounting Scheme 1 employs a certainty-equivalent social rate of

time preference (SRTP) derived by assuming the regional growth rate is equally likely to be 1% above or below the

original forecast growth rate. Gollier Discounting Scheme 2 calculates a certainty-equivalent social rate of time

preference (SRTP) using five possible growth rates, and applies the new SRTP instead of the original. Hope (2008)

conducts Monte Carlo analysis on the PRTP component of the discount rate. The PRTP is modeled as a triangular

distribution with a min value of 1%/yr, a most likely value of 2 %/yr, and a max value of 3 %/yr.

325 It should be noted that reported discount rates may not be consistently derived across models or specific

applications of models: while the discount rate may be identical, it may reflect different assumptions about the

individual components of the Ramsey equation identified earlier.
929

appeal and at this time a clear preference for one over the other is not warranted. Thus, we have

also included – and centered our current attention on – the average of the estimates associated

with these discount rates, which is $20. (Based on the $20 global value, the approximate

domestic fraction of these benefits would be $1.20 per metric ton of CO2 assuming that domestic

benefits are 6% of the global benefits.

The distinctions between sets of estimates generated using different discount rates are due

only in part to discount rate differences, because the models and parameters used to generate the

estimates in the sets associated with different discount rates also vary.

It is true that there is uncertainty about interest rates over long time horizons.

Recognizing that point, Newell and Pizer (2003) have made a careful effort to adjust for that

uncertainty. The Newell-Pizer approach models discount rate uncertainty as something that

evolves over time.326 This is a different way to model discount rate uncertainty than the approach

outlined above, which assumes there is a single discount rate with equal probability of 3 percent

and 5 percent.

Table 5.3.2.6-2 reports on the application of the Newell-Pizer adjustments. The precise

numbers depend on the assumptions about the data generating process that governs interest rates.

Columns (1a) and (1b) assume that “random walk” model best describes the data and uses 3

percent and 5 percent discount rates, respectively. Columns (2a) and (2b) repeat this, except that

it assumes a “mean-reverting” process. While the empirical evidence does not rule out a meanreverting model, Newell and Pizer find stronger empirical support for the random walk model.

326 In contrast, an alternative approach based on Weitzman (2001) would assume that there is a constant discount

rate that is uncertain and represented by a probability distribution. The Newell and Pizer, and Weitzman approaches

are relatively recent contributions to the literature. .
930

Table 5.3.2.6-2.

Global Social Cost of Carbon (SCC) Estimates ($ per metric ton CO2 in 2007 (2007$))*,

Using Newell & Pizer (2003) Adjustment for Future Discount Rate Uncertainty**

Randomwalk modelMean-reverting

model

Model Study

Climate

Scenario 3% 5% 3% 5%

(1a) (1b) (2a) (2b)

1 FUND Anthoff et al. 2009 FUND default 10 0 7 -1

2 FUND Anthoff et al. 2009 SRES A1b 2 0 1 -1

3 FUND Anthoff et al. 2009 SRES A2 15 0 10 -1

4 FUND Link and Tol 2004 No THC 21 6 13 4

5 FUND Link and Tol 2004 THC continues 21 4 13 2

6 FUND Guo et al. 2006 Constant PRTP 9 0 6 -1

7 FUND Guo et al. 2006 Gollier discount 1 14 0 14 0

8 FUND Guo et al. 2006 Gollier discount 2 7 -1 7 -1

FUND Mean 12 1 9 0

9 PAGE

Wahba & Hope

2006 A2-scen 100 13 65 8

10 PAGE Hope 2006 13 8

11 DICE Nordhaus 2008 15 9

Summary

Model-weighted

Mean 56 10 37 6

*The sample includes all peer reviewed, non-equity-weighted estimates included in Tol (2008), Nordhaus (2008),

Hope (2008), and Anthoff et al. (2009), that are based on the most recent published version of FUND, PAGE, or

DICE and use business-as-usual climate scenarios. All values are based on the best available information from the

underlying studies about the base year and year dollars, rather than the Tol (2008) assumption that all estimates

included in his review are 1995 values in 1995$. All values were updated to 2007 using a 3% annual growth rate in

the SCC, and adjusted for inflation using GDP deflator. See the Notes to Table 1 for further details.

**Assumes a starting discount rate of 3% or 5%. Newell and Pizer (2003) based adjustment factors are not applied

to estimates from Guo et al. (2006) that use a different approach to account for discount rate uncertainty (rows 7-8).

Note that the correction factor from Newell and Pizer is based on the DICE model. The proper adjustment may

differ for other integrated assessment models that produce different time schedules of marginal damages. We would

expect this difference to be minor.

The resulting estimates of the social cost of carbon are necessarily greater. When the

adjustments from the random walk model are applied, the estimates of the social cost of carbon

are $10 and $56 per ton of CO2, with the 5 percent and 3 percent discount rates, respectively.

The application of the mean-reverting adjustment yields estimates of $6 and $37. Relying on the

random walk model, analyses are also conducted with the value of the SCC set at $10 and $56.

5.3.2.7 Caveats

There are at least four caveats to the approach outlined above.

First, the impacts of climate change are expected to be widespread, diverse, and

heterogeneous. In addition, the exact magnitude of these impacts is uncertain, because of the

inherent randomness in the Earth’s atmospheric processes, the U.S. and global economies, and
931

the behaviors of current and future populations. The existing IAMs do not currently individually

account for and assign value to all of the important physical and other impacts of climate change

that are recognized in the climate change literature.327 Therefore, as noted by the IPCC, SCC

estimates are “very likely” underestimated.328 In addition, the SCC approach also likely

underestimates the value of GHG reductions because the marginal values apply only to CO2

emissions, which have different impacts than non-CO2 emissions because of variances in

atmospheric lifetimes and radiative forcing.329 Although it is likely that our capability to quantify

and monetize impacts will improve with time, it is also likely that even in future applications, a

number of potentially significant benefits categories will remain unmonetized. In order to more

fully characterize of benefits of mitigation these non-monetized benefits should be discussed

along with monetized benefits based on the SCC.

Second, in the opposite direction, it is unlikely that the damage estimates adequately

account for the directed technological change that climate change will cause. In particular,

climate change will increase the return on investment to develop technologies that allow

individuals to better cope with climate change. For example, it is likely that scientists will

develop crops that are better able to withstand high temperatures. In this respect, the current

estimates may overstate the likely quantified damages, though the costs associated with the

investments in adaptive technologies must also be considered (technologies must also be

included in the calculations, as the benefits should reflect net welfare changes to society).

Third, there has been considerable recent discussion of the risk of catastrophic impacts

and of how best to account for worst-case scenarios. Recent research by Weitzman (2009)

specifies some conditions under which the possibility of catastrophe would undermine the use of

IAMs and conventional cost-benefit analysis. This research requires further exploration before its

generality is known and the proper way to incorporate it into regulatory reviews is understood.

Fourth, it is also worth noting that the SCC estimates are only relevant for incremental

policies relative to the projected baselines, which capture business-as-usual scenarios. To

evaluate non-marginal changes, such as might occur if the U.S. acts in tandem with other

nations, then it might be necessary to go beyond the simple expedient of using the SCC along the

BAU path. This would require explicitly calculating the total benefits in a move from the BAU

scenario to the policy scenario, without imposing the restriction that the marginal benefit remains

constant over this range.

5.3.2.8 Other Options

327Examples of impacts that are difficult to monetize, and have generally not been included in SCC estimates,

include risks from extreme weather (death, disease, agricultural damage, and other economic damage from droughts,

floods and wildfires) and possible long-term catastrophic events, such as collapse of the West Antarctic ice sheet or

the release of large amounts of methane from melting permafrost.

328 IPCC WGII. 2007. Climate Change 2007 - Impacts, Adaptation and Vulnerability Contribution of Working

Group II to the Fourth Assessment Report of the IPCC.

329Radiative forcing is the change in the balance between solar radiation entering the atmosphere and the Earth's

radiation going out. On average, a positive radiative forcing tends to warm the surface of the Earth while negative

forcing tends to cool the surface. Greenhouse gases have a positive radiative forcing because they absorb and emit

heat. See http://www.epa.gov/climatechange/science/recentac.html for more general information about GHGs and

climate science.
932

The Administration considered other interim SCC options in addition to the approach

described above. Similar to May 2009 RFS2 Proposal, one alternative option was to bring in

SCC estimates in studies published after 1995, rather than limiting the estimates to those in

studies relying on the most recent published version of each of the three major integrated

assessment models: PAGE, FUND, and DICE. Although some older model versions (and old

versions of other models) were used to produce estimates between 1996 and 2001, there have

been no significant modeling paradigm changes since 1996. Rather, improvements to PAGE,

FUND, and DICE since 1996 have reflected incremental technical enhancements.

Another option was to select a range of SCC values for separate discount rates. For

example, sensitivity analysis could be conducted at the lowest and highest SCC values reported

in the filtered set of estimates for each discount rate considered. If considering SCC estimates

from studies published after 1995 and a discount rate of 2 percent, this option would result in a

range of SCC values of $5/t-CO2 to $260/t-CO2 (2007 emissions in 2007 dollars); at a 3 percent

discount rate, the range would be $0 to $58/ t-CO2.

Finally, we considered that the use of certain key assumptions under the Ramsey

framework, such as placing approximately equal weight on the welfare of current and future

generations, would imply use of a 2% discount rate. The Newell and Pizer (2003) method

applied to recent long-term risk free rates would likewise be approximately consistent with a rate

of 2 percent.330

5.3.2.9 Ongoing SCC Development

As noted, this is an emphatically interim SCC value. The judgments herein will be

subject to further scrutiny and exploration.

5.3.3 Application of Interim SCC Estimates to GHG Emissions Reductions from this

Final Rule

While no single rule or action can independently achieve the deep worldwide emissions

reductions necessary to halt and reverse the growth of GHGs, the combined effects of multiple

strategies to reduce GHG emissions domestically and abroad could make a major difference in

the climate change impacts experienced by future generations.331 The projected net GHG

emissions reductions associated with this final rule reflect an incremental change to projected

total global emissions. Given that the climate response is projected to be a marginal change

relative to the baseline climate, we estimate the marginal value of changes in climate change

impacts over time and use this value to measure the monetized marginal benefits of the GHG

emissions reductions projected for this rule.

330 Specifically, Newell and Pizer (2003) found that modeling of uncertainty in economic growth causes the

effective discount rate to decline over time. When starting at a 4% discount rate, the effective discount rate is 2% at

100 years and 1% at 200 years.

331 The Supreme Court recognized in Massachusetts v. EPA that a single action will not on its own achieve all

needed GHG reductions, noting that “[a]gencies, like legislatures, do not generally resolve massive problems in one

fell regulatory swoop.’’ See Massachusetts v. EPA, 549 U.S. at 524 (2007).
933

Accordingly, EPA has used the set of interim, global SCC values described above to

estimate the benefits resulting from the renewable fuel volumes mandated by EISA. The interim

SCC values, which reflect the Administration’s interim interpretation of the current literature, are

$5, $10, $20, $34, and $56, in 2007 dollars, and are based on a CO2 emissions change of 1 metric

ton in 2007. Table 5.3.3-1 presents the interim SCC values for the years 2007 and 2022 in 2007

dollars.

Table 5.3.3-1. Interim SCC Schedule (2007$ per metric tonne of CO2)

Year 5% 5% (NewellPizer)*Average SCC

from 3% and 5%

3% 3% (NewellPizer)*

2007 $5 $10 $20 $34 $56

2022 $8 $16 $30 $53 $88

Note: The SCC values are dollar-year and emissions-year specific. These values are presented in 2007$, for

individual year of emissions. To determine values for years not presented in the table, use a 3% growth rate. SCC

values represent only a partial accounting for climate impacts.

*SCC values are adjusted based on Newell and Pizer (2003) to account to future uncertainty in discount rates.

Tables 5.3.3-2 through 5.3.3-4 provide, for the high, base, and low cases, the average

annual GHG emissions reductions in 2022. The annualized emissions reductions are multiplied

by the SCC estimates for 2022 from Table 5.3.3-1 to produce the average annual monetized

benefit from the emissions reductions from the rule for CO2-equivalent GHGs. This is

equivalent to taking the time stream of emissions from the increase in renewable fuel volumes,

multiplying them by the SCC (which is increasing at a rate of 3 percent per year), and then

discounting the stream of benefits by 3 percent.

Table 5.3.3-2.

Average Annual Emissions Reduction (Million Metric Tonnes CO2-e) and Monetized

Benefits (Million 2007$) of RFS-2 Volumes in 2022, High Case

CO2 Non-CO2 GHG Total GHG

Emissions

Reductions

148.525 -8.234 140.291

5% $1,188 -$66 $1,122

5% (Newell-Pizer) $2,376 -$132 $2,245

Average SCC from

3% and 5% $4,515 -$250 $4,265

3% $7,842 -$435 $7,407

3% (Newell-Pizer) $13,069 -$725 $12,344
934

Table 5.3.3-3.

Average Annual Emissions Reduction (Million Metric Tonnes CO2-e) and Monetized

Benefits (Million 2007$) of RFS-2 Volumes in 2022, Base Case

CO2 Non-CO2 GHG Total GHG

Emissions

Reductions

146.645 -8.234 138.411

5% $1,173 -$66 $1,107

5% (Newell-Pizer) $2,346 -$132 $2,215

Average SCC from

3% and 5% $4,458 -$250 $4,208

3% $7,743 -$435 $7,308

3% (Newell-Pizer) $12,903 -$725 $12,179

Table 5.3.3-4.

Average Annual Emissions Reduction (Million Metric Tonnes CO2-e) and Monetized

Benefits (Million 2007$) of RFS-2 Volumes in 2022, Low Case

CO2 Non-CO2 GHG Total GHG

Emissions

Reductions

144.338 -8.234 136.104

5% $1,155 -$66 $1,089

5% (Newell-Pizer) $2,309 -$132 $2,178

Average SCC from

3% and 5% $4,388 -$250 $4,138

3% $7,621 -$435 $7,186

3% (Newell-Pizer) $12,700 -$725 $11,976

Table 5.3.3-5 provides, for the high, base, and low cases, the monetized benefits from the

emissions reductions from the increase in renewable fuel volumes for CO2-equivalent GHGs in

2022. The SCC estimates for 2022 increase at a rate of 3 percent per year, and are then

multiplied by the stream of emissions for each respective year for 30 years. The monetized

benefits in table 5.3.3-5 represent the net present value of these emissions for 30 years using a

discount rate of 7 percent.

Table 5.3.3-5.

Monetized Benefits (Million 2007$) of RFS-2 Volumes in 2022

Using a 7% Discount Rate

High Base Low

5% $606 $620 $631

5% (Newell-Pizer) $1,212 $1,239 $1,262

Average SCC from

3% and 5% $2,302 $2,355 $2,397

3% $3,999 $4,089 $4,163

3% (Newell-Pizer) $6,665 $6,816 $6,939
935

5.4 Quantified and Monetized Co-pollutant Health and Environmental

Impacts

5.4.1 Overview

This section describes EPA’s analysis of the co-pollutant health and environmental

impacts that can be expected to occur as a result of the increase in renewable fuel use throughout

the period from initial implementation through 2022. Although the purpose of this final rule is to

implement the renewable fuel requirements established by the Energy Independence and

Security Act (EISA) of 2007, the increased use of renewable fuels will also impact emissions of

criteria and air toxic pollutants and their resultant ambient concentrations. The fuels changes

detailed in Section 3.1 of the RIA will influence emissions of VOCs, PM, NOX, and SOX and air

toxics and affect exhaust and evaporative emissions of these pollutants from vehicles and

equipment. They will also affect emissions from upstream sources such as fuel production,

storage, distribution and agricultural emissions. Any decrease or increase in ambient ozone,

PM2.5, and air toxics associated with the increased use of renewable fuels will impact human

health in the form of a decrease or increase in the risk of incurring premature death and other

serious human health effects, as well as other important public health and welfare effects.

This analysis reflects the impact of the 2022 mandated renewable fuel volumes (the

“RFS2 control case”) compared with two different reference scenarios that include the use of

renewable fuels: a 2022 baseline projection based on the RFS1-mandated volume of 7.1 billion

gallons of renewable fuels, and a 2022 baseline projection based on the AEO 2007 volume of

roughly 13.6 billion gallons of renewable fuels.332 Thus, the results represent the impact of an

incremental increase in ethanol and other renewable fuels. We note that the air quality modeling

results presented in this final rule do not constitute the “anti-backsliding” analysis required by

Clean Air Act section 211(v). EPA will be analyzing air quality and health impacts of increased

renewable fuel use through that study and will promulgate appropriate mitigation measures under

section 211(v), separate from this final action.

As can be seen in Section 3.4 of this RIA, there are both increased and decreased

concentrations of ambient criteria pollutants and air toxics. Overall, we estimate that the final

rule will lead to a net increase in criteria pollutant-related health impacts. By 2022, the final

RFS2 rule volumes relative to both reference case scenarios (RFS1 and AEO2007), are projected

to adversely impact PM2.5 air quality over parts of the U.S., while some areas will experience

decreases in ambient PM2.5. As described in Section 3.4, ambient PM2.5 is likely to increase as a

result of emissions at renewable fuel production plants and from renewable fuel transport, both

of which are more prevalent in the Midwest. PM concentrations are also likely to decrease in

some areas. While the PM-related air quality impacts are relatively small, the increase in

population-weighted national average PM2.5 exposure results in a net increase in adverse PMrelated human health impacts. (the increase in national population weighted annual average

332 The 2022 modeled scenarios assume the following: RFS1 reference case assumes 6.7 Bgal/yr ethanol and 0.38

Bgal/yr biodiesel; AEO2007 reference case assumes 13.18 Bgal/yr ethanol and 0.38 Bgal/yr biodiesel; RFS2 control

case assumes 34.14 Bgal/yr ethanol, 0.81 Bgal/yr biodiesel, and 0.38 Bgal/yr renewable diesel. Please refer to

Chapter 3.3 and Table 3.3-1 for more information about the renewable fuel volumes assumed in the modeled

analyses and the corresponding emissions inventories.
936

PM2.5 is 0.006 μg/m3 and 0.002 μg/m3 relative to the RFS1 and AEO2007 reference cases,

respectively).

The required renewable fuel volumes, relative to both reference scenarios, are also

projected to adversely impact ozone air quality over much of the U.S., especially in the Midwest,

Northeast and Southeast. These adverse impacts are likely due to increased upstream emissions

of NOx in many areas that are NOx-limited (acting as a precursor to ozone formation). There

are, however, ozone air quality improvements in some highly-populated areas that currently have

poor air quality. This is likely due to VOC emission reductions at the tailpipe in urban areas that

are VOC-limited (reducing VOC’s role as a precursor to ozone formation). Relative to the RFS1

mandate reference case, the RFS2 volumes result in a small increase in ozone-related health

impacts (population weighted maximum 8-hour average ozone increases by 0.177 ppb). Relative

to the AEO2007 reference case, the RFS2 volumes result in a small increase in ozone-related

health impacts (population weighted maximum 8-hour average ozone increases by 0.116 ppb).

The analysis of national-level PM2.5- and ozone-related health and environmental impacts

associated with the final rule is based on peer-reviewed studies of air quality and human health

effects (see US EPA, 2006 and US EPA, 2008).1180,1181 We are also consistent with the benefits

analysis methods that supported the recently proposed Portland Cement National Emissions

Standards for Hazardous Air Pollutants (NESHAP) RIA (U.S. EPA, 2009a),1182 the proposed

NO2 primary NAAQS RIA (U.S. EPA, 2009b),1183 and the proposed Category 3 Marine Diesel

Engines RIA (U.S. EPA, 2009c).1184 To model the ozone and PM air quality impacts of the 2022

renewable fuel volumes, we used the Community Multiscale Air Quality (CMAQ) model (see

Chapter 3.4). The modeled ambient air quality data serves as an input to the Environmental

Benefits Mapping and Analysis Program (BenMAP).333 BenMAP is a computer program

developed by the U.S. EPA that integrates a number of the modeling elements used in previous

analyses (e.g., interpolation functions, population projections, health impact functions, valuation

functions, analysis and pooling methods) to translate modeled air concentration estimates into

health effects incidence estimates and monetized benefits estimates.

Emissions and air quality modeling decisions were made early in the analytical process

and as a result, there are a number of important limitations and uncertainties associated with the

air quality modeling analysis that must be kept in mind when considering the results. A key

limitation of the analysis is that it employed interim emission inventories, which were enhanced

compared to what was described in the proposal, but did not include some of the later

enhancements and corrections of the final emission inventories presented in this FRM (see

Section 3.3 of this RIA). Most significantly, our modeling of the air quality impacts of RFS2

relied upon interim inventories that assumed that ethanol will make up 34 of the 36 billion gallon

renewable fuel mandate, that approximately 20 billion gallons of this ethanol will be in the form

of E85, and that the use of E85 results in fewer emissions of direct PM2.5 from vehicles. The

emission impacts, air quality results and benefits analysis would be different if, instead of E85,

more non-ethanol biofuels are used or mid-level ethanol blends are approved and utilized.

In fact, as explained in Chapter 1, our more recent analyses indicate that ethanol and E85

volumes are likely to be significantly lower than what we assumed in the interim inventories.

333 Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/

benmodels.html.
937

Furthermore, the final emission inventories do not include vehicle-related PM reductions

associated with E85 use, as discussed in Chapters 3.1-3.3 of this RIA. There are additional,

important limitations and uncertainties associated with the interim inventories that must be kept

in mind when considering the results, which are described in more detail in Chapter 3.4. While it

is difficult to describe the overall impact of these limitations and uncertainties on the quantified

and monetized health impacts of the increased renewable fuel volumes without updating the air

quality modeling analysis, we believe the results are still useful for describing potential nationallevel health impacts.

Additionally, after the air quality modeling was completed, we discovered an error in the

way that PM2.5 emissions from locomotive engines were allocated to counties in the inventory.

The mismatched allocations between the reference and control scenarios resulted in PM2.5

emission changes that were too high in some counties and too low in others, by varying degrees.

As a result, we did not present the modeling results for specific localized PM2.5 impacts in

Section 3.4. However, because the error was random and offsetting, there was very little impact

on national-level PM2.5 emissions. An analysis of the error's impact on the national emission

inventories found that direct PM2.5 emissions were inflated by 8% relative to the AEO reference

case and by 0.6% relative to the RFS1 reference case, leading to a small overestimation of

national PM-related adverse health impacts. Note that this error did not impact other PM

precursor inventories such as NOx and SO2. As a result, we have concluded that PM2.5

modeling results are still informative for national-level benefits assessment, particularly given

that other uncertainties in the PM2.5 inventory (such as E85 usage, discussed below) have a more

important (and offsetting) effect.
938

Table 5.4-1.

Estimated 2022 Monetized PM-and Ozone-Related Health Impacts

from the Mandated Renewable Fuel Volumesa

2022 Total Ozone and PM Benefits, RFS2 Control Case Compared to RFS1 Reference Casea

Premature Ozone

Mortality Function

Reference Total Benefits

(Billions, 2007$, 3%

Discount Rate)b,c

Total Benefits

(Billions, 2007$, 7%

Discount Rate) b,c

Bell et al., 2004 Total: -$1.4 to -$2.8

PM: -$0.92 to -$2.3

Ozone: -$0.52

Total: -$1.4 to -$2.6

PM: -$0.84 to -$2.0

Ozone: -$0.52

Huang et al., 2005 Total: -$1.8 to -$3.1

PM: -$0.92 to -$2.3

Ozone: -$0.83

Total: -$1.7 to -$2.9

PM: -$0.84 to -$2.0

Ozone: -$0.83

Multi-city analyses

Schwartz, 2005 Total: -$1.7 to -$3.0

PM: -$0.92 to -$2.3

Ozone: -$0.77

Total: -$1.6 to -$2.8

PM: -$0.84 to -$2.0

Ozone: -$0.77

Bell et al., 2005 Total: -$2.5 to -$3.8

PM: -$0.92 to -$2.3

Ozone: -$1.6

Total: -$2.4 to -$3.6

PM: -$0.84 to -$2.0

Ozone: -$1.6

Ito et al., 2005 Total: -$3.1 to -$4.5

PM: -$0.92 to -$2.3

Ozone: -$2.2

Total: -$3.0 to -$4.2

PM: -$0.84 to -$2.0

Ozone: -$2.2

Meta-analyses

Levy et al., 2005 Total: -$3.1 to -$4.5

PM: -$0.92 to -$2.3

Ozone: -$2.2

Total: -$3.1 to -$4.3

PM: -$0.84 to -$2.0

Ozone: -$2.2

2022 Total Ozone and PM Benefits, RFS2 Control Case Compared to AEO Reference Casea

Premature Ozone

Mortality Function

Reference Total Benefits

(Millions, 2007$, 3%

Discount Rate)b,c

Total Benefits

(Millions, 2007$, 7%

Discount Rate) b,c

Bell et al., 2004 Total: -$0.63 to -$1.0

PM: -$0.29 to -$0.70

Ozone: -$0.34

Total: -$0.60 to -$0.98

PM: -$0.26 to -$0.63

Ozone: -$0.34

Huang et al., 2005 Total: -$0.84 to -$1.3

PM: -$0.29 to -$0.70

Ozone: -$0.55

Total: -$0.81 to -$1.2

PM: -$0.26 to -$0.63

Ozone: -$0.55

Multi-city analyses

Schwartz, 2005 Total: -$0.80 to -$1.2

PM: -$0.29 to -$0.70

Ozone: -$0.51

Total: -$0.77 to -$1.1

PM: -$0.26 to -$0.63

Ozone: -$0.51

Bell et al., 2005 Total: -$1.3 to -$1.8

PM: -$0.29 to -$0.70

Ozone: -$1.0

Total: -$1.3 to -$1.7

PM: -$0.26 to -$0.63

Ozone: -$1.0

Ito et al., 2005 Total: -$1.7 to -$2.2

PM: -$0.29 to -$0.70

Ozone: -$1.5

Total: -$1.7 to -$2.1

PM: -$0.26 to -$0.63

Ozone: -$1.5

Meta-analyses

Levy et al., 2005 Total: -$1.8 to -$2.2

PM: -$0.29 to -$0.70

Ozone: -$1.5

Total: -$1.7 to -$2.1

PM: -$0.26 to -$0.63

Ozone: -$1.5

a Total includes premature mortality-related and morbidity-related ozone and PM2.5 benefits. Range was developed

by adding the estimate from the ozone premature mortality function to the estimate of PM2.5-related premature

mortality derived from either the ACS study (Pope et al., 2002) or the Six-Cities study (Laden et al., 2006).

b Note that total benefits presented here do not include a number of unquantified benefits categories. A detailed

listing of unquantified health and welfare effects is provided in Table 5.4-2.
939

c Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines for Preparing

Economic Analyses and OMB Circular A-4. Results are rounded to two significant digits for ease of presentation

and computation.

The monetized estimates in Table 5.4-1 include all of the human health impacts we are

able to quantify and monetize at this time. However, the full complement of human health and

welfare effects associated with PM and ozone remain unquantified because of current limitations

in methods or available data. We have not quantified a number of known or suspected health

effects linked with ozone and PM for which appropriate health impact functions are not available

or which do not provide easily interpretable outcomes (i.e., changes in heart rate variability).

Additionally, we are unable to quantify a number of known welfare effects, including acid and

particulate deposition damage to cultural monuments and other materials, and environmental

impacts of eutrophication in coastal areas. These are listed in Table 5.4-2.
940

Table 5.4-2.

Unquantified and Non-Monetized Potential Effects from the

Mandated Renewable Fuel Volumes

POLLUTANT/EFFECTS EFFECTS NOT INCLUDED IN ANALYSIS - CHANGES IN:

Ozone Healtha Chronic respiratory damageb

Premature aging of the lungsb

Non-asthma respiratory emergency room visits

Exposure to UVb (+/-)e

Ozone Welfare Yields for

-commercial forests

-some fruits and vegetables

-non-commercial crops

Damage to urban ornamental plants

Impacts on recreational demand from damaged forest aesthetics

Ecosystem functions

Exposure to UVb (+/-)e

PM Healthc

Premature mortality - short term exposuresd

Low birth weight

Pulmonary function

Chronic respiratory diseases other than chronic bronchitis

Non-asthma respiratory emergency room visits

Exposure to UVb (+/-)e

PM Welfare Residential and recreational visibility in non-Class I areas

Soiling and materials damage

Damage to ecosystem functions

Exposure to UVb (+/-)e

Nitrogen and Sulfate

Deposition Welfare

Commercial forests due to acidic sulfate and nitrate deposition

Commercial freshwater fishing due to acidic deposition

Recreation in terrestrial ecosystems due to acidic deposition

Existence values for currently healthy ecosystems

Commercial fishing, agriculture, and forests due to nitrogen deposition

Recreation in estuarine ecosystems due to nitrogen deposition

Ecosystem functions

Passive fertilization

CO Health Behavioral effects

HC/Toxics Healthf Cancer (benzene, 1,3-butadiene, formaldehyde, acetaldehyde)

Anemia (benzene)

Disruption of production of blood components (benzene)

Reduction in the number of blood platelets (benzene)

Excessive bone marrow formation (benzene)

Depression of lymphocyte counts (benzene)

Reproductive and developmental effects (1,3-butadiene)

Irritation of eyes and mucus membranes (formaldehyde)

Respiratory irritation (formaldehyde)

Asthma attacks in asthmatics (formaldehyde)

Asthma-like symptoms in non-asthmatics (formaldehyde)

Irritation of the eyes, skin, and respiratory tract (acetaldehyde)

Upper respiratory tract irritation and congestion (acrolein)

HC/Toxics Welfare Direct toxic effects to animals

Bioaccumulation in the food chain

Damage to ecosystem function

Odor
941

a

The public health impact of biological responses such as increased airway responsiveness to stimuli, inflammation

in the lung, acute inflammation and respiratory cell damage, and increased susceptibility to respiratory infection are

likely partially represented by our quantified endpoints.

b The public health impact of effects such as chronic respiratory damage and premature aging of the lungs may be

partially represented by quantified endpoints such as hospital admissions or premature mortality, but a number of

other related health impacts, such as doctor visits and decreased athletic performance, remain unquantified.

c

In addition to primary economic endpoints, there are a number of biological responses that have been associated

with PM health effects including morphological changes and altered host defense mechanisms. The public health

impact of these biological responses may be partly represented by our quantified endpoints.

d While some of the effects of short-term exposures are likely to be captured in the estimates, there may be

premature mortality due to short-term exposure to PM not captured in the cohort studies used in this analysis.

However, the PM mortality results derived from the expert elicitation do take into account premature mortality

effects of short term exposures.

e May result in benefits or adverse impacts.

f Many of the key hydrocarbons related to this rule are also hazardous air pollutants listed in the Clean Air Act.

While there will be impacts associated with air toxic pollutant emission changes that

result from the increased use of renewable fuels, we do not attempt to monetize those impacts.

This is primarily because currently available tools and methods to assess air toxics risk from

mobile sources at the national scale are not adequate for extrapolation to incidence estimations or

benefits assessment. The best suite of tools and methods currently available for assessment at

the national scale are those used in the National-Scale Air Toxics Assessment (NATA). The EPA

Science Advisory Board specifically commented in their review of the 1996 NATA that these

tools were not yet ready for use in a national-scale benefits analysis, because they did not

consider the full distribution of exposure and risk, or address sub-chronic health effects.334

While EPA has since improved the tools, there remain critical limitations for estimating

incidence and assessing benefits of reducing mobile source air toxics. EPA continues to work to

address these limitations; however, we did not have the methods and tools available for nationalscale application in time for the analysis of the final rule.335

5.4.2 Quantified Human Health Impacts

Tables 5.4-3 and 5.4-4 present the annual PM2.5 and ozone health impacts in the 48

contiguous U.S. states associated with the RFS2 volumes relative to both the RFS1 and AEO

reference cases for 2022. For each endpoint presented in Tables 5.4-3 and 5.4-4, we provide

both the mean estimate and the 90% confidence interval.

Using EPA’s preferred estimates, based on the ACS and Six-Cities studies and no

threshold assumption in the model of mortality, we estimate that the RFS2 volumes will result in

334 Science Advisory Board. 2001. NATA – Evaluating the National-Scale Air Toxics Assessment for 1996 – an

SAB Advisory. http://www.epa.gov/ttn/atw/sab/sabrev.html.

335 In April, 2009, EPA hosted a workshop on estimating the benefits or reducing hazardous air pollutants. This

workshop built upon the work accomplished in the June 2000 Science Advisory Board/EPA Workshop on the

Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on

approaches to estimating human health benefits from reductions in air toxics exposure, but no consensus was

reached on methods that could be implemented in the near term for a broad selection of air toxics. Please visit

http://epa.gov/air/toxicair/2009workshop.html for more information about the workshop and its associated materials.
942

between 110 and 270 cases of PM2.5-related premature deaths annually in 2022 when compared

to the RFS1 reference case. When compared to the AEO reference scenario, we estimate that the

RFS2 volumes will result in between 33 and 85 cases of avoided PM2.5-related premature deaths

annually in 2022. As a sensitivity analysis, when the range of expert opinion is used we estimate

that in 2022 the RFS2 volumes will result in between 34 and 360 PM-related premature

mortalities when compared to the RFS1 reference case and between 11 and 110 PM-related

premature mortalities when compared to the AEO reference case.

The range of ozone impacts associated with the RFS2 volumes is based on changes in

risk estimated using several sources of ozone-related mortality effect estimates. This analysis

presents six alternative estimates for the association based upon different functions reported in

the scientific literature, derived from both multi-city studies, such as the National Morbidity,

Mortality, and Air Pollution Study (NMMAPS) (Bell et al., 2004, Huang et al., 2005, and

Schwartz et al., 2005) and from a series of recent meta-analyses (Bell et al., 2005, Ito et al.,

2005, and Levy et al., 2005). This approach is not inconsistent with recommendations provided

by the NRC in their recent report (NRC, 2008) on the estimation of ozone-related mortality risk,

“The committee recommends that the greatest emphasis be placed on estimates from new

systematic multicity analyses that use national databases of air pollution and mortality, such as in

the NMMAPS, without excluding consideration of meta-analyses of previously published

studies.” For ozone-related premature mortality, we estimate that national changes in ambient

ozone will contribute to between 54 to 250 additional premature mortalities in 2022 as a result of

the RFS2 volumes relative to the RFS1 scenario. When compared to the AEO reference

scenario, we estimate that the RFS2 volumes will contribute to between 36 to 160 additional

ozone-related premature mortalities in 2022.

Following these tables, we also provide a more comprehensive presentation of the

distributions of mortality-related incidence generated using the available information from

empirical studies and expert elicitation associated with the RFS2 volumes compared to each

reference scenario. Tables 5.4-5 and 5.4-6 present the distributions of PM2.5-related premature

mortality based on the C-R distributions provided by each expert, as well as that from the dataderived health impact functions, based on the statistical error associated with the ACS study

(Pope et al., 2002) and the Six-cities study (Laden et al., 2006). The 90% confidence interval for

each separate estimate of PM-related mortality is also provided.

When comparing the RFS2 fuel volume scenario to the RFS1 reference case, the effect

estimates of nine of the twelve experts included in the elicitation panel fall within the

empirically-derived range provided by the ACS and Six-Cities studies. Only one expert falls

below this range, while two of the experts are above this range. This same relationship occurs

when comparing the RFS2 fuel volume scenario to the AEO reference case. Although the overall

range across experts is summarized in these tables, the full uncertainty in the estimates is

reflected by the results for the full set of 12 experts. The twelve experts’ judgments as to the

likely mean effect estimate are not evenly distributed across the range illustrated by arraying the

highest and lowest expert means.
943

Table 5.4-3.

Estimated PM2.5-Related Health Impacts Associated with the

Mandated Renewable Fuel Volumesa

Health Effect

2022 RFS2 Control Case

Compared to RFS1

Reference Case

(5th% - 95th%ile)

2022 RFS2 Control Case

Compared to AEO Reference

Case

(5th% - 95th%ile)

Premature Mortality – Derived from Epidemiology

Literatureb

Adult, age 30+, ACS Cohort Study (Pope et al., 2002)

Adult, age 25+, Six-Cities Study (Laden et al., 2006)

Infant, age <1 year (Woodruff et al., 1997)

-110

(-42 - -170)

-270

(-150 - -400)

0

(0 - -1)

-33

(-13 - -53)

-85

(-46 - -120)

0

(0 - -1)

Chronic bronchitis (adult, age 26 and over) -65

(-26 - -110)

-19

(-4 - -18)

Non-fatal myocardial infarction (adult, age 18 and over) -180

(-65 - -290)

-51

(-19 - -84)

Hospital admissions - respiratory (all ages)c-26

(-25 - -26)

-7

(-5 - -8)

Hospital admissions - cardiovascular (adults, age >18)d-55

(-44 - -70)

-12

(-9 - -16)

Emergency room visits for asthma (age 18 years and

younger)

-180

(-110 - -260)

-99

(-58 - -140)

Acute bronchitis, (children, age 8-12) -160

(0 - -330)

-50

(0 - -100)

Lower respiratory symptoms (children, age 7-14) -1,900

(-910 - -2,900)

-600

(-290 - -910)

Upper respiratory symptoms (asthmatic children, age 9-

18)

-1,400

(-450 - -2,400)

-450

(-140 - -750)

Asthma exacerbation (asthmatic children, age 6-18) -1,700

(-190 - -4,800)

-540

(-60 - -1,500)

Work loss days -11,000

(-10,000 - -13,000)

-3,200

(-2,800 - -3,700)

Minor restricted activity days (adults age 18-65) -68,000

(-57,000 - -78,000)

-19,000

(-16,000 - -22,000)

a Note that negative incidence expressed in this table reflects “disbenefits”; in other words, an increase in total

aggregated national-level ozone-related health impacts. Incidence is rounded to two significant digits. Estimates

represent incidence within the 48 contiguous United States.

b

PM-related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 2002) and

the Six-Cities Study (Laden et al., 2006). Note that these are two alternative estimates of adult mortality and should

not be summed. PM-related infant mortality based upon a study by Woodruff, Grillo, and Schoendorf, (1997).336

c Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COPD),

pneumonia and asthma.

d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart

disease, dysrhythmias, and heart failure.

336 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected Causes of

Postneonatal Infant Mortality and Particulate Air Pollution in the United States.” Environmental Health

Perspectives 105(6):608-612.
944

Table 5.4-4.

Estimated Ozone-Related Health Impacts Associated with the

Mandated Renewable Fuel Volumesa

Health Effect

2022 RFS2 Control Case

Compared to RFS1 Reference

Case

(5th% - 95th%ile)

2022 RFS2 Control Case

compared to AEO Reference

Case

(5th% - 95th%ile)

Premature Mortality, All agesb

Multi-City Analyses

Bell et al. (2004) – Non-accidental

Huang et al. (2005) – Cardiopulmonary

Schwartz (2005) – Non-accidental

Meta-analyses:

Bell et al. (2005) – All cause

Ito et al. (2005) – Non-accidental

Levy et al. (2005) – All cause

-54

(-17 - -92)

-90

(-31 - -149)

-83

(-24 - -140)

-180

(-80 - -270)

-240

(-140 - -350)

-250

(-170 - -330)

-36

(-10 - -62)

-59

(-18 - -100)

-55

(-13 - -97)

-120

(-49 - -180)

-160

(-90 - -230)

-160

(-110 - -220)

Hospital admissions- respiratory causes (adult, 65

and older)c

-470

(-20 - -860)

-310

(-5 - -580)

Hospital admissions -respiratory causes (children,

under 2)

-83

(-24 - -140)

-190

(-52 - -330)

Emergency room visit for asthma (all ages) -260

(0 - -740)

-180

(0 - -510)

Minor restricted activity days (adults, age 18-65) -300,000

(-110,000 - -500,000)

-200,000

(-59,000 - -340,000)

School absence days -110,000

(-35,000 - -180,000)

-75,000

(-19,000 - -120,000)

a Note that negative incidence expressed in this table reflects “disbenefits”; in other words, an increase in total

aggregated national-level ozone-related health impacts. Incidence is rounded to two significant digits. Estimates

represent incidence within the 48 contiguous United States. Note that negative incidence estimates represent

additional cases of an endpoint related to pollution increases associated with the rule.

b Estimates of ozone-related premature mortality are based upon incidence estimates derived from several alternative

studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. (2005); Levy et al.

(2005). The estimates of ozone-related premature mortality should therefore not be summed.

c Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for

COPD and pneumonia.
945

Table 5.4-5. Results of Application of Expert Elicitation: Annual Reductions in Premature

Mortality in 2022 Associated with the Mandated Renewable Fuel Volumes

Source of Mortality 2022 RFS2 Control Case Compared to the RFS1 Reference Case

Estimate

5th Percentile Mean 95th Percentile

Pope et al. (2002) -42 -110 -170

Laden et al. (2006) -150 -270 -400

Expert A -53 -290 -530

Expert B -11 -210 -480

Expert C -39 -220 -470

Expert D -32 -150 -250

Expert E -180 -360 -550

Expert F -130 -200 -260

Expert G 0 -130 -240

Expert H -1 -160 -380

Expert I -34 -220 -390

Expert J -52 -180 -390

Expert K 9 -34 -180

Expert L 4 -130 -310

Table 5.4-6. Results of Application of Expert Elicitation: Annual Reductions in Premature

Mortality in 2022 Associated with the Mandated Renewable Fuel Volumes

Source of Mortality 2022 RFS2 Control Case Compared to the AEO Reference Case

Estimate

5th Percentile Mean 95th Percentile

Pope et al. (2002) -13 -33 -53

Laden et al. (2006) -46 -85 -120

Expert A -17 -91 -170

Expert B 7 -65 -160

Expert C -12 -68 -150

Expert D -10 -48 -79

Expert E -57 -110 -170

Expert F -35 -59 -69

Expert G 0 -40 -74

Expert H 0 -51 -120

Expert I -11 -68 -121

Expert J -16 -55 -122

Expert K 0 -11 -54

Expert L 8 -38 -101
946

5.4.3 Monetized Benefits

Monetized values for each quantified health endpoint are presented in Table 5.4-7. For

each endpoint presented in Table 5.4-7, we provide both the mean estimate and the 90%

confidence interval. Total aggregate monetized benefits are presented in Tables 5.4-8 and 5.4-9

using either a 3 percent or 7 percent discount rate, respectively. All of the monetary benefits are

in constant-year 2007 dollars.

In addition to omitted benefits categories such as air toxics and various welfare effects,

not all known PM2.5- and ozone-related health and welfare effects could be quantified or

monetized. The estimate of total monetized health impacts from the renewable fuel volumes are

thus equal to the subset of monetized PM2.5- and ozone-related health impacts we are able to

quantify plus the sum of the nonmonetized health and welfare impacts.

Our estimate of monetized adverse health impacts in 2022 for the RFS2 fuel volume

scenario compared to the RFS1 reference case, using the ACS and Six-Cities PM mortality

studies and the range of ozone mortality assumptions, is between $1.4 billion and $4.5 billion,

assuming a 3 percent discount rate, or between $1.4 billion and $4.3 billion, assuming a 7

percent discount rate. When compared to the AEO reference case, we estimate the monetized

adverse health impacts to be between $0.63 billion and $2.2 billion, assuming a 3 percent

discount rate, or between $0.60 billion and $2.1 billion, assuming a 7 percent discount rate. The

monetized impacts associated with an increase in the risk of both ozone- and PM2.5-related

premature mortality ranges between 90 to 98 percent of total monetized health impacts, in part

because we are unable to quantify a number of health and environmental impact categories (see

Table 5.4-2). These unquantified impacts may be substantial, although their magnitude is highly

uncertain.

The next largest adverse health impact is for increased incidence of PM-related chronic

illness (chronic bronchitis and nonfatal heart attacks), although this value is more than an order

of magnitude lower than for PM-related premature mortality. Hospital admissions for

respiratory and cardiovascular causes, minor restricted activity days, and work loss days account

for the majority of the remaining adverse health impacts. The remaining categories each account

for a small percentage of total adverse health impacts. A comparison of the incidence table to

the monetized health impacts table reveals that there is not always a close correspondence

between the number of incidences for a given endpoint and the monetary value associated with

that endpoint. For example, there are over 100 times more work loss days than PM-related

premature mortalities (based on the ACS study), yet work loss days account for only a very small

fraction of total monetized adverse health impacts. This reflects the fact that many of the less

severe health effects, while more common, are valued at a lower level than the more severe

health effects. Also, some effects, such as hospital admissions, are valued using a proxy measure

of willingness-to-pay (e.g., cost-of-illness). As such, the true value of these effects may be

different than that reported here.
947

Table 5.4-7.

Estimated Monetary Value of Health and Welfare Effect Incidence (in millions of 2007$) a,b

2022 RFS2

Control Case

Compared to

RFS1 Reference

Case

2022 RFS2 Control

Case Compared to

AEO Reference Case

PM2.5-Related Health Effect Estimated Mean Value of Reductions

(5th and 95th %ile)

Premature Mortality –

Derived from Epidemiology

Studiesc,d,

Adult, age 30+ - ACS study

(Pope et al., 2002)

3% discount rate

7% discount rate

-$860

(-$100 - -$2,300)

-$770

(-$91 - -$2,000)

-$270

(-$32 - -$700)

-$240

(-$28 - -$630)

Adult, age 25+ - Six-cities

study (Laden et al., 2006)

3% discount rate

7% discount rate

-$2,200

(-$29 - -$5,500)

-$2,000

(-$26 - -$5,000)

-$680

(-$90 - -$1,700)

-$620

(-$81 - -$1,600)

Infant Mortality, <1 year –

(Woodruff et al. 1997)

-$4.0

(-$3.0 - -$15)

-$1.7

(-$1.3 - -$6.7)

Chronic bronchitis (adults, 26 and over) -$32

(-$2.5 - -$110)

-$9.4

(-$0.72 - -$33)

Non-fatal acute myocardial infarctions

3% discount rate

7% discount rate

-$23

(-$4.1 - -$58)

-$23

(-$3.8 - -$58)

-$6.6

(-$1.0 - -$17)

-$6.4

(-$0.95 - -$16)

Hospital admissions for respiratory causes -$0.39

(-$0.19 - -$0.57

-$0.11

(-$0.06 - -$0.17)

Hospital admissions for cardiovascular causes -$1.5

(-$0.96 - -$2.1)

-$0.33

(-$0.20 - -$0.45)

Emergency room visits for asthma -$0.07

(-$0.04 - -$0.10)

-$0.04

(-$0.02 - -$0.06)

Acute bronchitis (children, age 8–12) -$0.01

($0 - -$0.03)

-$0.004

($0 - -$0.01)

Lower respiratory symptoms (children, 7–14) -$0.04

(-$0.01 - -$0.07)

-$0.01

(-$0.004 - -$0.02)

Upper respiratory symptoms (asthma, 9–11) -$0.04

(-$0.01 - -$0.10)

-$0.01

(-$0.004 - -$0.03)

Asthma exacerbations -$0.09

(-$0.009 - -$0.28)

-$0.03

(-$0.003 - -$0.09)

Work loss days -$1.7

(-$1.5 - -$1.9)

-$0.49

(-$0.42 - -$0.55)

Minor restricted-activity days (MRADs) -$4.3

(-$2.5 - -$6.2)

-$1.2

(-$0.69 - -$1.7)

Ozone-related Health Effect

Bell et al., 2004 -$480

(-$51 - -$1,300)

-$320

(-$32 - -$880)

Premature Mortality, All ages

– Derived from Multi-city

analyses Huang et al., 2005 -$800

(-$90 - -$2,200)

-$530

(-$56 - -$1,400)
948

Schwartz, 2005 -$740

(-$76 - -$2,000)

-$490

(-$48 - -$1,300)

Bell et al., 2005 -$1,600

(-$200 - -$4,000)

-$1,000

(-$130 - -$,700)

Ito et al., 2005 -$2,200

(-$290 - -$5,400)

-$1,400

(-$190 - -$3,600)

Premature Mortality, All ages

– Derived from Metaanalyses

Levy et al., 2005 -$2,200

(-$300 - -$5,300)

-$1,400

(-$200 - -$3,500)

Hospital admissions- respiratory causes (adult, 65 and older) -$11

(-$0.49 - -$20)

-$7.4

(-$0.13 - -$14)

Hospital admissions- respiratory causes (children, under 2) -$3.0

(-$1.0 - -$4,9)

-$1.9

(-$0.52 - -$3.3)

Emergency room visit for asthma (all ages) -$0.10

(-$0.009 - -

$0.26)

-$0.07

(-$0.008 - -$0.18)

Minor restricted activity days (adults, age 18-65) -$19

(-$6.4 - -$35)

-$13

(-$3.6 - -$24)

School absence days -$10

(-$3.1 - -$16)

-$6.7

(-$1.7 - -$11)

a

Negatives indicate a disbenefit, or an increase in health effect incidence. Monetary impacts are rounded to two

significant digits for ease of presentation and computation. PM and ozone impacts are nationwide.

b Monetary impacts adjusted to account for growth in real GDP per capita between 1990 and the analysis year

(2022)

c

Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results reflect the

use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic

analyses.
949

Table 5.4-8. Total Monetized Impacts Associated with the Mandated Renewable Fuel

Volumes – 3% Discount Rate

Total Ozone and PM Monetized Impacts (billions, 2007$) –PM Mortality Derived from the ACS

and Six Cities Studies

2022 RFS2 Control Case Compared to RFS1

Reference Case

2022 RFS2 Control Case Compared to AEO

Reference Case

Ozone

Mortality

Function

Reference Mean Total

Benefits

Ozone

Mortality

Function

Reference Mean Total

Benefits

Bell et al.,

2004

-$1.4 to -$2.8 Bell et al.,

2004

-$1.3 to -$1.8

Huang et al.,

2005

-$1.8 to -$3.1 Huang et al.,

2005

-$0.84 to -$1.3 Multi-city

Schwartz,

2005

-$1.7 to -$3.0

Multi-city

Schwartz,

2005

-$0.80 to -$1.2

Bell et al.,

2005

-$2.5 to -$3.8 Bell et al.,

2005

-$1.3 to -$1.8

Meta-analysis Ito et al., 2005 -$3.1 to -$4.5 Ito et al., 2005 -$1.7 to -$2.2

Levy et al.,

2005

-$3.1 to -$4.5

Meta-analysis

Levy et al.,

2005

-$1.8 to -$2.2

Total Ozone and PM Monetized Impacts (millions, 2007$) –

PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate)

2022 RFS2 Control Case Compared to RFS1

Reference Case

2022 RFS2 Control Case Compared to AEO

Reference Case

Ozone

Mortality

Function

Reference Mean Total

Benefits

Ozone

Mortality

Function

Reference Mean Total

Benefits

Bell et al.,

2004

-$0.86 to -$3.5 Bell et al.,

2004

-$0.45 to -$1.3

Huang et al.,

2005

-$1.2 to -$3.8 Huang et al.,

2005

-$0.66 to -$1.5 Multi-city

Schwartz,

2005

-$1.1 to -$3.8

Multi-city

Schwartz,

2005

-$0.62 to -$1.4

Bell et al.,

2005

-$1.9 to -$4.6 Bell et al.,

2005

-$1.2 to -$2.0

Meta-analysis Ito et al., 2005 -$2.5 to -$5.2 Ito et al., 2005 -$1.6 to -$2.4

Levy et al.,

2005

-$2.6 to -$5.2

Meta-analysis

Levy et al.,

2005

-$1.6 to -$2.4
950

Table 5.4-9. Total Monetized Impacts Associated with the Mandated Renewable Fuel

Volumes – 7% Discount Rate

Total Ozone and PM Monetized Impacts (billions, 2007$) –

PM Mortality Derived from the ACS and Six Cities Studies

2022 RFS2 Control Case Compared to RFS1

Reference Case

2022 RFS2 Control Case Compared to AEO

Reference Case

Ozone

Mortality

Function

Reference Mean Total

Benefits

Ozone

Mortality

Function

Reference Mean Total

Benefits

Bell et al.,

2004

-$1.4 to -$2.6 Bell et al.,

2004

-$0.60 to -$0.98

Huang et al.,

2005

-$1.7 to -$2.9 Huang et al.,

2005

-$0.81 to -$1.2 Multi-city

Schwartz,

2005

-$1.6 to -$2.8

Multi-city

Schwartz,

2005

-$0.77 to -$1.1

Bell et al.,

2005

-$2.4 to -$3.6 Bell et al.,

2005

-$1.3 to -$1.7

Meta-analysis Ito et al., 2005 -$3.0 to -$4.2 Ito et al., 2005 -$1.7 to -$2.1

Levy et al.,

2005

-$3.1 to -$4.3

Meta-analysis

Levy et al.,

2005

-$1.7 to -$2.1

Total Ozone and PM Monetized Impacts (millions, 2007$) –

PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate)

2022 RFS2 Control Case Compared to RFS1

Reference Case

2022 RFS2 Control Case Compared to AEO

Reference Case

Ozone

Mortality

Function

Reference Mean Total

Benefits

Ozone

Mortality

Function

Reference Mean Total

Benefits

Bell et al.,

2004

-$0.83 to -$3.5 Bell et al.,

2004

-$0.44 to -$1.2

Huang et al.,

2005

-$1.1 to -$3.5 Huang et al.,

2005

-$0.65 to -$1.4 Multi-city

Schwartz,

2005

-$1.1 to -$3.5

Multi-city

Schwartz,

2005

-$0.61 to -$1.4

Bell et al.,

2005

-$1.9 to -$4.3 Bell et al.,

2005

-$1.1 to -$1.9

Meta-analysis Ito et al., 2005 -$2.5 to -$4.9 Ito et al., 2005 -$1.6 to -$2.3

Levy et al.,

2005

-$2.5 to -$4.9

Meta-analysis

Levy et al.,

2005

-$1.6 to -$2.3

5.4.4 Methodology

Human Health Impact Functions

Health impact functions measure the change in a health endpoint of interest, such as

hospital admissions, for a given change in ambient ozone or PM concentration. Health impact

functions are derived from primary epidemiology studies, meta-analyses of multiple

epidemiology studies, or expert elicitations. A standard health impact function has four

components: 1) an effect estimate from a particular study; 2) a baseline incidence rate for the

health effect (obtained from either the epidemiology study or a source of public health statistics

such as the Centers for Disease Control); 3) the size of the potentially affected population; and 4)
951

the estimated change in the relevant ozone or PM summary measures.

A typical health impact function might look like:

 1 0 

 x

eyy



,

where y0 is the baseline incidence (the product of the baseline incidence rate times the potentially

affected population), β is the effect estimate, and Δx is the estimated change in the summary 

pollutant measure. There are other functional forms, but the basic elements remain the same.

The following subsections describe: the size of the potentially affected populations; the PM2.5

and ozone effect estimates; and the treatment of potential thresholds in PM2.5-related health

impact functions. Chapter 5.4.6 describes the ozone and PM air quality inputs to the health

impact functions.

Potentially Affected Populations

The starting point for estimating the size of potentially affected populations is the 2000

U.S. Census block level dataset.1185 The Benefits Modeling and Analysis Program (BenMAP)

incorporates 250 age/gender/race categories to match specific populations potentially affected by

ozone and other air pollutants. The software constructs specific populations matching the

populations in each epidemiological study by accessing the appropriate age-specific populations

from the overall population database. BenMAP projects populations to 2022 using growth

factors based on economic projections.1186

Effect Estimate Sources

The most significant quantifiable impacts of exposure to ambient concentrations of ozone

and PM are attributable to human health risks. EPA’s Ozone and PM Criteria Documents1187,1188

and the World Health Organization’s 2003 and 20041189,1190 reports outline numerous human

health effects known or suspected to be linked to exposure to ambient ozone and PM. US EPA

recently evaluated the ozone and PM literature for use in the benefits analysis for the final 2008

Ozone NAAQS and final 2006 PM NAAQS analyses. We use the same literature in this

analysis; for more information on the studies that underlie the health impacts quantified in this

RIA, please refer to those documents.

It is important to note that we are unable to separately quantify all of the possible PM and

ozone health effects that have been reported in the literature for three reasons: (1) the possibility

of double counting (such as hospital admissions for specific respiratory diseases versus hospital

admissions for all or a sub-set of respiratory diseases); (2) uncertainties in applying effect

relationships that are based on clinical studies to the potentially affected population; or (3) the

lack of an established concentration-response (CR) relationship. Table 5.4-10 lists the health

endpoints included in this analysis.
952

Table 5.4-10. Ozone- and PM-Related Health Endpoints

Endpoint Pollutant Study Study Population

Premature Mortality

Premature mortality –

daily time series

O3 Multi-city

Bell et al (2004) (NMMAPS study)1191 –

Non-accidental

Huang et al (2005)1192 - Cardiopulmonary

Schwartz (2005)1193 – Non-accidental

Meta-analyses:

Bell et al (2005)1194 – All cause

Ito et al (2005)1195 – Non-accidental

Levy et al (2005)1196 – All cause

All ages

Premature mortality —

cohort study, all-cause

PM2.5 Pope et al. (2002)1197

Laden et al. (2006)1198

>29 years

>25 years

Premature mortality,

total exposures

PM2.5 Expert Elicitation (IEc, 2006)1199 >24 years

Premature mortality —

all-cause

PM2.5 Woodruff et al. (1997)1200 Infant (<1 year)

Chronic Illness

Chronic bronchitis PM2.5 Abbey et al. (1995)1201 >26 years

Nonfatal heart attacks PM2.5 Peters et al. (2001)1202 Adults (>18 years)

Hospital Admissions

Pooled estimate:

Schwartz (1995) - ICD 460-519 (all resp)1203

Schwartz (1994a; 1994b) - ICD 480-486

(pneumonia)1204,1205

Moolgavkar et al. (1997) - ICD 480-487

(pneumonia)1206

Schwartz (1994b) - ICD 491-492, 494-496

(COPD)

Moolgavkar et al. (1997) – ICD 490-496

(COPD)

>64 years

O3

Burnett et al. (2001)1207 <2 years

PM2.5 Pooled estimate:

Moolgavkar (2003)—ICD 490-496

(COPD)1208

Ito (2003)—ICD 490-496 (COPD)1209

>64 years

PM2.5 Moolgavkar (2000)—ICD 490-496

(COPD)1210

20–64 years

PM2.5 Ito (2003)—ICD 480-486 (pneumonia) >64 years

Respiratory

PM2.5 Sheppard (2003)—ICD 493 (asthma)1211 <65 years

PM2.5 Pooled estimate:

Moolgavkar (2003)—ICD 390-429 (all

cardiovascular)

Ito (2003)—ICD 410-414, 427-428 (ischemic

heart disease, dysrhythmia, heart failure)

Cardiovascular >64 years

PM2.5 Moolgavkar (2000)—ICD 390-429 (all

cardiovascular)

20–64 years

Asthma-related ER

visits

O3 Pooled estimate:

Jaffe et al (2003)1212

Peel et al (2005)1213

Wilson et al (2005)1214

5–34 years

All ages

All ages

Asthma-related ER

visits (con’t)

PM2.5 Norris et al. (1999)1215 0–18 years
953

Other Health Endpoints

Acute bronchitis PM2.5 Dockery et al. (1996)1216 8–12 years

Upper respiratory

symptoms

PM2.5 Pope et al. (1991)1217 Asthmatics, 9–11

years

Lower respiratory

symptoms

PM2.5 Schwartz and Neas (2000)1218 7–14 years

Asthma exacerbations PM2.5 Pooled estimate:

Ostro et al. (2001)1219 (cough, wheeze and

shortness of breath)

Vedal et al. (1998)1220 (cough)

6–18 yearsa

Work loss days PM2.5 Ostro (1987)1221 18–65 years

School absence days

O3

Pooled estimate:

Gilliland et al. (2001)1222

Chen et al. (2000)1223

5–17 yearsb

Minor Restricted O3 Ostro and Rothschild (1989)1224 18–65 years

Activity Days

(MRADs)

PM2.5 Ostro and Rothschild (1989) 18–65 years

Notes:

a The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998)

study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB-HES), we extended

the applied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader age

group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the Analytical Plan

for EPA’s Second Prospective Analysis –Benefits and Costs of the Clean Air Act, 1990—2020. EPA-SABCOUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the Public Health Benefits

of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press.

b Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on

recent advice from the National Research Council and the EPA SAB-HES, we have calculated reductions in school

absences for all school-aged children based on the biological similarity between children aged 5 to 17.

In selecting epidemiological studies as sources of effect estimates, we applied several criteria to develop a set of

studies that is likely to provide the best estimates of impacts in the U.S. To account for the potential impacts of

different health care systems or underlying health status of populations, we give preference to U.S. studies over nonU.S. studies. In addition, due to the potential for confounding by co-pollutants, we give preference to effect

estimates from models including both ozone and PM over effect estimates from single-pollutant models.1225,1226

Treatment of Potential Thresholds in PM2.5-Related Health Impact Functions

In recent analyses, OTAQ has estimated PM2.5-related benefits assuming that a threshold

exists in the PM-related concentration-response functions (at 10 μg/m3) below which there are no

associations between exposure to PM2.5 and health impacts. For the analysis of the final rule,

however, we have revised this assumption. As explained in the recently proposed Portland

Cement MACT RIA, EPA’s preferred benefits estimation approach assumes a no-threshold

model that calculates incremental benefits down to the lowest modeled PM2.5 air quality levels.

EPA strives to use the best available science to support our benefits analyses, and we

recognize that interpretation of the science regarding air pollution and health is dynamic and

evolving. Based on our review of the body of scientific literature, EPA applied the no-threshold

model in this analysis. EPA's draft Integrated Science Assessment,1227,1228 which was recently

reviewed by EPA’s Clean Air Scientific Advisory Committee,1229,1230 concluded that the

scientific literature consistently finds that a no-threshold log-linear model most adequately

portrays the PM-mortality concentration-response relationship while recognizing potential
954

uncertainty about the exact shape of the concentration-response function.337 Although this

document does not represent final agency policy that has undergone the full agency scientific

review process, it provides a basis for reconsidering the application of thresholds in PM2.5

concentration-response functions used in EPA’s RIAs.338 It is important to note that while

CASAC provides advice regarding the science associated with setting the National Ambient Air

Quality Standards, typically other scientific advisory bodies provide specific advice regarding

benefits analysis.339,1231

As can be seen in Table 5.4-11, we conducted a sensitivity analysis for premature

mortality, with alternative thresholds at 3 μg/m3(the “background,” or no-threshold,

assumption), 7.5 μg/m3, 10 μg/m3, 12 μg/m3, and 14 μg/m3. By replacing the no-threshold

assumption in the ACS premature mortality function with a 10 μg/m3threshold model, the

number of avoided incidences of premature mortality would change dramatically.

337 It is important to note that uncertainty regarding the shape of the concentration-response function is conceptually

distinct from an assumed threshold. An assumed threshold (below which there are no health effects) is a

discontinuity, which is a specific example of non-linearity.

338 The final PM ISA, which will have undergone the full agency scientific review process, is scheduled to be

completed in late December 2009.

339 In the Portland Cement RIA, EPA solicited comment on the use of the no-threshold model for benefits analysis

within the preamble of that proposed rule. The comment period for the Portland Cement proposed NESHAP closed

on September 4, 2009 (Docket ID No. EPA–HQ–OAR–2002–0051 available at http://www.regulations.gov). EPA

is currently reviewing those comments.
955

Table 5.4-11. PM-Related Mortality Impacts Associated with the Mandated Renewable

Fuel Volumes: Threshold Sensitivity Analysis Using the ACS Study (Pope et al., 2002)a

PM Mortality Incidence Level of

Assumed

Threshold 2022 RFS2 Control

Case Compared to

RFS1 Reference Case

2022 RFS2 Control

Case Compared to

AEO Reference Case

15 μg/m3 b

9 -6

12 μg/m3 13 -14

10 μg/m3 c

-2 1

7.5 μg/m3 d

-66 -11

3 μg/m3 e-110 -33

Notes:

a Note that this table only presents the effects of a threshold on PMrelated mortality incidence based on the ACS study. Negative values

indicate a disbenefit, or additional mortality incurred.

b Alternative annual PM NAAQS.

c

Previous threshold assumption

d

SAB-HES (2004)86

e NAS (2002)87

5.4.5 Economic Values for Health Outcomes

Reductions in ambient concentrations of air pollution generally lower the risk of future

adverse health effects for a large population, while the reverse is also generally true. Therefore,

the appropriate economic measure is willingness-to-pay (WTP) for changes in risk of a health

effect rather than WTP for a health effect that would occur with certainty (Freeman, 1993).

Epidemiological studies generally provide estimates of the relative risks of a particular health

effect that is avoided because of a reduction in air pollution. We converted those to units of

avoided statistical incidence for ease of presentation. We calculated the value of avoided

statistical incidences by dividing individual WTP for a risk reduction by the related observed

change in risk. For example, suppose a pollution-reduction regulation is able to reduce the risk

of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual

WTP for this risk reduction is $100, then the WTP for an avoided statistical premature death is

$1 million ($100/0.0001 change in risk).

WTP estimates generally are not available for some health effects, such as hospital

admissions. In these cases, we used the cost of treating or mitigating the effect as a primary

estimate. These cost-of-illness (COI) estimates generally understate the true value of reducing

the risk of a health effect, because they reflect the direct expenditures related to treatment, but
956

not the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987). We

provide unit values for health endpoints (along with information on the distribution of the unit

value) in Table 5.4-12. All values are in constant year 2006 dollars, adjusted for growth in real

income out to 2022 using projections provided by Standard and Poor’s. Economic theory argues

that WTP for most goods (such as environmental protection) will increase if real income

increases. Many of the valuation studies used in this analysis were conducted in the late 1980s

and early 1990s. Because real income has grown since the studies were conducted, people’s

willingness to pay for reductions in the risk of premature death and disease likely has grown as

well. We did not adjust cost of illness-based values because they are based on current costs.

Similarly, we did not adjust the value of school absences, because that value is based on current

wage rates. For details on valuation estimates for PM-related endpoints, see the 2006 PM

NAAQS RIA. For details on valuation estimates for ozone-related endpoints, see the 2008

Ozone NAAQS RIA.
957

Table 5.4-12. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a

Central Estimate of Value Per Statistical Incidence

Health Endpoint

1990 Income

Level

2020 Income

Levelb

2030 Income

Levelb Derivation of Estimates

Premature Mortality (Value of a

Statistical Life): PM2.5- and

Ozone-related

$6,320,000 $7,590,000 $7,800,000 EPA currently recommends a default central VSL of $6.3 million

based on a Weibull distribution fitted to twenty-six published VSL

estimates (5 contingent valuation and 21 labor market studies). The

underlying studies, the distribution parameters, and other useful

information are available in Appendix B of EPA’s current Guidelines

for Preparing Economic Analyses. The guidelines can be accessed at:

http://yosemite.epa.gov/ee/epa/eermfile.nsf/vwAN/EE-0516-

01.pdf/$File/EE-0516-01.pdf

Chronic Bronchitis (CB) $340,000 $420,000 $430,000 Point estimate is the mean of a generated distribution of WTP to avoid

a case of pollution-related CB. WTP to avoid a case of pollutionrelated CB is derived by adjusting WTP (as described in Viscusi et al.,

[1991]1232) to avoid a severe case of CB for the difference in severity

and taking into account the elasticity of WTP with respect to severity

of CB.

Nonfatal Myocardial Infarction

(heart attack)

3% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–65

Age 66 and over

7% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–65

Age 66 and over

$66,902

$74,676

$78,834

$140,649

$66,902

$65,293

$73,149

$76,871

$132,214

$65,293

$66,902

$74,676

$78,834

$140,649

$66,902

$65,293

$73,149

$76,871

$132,214

$65,293

$66,902

$74,676

$78,834

$140,649

$66,902

$65,293

$73,149

$76,871

$132,214

$65,293

Age-specific cost-of-illness values reflect lost earnings and direct

medical costs over a 5-year period following a nonfatal MI. Lost

earnings estimates are based on Cropper and Krupnick (1990).1233

Direct medical costs are based on simple average of estimates from

Russell et al. (1998)1234 and Wittels et al. (1990).1235

Lost earnings:

Cropper and Krupnick (1990). Present discounted value of 5 years of

lost earnings:

age of onset: at 3% at 7%

25-44 $8,774 $7,855

45-54 $12,932 $11,578

55-65 $74,746 $66,920

Direct medical expenses: An average of:

1. Wittels et al. (1990) ($102,658—no discounting)

2. Russell et al. (1998), 5-year period ($22,331 at 3% discount rate;

$21,113 at 7% discount rate)
958

Table 5.4-12. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a(continued)

Central Estimate of Value Per Statistical

Incidence

Health Endpoint

1990 Income

Level

2020 Income

Levelb

2030 Income

Levelb Derivation of Estimates

Hospital Admissions

Chronic Obstructive Pulmonary

Disease (COPD)

(ICD codes 490-492, 494-496)

$12,378 $12,378 $12,378 The COI estimates (lost earnings plus direct medical costs) are based

on ICD-9 code-level information (e.g., average hospital care costs,

average length of hospital stay, and weighted share of total COPD

category illnesses) reported in Agency for Healthcare Research and

Quality (2000)1236 (www.ahrq.gov).

Pneumonia

(ICD codes 480-487)

$14,693 $14,693 $14,693 The COI estimates (lost earnings plus direct medical costs) are based

on ICD-9 code-level information (e.g., average hospital care costs,

average length of hospital stay, and weighted share of total pneumonia

category illnesses) reported in Agency for Healthcare Research and

Quality (2000) (www.ahrq.gov).

Asthma Admissions $6,634 $6,634 $6,634 The COI estimates (lost earnings plus direct medical costs) are based

on ICD-9 code-level information (e.g., average hospital care costs,

average length of hospital stay, and weighted share of total asthma

category illnesses) reported in Agency for Healthcare Research and

Quality (2000) (www.ahrq.gov).

All Cardiovascular

(ICD codes 390-429)

$18,387 $18,387 $18,387 The COI estimates (lost earnings plus direct medical costs) are based

on ICD-9 code-level information (e.g., average hospital care costs,

average length of hospital stay, and weighted share of total

cardiovascular category illnesses) reported in Agency for Healthcare

Research and Quality (2000) (www.ahrq.gov).

Emergency Room Visits for

Asthma

$286 $286 $286 Simple average of two unit COI values:

(1) $311.55, from Smith et al. (1997)1237 and

(2) $260.67, from Stanford et al. (1999).1238
959

Table 5.4-12. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a(continued)

Central Estimate of Value Per Statistical

Incidence

Health Endpoint

1990 Income

Level

2020 Income

Levelb

2030 Income

Levelb Derivation of Estimates

Respiratory Ailments Not Requiring Hospitalization

Upper Respiratory Symptoms

(URS)

$25 $27 $27 Combinations of the three symptoms for which WTP estimates are

available that closely match those listed by Pope et al. result in seven

different “symptom clusters,” each describing a “type” of URS. A

dollar value was derived for each type of URS, using mid-range

estimates of WTP (IEc, 1994)1239 to avoid each symptom in the

cluster and assuming additivity of WTPs. The dollar value for URS is

the average of the dollar values for the seven different types of URS.

Lower Respiratory Symptoms

(LRS)

$16 $17 $17 Combinations of the four symptoms for which WTP estimates are

available that closely match those listed by Schwartz et al. result in 11

different “symptom clusters,” each describing a “type” of LRS. A

dollar value was derived for each type of LRS, using mid-range

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster

and assuming additivity of WTPs. The dollar value for LRS is the

average of the dollar values for the 11 different types of LRS.

Asthma Exacerbations $42 $45 $45 Asthma exacerbations are valued at $42 per incidence, based on the

mean of average WTP estimates for the four severity definitions of a

“bad asthma day,” described in Rowe and Chestnut (1986).1240 This

study surveyed asthmatics to estimate WTP for avoidance of a “bad

asthma day,” as defined by the subjects. For purposes of valuation, an

asthma attack is assumed to be equivalent to a day in which asthma is

moderate or worse as reported in the Rowe and Chestnut (1986) study.

Acute Bronchitis $360 $380 $390 Assumes a 6-day episode, with daily value equal to the average of low

and high values for related respiratory symptoms recommended in

Neumann et al. (1994).1241
960

Table 5.4-12. Unit Values Used for Economic Valuation of Health Endpoints (2000$)a(continued)

Central Estimate of Value Per Statistical Incidence

Health Endpoint

1990 Income

Level

2020 Income

Levelb

2030 Income

Levelb Derivation of Estimates

Restricted Activity and Work/School Loss Days

Work Loss Days (WLDs) Variable

(national

median = )

County-specific median annual wages divided by 50 (assuming 2 weeks

of vacation) and then by 5—to get median daily wage. U.S. Year 2000

Census, compiled by Geolytics, Inc.

School Absence Days $75 $75 $75 Based on expected lost wages from parent staying home with child.

Estimated daily lost wage (if a mother must stay at home with a sick

child) is based on the median weekly wage among women age 25 and

older in 2000 (U.S. Census Bureau, Statistical Abstract of the United

States: 2001, Section 12: Labor Force, Employment, and Earnings, Table

No. 621). This median wage is $551. Dividing by 5 gives an estimated

median daily wage of $103.

The expected loss in wages due to a day of school absence in which the

mother would have to stay home with her child is estimated as the

probability that the mother is in the workforce times the daily wage she

would lose if she missed a day = 72.85% of $103, or $75.

Worker Productivity $0.95 per

worker per 10%

change in ozone

per day

$0.95 per

worker per

10% change in

ozone per day

$0.95 per

worker per 10%

change in ozone

per day

Based on $68 – median daily earnings of workers in farming, forestry and

fishing – from Table 621, Statistical Abstract of the United States (“FullTime Wage and Salary Workers – Number and Earnings: 1985 to 2000")

(Source of data in table: U.S. Bureau of Labor Statistics, Bulletin 2307

and Employment and Earnings, monthly).

Minor Restricted Activity Days

(MRADs)

$51 $54 $55 Median WTP estimate to avoid one MRAD from Tolley et al. (1986).1242

a

All monetized annual benefit estimates associated with the coordinated strategy are presented in year 2000 dollars. We use the Consumer Price Indexes to

adjust both WTP- and COI-based benefits estimates to 2007 dollars from 2000 dollars.1243 For WTP-based estimates, we use an inflation factor of 1.20 based on

the CPI-U for “all items.” For COI-based estimates, we use an inflation factor of 1.35 based on the CPI-U for medical care.

b

Our analysis accounts for expected growth in real income over time. Economic theory argues that WTP for most goods (such as environmental protection) will

increase if real incomes increase. Benefits are therefore adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor to account for

income growth over time. For a complete discussion of how these adjustment factors were derived, we refer the reader to the PM NAAQS regulatory impact

analysis. Note that similar adjustments do not exist for cost-of-illness-based unit values. For these, we apply the same unit value regardless of the future year of

analysis.
961

5.4.6 Manipulating Air Quality Modeling Data for Health Impacts Analysis

In Chapter 3.4, we summarized the methods for and results of estimating air quality

impacts for the mandated renewable fuel volumes. These air quality results are in turn associated

with human populations to estimate changes in health effects. For the purposes of this analysis,

we focus on the health effects that have been linked to ambient changes in ozone and PM2.5

related to emissions associated with the RFS2 mandated fuel volumes. We estimate ambient

PM2.5 and ozone concentrations using the Community Multiscale Air Quality model (CMAQ).

This section describes how we converted the CMAQ modeling output into full-season profiles

suitable for the health impacts analysis.

General Methodology

First, we extracted hourly, surface-layer PM and ozone concentrations for each grid cell

from the standard CMAQ output files. For ozone, these model predictions are used in

conjunction with the observed concentrations obtained from the Aerometric Information

Retrieval System (AIRS) to generate ozone concentrations for the entire ozone season.340,341 The

predicted changes in ozone concentrations from the future-year base case to future-year control

scenario serve as inputs to the health and welfare impact functions of the benefits analysis (i.e.,

BenMAP).

To estimate ozone-related health effects for the contiguous United States, full-season

ozone data are required for every BenMAP grid-cell. Given available ozone monitoring data, we

generated full-season ozone profiles for each location in two steps: (1) we combined monitored

observations and modeled ozone predictions to interpolate hourly ozone concentrations to a grid

of 12-km by 12-km population grid cells for the contiguous 48 states, and (2) we converted these

full-season hourly ozone profiles to an ozone measure of interest, such as the daily maximum 8-

hour average.342,343

For PM2.5, we also use the model predictions in conjunction with observed monitor data.

CMAQ generates predictions of hourly PM species concentrations for every grid. The species

include a primary coarse fraction (corresponding to PM in the 2.5 to 10 micron size range), a

primary fine fraction (corresponding to PM less than 2.5 microns in diameter), and several

secondary particles (e.g., sulfates, nitrates, and organics). PM2.5 is calculated as the sum of the

primary fine fraction and all of the secondarily formed particles. Future-year estimates of PM2.5

were calculated using relative reduction factors (RRFs) applied to 2005 ambient PM2.5 and PM2.5

species concentrations. A gridded field of PM2.5 concentrations was created by interpolating

Federal Reference Monitor ambient data and IMPROVE ambient data. Gridded fields of PM2.5

species concentrations were created by interpolating US EPA speciation network (ESPN)

340 The ozone season for this analysis is defined as the 5-month period from May to September.

341 Based on AIRS, there were 961 ozone monitors with sufficient data (i.e., 50 percent or more days reporting at

least nine hourly observations per day [8 am to 8 pm] during the ozone season).

342 The 12-km grid squares contain the population data used in the health benefits analysis model, BenMAP.

343 This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi

Neighbor Averaging (EVNA) spatial interpolation. See the BenMAP manual for technical details, available for

download at http://www.epa.gov/air/benmap.
962

ambient data and IMPROVE data. The ambient data were interpolated to the CMAQ 12 km

grid.

The procedures for determining the RRFs are similar to those in US EPA’s draft guidance

for modeling the PM2.5 standard (EPA, 1999). The guidance recommends that model predictions

be used in a relative sense to estimate changes expected to occur in each major PM2.5 species.

The procedure for calculating future-year PM2.5 design values is called the “Speciated Modeled

Attainment Test (SMAT).” EPA used this procedure to estimate the ambient impacts of the

coordinated strategy to control ship emissions.

Table 5.4-13 provides those ozone and PM2.5 metrics for grid cells in the modeled

domain that enter the health impact functions for health benefits endpoints. The populationweighted average reflects the baseline levels and predicted changes for more populated areas of

the nation. This measure better reflects the potential benefits through exposure changes to these

populations.

Table 5.4-13.

Summary of CMAQ-Derived Population-Weighted Ozone and PM2.5 Air Quality Metrics

for Health Impact Endpoints Associated with the Mandated Renewable Fuel Volumes

2022 RFS2 control case

compared to the RFS1

reference case

2022 RFS2 control case

compared to the AEO

reference case

Statistica Reference Changeb Reference Changeb

Ozone Metric: National Population-Weighted Average (ppb)c

Daily Maximum 8-Hour Average

Concentration

44.513 -0.177 44.575 -0.116

PM2.5 Metric: National Population-Weighted Average (ug/m3)

Annual Average Concentration 9.658 -0.006 9.662 -0.002

Notes:

a Ozone and PM2.5 metrics are calculated at the CMAQ grid-cell level for use in health effects estimates.

Ozone metrics are calculated over relevant time periods during the daylight hours of the “ozone season”

(i.e., May through September).

b The change is defined as the reference case value minus the final rule value. A negative change means

that the population-weighted average has increased from the reference scenario to the final rule scenario.

c Calculated by summing the product of the projected CMAQ grid-cell population and the estimated

CMAQ grid cell seasonal ozone concentration and then dividing by the total population.

5.4.7 Methods for Describing Uncertainty

The National Research Council (NRC)1244 highlighted the need for EPA to conduct

rigorous quantitative analysis of uncertainty in its benefits estimates and to present these

estimates to decision makers in ways that foster an appropriate appreciation of their inherent

uncertainty. In response to these comments, EPA’s Office of Air and Radiation (OAR) is

developing a comprehensive strategy for characterizing the aggregate impact of uncertainty in

key modeling elements on both health incidence and benefits estimates. Components of that

process include emissions modeling, air quality modeling, health effects incidence estimation,

and valuation.
963

In benefit analyses of air pollution regulations conducted to date, the estimated impact of

reductions in premature mortality has accounted for 85% to 95% of total benefits. Therefore, it

is particularly important to characterize the uncertainties associated with reductions in premature

mortality. The health impact functions used to estimate avoided premature deaths associated

with reductions in ozone have associated standard errors that represent the statistical errors

around the effect estimates in the underlying epidemiological studies.344 In our results, we report

credible intervals based on these standard errors, reflecting the uncertainty in the estimated

change in incidence of avoided premature deaths. We also provide multiple estimates, to reflect

model uncertainty between alternative study designs.

For premature mortality associated with exposure to PM, we follow the same approach

that has been used in several recent RIAs.1245,1246,1247 First, we use Monte Carlo methods for

estimating random sampling error associated with the concentration response functions from

epidemiological studies and economic valuation functions. Monte Carlo simulation uses random

sampling from distributions of parameters to characterize the effects of uncertainty on output

variables, such as incidence of premature mortality. Specifically, we used Monte Carlo methods

to generate confidence intervals around the estimated health impact and dollar benefits.

Distributions for individual effect estimates are based on the reported standard errors in the

epidemiological studies. Distributions for unit values are described in Table 6-11.

Second, as a sensitivity analysis, we use the results of our expert elicitation of the

concentration response function describing the relationship between premature mortality and

ambient PM2.5 concentration.345, 1248 Incorporating only the uncertainty from random sampling

error omits important sources of uncertainty (e.g., in the functional form of the model; whether

or not a threshold may exist). This second approach attempts to incorporate these other sources

of uncertainty.

Use of the expert elicitation and incorporation of the standard errors approaches provide

insights into the likelihood of different outcomes and about the state of knowledge regarding the

benefits estimates. Both approaches have different strengths and weaknesses, which are fully

described in Chapter 5 of the PM NAAQS RIA.1249

These multiple characterizations, including confidence intervals, omit the contribution to

overall uncertainty of uncertainty in air quality changes, baseline incidence rates, populations

exposed and transferability of the effect estimate to diverse locations. Furthermore, the approach

presented here does not yet include methods for addressing correlation between input parameters

and the identification of reasonable upper and lower bounds for input distributions characterizing

uncertainty in additional model elements. As a result, the reported confidence intervals and range

of estimates give an incomplete picture about the overall uncertainty in the estimates. This

information should be interpreted within the context of the larger uncertainty surrounding the

entire analysis.

344 Health impact functions measure the change in a health endpoint of interest, such as hospital admissions, for a

given change in ambient ozone or PM concentration.

345 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually

of multiple experts, are obtained (Ayyb, 2002).
964

5.5 Impacts of Increasing Volume Requirements in the RFS2 Program

The displacement of gasoline and diesel with renewable fuels has a wide range of

environmental and economic impacts. As we describe in Chapters 2-6 of the RIA, we have

assessed many of these impacts for the final rule. It is difficult to ascertain how much of these

impacts might be due to the natural growth in renewable fuel use due to market forces as crude

oil prices rise versus what might be forced by the RFS2 standards. Regardless, these

assessments provide important information on the wider public policy considerations related to

renewable fuel production and use, climate change, and national energy security. Where

possible, we have tried to provide two perspectives on the impacts of the renewable fuel volumes

mandated in EISA – both relative to the RFS1 mandated volumes, and relative to a projection

from EIA (AEO 2007) of renewable fuel volumes that would have been expected without EISA.

Based on the results of our analyses, when fully phased in by 2022, the increased volume

of renewable fuel required by this final rule in comparison to the AEO 2007 forecast would

result in 138 million metric tons fewer CO2-equivalent GHG emissions (annual average over 30

years), the equivalent of removing 27 million vehicles from the road today. Below we report

GHG benefits in the year 2022. The benefit stream from GHG reductions through time after

2022 would show increasing GHG benefits.

At the same time, increases in emissions of hydrocarbons, nitrogen oxides, particulate

matter, and other pollutants are projected to lead to increases in population-weighted annual

average ambient PM and ozone concentrations, which in turn are anticipated to lead to up to 245

cases of adult premature mortality. The air quality impacts, however, are highly variable from

region to region. Ambient PM2.5 is likely to increase in areas associated with biofuel production

and transport and decrease in other areas; for ozone, many areas of the country will experience

increases and a few areas will see decreases. Ethanol concentrations will increase substantially;

for the other modeled air toxics there are some localized impacts, but relatively little impact on

national average concentrations. It is important to note that these air quality results represent the

impact of an incremental increase in ethanol and other renewable fuels and do not estimate the

total air quality impact of the RFS2 volumes of renewable fuels as compared to near-zero levels.

EPA will conduct that type of analysis as part of the “anti-backsliding” study required by Clean

Air Act section 211(v), separate from this final action. The “anti-backsliding” study will use

improved emissions data and consider different ethanol blend levels. Clean Air Act section

211(v) requires EPA to issue regulations that mitigate, to the greatest extent achievable, adverse

impacts on air quality, considering the results of the “anti-backsliding” study.

In addition to air quality, there are also expected to be adverse impacts on both water

quality and quantity as the production of biofuels and their feedstocks increase.

Also, the increased volumes of renewable fuels required by this final rule are projected to

have a number of other energy and economic impacts. The increased renewable fuel use is

estimated to reduce dependence on foreign sources of crude oil, increase domestic sources of

energy, and diversify our energy portfolio to help in moving beyond a petroleum-based

economy. The increased use of renewable fuels is also expected to have the added benefit of

providing an expanded market for agricultural products such as corn and soybeans and open new

markets for the development of cellulosic feedstock industries and conversion technologies.
965

Overall, we estimate that the renewable fuel standards will result in significant net benefits,

ranging between $13 and $26 billion in 2022. Table 5.5-1 summarizes the results of our impacts

analyses of the RFS2 standards relative to the AEO2007 reference case and identifies the section

where you can find further explanation of it. As we work to implement the requirements of

EISA, we will continue to assess these impacts.
966

Table 5.5-1

Impact Summary of the RFS2 Standards in 2022 Relative to the AEO2007 Reference Case

(2007 Dollars)

Category Impact in 2022 Chapter

Discussed

Emissions and Air Quality

GHG Emissions -138 million metric tons 2.7

Non-GHG Emissions

(criteria and toxic

pollutants)

-1% to +10% depending on the pollutant 3.2

Nationwide Ozone +0.12 ppb population-weighted seasonal max

8hr average

5.4

Nationwide PM2.5 +0.002 μg/m3population-weighted annual

average PM2.5

5.4

Nationwide Ethanol +0.409 μg/m3population-weighted annual

average

3.4

Other Nationwide

Air Toxics

-0.0001 to -0.023 μg/m3population-weighted

annual average depending on the pollutant

3.4

PM2.5-related Premature

Mortality

33 to 85 additional cases of adult mortality

(estimates vary by study)

5.4

Ozone-related Premature

Mortality

36 to 160 additional cases of adult mortality

(estimates vary by study)

5.4

Other Environmental Impacts

Loadings to the

Mississippi River from the

Upper Mississippi River

Basin

Nitrogen: +1.43 billion lbs. (1.2%)

Phosphorus: +132 million lbs. (0.7%)

6.4

Fuel Costs

Gasoline Costs -2.4¢/gal 4.4

Diesel Costs -12.1 ¢/gal 4.4

Overall Fuel Cost -$11.8 Billion 4.4

Gasoline and Diesel

Consumption

- 13.6 Bgal 4.4

Total Capital Costs Thru

2022

$90.5 Billion 4.4

Food Costs

Corn +8.2% 5.1

Soybeans +10.3% 5.1

Food +$10 per capita 5.1

Economic Impacts
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Energy Security +$2.6 Billion 5.2

Monetized Health Impacts -$0.63 to -$2.2 Billion 5.4

GHG Impacts (SCC)a+$0.6 to $12.2 Billion (estimates vary by SCC

assumption)

5.3

Oil Imports -$41.5 Billion 5.2

Farm Gate Food +$3.6 Billion 5.1

Farm Income +$13 Billion (+36%) 5.1

Corn Exports -$57 Million (-8%) 5.1

Soybean Exports -$453 Million (-14%) 5.1

Total Net Benefitsb

+$13 to $26 Billion (estimates vary by SCC

assumption)

5.5

a The models used to estimate SCC values have not been exercised in a systematic manner that would allow

researchers to assess the probability of different values. Therefore, the interim SCC values should not be considered

to form a range or distribution of possible or likely values. See Chapter 5.3 for a complete summary of the interim

SCC values.

b

Sum of Overall Fuel Costs, Energy Security, Monetized Health Impacts, and GHG Impacts (SCC).
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Chapter 6: Impacts on Water

6.1 Feedstock Production and Water Quality

As the production and price of corn and other biofuel feedstocks increase, there may be

substantial impacts to both water quality and water quantity. To analyze these impacts, EPA

focused on corn production for several reasons. Corn acres have increased dramatically, 20% in

2007 from 2006, an increase of over 15 million additional corn acres for a total of 93.6 million

acres. Over two-thirds of the new corn acres came from soybean production. Most of the

remaining acres came from the conversion to corn from cotton. Although corn acres declined to

87 million acres in 2009 due to strong prices for other commodities including soybeans, total

corn acres remained the second highest since 1946.1250

There are three major pathways for contaminants to reach water from agricultural lands:

runoff from the land’s surface, subsurface tile drains, or leaching to ground water. A variety of

management factors influence the potential for contaminants such as fertilizers, sediment, and

pesticides to reach water from agricultural lands. These factors include nutrient and pesticide

application rates and application methods, use of conservation practices and crop rotations by

farmers, and acreage and intensity of tile drained lands. Additional factors outside an agricultural

producers control include soil characteristics, climate, and proximity to waterbodies.

6.1.1 Corn Production and Water Quality

The rapid growth in corn acres may have major implications for water quality. Unlike

soybeans and other legumes, corn needs large amounts of fertilizer, especially nitrogen fertilizer,

to produce economic yields. Of all current and potential feedstocks for biofuels, corn has the

greatest application rates of both fertilizer and pesticides per acre and accounts for the largest

share of nitrogen use among all crops.1251 If fertilizers are applied at rates or times when the corn

cannot use them, they are available to runoff or leach to water. Corn generally utilizes only 40 to

60% of applied nitrogen. The remaining nitrogen is available to leave the field and runoff to

surface waters, leach into ground water, or volatilize to the air where it can return to water

through depositional processes. Farmers were expected to apply an additional one million tons of

nitrogen fertilizer to the 2007 corn crop.1252

Historically, corn has been grown in rotation with other crops such as wheat, hay, oats,

and especially soybeans. As corn prices increase relative to prices for other crops, farmers chose

to grow corn every year (continuous corn). Much of the recent growth in corn acres has come

from reductions in a corn-soybean rotation to continuous corn. Although the amount of losses of

nitrogen fertilizers to ground and surface water vary, continuous corn loses significantly more

nitrogen annually than a corn-soybean rotation.1253 In 2005, the latest year for which data was

analyzed, the U.S. average nitrogen fertilization rate for corn was 138 pounds per acre. For

soybeans the average rate was 16 pounds per acre.1254 Soybeans fix nitrogen, so they do not

require as much fertilizer for adequate growth.
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Continuous corn may have additional impacts on the rates of fertilizer and pesticide use.

Continuous corn has lower yields per acre than corn grown in rotation. In response, farmers may

add higher rates of nitrogen fertilizer to try to match yields of corn grown in rotation.

Alternatively, if farmers maintain fertilization rates with these reduced yields, the amount of

unused nutrients will increase and eventually be lost to the environment. Growing continuous

corn also increases population densities of pests such as corn rootworm. Farmers may increase

use of pesticides to control these pests. Total corn herbicide use may also increase due to the

additional corn acres, especially for atrazine, the most commonly used herbicide on corn.

There are potential toxicity concerns with volatilization of pesticide active ingredients1255

in addition to concerns with contamination of foods and drinking water. Furthermore, raising

acreage under corn production will increase the quantity of pesticide products in use. Further

assessment is necessary to determine whether there is the potential for adverse human health

effects from any increase in pesticide use associated with increased domestic corn production.

The most commonly used types of pesticides associated with corn production and storage

largely belong to two broad use categories, herbicides and insecticides. The majority of the more

common corn herbicide products presently on the market contain an organochlorine-type (OC)

active ingredient (AI). For the most part, OC herbicides inhibit cell division and growth while a

subgroup of these products, the atrazine-containing OC herbicides, inhibit plant photosynthesis.

Another type of common corn herbicide, the phosphonoglycene or glyphosate–containing

organophosphate (OP) herbicides, inhibit protein synthesis in plants. Several of the common corn

herbicide compounds, such as acetochlor, carbaryl and alachlor, are classified by EPA as known

or likely human carcinogens and oral exposure to some of these AI compounds at high enough

levels has resulted in adverse health effects, on organs such as the liver or kidney in animals.1256,

1257, 1258

The majority of common corn insecticides are split fairly evenly between OP- and

carbamate-type AI compounds, with the top selling corn insecticide products, by sales of AI by

weight, generally contain methomyl. Methomyl is an N-methyl-carbamide compound which

inhibits the acetylcholinesterase enzyme, causing neurotoxicity in both insects and humans.1259

Methomyl is classified by EPA as an E/unlikely human carcinogen1260 and its use is regulated as

a compound highly toxic to most aquatic and land animals.

High corn prices may encourage farmers to grow corn on land where row crops are not

currently grown. If land is not in row crop production, it generally is an indication that the land is

marginal for row crop production though the land may still be used for agriculture, such as

pasture land. Typically, agricultural producers apply far less fertilizer and pesticide on pasture

land than land in row crops. Corn yield on these marginal lands will be lower, limiting nutrient

uptake and causing a higher percentage of nutrients under standard fertilization rates to be

underutilized and ultimately lost to the environment. However since nitrogen fertilizer prices are

tied to natural gas prices, fertilizer costs have increased significantly. According to U. S.

Department of Agriculture (USDA) Economic Research Service (ERS), fertilizer prices have

been rising steadily since 2002. Through 2008, the annual average prices paid for fertilizers rose

264%. The annual average prices paid for fertilizers were up 82% in 2008 alone. In 2009, ERS

predicts that the annual average prices paid for fertilizers will fall 26.5%. In October 2009
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fertilizer prices returned to the December 2007 level, when the run-up in prices started.1261 It is

unclear how agricultural producers responded to these changes in both corn and fertilizer prices.

EPA has no data indicating US farmers will increase their corn fertilization rates in

response to higher corn prices. However, as demand for corn expands, additional acres panted to

corn will likely results in increased amounts of fertilizer applied. The USDA National

Agricultural Statistics Service has announced that it will discontinue its national Agriculture

Chemical Use reports, collected since 1990, the only survey of its kind. Therefore, it will be very

difficult to obtain future information on fertilizer and pesticide application rates.

Artificial drainage is another important factor in determining the losses of nutrients from

cropland. Artificial drainage consists either of subsurface tiles/pipes or man-made ditches that

move water from wet soils to surface waters so crops can be planted. In a few areas, drains

move water to wells and then groundwater instead of to surface water. Artificial drainage has

transformed large expanses of historic wetland soils into productive agriculture lands. However,

the artificial drains or ditches also move nutrients and pesticides more quickly to surface waters

without any of the attenuation that would occur if these contaminants moved through soils or

wetlands. The highest proportion of tile drainage occurs in the Upper Mississippi and the OhioTennessee River basins in areas of intensive corn production.1262 Manmade ditches predominate

in areas like the Eastern Shore of the Chesapeake Bay.

6.1.2 Impact on Farm Bill Conservation Programs

The increase in corn production and prices may also have significant impacts on

conservation programs funded by the USDA. USDA funds a variety of voluntary programs to

help agricultural producers implement conservation practices on their operations. These

programs fall into two basic categories: land retirement and working lands.

USDA’s largest land retirement program and its largest conservation program is the

Conservation Reserve Program (CRP). Under CRP farmers receive annual rental payments under

10- to 15- year contracts to take land out of agricultural production and plant grasses or trees on

those acres. Generally farmers put land into CRP because it is not as productive and has other

characteristics that make the cropland more environmentally sensitive, such as high erosion rates.

The 2008 Farm Bill (Food, Conservation and Energy Act of 2008) lowered the cap on CRP acres

from 39.2 million acres to 32 million acres. Prior to the passage of the new farm bill, farmers had

already not renewed their contracts on over two million acres of CRP in response to higher crop

prices. USDA expects another 4.6 million acres to come out of CRP between 2007 and 2010, 1.4

million acres in major corn producing states.1263

CRP acres provide valuable environmental benefits both for water quality and for wildlife

habitat. CRP is an important component of rare grassland habitats in the Midwest and Great

Plains.1264 CRP payments are based on the average agricultural land rental rates in the area. As

land values increase due to increase in crop prices, CRP payments are not keeping up with the

higher land rental rates. Farmland in Iowa increased an average of 18% in 2007 from 2006

prices.1265 Midwestern states, where much of the nation’s corn is grown, tend to have
971

reenrollment rates lower than the national average. We note that based on input from USDA,

EPA has modeled assuming the 32 million acres of eligible CRP land will remain protected.

The largest USDA conservation program on working lands is the Environmental Quality

Incentives Program (EQIP). About $1 billion is given to farmers annually to implement

conservation practices on their farms. Farmers are paid a percentage of the cost of installing the

practices, generally ranging from 50 to 90%. Conservation practices encompass a wide range

that can have a significant impact on pollutants reaching ground or surface water from crop

production. EQIP cost-shares with farmers for important practices such as nutrient management,

cover crops, livestock manure storage, and riparian buffers. Like CRP, high corn prices may

have an impact on the willingness of agricultural producers to participate in EQIP. Producers

may require higher payments to offset potential loss of profits through implementation of

conservation practices.

The effectiveness of agricultural conservation practices in controlling runoff and/or

leaching of nutrients, sediment, and pesticides at the field level has been established by

numerous scientific studies across many geographic areas. However, the usefulness of these

practices in achieving water quality goals is dependent on their placement within watersheds. To

most effectively protect water quality, conservation practices should be targeted to the most

vulnerable areas of watersheds. Conservation practices designed to meet wildlife goals will need

different targeting mechanisms to ensure adequate habitat. USDA through the Conservation

Effects Assessment Project (CEAP) is trying to evaluate the effectiveness of controlling

pollution from agricultural lands at the watershed level.346 In order to ensure that Farm Bill

conservation programs meet their environmental quality goals, the EPA’s Science Advisory

Board report to the Gulf of Mexico Task Force (SAB) also recommends implementing the

practices through competitive bidding to ensure that the highest environmental benefit is

achieved at the least cost.1266 It also warns that voluntary programs without economic incentives

are unlikely to be effective to control nitrogen and phosphorus, except for a few practices.

The most cost-effective practices on working lands include: riparian buffers; crop

rotation; appropriate rate, timing, and method of nutrient application; cover crops; and, on tiledrained lands, treatment wetlands and controlled drainage. These practices have significant water

pollution reduction benefits that vary based on the site-specific conditions and on the

implementation and operation and maintenance of the practice. For example, controlled drainage

can reduce nitrogen loads by 30%; treatment wetlands by 40% to 90%; vegetative buffers by

12% to 90%.

6.1.3 Other Agricultural Biofuel Feedstocks

While corn is the most common feedstock for biofuel production by far, under this

proposal, in later years other agricultural feedstocks will become increasingly important. These

feedstocks will have dramatically different impacts on water quality. Biodiesel feedstocks,

primarily soybeans, as well as cellulosic feedstock such as switchgrass or popular trees are not

expected to have significant water quality impacts. As noted previously, soybeans require little to

no additional nitrogen fertilizer. However, soybeans have less residue remaining after harvest

346 See http://www.nrcs.usda.gov/technical/nri/ceap/index.html
972

compared to corn, so sediment runoff could be more of a concern, depending on how each crop

is managed. Switchgrass may be a more favorable biofuels crop for reducing water impacts. It is

a native plant which does not require high inputs of fertilizers or pesticides and since it is a

perennial crop, there is limited sediment runoff compared to annual crops. There is very minimal

acreage of switchgrass grown commercially at the present time, so it is difficult to predict what

inputs farmers will use to cultivate it. Some concern has been expressed about the potential in the

future for farmers to increase switchgrass fertilizer application rates and irrigation rates to

dramatically increase yields.

Corn stover, at the present time, appears to be one of the more viable feedstocks for

cellulosic ethanol, especially in the Corn Belt states. Corn stover is the above ground stalks,

husks, and corn cobs that remain once the corn grain is harvested. Farmers keep the corn stover

on their cropland to maintain the productivity of the soil. Corn stover maintains the soil organic

carbon which has many benefits as a source of nutrients, preventing erosion by wind and water,

and increasing soil aeration and water infiltration. Wilhelm, et al.1267 evaluated the amount of

corn stover that could be harvested for biofuel production and still maintain soil carbon. In all the

soils they evaluated more stover was needed to maintain the soil carbon than for controlling

erosion. For a more general discussion of cellulosic ethanol production, see Chapter 5, Section

V.B.2. More research is needed to identify the amount of stover that can be removed and retain

these important productivity and environmental benefits.

Different conservation systems and conservation practice standards will need to be

developed and adopted for cellulosic feedstocks, such as corn stover, switchgrass, and trees for

biofuels production. USDA will need to continue to adjust current standards and develop

additional standards, where needed, to permit cellulosic feedstocks to be produced and utilized in

a sustainable manner.

6.2 Ecological Impacts

6.2.1 Nutrients

Nitrogen and phosphorus enrichment due to human activities is one of the leading

problems facing our nation’s lakes, reservoirs, and estuaries. Nutrient enrichment is also a

contributing factor to stream degradation. It has negative impacts on aquatic life in streams;

adverse health effects on humans and domestic animals; aesthetic and recreational use

impairment; and excessive nutrient input into downstream waterbodies, such as lakes. Excess

nutrients in streams can lead to excessive growth of phytoplankton (free-floating algae) in slowmoving rivers, periphyton (algae attached to a surface) in shallow streams, and macrophytes

(aquatic plants large enough to be visible to the naked eye) in all waters. Unsightly filamentous

algae can impair the aesthetic enjoyment of streams. In more extreme situations, excessive

growth of aquatic plants can slow water flow in flat streams and canals, interfere with

swimming, and clog the screens on water intakes of water treatment plants and industries.

Nutrient enrichment in streams has also been demonstrated to affect animal communities

in these waterbodies. For example, declines in invertebrate community structure have been
973

correlated directly with increases in phosphorus concentration. High concentrations of nitrogen

in the form of ammonia are known to be toxic to aquatic animals. Excessive levels of algae have

also been shown to be damaging to invertebrates. Finally, fish and invertebrates will experience

growth problems and can even die if either oxygen is depleted or pH increases are severe; both

of these conditions are symptomatic of eutrophication. As a biologic system becomes more

enriched by nutrients, different species of algae may spread and species composition can shift;

however, unless such species shifts cause clearly demonstrable symptoms of poor water

quality—such as fish kills, toxic algae, or very long streamers of filamentous algae—the general

public is unlikely to be aware of this potential ecological concern.

Nutrient pollution is widespread. The most widely known examples of significant

nutrient impacts include the Gulf of Mexico and the Chesapeake Bay. For these two areas alone,

there are 35 states that contribute the nutrient loadings. There are also known impacts in over 80

estuaries/bays, and thousands of rivers, streams, and lakes. The significance of these impacts has

led EPA, States, and the public to come together to place an unprecedented priority on public

partnerships, collaboration, better science, and improved tools to reduce nutrient pollution.

Virtually every state and territory is impacted by nutrient-related degradation of our

waterways. All but one state and two territories have waterbodies that are polluted by nutrients.

States have listed over 10,000 waterbodies that have nutrient and nutrient-related impairments.

Fifteen states have more than 200 nutrient-impaired waterbodies each. Reducing nutrient

pollution is a priority for EPA.

EPA’s Wadeable Streams Assessment provided the first statistically defensible summary

of the condition of the nation’s streams and small rivers.1268 To perform the assessment, EPA,

states, and tribes collected chemical, physical, and biological data at 1,392 perennial stream

locations to determine the biological condition of these waters and the primary stressors affecting

their quality. Research teams collected samples at sites chosen using a statistical design to ensure

representative results. The results of the analysis provide a clear assessment of the biological

quality of wadeable, perennial streams and rivers across the country.

The Wadeable Streams Assessment found that excess total nitrogen is the most pervasive

biological stressor for the nation. Approximately 32% of the nation’s stream length shows high

concentrations of nitrogen compared to reference conditions. Phosphorus exhibits comparable

patterns to nitrogen and is the second most-pervasive stressor for the nation’s stream length.

Streams with relatively high concentrations of nutrients or excess streambed sediments are two

to four times more likely to exhibit poor biological conditions.

The National Water Quality Inventory: Report to Congress, prepared under section

305(b) of the Clean Water Act, summarizes water quality reports submitted by the states and

territories to EPA. Historically, the National Water Quality Inventories have repeatedly shown

that nutrients are a major cause of ambient water quality use impairments. In the most recent

report summarizing the 2002 reports from state, nutrients are identified as the leading cause of

water pollution in assessed lakes and the second leading cause of pollution in assessed estuaries

and bays.1269 Sediment is the leading cause of pollution in assessed rivers and streams.
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Agriculture is the largest known source of water quality impairment to both assessed rivers and

streams and lakes and reservoirs.

6.2.2 Air Deposition of Nitrogen to Water

Nitrogen oxide (NOx) emissions from fossil fuel combustion from both stationary

sources and vehicles can add to the load of nitrogen to waterbodies around the country.

Depending on climate and other variables, the atmospheric NOx falls back to the ground as rain,

snow, fog, or dry deposition. NOx is deposited directly on waterbodies or falls on the land and

can run off to waterbodies. NOx from both stationary sources and vehicles results in significant

loadings of nitrogen from air deposition to waterbodies around the country1270, including the

Chesapeake Bay1271, Long Island Sound1272, and Lake Tahoe.1273 The majority of the new

biofuel production facilities are expected to be located in the Corn Belt in the Mississippi River

Basin, therefore the NOx emissions will add to the nutrient loads to local water bodies and the

Gulf of Mexico. Much of the nitrogen deposition from vehicles falls on impervious surfaces,

such as roads and parking lots where it runs off into streams. Road drainage systems generally

channel runoff quickly and accelerate the nitrogen loadings downstream. In the Chesapeake

region, vehicle exhaust remains the single largest source of fossil-fuel derived nitrogen

pollution.1274 Air deposition of nitrogen accounts for more than half of all nitrogen loadings to

Lake Tahoe.1275

6.3 Gulf of Mexico

Production of corn for ethanol may exacerbate existing serious water quality problems in

the Gulf of Mexico. Nitrogen fertilizer applications to corn are already the major source of total

nitrogen loadings to the Mississippi River.1276 A large area of low oxygen, or hypoxia, forms in

the Gulf of Mexico every year, often called the “dead zone”. Hypoxia threatens commercial and

recreational fisheries in the Gulf because fish and other aquatic species cannot live in the low

oxygen waters. The primary cause of the hypoxia is excess nutrients (nitrogen and phosphorus)

from the Upper Midwest flowing into the Mississippi River to the Gulf. These nutrients trigger

excessive algal growth (or eutrophication) resulting in reduced sunlight, loss of aquatic habitat,

and a decrease in oxygen dissolved in the water.

The 2008 hypoxic zone was measured at 8,000 square miles, the second largest since

measurements began in 1985 and an area the size of Massachusetts.1277 In 2009 models predicted

an even larger hypoxic zone, but it was measured at only 3,000 square miles. A combination of

below average high flows on the Mississippi River and winds that mixed Gulf waters are the

likely causes of the reduced size of the 2009 zone. The average size of the hypoxic zone over the

past five years has been 6,600 square miles.

The Mississippi River/Gulf of Mexico Watershed Nutrient Task Force’s “Gulf Hypoxia

Action Plan 2008” lays out two major goals for reducing water quality problems in the

Mississippi River/Atchafalaya River Basin: 1) reduce the five-year running average areal extent

of the Gulf of Mexico hypoxic zone to 2,000 square miles by 2015 and 2) implement nutrient

and sediment reductions to protect public health and aquatic life and reduce negative impacts of
975

water pollution. The Gulf of Mexico Action Plan calls for an acceleration of actions to reduce the

hypoxia in the Gulf. In order to meet these goals, the Action Plan calls for a 45% reduction in

both nitrogen and phosphorus reaching the Gulf.1278 EPA’s Science Advisory Board (SAB)

report to the Task Force said that an additional reduction in nitrogen and phosphorus reduction

will be necessary as a result of increased corn production for ethanol and climate change

impacts.1279 The SAB also found that the Gulf of Mexico ecosystem appeared to have undergone

a shift so that now the system is more sensitive to nutrient inputs than in the past, inducing a

larger response in hypoxia.

Under the Gulf Hypoxia Action Plan, “USDA will encourage the increased use of its

nutrient management standard to minimize nutrient loss from fields to help alleviate the impact

of increased biofuels production on nutrient loads to the Gulf”.1280 The nutrient management

standard requires farmers to account for all plant-available nutrient sources immediately

available or rendered available throughout the crop production cycle.

6.3.1 Nutrient Loads to the Gulf of Mexico

The U.S. Geological Survey (USGS) has estimated that the spring delivery of nutrients to

the Gulf of Mexico in 2008 was among the highest since the early 1980s. Spring nutrient

delivery is one of the main factors that control the size of the hypoxic zone. In relation to the

long-term spring average, total nitrogen was about 35 to 40% higher (817,000 tons) and total

phosphorus was a record 60 to 85% higher (83,000 tons). The large nutrient contributions are

primarily due to near record-breaking streamflows in spring 2008 in the Mississippi River Basin.

Streamflows were about 50% higher this year compared to the long-term spring average flows

since about 1980. Nutrient contributions for a given spring vary depending on the amount of

flow in the Mississippi-Atchafalaya River Basin, as well as average stream water nutrient

concentrations.

Alexander, et al. modeled the sources of nutrient loadings to the Gulf of Mexico using the

USGS SPARROW (spatially referenced regression on watershed attributes) model.1281 They

estimated that agricultural sources contribute more than 70% of the delivered nitrogen and

phosphorus. Corn and soybean production alone accounted for 52% of the total nitrogen delivery

to the Gulf. Atmospheric deposition was the second largest nitrogen source at 16%. Animal

manure on pasture and rangeland are the main sources of phosphorus loadings, contributing

37%. Corn and soybean contributed 25% of the phosphorus; other crops 18%, and urban areas,

12%.

6.3.2 Recent Analyses of Impact of Corn Ethanol Production on Nutrient Loadings to the

Gulf

Since over 80% of corn grown in the U.S. is produced in the Gulf of Mexico watershed,

concern has been expressed about the impact on Gulf hypoxia of increasing corn production for

ethanol. Several recent scientific reports have estimated the water quality impact of that increase

in corn production. Donner and Kucharik modeled increases in nitrogen export to the Gulf as a

result of corn ethanol volumes increasing from 2007 production levels to 15 billion gallons in

2022.1282 They concluded that the expansion of corn-based ethanol production could make it
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almost impossible to meet the Gulf of Mexico nitrogen reduction goals without “radical shift” in

feed production, livestock diet, and management of agricultural lands. The study estimated a

mean dissolved inorganic nitrogen load increase of 10 to18% from 2007 to 2022 to meet the 15

billion gallon corn ethanol goal, depending on the rate of corn yield increases and potential

efficiency increases in the conversion of corn to ethanol.

EPA’s Science Advisory Board (SAB) report to the Mississippi River/Gulf of Mexico

Watershed Task Force estimated the additional annual nitrogen loadings to the Gulf due to the

increase in corn acres from 78.3 million acres in 2006 to 93.7 million acres in 2013.1283 The SAB

estimated that this scenario will result in an additional national annual loading of almost 300

million pounds of nitrogen. An estimated 80% of that nitrogen loading or 238 million pounds

will occur in the Mississippi-Atchafalaya River basin and contribute nitrogen to the “dead zone”

in the Gulf of Mexico.

6.4 Upper Mississippi River Basin Analysis

To provide a quantitative estimate of the impact of this regulation and production of corn

ethanol generally on water quality, EPA conducted an analysis that focused on agricultural

production in the Upper Mississippi River Basin (UMRB). The UMRB drains approximately

189,000 square miles, including large parts of the states of Illinois, Iowa, Minnesota, Missouri,

and Wisconsin. Small portions of Indiana, Michigan, and South Dakota are also within the basin.

EPA selected the UMRB because it is representative of the many potential issues associated with

ethanol production, including its connection to major water quality concerns such as Gulf of

Mexico hypoxia, large corn production, and numerous ethanol production plants.

In 2007, there were approximately 23.7 million acres of corn in the UMRB. About 75%

of ethanol production is expected to be in the states in the Corn Belt region, of which the UMRB

is a part.1284 Additional discussion about corn production can be found in Section 1.5.1. On

average the UMRB contributes about 39% of the total nitrogen loads and 26% of the phosphorus

loads to the Gulf of Mexico.1285 The Ohio/Tennessee River Basin is the highest contributor of

nitrogen loads to the Gulf at 41%. The high percentage of nitrogen from these two basins is

primarily due to the large inputs of fertilizer for agriculture and the extensive systems of tile

drains. According to USGS, nitrogen loads to the Gulf ranged from 810,000 metric tons to 2.2

million metric tons between 1985 and 2005. Phosphorus loads to the Gulf ranged from 80,700

metric tons to 180,000 metric tons during that same 20-year period.1286 Although nitrogen inputs

to the UMRB in recent years is fairly level, there is a 21% decline in loads to the Gulf. The

Science Advisory Board report attributes this decline to higher amount of nitrogen removed

during harvest, due to higher crop yields.1287 However, most of the reduction in the spring was

from nitrogen forms other than nitrate. Nitrate is an important nitrogen form fueling the algal

growth which leads to hypoxia.1288 For the same period phosphorus inputs increased 12%.

In 2007, the U.S. produced approximately seven billion gallons of ethanol, mostly from

corn kernels. Corn-based ethanol production is expected to reach at least 15 billion gallons in

order for industry to comply with the RFS2 standards. Of the potential crops for biofuels

production, corn has the highest rates of fertilizer and pesticide application, leading to the
977

concern that higher corn production will result in increased loading of nutrients, pesticides, and

sediment to water bodies, including major rivers and estuaries.

6.4.1 SWAT Model

EPA selected the SWAT (Soil and Water Assessment Tool) model to assess nutrient

loads from changes in agricultural production in the UMRB. Models are the primary tool that can

be used to predict future impacts based on alternative scenarios. SWAT is a physical process

model developed to quantify the impact of land management practices in large, complex

watersheds. SWAT, primarily developed by USDA’s Agricultural Research Service and the

Texas A&M University Blackland Research and Extension Center, is a public domain model.

EPA determined that SWAT was the most appropriate model to use for this analysis

because it has been widely used and validated in watersheds both nationally and

internationally.1289 SWAT has been applied extensively to support water quality and Total

Maximum Daily Load (TMDL) planning throughout the United States. SWAT is a basin-scale

continuous simulation model that operates on a daily time step and is designed to predict the

nonpoint source loadings and resulting water quality impacts of water, sediment, and agricultural

chemicals (nutrients and pesticides) from a watershed. The model can assess a wide variety of

impacts of alternative management practices and land use changes. The model is physically

based, computationally efficient, and capable of continuous simulations over long periods of

time, ranging from days to years to decades. Major model components include weather,

hydrology, erosion/sedimentation, soil temperature, plant growth, nutrients, pesticides, bacteria,

agricultural management, stream routing and pond/reservoir routing.

SWAT has several very important strengths that enabled EPA to develop a robust

representation of the hydrology and water quality of the UMRB:

1) Watersheds can be modeled to evaluate the relative impact of changes in management

practices, climate, and vegetation on water quality or other variables of interest;

2) SWAT uses readily available inputs commonly available from various government

agencies;

3) It can simulate crop and plant communities and provide crop yield and plant biomass,

essential to estimate past trends and project accurately into the future;

4) Simulation of very large basins or a variety of management strategies can be

performed expeditiously;

5) Long-term impacts spanning several decades can be studied. Time- and climatevariable pollutant contributions can be simulated along with the impact on downstream

water bodies spanning several decades; and

6) The model code has been validated on hundreds of basins throughout the United States

and abroad.
978

In addition, prior applications of SWAT for hydrology and nutrient simulation in the

UMRB had been completed and were available as a starting foundation for the modeling efforts

and focus of this study.1290 1291 Further technical information regarding SWAT can be found at:

http://www.brc.tamus.edu/swat.

6.4.1.1 AEO 2007 Reference Case

In order to assess alternative potential future conditions within the UMRB, such as

alternative levels of increased corn production as feedstock for ethanol, we had to establish

baseline conditions for SWAT. EPA developed a SWAT model for a reference case using the

ethanol fuel volumes predicted in the AEO 2007 report through 2022 to which the results of the

RFS1 mandate reference case, and the RFS2 control cases could be compared. As in the NPRM,

we selected 2005 as the mid-point of the target period for baseline conditions in the watershed.

However, for the analysis for this final rule, we used the 2007 corn yield value to correspond

with the agricultural analysis described in Chapter 5. We assumed that 33% of corn produced in

the UMRB was converted to corn ethanol, based on estimates from USDA.1292 This baseline

does not include corn ethanol produced as a result of this rulemaking.

Like most water quality modeling, we had to use a range of data sets for the base case

scenario inputs. In developing this scenario, it was necessary to select a target year, or window of

years, that represent the conditions on the watershed. For this study the year 2005 was selected as

the target period for baseline conditions. As with most models of this scale, it was not possible to

have all of the data sources come from the exact same time period. It is a common modeling

practice to combine the best available data sources for model development in an attempt to

characterize the baseline condition within a short time window or period. The majority of the

data sources were from the years 2000 through 2006. In addition, selected assumptions about the

baseline were made using 2007 as the reference year. In particular, the baseline value for average

corn yield (144.2 bushels per acre) was based on the year 2007. In reality, the base case

represents watershed conditions within a two to three year period.

Since one of the driving forces in the SWAT model is the water balance, climate data is

key to accurately predicting the movement of nutrients and sediment. SWAT was applied (i.e.

calibrated) to the UMRB using weather data from the NRCS climatic data center for a 40-year

period from 1960 to 2001 and flow and water quality data from 13 USGS gauges on the

mainstem of the Mississippi River, spatially distributed from the upper reaches in Minnesota and

Wisconsin to the UMRB outlet below Grafton, Illinois. In addition, the weather data has been

spatially interpolated to assign one weather station per subwatershed.

To establish the land use for the baseline scenario, SWAT was setup on 131

subwatersheds [8-digit Hydrologic Unit Code (HUC)] for the entire UMRB using the 2001

National Land Cover Data (NLCD)1293 and Cropland Data Layer (CDL).1294 The CDL contains

crop specific digital data layers, suitable for us in geographic information system applications.

The CDL program focuses on classifying corn/soybean/rice/cotton agricultural regions in many

of the Midwestern and Mississippi delta states using remote-sensing imagery and on-the-ground

monitoring. The USDA-NRCS STATSGO provided the soils data for the entire analyses. The
979

primary input data is the USDA 1997 National Resource Inventory (NRI), which provided land

use, soil, and data on management practices on the land.1295 1997 is the most current year for

which this data is available.

In addition, information from the Conservation Tillage Information Center and USDANASS Census of Agriculture 2002/1997 were used to identify the cropping rotation and

management practices for the agricultural land areas by these same 131 subwatersheds. Based on

the management information at this level, each sub-watershed was assigned appropriate

management and tillage practices.

Drainage tiles are one of the critical man-made hydrology structures that changes the

natural hydrological cycle significantly at both surface and subsurface (lateral flow) levels. There

are no clear records of where the tiles are within the UMRB, other than a few research articles

that attempted to estimate the location and extent of the tile drainage coverage. In this study,

similar literature values were used to estimate and identify the areas that have the tile system to

drain the excess water and nutrients in a timely manner. First, the STATSGO database was used

to identify the very poorly drained soils, somewhat poorly drained soils, and poorly drained soils.

Then, slope and land use maps were overlaid on these poorly drained soils to identify the

potential tile drainage system. Only slopes <1% and agricultural land uses were identified as

areas that may potentially be served with tile drainage system.

The tillage practice information in the UMRB was obtained at the county level from

Conservation Technology Information Center.1296 There are five major tillage types. Three of

them (no-tillage, ridge-tillage, and mulch-tillage) belong to conservation tillage, and the other

two types of tillage (reduced-tillage and intensive-tillage) are non-conservation tillage. The

county acreages of this tillage information were overlaid on 8-digit HUCs to estimate the percent

of each tillage practices by crop within each HUC.

To estimate nutrient applications on cropland, we started by estimating the livestock and

the amount of manure produced. The livestock numbers came from the agricultural statistics for

each county based on the 2002 Census of Agriculture for each 8-digit HUC. (Only cattle and

hogs numbers were used since they are the dominant livestock types in the UMRB.) Then, the

manure production of each 8-digit HUC was obtained through multiplying the number of cattle

and hogs and the manure production rates as outlined in ASABE, 2005.1297 If the total amount of

the manure production exceeded 20% of the estimated total fertilizer application in one HUC,

manure application and chemical fertilizer application were used as SWAT model inputs to

simulate nutrient applications in that HUC. The manure was applied to only those areas that are

agricultural land use, even during rotation. For example, only hay, corn, and row crops get

manure application, not legume crops such as alfalfa or soybean. So, if an area had a corn and

soybean rotation, manure was only applied during the corn growing period. Even when manure

was applied, chemical fertilizer was used to supplement the manure application where and when

needed. In areas where the manure was not applied, chemical fertilizer was applied to grow the

agricultural crops. Chemical nitrogen fertilizer at applied at 1.3 times the amount of nitrogen

taken off at harvest.

For the UMRB analysis we used the auto-fertilization feature in SWAT. Any time actual

plant growth fell below the specified nitrogen stress threshold, the model automatically applied
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fertilizer. The user specifies the type of fertilizer, the fraction of total fertilizer applied to the soil

surface, the maximum amount of fertilizer that can be applied during the year, the maximum

amount of fertilizer that can be applied in any one application, and the application efficiency.

Fertilizer is applied to match the difference between soil available nitrogen and the crop yield

nitrogen that is removed during harvest. The auto-fertilization used in the UMRB study was set

up using default parameter values for fertilizer application rate (200 kg N /ha), maximum per

year fertilization rate (300 kg N /ha), application efficiency (1.3, ratio, unitless), and fraction of

fertilizer applied to soil surface (0.2). The nitrogen stress factor was set to 0.75 (ratio, unitless).

The 42-year SWAT model runs were performed and the results analyzed to

establish runoff, sediment, nitrogen, and phosphorous loadings from each of the 131 8-

digit HUC subwatersheds and the larger 4-digit subbasins, along with the total outflow

from the UMRB and at the various USGS gage sites distributed along the Mississippi

River mainstem. These results provided the Reference Case model values to which the

RFS1 and RFS2 future alternatives are compared.

The current national average for corn yield of 150 bushels per acre (bu/ac) was used to

establish baseline yield levels. The baseline average yield for the UMRB was established at

144.2 bushels per acre. This baseline yield is due to the significant amount of crop area in

northern states where yield values are lower than the national average. National average corn

yields have been increasing primarily due to favorable weather conditions and improvement in

practices to reduce stress on the corn plants from excess water, drought, and pests.

6.4.1.2 Reference Cases and RFS2 Control Case

To assess the impacts of the increased use of corn ethanol, we modeled an RFS2 control

case and compared it to both the AEO2007 reference case and the RFS1 mandate reference case.

For the AEO20071298 reference case we modeled: 10.49 billion gallons a year (BGY) in 2010,

11.1 BGY in 2015, 11.83 BGY in 2020, and 12.29 BGY in 2022. For the RFS1 mandate

reference case we modeled a constant national ethanol goal of 7.05 billion gallons a year (BGY)

starting in 2012. For this analysis, the reference cases assumed that no cellulosic ethanol was

produced from corn stover. For the RFS2 control case we modeled a steadily increasing volume

of corn ethanol in keeping with the EISA standards; 11.24 BGY in 2010, 14.79 BGY in 2015,

and 15 BGY in 2016 and beyond. We were not able to model the impacts of corn stover removal

at this time, so the analysis only reflects the impacts of increased use of corn grain for renewable

fuel use.

For SWAT analyses of these three scenarios, national corn ethanol volumes were

adjusted for the UMRB based on a 42.3% ratio of ethanol production capacity within the UMRB

compared to national capacity. This fraction was determined by overlaying coverage of

nationwide ethanol plants with a coverage of the UMRB. Production from ethanol plants within

the study area were totaled and then divided by the nationwide production. Both current

production and planned expansion were included in the totals. Ethanol location and production

information were taken from the Renewable Fuels Association table of ethanol refinery locations

in April 2008.1299 We assumed an average of 2.7 gallons of ethanol per bushed of corn and a

moisture content of 20% when converting corn grain mass to bushels. The resulting UMRB
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ethanol production goals were converted into the corresponding required corn production

acreage, i.e. the extent of corn acreage needed to meet those ethanol production goals.

The SWAT model was run with the available input climate record, 1960-2001, with the

model run under conditions of the increased corn production and yields noted above. Separate

model runs were performed for each of the three projection years, and the model results were

analyzed to provide loadings for comparison with the baseline loadings.

6.4.1.2.1 Corn

Increases in corn yield were built into the future scenarios, with an annual increase of

1.23%. This produced yield increases to 149.6 bushels per acre (bu/ac) (3.7% in 2010), 159 bu/ac

(10.3% in 2015), 169 bu/ac (17.2% in 2020), and 173.2 bu/ac (20.1% in 2022). Table 6.4-1

shows the corn acreage in the Upper Mississippi River Basin for each case. Corn acres increased

9% in 2022 between the AEO 2007 case and the RFS2 (no stover) cae.

Table 6.4-1.

Corn Acres in the Upper Mississippi River Basin for

AEO 2007, RFS1, and RFS2 Cases (millions of acres)

AE0 2007 RFS1 RFS2 (no stover)

2010 26.83 23.65 27.61

2015 26.78 22.78 30.40

2020 26.80 22.35 29.73

2022 26.96 22.20 29.40

6.4.1.2.2 RFS2 (No Stover) Control Case Pollutant Loadings

Tables 6.4-2 through 6.4-4 compare the model outputs for nitrogen, phosphorus, and

sediment between the AEO 2007 Reference Case and the RFS2 (no stover) Case scenarios for

the years 2010, 2015, 2020, and 2022. Land load is the total amount of nitrogen or phosphorus

that reaches a stream within the UMRB. The total outflow is the remaining amount measured at

the outlet of the UMRB at Grafton, Illinois after accounting for in-stream loses due to uptake or

assimilation.

These results only estimate loadings from the Upper Mississippi River basin, not the

entire Mississippi River watershed. As noted earlier, the UMRB contributes about 39% of the

total nitrogen loads and 26% of total phosphorus loads to the Gulf of Mexico. The decreasing

nutrient load over time is likely attributable to the increased average corn yield per acre, resulting

in greater plant uptake of nitrogen and fewer corn acres planted for ethanol production goals in

this rule.
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Table 6.4-2.

Average annual nitrogen loads: Comparison of AEO 2007 Reference Case

to the 2022 RFS2 (No Stover) Case (% difference in parentheses)

AEO 2007 Reference Case RFS2 (No Stover) Case

Model

Run

Total Land Load,

million lbs

Total Outflow,

million lbs

Total Land Load,

million lbs

Total Outflow,

million lbs

2010 1948 1470 1944 (-0.21) 1467 (-0.20)

2015 1911 1441 1946 (1.83) 1469 (1.94)

2020 1887 1421 1912 (1.32) 1442 (1.48)

2022 1877 1413 1897 (1.07) 1430 (1.20)

Approximately 24 to 25% of nitrogen leaving agricultural fields was either taken up by

aquatic plants or volatilized before reaching the outlet of the UMRB at Grafton, Illinois. Even

though much of the nitrogen that is volatilized from streams and rivers and near-coastal waters is

removed from the total loading to water, it is not necessarily eliminated as an environmental

concern. Conversion of the nitrate to nitrogen gas through denitrification is generally an

incomplete chemical process. 5% or more of the nitrogen can be converted to nitrous gas, a

powerful greenhouse gas that is 300 times the climate-warming potential of carbon dioxide, the

major greenhouse gas of environmental concern. Thus, a water pollutant becomes an air

pollutant until it is either captured through biological sequestration or converted fully to

elemental nitrogen.

The scenarios showed an increase in phosphorous loads at a slightly lower percentage

than nitrogen.

Table 6.4-3.

Average annual phosphorus loads: Comparison of AEO 2007 Reference Case

to the 2022 RFS2 (No Stover) Case (% difference in parentheses)

AEO 2007 Reference Case RFS2 (No Stover) Case

Model

Run

Total Land Load,

million lbs

Total Outflow,

million lbs

Total Land Load,

million lbs

Total Outflow,

million lbs

2010 180.0 133.8 179.9 (-0.06) 133.7 (-0.07)

2015 178.2 132.3 179.6 (0.79) 133.6 (0.98)

2020 177.0 131.3 178.2 (0.68) 132.4 (0.84)

2022 176.5 130.9 177.6 (0.62) 131.8 (0.69)

Total sediment outflow showed very little change over all scenarios. This result is

primarily due to corn stover remaining on the field following harvest and therefore reducing

sediment transport to water.
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Table 6.4-4.

Average annual sediment loads: Comparison of AEO 2007 Reference Case

to the 2022 RFS2 (No Stover) Case (% difference in parentheses)

AEO 2007

Reference Case

RFS2 (No

Stover) Case

Model

Run

Total Outflow,

million tons

Total Outflow,

million tons

2010 6.231 6.232 (0.02)

2015 6.221 6.233 (0.19)

2020 6.214 6.224 (0.16)

2022 6.211 6.220 (0.14)

6.4.1.2.3 RFS1 Mandate Reference Case Pollutant Loadings

Tables 6.4-5 through 6.4-7 compare the models outputs for nitrogen, phosphorus, and

sediment between the RFS1 Mandate Reference Case and the RFS2 (No Stover) Case scenarios

for the years 2010, 2015, 2020, and 2022. Land load is the total amount of nitrogen or

phosphorus that reaches a stream within the UMRB. The total outflow is the remaining amount

measured at the outlet of the UMRB at Grafton, Illinois after accounting for in-stream loses due

to uptake or assimilation.

Table 6.4-5.

Average annual nitrogen loads: Comparison of RFS1 Mandate Reference Case

to the RFS2 (No Stover) Case (% difference in parentheses)

RFS1 Mandate Reference Case RFS2 (No Stover) Case

Model

Run

Total Land Load,

million lbs

Total Outflow,

million lbs

Total Land Load,

million lbs

Total Outflow,

million lbs

2010 1878 1414 1944 (3.5) 1467 (3.7)

2015 1838 1382 1946 (5.8) 1469 (6.3)

2020 1806 1357 1912 (5.9) 1442 (6.3)

2022 1794 1347 1897 (5.7) 1430 (6.2)
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Table 6.4-6.

Average annual phosphorus loads: Comparison of RFS1 Mandate Reference Case

to the RFS2 (No Stover) Case (% difference in parentheses)

RFS1 Mandate Reference Case RFS2 (No Stover) Case

Model

Run

Total Land Load,

million lbs

Total Outflow,

million lbs

Total Land Load,

million lbs

Total Outflow,

million lbs

2010 175.6 130.1 179.9 (2.4) 133.7 (2.8)

2015 173.5 128.4 179.6 (3.5) 133.6 (4.0)

2020 171.6 126.9 178.2 (3.8) 132.4 (4.3)

2022 170.8 126.3 177.6 (4.0) 131.8 (4.4)

Table 6.4-7.

Average annual sediment loads: Comparison of RFS1 Mandate Reference Case

to the 2022 RFS2 (No Stover) Case (% difference in parentheses)

RFS1 Mandate

Reference Case

RFS2 (No

Stover) Case

Model

Run

Total Outflow,

million tons

Total Outflow,

million tons

2010 6.190 6.232 (.07)

2015 6.187 6.233 (.07)

2020 6.178 6.224 (.07)

2022 6.174 6.220 (.07)

6.4.1.2.4 Case Study

To evaluate local water quality impacts that are impossible to ascertain at the scale of the

UMRB, we also modeled the Raccoon River watershed in central Iowa. The criteria for

choosing this watershed included: percentage of corn area representative of the UMRB, stream

segments included in EPA’s 303(d) list of impaired waters due to high nutrient levels,

biorefinery plants, drinking water intakes, and observed streamflow and water quality data.

Nearly 88% of the watershed is in agriculture. 75% of the watershed produces corn and

soybeans, mostly in rotation. Hay and other row crops are produced on the remaining agriculture

land. The city of Des Moines makes up about 8% of the watershed. The state of Iowa has listed

numerous stream segments of the Raccoon River as impaired. In particular, the two stream

segments from the confluence of the North and South branches of the Raccoon River to the

watershed’s outlet were listed in 2006 for having more than 25% of the collected water samples

exceed the drinking water standard for nitrate.

As part of the UMRB and by itself, the Raccoon River has been the focus of numerous

modeling studies. As a result, there is a substantial amount of observed data throughout the

watershed, primarily from the U.S. Geological Survey gaging stations.
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The case study used the same assumptions and scenarios as those used for the UMRB

analysis. SWAT-simulated streamflow and water quality (total nitrogen and phosphorus, and

sediment loadings) were calibrated against observed data at both monthly and yearly time steps.

As in the UMRB study, nitrogen loads to water increased for the future scenarios, though

at a greater rate. Future phosphorus loads decreased in the Raccoon River model, where they

had shown minor increases in the UMRB model. For the Raccoon River, there was a greater

decrease in sediment load, which is the likely cause for the decrease in phosphorus loadings. As

with the UMRB model, there was minimal change in streamflow.

6.4.1.5 Sensitivity Analysis

Using the existing UMRB SWAT model, a sensitivity analysis was conducted on a

number of important meteorological and management related factors. The goal was to further

understand the model characteristics and sensitivities to parameters and input forcing functions

that control the model response for the key environmental indicators of concern. Scenarios were

constructed using four factors: fertilization application threshold, corn residue removal, daily air

temperature, and daily precipitation. The results of the analysis showed that rainfall and

temperature are the most influential factors for all model outputs: water yield, total nitrogen and

phosphorus loadings, and sediment loadings. These results underscored the importance of

representing these two driving factors accurately in hydrologic modeling. Corn residue removal

noticeably reduced nutrient loading into streams while increasing sediment loads. However,

since corn residue is the main source of organic nitrogen and phosphorus, the removal of the

residue leads to the need for higher nutrient inputs in the growing season. The fertilization

application threshold scenario did not tangibly impact water yield and sediment loading. The

findings from this study indicated that future climate change could greatly influence water

availability and pollution from corn cropland.

6.5 Climate Change Impacts

Although climate change is expected to be an important factor in future crop production

in the Upper Mississippi River Basin, EPA has not modeled the impact of climate change on

corn yields for a variety of reasons. Climate change requires a long period of observation. Over

the short time frame reflected in this proposal, precipitation and temperature increases will be

small and indistinguishable from the natural variability of the climate.

Crop yield changes resulting from climate change depend on the atmospheric carbon

dioxide level, the crop, and the base temperature. Yield also depends on the characteristics of the

crop relative to the timing of precipitation and of extreme temperature events. All of these

variables make an estimation of actual climate-induced yield loss very difficult to develop.

Farmer adaptation may mitigate the effects of climate change on agriculture to some degree.

Adaptations are influenced by many unpredictable factors, including government policy, prices,

research and development, and technical assistance. Climate model simulations generally

indicate that most locations in the upper Midwest will warm more than the global average and
986

will receive more precipitation than current – though estimates vary considerably depending on

the model used and initial conditions.

6.6 Chesapeake Bay Watershed

The Chesapeake Bay Commission and others have expressed concerns about the water

quality impact of increased corn production for ethanol may have on the Chesapeake Bay.1300,

1301 The Chesapeake Bay watershed stretches across more than 64,000 square miles,

encompassing parts of six states--Delaware, Maryland, New York, Pennsylvania, Virginia and

West Virginia—and the entire District of Columbia. The Chesapeake's land-to-water ratio (14:1)

is the largest of any coastal water body in the world. This is why land use and land management

have such significant influences on the health of the Bay. In its annual State of the Bay report in

2007, the Chesapeake Bay Foundation gave the Bay a score of 28 on a scale where 70 means the

Bay is “saved” and 100 is pristine. The Foundation said that “the health of the Chesapeake Bay is

dangerously out of balance”.1302

In 2000, Chesapeake Bay Program partners (states, federal agencies, universities,

nongovernmental agencies) agreed to reduce nitrogen pollution from an estimated 285 million

pounds per year to no more than 175 million pounds by 2010. Similarly they pledged to reduce

phosphorus from about 19 million pounds per year to less than 13 million pounds. While there

have been steady declines in nitrogen and phosphorus, they have not been adequate to meet the

established goals. The watershed must essentially quadruple the pace of the Bay cleanup to meet

the 2010 commitment. To restore water quality in the Bay, all of the basin’s more than 87,000

farms will need to implement best management practices (BMPs) at levels never before seen in

this country. The states have committed to implement close to 30 different agricultural BMPs as

part of their restoration strategies.

In May 2009, President Obama issued Executive Order 13508 on Chesapeake Bay

Restoration and Protection. The order established a Federal Leadership Committee, chaired by

EPA, and with senior representatives from the departments of Agriculture, Commerce, Defense,

Homeland Security, Interior, and Transportation. In November 2009, these federal agencies

released a draft strategy which contains a range of approaches for accelerating cleanup of the

nation’s largest estuary and its vast watershed.1303 The draft strategy calls for increased

accountability and performance from pollution control, habitat protection and land conservation

programs at all levels of government, including an expanded use of regulatory authorities to

address pollution control and additional voluntary and market-based solutions – particularly

when it comes to habitat protection and land conservation programs. The proposed actions are in

response to overwhelming scientific evidence that the health of the Chesapeake Bay remains

exceptionally poor, despite the concerted restoration efforts of the past 25 years.

Agricultural lands account for nearly a quarter of the watershed, and contribute more

nutrients to the Bay than any other land use. Agricultural operations produce about 41% of the

nitrogen and 47% of the phosphorus loads going to the Bay. Agriculture also contributes about

63% of the Bay’s sediment. Municipal and industrial wastewater treatment plants throughout the
987

watershed are responsible for 21% of the total nitrogen pollution and 22% of the total

phosphorus pollution delivered to the Bay.

At least 25% and possibly a third of the nitrogen entering the Bay comes from air

deposition. The principal sources of emissions are power plants, cars and trucks, agriculture, and

off-road sources such as construction equipment, lawn mowers and aircraft. While population

increased about 8% during the last decade, vehicle miles traveled rose 26%. More discussion

about nitrogen oxides emission impacts can be found in Chapter 3.2.

The Bay watershed receives significant levels of nitrogen oxides and other airborne

pollutants from its large airshed (which is about six and a half times the size of the watershed), as

far west as Ohio and Indiana. Air deposition of nitrogen on the land adds to the burden that must

be dealt with by farmers, local governments and other landowners.

6.6.1 Agricultural Production Effects

Due to the significant acreage within the Chesapeake Bay watershed that is devoted to

agricultural production (approximately 22%), increases in corn acreage can potentially contribute

to changes in nutrient loads to the Bay. High demand for corn reflected in record corn prices

have played a substantial role in encouraging producers to alter their typical crop production

rotations and crop acreage, contributing to noteworthy changes in crop acreages across the

watershed. A technical review committee convened by the Chesapeake Bay Commission

estimated that 300,000 new acres of corn could be added in the Bay watershed in the coming

years.1304 This new corn acreage could potentially contribute an additional five million pounds of

nitrogen to the Bay. The Bay Program partners are trying to reach a 90 million pound reduction

in nitrogen from all sources. However, it is estimated that 17 million pounds of nitrogen could be

offset if all agriculture acres used cover crops as a conservation practice after harvest.

Strong market forces also encourage agricultural operators to increase grain production

possibly by increasing the conversion of non-row crop acreage (hay, pasture and fallow or idle

lands) to row crop production. Grain row crops can add more nutrients per acre to the Bay than

hay and pasture due to production intensity, management systems, and nutrient efficiency of the

crop.

6.6.1.1 Base Analysis Assumptions

The Chesapeake Bay Program Watershed Model Phase 4.3 (CBWM) and Vortex were

utilized in the analysis of potential shifts in nutrient loading to the Bay based on reported

changes to agricultural crop production from 2005 to 2008. These agricultural production

changes are partially the result of the rapid expansion of biofuel production within the United

States, supported by market-driven commodity price increases, government policies, or a

combination of both. The CBWM is a dynamic watershed model used to characterize nutrient

and sediment loads, and changes in these loads, due to management actions for decision

support.347

.

347 For more information on the CBWM see http://www.chesapeakebay.net/model.htm
988

In developing the agricultural production trend analysis within the Chesapeake Bay

watershed, the USDA’s National Agricultural Statistics Service’s (NASS) 2007 and 2008

Projected Plantings report on reported crop acreages was modified to target only the Bay

watershed.

6.6.1.2 Corn Production Analysis

Analyzing corn production acreage figures for the period from 2005 to 2008 from the

NASS 2007 and 2008 Projected Planting reports, a measurable upward trend was evident for

corn acreage plantings across the Bay watershed over the analysis period. This upward trend

increased sharply between 2006 and 2007 and decreased for the 2007 and 2008 period. Despite

the recent downward trend, total corn acreage increased over the analysis period by almost

66,000 acres.

6.6.1.3 Corn Nutrient Load Analysis

Employing a modeled analysis of the USDA-NASS Prospective Plantings report using

the Chesapeake Bay Watershed Model Version 4.3 and Vortex, considerable increases of

potential nitrogen loads to the Bay are associated with increased corn acreage. The decrease of

corn acreage in the 2007 to 2008 period does not offset the total increase in acreage and nitrogen

yields between 2005 and 2008. Total nitrogen loads increased by almost 2.4 million pounds.

6.6.1.4 Land Use Conversion Analysis

The agricultural production trends between 2005 and the present not only indicate an

overall increase in the number of acres under corn production, but also an increase in the total

acres of land under row crop production by over 355,000 acres. Since agricultural land uses

within the Chesapeake Bay watershed are continuously decreasing due to urban development,

the increase in row crop acreage may come at the expense of other cropping systems, or

agricultural land uses such as hay, pasture or idle lands.

6.6.1.5 Land Use Conversion Nitrogen Load Analysis

The USDA-NASS Prospective Plantings reports and the Chesapeake Bay Program

Watershed Model indicate a continuous total conversion of non-row crop agricultural lands over

the period from 2005 to 2008 to more intensive row crop production. The non-row crop land

uses typically produce less nitrogen yields to the Bay, thus additional acres converted to grain

production can also increase nitrogen loads significantly. This analysis estimates that nitrogen

loads increase by 8.8 million pounds.

If time and resources allow, the Chesapeake Bay Program proposes to analyze the

potential impacts within the Chesapeake Bay watershed of the implementation of the RFS2 for

the FRM using available systems and models at our disposal. The models that would potentially

be used in the analysis would include Phase 5.2 of the Chesapeake Bay Watershed Model

(CBWM), the Nutrient and Sediment Scenario Builder (NSSB), the Chesapeake Bay Estuarine
989

Water Quality Sediment Transport Model (CBEWQSTM) and the Chesapeake Bay Land Change

Model (CBLCM). The CBWM is a dynamic watershed model used to characterize nutrient and

sediment loads, and changes in these loads, due to management actions for decision support. The

NSSB is being developed to determine nutrient and sediment loads under multiple land uses and

crop types with variable organic and inorganic nutrient inputs. The CBEWQSTM determines the

effects of nutrient and sediment load changes to the attainment of water quality standards. The

CBLCM simulates changes in land use as a result of locally projected increases in population out

to the year 2030.

The scope of the analysis is proposed to include incremental and delivered nitrogen,

phosphorus and sediment to the Chesapeake Bay, and the effect of management changes to the

attainment of water quality standards.

6.7 Ethanol Production and Distribution

Under the Clean Water Act, all point sources of pollution, including ethanol plants, must

have a permit to discharge to water bodies or to municipal wastewater treatment plants for both

industrial process water and stormwater. The permit regulates the amount of pollutants that can

be discharged. There are three principle sources of discharges to water from ethanol plants: reject

water from water purification, cooling water blowdown, and off-batch ethanol.

6.7.1 Water Discharges

Water is required at ethanol facilities for processing and for the production of steam that

is typically used in biomass pretreatment and ethanol distillation processes. An ethanol plant’s

wastewater is typically comprised of cooling tower blowdown, boiler blowdown, and water

softener discharge. The majority of the process water is lost as steam in the distillation process.

In addition, stormwater runoff from the facility may be contaminated from precipitation (rain or

snow) coming in contact with plant operations (industrial plant yards, material and waste

handling, storage areas, shipping and receiving areas, residuals sites) and requires adequate

control and management.1305

While some ethanol facilities get their process water from municipal water supplies, most

use on-site wells to produce the process water for the ethanol process. Most groundwater sources

are not suitable for process water because of their mineral content. Therefore, the water must be

treated for use in ethanol production. The most common method of groundwater treatment is

reverse osmosis. Reverse osmosis uses specialized filtration and pressure to produce pure water

while concentrating the groundwater minerals into reject water. The minerals in the reject water

are site-specific, but they can include: calcium carbonate, magnesium carbonate, sulfate, iron,

and sodium. For every two gallons of pure water produced, about a gallon of brine is discharged

as reject water. Most estimates of water consumption in ethanol production are based on the use

of clean process water and neglect the water discharged as reject water.

The largest source of wastewater discharge is reverse osmosis reject water from process

water purification. The reverse osmosis process concentrates groundwater minerals to levels
990

where they can have water quality impacts. The concentrated minerals can show toxicity due to

osmotic concentration and the presence of some ions such as sulfate or copper. There is really no

means of “treating” these ions to reduce toxicity, other than further concentration and disposal,

or use of in-stream dilution. Some facilities have had to construct long pipelines to get access to

dilution so they can meet water quality standards.

Ethanol plants also discharge cooling water blowdown, where some cooling water is

discharged to avoid the buildup of minerals in the cooling system. These brines are similar to the

reject water described above. In addition, if off-batch ethanol product or process water is

discharged, the waste stream can have high Biochemical Oxygen Demand (BOD) levels. BOD

directly affects the amount of dissolved oxygen in rivers and streams. The greater the BOD, the

more rapidly oxygen is depleted in the stream. This means less oxygen is available to higher

forms of aquatic life. The consequences of high BOD are the same as those for low dissolved

oxygen: aquatic organisms become stressed, suffocate, and die.

.

Ethanol production facilities are important transportation hubs. For instance, a facility in

Iowa produces about 130 million gallons of ethanol in a year. On an average workday, 175

tractor-trailers bring in corn, ethanol goes out in 12 rail tankers, and 8 rail cars are filled with

dried distillers grain to be used as animal feed. This intensity of vehicle travel can have local

water impacts from stormwater runoff, spills, etc. similar to any other rail and trucking terminal.

6.7.2 Water Use

Older generation production facilities used 4-6 gallons of process water to produce a

gallon of ethanol, but newer facilities use less than 3 gallons of water in the production process.

Most of this water savings is gained through improved recycling of water and heat in the process:

the conservation of heat energy and water go hand-in-hand. This energy savings is a key

economic advantage for newer plants. A gallon of ethanol contains about 70,000 Kcal of energy.

Older plants used 35-40,000 Kcal of energy to produce a gallon of ethanol, but newer facilities

use only 25-28,000 Kcal per gallon.

The abundance or lack of water supply is a local issue, and there have been concerns with

water consumption as new plants go online. Some facilities are tapping into deeper aquifers as a

source of water. These deeper water resources tend to contain higher levels of minerals and this

can further increase the concentration of minerals in reverse osmosis reject water.

6.7.3 Distillers Grain with Solubles

One important co-product of ethanol production is distillers grain with solubles (DGS).

Due to the increase in ethanol production and the price of corn, DGS has become an increasing

important feed component for confined livestock. About one-third of the corn processed into

ethanol is converted into DGS. Therefore approximately 45 million tons of DGS will be

produced for the 15 billion gallons of corn ethanol produced by 2015. Concerns have been raised

about the relatively higher phosphorus content of DGS compared to traditional feeds.
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Livestock producers may partially replace corn or other feeds with DGS for both

economic and production reasons. Different livestock species can tolerate varying amounts of

DGS in their diets. The majority of DGS are fed to beef and dairy cows. Current

recommendations allow beef and dairy cows diets to include from 15 to 40% DGS.

Recommendations for poultry and swine diets are generally less than 15% DGS. Although

specific analysis of DGS can vary between ethanol plants, compared to corn, DGS are higher in

crude protein (nitrogen) and three to four times higher in phosphorus.1306

The increase in nitrogen and phosphorus from DGS in livestock feed has potential

implications for water quality. When nitrogen and phosphorus are fed in excess of the animal’s

needs, these nutrients are excreted in the manure. Most livestock manure is applied to crops,

especially corn, as a source of nutrients. When manure is applied at rates above the nutrient

needs of the crop or at times the crop can not use the nutrients, the nitrogen and phosphorus can

runoff to surface waters or leach or ground waters. Excess nutrients from manure nutrients have

the same impact on water quality as excess nutrients from other sources.

Several recent studies have indicated that DGS may have an impact on food safety. Cattle

fed DGS have a higher prevalence of a major food-borne pathogen, E. coli O157, than cattle

without DGS in their diets.1307 More research is needed to confirm these studies and devise

methods to eliminate the potential risks.

Livestock producers can limit the potential pollution from manure applications to crops

through a variety of techniques. USDA’s Natural Resources Conservation Service (NRCS) has

developed a standard for a comprehensive nutrient management plan (CNMP) to address the

issue of proper use of livestock manure.1308 Agricultural producers who use manure should test

the nitrogen and phosphorus content before application of the manure. Due to the substantially

higher phosphorus content of manure from livestock fed DGS, producers will potentially need

significantly more acres to apply the manure so that phosphorus will not be applied at rates

above the needs of the crops. This is a particularly important concern in areas where

concentrated livestock production already produces more phosphorus in the manure than can be

taken up by crops or pasture land in the vicinity.

6.7.4 Water Quality Impact from Ethanol Leaks and Spills

The potential for exposure to fuel components and/or additives can occur when

underground fuel storage tanks leak fuel into ground water that is used for drinking water

supplies or when spills occur from above ground tanks or distribution systems that contaminate

surface drinking water supplies or surface waters. Additionally, in surface waters, rapid

biodegradation of ethanol can result in depletion of dissolved oxygen with potential mortality to

aquatic life.

Regarding leaks or spills and drinking water impacts, ethanol biodegrades quickly and is

not necessarily the pollutant of greatest concern in these situations. Instead, ethanol’s high

biodegradability shifts the subsurface geochemistry, which can cause reduced biodegradation of

benzene, toluene, and xylene (up to 50% for toluene and 95% for benzene).1309 The plume of

BTEX (benzene, toluene, ethylbenzene and xylenes) compounds in gasoline from a fuel spill can
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extend as much as 70% farther in groundwater and can persist longer, thereby increasing

potential human exposures to these compounds.1310 Particularly large plumes of benzene can be

expected when: there is a large area of the aquifer that is contaminated with liquid phase

gasoline; the background concentration of sulfate-reducing bacteria (which biodegrade ethanol

and benzene) in the ground water is low; the rate of ethanol biodegradation is low; and the flow

velocity of the ground water is high. More detail on ethanol biodegradation and a summary of

laboratory and field studies of ethanol spills will be forthcoming in the EPAct 2005 Report to

Congress on Fuel Additive Replacements for MTBE in 2010.

Ethanol leak and spills from the approximately 600,000 gas stations in the U.S, could

have a significant impact on water quality and drinking water supplies. Urban areas, that rely on

ground water for drinking water would be affected most, especially where are existing water

shortages

With the increasing use of ethanol in the fuel supply nationwide, it is important to

understand the impact of ethanol on the existing tank infrastructure. Federal regulations require

that underground storage tank (UST) systems be compatible with the fuel stored. Because much

of the current underground storage tank equipment was designed and tested for use with

petroleum fuels, there may be many UST systems currently in use that contain materials that are

incompatible with ethanol blends greater than 10%. Combined with the fact that ethanol is more

corrosive than petroleum, there is concern regarding the increased potential for leaks from

existing distribution systems, terminals and gas stations and subsequent impacts on water

supplies. Given the practical challenges of determining the age and materials of underground

storage equipment at approximately 233,000 federally regulated facilities, it may be difficult or

impossible to confirm the compatibility of current underground storage tanks and other tankrelated hardware with ethanol blends. Further discussion of challenges in retail distribution are

discussed in Section 1.6 of the RIA.

In 2007, there were 7,500 reported releases from underground storage tanks. Since

approximately 50% of the gasoline used in the U.S. contains ethanol, approximately 3,750 of

those releases likely contained some amount of ethanol. Therefore, EPA is undertaking analyses

designed to assess the potential impacts of ethanol blends on tank infrastructure and leak

detection systems and determine the resulting water quality impacts.

An additional hazard from spills from fuels containing ethanol is risk of potential

explosions. Laboratory and field studies have found biodegradation of ethanol can produce

concentrations of methane in excess of the water solubility of methane (i.e., more methane was

produced than could be dissolved by the available water). This methane could bubble out of the

ground water and enter the soil gas at explosive concentrations, although it is not possible to

quantify the risk at this time. EPA is beginning development of modeling software for the

assessment of fuels of varying composition on ground water, with simulation of methane

production being one component of this work.

6.8 Water Use and Wastewater from Biodiesel Plants
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Biodiesel plants use much less water than ethanol plants in production of biofuel. Water

is not used in conversion of oil to biodiesel, but is used for washing impurities from the finished

product. Water use is variable, but is usually less than one gallon of water for each gallon of

biodiesel produced. Larger well-designed plants use water more sparingly, while smaller

producers and hobbyists use more water. Some facilities recycle washwater, which reduces water

consumption.

The strength of process wastewater from biodiesel plants is highly variable. Most

production processes produce washwater that has very high BOD levels. Essentially the strength

of the wastewater is based on glycerin and methanol content. Larger facilities are segregating

glycerin as a side product and have efficient methanol recovery, while smaller plants are more

likely to dispose of glycerin, excess methanol, and washwater as a single waste stream. Crude

glycerin is an important side product from the biodiesel process and has many uses. It is about

10% of the final product. Although there is a commercial market for glycerin, the rapid

development of the biodiesel industry has caused a glut of glycerin production and many

facilities dispose of glycerin.

The high strength of these wastes can overload and disrupt the biological processes in

municipal treatment plants. The normal wastewater going into a municipal sewage treatment

plant has a BOD 200mg/l. Washwater from the biodiesel process with efficient recovery of

methanol, containing small amounts of glycerin, can have a BOD of 10,000 – 15,000 mg/L. Pure

glycerin has a BOD of nearly 1,000,000 mg/L. There have been several cases of wastewater

treatment plant upsets due to these shock loadings from releases of glycerin from biodiesel

production facilities. Unfortunately, these have been due to slug loadings to small wastewater

treatment plants. Other states such as Illinois and Alabama have also had problems with

discharges from small biodiesel plants. In addition, there have been incidences of outright

dumping of glycerin. One such event resulted in a large fish kill in Missouri.

Producers that choose to dispose of glycerin can be regulated under several EPA

programs, depending on the practice. EPA strongly supports the beneficial use of glycerin as a

product. While the market for refined glycerin is glutted with an excess supply, there are many

known uses for glycerin feedstock. As prices for glycerin go down, many of these known

products will show a better profit margin and demand for glycerin will increase. Most larger

facilities are segregating crude glycerin for refining into usable feedstock for other products.

Refining can range from minimal processing up to creation of a food grade product. Nationally,

there is a lot of research on the creation of new value added products (ethanol, propylene glycol,

etc.) using glycerin as a feedstock. Most of these projects are in university labs, but a few are up

to pilot scale. These new technologies will go online at full scale within the next few years, and

are an important part of the profit stream for the industry.

6.9 Potential Impacts to Drinking Water and Public Health

Under the Safe Drinking Water Act (SDWA), EPA establishes enforceable safety

standards for drinking water provided by public water systems (PWS). For chemicals, the

standard is typically called a maximum contaminant level (MCL). A PWS is “a system for the

provision to the public of water for human consumption through pipes or other constructed
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conveyances, if such system has at least fifteen service connections or regularly serves at least

twenty-five individuals.” If the source water for a PWS does not meet the MCL, the PWS must

take measures to reduce the contamination to safe levels and that may entail installing expensive

drinking water treatment technology e.g., ion exchange (IE), granulated activated carbon (GAC)

or reverse osmosis (RO).

EPA anticipates that increased corn production for ethanol will increase the occurrence of

nitrate, nitrite, and atrazine in sources of drinking water. New corn acreage may result in increase

in the application of fertilizers and herbicides, especially on marginal lands that are not as

productive. The ethanol production process may generate new or increased discharges, injection

or infiltration of process waste water that could adversely affect the nation’s surface water and

ground water used for drinking water.

In addition to potential additional contamination of sources of drinking water, surface and

ground water supplies may be strained by increased production of irrigated corn for ethanol and

the ethanol production process itself in local and regional areas. Increased pumping from

agricultural aquifers to support ethanol production may accelerate the long running depletion of

aquifers which has been documented by the USGS. According to U. S. Geological Survey

(USGS) data, more than 72 billion gallons a day are already being pumped from the “thirty

regional principle aquifers with the greatest amount of ground water use”, with irrigation

accounting for slightly more than 75% of those withdrawals.1311 The water table of the Ogallala

aquifer has declined by over 150 feet in some areas since the 1950s due to increasingly large

withdrawals.1312 Aquifers provide water for domestic and other uses, and contribute to the base

flow of many streams and lakes that support aquatic habitats and other ecosystem services such

as fishing and swimming. Lower stream levels combined with the increased pollutant loadings

may concentrate pollutants. Higher pollutant concentrations may require increased drinking

water treatment. The accelerated depletion of agricultural aquifers and surface water supplies

may be exacerbated by an increase in the incidence of droughts that are predicted under many

climate change scenarios.

6.9.1 Nitrogen

The nitrogen fertilizers that are applied to corn and other agricultural crops can end up in

drinking water sources where they can impact human health. The two nitrogen compounds of

concern are nitrate and nitrite. Nitrate is the most stable form of nitrogen in water.

EPA has established the MCL for nitrate-nitrogen at 10 parts per million (ppm) and for

nitrite at 1 ppm. Infants below six months who drink water containing nitrate and/or nitrite in

excess of the MCL could become seriously ill and, if untreated, may die.1313 Symptoms include

shortness of breath and blue baby syndrome. This health effects language is not intended to

catalog all possible health effects for nitrate. Rather, it is intended to inform people of the most

significant and probable health effects, associated with nitrate and nitrite in drinking water.

Most nitrogen in water is converted to nitrates. Since nitrates are very soluble and do not

bind to soils, they have a high potential to migrate to ground water. Because they do not

evaporate, nitrates and nitrites are likely to remain in water until consumed by plants or other
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organisms. Primary sources of nitrate which may contaminate drinking water are human sewage,

livestock manure, and fertilizers.

In 2007, there were 562 public water systems, serving 257, 558 people, reporting

violations of the nitrate MCL.1314 If a utility’s routine compliance monitoring indicates that

nitrate or nitrite concentrations are above the MCL, the water system must implement measures

such as treatment or blending to reduce the concentration so that it is below the MCL (e.g., find a

new source of water, adjust existing treatment or install new treatment). Also, utilities must

monitor the finished water every quarter and provide notification to consumers of the MCL

exceedance.

Since there is no nationally consistent sampling of ambient water used by public water

systems, the relative contribution of nitrate detections from the various sources is generally

unknown.

6.9.2 Pesticides

The U.S. Geological Survey evaluated the fate and transport of herbicides in surface

water, ground water, and in precipitation in the Midwest during the 1990s. Results of these

studies showed the occurrence and temporal distribution of herbicides and their associated

degradation products in reservoir outflows.1315

Atrazine is estimated to be the most widely used herbicide in the United States for control

of weeds. Atrazine was the second most frequently detected pesticide in EPA's National Survey

of Pesticides in Drinking Water Wells. EPA's Pesticides in Ground Water Database indicates

numerous detections of atrazine at concentrations above the MCL in ground water in several

states, including Delaware, Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri,

Nebraska, and New York.1316 In 1993, EPA and the atrazine registrants initiated a monitoring

program to focus on the most significant exposures associated with agricultural and residential

uses -- exposures through drinking water. To this point, levels found in PWS have been low.

Through the PWS monitoring program, EPA is ensuring that exposures to atrazine in drinking

water do not reach levels that pose a risk to public health.

The MCL for atrazine is three parts per billion (ppb). MCL violations are not triggered by

single measurement above the MCL but by the running annual average concentration from four

quarterly samples in which at least one measurement during that period exceeds 3 ppb. Some

people who drink water containing atrazine well in excess of the MCL over a period of many

years could experience problems with their cardiovascular system or reproductive difficulties.

This health effects language is not intended to catalog all possible health effects for atrazine.

Rather, it is intended to inform people of the most significant and probable health effects,

associated with atrazine in drinking water.

Atrazine may be released to the environment in wastewater from herbicide manufacturing

facilities and through its use as an herbicide. Microbial activity and other chemicals may

breakdown atrazine in soil and water, particularly in alkaline conditions. Sunlight and
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evaporation do not reduce its presence. It may bind to some soils, but generally tends to leach to

ground water. Atrazine is not likely to be taken up in the tissues of plants or animals.1317

In A Review of Contaminant Occurrence in Public Water Systems, published in 1999,

EPA found atrazine in the finished water of 21% of the surface water systems.1318 Atrazine was

found at concentrations exceeding the MCL in 10.7% of the surface water systems and, in 83%

of those systems, atrazine was found at concentrations that would have been in violation of the

MCL. As noted above, MCL violations are not triggered by single excursions above the MCL

but by the running annual average concentration from four quarterly samples in which the

measurement of at least one of those samples exceeds three ppb. However in one of the states

where atrazine is widely used e.g., for corn production, the percentage of single samples

exceeding the MCL was as high as 77.8% for surface water systems serving less than 500

people; see Table 6.1.

Table 6.9-1.

Percentage of Surface Water Systems with Detections of Atrazine

for a High Occurrence State, 1999

POPULATION <500 500 – 3,300 3,301 –

10,000

10,001 –

50,000 > 50,000

> MRLa100% 100% 96.2% 96.3% 55.6%

> MCLb77.8% 71.1% 57.7% 18.5% 22.2%

a The MRL, or minimum reporting level, is the lowest concentration at which the contaminant can be

consistently and reliably detected. U.S. Environmental Protection Agency, A Review of Contaminant

Occurrence in Public Water Systems, EPA 816-R-99-006, 1999, Table V.A.2, page D-2.

b

U.S. Environmental Protection Agency, ibid, p. D-3.

In 2003, EPA estimated that single atrazine measurements greater than the MCL would

be observed in 26 to 57 public water systems serving a range of 24,400 – 260,300 people.1319

Because atrazine is used mostly as a pre-emergent herbicide on corn, the surface water

concentrations typically spike during growing season then taper off for the rest of the year. Even

though many surface water systems encounter concentrations above the MCL during the growing

season, very few experience MCL violations based on the average concentration over four

consecutive quarters. In 2007, only one water system serving 740 people officially reported a

MCL violation.1320

From 1992 through 2001, the USGS observed atrazine in 90% of the samples it took from

83 stream sites in agricultural areas as part of its National Water Quality Assessment (NAWQA).

Although it does not target exclusively drinking water intakes or wells, the NAWQA program

“provides an understanding of water-quality conditions and how those conditions may vary

locally, regionally, and nationally...”.1321 Atrazine was observed in 71% of the samples from 30

urban stream sites during the same period. For ground water, USGS observed atrazine in 42% of

the samples it took from wells in agricultural areas and in 31% of the samples from urban wells.
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The detection limits for this study were very low and 95% of the sampling results from streams

in agricultural areas, where the highest concentrations of atrazine were found, were below 2.4

ppb which is 80% of the MCL of 3 ppb.

6.9.3 Future availability of more recent occurrence data

EPA anticipates releasing the chemical occurrence data covering the years 1999 – 2006

from states for publication in 2010 as part of the six year review of drinking water standards.

Once those data sets are publicly available, they will be useful in updating the occurrence data

published here for nitrate and atrazine.

6.10 Water Quantity Concerns

Biofuel production based on current and projected approaches and processes, future

alternative fuel development and production could markedly increase the demand for various

fresh water resources. Two potential needs could increase water demand: quantities of water to

produce biomass as a feedstock, and the additional water demand for refining of bio-ethanol and

biodiesel fuels (by up to a factor of three relative to traditional refining). From a regional

perspective, water demand for crop production would be relatively much larger than biorefinery

demand; crop production needs would be approximately 200 times the water needed to refine

biofuels.1322

With growth of ethanol production, water supply reliability related to crop demand for

biomass feedstock will remain an issue. The amount of water needed to grow feedstocks for

biofuels can be considerable – for example, the ratio of water consumed to produce the corn

itself for ethanol is nearly one thousand gallons per gallon of corn ethanol. Large scale

production of perennial energy crops involving tens of millions of acres, even when rain-fed, can

have water resource impacts and unintended local consequences due to alterations of hydrologic

flows. The timing of the water demand may also be critical; water is often plentiful in one season

but scarce in another.

Growing crops for biofuel production is likely to have significant regional and local

impacts, including the potential to change irrigation water use, and thus local water availability.

The feasibility and sustainability of water diversions for biomass irrigation will vary depending

on the region. Moreover, some ethanol plants are being sited where water resources are already

under duress, for example on the High Plains aquifer.

Biofuel refineries create additional local scale demand for water withdrawals and

consumption. It is difficult to generalize about the impact on local water supplies, however, some

community supplies have been stressed by the water requirements of ethanol facilities.

However, from a national and regional perspective, relative to the water incorporated in the

feedstock, water use in biorefineries is quite small. A typical corn ethanol plant consumes

slightly more than four gallons water per gallon of ethanol produced; biodiesel refining even

less, about one gallon of water per gallon of biodiesel, which on an energy-equivalent basis is

even less in comparison to ethanol. (Petroleum refining consumes about 1.5 gallons of water per
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gallon fuel produced.) Biodiesel refining consumes about one gallon water per gallon, but may

be up to three gallons per gallon. However, biofuel crops may be irrigated with wastewater that

is biologically and chemically unsuitable for use with food crops. On the other hand, cellulosic

materials require a different process, and are thought to use 9.5 gallons water per gallon fuel

produced--but this would be expected to decline as efficiency increases (currently projected to be

lowered to two to six gallons per gallon).

Geographic impacts of biofuel refining vary. Currently, the Midwest and Southeast have

most of the production. In Iowa, water consumption alone from ethanol refining already accounts

for about 7% of all industrial water use, and is projected to be 14% by 2012--or about 50 million

gallons per day. A typical ethanol plant now producing 50 million gallons per year means a

minimum of 175 million gallons (nearly 480,000 per day) used in a year. In the Great Lakes-St.

Lawrence region, newer facilities under construction will have capacities of 100 million gallons

per year.1323 For a 100 million gallon per year corn ethanol plant, water consumption is one

million gallons of water/day (equates to daily water consumption of a town of 20,000).

Research is needed to establish water use requirements across the entire biofuel

production chain. Information needs related to biomass feedstock production include the

assessment and quantification of impacts of increased irrigation of energy crops and resulting

biofuel cost/benefit tradeoffs for both starch/sugar/oil biofuel crops, and the lignocellulosic

biofuel crops. An assessment is also needed of the impacts on hydrologic flows of regional

expansion of perennial energy crop production. These include the impacts and risks tradeoffs,

e.g., altered flows due to deep extensive root systems and dense canopies, as well as a need for

management practices/metrics, e.g. relatively large absolute water consumption, and additional

irrigation necessary. Changing climate adds an additional element of uncertainty in making

assessments of water use.

Many uncertainties exist regarding estimating water needs for irrigating cellulosic

feedstocks in particular. Reasons include: water data is less available for proposed cellulosic

feedstock than for common crops, evapotranspiration rates of marginal lands used for these crops

are unknown, and water demand by heretofore unirrigated native grasses is unknown.

Some practices can mitigate the increased demand for water by biofuels. Both the

impacts and regulatory opportunities for mitigation of water impacts are likely to be at the state

and local levels. For example, rainfall harvesting, efficient irrigation water transport and use of

reclaimed water can lead to more efficient agricultural water use for both corn and cellulosic

ethanol crops. Also, biorefineries are increasingly incorporating water recycling.

The economics of the energy-water distribution linkage are important in biofuels

production. At a macroscale, the high prices of energy driving the increased production of

biofuels will likely affect water availability and use, e.g., conveyance costs related to irrigation

waters will also increase with energy costs, possibly leading to water conservation that may

counter the expanded water use for crops. Also, the value of crops relative to their water demand

matters: water rights can often be bought and sold if the value of the crop is sufficiently high.
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Finally, there is the potential for a low water use alternative biomass feedstock to

develop: oil-producing macro-algae. These algae can be grown without land, using

nontraditional waters, and CO2 waste streams as a nutrient source. Such fuels can have

significantly higher energy density and are potentially more fungible within existing

transportation fuel infrastructure than ethanol.
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Chapter 7: Final Regulatory Flexibility Analysis

This chapter discusses our Final Regulatory Flexibility Analysis (FRFA) which evaluates

the potential impacts of the standards on small entities. The Regulatory Flexibility Act, as

amended by the Small Business Regulatory Enforcement Fairness Act of 1996 (SBREFA),

generally requires an agency to prepare a regulatory flexibility analysis of any rule subject to

notice and comment rulemaking requirements under the Administrative Procedure Act or any

other statute unless the agency certifies that the rule will not have a significant economic impact

on a substantial number of small entities. Prior to issuing a proposal for this rulemaking, we

analyzed the potential impacts of these regulations on small entities. As a part of this analysis,

we convened a Small Business Advocacy Review Panel (SBAR Panel, or ‘the Panel’). During

the Panel process, we gathered information and recommendations from Small Entity

Representatives (SERs) on how to reduce the impact of the rule on small entities, and those

comments are detailed in the Final Panel Report which is located in the public record for this

rulemaking (Docket EPA-HQ-OAR-2005-0161).

7.1 Overview of the Regulatory Flexibility Act

In accordance with section 609(b) of the Regulatory Flexibility Act, we convened an

SBAR Panel before conducting the FRFA. A summary of the Panel’s recommendations can be

found in the preamble to the proposed rule. Further, the Final Panel Report contains a detailed

discussion of the Panel’s advice and recommendations (as well as comments from the Small

Entity Representatives (SERs)). The regulatory alternatives that are being adopted in this final

rule are described below.

Section 609(b) of the Regulatory Flexibility Act further directs the Panel to report on the

comments of small entity representatives and make findings on issues related to identified

elements of the Regulatory Flexibility Analysis under section 603 of the Regulatory Flexibility

Act. Key elements of a Regulatory Flexibility Analysis are:

- a description of and, where feasible, an estimate of the number of small entities to which

the rule will apply;

- projected reporting, record keeping, and other compliance requirements of the rule,

including an estimate of the classes of small entities which will be subject to the

requirements and the type of professional skills necessary for preparation of the report or

record;

- an identification, to the extent practicable, of all other relevant Federal rules which may

duplicate, overlap, or conflict with the rule;

- any significant alternatives to the rule which accomplish the stated objectives of

applicable statutes and which minimize any significant economic impact of the rule on

small entities.

The Regulatory Flexibility Act was amended by SBREFA to ensure that concerns

regarding small entities are adequately considered during the development of new regulations
1001

that affect those entities. Although we are not required by the Clean Air Act to provide special

treatment to small businesses, the Regulatory Flexibility Act requires us to carefully consider the

economic impacts that our rules may have on small entities. The recommendations made by the

Panel may serve to help lessen these economic impacts on small entities when consistent with

Clean Air Act requirements.

7.2 Need for the Rulemaking and Rulemaking Objectives

A detailed discussion on the need for and objectives of this rule are located in the

preamble to the final rule. As previously stated, section 1501 of the Energy Policy Act of 2005

(EPAct) amended section 211 of the Clean Air Act (CAA) by adding section 211(o) which

required the Environmental Protection Agency (EPA) to promulgate regulations implementing a

renewable fuel program. The final Renewable Fuels Standard (RFS1) program, which began on

September 1, 2007, created a specific annual level for minimum renewable fuel use that

increases over time — resulting in a requirement that 7.5 billion gallons of renewable fuel be

blended into gasoline by 2012.

The Energy Independence and Security Act of 2007 (EISA) amended section 211(o), and

the RFS program, by requiring higher volumes of renewable fuels, to result in 36 billion gallons

of renewable fuel by 2022. EISA also expanded the purview of the RFS1 program by requiring

that these renewable fuels be blended into diesel fuel (both highway and nonroad) in addition to

gasoline. This expanded the volume obligation of parties that were already regulated under

RFS1. It also expanded the pool of regulated entities, so the obligated parties under the RFS2

rule will now include certain refiners, importers, and blenders of these fuels that were not

previously covered by the RFS1 program. In addition to the total renewable fuel standard

required by EPAct, EISA added standards for three additional types of renewable fuels to the

program (advanced biofuel, cellulosic biofuel, and biomass-based diesel) and requires

compliance with all four standards.

7.3 Definition and Description of Small Entities

Small entities include small businesses, small organizations, and small governmental

jurisdictions. For the purposes of assessing the impacts of the rule on small entities, a small

entity is defined as: (1) a small business that meets the definition for business based on the Small

Business Administration’s (SBA) size standards; (2) a small governmental jurisdiction that is a

government of a city, county, town, school district or special district with a population of less

than 50,000; and (3) a small organization that is any not-for-profit enterprise which is

independently owned and operated and is not dominant in its field.

Small businesses (as well as large businesses) would be regulated by this rulemaking, but

not small governmental jurisdictions or small organizations as described above. As set by SBA,

the categories of small entities that will potentially be affected by this rulemaking are defined in

Table 7.3-1 provides an overview of the primary SBA small business categories potentially

affected by this regulation.
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Table 7.3-1. Small Business Definitions

Industry

Defined as small entity

by SBA if less than or

equal to:

NAICSacodes

Gasoline and diesel fuel refiners 1,500 employeesb324110

a

North American Industrial Classification System

b

EPA has included in past fuels rulemakings a provision that, in order to qualify for the small refiner

flexibilities, a refiner must also produce no greater than 155,000 bpcd crude capacity

EPA used a variety of sources to identify which entities are appropriately considered

“small.” EPA used the criteria for small entities developed by the Small Business

Administration under the North American Industry Classification System (NAICS) as a guide.

Information about the characteristics of refiners comes from sources including the Energy

Information Administration (EIA) within the U.S. Department of Energy, oil industry literature,

and previous rulemakings that have affected the refining industry. EPA then found employment

information for these companies using the business information database Hoover’s Online (a

subsidiary of Dun and Bradstreet). These refiners fall under the Petroleum Refineries category,

324110, as defined by NAICS.

Small entities that will be subject to the renewable fuel standard include: domestic

refiners that produce gasoline and/or diesel, and importers of gasoline and/or diesel into the U.S.

Based on 2007 data, EPA believes that there are about 95 refiners of gasoline and diesel fuel. Of

these, EPA believes that there are currently 17 refiners owning 20 refineries producing gasoline

and/or diesel fuel that meet the SBA small entity definition of having 1,500 employees or less.

Further, we believe that three of these refiners own refineries that do not meet the definition of a

“small refinery” that Congress specified under section 211(o). It should be noted that because of

the dynamics in the refining industry (i.e., mergers and acquisitions), the actual number of

refiners that ultimately qualify for small refiner status under the RFS2 program could be different

from this initial estimate.

7.4 Steps to Minimize Impacts on Small Entities

As a part of the SBREFA process, we conducted outreach to small refiners of gasoline

and/or diesel fuels and convened a Panel to gain feedback and advice from these entities. Prior

to convening the Panel, we held outreach meetings with the SERs to learn the needs of small

entities and potential challenges that these entities may face. The outreach meetings also helped

to provide the SERs an opportunity to gain a better understanding of the new requirements under

EISA and how it would change the RFS program (including those small refiners who only

produce diesel and were not regulated entities ). The feedback that we received from SERs as a

result of these meetings was used during the Panel process to develop regulatory alternatives to

mitigate the impacts of the rulemaking on small businesses. General concerns raised by SERs

during the SBREFA process were potential costs and access to RINs for compliance with the

program.
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The Panel consisted of members from EPA, the Office of Management and Budget

(OMB), and the Small Business Administration’s Office of Advocacy. Following the Panel

convening, a Final Panel Report detailing all of the alternatives that were recommended by the

Final Regulatory Support Document Panel (as well as individual Panel members) was issued.

We either proposed or requested comment on the various recommendations put forth by the

Panel. Below we discuss those flexibility options recommended in the Panel Report, our

proposed regulatory alternatives, and those provisions which are being finalized.

7.4.1 Panel Recommendations

The purpose of the Panel process is to solicit information as well as suggested flexibility

options from the SERs, and the Panel recommended that EPA continue to do so during the

development of the RFS2 rule. Recognizing the concerns about EPA’s authority to provide

extensions to a subset of small refineries (i.e., those that are owned by small refiners) different

from that provided to small refineries in section 211(o)(9), the Panel recommended that EPA

continue to evaluate this issue, and that EPA request comment on its authority and the

appropriateness of providing extensions beyond those authorized by section 211(o)(9) for small

refineries operated by a small refiner. The Panel also recommended that EPA propose to provide

the same extension provision of 211(o)(9) to small refiners who do not own small refineries as is

provided for small refiners who do own small refineries.

7.4.2 Extensions of the Temporary Exemption Based on a Study of Small Refinery

Impacts

Panel Recommendations

The Panel recommended that EPA propose in the RFS2 program the provision at 40 CFR

80.1141(e) extending the RFS1 temporary exemption for at least two years for any small refinery

that DOE determines would be subject to disproportionate economic hardship if required to

comply with the RFS2 requirements.

Section 211(o)(9)(A)(ii) requires DOE to perform a study of the economic impacts of the

RFS requirements on small refineries. The study, which was required to be completed by

December 31, 2008, must assess and determine whether the RFS requirements would impose a

disproportionate economic hardship on small refineries. Small refineries that are found to be in a

disproportionate economic hardship situation will receive an extension of the temporary

exemption for at least two years.

The Panel also recommended that EPA work with DOE in the development of the small

refinery study, specifically to communicate the comments that SERs raised during the Panel

process.

What We Proposed and Public Comments Received on the NPRM

We did not propose this hardship provision given the outcome of the DOE small refinery

study. In the small refinery study, “EPACT 2005 Section 1501 Small Refineries Exemption
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Study”, DOE’s finding was that there is no reason to believe that any small refinery would be

disproportionately harmed by inclusion in the proposed RFS2 program. This finding was based

on the fact that there appeared to be no shortage of RINs available under RFS1, and EISA has

provided flexibility through waiver authority (per section 211(o)(7)). Further, in the case of the

cellulosic biofuel standard, cellulosic biofuel allowances can be provided from EPA at prices

established in EISA (see regulation section 80.1455). DOE thus determined that no small

refinery would be subject to disproportionate economic hardship under the proposed RFS2

program, and that the small refinery exemption should not be extended beyond December 31,

2010. DOE noted in the study that, if circumstances were to change and/or the RIN market were

to become non-competitive or illiquid, individual small refineries have the ability to petition

EPA for an extension of their small refinery exemption (as stated in regulation section 80.1441).

In their written comments, as well as in discussions we had with them on the proposed

rule, small refiners indicated that they did not believe that EPA should rely on the results of the

DOE small refinery study to inform any decisions on small refiner provisions. Small refiners

generally commented that they believe that the study was flawed and that the conclusions of the

study were reached without adequate analysis of, or outreach with, small refineries (as the

majority of the small refiners own refineries that meet the Congressional small refinery

definition). One commenter stated that such a limited investigation into the impact on small

refineries could not have resulted in any in-depth analysis on the economic impacts of the

program on these entities. Another commenter stated that it believes that DOE should be

directed to reopen and reassess the small refinery study be June 30, 2010, as suggested by the

Senate Appropriations Committee.

What We’re Finalizing

As discussed more in section III.E of the preamble to the final rule, since the only small

refinery study available for us to use as a basis for whether or not to grant small refineries an

automatic two-year extension of the exemption is the study that was performed in 2008, we had

to use this study to develop this final rule. EPAct directs EPA to consider the DOE small

refinery study in assessing the impacts to small refineries, and we interpret this to mean that any

extension past December 31, 2010 has to be tied to the DOE Study. Further, since that study

found that there was no disproportionate economic impact on small refineries, we cannot grant

an automatic additional extension for small refineries or small refiners (except on a case-by-case

hardship basis). However, this does not preclude small refiners from applying for case-by-case

extensions of the small refiner temporary exemption.

We are aware that there have been expressions of concern from Congress regarding the

DOE Study. Specifically, in Senate Report 111-45, the Senate Appropriations Committee

“directed [DOE] to reopen and reassess the Small Refineries Exemption Study by June 30,

2010,” noting a number of factors that the Committee intended that DOE consider in the revised

study. The Final Conference Report 111-278 to the Energy & Water Development

Appropriations Act (H.R. 3183), referenced the language in the Senate Report, noting that the

conferees “support the study requested by the Senate on RFS and expect the Department to

undertake the requested economic review.” The DOE study has not been revised at this time;

however, if DOE prepares a revised study and the revised study finds that there is a
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disproportionate economic impact, we will revisit the exemption extension at that point in

accordance with section 211(o)(9)(A)(ii).

7.4.3 Delay in Standards for Small Refiners

Panel Recommendations

The RFS1 program regulations provide small refiners who operate small refineries as

well as small refiners who do not operate small refineries with a temporary exemption from the

standard through December 31, 2010. Small refiner SERs suggested that an additional

temporary exemption for the RFS2 program would be beneficial to them in meeting the RFS2

standards. EPA evaluated a temporary exemption for at least some of the four required RFS2

standards for small refiners. The Panel recommended that EPA propose a delay in the effective

date of the standards until 2014 for small entities, to the maximum extent allowed by the statute.

However, the Panel recognized that EPA has serious concerns about its authority to provide an

extension of the temporary exemption for small refineries that is different from that provided in

CAA section 211(o)(9), since Congress specifically addressed an extension for small refineries

in that provision.

The Panel did recommend that EPA propose other avenues through which small

refineries and small refiners could receive extensions of the temporary exemption. These

avenues were a possible extension of the temporary exemption for an additional two years

following a study of small refineries by the Department of Energy (DOE) (as discussed above)

and provisions for case-by-case economic hardship relief.

What We Proposed and Public Comments Received on the NPRM

We proposed and took comment on the recommendations of the Panel and SERs. We

proposed to continue the temporary exemption finalized in RFS1, through December 31, 2010,

for small refiners (and small refineries), extending it to include an exemption for diesel volume

and diesel refiners, importers, and blenders, as required by EISA.

Commenters that oppose an extension of the temporary exemption generally stated that

they believe an extension is not warranted, and some of these commenters expressed concerns

about allowing provisions for small refiners. One commenter also stated that the small refinery

exemption should not be extended and that the small refiner exemption should be eliminated

completely. A couple commenters supported the continuation of the exemption through

December 31, 2010 only; one of those commenters stated that it does not support an extension as

it believes that all parties have been well aware of the passage of EISA and small refineries and

small refiners should have been striving to achieve compliance by the end of 2010. Two

commenters also expressed views that the exemption should not have been offered to small

refiners in RFS1 as this was not provided by EPAct, and that an extension of the exemption

should not be finalized for small refineries at all. The commenters further commented that an

economic hardship provision was included in EPAct, and any exemption extension should be

limited to such cases, and only to the specific small refinery (not small refiner) that has

petitioned for such an extension.
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Commenters supporting an extension of the exemption commented that they believe that

the statutes (EPAct and EISA) do not prohibit EPA from providing relief to regulated small

entities on which the rule will have a significant economic impact, and that such a delay could

lessen the burden on these entities. One commenter stated that it believes EPA denied or ignored

much of the relief recommended by the Panel in the proposal. Another commenter stated that it

believes EPA’s concerns regarding the legal authority are unsustainable considering EPA’s past

exercises of discretion under the RFS1 program, and with the discretion afforded to EPA under

section 211(o) of the CAA. Some commenters requested a delay until 2014 for small refiners.

One additional commenter expressed support for an extension of the small refinery exemption

only, and stated that these small refineries should be granted a permanent exemption.

What We’re Finalizing

The RFS1 program regulations exempt gasoline produced by small refineries from the

renewable fuels standard through December 31, 2010 (at 40 CFR 80.1141), per EPAct. As EISA

did not alter the small refinery exemption in any way, we are retaining this small refinery

temporary exemption in the RFS2 program, extending the relief to diesel fuel volumes produced

or imported in addition to gasoline, and extending the relief to those small refineries of diesel

fuel that were not covered under RFS1. Likewise, as we extended under RFS1 the small refinery

temporary exemption to the few remaining small refiners that met the Small Business

Administration’s (SBA) definition of a small business (1,500 employees or less company-wide),

we are also finalizing a continuation of the small refiner temporary exemption through December

31, 2010 for all gasoline and diesel small refiners.

As described in the Final Panel Report, EPA early-on identified limitations on its

authority to issue additional flexibility and exemptions to small refineries. In section 211(o)(9)

Congress specifically addressed the issue of an extension of time for compliance for small

refineries, temporarily exempting them from renewable fuel obligations through December 31,

2010. As discussed above, the statute also includes two specific provisions describing the basis

and manner in which further extensions of this exemption can be provided. In the RFS1

rulemaking, EPA considered whether it should provide additional relief to the limited number of

small refiners who were not covered by the small refinery provision, by providing them a

temporary exemption consistent with that provided by Congress for small refineries. EPA

exercised its discretion under section 211(o)(3) and provided such relief. Thus, in RFS1, EPA

did not modify the relief provided by Congress for small refineries, but did exercise its discretion

to provide the same relief specified by statute to a few additional parties.

In RFS2 we are faced with a different issue—the extent to which EPA should provide

additional relief to small refineries beyond the relief specified by statute, and whether it should

provide such further relief to small refiners as well. There is considerable overlap between

entities that are small refineries and those that are small refiners. Providing additional relief just

to small refiners would, therefore, also extend additional relief to at least a number of small

refineries. Congress spoke directly to the relief that EPA may provide for small refineries,

including those small refineries operated by small refiners, and limited that relief to a blanket

exemption through December 31, 2010, with additional extensions if the criteria specified by
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Congress are met. EPA believes that an additional or different extension, relying on a more

general provision in section 211(o)(3) would be inconsistent with Congressional intent. Further,

we do not believe that the statute allows us the discretion to give relief to small refiners only—as

this would result in a subset of small refineries (those that also qualify as small refiners)

receiving relief that is greater than the relief already given to all small refineries under EISA.

EPA also notes that the criteria specified by statute for providing a further compliance

extension to small refineries is a demonstration of “disproportionate economic hardship.” The

statute provides that such hardship can be identified through the DOE study, or in individual

petitions submitted to the Agency. However, the DOE study has concluded that no

disproportionate economic hardship exists, at least under current conditions and for the

foreseeable future under RFS2. Therefore, absent further information that may be provided

through the petition process, there does not currently appear to be a basis under the statute for

granting further compliance extensions to small refineries. If DOE revises its study and comes to

a different conclusion, EPA can revisit this issue.

During the development of this final rule, we again evaluated the various options

recommended by the Panel, the legality of offering an extension of the exemption to small

refiners only, and also comments on the proposed rule. Specifically in the case of an extension

of the exemption for small refiners, we also consulted the small refinery study prepared by DOE,

as the statute directs us to use this as a basis for providing an additional two year exemption. As

discussed in Section III.E of the preamble to the final rule, we do not believe that we can provide

an extension of the exemption considering the outcome of the DOE small refinery study, which

did not find that there was a disproportionate economic hardship. Further, we do not believe that

the statute allows us the discretion to give relief to a subset of small refineries (those that also

qualify as small refiners) that is greater than the relief already given to all small refineries under

EPAct. However, it is important to recognize that the 211(o)(9) small refinery provision does

allow for extensions beyond December 31, 2010, as discussed in preamble Section III.E.2. Thus,

refiners may apply for individual hardship relief.

7.4.4 Phase-in

Panel Recommendations

Small refiner SERs suggested that a phase-in of the obligations applicable to small

refiners would be beneficial for compliance, such that small refiners would comply by gradually

meeting the standards on an incremental basis over a period of time, after which point they

would comply fully with the RFS2 standards, EPA has serious concerns about its authority to

allow for such a phase-in of the standards. CAA section 211(o)(3)(B) states that the renewable

fuel obligation shall “consist of a single applicable percentage that applies to all categories of

persons specified” as obligated parties. This kind of phase-in approach would result in different

applicable percentages being applied to different obligated parties. Further, such a phase-in

approach would provide more relief to small refineries operated by small refiners than that

provided under the small refinery provision. Thus the Panel recommended that EPA should

invite comment on a phase-in, but not propose such a provision.
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What We Proposed and Public Comments Received on the NPRM

While we did not propose it, we did request comment on the concept of a phase-in for

small refiners only. Specifically, we requested comments on a phase-in for some or all of the

applicable standards for small refiners.

With respect to our request for comments on the concept of a phase-in of the RFS

standards for small refiners, some commenters stated that they believe that EPA has the ability to

consider a phase-in of the standards for small refiners. One commenter suggested that a

temporary phase-in could help lessen the burden of regulation on small entities and promote

compliance. Another commenter stated that it believes EPA’s legal concerns regarding a phasein are unsustainable considering EPA’s past exercises of discretion under the RFS1 program and

with the discretion afforded to EPA under section 211(o) of the CAA.

What We’re Finalizing

After considering the comments on this issue, EPA continues to believe that allowing a

phase-in of regulatory requirements for small refineries and/or small refiners would be

inconsistent with the statute, for the reasons mentioned above. Any individual entities that are

experiencing hardship that could justify a phase-in of the standards have the ability to petition

EPA for individualized relief. Therefore we are not including a phase-in of standards for small

refiners in the final rule.

7.4.5 RIN-related Flexibilities

Panel Recommendations

The small refiner SERs requested that the proposed rule contain provisions for small

refiners related to the RIN system, such as flexibilities in the RIN rollover cap percentage and

allowing all small refiners to use RINs interchangeably. In the RFS1 program, EPA allows for

20% of a previous year’s RINs to be “rolled over” and used for compliance in the following year.

We noted during the Panel process that a provision to allow for flexibilities in the rollover cap

could include a higher RIN rollover cap for small refiners for some period of time or for at least

some of the four standards. Further, we noted our belief that since the concept of a rollover cap

was not mandated by section 211(o), EPA believes that there may be an opportunity to provide

appropriate flexibility in this area to small refiners under the RFS2 program but only if it is

determined in the DOE small refinery study that there is a disproportionate effect warranting

relief. The Panel recommended that EPA request comment on increasing the RIN rollover cap

percentage for small refiners, and further that EPA should request comment on an appropriate

level of that percentage. The Panel also recommended that EPA invite comment on allowing

RINs to be used interchangeably for small refiners, but not propose this concept because under

this approach small refiners would arguably be subject to a different applicable percentage than

other obligated parties.
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What We Proposed and Public Comments Received on the NPRM

We proposed a change to the RIN rollover cap for small refiners only, and we requested

comment on appropriate level to set the rollover cap for these entities. We also took comment on

the concept of allowing RINs to be used interchangeably for small refiners only.

We also requested comment on the concept of RIN-related flexibilities for small refiners.

In their comments on the proposed rule, one small refiner commented that, in regards to small

refiners’ concerns about RIN pricing and availability, there is no mechanism in the rule to

address the possibility that the RIN market will not be viable. The commenter further suggested

that more “durable” RINs are needed for small refiners that can be carried over from year to

year, to alleviate some of the potentially market volatility for renewable fuels. Another

commenter suggested that RINs should be interchangeable for small refiners, or alternatively,

some mechanism should be implemented to ensure that RIN prices are affordable for small

refiners. Further, with regard to interchangeable RINs, one commenter stated that small refiners

do not have the staff or systems to manage and account for four different categories of RINs and

rural small refiners will suffer economic hardship and disadvantage because of the unavailability

of biofuels. The commenter also requested an increase in the rollover cap to 50% for small

refiners.

What We’re Finalizing

We are not finalizing RIN-related provisions in today’s action. As highlighted in the

NPRM, we continue to believe that the concept of interchangeable RINs for small refiners only

fails to require the four different standards mandated by Congress (e.g., conventional biofuel

could not be used instead of cellulosic biofuel or biomass-based diesel). Further, given the

findings from the DOE study, if small refineries and small refiners do not face disproportionate

economic hardship, then we do not believe that we have the basis for granting such additional

relief beyond what Congress already provided. Thus, small refiners will be held to the same RIN

rollover cap as other obligated parties.

7.4.6 Program Review

Panel Recommendations

During the SBREFA process, SERs raised concerns over uncertainty with acquiring

RINs, and the potential pricing of RINs. They commented that an annual program review would

be beneficial to small refiners as it could provide information about the RIN system. EPA raised

the concern that this could lead to some redundancy since EPA is required to publish a notice of

the applicable RFS standards in the Federal Register annually, and that this annual process will

inevitably include an evaluation of the projected availability of renewable fuels. Nevertheless,

the SBA and OMB Panel members stated that they believe that a program review could be

helpful to small entities in providing them some insight to the RFS program’s progress and

alleviate some uncertainty regarding the RIN system. As EPA will be publishing a Federal

Register notice annually, the Panel recommended that EPA include an update of RIN system

progress (e.g., RIN trading, RIN availability, etc.) in this notice and that the results of this
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evaluation be considered in any request for case-by-case hardship relief.

What We Proposed and Public Comments Received on the NPRM

In the NPRM, we proposed that we would include information to help inform industry

about the RIN system in the annual notice of the RFS standards that EPA must publish in the

Federal Register. We also proposed that information from the annual Production Outlook

Reports that producers and importers must submit to EPA, as well as information required in

EMTS reports, could be used in the annual Federal Register notice to update RIN system

progress.

A group of commenters stated that they support the concept of an annual review. They

commented that EPA should include a review of the RIN system in annual review procedures,

and further suggested that EPA invite small refiner participation in the development of the

review process. (For more information on the comments received on Production Outlook

Reports specifically, please see Chapter 3 of the Summary and Analysis of Comments.)

What We’re Finalizing

Based on comments received on the proposed rule, we believe that such information

could be helpful to industry, especially to small businesses to help aid the proper functioning of

the RIN market, especially in the first years of the program. However, during the development

of the final rule, it became evident that there could be instances where we would want to report

out RIN system information on a more frequent basis than just once a year. Thus, we are

finalizing that we will report out elements of RIN system progress; but such information will be

reported via other means (e.g., the RFS website (www.epa.gov/otaq/renewablefuels/index.htm),

EMTS homepage, etc.). Additionally, we will also publish annual summaries of the Production

Outlook Reports.

7.4.7 Extensions of the Temporary Exemption Based on Disproportionate Economic

Hardship

Panel Recommendations

While SERs did not specifically comment on the concept of hardship provisions for the

upcoming proposal, the Panel noted that under CAA section 211(o)(9)(B) small refineries may

petition EPA for case-by-case extensions of the small refinery temporary exemption on the basis

of disproportionate economic hardship. Refiners may petition EPA for this case-by-case

hardship relief at any time.

The Panel recommended that EPA propose in the RFS2 program a case-by-case hardship

provision for small refineries similar to that provided at 40 CFR 80.1141(e)(1). The Panel also

recommended that EPA propose a case-by-case hardship provision for small refiners that do not

operate small refineries that is comparable to that provided for small refineries under section

211(o)(9)(B), using its discretion under CAA section 211(o)(3)(B). This would apply if EPA

does not adopt an automatic extension for small refiners, and would allow those small refiners
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that do not operate small refineries to apply for the same kind of extension as a small refinery.

The Panel recommended that EPA take into consideration the results of the annual update of RIN

system progress and the DOE small refinery study in assessing such hardship applications.

What We Proposed and Public Comments Received on the NPRM

We did propose hardship provisions for small refineries and small refiners in the RFS2

proposal program similar to those provided at 40 CFR 80.1141 and 80.1142. We propose to

extend the temporary exemption for at least two years for any small refinery that DOE’s small

refinery study determines would face disproportionate economic hardship in meeting the

requirements of the RFS2 program (per CAA section 211(o)(9)(A)(ii)(I)), and that any small

refinery could apply for a case-by-case hardship at any time on the basis of disproportionate

economic hardship per section 211(o)(9)(B). For those small refiners that do not operate small

refineries, we also proposed the same case-by-case hardship provision using our discretion under

CAA section 211(o)(3)(B).

While the findings from DOE’s small refinery study indicate that no small refineries

would be subject to disproportionate economic hardship under the proposed RFS2 program and

that the small refinery exemption should not be extended beyond December 31, 2010, DOE

noted in the study that if circumstances were to change and/or the RIN market became noncompetitive or illiquid, individual small refineries have the ability to petition EPA for an

extension of their small refinery exemption.

Two commenters noted that an economic hardship provision was included in EPAct, and

commented that any extension of the exemption past 2010 should be limited to such cases, and

only to the specific small refinery (not small refiner) that has petitioned for such an extension. A

group of commenters also stated that they believe that small refiners that may be subject to

disproportionate hardship should be granted a two-year extension of the existing RFS1

temporary exemption. The commenters further suggested that EPA tailor the case-by-case

hardship provisions to include a general hardship exemption of up to five years for any and all

small refiners meeting certain specified hardship criteria. The commenters stated that such

criteria should be developed with small refiner participation.

What We’re Finalizing

We believe that these avenues of relief can and should be fully explored by small refiners

who are covered by the small refinery provision. In addition, we believe that it is appropriate to

allow petitions to EPA for an extension of the temporary exemption based on disproportionate

economic hardship for those small refiners who are not covered by the small refinery provision

(again, per our discretion under section 211(o)(3)(B)); this would ensure that all small refiners

have the same relief available to them as small refineries do. Thus, we are finalizing a hardship

provision for small refineries in the RFS2 program, that any small refinery may apply for a caseby-case hardship at any time on the basis of disproportionate economic hardship per CAA

section 211(o)(9)(B). We are also finalizing a case-by-case hardship provision for those small

refiners that do not operate small refineries (section 80.1442(h)) using our discretion under CAA

section 211(o)(3)(B). This provision will allow those small refiners that do not operate small
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refineries to apply for the same kind of extension as a small refinery. In evaluating applications

for this hardship provision EPA will take into consideration information gathered from annual

reports and RIN system progress updates, as recommended by the SBAR Panel.

7.5 Reporting, Recordkeeping, and Other Compliance Requirements

Registration, reporting, and recordkeeping are necessary to track compliance with the

RFS standards and transactions involving RINs. As discussed in Sections II.J and III.A of the

preamble to the final rule, the compliance requirements under the RFS2 rule are in many ways

similar to those required under the RFS1 rule, with some modifications (e.g., those to account for

the new requirements of EISA). New provisions being finalized in today’s action include the

new EPA Moderated Transaction System (EMTS) to aid industry in their reporting and ensure

validity of RINs in the marketplace. EMTS allows for “real-time” reporting of RIN generation

transactions, and the ability for small blenders to “delegate” their RIN-separation responsibilities

to the party directly upstream. Please see Sections II and III of the final preamble for more

detailed information on these and other registration, recordkeeping, reporting, and compliance

requirements of the final rule.

7.6 Related Federal Rules

We are aware of a few other current or proposed Federal rules that are related to this rule.

The primary related federal rules are: the first Renewable Fuel Standard (RFS1) rule (72 FR

23900, May 1, 2007), the RFS1 Technical Amendment Direct Final Rulemaking (73 FR 57248,

October 2, 2008), and Control of Emissions from New Marine Compression-Ignition Engines at

or Above 30 Liters per Cylinder (proposed rule: 74 FR 44442, August 28, 2009; final rule: signed

December 22, 2009).

7.7 Conclusions

Based on our outreach, fact-finding, and analysis of the potential impacts of our

regulations on small businesses, we were able to estimate annual costs, and thus use this

information to complete a preliminary screening analysis. To perform this analysis, we used a

cost-to-sales ratio test (a ratio of the estimated annualized compliance costs to the value of sales

per company). Costs were analyzed using average gasoline + diesel costs for the RFS2 program

referenced to the AEO 2007 reference case for 2022, and also for 2010 and 2012.

For 2022, the cost-to-sales test indicated that all 17 small refiners would be affected at

less than 1 percent of their sales (i.e., the estimated costs of compliance with the rule would be

less than 1 percent, of their sales), and that these costs would actually be negative (or, a cost

savings)—ranging from -3.15% to -0.94%. The gasoline and diesel costs for the 2022 scenario

were estimated to be -2.35 and -12.07 cents per gallon, respectively.
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Under the 2010 and 2012 scenarios, all small refiners were still affected at less than 1

percent, however the costs were no longer negative. For 2010, costs ranged from 0.24 to 0.84

percent of small refiners’ sales, with estimated gasoline and diesel costs of 0.29 and 1.29 cents

per gallon, respectively. For 2012, the costs were relatively similar for all small refiners—at

0.24-0.25 percent of their sales. The gasoline and diesel costs for 2012 were estimated to be 0.51

and 0.58 cents per gallon, respectively (the similarity in small refiners’ costs-to-sales was due to

this similarity in the estimated gasoline and diesel costs). Thus, costs for small refiners are

generally anticipated to be less than one percent of their sales, and are expected to decrease over

time, ultimately resulting in a cost savings by 2022. Note that while we did analyze a 2010

scenario, small refiners would not be participating in the program during this time, as the small

refiner temporary exemption runs through December 31, 2010.

The cost estimates for all 3 scenarios do include the current available subsidies for the

blending of ethanol of 45 cents per gallon for ethanol and $1.00 per gallon for

biodiesel/renewable diesel, and the $1.01 per gallon producer credit for cellulosic biofuel, which

depress the true cost of these renewable fuels in the marketplace.

For a complete discussion of the costs of the RFS2 rulemaking please see Chapter 4 of

this Regulatory Impact Analysis.
1014

Appendix A: Biodiesel Effects on Heavy-Duty Highway Engines and

Vehicles

Executive Summary

Due to the continuing interest in the use of biodiesel fuels, the Environmental Protection

Agency (EPA) has conducted a comprehensive analysis of the emission impacts of biodiesel

using publicly-available heavy-duty, in-use diesel chassis and engine exhaust emissions data.

We investigated the emission impacts on NOx, PM, HC, and CO of 20 volume percent

biodiesel fuels produced from various animal- and plant-based feedstock materials tested under

several cycles in this analysis. Average NOx emissions were found to increase 2.2%, while PM,

HC, and CO were found to decrease 15.6%, 14.1%, and 13.8% respectively, for all test cycles

run on 20 vol% soybean-based biodiesel fuel at a significance level of P < 0.05 (See Table ESA).

Table ES-A.

Emission impacts for all cycles tested on 20 vol% soybean-based

biodiesel fuel relative to an average base fuel

Emissions Percent Change in

Emissions

NOx

PM

HC

CO

+2.2%

-15.6%

-14.1%

-13.8%

These results are consistent with the exhaust emission impacts for heavy-duty, in-use

diesel engines found in our 2002 Draft Technical Report, entitled "A Comprehensive Analysis of

Biodiesel Impacts on Exhaust Emissions."

The current analysis also found that heavy-duty engine dynamometer data was

statistically indistinguishable from heavy-duty chassis dynamometer data for NOx and HC at a

significance level of p < 0.05. Likewise, results for Detroit Diesel Corporation (DDC) engines,

used in many test programs, were found to be statistically similar to results for other engines for

NOx, CO, and HC at a significance level of p < 0.05.

The results of the current analysis also point to a load-dependence of NOx emissions for

heavy-duty highway engines and chasses. The difference in NOx emissions between our results

here and those of other researchers appears to be attributable to an artifact of the selected test

cycle profile. Analyzing the NOx emissions data as a function of load, as we do here -- as

opposed to a particular test cycle profile -- reconciles the difference and supports the NOx

emission-load-dependence hypothesis for heavy-duty highway engines and chasses posited by

Sze et al. and corroborated by Eckerle et al.
1015

A1. Introduction

We investigated the emission impacts on NOx, PM, CO, and HC of 20 volume percent

(vol%) biodiesel fuels produced from various feedstock materials tested under several vehicle

and engine test cycles. The data used in this analysis is comprised of data used in EPA's 2002

Draft Technical Report, entitled "A Comprehensive Analysis of Biodiesel Impacts on Exhaust

Emissions Draft Technical Report", hereafter referred to as the 2002 Draft Technical Report.

Data from that report was supplemented with pertinent data sources published in the scientific

and automotive literature between 2002 and 2007. The supplemental data was comprised of late

model year engines, vehicles, and technology groups. A list of all data sources used in this

analysis appears in the appendix to this document as does the 2002 Draft Technical Report.

The focus of the analysis proceeded from general to specific terms, through seven fuelcycle combinations, summarized below. In Case 1, the most-general fuel-cycle combination, we

examined all heavy-duty engine and chassis cycles run on plant- and animal-based biodiesel

fuels; in Case 5a, 5b, and 5c, the most-specific fuel-cycle combinations, we examined heavyduty engine and chassis data for light-, medium-, and heavy-duty cycles using soybean-based

biodiesel. The latter analysis was designed to examine load-dependence of NOx emissions for

heavy-duty highway engines and chasses first posited by EPA in 2007 (see Sze et al.). This

research was further elucidated by Eckerle et al. While feedstock materials varied for the seven

fuel-cycle combinations presented here, all analyses were conducted using 20 vol% biodiesel

fuels.

A summary of fuel-cycle combinations used in the analysis appears below.

 Case 1: All cycles tested on plant-based (soybean, rapeseed/canola, and coconut) and

animal-based (tallow, lard, and grease) biodiesel fuels,

 Case 2: All cycles tested on soybean-based biodiesel fuel,

 Case 3: FTP and UDDS cycles tested on soybean-based biodiesel fuel,

 Case 4: Detroit Diesel Corporation (DDC) and non-DDC engines tested on

soybean-based biodiesel fuel,

 Case 5a: Engines and chasses tested on soybean-based biodiesel fuel run on light-duty

cycles,

 Case 5b: Engines and chasses tested on soybean-based biodiesel fuel run on

medium-duty cycles, and

 Case 5c: Engines and chasses tested on soybean-based biodiesel fuel run on heavy-duty

cycles.

The results of the analysis of the seven fuel-cycle combinations appear in Section A3.2.

The results of the analysis of the NOx emissions load-dependence appear in Section A3.3 and a
1016

discussion of the observed load-dependence impacts in the context of relevant literature and the

2002 Draft Technical Report appear in Section A3.4.

In the 2002 Draft Technical Report, we focused our analysis on data from heavy-duty

highway engines, since this data was the most abundant in our database and since it was unclear

to what extent testing on a chassis dynamometer might differ from testing on an engine

dynamometer. However, some researchers criticized the conclusions of the report for its

disproportionate reliance on engine data. These researchers argued that these engines may not

behave in a manner indicative of the actual, in-use fleet or that chassis-generated data may be

better-suited for NOx emissions testing.

Some researchers also criticized the conclusions of our 2002 Draft Technical Report,

citing its disproportionate reliance on DDC engine data. These researchers argued that these

engines may not behave in a manner indicative of the actual, in-use fleet as a whole. To help

address these concerns, we supplemented the database for the 2002 Draft Technical Report with

non-DDC engine data. In the current analysis, non-DDC engines represent 59.0% of all engines

present in the supplemented database.

To investigate these concerns, we carried out an analysis to determine the compatibility

of heavy-duty highway engine data with heavy-duty highway chassis data. Establishing

compatibility between heavy-duty highway engine data and heavy-duty highway chassis data

would allow us to make more-complete use of all emissions data in the database. In turn, this

would allow us to perform more robust statistical analyses. The results of this engine and chassis

data compatibility analysis are presented in Section A3.1. Section A2 contains a discussion of

the data screening criteria and methodology used in this analysis.

A2. Data Screening and Methodology

The data used in this analysis is comprised of data initially used in our 2002 Draft

Technical Report, supplemented by pertinent data published between 2002 and 2007. The

supplemental data included late model year engines, vehicles, and technology groups. A list of

data sources used in this analysis appears in the appendix to this document.

A criticism raised by the 2002 Draft Technical report was that its analysis relied too

heavily upon data from early model year engines and that these engines may not behave in a

manner indicative of the actual, in-use fleet as a whole. To help address this concern, we

supplemented the existing database of over 800 observations with approximately 560 additional

observations comprised of late model year engines, vehicle, and technology groups.

Candidate data were first screened to verify that they met EPA data QC/QA requirements

as well as criteria consistent with the goals of the analysis before inclusion into the database (See

Section II of the 2002 Draft Technical Report for a discussion of EPA data QC/QA

considerations). New data meeting these criteria were entered into the database developed for

the 2002 Draft Technical Report. These criteria are described in Section A2.1.
1017

A2.1 Criteria for Selecting Data

Candidate data were screened to verify that they met criteria consistent with the goals of

the analysis before inclusion into the database. For instance, the analysis was limited to No. 1

and No. 2 diesel fuel and related blends that can be used in typical heavy-duty diesel engines

without engine modifications. As a result, all emulsions and non-biodiesel oxygenated blends

with more than 20 vol% oxygenate were excluded from the final database used in the analysis.

Also, synthetic fuels, such as those produced using the Fischer-Tropsch process, rather than

refinery streams, were excluded from the final database.

We also limited this study to vehicles and engines that had already been sold

commercially or had a high probability of being sold in the future. Vehicles and engines with

experimental technologies that had no immediate plans for commercialization, such as those with

innovative combustion chamber geometries, were excluded from the database. Likewise, singlecylinder research engines were excluded from consideration, even though the associated full-size

parent engine might have been appropriately included in the database, had it been tested. Singlecylinder engines do not appear in heavy-duty applications. By definition, such engines have

lower total horsepower and displacement, both of which may influence the way in which

biodiesel impacts emissions.

The pairing of diesel and biodiesel fuels used in a particular study also played a role in

determining if data from that study would be included in our analysis. For example, we excluded

data from all studies that did not test at least two different biodiesel concentrations on the same

engine, one of which could be 0 vol% biodiesel.

There were a number of instances in which data from one study was repeated in other

studies. This might occur if the authors published the same dataset in multiple scientific journals

to maximize exposure, or if the authors presented a previously-published set of data in a new

publication for the purposes of comparing the two datasets. Such duplicative data was also

excluded from our database.

Also, each prospective data source was screened to verify that it contained raw, not

aggregated, data. In cases where raw data was not published in a study, attempts were made to

obtain it from the study author(s). Raw data obtained from author(s) were included in our

database after successful screening.

A2.2 Criteria for Selecting Test Cycles

We selected cycles which were representative of actual, in-use operating conditions.

While the Federal Test Procedure (FTP) transient cycle most-closely reflects actual, in-use

operating conditions, we included data from a number of other studies that used atypical test

cycles which were adequately comprehensive in their number, selection of modes, and/or in their

transient speed-load traces, so that the resulting emission measurements may still be informative.
1018

Data collected under test cycles that were unique, contained only a single steady-state

mode, or used two- or three-nonstandard modes for testing, were typically excluded from the

database. Non-FTP/UDDS test cycles represented about 24 percent of all data in the database.

A total of eight different cycles, with two variants, representing a variety of load levels

were included in our database. A description of the test cycles included in our analysis appears

below.

 AVL 8-Mode Test – An eight-mode steady-state engine test procedure, designed to

correlate with FTP cycle exhaust emission results. Only NOx emissions data generated

by the AVL 8-Mode test was included in our database.

 Combined International Local Cycle and Commuter (CILCC) – A transient cycle

developed by NREL for testing Class 4 to Class 6 vehicles. It is intended to simulate

urban delivery driving conditions for heavy-duty vehicles.

 City-Suburban Heavy-Vehicle Cycle (CSHVC) – A transient cycle developed by West

Virginia University. It is intended to simulate low-speed urban/ suburban driving

conditions of heavy-duty vehicles and is punctuated with frequent stops.

 Freeway Cycle – A transient cycle intended to simulate four-lane highway driving

conditions of heavy-duty vehicles, including entrance and exit ramps.

 Federal Test Procedure (FTP) – The heavy-duty transient cycle currently used by EPA

for emission, certification, and other testing of heavy-duty on-road engines; the cycle

most-closely reflects actual, in-use operating conditions and was developed to simulate a

variety of heavy-duty truck and bus driving conditions in cities and on expressways.

 Highway Cycle (HWY) – A high-speed highway cruise cycle based on the Heavy

Heavy-Duty Diesel Truck chassis cycle developed by the California Air Resources Board

and previously used in the Coordinating Research Council E-55 program.

 Rowan University Composite School Bus Cycle (RUCSBC) – A school bus cycle

developed by Rowan University.

 Urban Dynamometer Driving Schedule (UDDS) – A heavy-duty chassis dynamometer

test. Our database includes data from the UDDS cycle and two variants simulating light

(6,000 lbs) and heavy (28,000 lbs) test weight conditions.

The summary of the test cycles included in our analysis appears in Table A2-A.
1019

Table A2-A. Test cycles included in this analysis

Test Cycle Description Duration

AVL 8 Eight-mode steadystate cycle n/a

CILCC Heavy-duty urban

delivery cycle

53 min

12 sec

CSHVC

Heavy-duty citysuburb low-speed

cycle

28 min

20 sec

Freeway Heavy-duty highway

cycle

27 min

20 sec

FTP Heavy-duty engine

certification cycle 20 min

HWY High-speed cruise

cycle from CRC E-55

12 min

40 sec

RUCSBC School bus cycle 21 min

50 sec

UDDS Heavy-duty chassis

cycle

17 min

40 sec

UDDS 6k UDDS variant based

on EPA data

17 min

40 sec

UDDS 28k UDDS variant based

on CRC E-55 data

17 min

40 sec

A2.3 Criteria for Selecting Feedstock Materials

Biodiesel fuel can be produced from a wide variety of feedstock materials. While the

studies that comprise our database included only a portion of the many feedstock materials

possible, they do represent the most-common feedstock materials. The biodiesel feedstock

materials found in our database and their percentages are listed in Table A2-B.
1020

Table A2-B.

Biodiesel feedstock material observations in the database

Feedstock

Materials

Number of

Observations

Percentage of

Observations

Soybean 556 77.1%

Rapeseed/Canola 95 13.2%

Grease*42 5.8%

Tallow 19 2.6%

Coconut 6 0.8%

Lard 3 0.4%

* Includes high free fatty acid (HFFA) and low free fatty acid (LFFA)

Given the limited data available for some feedstock materials, we aggregated all biodiesel

feedstock materials into three general categories: plant-based biodiesel, soybean-based biodiesel

(a subset of plant-based biodiesel), and animal-based biodiesel (See Table A2-C for a listing of

biodiesel feedstock materials aggregated into the categories used in our database).

Table A2-C.

Biodiesel feedstock materials aggregated

into categories used in the database

Aggregated Feedstock

Material Category

Number of Observations

in Category

Plant-based 657

Soybean-based 556

Animal-based 64

A2.4 Overview of methodology

This section summarizes the statistical approach used in this analysis, which employed

the SAS/STAT software procedure PROC MIXED. This procedure can treat some variables as

fixed-effects and others as random-effects.

For instance, the NOx fixed-effect was expressed as a function of percent biodiesel (0 or

20 vol%), vehicle class (Class 1-2a or Class 2b-8), the interaction of percent biodiesel and
1021

vehicle class, test cell type (chassis test cell or engine test cell), and the interaction between test

cell type and percent biodiesel. Each fixed-effect term is tested and removed if found not

significant. Additional fixed-effect terms were added to the model when examining DDC and

non-DDC engines. The random-effects examined were the test cell type and its interaction with

percent biodiesel, test cycle, and the biodiesel source.

After creating the initial model, the distribution of mixed-model residuals are examined;

residuals with absolute values greater than four standard deviations from a mean of 0 are

considered outliers and removed from further consideration.

The final model evaluates the statistical significance of the difference between fuels

containing 20 vol% biodiesel and the base fuel, containing no biodiesel. A significance criterion

of p < 0.05 was used for all analysis. See Section II of the 2002 Draft Technical Report for

additional discussion and derivations.

A3. Results

This section contains the results of the biodiesel emissions impact analysis described in

Section A2. The results of the analysis of the compatibility of heavy-duty highway chassis and

engine data appear in Section A3.1. The results of the analysis of the seven fuel-cycle

combinations appear in Section A3.2, including the analysis of DDC engines versus non-DDC

engines. The results of the analysis of the NOx emissions load-dependence appear in Section

A3.3 and a discussion of the observed load-dependence impacts placed in the context of relevant

literature and the 2002 Draft Technical Report appear in Section A3.4.

A3.1 Compatibility of Heavy-duty Highway Chassis and Engine Data

The primary objective of the analysis was to quantify the impacts of biodiesel fuels. One

aspect of the analysis was to determine if heavy-duty highway emissions engine data in our

database was comparable to heavy-duty highway chassis data for purposes of our statistical

analysis. Much of the database (66%) consisted of heavy-duty highway engine data, with the

balance of the data comprising heavy-duty highway chassis data. Establishing compatibility

between engine and chassis data would allow us to make more complete use of all emissions data

in the database, which, in turn, would allow us to perform more robust statistical analyses.

Moreover, some researchers criticized the conclusions of our 2002 Draft Technical

Report, citing its disproportionate reliance on engine data. These researchers argued that these

engines may not behave in a manner indicative of the actual, in-use fleet as a whole or that

chassis-generated data might be better-suited for purposes of biodiesel emissions testing. To

investigate these claims, we undertook an analysis to determine the compatibility of heavy-duty

highway engine data with heavy-duty highway vehicle data.

Using a significance level of p < 0.05 for all statistical analysis, engine data was found to

be statistically comparable to chassis data for NOx and CO emissions. This finding is supported

by the research of NREL, whose examination of published data suggests that there exists no

discrepancy between engine and vehicle testing data (See NREL Milestone 10.4). See Table A3-
1022

A for a summary of fuel-cycle combinations for which heavy-duty highway engine data was

statistically comparable to heavy-duty highway chassis data for regulated pollutants.

Table A3-A.

Fuel-cycle combinations for which heavy-duty highway

engine data was statistically comparable to heavy-duty

highway chassis data for regulated pollutants

Regulated Pollutants

NOx PM CO HC

Case 1 x x

Case 2 x x

Case 3 x x

Case 4 x x

Case 5a x x

Case 5b x x

Fuel-Cycles

Case 5c x x

x Denotes heavy-duty highway engine data that is statistically comparable

to heavy-duty highway chassis data.

Emissions of PM and CO for heavy-duty highway engines and chasses were not found to

be statistically comparable to each other for any of the seven fuel-cycle combinations. Based

upon our experience with chassis test cells, it is difficult to accurately quantify PM exhaust

emissions, as chassis-based testing is often deficient and non-standardized vis-à-vis 40CFR86-

2007 and 40CFR1065 for test specification and test equipment, respectively. Deficiencies

associated with non-standardized chassis test cells may produce high test-to-test variability.

Likewise, CO emissions are difficult to accurately quantify on both heavy-duty engine and

chassis test cells and may also produce high test-to-test variability.

High test-to-test variability curtails the ability to accurately capture and discern small

differences in exhaust emissions, particularly PM and CO exhaust emissions. This variability

may help to explain why engine and chassis data are not comparable in our analysis for CO and

PM emissions.

In cases where heavy-duty highway engine data is statistically comparable to heavy-duty

highway chassis data, engine and chassis data are pooled to produce the appropriate statistic. In

other cases, we believe that the use of engine data alone results in a more-representative statistic,

which we report here. The results for the following analysis reflect this approach.

A3.2 Fuel-cycle Results

Seven fuel-cycle combinations were identified for our statistical analysis, ranging from a

general case, combining testing of all test cycles and all cycles and on all biodiesel fuel feedstock
1023

materials to cycles aggregated into light-, medium-, and heavy-duty test cycle categories, which

were run exclusively on 20 vol% soybean-based biodiesel fuel. Heavy-duty highway engine and

chassis emissions data were statistically comparable for NOx and HC and were subsequently

pooled for our analysis. Heavy-duty highway engine and chassis emissions data were not

statistically comparable for PM and CO, so only engine results are presented here. The results

for each of the seven fuel-cycle combinations appear below. All results presented are

statistically significant at a significance level of p < 0.05.

Case 1: All cycles tested on all biodiesel fuels

Our first fuel-cycle combination examined data collected on all cycles included in the

database and from all vehicles and engines tested on both plant-based (soybean, rapeseed/canola

and coconut) and animal-based (tallow, lard, and grease) biodiesel fuels (See Table A2-A for a

listing of all cycles used in the Case 1, Case 2, and Case 4 analyses).

For plant-based and animal-based biodiesel fuels, NOx emissions were found to increase

a statistically-significant 2.0% relative to the base fuel, whereas, PM, CO, and HC emissions

were found to decrease by 13.6%, 13.5%, and 18.7%, respectively, relative to the base fuel.

Case 2: All cycles tested on soybean-based biodiesel fuels

Our second fuel-cycle combination examined all cycles specified in Case 1, but only

involved vehicles and engines tested on soybean-based biodiesel fuels.

For soybean-based biodiesel, NOx emissions were found to increase by 2.2%, whereas

PM, CO, and HC emissions were found to decrease by 15.6%, 13.8%, and 14.1%%, respectively,

relative to the base fuel. Case 1 and Case 2 differ in their fuel composition. The biodiesel tested

in Case 1 is composed of 20 vol% animal- and plant-based biodiesel, whereas in Case 2, the fuel

is composed of 20 vol% soybean-based biodiesel only. The results suggest that the removal of

animal-based and/or rapeseed/canola/coconut-based biodiesel fuel feedstock materials may have

a slight impact on some exhaust emissions. Increases in emissions of 0.2 %, 2.0%, and 0.3% are

observed for NOx, PM, and CO, respectively, relative to a soybean-based biodiesel discussed in

Case 2. HC emissions decrease by 4.6% relative to a soybean-based biodiesel.

Several hypotheses have been advanced by researchers in an attempt to help explain the

differences in exhaust emissions between plant-based and animal-based biodiesel feedstock

materials (See Graboski et al. and Goetz); these are, however, outside the scope of the current

analysis and are not discussed here.

Case 3: FTP and UDDS cycles tested on soybean-based biodiesel fuels

Our third fuel-cycle combination examined only engines and vehicles tested on soybeanbased biodiesel fuel over only the FTP and UDDS cycles. Together, FTP and UDDS cycles

comprise 76% of the database observations.

For soybean-based biodiesel, NOx emissions were found to increase by 3.2%, whereas

PM, CO, and HC emissions were found to decrease by 15.6%, 15.9%, and 13.7%, respectively,

relative to the base fuel.
1024

The results of this analysis suggest that the emission impacts associated with heavy-duty

highway engines and chasses tested on 20 vol% soybean-based biodiesel and run on FTP/UDDS

cycles produce an increase in exhaust emissions of 1.0% for NOx and 2.1% for CO, relative to

all engine and chassis cycles run on the same fuels. HC emissions decrease by 0.4% relative to

all engine and chassis cycles and PM emissions appear to be relatively unaffected. These results

were statistically significant at a significance level of p < 0.05.

As the FTP and UDDS cycles may more-closely represent actual, in-use operating

conditions encountered by heavy-duty highway engines and vehicles, it is possible that the Case

3 results may be a better indicator of actual, in-use biodiesel emissions impacts.

Case 4: DDC vs. non-DDC engines tested on soybean-based biodiesel fuels

Our fourth fuel-cycle combination separately examined DDC and non-DDC engines

tested on the cycles specified in Case 1 and Case 2 using soybean-based biodiesel fuel.

Our analysis found that DDC heavy-duty engine data was statistically comparable to nonDDC heavy-duty engine data for NOx, CO, and HC emissions at a significance level of p < 0.05.

DDC heavy-duty engines and non-DDC heavy-duty engines did not behave in a statistically

similar manner for PM emissions, however. In this regard, the results of our analysis suggest

that DDC heavy-duty engines behave in the same manner in which non-DDC heavy-duty

engines behave in our database for NOx, CO, and HC emissions. As such, this finding should

help alleviate earlier concerns that the disproportionate representation of DDC engines may

produce results which are not indicative of the database as a whole or the in-use fleet.

Case 5a: Light-duty cycles tested on soybean-based biodiesel fuels

Our fifth fuel-cycle combination examined heavy-duty engine and chassis data for lightduty cycles specified in Table A3-B and tested on soybean-based biodiesel fuel.

For soybean-based biodiesel tested on light-duty cycles, NOx, PM, CO, and HC

emissions were found to decrease by 1.0%, 19.0%, 9.9%, and 14.2% respectively, relative to the

base fuel.
1025

Table A3-B. Cycle composition by case

Case 5b: Medium-duty cycles tested on soybean-based biodiesel fuels

Our sixth fuel-cycle combination examined heavy-duty engine and chassis data for

medium-duty cycles specified in Table A3-B and tested on soybean-based biodiesel fuel.

For soybean-based biodiesel tested on medium-duty cycles, NOx emissions were found

to increase by 2.5%, relative to the base fuel, whereas PM, CO, and HC emissions were found to

decrease by 19.0%, 14.0%, and 14.2%, and respectively, relative to the base fuel.

Case 5c: Heavy-duty cycles tested on soybean-based biodiesel fuels

Our seventh fuel-cycle combination examined heavy-duty engine and chassis data for

heavy-duty cycles specified in Table A3-B and tested on soybean-based biodiesel fuel.

For soybean-based biodiesel tested on heavy-duty cycles, NOx emissions were found to

increase by 5.1%, relative to the base fuel, whereas PM, CO, and HC emissions were found to

decrease by 32.6%, 22.0%, and 14.2%, respectively, relative to the base fuel. Unlike Case 5a

and 5b, the PM emissions results were significant and greater than those of the light-duty and

medium-duty cases. A summary of these results appears in Table A3-C.

Case Number Case Description Individual

Cycles

Case 5a Light-duty cycles

CILCC

CSHVC

UDDS6k

Case 5b Medium-duty

cycles

AVL8 (NOx)

Freeway

FTP

RUCSBC

UDDS

UDDS28k

Case 5c Heavy-duty cycles HWY55
1026

Table A3-C.

Summary of emissions results for seven fuel-cycle combinations

for heavy-duty highway engines and chasses

Regulated Pollutants

NOx PM* CO* HC

Case 1 +2.0% -13.6% -13.5%+-18.7%

Case 2 +2.2% -15.6% -13.8% -14.1%

Case 3 +3.2% -15.6% -15.9% -13.7%

Case 4 +2.4% -16.9% -13.9% -14.3%

Case 5a -1.0% -9.9%

Case 5b +2.5%

-19.0%

-14.0%

Fuel-Cycles

Case 5c +5.1% -32.6% -22.0%

-14.2%

* Only engine data.

+ Not significant.

A3.3 Load-dependent Emissions Impacts

We initially identified the load-dependence of NOx emissions in heavy-duty highway

engines in 2007 (See Sze et al.) and these results were later corroborated by Eckerle et al. The

results of Sze et al. and Eckerle et al. were based largely upon new engine and chassis studies (as

well as modeling efforts), which were aimed specifically at examining the load-dependent NOx

emissions phenomenon.

In the current research, however, our work is retrospective insofar as we examined data

from a broad array of pre-existing studies, none of which were designed to examine the loaddependent NOx emissions impacts. As such, this analysis occasionally suffers from the

experimental design limitations associated with the pre-existing studies. One such limitation is

the use of test cycles which do not realistically reflect actual, in-use operating conditions. Such

data can skew results and obscure evidence of the load-dependence of NOx emissions. Such a

situation is discussed in Case 5a.

Case 5a, 5b, and 5c: Load-dependent emissions impact on NOx

The load-dependence of NOx emissions observed in this analysis is apparent when

comparing Case 5a (light-load cycles) to Case 5b (medium-load cycles) to Case 5c (heavy-load

cycles). However, results for the light-load conditions in Case 5a may not be representative of

in-use vehicle operation.

Case 5a: Confounding effects of lightly-loaded conditions

The load-dependence of NOx emissions evident in the research of Sze et al. and Eckerle

et al. was based upon medium- and heavy-duty cycles, not lightly-loaded cycles as in Case 5a.
1027

We believe that our current findings for NOx emissions under lightly-loaded conditions

may not be representative of the operating conditions typically encountered in actual, in-use fleet

operations. As in-use heavy-duty highway engines and vehicles do not typically operate under

the lightly-loaded conditions encountered in the cycles which comprise our light-duty category,

the practical significance of the NOx emissions results for Case 5a is questionable.

Further, the NOx emissions results for Case 5a are also suspect when compared to Cases

1-4 and 5b-5c, all of which indicate that there is a statistically-significant increase in NOx

emissions of between 2.0% and 5.1%.

As such, we place greater significance on the results obtained under medium- (Case 5b)

and heavy-load (Case 5c) conditions, since they more-accurately mirror typical, in-use fleet

operations and, as a result, provide a more realistic representation of operating conditions

encountered in-use.

Case 5b vs. Case 5c: Load-dependent NOx emission impacts

Since the medium-loading conditions associated with these cycles are typical of actual,

in-use fleet operations, these cycles provide a more realistic representation of operating

conditions encountered by actual fleet usage. As such, we place greater significance on these

results.

The difference in NOx emissions between our results here and those of other researchers

appears to be attributable to an artifact of the selected test cycle profile. Analyzing the NOx

emissions data as a function of load, as we do here -- as opposed to a particular test cycle profile

-- reconciles the difference and supports the NOx emission-load-dependence hypothesis for

heavy-duty highway engine and chasses posited by Sze et al. and corroborated by Eckerle et al.

The discussion of load-dependent NOx emission impacts in the context of our research appears

in Section A3.4.

A3.4 Relevant Studies

We initially identified the load-dependence of NOx emissions in heavy-duty highway

engines in 2007 (See Sze et al.) and these results were later corroborated by Eckerle et al. The

results of Sze et al. and Eckerle et al. were based largely upon new engine and chassis studies (as

well as modeling efforts), which were aimed specifically at examining the load-dependent NOx

emissions phenomenon.

In the current research, however, our work is retrospective insofar as we examined data

from a broad array of pre-existing studies, none of which were designed to examine the loaddependent NOx emissions impacts. As such, this analysis occasionally suffers from the

experimental design limitations associated with the pre-existing studies. One such limitation is

the use of test cycles which do not realistically reflect actual, in-use operating conditions. Such

data can skew results and obscure evidence of the load-dependence of NOx emissions. Such a

situation is discussed in Case 5a.
1028

2002 Draft Technical Report

In 2002, the EPA conducted a comprehensive analysis of the emission impacts of

biodiesel using publicly available data. Entitled "A Comprehensive Analysis of Biodiesel

Emissions Impacts on Exhaust Emissions," the 2002 Draft Technical Report made use of

statistical regression analysis to correlate the concentration of biodiesel in conventional diesel

fuel with changes in regulated and unregulated pollutants for heavy-duty highway engines.

Figure A3-A presents basic emission correlations for NOx, PM, CO, and HC developed

in the 2002 Draft Technical Report as a function of soybean-based biodiesel concentration.

Table A3-D presents results specifically for 20 vol% soybean-based biodiesel.
1029

Figure A3-A.

Emission impacts from the 2002 Draft Technical Report by percent biodiesel

content for soybean-based biodiesel added to an average base fuel

0 20 40 60 80 100

Percent biodiesel

-80%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

Percent change in emissions

NOx

HC

CO

PM

Table A3-D.

Emission impacts from the 2002 Draft Technical Report for

20 vol% soybean-based biodiesel added to an average base fuel

Emissions Percent Change in

Emissions

NOx

PM

HC

CO

+2.0%

-10.1%

-21.1%

-11.0%

We found that the results of the current analysis, which examined heavy-duty highway

engine and chassis data, are consistent with the findings of our 2002 Draft Technical Report,

which examined heavy-duty highway engine data only. Compared to the 2002 Draft Technical

Report, NOx emissions were found to increase 2.2% while PM, HC, and CO emissions were

found to decrease by 15.6%, 14.1%, and 13.8%, respectively, in the current study. These are

shown in Figure A3-B as points overlaid on the results of the 2002 Draft Technical Report.
1030

Figure A3-B.

Emission impacts from the 2002 Draft Technical Report by percent biodiesel content for

soybean-based biodiesel added to an average base fuel with new results overlaid

SAE paper by Sze et al.

Sze et al. conducted a series of paired fuel tests comparing certification-grade highway

diesel fuels with 5 to 50 vol% soybean-based biodiesel blends. Each fuel pair was tested for up

to seven transient cycles representing various load conditions, using a 2006 model year Cummins

ISB compression ignition engine.

The authors concluded that biodiesel NOx impact on the test engine is directly

proportional to average cycle power or fuel consumption and biodiesel content (See Figure A3-

C).

0 20 40 60 80 100

Percent biodiesel

-80%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

Percent change in emissions

NOx

HC

CO

PM
1031

Figure A3-C.

NOx emissions for 20 vol% and 50 vol% soybean-based biodiesel fuel versus average

cycle power for various heavy-duty highway engine and chassis cycles

Except for the most lightly-loaded cycle, the results show statistically significant

differences in NOx emissions for all fuel pairs. The average NOx emissions due to biodiesel

were found to increase over each cycle, ranging from 0.9 to 6.6% for 20 vol% and 2.2 to 17.2%

for 50 vol% biodiesel fuels. The load-dependence of NOx emissions observed by Sze et al. is

consistent with the findings presented in this report for Cases 5a, 5b, and 5c as well as those of

Eckerle et al.

To further elucidate the load-dependent nature of NOx emissions, Sze et al. reanalyzed

chassis-generated NOx emissions data from NREL as a function of fuel consumption (a

surrogate for average cycle power) and found it to be in close agreement with EPA engine test

data. This is depicted in Figure A3-D. Data from NREL using the same engines tested instead

on a chassis dynamometer are also shown in the figure and follow the same trend, with an R2=

0.99 with and without the inclusion of the NREL dataset.

R

2

= 0.99

R

2

= 0.99

0

5

10

15

20

25

30

35

40

45

0 20 40 60 80 100 120 140 160

Average Cycle Power (hp)

NOx (Biodiesel Blend) - NOx (B0), (g/hr)

B20 HWY

B20 FTP

B20 WHTC

B20 UDDS 28k

B20 UDDS 6k

B20 HWY 172

B20 NRTC

B50 HWY

B50 FTP

B50 UDDS 28k

B50 UDDS 6k
1032

Figure A3-D.

NOx emissions for 20 vol% soybean-based biodiesel fuel versus average cycle power for

various heavy-duty highway engine and chassis cycles, including NREL chassis testing data

SAE paper by Eckerle et al.

The load-dependence of NOx emissions was also examined by Eckerle et al., who

generated engine data using 20 vol% soybean-based biodiesel to calibrate chemical kinetic

models. These models were used to examine NOx production during the combustion process.

The authors concluded that the NOx effect associated with burning biodiesel blends over a duty

cycle depends, in part, on the duty cycle average power and that higher duty cycle average power

corresponded to larger increases in NOx emissions.

R

2

= 0.99

R

2

= 0.996

0

5

10

15

20

25

30

35

40

45

0 5 10 15 20 25 30

Fuel Consumption (kg/hr)

NOx (Biodiesel Blend) - NOx (B0), (g/hr)

B20 NREL chassis data

B20 HWY

B20 FTP

B20 WHTC

B20 UDDS 28k

B20 UDDS 6k

B20 HWY 172

B20 NRTC

B50 HWY

B50 FTP

B50 UDDS 28k

B50 UDDS 6k

With NREL chassis data

Without NREL chassis data

R

2

= 0.99
1033

References

Studies included in database

Aako P. et al., "Emission Performance of Selected Biodiesel Fuels - VTT's Contribution"

Research Report ENE/33/2000, October 2000

Alam M. et al., "Combustion and Emissions Performance of Low Sulfur, Ultra Low Sulfur and

Biodiesel Blends in a DI Diesel Engine," SAE paper no. 2004-01-3024

Bouché, T., M. Hinz, R. Pitterman, "Optimising Tractor CI Engines for Biodiesel Operation,"

SAE paper no. 2000-01-1969

Callahan, T.J., C.A. Sharp, "Evaluation of Methyl Soyate/Diesel Fuel Blends as a Fuel for Diesel

Engines," Southwest Research Institute Final Report to the American Biofuels Association,

December 1993

Clark, N.N., C.M. Atkinson, G.J. Thompson, R.D. Nine, "Transient Emissions Comparisons of

Alternative Compression Ignition Fuels," SAE paper no. 1999-01-1117

Clark N. N. et al., "Class 8 Truck Emission Testing: Effects of Test Cycles and Data on

Biodiesel Operation," ASAE, 1999, 42(5), 1211-1219

Durbin, T.D., J.R. Collins, J.M. Norbeck, M.R. Smith, "Evaluation of the Effects of Alternative

Diesel Fuel Formulations on Exhaust Emissions Rates and Reactivity," Final Report from the

Center for Environmental Research and Technology, University of California, April 1999

Durbin, T.D., et al., Final Report: "Evaluation of the Effects of Biodiesel and Biodiesel Blends

on Exhaust Emission Rates and Reactivity - 2", Center for Environmental Research and

Technology; College of Engineering; University of California, Riverside, August 2001

Durbin, T.D., J.M. Norbeck, “Effects of Biodiesel Blends and Arco EC-Diesel on Emissions

from Light Heavy-Duty Diesel Vehicles,” Environ. Sci. Technol., 2002, 36, 1686-1691

Durbin T. D. et al., "Effects of Biodiesel, Biodiesel Blends, and a Synthetic Diesel on Emissions

from Light Heavy-Duty Diesel Vehicles," Environ. Sci. Technol., 2000, 34, 349-355

Environment Canada, "Emissions Characterization of a Caterpillar 3126E Equipped with a

Prototype SCRT System with Ultra Low Sulfur Diesel and a Biodiesel Blend," ERMD Report

#2005-32, 2005

Eckerle, W., Lyford-Pike, E., Stanton, D., LaPointe, L., Whitacre, S., Wall, J., “Effects of

Methyl Ester Biodiesel Blends on NOx Emissions,” SAE paper no. 2008-01-0078.

Fosseen, D., "DDC 6V-71N Emission Testing on Diesel and Biodiesel Blend," Fosseen

Manufacturing and Development, Ltd., report no. NSDB4F15, July 14, 1994
1034

Fosseen, D., "DDC 6V-92TA MU1 Coach Upgrade Emission Optimization on 20%/80%

Soy/Diesel Blend," Fosseen Manufacturing & Development, Ltd., report no. 260-2 and 24 1-1,

September 30, 1994

Frank B.P. et al., "A Study of the Effects of Fuel Type and Emission Control Systems on

Regulated Gaseous Emissions from Heavy-Duty Diesel Engines," SAE paper no. 2004-01-1085

Goetz, W., "Evaluation of Methyl Soyate/Diesel Blend in a DDC 6V-92TA Engine:

Optimization of NOx Emissions," Ortech International, Report No. 93-E14-36, July 20, 1993

Goetz, W., "Evaluation of a Tallow/Diesel Blend in a DDC 6V-92TA Engine," Ortech

International report no. 93-E14-37, July 21, 1993

Graboski, M.S., J.D. Ross, R.L. McCormick, "Transient Emissions from No. 2 Diesel and

Biodiesel Blends in a DDC Series 60 Engine," SAE paper no. 961166

Graboski, M.S., R.L. McCormick, T.L. Alleman, A.M. Herring, "The Effect of Biodiesel

Composition on Engine Emissions from a DDC Series 60 Diesel Engine," Colorado School of

Mines, Final Report to National Renewable Energy Laboratory, June 8, 2000

Hansen, K.F., M.G. Jensen, "Chemical and Biological Characteristics of Exhaust Emissions from

a DI Diesel Engine Fuelled with Rapeseed Oil Methyl Ester (RME)," SAE paper no. 971689

Holden, Bruce et al., Final Report: "Effect of Biodiesel on Diesel Engine Nitrogen Oxide and

Other Regulated Emissions," Naval Facilities Engineering Command (NAVFAC) Technical

Report TR-2275-ENV, May 2006

Howes, P., G. Rideout, "Evaluation of Biodiesel in an Urban Transit Bus Powered by a 1988

DDECII6V92 TA Engine," National Biodiesel Board, MSED Report # 96-26743-1, June 1995

Howes, P., G. Rideout, "Evaluation of Biodiesel in an Urban Transit Bus Powered by a 1981

DDCC8V71 Engine," National Biodiesel Board, MSED Report # 95-26743-2

Liotta, F.J., D.M. Montalvo, "The Effect of Oxygenated Fuels on Emissions from a Modern

Heavy-Duty Diesel Engine," SAE paper no. 932734

Lyons D. W., "Biodiesel Fuel Comparison Final Data Report for Washington Metropolitan Area

Transit Authority," West Virginia University, August 2002

Manicom, B., C. Green, W. Goetz, "Methyl Soyate Evaluation of Various Diesel Blends in a

DDC 6V-92 TA Engine," Ortech International, April 21, 1993

Marshall, W., L.G. Schumacher, S. Howell, "Engine Exhaust Emissions Evaluation of a

Cummins L10E When Fueled With a Biodiesel Blend," University of Missouri
1035

McCormick, R.L., J.D. Ross, M.S. Graboski, "Effect of Several Oxygenates on Regulated

Emissions from Heavy-Duty Diesel Engines," Environ. Sci. Technol., 1997, 31, 1144-1150

McCormick, R.L., J.R. Alvarez, M.S. Graboski, "NOx Solutions for Biodiesel," Colorado School

of Mines, August 31, 2001

McCormick R.L. et al., "Fuel Additive and Blending Approaches to Reducing NOx Emissions

from Biodiesel," SAE paper no. 2002-01-1658

McCormick R.L. et al., "Regulated Emissions from Biodiesel Tested in Heavy-Duty Engines

Meeting 2004 Emission Standards," SAE paper no. 2005-01-2200

McCormick, R.L. et al., "Effects of Biodiesel Blends on Vehicle Emissions, FY 2006 Annual

Operating Plan Milestone 10.4," October 2006

McDonald, J.F., D.L. Purcell, B.T. McClure, D.B. Kittelson, "Emissions Characteristics of Soy

Methyl Ester Fuels in an IDI Compression Ignition Engine," SAE paper no. 950400

McGill, R., Storey, J., Wagner, R., Irick, D., Aakko, P., Westerholm, M., Nylund, N., Lappi, M.,

“Emission Performance of Selected Biodiesel Fuels,” SAE paper no. 2003-01-1866

National Resources Canada, "National Resources Canada/Fiba Canning - Biodiesel

Freightliners," National Resources Canada draft report, October 24, 2006

National Institute for Petroleum and Energy Research, "Effects of Methyl Esters of Tallow and

Grease on Exhaust Emissions and Performance of a Cummins L10 Engine," September 16, 1993

ORYXE Energy International, Inc., Biodiesel Emissions Test Summary, ORYXE presentation,

2006

Peterson, C., D. Reece, J. Thompson, S. Beck, C. Chase, "Development of Biodiesel for Use in

High Speed Diesel Engines," University of Idaho, presentation at Sixth National Bioenergy

Conference, Oct. 2-6, 1994

Peterson, C.L., D.L. Reece, "Emissions Tests with an On-Road Vehicle Fueled with Methyl and

Ethyl Esters of Rapeseed Oil," ASAE paper no. 946532

Peterson C. L., et al., "The Effect of Biodiesel Feedstock on Regulated Emissions in Chassis

Dynamometer Tests of a Pickup Truck," ASAE, 2000, 43 (6), 1371-1381

Peterson, C.L., D.L. Reece, "Emissions Testing with Blends of Esters of Rapeseed Oil Fuel With

and Without a Catalytic Converter," SAE paper no. 961114

Peterson, C.L., "Truck-In-The-Park Biodiesel Demonstration with Yellowstone National Park,"

University of Idaho, August 1999
1036

Rantanen, L., S. Mikkonen, L. Nylund, P. Kociba, M. Lappi, N. Nylund, "Effect of Fuel on the

Regulated, Unregulated and Mutagenic Emissions of DI Diesel Engines," SAE paper no. 932686

Schumacher, L.G., S.C. Borgelt, W.G. Hires, D. Fosseen, W. Goetz, "Fueling Diesel Engines

with Blends of Methyl Ester Soybean Oil and Diesel Fuel," University of Missouri. See

http://web.missouri.edu/~pavt0689/ASAED94.htm.

Schumacher, L., S.C. Borgelt, W.G. Hires, W. Wetherell, A. Nevils, "100,000 Miles of Fueling

5.9L Cummins Engines with 100% biodiesel," SAE paper no. 962233

Schumacher, L., N.N. Clark, D.W. Lyons, W. Marshall, “Diesel Engine Exhaust Emissions

Evaluation of Biodiesel Blends Using a Cummins L10E Engine,” ASAE, 44(6), 1461-1464

Sharp, C.A., "Transient Emissions Testing of Biodiesel in a DDC 6V-92TA DDEC Engine,"

Southwest Research Institute, Final Report to the National Biodiesel Board, October 1994

Sharp, C.A., "Transient Emissions Testing of Biodiesel and Other Additives in a DDC Series 60

Engine," Southwest Research Institute, Final Report to the National Biodiesel Board, December

1994

Sharp, C.A., "Emissions and Lubricity Evaluation of Rapeseed Derived Biodiesel Fuels,"

Southwest Research Institute, Final Report for Montana Department of Environmental Quality,

November 1996

Sharp, C.A., "Characterization of Biodiesel Exhaust Emissions for EPA 211(b)," Southwest

Research Institute report no. 08-1039A, January 1998

Sharp, C.A., S.A. Howell, J. Jobe, "The Effect of Biodiesel Fuels on transient Emissions from

Modern Diesel Engines, Part I Regulated Emissions and Performance," SAE paper no. 2000-01-

1967

Sirman, M.B., E.C. Owens, K.A. Whitney, "Emissions Comparison of Alternative Fuels in an

Advanced Automotive Diesel Engine," Southwest Research Institute, AD A353968/PAA,

November 1998

Smith, J.A., D.L. Endicott, R.R. Graze, "Biodiesel Engine Performance and Emissions Testing,"

Caterpillar Technical Center, May 1998

Souligny M. et al., "Heavy-Duty Engine Performance and Comparative Emission Measurements

for Different Biodiesel Blends Used in the Montreal Project," SAE paper no. 2004-01-1861

Spataru, A., C. Romig, "Emissions and Engine Performance from Blends of Soya and Canola

Methyl Ester with ARB #2 Diesel in a DDC 6V92TA MUI Engine," SAE paper no. 952388

Starr, M.E., "Influence on Transient Emissions at Various Injection Timings, Using Cetane

Improvers, Bio-Diesel, and Low Aromatic Fuels," SAE paper no. 972904
1037

Stotler, R.W., D.M. Human, "Transient Emission Evaluation of Biodiesel Fuel Blend in a 1987

Cummins L10 and DDC 6V-92-TA," Engineering Test Services, report no. ETS-95-128, Nov.

30, 1995.

Sze, C., Whinihan, J, Olson, B., Schenk, C., Sobotowski, R., “Impact of Test Cycle and

Biodiesel Concentration on Emissions,” SAE paper no. 2007-01-4040.

Ullman, T.L., C.T. Hare, T.M. Baines, "Heavy-Duty Diesel Emissions as a Function of Alternate

Fuels," SAE paper no. 830377.

Wang W. G., "Emissions from Nine Heavy-Duty Trucks Fueled by Diesel and Biodiesel Blend

Without Engine Modification," Environ. Sci. Technol., 2000, 34, 933-939.

Studies not included because data collected on single cylinder/experimental engine

Murayama, T., Y. Oh, A. Kido, T. Chikahisa, N. Miyamoto, K. Itow, "Effects of Super Heating

of Heavy Fuels on Combustion and Performance in DI Diesel Engines," SAE paper no. 860306

Murayama, T., Y. Oh, N. Miyamoto, T. Chikahisa, N. Takagi, K. Itow, "Low Carbon Flower

Buildup, Low Smoke, and Efficient Diesel Operation with Vegetable Oils by Conversion to

Mono-Esters and Blending with Diesel Oil or Alcohols," SAE paper no. 841161

Shaheed, A., E. Swain, "Performance and Exhaust Emission Evaluation of a Small Diesel Engine

Fuelled with Coconut Oil Methyl Esters," SAE paper no. 981156

Suda, K.J., "Vegetable Oil or Diesel Fuel - A Flexible Option," SAE paper no. 840004

Ziejewski, M., H.J. Goettler, "Comparative Analysis of the Exhaust Emissions for Vegetable Oil

Based Alternative Fuels," SAE paper no. 920195

Studies not included because data not readily available

Alfuso. S., M. Auriemma, G. Police, M.V. Prati, "The Effect of Methyl-Ester of Rapeseed Oil on

Combustion and Emissions of DI Diesel Engines," SAE paper no. 932801

Hemmerlain, N., V. Korte, H. Richter, G. Schröder, "Performance, Exhaust Emissions and

Durability of Modern Diesel Engines Running on Rapeseed Oil," SAE paper no. 910848

Humke, A.L., N.J. Barsic, "Performance and Emissions Characteristics of a Naturally Aspirated

Diesel Engine with Vegetable Oil Fuels - (Part 2)," SAE paper no. 810955

Knothe, G., C.A. Sharp, T.W Ryan, “Exhaust Emissions of Biodiesel, Petrodiesel, Neat Methly

Esters, and Alkanes in a New Technology Engine,” Energy and Fuels, 2006, 20, 403-408

Lapuerta, M., Armas, O., Ballesteros, R., Fernandez, J., "Diesel Emissions from Biofuels

Derived from Spanish Potential Vegetable Oils," Fuel, 2005, 84(6), 773-780
1038

Last, R.J., M. Krüger, M. Dürnholz, "Emissions and Performance Characteristics of a 4-Stroke,

Direct Injected Diesel Engine Fueled with Blend of Biodiesel and Low Sulfur Diesel Fuel," SAE

paper no. 950054

Ma, W., L.I. Leviticus, F.G. Ullman, "On-Line Measurement of Formaldehyde in Tailpipe

Emissions by Tunable Diode Laser Spectroscopy," SAE paper no. 941702

Martin, B., P. Aakko, D. Beckman, N. D. Giacomo, F. Giavazzi, "Influence of Future Fuel

Formulations on Diesel Engine Emissions - A Joint European Study," SAE paper no. 972966

McDonald, J.F., "Evaluation of a Yellow Grease Methyl Ester and Petroleum Diesel Fuel

Blend," University of Minnesota Final Report to the Agricultural Utilization Research Institute,

August 11, 1997

Montagne, X., "Introduction of Rapeseed Methyl Ester in Diesel Fuel - The French National

Program," SAE paper no. 962065

Reece, D.L., C.L. Peterson, "Biodiesel Testing in Two On-Road Pickups," SAE paper no.

952757

Staat, F., P. Gateau, "The Effects of Rapeseed Oil Methyl Ester on Diesel Engine Performance,

Exhaust Emissions and Long-Term Behavior - A Summary of Three Years of Experimentation,"

SAE paper no. 950053

Wang, W.G., D.W. Lyons, N.N. Clark, M. Gautam, "Emissions from Nine Heavy Trucks Fueled

by Diesel and Biodiesel Blend without Engine Modification," Environ. Sci. Technol., 2000, 34,

933-939

Studies not included because test cycle nonstandard/unrepresentative

Adelman, A.J., "Emission Evaluation Test Report for a Comparative Analysis of Emissions

Among Petroleum Diesel and Biodiesel Blends fired in a Large Diesel Engine," AirNova, Inc.,

January 1998

Akasaka, Y., T. Suzuki, Y. Sakurai, "Exhaust Emissions of a DI Diesel Engine Fueled with

Blends of Biodiesel and Low Sulfur Diesel Fuel," SAE paper no. 972998

Bagley, S.T., L.D. Gratz, J.H. Johnson, J.F. McDonald, "Effects of an Oxidation Catalytic

Converter and a Biodiesel Fuel on the Chemical, Biological, and Particulate Size Characteristics

of Emissions from an IDI Diesel Engine," Michigan Technological University, December 1995

Chang, D.Y.Z., J.H. Van Gerpen, "Fuel Properties and Engine Performance for Biodiesel

Prepared from Modified Feedstocks," SAE paper no. 971684
1039

Chang, D.Y., J.H. Van Gerpen, "Determination of Particulate and Unburned Hydrocarbon

Emissions from Diesel Engines Fueled with Biodiesel," SAE paper no. 982527

Choi, C.Y., G.R. Bower, R.D. Reitz, "Effects of Biodiesel Blended Fuels and Multiple Injections

on D.I. Diesel Engines," SAE paper no. 970218

Czerwinski, J., "Performance of HD-DI-Diesel Engine with Addition of Ethanol and Rapeseed

Oil," SAE paper no. 940545

Desantes, J.M., J. Arrègle, S. Ruiz, A. Delage, "Characterization of the Injection-Combustion

Process in a D.I. Diesel Engine Running with Rape Oil Methyl Ester," SAE paper no. 1999-01-

1497

Fort, E.F., P.N. Blumberg, H.E. Staph, J.J. Staudt, "Evaluation of Cottonseed Oil as Diesel Fuel,"

SAE paper no. 820317

Jacobus, M.J., S.M. Geyer, S.S. Lestz, W.D. Taylor, T.H. Risby, "Single-Cylinder Diesel Engine

Study of Four Vegetable Oils," SAE paper no. 831743

Masjuki, H., M.Z. Abdulmuin, H.S. Sii, "Investigations on Pre-Heated Palm Oil Methyl Esters in

the Diesel Engine," IMechE 1996, Part A: Journal of Power and Energy

Needham, J.R., D.M. Doyle, "The Combustion and Ignition Quality of Alternative Fuels in Light

Duty Diesels," SAE paper no. 852101

Schmidt, K., J. Van Gerpen, "The Effect of Biodiesel Fuel Composition on Diesel Combustion

and Emissions," SAE paper no. 961086

Scholl, K.W., S.C. Sorenson, "Combustion of Soybean Oil Methyl Ester in a Direct Injection

Diesel Engine," SAE paper no. 930934

Schramm, J., I. Foldager, N. Olsen, L. Gratz, "Emissions from a Diesel Vehicle Operated on

Alternative Fuels in Copenhagen," SAE paper no. 1999-01-3603

Schröder, O., J. Krahl, A. Munack, J. Krahl, J. Bünger, "Environmental and Health Effects

Caused By the Use of Biodiesel," SAE paper no. 1999-01-3561

Senatore, A., M. Cardone, V. Rocco, M.V. Prati, "A Comparative Analysis of Combustion

Process in D.I. Diesel Engine Fueled with Biodiesel and Diesel Fuel," SAE paper no. 2000-01-

0691

Uchida, M., Y. Akasaka, "A Comparison of Emissions from Clean Diesel Fuels," SAE paper no.

1999-01-1121

Zhang, Y., J.H. Van Gerpen, "Combustion Analysis of Esters of Soybean Oil in a Diesel

Engine," SAE paper no. 960765
1040

Studies not included because they lack new emissions data for regulated pollutants

Culshaw, F.A., "The Potential of 'Biodiesel' from Oilseed Rape," IMechE 1993, Part A: Journal

of Power and Energy

Durbin, T.D., J.R. Collins, H. Galdamez, J.M. Norbeck, M.R. Smith, R.D. Wilson, T.

Younglove, "Evaluation of the Effects of Biodiesel Fuel on Emissions from Heavy-Duty NonRoad Engines," University of California Final Report submitted to South Coast Air Quality

Management District, May 2000

Peterson, C.L., D.L. Auld, "Technical Overview of Vegetable Oil as a Transportation Fuel,"

University of Idaho, 1991

Sharp, C.A., S.A. Howell, J. Jobe, "The Effect of Biodiesel Fuels on Transient Emissions from

Modern Diesel Engines, Part II Unregulated Emissions and Chemical Characterization," SAE

paper no. 2000-01-1968

Exclusion of duplicate datasets

Colorado Institute for Fuels and High Altitude Engine Research, "Emissions from Biodiesel

Blends and Neat Biodiesel from a 1991 Model Series 60 Engine Operating at High Altitude,"

Final Report to National Renewable Energy Laboratory, September 1994

Note: CO2 values were not duplicative.

Peterson, C.L., D.L. Reece, "Emissions Testing with Blends of Esters of Rapeseed Oil Fuel With

and Without a Catalytic Converter," SAE paper no. 961114

Note: Only Table 6 data is duplicative.

Taberski, J.S., C.L. Peterson, "Dynamometer Emissions test Comparisons on a 5.9L Direct

Injected Diesel Powered Pickup," BioEnergy '98: Expanding BioEnergy Partnerships

Taberski, J.S., C.L. Peterson, J. Thompson, H. Haines, "Using Biodiesel in Yellowstone National

Park - Final Report of the Truck in the Park Project," SAE paper no. 1999-01-2798

McCormick, R.L., M.S. Graboski, T.L. Alleman, A.M. Herring, "Impact of Biodiesel Source

Material and Chemical Structure on Emissions of Criteria Pollutants from a Heavy-Duty

Engine," Environ. Sci. Technol., 2001, 35, 1742-1747

Studies not included for other reasons

Watts, W.F., M. Spears, J Johnson, "Evaluation of Biodiesel Fuel and Oxidation Catalyst in an

Underground Metal Mine," University of Minnesota, September 24, 1998

Reason: Emission measurements were made in the field with portable emission

measurement devices
1041

Yoshimoto, Y., M. Onodera, H. Tamaki, "Reduction of NOx, Smoke, and BSFC in a Diesel

Engine Fueled by Biodiesel Emulsion with Used Frying Oil," SAE paper no. 1999-01-3598

Reason: Biodiesel blend included water in an emulsion

Frank B.P. et al., "A Study of the Effects of Fuel Type and Emission Control Systems on

Regulated Gaseous Emissions from Heavy-Duty Diesel Engines," SAE paper no. 2004-01-1085

Reason: Effect of oxycat & CRDPF greater than effect of B20.

Durbin T. D. et al., "Evaluation of the Effects of Biodiesel Fuel on Emissions from Heavy-Duty

Non-Road Vehicles", Final Report for the SCAQMD under contract No. 99132 by the University

of California, Riverside, May 2000

Reason: Non-road vehicles not considered here.

Guerra S. A. et al., "Comparison Study of NOx and CO2 Emissions from an Off-Road Diesel

Compactor Running on Regular Diesel, Biodiesel and Ultra-Low Sulfur Diesel," University of

Kansas draft report, March 17, 2006

Reason: Non-road equipment not considered here.
1042

Endnotes

1

See the following documents: Iowa State University Center for Agricultural Research and

Development, “Emerging Biofuels: Outlook of Effects on U.S. Grain, Oilseed, and Livestock

Markets,” May 2007 (a revised version is available from July 2007); USDA Agricultural

Projections to 2018, Report OCE-2009-1, February 2009 (also USDA Agricultural Projections to

2016, Report OCE-2007-1, February 2007 for details on ethanol conversion yield); NCGA,

“How Much Ethanol Can Come From Corn?” 2006; USDA, “An Analysis of the Effects of an

Expansion in Biofuel Demand on U.S. Agriculture,” May 2007.

2 USDA/NASS. “Quick Stats: U.S. and All States Data- Corn field”

3 USDA/NASS. “Corn: Yield by year, US,”

http://www.nass.usda.gov/Charts_and_Maps/Field_Crops/cornyld.asp, November 10, 2009.

4 USDA Agricultural Projections to 2018, Report OCE-2009-1, February 2009

5 USDA, National Agricultural Statistics Service, Crop Production and Grain Stocks; USDA,

World Agricultural Outlook Board, World Supply and Demand Estimates; and U.S. Department

of Commerce, Bureau of the Census.

6 USDA Agricultural Projections to 2016, Report OCE-2007-1, February 2007, page 22.

7

Shapouri, H., Gallagher, P., “USDA’s 2002 Ethanol Cost-of-Production Survey,” July 2005,

p.11.

8

F.O. Licht, “World Ethanol Markets: The Outlook to 2015,” 2006, page 45.

9 Renewable Fuels Association. “2008 World Fuel Ethanol Production”.

http://www.ethanolrfa.org/industry/statistics/

10 “Sugarcane Industry in Brazil: Ethanol, Sugar, Bioelectricity” brochure from UNICA

11 UNICA. “Sugarcane Production – Brazil” Spreadsheet available

http://english.unica.com.br/dadosCotacao/estatistica/, Accessed December 8, 2009.

12 Agra FNP. “Sugar and Ethanol in Brazil: A Study of the Brazilian Sugarcane, Sugar and

Ethanol Industries,” 2007.

13 “Sugarcane Industry in Brazil: Ethanol, Sugar, Bioelectricity” brochure from UNICA

14 Scaramucci, Jose & Cunha, Marcelo, “Bioethanol as Basis for Regional Development in

Brazil: An Input-Output Model with Mixed Technologies.”

15 CGEE, ABDI, Unicamp, and NIPE, “Scaling Up the Ethanol Program in Brazil,” n.d. cited in

Rothkopf, Garten, “A Blueprint for Green Energy in America,” 2006.

16 CGEE, ABDI, Unicamp, and NIPE, “Scaling Up the Ethanol Program in Brazil,” n.d. cited in

Rothkopf, Garten, “A Blueprint for Green Energy in America,” 2006.

17 USDA, “The Economic Feasibility of Ethanol Production from Sugar in the United States,”

July 2006.

18 Kline, K. et. al., “Biofuel Feedstock Assessment for Selected Countries,” Oak Ridge National

Laboratory, February 2008.

19 USDA, “The Economic Feasibility of Ethanol Production from Sugar in the United States,”

July 2006.

20 Personal communication with Nathalie Hoffman, Managing Member of California Renewable

Energies, LLC, August 27, 2008.

21 Christiansen, Ryan, “Analysis: Sugarcane Ethanol Has 95% Less Emissions,” Ethanol

Producer Magazine, March 2009.

22 Voegele, Erin, “Sugarcane Economics,” Ethanol Producer Magazine, March 2009.
1043

23 Voegele, Erin, “Sugarcane plays role in development of green polyethylene, Sugarcorn,”

Ethanol Producer Magazine, March 2009.

24 Saballos, Ana. in W. Vermerris (ed.), (2008) “Development and Utilization of Sorghum as a

Bioenergy Crop,” Genetic Improvement of Bioenergy Crops DOI: 10.1007/978-0-387-70805-

8_8

25 W. W. Wilhelm, USDA-ARS, Univ. of Nebraska, Lincoln, NE 68583-0934, J. M. F. Johnson,

USDA-ARS, Morris, MN 56267-1065, J. L. Hatfield, 108 Natl. Soil Tilth Lab., Ames, IA 50011-

3120, W. B. Voorhees, USDA-ARS (retired), Morris, MN 56267-1065, and D. R. Linden,

USDA-ARS (retired), St. Paul, MN 55108-0000, Crop and Soil Productivity Response to Corn

Residue Removal A Literature Review; FORUM, © American Society of Agronomy, Madison,

WI 53711 USA, Published in Agron. J. 96:1-17 (2004)

26 Predicting Soil Erosion by Water. A Guide to Conservation Planning with the Revised

Universal Soil Loss Equation (RUSLE). United States Department of Agriculture, Agricultural

Research Service, Agricultural Handbook Number 703, 1997.

27 Richard G. Nelson, Enersol Resources, Manhattan, KS 66502, USA, Resource Assessment

and Removal Analysis for Corn Stover and Wheat Straw in the Eastern and Midwestern United

States—Rainfall and Wind-induced Soil Erosion Methodology, Biomass and Bioenergy 22

(2002) 349 – 363, Published by Elsevier Science Ltd

28 Linda Mann, Virginia Tolbert, Janet Cushman, Environmental Sciences Division, Oak Ridge

National Laboratory, Oak Ridge, TN 37831-6038, Potential Environmental Effects of Corn

Stover Removal With Emphasis on Soil Organic Matter and Erosion, Agriculture, Ecosystems

and Environment 89: 149-166, ISSN 0167-8809 Elsevier Science, Amsterdam. June 2002.

29 http://www.nrcs.usda.gov/technical/Standards/nhcp.html

30 Richard G. Nelson, Enersol Resources, Manhattan, KS 66502, USA, Resource Assessment

and Removal Analysis for Corn Stover and Wheat Straw in the Eastern and Midwestern United

States—Rainfall and Wind-induced Soil Erosion Methodology, Biomass and Bioenergy 22

(2002) 349 – 363, Published by Elsevier Science Ltd.

31 W.W. Wilhelm, USDA-ARS; J.M.F. Johnson, USDA-ARS; D.L. Karlen, USDA-ARS; D.T.

Lightle, National Soil Survey; Corn Stover to Sustain Soil Organic Carbon Further Constrains

Biomass Supply, Published in Agron. J. 99:1665–1667 (2007); American Society of Agronomy,

Madison, WI 53711.

32 Susan S. Andrews, Ph.D., Leader, Soil Quality National Technology Development Team

USDA-Natural Resource Conservation Service Crop, White Paper: Residue Removal for

Biomass Energy Production: Effects on Soils and Recommendations, Updated February 22, 2006

33 Graham, R.L. et. al., “Current and Potential U.S. Corn Stover Supplies,” American Society of

Agronomy 99:1-11, 2007.

34 Perlack, Robert D.; Biomass as Feedstocks for a Bioenergy and Bioproducts Industry: The

Technical Feasibility of a Billion Ton Annual Supply; Sponsored by the U.S. Department of

Energy and the U.S. Department of Agriculture; April 2005.

35 Elbehri, Aziz. USDA, ERS, “An Evaluation of the Economics of Biomass Feedstocks: A

Synthesis of the Literature,” Prepared for the Biomass Research and Development Board, 2007.

36 Milbrandt, A., “Geographic Perspective on the Current Biomass Resource Availability in the

United States,” NREL Report No. TP-560-39181, 2005.

37 Gallagher, P. et. al., “Supply and Social Cost Estimates for Biomass from Crop Residues in the

United States,” Environmental and Resource Economics 24: 335-358, 2003.
1044

38 Walsh, Marie, “U.S. Cellulosic Biomass Feedstock Supplies and Distribution,” Unpublished

Manuscript, January 10, 2008.

39 Graham, R.L. et. al., “Current and Potential U.S. Corn Stover Supplies,” American Society of

Agronomy 99:1-11, 2007.

40 Wright, Lynn. (July 2007) “Historical Perspective on How and Why Switchgrass was Selected

as a “Model” High-Potential Energy Crop,” Oak Ridge National Laboratory.

41 U.S Department of Energy. “Breaking the Biological Barriers to Cellulosic Ethanol: A Joint

Research Agenda.” (2006).

42 Kumar, A., & Sokhansanj, S., “Switchgrass (Panicum vigratum, L.) delivery to a biorefinergy

using integrated biomass supply analysis and logistics (IBSAL) model.” Bioresource

Technology, (2006).

43 McLaughlin, S.B. & Adams Kszos, L., “Development of switchgrass (Panicum virgatum) as a

bioenergy feedstock in the United States,” Biomass and Bioenergy, 28: 515-535, 2005.

44 Kumar, op. cit.

45 McLaughlin, op. cit.

46 Lewandowski, I., Clifton-Brown, J.C., Scurlock, J.M.O., & Huisman, W., “Miscanthus:

European experience with a novel energy crop,” Biomass and Bioenergy, 19: 209-227, 2000.

47 Pyter, R., Voigt, T., Heaton, H., Dohleman, F., & Long, S. Growing giant miscanthus in

Illinois. University of Illinois. (2006).

48 Ibid.

49 Ibid.

50 Lewandowski, op. cit.

51 Pyter, op. cit.

52 Pyter, op. cit.

53 Lewandowski, op. cit.

54 Pyter, op. cit.

55 U.S Department of Energy. “Breaking the Biological Barriers to Cellulosic Ethanol: A Joint

Research Agenda,” 2006.

56 Saballos, op. cit.

57 McCutchen, Bill et. al. High-Tonnage Dedicated Energy Crops: The Potential of Sorghum and

Energy Cane.

58 U.S Department of Energy. “Breaking the Biological Barriers to Cellulosic Ethanol: A Joint

Research Agenda,” 2006.

59 Ibid.

60 Campbell, J.E. at al., ”The Global Potential of Bioenergy on Abandoned Agriculture Lands,”

Environ. Sci. Technology, 2008.

61 Adapted from additional information received from J. Elliot Campbell. “The Global Potential

of Bioenergy on Abandoned Agriculture Lands,” Environ. Sci. Technology, 2008.

62 USDA, Farm Services Agency, “Conservation Reserve Program Summary and Enrollment

Statistics,” FY 2008.

63 USDA, Farm Services Agency, “Conservation Reserve Program Statistics: Monthly Summary

November 2009.”

64 Ibid.

65 Perlack, R.D., Wright, L.L., Turhollow, A.F, Graham, R.L., Environmental Sciences Division,

Oak Ridge National Laboratory; Stokes, B.J., Forest Service, USDA.; Erbach, D.C., Agricultural
1045

Research Service, USDA, “Biomass as Feedstock for a Bioenergy & Bioproducts Industry: The

Technical Feasibility of a Billion-Ton Annual Supply (A Joint Study Sponsored by DOE and

USDA, Prepared by Oak Ridge National Laboratory),” 2005

66 Walsh, M.E. (2007) Sungrant Bioweb. Forest Resources.

http://bioweb.sungrant.org/Technical/Biomass+Resources/Forest+Resources/Default.htm

67 Ibid.

68 Perlack, R.D., Wright, L.L., Turhollow, A.F, Graham, R.L., Environmental Sciences Division,

Oak Ridge National Laboratory; Stokes, B.J., Forest Service, USDA.; Erbach, D.C., Agricultural

Research Service, USDA, “Biomass as Feedstock for a Bioenergy & Bioproducts Industry: The

Technical Feasibility of a Billion-Ton Annual Supply (A Joint Study Sponsored by DOE and

USDA, Prepared by Oak Ridge National Laboratory),” 2005.

69 Ibid.

70 Dickerson, K., et. al. (revised November 2007). Biomass and Biofuels in Maine: Estimating

Supplies for Expanding the Forest Products Industry. Margaret Chase Smith Policy Center.

71 Puttock, G.D. (1995). Estimating cost for integrated harvesting and related forest management

activities. Biomass and Bioenergy 8(2), 73-79.

72 Perlack, R.D., Wright, L.L., Turhollow, A.F, Graham, R.L., Environmental Sciences Division,

Oak Ridge National Laboratory; Stokes, B.J., Forest Service, USDA.; Erbach, D.C., Agricultural

Research Service, USDA, “Biomass as Feedstock for a Bioenergy & Bioproducts Industry: The

Technical Feasibility of a Billion-Ton Annual Supply (A Joint Study Sponsored by DOE and

USDA, Prepared by Oak Ridge National Laboratory),” 2005.

73 Dickerson, K., et. al. (revised November 2007). Biomass and Biofuels in Maine: Estimating

Supplies for Expanding the Forest Products Industry. Margaret Chase Smith Policy Center.

74 Ibid.

75 Timko, B., 2003. USDA, Forest Service, personal communication as noted in the “Billion-ton

Study” by Perlack, R.D., et. al.

76 F. Bohlin and A. Roos, Wood fuel supply as a function of forest owner preferences and

management styles, Biomass & Bioenergy 22 (2002), pp. 237–249.

77 Sanchez, Felipe G., Effects of organic matter removal and soil compaction on fifth-year

mineral soil carbon and nitrogen contents for sites across the United States and Canada,

Canadian Journal of Forestry Research, 36;:565 – 576, 2006.

78 Powers, Robert F., The North American long-term soil productivity experiment: Findings

from the first decade of research, Forest Ecology and Management, 220: 31 – 50, 2005.

79 Zobrist, K.W.; T. M. Hinckley; M.G. Andreu. A literature review of management practices to

support increased biodiversity in intensively managed loblolly pine plantations. Final Technical

Report to the National Commission on Science for Sustainable Forestry (NCSSF). July 31, 2005.

16 p.

80 Jonsell, M. 2007. Effects on Biodiversity of Forest Fuel Extraction, Governed by Processes

Working on a Large Scale. Biomass and Bioenergy, Issue 31 pg. 726-732.

81 T.B. Wigley and T. H. Roberts. 1994. Forest management and wildlife in forested wetlands

of the southern Appalachians. Water, Air and Soil Pollution 77: 445--456.

82 Kozlowski, T.T., 1999. Soil Compaction and Growth of Woody Plants. Scandinavian Journal

of Forest Research, Volume 14, Number 6. Taylor and Francis Ltd.

83 Rummer, B.; Len, D.; and O’Brien, O. (2004). Forest Residues Bundling Project-New

Technology for Residue Removal. USDA Forest Service, Southern Research Station
1046

84 Energy content of hardwood; www.eere.energy.gov/biomass/progs/search3.cgi?8828;

Biomass Feedstock Composition and Property Database; Energy Efficiency and Renewable

Energy, U.S. Department of Energy.

85 Aden, Andy. December 10, 2009. “Feedstock Considerations and Impacts on Biorefining.”

National Renewable Energy Laboratory

86 Perlack, Robert D.; Biomass as Feedstocks for a Bioenergy and Bioproducts Industry: The

Technical Feasibility of a Billion Ton Annual Supply; Sponsored by the U.S. Department of

Energy and the U.S. Department of Agriculture; April 2005.

87 Walsh, Marie E., U.S. Cellulosic Biomass Feedstock Supplies and Distribution; January 10,

2008.

88 Gordon, Gayle of the Western Governors Association; Strategic Assessment of Bioenergy

Development in the West, Kansas State University and the US Forest Service, multiple reports

dated September 1, 2008.

89 Data provided by e-mail by Perlack, Robert D., May 2008.

90 EPA. “Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts

and Figures for 2008,”

91 USEPA. “Municipal Solid Waste in the United States,” 2007; See Tables 1-3.

92 Biocycle, “The State of Garbage in America,” Vol. 49, No. 12, December 2008, page 22.

93 Midwest Assistance Program, Inc. “The 2006-2007 Missouri Municipal Solid Waste

Composition Study,” October, 2007.

94 R. W. Beck, “Georgia Statewide Characterization Study,” June 2005.

95 Engineering Solutions and Design, Inc., “State of Ohio Waste Characterization Study,” April

2004.

96 NYC Department of Sanitation, “2004-2005 NYC Waste Characterization Study.”

97 R.W. Beck, “Statewide MSW Composition Study: A Study of Discards in the State of

Minnesota,” 2000.

98 R.W. Beck, “Statewide Waste Composition Study: Pennsylvania State-wide Waste

Composition,” April 2003.

99 DSM Environmental Services, Inc, “Vermont Waste Composition Study,” June 2002.

100 R.W. Beck, “1992 Washington State Waste Characterization Study.”

101 R.W. Beck, “Wisconsin Statewide Waste Characterization Study,” May 2003.

102 Department of Environmental Quality, “Oregon 2002 Waste Composition Study,” 2002.

103 Williams, Robert and California Biomass Collaborative, “Biofuels from Municipal WastesBackground Discussion Paper,” March 28, 2007.

104 Ibid.

105 Walsh, Marie, “U.S. Cellulosic Biomass Feedstock Supplies and Distribution,” Unpublished

Manuscript, January 10, 2008.

106 Falk, Robert and McKeever, David. 2004. “Recovering Wood for Reuse and Recycling A

United States Perspective” USDA Forest Service

107 Fricke, K. (1989). “Technology and undesirable components on compost of separately

collected organic wastes” Agriculture, Ecosystem, Environment.

108 McKeever, David. (December 1999). “How Woody Residues Are Recycled in the United

States" BioCycle

109 Center for Sustainable Systems “U.S. Food System Fact Sheets,”2008
1047

110 USEPA, Construction and Demolition (C&D) Materials.

http://www.epa.gov/osw/nonhaz/industrial/cd/basic.htm Accessed August 10, 2008.

111 Fehrs, J., “Secondary Mill Residues and Urban Wood Waste Quantities in the United StatesFinal Report,” Northeast Regional Biomass Program Washington, D.C., December 1999.

112 Wiltsee, G., “Urban Wood Waste Resource Assessment,” Appel Consultant, Inc. Valencia,

CA. November, 1998.

113 Seabra, Joaquim. (July 2009). “Comparison of options for advanced ethanol production in

Brazilian sugarcane biorefineries” draft manuscript, anticipated to be available in Biomass and

Bioenergy soon.

114 Kline, K. et. al. Biofuel Feedstock Assessment for Selected Countries. Oak Ridge National

Laboratory. February 2008.

115 See F.O. Licht World Ethanol and Biofuels Report, vol 7, no 21, p 434 (July 8, 2009).

116 Mueller, Steffen. An analysis of the projected energy use of future dry mill corn ethanol

plants (2010-2030). October 10, 2007. Available at

http://www.chpcentermw.org/pdfs/2007CornEethanolEnergySys.pdf

117 “Essential Rendering”, published by the National Renderer’s Association, edited by David

Meeker. 2006. Available at http://nationalrenderers.org/assets/essential_rendering_book.pdf

118 “Statewide Feasibility Study for a Potential New York State Biodiesel Industry”, prepared by

LECG, LLC. Report number 04-02, April 2004. Available at

http://www.nyserda.org/publications/biodieselreport.pdf

119 A copy of this letter is available in the docket.

120 Sheehan, John, Terri Dunahay, John Benemann, and Paul Roessler, “A Look Back at the U.S.

Department of Energy’s Aquatic Species Program—Biodiesel from Algae,” National Renewable

Energy Laboratory. Office of Fuels Development. Golden, Colorado, 1998.

121 Ibid.

122 Ryan, Catie. op. sit.

123 Ryan, Catie, “Cultivating Clean Energy: The Promise of Algae Biofuels,” NRDC and

Terrapin Bright Green, LLC. October 2009.

124 Davis, Ryan. November 2009. Techno-economic analysis of microalgae-derived biofuel

production. National Renewable Energy Laboratory. Technical Memorandum to EPA.

125 Davis, Ryan. op. sit.

126 Chisti, Yusuf, "Biodiesel from Microalgae." Biotechnology Advances 25, 294-306, 2007.

127 Schenider, David, "Grow Your Own?" American Scientist, Sept.-Oct. 2006.

128 Campbell, Matthew N., "Biodiesel: Algae as a Renewable Source for Liquid Fuel." Guelph

Engineering Journal, 1, 2-7, 2008.

129 Chisti, op. sit.

130 Pienkos, Philip T., "The Potential for Biofuels from Algae," Algae Biomass Summit. San

Francisco, CA. Nov. 15, 2007.

131 Campbell, op. sit.

132 Pienkos, op. sit.

133 Pienkos, op. sit.

134 Campbell, op. sit.

135 Putt, Ron. “Algae as a Biodiesel Feedstock,” Nov. 20, 2007.

136 Sheehan, op. sit.

137 "Algae Bloom Again." Nature, 447, 520-21 2007.
1048

138 Deshayes, Pierre-Henry, "As planet swelters, are algae unlikely saviour?" AFP, July 10, 2008.

139 Sheehan, op. sit.

140 Pienkos, op. sit.

141 “PV Solar Radiation,” Digital image. National Renewable Energy Laboratory. May 2004.

142 Bullock, Cary, "Algae Corrals Carbon Dioxide," EnergyBiz Magazine, 68-70, Mar.-Apr.

2007.

143 Chisti, op. sit.

144 Sheehan, op. sit.

145 Davis, Ryan. op sit.

146 Ryan, Catie, op. sit.

147 Jongschaap, R.E.E, W.J. Corre, P.S Bindraban, and W.A. Brandenburg, “Claims and Facts on

Jatropha curcas L,” Plant Research International, Vers. Report 158, Oct. 2007.

148 Euler, Hartlieb, and David Gorriz, “Case Study "Jatropha Curcas"” Rep. No. Global

Facilitation Unit for Underutilized Species, Frankfurt, 2004.

149 Jongschaap, op. sit.

150 Ibid.

151 Fairless, Daemon. "The little shrub that could - maybe." Nature 449 (2007): 652-55.

152 Francis, G., R. Edinger, and K. Becker. "A concept for simultaneous wasteland reclamation,

fuel production, and socio-economic development in degraded areas in India: need, potential and

perspectives of Jatropha plantations." Natural Resources Forum 29 (2005): 12-24. As sited in

Jongschaap, R.E.E, W.J. Corre, P.S Bindraban, and W.A. Brandenburg, “Claims and Facts on

Jatropha curcas L,” Plant Research International, Vers. Report 158, Oct. 2007.

153 Woltjer, Lyanne. Size does matter. Expertise Centrum voor Duurzame Ontwikkeling

(ECDO). Dec. 2006.

154 Jongschaap, op. sit.

155 Li Guo, T. "The photosynthesis and water use efficiency of eight garden tree species." Forest

Research 15 (2002): 291-96. As sited in Jongschaap, R.E.E, W.J. Corre, P.S Bindraban, and

W.A. Brandenburg, “Claims and Facts on Jatropha curcas L,” Plant Research International,

Vers. Report 158, Oct. 2007.

156 Jongschaap, op. sit.

157 Fairless, op. sit.

158 Jongschaap, op. sit.

159 Ghosh, A., J.S. Patolia, D.R. Chaudharry, J. Chikara, S.N. Rao D. Kumar, G.N. Boricha, and

A. Zala. Response of Jatropha curcas under different spacing to Jatropha de-oiled cake.

Discipline of Phytosalinity, Central Salt and Marine Chemicals Research Institute. 2007.

160 Jongschaap, op. sit.

161 Kumar, op. sit.

162 Ibid.

163 Pant, K.S., V. Khosla, D. Kumar, and S. Gairola, "Seed oil content variation in Jatropha

curcas Linn. in different altitudinal ranges and site conditions in H.P. India," Lyonia 11 (2006):

31-34.

164 Jongschaap, op. sit.

165 USDA, "PLANTS Profile for Jatropha curcas L." USDA Natural Resources Conservation

Service. http://plants.usda.gov/java/profile?symbol=jacu2, November 3, 2008.

166 Jongschaap, op. sit.
1049

167 Oregon Climate Service, “State Annual Precip Maps,” http://www.ocs.oregonstate.edu.,

November 3, 2008.

168 Carney, Dan. "Second-generation biodiesel." Automotive Engineering, May 2008: 20-22.

169 Koch, Blair. “Canola yield light, but growers pleased with first crop.” Ag Weekly August

21, 2007. <http://www.agweekly.com/articles/2007/09/10/news/ag_news/news32.txt>

170 W.M. Breene, J.T. Budin, L.A. Field, and D.H. Putnam. “Camelina: A Promising Low-Input

Oilseed.” New Crops. 1993. 314-322. <

http://www.hort.purdue.edu/newcrop/proceedings1993/v2-314.html>

171 Great Plains: The Camelina Company <

https://www.camelinacompany.com/marketing/agronomics.aspx#Facts>

172 W.M. Breene, et. al. op sit.

173 McCormick, Margaret. “Oral Comments of Targeted Growth, Incorporated” Submitted to the

EPA on June 9, 2009.

174 Schill, Susanne R. “Making Pennycress Pay Off.” Biodiesel Magazine February 2008.

175 Biofuel Manufacturers of Illinois. http://www.growpennycress.com/

176 Schill, Susanne R. op sit.

177 Figures taken from European Biodiesel Board website as of December 2009. See

http://www.ebb-eu.org/stats.php.

178 Food and Agricultural Policy Research Institute, 2009 Agricultural Outlook. See

http://www.fapri.iastate.edu/outlook/2009/text/14WorldBiofuels.pdf.

179 Biofuels Barometer. Eurobserv’er, July 2009. See http://www.eurobserver.org/pdf/baro192.pdf

180 DTN Ethanol Center, ”EU Biodiesel Feedstock Challenge,” October 11, 2007,

http://www.dtnethanolcenter.com/index.cfm?show=10&mid=66&pid=27

181 Food and Agricultural Policy Research Institute, 2009 Agricultural Outlook. See

http://www.fapri.iastate.edu/outlook/2009/text/14WorldBiofuels.pdf.

182 National Renewable Energy Laboratory estimate based on biomass portion available at $45-

$55/dry ton.. Using POLYSYS Policy Analysis System, Agricultural Policy Analysis Center,

University of Tennessee. http://www.agpolicy.org/polysys.html. Accessed May 2008.

183 Milbrandt, A. (2005). “Geographic Perspective on the Current Biomass Resource Availability

in the United States.” 70 pp.; NREL Report No. TP-560-39181

184 Kerstetter, J. and Lyons, J., (2001) Logging and Agricultural Residue Supply Curves for the

Pacific Northwest. Washington State University Energy Program for U.S. DOE.

185 Rummer, B.; Len, D.; and O’Brien, O. (2004). Forest Residues Bundling Project-New

Technology for Residue Removal. USDA Forest Service, Southern Research Station

186 Ibid.

187 Rawlings, C., et. al. (2004). A Study of How to Decrease the Costs of Collecting, Processing

and Transporting Slash. USDA Forest Service.

188 D. Montross, Associate Professor, Scott A. Shearer, Professor, Timothy S. Stombaugh,

Associate Extension Professor, Stephen F. Higgins, Research Specialist, and Samuel G. McNeill,

Associate Extension Professor, Department of Biosystems and Agricultural Engineering,

University of Kentucky, Lexington, Kentucky; and Shahab Sokhansanj, Professor, Oak Ridge

National Laboratory, Environmental Sciences Division, Oak Ridge, Tennessee, Corn Stover

Availability and Collection Efficiency Using Typical Hay Equipment, This article is published

with the approval of the Director of the Kentucky Agricultural Experiment Station and
1050

designated Paper No. 04-05-066. Transactions of the ASABE, Vol. 50(3): 705−711 – 2007

American Society of Agricultural and Biological Engineers, ISSN 0001−2351.

189 S. Sokhansanj1, A. Turhollow, and R. Perlack, Oak Ridge National Laboratory, Tennessee,

USA 37830, Stochastic Modeling of Costs of Corn Stover Costs Delivered to an Intermediate

Storage Facility, Paper Number: 024190, Presented at: 2002 ASAE Annual International

Meeting / CIGR XVth World Congress, Sponsored by ASAE and CIGR, Hyatt Regency

Chicago, Chicago, Illinois, USA, July 28-July 31, 2002

190 P. Savoie, L. D’Amours, F. Lavoie, G. Lechasseur, H. Joannis. Development of a CutterShredder-Baler to Harvest Long-Stem Willow. Paper Number: 061016, Presented at: 2006

ASABE Annual International Meeting, Sponsored by ASABE, Portland Convention Center,

Portland, Oregon, USA, July 9 – July 12, 2006

191 Kevin J. Shinners, Professor of Agricultural Engineering, Ben N. Binversie, Graduate

Research Assistant, Department of Biological Systems Engineering, University of Wisconsin,

Madison, WI, Philippe Savoie, Research Scientist, Agriculture and Agri-Food Canada, Quebec,

Canada; Harvest and Storage Of Wet and Dry Corn Stover As A Biomass Feedstock, Paper

Number: 036088, 2003 ASAE Annual International Meeting

Riviera Hotel and Convention Center, Las Vegas, Nevada, USA, 27- 30 July 2003

192 Christopher T. Wright, Peter A. Pryfogle, Nathan A. Stevens, J. Richard Hess, Corey W.

Radtke, Idaho National Laboratory, Idaho Falls, ID, USA, Value of Distributed Preprocessing of

Biomass Feedstocks to a Bioenergy Industry, Paper No: 066151, 2006 ASABE Annual

International Meeting, Oregon Convention Center, Portland, Oregon, 9 - 12 July 2006

193 Biomass Research Development Board. The Economics of Biomass Feedstocks in the United

States. Occasional Paper 1, October 2008.

194 ICM website, Ethanol Production Process,

http://www.icminc.com/ethanol/production_process (Supplemented with information provided

by EPA’s Region 7).

195 Renewable Fuels Association website, How Ethanol is Made: The Ethanol Production

Process –Wet Milling, http://www.ethanolrfa.org/resource/made.

196 Renewable Fuels Association, Annual Energy Outlook 2006

(http://www.ethanolrfa.org/objects/pdf/outlook/outlook_2006.pdf)

197 POET website, Technology - BPXTM, http://www.poetenergy.com/learn/bpx.asp.

198 Kirkbride McElroy, Anduin , “Poet, ISU partner for BPX research,” Ethanol Producer

Magazine, http://www.ethanol-producer.com/article.jsp?article_id=3770, March 2008.

199 U.S. Environmental Protection Agency, Combined Heat & Power Partnership website, Basic

Information, http://www.epa.gov/chp/basic/index.html.

200 Smeets, Edward, et. al. “Sustainability of Brazilian bio-ethanol”. August 2006

201 Saeman, J.F., Kinetics of Wood Saccharification: Hydrolysis of Cellulose and Decomposition

of Sugars in dilute Acid at High Temperature, Industrial and Engineering Chemistry, 37(1): 43–

52 (1945).

http://pubs3.acs.org/acs/journals/toc.page?incoden=iechad&indecade=3&involume=37&inissue=

1]

202 Harris, E.E., Beglinger, E., Hajny, G.J., and Sherrard, E.C., , Industrial and Engineering

Chemistry, 37(1): 12–23 (1945)

http://pubs3.acs.org/acs/journals/toc.page?incoden=iechad&indecade=3&involume=37&inissue=

1]
1051

203 DOE National Laboratories. “30x30 A Scenario for Supplying 30% of 2004 Motor Gasoline

with Ethanol by 2030: A Production Scenario for Cellulosic Ethanol from Corn Stover,” June 23,

2006.

204 LaMonica, Martin. “Investor Vinod Khosla: Advanced biofuels are closer than you think”.

Accessed at: http://www.news.com/8301-11128_3-9811702-54.html

205 DOE. “Biomass Program: ABC’s of Biofuels”. Accessed at:

http://www1.eere.energy.gov/biomass/abcs_biofuels.html#content

206 Starch Hydrolysis; http://www.bio-link.org/pdf/starch.pdf

207 Glossary of Biomass Terms, National Renewable Energy Laboratory, Golden, CO.

http://www.nrel.gov/biomass/glossary.html

208 Glossary of Biomass Terms, National Renewable Energy Laboratory, Golden, CO.

http://www.nrel.gov/biomass/glossary.html

209 Condensation and Hydrolysis; http://www.biotopics.co.uk/as/disaccharideformation.html

210 BlueFire Ethanol. “Review of Competing Processes,” May 2006. Accessed at:

http://www.bluefireethanol.com/images/Technology_Comparison_051206.ppt#295,1, 31 Musick

Irvine, California 92618.

211 Stephanopoulos, Gregory, et. al., “Challenges in Engineering Microbes for Biofuels

Production,” Science, 315, 801, February 9, 2007.

212 Dipardo, Joseph, “Outlook for Biomass Ethanol Production and Demand,” Energy

Information Administration, 2002.

213 Tao, Ling and Aden, Andy. November 2008. Techno-economic Modeling to Support the EPA

Notice of Proposed Rulemaking (NOPR). National Renewable Energy Laboratory (NREL)

214 Aden, Andy, Ethanol Process Design and Economics Utilizing Co-current Dilute Acid

Prehydrolysis and Enzymatic Hydrolysis for Corn Stover, Contract DE-AC36-99-GO10337,

National Renewable Energy Laboratory, June 2002.

215 Kim S, Holtzapple MT., “Effect of Structural Features on Enzyme Digestibility of Corn

Stover,” Bioresource Technology, 97: 583-591, 2006. As sited in Gray, Kevin, et. al.,

“Bioethanol,” Current Opinion in Chemical Biology, 10:141-146, 2006.

216 A. Aden, M. Ruth, K. Ibsen, J. Jechura, K. Neeves, J. Sheehan, and B. Wallace

National Renewable Energy Laboratory (NREL); L. Montague, A. Slayton, and J. Lukas Harris

Group, Seattle, Washington, Ethanol Process Design and Economics Utilizing Co-Current Dilute

Acid Prehydrolysis and Enzymatic Hydrolysis for Corn Stover; June 2002;NREL is a U.S.

Department of Energy Laboratory Operated by Midwest Research Institute • Battelle • Bechtel;

Contract No. DE-AC36-99-GO10337

217 Graf, Angela, et. al., “Oregon Cellulose-Ethanol Study: An evaluation of the potential for

ethanol production in Oregon using cellulose-based feedstocks,” Bryan & Bryan Inc and Oregon

Office of Energy, 2000.

218 Tim Eggeman, Neoterics International, Lakewood, CO 80228, USA, Richard T. Elander,

National Renewable Energy Laboratory, Golden, CO 80401, USA, Process and economic

analysis of pretreatment technologies, Bioresource Technology 96 (2005) 2019–2025

219 Gray, K., et. al., “Bioethanol,” Current Opinion in Chemical Biology, 10:141-146, 2006.

220 Graf, op. sit.

221 Aden, Andy. December 10, 2009. “Feedstock Considerations and Impacts on Biorefining.”

National Renewable Energy Laboratory
1052

222 DOE, “From Biomass to Biofuels: A Roadmap to the Energy Future.” based on a workshop

held in Rockville, MD, on December 7-9, 2005. As sited in Stephanopoulos, Gregory, et. al.,

“Challenges in Engineering Microbes for Biofuels Production,” Science, 315, 801, February 9,

2007.

223 Presentations of K.C. McFarland (Novozymes) and G. Anderl (Genencor International) at the

World Congress on Industrial Biotechnology and Bioprocessing, Orlando, FL, July 2006. As

sited in Stephanopoulos, Gregory, et. al., “Challenges in Engineering Microbes for Biofuels

Production,” Science, 315, 801, February 9, 2007.

224 Genencor Launches First Ever Commercial Enzyme Product for Cellulosic Ethanol,

Rochester, NY, World-Wire, October 22, 2007 Copyright © 2007,. All rights reserved. WorldWire is a resource provided by Environment News Service. http://worldwire.com/news/0710220001.html

225 Purdue yeast makes ethanol from agricultural waste more effectively, Purdue News, June 28,

2004

http://www.purdue.edu/UNS/html4ever/2004/040628.Ho.ethanol.html

226 Stephanopoulos, op. sit.

227 US. DOE. Technologies: Processing and Conversion. Accessed at:

http://www1.eere.energy.gov/biomass/processing_conversion.html on October 28, 2008

228 EERE, DOE, Thermochemical Conversion, & Biochemical Conversion, Biomass Program

Thermochemical R&D .

http://www1.eere.energy.gov/biomass/thermochemical_conversion.html

http://www1.eere.energy.gov/biomass/biochemical_conversion.html

229 Aden, Andy, Mixed Alcohols from Woody Biomass – 2010, 2015, 2022, National Renewable

Energy Laboratory (NREL), September, 23 2009.

230 Lin Wei, Graduate Research Assistant, Lester O. Pordesimo, Assistant Professor

Willam D. Batchelor, Professor, Department of Agricultural and Biological Engineering,

Mississippi State University, MS 39762, USA, Ethanol Production from Wood: Comparison of

Hydrolysis Fermentation and Gasification Biosynthesis, Paper Number: 076036, Written for

presentation at the 2007 ASABE Annual International Meeting. Minneapolis Convention Center,

Minneapolis, MN, 17 - 20 June 2007

231 J. Phillips, “Different Types of Gasifiers and Their Integration with Gas Turbines,”

EPRI/Advanced Coal Generation,

http://www.netl.doe.gov/technologies/coalpower/turbines/refshelf/handbook/1.2.1.pdf, October

30, 2008.

232 Ciferno, Jared P., Benchmarking Biomass Gasification Technologies for Fuels, Chemicals

and Hydrogen Production, National Energy Technology Laboratory, Department of Energy, June

2002.

233 S. Phillips, A. Aden, J. Jechura, and D. Dayton, National Renewable Energy Laboratory,

Golden, Colorado 80401-3393, T. Eggeman, Neoterics International, Inc., Thermochemical

Ethanol via Indirect Gasification and Mixed Alcohol Synthesis of Lignocellulosic Biomass,

Technical Report, NREL/TP-510-41168, April 2007

234 Aden, Andy, Mixed Alcohols from Woody Biomass – 2010, 2015, 2022, National Renewable

Energy Laboratory (NREL), September, 23 2009.
1053

235 BRI Energy Bioengineering Resources for Renewable Energy. “The Co-Production of

Ethanol and Electricity From Carbon-based Wastes,” A Report from BRI Energy, Inc. Regarding

a New Technology That Addresses Multiple Energy and Waste Disposal Solutions, March 2006.

236 DOE EERE. “Biomass Program: Thermochemical Conversion of Corn Stover,” April 2006.

Accessed at: http://www1.eere.energy.gov/biomass/pdfs/thermochemical_stover.pdf.

237 S. Phillips, A. Aden, J. Jechura, and D. Dayton, National Renewable Energy Laboratory,

Golden, Colorado 80401-3393, T. Eggeman, Neoterics International, Inc., Thermochemical

Ethanol via Indirect Gasification and Mixed Alcohol Synthesis of Lignocellulosic Biomass,

Technical Report, NREL/TP-510-41168, April 2007

238 Davis, Ryan; Techno-economic analysis of current technology for Fischer-Tropsch fuels

production, National Renewable Energy Laboratory for EPA, August 14, 2009.

239 DOE EERE Biomass Program. “Thermochemical Conversion Processes: Pyrolysis”

http://www1.eere.energy.gov/biomass/thermochemical_processes.html, November 6, 2008.

240 Zeman, Nicholas, “Thermochemical Versus Biochemical,” Biomass Magazine, June 2007.

241 Information derived from staff-level communication with National Biodiesel Board

personnel.

242 “Tyson ConocoPhillips Venture in Jeopardy.” The Morning News, article dated 9/26/08.

http://www.nwaonline.net/articles/2008/09/26/business/092708fuelcredits.txt

243 See Syntroleum Investor Presentation, dated November 2009. Available here:

http://www.syntroleum.com/Presentations/SyntroleumInvestorPresentation.November%205.200

9.FINAL.pdf. Accessed December 16, 2009.

244 “Tyson's Fats to Fuel Plant on Schedule.” The Morning News, article dated 10/6/08.

http://www.nwaonline.net/articles/2008/10/06/business/100708tysonfuelplant.txt

245 Based on presentation materials by Lars Peter Lindfors dated 29 September 2009, available

here: http://www.nesteoil.com/default.asp?path=1,41,538,2496,12947. Accessed December 16,

2009.

246 Total ethanol usage from EIA, Monthly Energy Review, September 2009 (Table 10.2) and

EIA imports reported online at: http://tonto.eia.doe.gov/dnav/pet/hist/mfeimus1a.htm.

247 Total projected ethanol consumption according to a letter from Richard Newell, EIA

Administrator to Lisa Jackson, EPA Administrator dated October 29, 2009 (Table 1) and

projected imports according to 2009 YTD reported online at:

http://tonto.eia.doe.gov/dnav/pet/hist/mfeimus1a.htm.

248 110th Congress of the United States of America, First Session, The Energy Independence and

Security Act of 2007, House Rule 6 (December 19, 2007) available at:

http://www.govtrack.us/congress/billtext.xpd?bill=h110-6.

249Total ethanol usage from EIA, Monthly Energy Review, September 2009 (Table 10.2) and

EIA imports reported online at: http://tonto.eia.doe.gov/dnav/pet/hist/mfeimus1a.htm.

250 Sources include Renewable Fuels Association (RFA), Ethanol Biorefinery Locations (updated

October 22, 2009) and Ethanol Producer Magazine (EPM), Producing plant list (last modified on

October 22, 2009) in addition to information gathered from producer websites and follow-up

correspondence.

251 Form 10-Q/A submitted by Greenshift Corporation to the United States Securities and

Exchange Commission for the quarterly period ending June 30, 2009. The Q2 2009 SEC filing

can be found at: http://www.greenshift.com/pdf/GERS_Form10Q_Q209_FINAL.pdf
1054

252 Spreadsheet provided to EPA by Neeharika Naik-Dhungel at EPA’s Combined Heat & Power

Partnership on December 17, 2009.

253 Spreadsheet provided to EPA by Bruce Woerner at Airgas on August 14, 2009.

254 Total plants less those denoted as “farmer owned” based on Renewable Fuels Association

(RFA) Ethanol Biorefinery Locations (updated October 22, 2009).

255 “Valero Renewables to Purchase Three Ethanol Plants,” December 15, 2009. Valero press

release available at: http://www.valero.com/NewsRoom/Pages/PR_20091215_0.aspx

256 Based on Valero refinery information provided at

http://www.valero.com/OurBusiness/OurLocations/

257 EIA, Annual Energy Outlook 2009 – ARRA Update (Table 12).

258 Sources include Renewable Fuels Association (RFA), Ethanol Biorefinery Locations (updated

October 22, 2009) and Ethanol Producer Magazine (EPM), Producing, Not Producing, Under

Construction, and Expansions lists (last modified on October 22, 2009) in addition to

information gathered from producer websites and follow-up correspondence.

259 Data based on “An Analysis of the Projected Energy Use of Future Dry Mill Corn Ethanol

Plants (2010-2030)”; Steffen Mueller, University of Illinois at Chicago Energy Resource Center,

October 10, 2007, Available online at:

http://www.chpcentermw.org/pdfs/2007CornEethanolEnergySys.pdf

260 Data based on “An Analysis of the Projected Energy Use of Future Dry Mill Corn Ethanol

Plants (2010-2030)”; Steffen Mueller, University of Illinois at Chicago Energy Resource Center,

October 10, 2007

261 Data based on “An Analysis of the Projected Energy Use of Future Dry Mill Corn Ethanol

Plants (2010-2030)”; Steffen Mueller, University of Illinois at Chicago Energy Resource Center,

October 10, 2007, with the exception of Membrane Separation data based on conversation with

Leland Vane, EPA

262 According to an e-mail from ADM’s Dean Frommelt dated May 7, 2009, “The new dry mills

for Cedar Rapids, IA and Columbus, NE commenced construction July 2007 and October 2006,

respectively.” According to information posted on Riverland Biofuels website, the Canton, IL

plant broke ground in October 2006. According to the Southwest Iowa Renewable Energy

website, the Council Bluffs, IA plant had a groundbreaking ceremony on November 11, 2006

(plant was assumed to break ground sometime shortly after that).

263 Ministry of Agriculture in Brazil (MAPA), http://www.agricultura.gov.br/ Accessed: April 3,

2009

264 F.O. Licht’s, “World Ethanol Markets: The Outlook to 2015,” pg. S-22 to S-23, 2006.

265 F.O. Licht’s, “World Ethanol and Biofuels Report,” Vol. 5, No. 22, July 23, 2007.

266 Agra FNP, “Sugar and Ethanol in Brazil: A Study of the Brazilian Sugar Cane, Sugar and

Ethanol Industries,” 2007.

267 Agra FNP, op. sit.

268 ANFAVEA (Associação Nacional dos Fabricantes de Veículos Automotores

Assessoria de Planejamento Econômico e Estatístico Indústria Automobilística Brasileira -

Empresas Associadas). Internal Sales in Brazil 2009. http://www.anfavea.com.br/tabelas.html,

November 24, 2009.

269 Ministerio da Agricultura, Pecularia E Abastecimento (MAPA), “Brazilian Ethanol

Production,”
1055

http://www.agricultura.gov.br/pls/portal/docs/PAGE/MAPA/ESTATISTICAS/PRODUCAO/06-

PROD.%20BR%20%C1LCOOL.PDF, December 9, 2009.

270 Ewing, Elizabeth, “Pipeline Projects on Hold.” Ethanol Producer Magazine, July 2008.

271 Rothkopf, Garten, “A Blueprint for Green Energy in the Americas,” 2006.

272 Ibid.

273 FAPRI 2009 U.S. and World Agricultural Outlook; OECD-FAO Agricultural Outlook 2007-

2016, 2007 (note: this was interpreted from a graph); Bioenergia: Liquid Biofuels, “18 Billion

Liters a Year!” March 2007; Rothkopf, op. sit. (note: years 2007-2019 were interpreted from a

graph); EPE, “Plano National de Energia 2030,” Presentation from Mauricio Tolmasquim, 2007

(note: years were interpreted from a graph); UNICA, “Sugarcane Industry in Brazil: Ethanol,

Sugar, Bioelectricity” brochure, 2008.

274 Agra FNP, op. sit.

275 Carpenter, Claudia, “Sugar Gains Capped by Brazil Ethanol Expansion, Czarnikow Says,”

Bloomberg, June 19, 2008.

276 UNICA, “Sugarcane as a key element against Global Warming to be highlighted at Green

Week in Brussels,” Press Release on June 23, 2009.

277 F.O. Licht GmbH, “89 New Ethanol Facilities to Come Online in 2007-11 – BNDES,” F.O.

Licht’s World Ethanol & Biofuels Report, Vol. 5, No. 22, July 23, 2007.

278 AFP, “Brazil says biofuel production not to blame for food crisis,” June 1, 2008.

279 Business News Americas, “Odebrecht to invest US$2.6 bn to Lead Ethanol Sector – Brazil,”

June 28, 2007.

280 The Wall Street Journal, “ADM Plans Entry Into Sugar-Cane Ethanol in Brazil,” June 22,

2007.

281 Agra FNP, op. sit.

282 Petrobras Magazine No. 122. “Ethanol: From Brazil to the World,” August 13, 2007.

283 Ibid.

284 Ewing, op. sit.

285 Rothkopf, op. sit.

286 Ibid.

287 F.O. Licht GmbH. “Ethanol Blend Rose to 25% on July 1, 2007,” F.O. Licht’s World Ethanol

& Biofuels Report, Vol. 5, No. 21, July 9, 2007.

288 Agra FNP, op. sit.

289 F.O. Licht GmbH. “Sobering Times for Biofuels in Seville,” F.O. Licht’s World Ethanol &

Biofuels Report, Vol. 5, No. 20, June 19, 2007. (note: only 2006, 2015 & 2025 values were

reported, other values are interpolated); FAPRI 2009 U.S. and World Agricultural Outlook; EPE,

op. sit. (note: years were interpreted from a graph); UNICA, “Sugarcane Industry in Brazil:

Ethanol, Sugar, Bioelectricity” brochure, 2008.

290 Ewing, op. sit.

291 European Bioethanol Fuel Association (eBio), “The Industry, Statistics: Production Data,”

http://www.ebio.org/statistics.php?id=4, September, 2009.

292 Walter, A. et. al., “Perspectives on Fuel Ethanol Consumption and Trade,” Biomass and

Bioenergy, 32, 730-748, 2008.

293 Flach, Bob, “EU-27 Biofuels Annual: Annual Report 2009,” USDA FAS GAIN Report, June

15, 2009.

294 Ibid.
1056

295 Energy Information Administration (EIA), “Delivered Energy Consumption in OECD Europe

by End-Use Sector and Fuel, 2006-2030: Table F6,” International Energy Outlook 2009,

http://www.eia.doe.gov/oiaf/ieo/index.html

296 BP, “BP Statistical Review of World Energy 2007 (revised with 2008 statistics) Historical

Data,” http://www.bp.com/statisticalreview.

297 Walter, A., op. sit.

298 Ayoub PM. Biofuels for Shell-biomass based blending components for transport fuels

(gasoline and diesel). In: World biofuels symposium-China, Beijing, 2006. As sited in Walter,

op. sit.

299 European Bioethanol Fuel Association (eBio), “Production Capacity Installed” and

“Production Capacity Under Construction,” http://www.ebio.org/statistics.php?id=5 and

http://www.ebio.org/statistics.php?id=6, September 9, 2009.

300 F.O. Licht GmbH. “Sobering Times for Biofuels in Seville,” F.O. Licht’s World Ethanol &

Biofuels Report, Vol. 5, No. 20, June 19, 2007.

301 Fukuda, Hisao, et. al.,“Japan Bio-fuels Production Report,” USDA, FAS, May 26, 2006.

http://www.fas.usda.gov/gainfiles/200605/146197881.pdf

302 Iijima, Midori. (2009) “Japan to Focus on Next Generation Biofuels,” USDA FAS GAIN

Report, June 1, 2009.

303 Rothkopf, op. sit.

304 Fukuda, op. sit.

305 Ibid.

306 Rothkopf, op. sit.

307 Jolly, Lindsay, “World Fuel Ethanol Outlook: Can ethanol be traded globally in a large

proportion?,” International Sugar Organization, Presentation to 9th World Beet and Cane

Growers Conference, 9-12 July 2007.

308 Fukuda, op. sit.

309 Rothkopf, op. sit.

310 Iijima, op sit.

311 Iijima, Midori. op. sit.

312 Ferreira, Lenilson, “Brazil Priming Ethanol Initiative to Supply Fuel-Thirsty Japan,” The

Japan Times, August 22, 2007. http://search.japantimes.co.jp/print/nb20070822a3.html

313 Ewing, op. sit.

314 Iijima, op sit.

315 Rothkopf, op. sit.

316 Singh, Santosh, “India: Biofuels Annual 2009,” USDA FAS GAIN Report, June 15, 2009.

317 Ibid.

318 Jagannathan, Prabha, “Fuel doping: Govt to rework pricing formula for ethanol” The

Economic Times, December, 23, 2009.

319 Nayak, D., “Weak Crude, Cane Shortage Hit India Ethanol Expansion” Wall Street Journal,

April, 1, 2009.

320Renewable Fuels Association (RFA), “2008 World Fuel Ethanol Production, ”

http://www.ethanolrfa.org/industry/statistics/#E, December 23, 2009.

321 Ohga K. & Tatsuji K. Biofuels Policies in Asia: Trade effects on World Agricultural and

Biofuels Trade. Presentation to USDA Agricultural Outlook Forum, March 2, 2007.
1057

322 Beckman, Chanda et. al., “China-Peoples Republic of: Biofuels Annual” USDA FAS GAIN

Report, July 17, 2009.

323 Rothkopf, op. sit.

324 AFX News Limited, Forbes, “China to Freeze Approvals for Corn Processing Projects for 3

Years,” September 21, 2007.

http://www.forbes.com/markets/feeds/afx/2007/09/21/afx4142281.html

325 F.O. Licht GmbH, “New Ethanol Plants Will Rely on Non-Grain Feedstocks,” F.O. Licht’s

World Ethanol & Biofuels Report, Vol. 5, No. 22, July 23, 2007.

326 Rothkopf, op. sit.

327 Beckman, op. sit. Conversions assume 1 million tonnes of ethanol equals 330 million gallons.

328 Rothkopf, op. sit.

329 Ibid.

330 Ohga, op. sit.

331 F.O. Licht GmbH, “New Ethanol Plants Will Rely on Non-Grain Feedstocks,” F.O. Licht’s

World Ethanol & Biofuels Report, Vol. 5, No. 22, July 23, 2007.

332 Rothkopf, op. sit.

333 F.O. Licht GmbH, “New Ethanol Plants Will Rely on Non-Grain Feedstocks,” F.O. Licht’s

World Ethanol & Biofuels Report, Vol. 5, No. 22, July 23, 2007.

334 EIA, “U.S. Oxygenates: Fuel Ethanol Imports,” Last updated on October 10, 2008.

http://tonto.eia.doe.gov/dnav/pet/pet_move_impcus_a2_nus_EPOOXE_im0_mbbl_m.htm

335 Joseph, Ken, “Argentina Biofuels Annual,” USDA FAS GAIN Report, June 19, 2009.

336 Deborah O’Connell, et. al., “Biofuels in Australia - Issues and Prospects. A Report for the

Rural Industries Research and Development Corporation,” CSIRO Australian Government Rural

Industries Research and Development Corporation, May 2007.

337 Roberts, Greg, “Rees scraps biofuel mandates,” The Australian: Online Newspaper of the

Year, September 20, 2008. http://www.theaustralian.news.com.au/story/0,25197,24373888-

11949,00.html

338 Sobolik, Jessica, “Canadian Government Passes Renewable Fuels Standard,” Ethanol

Producer Magazine, August 2008.

339 Pinzon, L., “Colombia Bio-fuels Annual 2008,” USDA FAS GAIN Report, August 20, 2008.

340 Chavez, L., “Mexico Biofuels Annual,” USDA FAS GAIN Report, June 15, 2009.

341 Darby, Mike, “Australia Biofuels Annual,” USDA FAS GAIN Report, June 1, 2009.

342 Dessureault, Darlene, “Canada Biofuels Annual,” USDA FAS GAIN Report, June 30, 2009.

343 Office of the United States Trade Representative (USTR), “Caribbean Basin Initiative,”

November 12, 2008.

http://www.ustr.gov/Trade_Development/Preference_Programs/CBI/Section_Index.html

344 Bryan, Tom, “Entering Tariff-Free,” Ethanol Producer Magazine, January 2004.

345 Office of the United States Trade Representative (USTR), “Ethanol Provisions in the

CAFTA-DR,” July 2007.

346 Bryan, op. sit.

347 Rapoza, Kenneth, “UPDATE: Tax Loophole Helps US Import Ethanol ‘Duty Free’- ED&F,”

INO News, Dow Jones Newswires, March 2008. http://news.ino.com/

348 Peter Rohde, “Senate Finance May Take Up Drawback Loophole As Part of Energy Bill,”

EnergyWashington Week, April 18, 2007. As sited in Yacobucci, Brent, “Ethanol Imports and
1058

the Caribbean Basin Initiative,” CRS Report for Congress, Order Code RS21930, Updated

March 18, 2008.

349 Perkins, Jerry, “BRAZIL: Loophole Hurt U.S. Ethanol Prices,” DesMoinesRegister.com,

October 18, 2007.

350 Public Law Version 6124 of the Farm Bill. 2008.

http://www.usda.gov/documents/Bill_6124.pdf.

351 Lundell, Drake, “Brazilian Ethanol Export Surge to End; U.S. Customs Loophole Closed Oct.

1,” Ethanol and Biodiesel News, Issue 45, November 4, 2008.

352 MacDonald, T., et. al., “Ethanol Supply Outlook for California,” California Energy

Commission, October 2003. http://www.ethanolrfa.org/objects/documents/100/castaffreport.pdf

353 Petrojam Limited, “Official Opening of Petrojam Ethanol Limited,” November 12, 2008.

http://www.pcj.com/petrojam/whats_new.htm

354 Petrojam Limited, “About Us,” November 12, 2008.

http://www.petrojambunkering.com/petrojam_aboutb.htm

355 F.O. Licht GmbH, “Company News- JBG Commences Fuel Ethanol Production,” F.O.

Licht’s World Ethanol & Biofuels Report, Vol. 5, No. 22, July 23, 2007.

356 Email Correspondence with Jeffrey Tuite, ED&F Man, September 6, 2007.

357 Yacobucci, Brent, “Ethanol Imports and the Caribbean Basin Initiative,” CRS Report for

Congress, Order Code RS21930, Updated March 18, 2008.

358 Email Correspondence with Jeffrey Tuite, ED&F Man, September 6, 2007.

359 Ethanol Producer Magazine, “Ethanol Dehydration Facility to be Built in Trinidad,”

December 2006.

360 Etter, Lauren, and Millman, Joel, “Ethanol Tariff Loophole Sparks a Boom in Caribbean

Islands Build Plants To Process Brazil’s Fuel; Farm Belt Cries Foul,” Wall Street Journal, March

9, 2007.

361 Homer, Louis, “Another Energy Sector Stimulus,” PetroleumWorld.com. November 22,

2006. http://www.petroleumworldtrinidad.com/storytrini06112606.htm

362 Etter, op. sit.

363 Browne, J., “Angostura Launches US$11m Ethanol Plant,” The Trinidad Guardian. August 6,

2004. http://www.guardian.co.tt/archives/2004-08-06/business1.html

364 Email Correspondence with Jeffrey Tuite, ED&F Man, September 6, 2007.

365 Ramjeet, Oscar, “Barbados to Build $72 Million Ethanol Dehydration Plant,” Caribbean Net

News, August 22, 2008.

366 Jamaica Gleaner News, “US$36m Ethanol Plant for Barbados,” August 23, 2008.

http://www.jamaica-gleaner.com/gleaner/20080823/business/business1.html

367 Email Correspondence with Jeffrey Tuite, ED&F Man, September 6, 2007.

368 Bryan, op. sit.

369 Etter, op. sit.

370 Ibid.

371 Email Correspondence with Jeffrey Tuite, ED&F Man, September, 7, 2007.

372 Etter, op. sit.

373 110th Congress of the United States of America, Second Session, The Food, Conservation, and

Energy Act of 2008, House Rule 6124 (June 18, 2008) available at:

http://www.usda.gov/documents/Bill_6124.pdf.
1059

374 Bell Bio-Energy funding based on Press Release, “Defense Energy Support Center and Army

Test Turning Waste into Fuel,” (October 2, 2008) available at:

http://www.bellbioenergy.com/html/newsletter/Press%20Releases/DLA_PR_10-3-08.pdf.

Clearfuels Technology funding based on DOE Press Release, “Recovery Act Announcement:

Secretaries Chu and Vilsack Announce More than $600 Million in Advanced Biorefinery

Projects,” (December 4, 2009) available at: http://www.energy.gov/8352.htm. Verenium

funding based on DOE Press Release, “DOE to Provide up to $40 Million in Funding for SmallScale Biorefinery Projects in Wisconsin and Louisiana,” (July 14, 2008) available at:

http://www.energy.gov/news/6413.htm.

375 Letter from Richard Newell, EIA Administrator to Lisa Jackson, EPA Administrator dated

October 29, 2009 (Table 2).

376 http://www.iogen.ca/company/demo_plant/index.html

377 DOE Press Release, “Recovery Act Announcement: Secretaries Chu and Vilsack Announce

More than $600 Million in Advanced Biorefinery Projects,” (December 4, 2009) available at:

http://www.energy.gov/8352.htm.

378 Based on the following DOE press releases, “U.S. Department of Energy Selects First Round

of Small-Scale Biorefinery Projects for Up to $114 Million in Federal Funding,” (January 29,

2008) available at: http://www.energy.gov/news/5903.htm; “DOE Selects 3 Small-Scale

Biorefinery Projects for Up to $86 Million of Federal Funding in Maine, Tennessee and

Kentucky,” (April 18, 2008) available at: http://www.energy.gov/news/6164.htm; “DOE to

Provide up to $40 Million in Funding for Small-Scale Biorefinery Projects in Wisconsin and

Louisiana,” (July 14, 2008) available at: http://www.energy.gov/news/6413.htm; “Recovery Act

Announcement: Secretaries Chu and Vilsack Announce More than $600 Million in Advanced

Biorefinery Projects,” (December 4, 2009) available at: http://www.energy.gov/8352.htm.

379 DOE Press Release, “DOE Selects Six Cellulosic Ethanol Plants for Up to $385 Million in

Federal Funding,” (February 28, 2007) available at: http://www.energy.gov/news/4827.htm.

380 109th Congress of the United States of America, First Session, The Energy Policy Act of 2005,

House Rule 6 (January 4, 2005) available at: http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=109_cong_bills&docid=f:h6enr.txt.pdf

381 DOE Press Release, “DOE Announces Final Rule for Loan Guarantee Program,” (October 4,

2007) available at: http://www.energy.gov/print/5568.htm.

382 111th Congress of the United States of America, First Session, The American Recovery and

Reinvestment Act of 2009, House Rule 1 (January 6, 2009) available at:

http://frwebgate.access.gpo.gov/cgi-bin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf.

383 DOE Press Release, “Department of Energy Issues Final Rule on Loan Guarantees,”

(December 7, 2009) available at: http://www.energy.gov/news2009/8369.htm

384 USDA Press Release, “USDA Approves First Ever Guaranteed Loan for Commercial-Scale

Cellulosic Ethanol Plant,” (January 16, 2009) available at:

http://www.usda.gov/wps/portal/!ut/p/_s.7_0_A/7_0_1RD?printable=true&contentidonly=true&

contentid=2009/01/0024.xml

385 DOE Press Release, “DOE Selects Five Ethanol Conversion Projects for $23 Million in

Federal Funding,” (March 27, 2007) available at: http://www.energy.gov/print/4896.htm.

386 DOE Press Release, “Energy and Agriculture Depts. Provide $8.3 Million in Funding for

Biofuels Research,” (June 7, 2007) available at: http://www.energy.gov/print/5115.htm.
1060

387 DOE Press Release, “Energy Department Selects Three Bioenergy Research Centers for $375

Million in Federal Funding,” (June 26, 2007), http://www.energy.gov/print/5172.htm.

388 DOE Press Release, “Department of Energy to Invest up to $7.7 Million for Four Biofuels

Projects,” (December 4, 2007) available at: http://www.energy.gov/print/5757.htm.

389 DOE Press Release, “U.S. Department of Energy to Invest up to $33.8 Million to Further

Development of Commercially Viable Renewable Fuels,” (February 26, 2008) available at:

http://www.energy.gov/print/6015.htm.

390 DOE Press Release, “USDA, DOE to Invest up to $18.4 Million for Biomass Research,

Development and Demonstration Projects,” (March 4, 2008) available at:

http://www.energy.gov/print/6035.htm.

391 DOE Press Release, “DOE and USDA Announce More than $10 Million in Bioenergy Plant

Feedstock Research,” (July 31, 2008) available at: http://www.energy.gov/print/6444.htm

392 DOE Press Release, “DOE Announces Up to $7 Million for Technology Commercialization

Acceleration,” (August 29, 2008) available at: http://www.energy.gov/news/6493.htm

393 DOE Press Release, “DOE to Invest up to $4.4 Million in Six Innovative Biofuels Projects at

U.S. Universities,” (September 10, 2008) available at: http://www.energy.gov/print/6525.htm.

394 DOE Press Release, “USDA & DOE Release National Biofuels Action Plan,” (October 7,

2008) available at: http://www.energy.gov/print/6633.htm.

395 DOE Press Release, “DOE Announces Additional Steps in Developing Sustainable Biofuels

Industry,” (October 7, 2008) available at: http://www.energy.gov/print/6638.htm.

396 DOE Press Release, “Energy Secretary Chu, Agriculture Secretary Vilsack Announce $6.3

million for Biofuels Research,” (July 22, 2009) available at:

http://www.energy.gov/news2009/7683.htm.

397 DOE Press Release, “DOE Awards $377 Million in Funding for 46 Energy Frontier Research

Centers,” (August 6, 2009) available at: http://www.energy.gov/news2009/7768.htm.

398 DOE Press Release, “DOE Selects Biofuels Projects to Receive up to $21 Million in

Funding,” (August 31, 2009) available at: http://www.energy.gov/news2009/7848.htm).

399 DOE Press Release, “DOE and USDA Select Projects for more than $24 Million in Biomass

Research and Development Grants,” (November 12, 2009) available at:

http://www.energy.gov/news2009/print2009/8283.htm

400Midwest Governors Association, “Energy Security and Climate Stewardship Platform for the

Midwest 2007,” (November 2007) available at:

http://www.midwesterngovernors.org/resolutions/Platform.pdf.

401 Midwest Governors Association, “The Midwestern Energy Infrastructure Accord,” (October

2009) available at: www.midwesterngovernors.org/publications.htm.

402 Plant information taken from Nation Biodiesel Board website as of November 2009.

403 Capacity data taken from National Biodiesel Board as of November 2009. Production,

import, and export figures taken from EIA Monthly Energy Review, Table 10.4 as of December

2009.

404 See http://www.biodiesel.org/buyingbiodiesel/plants/showall.aspx and

http://www.afdc.energy.gov/afdc/incentives_laws.html. Tax incentive information used here

was current as of October 2008, and plant statistics as of October 2009.

405 Industry data for 2008 taken from National Biodiesel Board fact sheets available at that time

(see docket for historical lists) and
1061

http://www.biodiesel.org/pdf_files/fuelfactsheets/Production_graph_slide.pdf (accessed April 27,

2009).

406 “Tyson ConocoPhillips Venture in Jeopardy.” The Morning News, article dated 9/26/08.

Available in the docket.

407 See Syntroleum Investor Presentation, dated November 2009. Available here:

http://www.syntroleum.com/Presentations/SyntroleumInvestorPresentation.November%205.200

9.FINAL.pdf. Accessed December 16, 2009.

408 “Tyson's Fats to Fuel Plant on Schedule.” The Morning News, article dated 10/6/08.

Available in the docket.

409 Syntroleum Investor Presentation dated November 5, 2009. Available in the docket or at:

http://www.syntroleum.com/Presentations/SyntroleumInvestorPresentation.November%205.200

9.FINAL.pdf

410 Ethanol and Pipeline Transportation Policy Paper, Association of Oil Pipelines (AOPL and

American Petroleum Institute (API), February 2007,

http://www.aopl.org/pdf/AOPL_API_Ethanol_Transportation_Final144839.pdf

411 Testimony of Tim Felt President and CEO Explorer Pipeline On Behalf of and the

Association of Oil Pipe Lines (AOPL) and API Before the House Committee on Energy and

Commerce Subcommittee on Energy and Air Quality March 12, 2008,

http://www.aopl.org/pdf/felt-testimony.pdf

412 Kinder Morgan announcement that their Central Florida Pipeline from Tampa to Orlando

ships batches of ethanol along with batches of gasoline.

http://www.kindermorgan.com/business/products_pipelines/

413 “POET Joins Magellan Midstream Partners to Assess Dedicated Ethanol Pipeline”, March

2009, http://www.poet.com/news/showRelease.asp?id=155

414 Kinder Morgan announcement that their Central Florida Pipeline from Tampa to Orlando

ships batches of ethanol along with batches of gasoline.

http://www.kindermorgan.com/business/products_pipelines/

415 “Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints”,

prepared for EPA by Oak Ridge National Laboratory, March 2009.

416 “Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints”,

prepared for EPA by Oak Ridge National Laboratory, March 2009.

417 “The Importance of Adequate Rail Investment”, Association of American Railroads,

http://www.aar.org/GetFile.asp?File_ID=150

418 American Association of State Highway and Transportation Officials (AASHTO) FreightRail Bottom-Line Report, 2003.

419 Association of American Railroads (AAR), National Rail Freight Infrastructure Capacity and

Investment Study, September 2007.

420 “Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints”,

prepared for EPA by Oak Ridge National Laboratory, March 2009.

421 Transportation activity data output spreadsheet that accompanied the report entitled “Analysis

of Fuel Ethanol Transportation Activity and Potential Distribution Constraints”, prepared for

EPA by Oak Ridge National Laboratory, March 2009.

422 “Kinder Morgan Announces $195 Million Acquisition and new Ethanol Venture”, download

from Kinder Morgan website.
1062

423 Transportation activity data output spreadsheet that accompanied the report entitled “Analysis

of Fuel Ethanol Transportation Activity and Potential Distribution Constraints”, prepared for

EPA by Oak Ridge National Laboratory, March 2009.

424 “Barge Transportation in the United States”. Timothy Casey, Presentation at the MARE

Forum USA, 2006,

http://www.mareforum.com/ENERGY_TRANSPORTATION_USA_2006_PRESENTATIONS_

files/CASEY.pdf

425 Energy Information Administration (EIA) historical company level ethanol and gasoline

import data:

http://www.eia.doe.gov/oil_gas/petroleum/data_publications/company_level_imports/cli_historic

al.html

426 “Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints”,

prepared for EPA by Oak Ridge National Laboratory, March 2009. The list of projected ethanol

import locations is contained in Table 3 in Section 3.3.2 of this report.

427 “Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints”,

prepared for EPA by Oak Ridge National Laboratory, March 2009.

428 Transportation activity data output spreadsheet that accompanied the report entitled “Analysis

of Fuel Ethanol Transportation Activity and Potential Distribution Constraints”, prepared for

EPA by Oak Ridge National Laboratory, March 2009.

429 “The U.S. Truck Driver Shortage: Analysis and Forecasts”, Prepared by Global Insights for

the American Trucking Association, May 2005.

http://www.truckline.com/NR/rdonlyres/E2E789CF-F308-463F-8831-

0F7E283A0218/0/ATADriverShortageStudy05.pdf

430 Our projections of the motor fuel demand can be found in chapter 1.2 of this RIA.

431 “Renewable Fuel Standard Challenges Faced by Terminals”, Andrea Grant, Independent Fuel

Terminal Operators Association, May, 13, 2008, presentation at the 2008 Society of Automotive

Engineers Government-Industry Meeting.

432 “Infrastructure Requirements for an Expanded Fuel Ethanol Industry”, Downstream

Alternative Incorporated, January 15, 2002.

433 A discussion of the volumes of biofuels we estimate would be used absent the RFS2 standards

is located in Section 1.2 of this RIA.

434 A discussion of the estimated number of terminals that carry diesel fuel is contained in

Chapter V of the Regulatory Impact Analysis to the final highway diesel fuel rule

http://www.epa.gov/otaq/highway-diesel/regs/ria-v.pdf, EPA Docket A-99-06.

435 “Renewable Fuel Standard Challenges Faced by Terminals”, Andrea Grant, Independent Fuel

Terminal Operators Association, May, 13, 2008, presentation at the 2008 Society of Automotive

Engineers Government-Industry Meeting.

436 “Renewable Fuel Standard Challenges Faced by Terminals”, Andrea Grant, Independent Fuel

Terminal Operators Association, May, 13, 2008, presentation at the 2008 Society of Automotive

Engineers Government-Industry Meeting.

437 Section 1.7 of this RIA contains a discussion of our projections regarding the prevalence of

flexible fuel vehicles in the vehicle fleet and the relative volumes of E85 versus E10 that would

be used in these vehicles.

438 “Summary Data on Diesel Fuel Retailers”, Memorandum to the Docket, Jeffery Herzog, U.S.

EPA, March 23, 2000, EPA Docket A-99-06, Docket Item II-B-07.
1063

439 2007 National Association of Convenience Stores State of the Industry survey data

www.nacsonline.com as reported in the National Petroleum News MarketFacts 2007, October

2007.

440 “Diesel Throughput Volume by Percentage of Diesel Fuel Retailers”, Memorandum to the

Docket, Jeffery Herzog, U.S. EPA, May 05, 2000, EPA Docket A-99-06, Docket Item II-B-14.

441 National Petroleum News, “2008 marketfacts”, www.npnweb.com

442 National Petroleum News, “2007 marketfacts”, Volume 99, Number 10, October 2007,

www.npnweb.com

443 The National Ethanol Vehicle Coalition estimate of E85 refueling facilities is located at

http://www.e85refueling.com/ estimates http://www.e85refueling.com/

444 The “NEAR85” estimate of E85 retail facilities is located at http://www.neare85.com/

445 Letter from Richard Newell, EIA Administrator to Lisa Jackson, EPA Administrator dated

October 29, 2009 (Table 1).

446 Based on comments provided by NPRA (EPA-HQ-OAR-2005-0161-2124.1)

447 Hart Energy Consulting, North American Ethanol Database Service, Revised November 2009.

Included under the permission of Fred Potter, Executive VP at Hart.

448 Quadrillion BTUs based on EIA, AEO 2007 (Table 2), EIA, AEO 2008 Final Release (Table

2), and EIA, AEO 2009 – ARRA Update (Table 2).

449 http://www.growthenergy.org/2009/e15/Waiver%20Cover%20Letter.pdf

450 1998-2005 FFV sales based on DOE’s EERE data available at:

www.eere.energy.gov/afdc/data/index.html

451 EIA, Annual Energy Outlook 2009 – ARRA Update (Table 57).

452 Rulemaking to Establish Light-Duty Vehicle GHG Emission Standards and Corporate

Average Fuel Economy Standards, 74 FR 49454 (September 28, 2009).

453 Historical car and light truck sales taken from ORNL, Transportation Energy Data Book,

Edition 28-2009 (Tables 4.5 and 4.6).

454 Vehicle survival rates taken from Rulemaking to Establish Light-Duty Vehicle GHG Emission

Standards and Corporate Average Fuel Economy Standards, 74 FR 49454 (September 28, 2009).

455 EIA, Annual Energy Outlook 2009 – ARRA Update (Table 57).

456 Ethanol Producer Magazine, “Automakers Maintain FFV Targets in Bailout Plans.” February

2009. FFV commitments reiterated in Chrysler Restructuring Plan for Long-Term Viability and

General Motors Corp. 2009-2014 Restructuring Plan presented to the U.S. Department of

Treasury on February 17, 2009.

457111th Congress of the United States of America, First Session, The Open Fuel Standard Act of

2009, House Rule 1476 (March 12, 2009) available at: http://www.opencongress.org/bill/111-

h1476/text.

458 EIA, Annual Energy Outlook 2007 (Table 47).

459 For more information, visit: http://www.afdc.energy.gov/afdc/ . The $100 per FFV estimate is

also cited by others, including Growth Energy:

http://www.growthenergy.org/2009/Brochures/Fueling_Ethanol.pdf.

460 National Ethanol Vehicle Coalition, 2009 FFV Purchasing Guide.

461 NEVC website, accessed on November 23, 2009

462 For more information, visit: http://www.midwesterngovernors.org/resolutions/Platform.pdf
1064

463 111th Congress of the United States of America, First Session, The American Recovery and

Reinvestment Act of 2009, House Rule 1 (January 6, 2009) available at:

http://frwebgate.access.gpo.gov/cgi-bin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf.

464 Proposed Rulemaking to Establish Light-Duty Vehicle GHG Emission Standards and

Corporate Average Fuel Economy Standards, 74 FR 49454 (September 28, 2009).

465 EIA, Annual Energy Outlook 2009 - ARRA Update (Table 17).

466 NEVC, “2008 Purchasing Guide for Flexible Fuel Vehicles.” Refers to all mass produced 3.5

and 3.9L Impalas. However, it is our understanding that consumers may still place special orders

for non-FFVs.

467 Based on average E85 and regular unleaded gasoline prices reported at

http://www.fuelgaugereport.com/ on November 23, 2009.

468 110th Congress of the United States of America, Second Session, The Food, Conservation, and

Energy Act of 2008, House Rule 6124 (June 18, 2008) available at:

http://www.usda.gov/documents/Bill_6124.pdf,

469 EIA, Annual Energy Outlook 2009 - ARRA Update (Table 17).

470 Brownstone, D., Small, K.A., Valuing Time and Reliability: Assessing the Evidence from

Road Pricing Demonstrations, Transportation Research Part A 39, 2005 (pages 293-279).

471 Greene, David, Survey Evidence on the Importance of Fuel Availability to Choice of

Alternative Fuels and Vehicles, Prepared by Oak Ridge National Laboratory (November 11,

1997), available at: http://ntl.bts.gov/lib/000/700/723/survey.pdf.

472 http://www.dtnethanolcenter.com/index.cfm?show=10&mid=32

473 http://www.growthenergy.org/2009/e15/Waiver%20Cover%20Letter.pdf. Additional

supporting documents are available on the Growth Energy website.

474 Refer to 74 FR 18228 (April 21, 2009).

475 Refer to 74 FR 23704 (May 20, 2009).

476 http://www.epa.gov/OMS/regs/fuels/additive/lettertogrowthenergy11-30-09.pdf

477 Highway and non-road gasoline information taken from ORNL, Transportation Energy Data

Book, Edition 28-2009 (Table 2.5). Off-road gasoline information taken from ORNL,

Transportation Energy Data Book, Edition 26-2007 (Table 2.5). This was the last year ORNL

reported. All calculations exclude aviation gasoline.

478 Ethanol Producer Magazine website, Ethanol Plant Lists (Producing, Not Producing, Under

Construction, Expansions), http://www.ethanolproducer.com/plant-list.jsp, Modified on April 14,

2008.

479 Renewable Fuels Association (RFA) website, Ethanol Biorefinery Locations,

http://www.ethanolrfa.org/industry/locations, Updated on April 2, 2008.

480 International Trade Commission 2005-2007 historical ethanol import data as reported by the

Renewable Fuels Association, http://www.ethanolrfa.org/industry/statistics/

481 Communication from Douglas Newman, International Trade Commission to Doris Wu, EPA,

May 20, 2008.

482 Energy Information Administration historical gasoline and ethanol import data:

http://www.eia.doe.gov/oil_gas/petroleum/data_publications/company_level_imports/cli_historic

al.html

483 NASS database. http://www.nass.usda.gov/

484 USDA, WGA, Bioenergy Strategic Assessment project findings upcoming as noted in report

WGA. Transportation Fuels for the Future Biofuels: Part I. 2008.
1065

485 NASS database. http://www.nass.usda.gov/

486 U.S. Department of Energy, Energy Efficiency and Renewable Energy, Transportation

Energy Book: Edition 26, Prepared by Oak Ridge National Laboratory, ORNL-6978, 2007

(Table 2.5).

487 U.S. Department of Energy, Energy Information Administration, Petroleum Marketing

Annual 2006, http://tonto.eia.doe.gov/FTPROOT/petroleum/048706.pdf, August 2007 (Tables

31, 34 & 48).

488 U.S. Environmental Protection Agency, Draft Regulatory Impact Analysis: Control of

Hazardous Air Pollutants from Mobile Sources, EPA420 D-06-004,

http://www.epa.gov/otaq/regs/toxics/chapter9.pdf, February 2006 (Chapter 9).

489 American Petroleum Institute, Gasoline Tax Report,

http://www.api.org/statistics/fueltaxes/upload/January_2008_gasoline_and_diesel_summary_pag

es-2.pdf, January 2008.

490 See http://www.afdc.energy.gov/afdc/fuels/biodiesel_laws.html. Incentives were evaluated as

of October 2008.

491 Status taken from the online plant database as of October 2009. Current data is available at

http://www.biodiesel.org/buyingbiodiesel/plants/showall.aspx

492 Industry data for 2008 taken from National Biodiesel Board fact sheets no longer available

(see docket for historical versions) and

http://www.biodiesel.org/pdf_files/fuelfactsheets/Production_graph_slide.pdf (accessed April 27,

2009).

493 Department of Energy, Alternative Fuels & Advanced Vehicles Data Center, Biodiesel

Incentives and Laws. Accessed July 2008.

http://www.afdc.energy.gov/afdc/fuels/biodiesel_laws.html

494 Energy Information Administration, 2005 Prime Supplier Sales Volumes for No. 2 Diesel.

Accessed 10/7/2008.

http://tonto.eia.doe.gov/dnav/pet/pet_cons_prim_a_EPD2D_P00_Mgalpd_a.htm

495 Energy Information Administration, Fuel Oil and Kerosene Sales 2006, Table 16. 2005 data

column used.

http://www.eia.doe.gov/pub/oil_gas/petroleum/data_publications/fuel_oil_and_kerosene_sales/c

urrent/pdf/table16.pdf

496 Product Life Cycle Accounting and Reporting Standard, Review Draft for Stakeholder

Advisory Group, November 2009, World Resources Institute & World Business Council for

Sustainable Development, 2009

497 Clean Air Act Section 211(o)(1)

498 Delucchi, M. 2005. Incorporating the Effect of Price Changes on CO2-Equivalent Emissions

from Alternative Fuel Lifecycles: Scoping the Issues. Working Paper UCD—ITS—RR—05—

19. http://pubs.its.ucdavis.edu/publication_detail.php?id=62

499 http://www.epa.gov/sequestration/greenhouse_gas.html

500 Heffer, Patrick (2009). Assessment of Fertilizer Use by Crop at the Global Level, 2006/07 –

2007/08. International Fertilizer Industry Association. Paris, France.

501 http://faostat.fao.org/

502 Macedo I.C., Seabra J.E.A., Silva J.E.A.R. (2008) “Greenhouse gases emissions in the

production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a

prediction for 2020.” Biomass and Bioenergy.
1066

503 Argonne Laboratory. 2008. Update of Distillers Grains Displacement Ratios for Corn Ethanol

Life-Cycle Analysis by Arora, Wu, and Wang

504 Shurson, J. 2009. Analysis of Current Feeding Practices of Distiller’s Grains with Solubles in

Livestock and Poultry Feed Relative to Land Use Credits Associated with Determining the Low

Carbon Fuel Standard for Ethanol. Dr. Jerry Shurson, Professor, Department of Animal Science,

University of Minnesota, March 25, 2009

505 Energy Life-Cycle Assessment of Soybean Biodiesel, A. Pradhan, D.S. Shrestha, A.

McAloon, W. Yee, M. Haas, J.A. Duffield, H. Shapouri, USDA, Agricultural Economic Report

Number 845, September 2009

506 Davis, Ryan. November 2009. Techno-economic analysis of microalgae-derived biofuel

production. National Renewable Energy Laboratory (NREL)

507 Aden, Andy. September 2009. Mixed Alcohols from Woody Biomass – 2010, 2015, 2022.

National Renewable Energy Laboratory (NREL)

508 Davis, Ryan. August 2009. Techno-economic analysis of current technology for FischerTropsch fuels. National Renewable Energy Laboratory (NREL)

509 An Analysis Of Modern Corn Ethanol Technologies, February 16, 2009, Energy Resources

Center, 1309 South Halsted Street, Chicago, IL 60607

510 The MODerate resolution Imagin Spectoradiometer (MODIS) land Products ( MCD12Q1)

Land Cover Type Product (Land Cover Type 1),

https://lpdaac.usgs.gov/lpdaac/products/modis_products_table/land_cover/yearly_l3_global_500

_m/mcd12q1

511 FAO/IIASA/ISRIC/ISSCAS/JRC, 2009. Harmonized World Soil Database (version 1.1).

FAO, Rome, Italy and IIASA, Laxenburg, Austria.

512 Lewis et al. 2009. Increasing carbon storage in intact African tropical forests. Nature.

457(19): 1003-1006

513 Phillips et al. 2008. The changing Amazon forest. Phil. Trans. R. Soc. (363): 1819-1827

514 Department of Energy: National Energy Technology Laboratory. 2009. NETL: PetroleumBased Fuels Life Cycle Greenhouse Gas Analysis - 2005 Baseline Model.

515 “N2O release from agro-biofuel production negates global warming reduction by replacing

fossil fuels” P.J. Crutzen, A.R. Mosier, K.A. Smith, and W. Winiwarter, Atmospheric Chemistry

and Physics, 8, 389–395, 2008.

516 U.S. Environmental Protection Agency. 2005. Inventory of U.S. Greenhouse Gas Emissions

and Sinks: 1990–2003. EPA 430-R-05-003. Available at

http://www.epa.gov/climatechange/emissions/downloads06/05CR.pdf.

517 U.S. Environmental Protection Agency. 2005. Inventory of U.S. Greenhouse Gas Emissions

and Sinks: 1990–2003. EPA 430-R-05-003. Available at

http://www.epa.gov/climatechange/emissions/downloads06/05CR.pdf.

518 U.S. Environmental Protection Agency. 2005. Inventory of U.S. Greenhouse Gas Emissions

and Sinks: 1990–2003. EPA 430-R-05-003. Available at

http://www.epa.gov/climatechange/emissions/downloads06/05CR.pdf.

519 Heffer, Patrick (2009). Assessment of Fertilizer Use by Crop at the Global Level, 2006/07 –

2007/08. International Fertilizer Industry Association. Paris, France.

520 Venezia, John et. al. Notable Revisions and Recalculations: International Agriculture GHG

Emissions and GHG Metrics. Memorandum to EPA on September 30, 2009.
1067

521 Ross and Associates Environmental Consulting, Ltd (2009). Peer Review of International

Greenhouse Gas Emissions and Factors as provided to EPA to support its RFS2 rulemaking.

522 Venezia, John et. al. Notable Revisions and Recalculations: International Agriculture GHG

Emissions and GHG Metrics. Memorandum to EPA on September 30, 2009.

523 FAO (2009). FAOStat Production (Crops). Retrieved Sept. 25, 2009 from

http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor. Assumes FAO

definitions for seed cotton, maize, sunflower seed, groundnuts, in shell, and rice, paddy equal

cotton; corn; sunflower; peanut; and rice, respectively.

524 Venezia, John et. al. Notable Revisions and Recalculations: International Agriculture GHG

Emissions and GHG Metrics. Memorandum to EPA on September 30, 2009.

525 USDA Economic Research Service. China Agricultural and Economic Data: National Data

Results. Retrieved from

http://www.ers.usda.gov/Data/China/NationalResults.aspx?DataType=6&DataItem=160&StrDat

atype=Agricultural+inputs&ReportType=0 on Sept 15, 2009.

526 Faroni, C.E.; Penatti, C.P.; Donzelli, J.L.; Paes, L.A.D. Simulação CTC para o uso de

fertilizantes pela cana-de-açúcar no ano de 2020. Relatório Técnico de Pesquisa e

Desenvolvimento (Planilha Microsoft Excel). CTC – Centro de Tecnologia Canavieira,

Piracicaba-SP, 2009.

527 2006 IPCC Guidelines for National Inventories, Volume 4, Chapter 11: N2O Emissions from

Managed Soils, and CO2 Emissions from Lime and Urea Application

528 http://wds.iea.org/wds/default.aspx

529 Macedo I.C., Seabra J.E.A., Silva J.E.A.R. (2008) “Greenhouse gases emissions in the

production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a

prediction for 2020.” Biomass and Bioenergy.

530 International Rice Research Institute. www.iri.org. Last accessed: October 15, 2008.

531 UNICA. (August 2009) Submission of comments document # EPA-HQ-OAR-2005-0161-

1761.1, pg. 20-21

532 Michael Wang, May Wu, Hong Huo and Jiahong Liu (2008) Life-cycle energy use and

greenhouse gas emissions implications of Brazilian sugarcane ethanol simulated with the

GREET model. International Sugar Journal 2008, Vol. 110, No. 1317.

533 Salil Arora, May Wu, and Michael Wang, “Update of Distillers Grains Displacement Ratios

for Corn Ethanol Life-Cycle Analysis,” September 2008. See

http://www.transportation.anl.gov/pdfs/AF/527.pdf

534 West, T.O., and W.M. Post. 2002. “Soil Organic Carbon Sequestration Rates by Tillage and

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535 See Adams et al. (2005) for additional detail on the FASOM forest carbon accounting

procedures.

536 Smith, J., L.S. Heath, and J. Jenkins. 2003. Forest Volume-to-Biomass Models and

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537 Haynes, R.W. (Technical coordinator). 2003. An Analysis of the Timber Situation in the

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538 Birdsey, R.A. 1996a. “Regional Estimates of Timber Volume and Forest Carbon for Fully

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539 Birdsey, R.A. 1996b. “Carbon Storage for Major Forest Types and Regions in the

Conterminous United States.” In Forests and Global Change. Volume II: Forest management

opportunities for mitigating carbon emissions, Chapter 1, D. Hair and Neil R. Sampson, eds.

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540 Smith, J., L.S. Heath, and J. Jenkins. 2003. Forest Volume-to-Biomass Models and

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541 Ibid.

542 Miles, P., U.S. Forest Service, electronic file 2002_RPA_Tables.xls to Brooks Depro, RTI

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543 Birdsey, R.A. 1992. Changes in forest carbon storage from increasing forest area and timber

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544 Birdsey, R.A. 1996a. “Regional Estimates of Timber Volume and Forest Carbon for Fully

Stocked Timberland, Average Management after Cropland and Pasture Revision to Forest.” In

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Carbon Emissions, D. Hair, and Neil R. Sampson, eds., Washington, DC: American Forests:

309-333.

545 Birdsey, R.A. 1996b. “Carbon Storage for Major Forest Types and Regions in the

Conterminous United States.” In Forests and Global Change. Volume II: Forest management

opportunities for mitigating carbon emissions, Chapter 1, D. Hair and Neil R. Sampson, eds.

Washington, DC: American Forests: 1-25.

546 Heath, L.S.; Birdsey, R.A.; Williams, D.W. 2002. “Methodology for estimating soil carbon

for the forest carbon budget model of the United States, 2001.” Environmental Pollution, 116:

373-380.

547 Birdsey, R.A. 1996a. “Regional Estimates of Timber Volume and Forest Carbon for Fully

Stocked Timberland, Average Management after Cropland and Pasture Revision to Forest.” In

Forests and Global Change. Volume II: Forest Management Opportunities for Mitigating

Carbon Emissions, D. Hair, and Neil R. Sampson, eds., Washington, DC: American Forests:

309-333.

548 Birdsey, R.A. 1996b. “Carbon Storage for Major Forest Types and Regions in the

Conterminous United States.” In Forests and Global Change. Volume II: Forest management

opportunities for mitigating carbon emissions, Chapter 1, D. Hair and Neil R. Sampson, eds.

Washington, DC: American Forests: 1-25.

549 Heath, L.S.; Birdsey, R.A.; Williams, D.W. 2002. “Methodology for estimating soil carbon

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373-380.

550 Birdsey, R.A. 1996a. “Regional Estimates of Timber Volume and Forest Carbon for Fully

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Forests and Global Change. Volume II: Forest Management Opportunities for Mitigating

Carbon Emissions, D. Hair, and Neil R. Sampson, eds., Washington, DC: American Forests:

309-333.
1069

551 Birdsey, R.A. 1996b. “Carbon Storage for Major Forest Types and Regions in the

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552 Smith, J., and L.S. Heath. 2002. A Model of Forest Floor Carbon Mass for United States

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553 Smith, J., USDA Forest Service, email communication to Brian Murray, RTI International.

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554 U.S. Environmental Protection Agency. 2003. Inventory of U.S. Greenhouse Gas Emissions

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555 U.S. Environmental Protection Agency. 2003. Inventory of U.S. Greenhouse Gas Emissions

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556 Skog, K, and G. Nicholson. 2000. Carbon Sequestration in Wood and Paper Products. In The

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557 Adams, D., R. Alig, B.A. McCarl, and B.C. Murray. 2005. “FASOMGHG Conceptual

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558 Skog, K, and G. Nicholson. 2000. Carbon Sequestration in Wood and Paper Products. In The

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559 Skog, K, and G. Nicholson. 2000. Carbon Sequestration in Wood and Paper Products. In The

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560 Source: Adams, D., R. Alig, B.A. McCarl, and B.C. Murray. 2005. “FASOMGHG

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562 Smith, W.B., J.S. Vissage, D.R. Darr, and R.M. Sheffield. 2001. Forest Resources of the

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570 ICF International, Inc. 2009. Biofuels Stochastic International Land Use Lifecycle Analysis

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575 Harris, NL, S. Grimland and S. Brown. 2009. Land Use Change and Emission Factors:

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576 U.S. EPA. 2009. Draft Regulatory Impact Analysis: Changes to Renewable Fuel Standard

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577 Ruesch, A. and H.K. Gibbs. 2008. New IPCC Tier-1 Global Biomass Carbon Map For the

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578 Saatchi, S.S., N.L. Harris, S. Brown, M. Lefsky and S. Petrova. New benchmark map of

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579 Mokany, K., R.J. Raison, and A.S. Prokushkin. 2006. Critical analysis of root:shoot ratios in

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584 Houghton, R.A., D. Butman, A.G. Bunn, O.N. Krankina, P. Schlesinger and T.A. Stone.

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588 Amaral W. A. N., J.P. Marinho, R. Tarasantchi, A. Beber, and E. Giuliani. 2008,

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597 Micales, J.A. and Skog, K.E.: 1997, ‘The decomposition of forest products in landfills’, Int.

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598 Lewis, S.L., G. Lopez-Gonzalez, B. Sonké, K. Affum Bafoe, T.R. Baker, L.O. Ojo, O.L.

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599 Stehman, S. 1997, Estimating Standard Errors of Accuracy Assessment Statistics under

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600 Macedo I.D.C., Leal M.R.L.V. and Seabra J.E.A. (2004) “Assessment of greenhouse gas

emissions in the production and use of fuel ethanol in Brazil.” Prepared for the State of Sao

Paulo, Brazil.

601 Macedo I.C., Seabra J.E.A., Silva J.E.A.R. (2008) “Greenhouse gases emissions in the

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prediction for 2020.” Biomass and Bioenergy.
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602 Michael Wang, May Wu, Hong Huo and Jiahong Liu (2008) “Life-cycle energy use and

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603 UNICA. (August 2009) Submission of comments document # EPA-HQ-OAR-2005-0161-

1761.1

604 Version 1.8c of the GREET model, released in March, 2009, was utilized to estimate GHG

emissions associated with sugarcane transport. Information on this release can be found at:

http://www.transportation.anl.gov/modeling_simulation/GREET/

605 Davis, Ryan. November 2009. Techno-economic analysis of microalgae-derived biofuel

production. National Renewable Energy Laboratory. Technical Memorandum to EPA.

606 Ryan, Catie, “Cultivating Clean Energy: The Promise of Algae Biofuels,” NRDC and

Terrapin Bright Green, LLC. October 2009.

607 Davis, Ryan. November 2009. Techno-economic analysis of microalgae-derived biofuel

production. National Renewable Energy Laboratory. Technical Memorandum to EPA.

608 A. Pradhan, D.S. Shrestha, A. McAloon, W. Yee, M. Haas, J.A. Duffield, H. Shapouri,

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Uses, Agricultural Economic Report Number 845

609 Tao, Ling and Aden, Andy. November 2008. Techno-economic Modeling to Support the EPA

Notice of Proposed Rulemaking (NOPR). National Renewable Energy Laboratory (NREL)

610 Aden, Andy. September 2009. Mixed Alcohols from Woody Biomass – 2010, 2015, 2022.

National Renewable Energy Laboratory (NREL)

611 Davis, Ryan. August 2009. Techno-economic analysis of current technology for FischerTropsch fuels. National Renewable Energy Laboratory (NREL)

612 Aden, Andy. December 10, 2009. Feedstock Considerations and Impacts on Biorefining.

National Renewable Energy Laboratory (NREL)

613 Michael Wang, May Wu, Hong Huo and Jiahong Liu (2008) Life-cycle energy use and

greenhouse gas emissions implications of Brazilian sugarcane ethanol simulated with the

GREET model. International Sugar Journal 2008, Vol. 110, No. 1317.

614 Macedo I.C., Seabra J.E.A., Silva J.E.A.R. (2008) “Greenhouse gases emissions in the

production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a

prediction for 2020.” Biomass and Bioenergy.

615 Swain, R.L., Molecular sieve dehydrators. How they became the industry standard and how

they work. Chapter 19, Alcohol Textbook.

616 Kaminski, W. (2008) Renewable energy source- Dehydrated ethanol. Chemical Engineering

Journal 135: 95-102.

617 Roy, Suzanne. Fuel Production: Using membrane integration for plant expansion. Vaperma

handout.

618 Vane, Leland. (2008). Separation technologies for the recovery and dehydration of alcohols

from fermentation broths. Biofuels, Bioproducts, and Biorefining 2.6: 553-588.

619 Kawaitkowski, J.R., McAloon, A., Taylor, F., Johnston, D.B., Modeling the process and costs

of fuel ethanol production by the corn dry grind process. Industrial Crops and Products 23

(2006) 288-296. A copy of the current USDA model can be obtained by contacting the

corresponding author. Model was adjusted based on conversations with technology providers.
1073

620 Fitch, George (2009) Caribbean Basin Ethanol Producers Group email correspondence,

"Caribbean Basin Ethanol Dehydration Energy Usage and CO2 Emissions”.

621 Fitch, George (2009) Caribbean Basin Ethanol Producers Group email correspondence,

"Caribbean Basin Ethanol Dehydration Energy Usage and CO2 Emissions”.

622 Fitch, George (2009) Caribbean Basin Ethanol Producers Group email correspondence,

"Caribbean Basin Ethanol Dehydration Energy Usage and CO2 Emissions”.

623 UNICA. (September 2009) Submission of comments document # EPA-HQ-OAR-2005-0161-

2137.1, pg. 21-29

624 Government of Brazil, “Executive Summary-National Plan on Climate Change”, pg. 10.

625 UNICA. (September 2009) Submission of comments document # EPA-HQ-OAR-2005-0161-

2137.1, pg. 28

626 Version 1.8c of the GREET model, released in March, 2009, was utilized to estimate the

GHG emissions associated with the transportation of domestic and imported ethanol. Information

on this release can be found at: http://www.transportation.anl.gov/modeling_simulation/GREET/

The method used to estimate the GHG impacts associated with biodiesel transportation has not

been changed since the proposal. This method utilized an earlier version of the GREET model.

627 “Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints,"

prepared for EPA by Oak Ridge National Laboratory, March 2009. Data files that support this

report are available in the docket.

628 Energy Information Administration (EIA) historical company level ethanol and gasoline

import data:

http://www.eia.doe.gov/oil_gas/petroleum/data_publications/company_level_imports/cli_historic

al.html

629 UNICA. (August 2009) Submission of comments document # EPA-HQ-OAR-2005-0161-

1761.1

630 UNICA. (September 2009) Submission of comments document # EPA-HQ-OAR-2005-0161-

2137.1

631 https://www.gtap.agecon.purdue.edu. Accessed on January 14, 2010.

632 See Burniaux, Jean-Marc & T. Truong, 2002. "GTAP-E: An Energy-Environmental Version

of the GTAP Model," GTAP Technical Papers #16, Center for Global Trade Analysis,

Department of Agricultural Economics, Purdue University. Available at

https://www.gtap.agecon.purdue.edu/resources/download/1203.pdf.

633 See Lee H, Hertel T, Sohngen B, Ramankutty N. Towards an Integrated Land Use Data Base

for Assessing the Potential for Greenhouse Gas Mitigation. GTAP Technical Paper # 25, Center

for Global Trade Analysis, Purdue University: 2005. Available at

https://www.gtap.agecon.purdue.edu/resources/download/2375.pdf

634 See Birur D, Hertel T, Tyner W. Impact of Biofuel Production on World Agricultural

Markets: A Computable General Equilibrium Analysis. GTAP Working Paper # 53, Department

of Agricultural Economics, Purdue University. Available at

https://www.gtap.agecon.purdue.edu/resources/download/4034.pdf.

635 See Kløverpris J., H. Wenzel, and A. Nielsen (2008) “Life Cycle Inventory Modeling of Land

Use Induced by Crop Consumption.” International Journal of Life Cycle Analysis, 13(1): 13-21.

636 Lee, H.-L., T.W. Hertel, S. Rose and M. Avetisyan (2009) “An Integrated Global Land Use

Data Base for CGE Analysis of Climate Change Policy Options.” Chapter 4 in T. Hertel, S. Rose

and R. Tol (eds.) Economic Analysis of Land Use in Global Climate Policy, Routledge
1074

Publishing, London, New York, pp: 72-88. Also available as GTAP Working Paper # 42 (2008),

Center for Global Trade Analysis, Purdue University, West Lafayette, IN, USA. Available at:

https://www.gtap.agecon.purdue.edu/resources/download/3673.pdf.

637 See https://www.gtap.agecon.purdue.edu/models/cge_gtap_n.asp. Accessed on January 14,

2010.

638 See http://www.arb.ca.gov/fuels/lcfs/030409lcfs_isor_vol1.pdf

639 See Taheripour, Farzad, T.W. Hertel, W.E. Tyner, J. F. Beckman, and D.K. Birur (2009).

“Biofuels and their By-Products: Global Economic and Environmental Implications.” Biomass

and Bioenergy, in press.

640 See Taheripour et al, (2009).

641 Villoria, Nelson and T. Hertel (2009). Geography Matters: International Trade Patterns and

the Indirect

Land Use Effects of Biofuels. Under review at American Journal of Agricultural Economics.

642 See Arndt, C. and K.R. Pearson (1998) “How to Carry Out Systematic Sensitivity Analysis

via Gaussian Quadrature and GEMPACK.” GTAP Technical Paper No. 3, Center for Global

Trade Analysis, Purdue University, West Lafayette, IN. Available at:

https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=474; See also

Keeney, Roman, and T.W. Hertel, The Indirect Land Use Impacts of United States Biofuel

Policies: The Importance of Acreage, Yield, and Bilateral Trade Responses (2009-01). American

Journal of Agricultural Economics, Vol. 91, Issue 4, pp. 895-909, November 2009. Available at

SSRN: http://ssrn.com/abstract=1483971.

643 See Keeney & Hertel (2009).

644 See FAPRI (2004) “Documentation the FAPRI Modeling System.” FAPRI-UMC Report #12-

04, Food and Agricultural Policy Research Institute, Iowa State University, University of

Missouri-Columbia, Ames, IA, USA. December 2004.

645 These values are based on the 2008 Ahmed, Hertel, and Lubowski paper. See Ahmed, Syud

Amer. T. Hertel & R. Lubowski, 2008. "Calibration of a Land Cover Supply Function Using

Transition Probabilities," GTAP Research Memoranda 14, Center for Global Trade Analysis,

Department of Agricultural Economics, Purdue University. Available at

https://www.gtap.agecon.purdue.edu/resources/download/4199.pdf.

646 The MODerate resolution Imagin Spectoradiometer (MODIS) land Products ( MCD12Q1)

Land Cover Type Product (Land Cover Type 1),

https://lpdaac.usgs.gov/lpdaac/products/modis_products_table/land_cover/yearly_l3_global_500

_m/mcd12q1

647http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/TriDecadal_Global_Landsat_

Orthorectified_Overview

648 https://lpdaac.usgs.gov/lpdaac/get_data/data_pool

649 Integrity Applications Incorporated. 2009. Renewable Fuel Lifecycle Analysis – High

Resolution Land Use Change Analysis, Final Report, BPA-09-03/GS-23F-0076M

650 Available in the docket, EPA-HQ-OAR-2005-0161, or on the NETL website at

http://www.netl.doe.gov/energy-analyses/pubs/NETL%20LCA%20PetroleumBased%20Fuels%20Nov%202008.pdf

651 Alex D. Charpentier, Joule A. Bergerson and Heather L. MacLean, “Understanding the

Canadian Oil Sands Industry’s Greenhouse Gas Emissions,” Environmental Research Letters 4

(2009).
1075

652 International Energy Outlook 2007, Report #:DOE/EIA-0484(2007), Release Date: May

2007, Tables G1 and G3.

653 U.S. EPA 2008, Inventory of U.S. Greenhouse Gas Emissions and Sinks (EPA 430-R-08-

005), Tables 3-23 and3-24 http://www.epa.gov/climatechange/emissions/usinventoryreport.html

654 U.S. EPA 2008, Inventory of U.S. Greenhouse Gas Emissions and Sinks (EPA 430-R-08-

005), Table ES-1 http://www.epa.gov/climatechange/emissions/usinventoryreport.html

655 Department of Energy: National Energy Technology Laboratory. 2009. NETL: PetroleumBased Fuels Life Cycle Greenhouse Gas Analysis - 2005 Baseline Model.

www.netl.doe.gov/energy-analyses.

656 IPCC WGI, 2007.

657 “Summary of recent findings for fuel effects of a 10% ethanol blend on light duty exhaust

emissions”, Memo from Aron Butler to Docket EPA-HQ-OAR-2005-0161.

658 “MOVES runs performed to support RFS2 final rule emission inventories”, Memo from John

Koupal to Docket EPA-HQ-OAR-2005-0161

659 MOVES (Motor Vehicle Emission Simulator)

http://www.epa.gov/otaq/models/moves/index.htm

660 U. S. EPA. 2007. Final Regulatory Impact Analysis: Control of Hazardous Air Pollutants

from Mobile Sources; EPA420-R-07-002; Office of Transportation and Air Quality, Ann Arbor,

MI. http://www.epa.gov/otaq/toxics.htm

661 U. S. EPA. 2007. Regulatory Impact Analysis: Renewable Fuel Standard Program;

EPA420-R-07-004; Office of Transportation and Air Quality, Ann Arbor, MI.

http://www.epa.gov/otaq/renewablefuels/index.htm

662 Overview Presentation to CRC on EPACT Testing (September 2008)

663 CRC E-74b report

664 Haskew, Harold M., Thomas F. Liberty and Dennis McClement, “Fuel Permeation from

Automotive Systems,” Final Report, for the Coordinating Research Council and the California

Air Resources Board, CRC Project E-65, September 2004. Available in Docket EPA-HQ-OAR2005-0161.

665 “Analysis of ethanol evaporative permeation effects from CRC E-77 and E-65 programs”,

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666Southwest Research Institute. 2007. Flex Fuel Vehicles (FFVs) VOC/PM Cold Temperature

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Environmental Protection Agency. Available in Docket EPA-HQ-OAR-2005-0161.

667 Environment Canada. 2007. Comparison of Emissions from Conventional and Flexible Fuel

Vehicles Operating on Gasoline and E85 Fuels. ERM Report No. 05-039, Emissions Research

Division. Available in Docket EPA-HQ-OAR-2005-0161.

668 EPA Certification and Fuel Economy Information System. http://www.epa.gov/otaq/cfeis.htm

669 Southwest Research Institute. 2007. Flex Fuel Vehicles (FFVs) VOC/PM Cold Temperature

Characterization When Operating on Ethanol (E10, E70, E85). Prepared for U. S.

Environmental Protection Agency. Available in Docket EPA-HQ-OAR-2005-0161.

670 Graham, L. A.; Belisle, S. L. and C. Baas. 2008. Emissions from light duty gasoline vehicles

operating on low blend ethanol gasoline and E85. Atmos. Environ. 42: 4498-4516.

671 Environment Canada. 2007. Comparison of Emissions from Conventional and Flexible Fuel

Vehicles Operating on Gasoline and E85 Fuels. ERM Report No. 05-039, Emissions Research

Division. Available in Docket EPA-HQ-OAR-2005-0161.
1076

672 Auto/Oil Air Quality Improvement Research Program. 1996. Phase I and II Test Data.

Prepared by Systems Applications International, Inc.

673 U.S. EPA, 2007. Modeling of Ethanol Blend Effects on Nonroad Fuel Hose and Tank

Permeation. Memorandum from Craig Harvey, U. S. EPA, March 26, 2007.

674 U.S. EPA. 2008. Work Plan for Work Assignment 1-07, Amendment 1, “Ethanol Evaluation

on Non-road Engines,” Contract EP-C-07-028, October 3, 2008.

675 U.S. EPA. 2008. Work Plan for Work Assignment 1-07, Amendment 2, “Ethanol Evaluation

on Nonroad Engines,” Contract EP-C-07-028, October 22, 2008.

676 “Effects of Intermediate Ethanol Blends on Legacy Vehicles and Small Non-Road Engines,

Report 1 – Updated,” National Renewable Energy Laboratory, NREL/TP-540-43543,

ORNL/TM-2008/117, February 2009.

677 U.S. EPA, 2008. Control of Emissions From Nonroad Spark-Ignition Engines and

Equipment; Final Rule, 73 FR 59034, Oct. 8, 2008.

678 U.S. EPA, 2008. Biodiesel Effects on Heavy-Duty Highway Engines and Vehicles; Office of

Transportation and Air Quality, Ann Arbor, MI.

679 U.S. EPA 2002 A Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions:

Draft Technical Report (EPA420-P-02-001)

680 U.S. EPA, 2008. Development of MOVES Rates for HC, CO and NOx Exhaust Emissions

from Light-duty Vehicles for use in the RFS2 NRPM, Memorandum from James Warila, U. S.

EPA, November 2008.

681 U.S. EPA. 2008. Kansas City Characterization Study: Final Report (EPA420-R-08-009)

http://www.epa.gov/otaq/emission-factors-research/420r08009.pdf

682 U.S. EPA. 2008. Analysis of Particulate Matter Emissions from Light-Duty Vehicles in

Kansas City (EPA420-R-08-010) http://www.epa.gov/otaq/emission-factorsresearch/420r08010.pdf

683 U.S. EPA, 2008. Modeling Evaporative Emissions in MOVES for the RFS2 NPRM.

Memorandum from Prashanth Gururaja, U.S. EPA, November 2008.

684 U.S. EPA. 2002. Technical Description of the Toxics Module for MOBILE6.2 and Guidance

on Its Use for Emission Inventory Preparation. (EPA420-R-02-029)

http://www.epa.gov/otaq/models/mobile6/r02029.pdf

685 Details about NMIM are available at http://www.epa.gov/otaq/nmim.htm.

686 Information about the 2005 NEI may be found at

http://www.epa.gov/ttn/chief/net/2005inventory.html.

687 Details concerning the Final Rule: Control of Emissions of Air Pollution from New Nonroad

Spark-Ignition Engines, Equipment, and Vessels may be found at

http://www.epa.gov/otaq/equip-ld.htm#.

688 Sze, C., Whinihan, J., Olson, B., Schenk, C., Sobotowski, R. Impact of Test Cycle and

Biodiesel Concentration of Emissions, SAE report 2007-01-4040 (2007).

689 U. S. EPA. 2007. Final Regulatory Impact Analysis: Control of Hazardous Air Pollutants

from Mobile Sources; EPA420-R-07-002; Office of Transportation and Air Quality, Ann Arbor,

MI. http://www.epa.gov/otaq/toxics.htm

690 Landman, L. C. (2007) Estimating Emissions Associated with Portable Fuel Containers

(PFCs). U. S. EPA, Assessment and Standards Division, National Vehicle and Fuel Emissions

Laboratory, Ann Arbor, MI, Report No. EPA420-R-07-001. http://www.epa.gov/otaq/toxics.htm
1077

691 U. S. EPA. 2007. Regulatory Impact Analysis: Renewable Fuel Standard Program; EPA420-

R-07-004; Office of Transportation and Air Quality, Ann Arbor, MI.

http://www.epa.gov/otaq/renewablefuels/index.htm

692 U. S. EPA. 1993. Final Regulatory Impact Analysis for Reformulated Gasoline. December

13, 1993. http://www.epa.gov/otaq/regs/fuels/rfg/

693 Haskew, H. M.; Liberty, T. F.; McClement, D. 2004. Fuel Permeation from Automotive

Systems. Prepared for the Coordinating Research Council by Harold Haskew and Associates

and Automotive Testing Laboratories, Inc. September 2004. CRC Project No. E-65.

http://www.crcao.com. This document is available in Docket EPA-HQ-OAR-2005-0036.

694 Hester, Charles. 2006. Review of Data on HAP Content in Gasoline. Memorandum from

MACTEC to Steve Shedd, U. S. EPA, March 23, 2006. This document is available in Docket

EPA-HQ-OAR-2003-0053.

695 U. S. EPA. 2006. Determination that Gasoline Distribution Stage 1 Area Source (GD AS)

Category Does Not Need to Be Regulated Under Section 112(c)6. Memoradum from Stephen

Shedd to Kent Hustvedt, May 9, 2006. This document is available in Docket EPA-HQ-OAR2005-0036.

696 U. S. EPA. SPECIATE Version 4.0.

http://www.epa.gov/ttn/chief/software/speciate/index.html

697 Haskew, H. M., Liberty, T. F., McClement, D. 2004. Fuel Permeation from Automotive

Systems. Report No. CRC E-65, Prepared for California Air Resources Board and Coordinating

Research Council. http://www.crcao.org/

698 United States Department of Agriculture, Amber Waves, February 2004, page 9, U.S.

Increasingly Imports Nitrogen and Potash Fertilizer.

http://www.ers.usda.gov/amberwaves/February04/

699 Wen Huang, United States Department of Agriculture, Economic Research Service, personal

communication to Craig Harvey, US EPA, November 9, 2007.

700 U.S. EPA. 2009. RFS2 Final Fuel Production/Distribution Emission Impacts Calculation

Spreadsheet, 12/23/2009.

701 40 CFR Part 63 National Emission Standards for Hazardous Air Pollutants for Source

Categories: Gasoline Distribution Bulk Terminals, Bulk Plants, Pipeline Facilities, and Gasoline

Dispensing Facilities; Proposed Rule, Thursday, November 9, 2006, 66064-66092.

702 E. H. Pechan. Documentation For The Final 2002 Nonpoint Sector (Feb 06 Version) National

Emission Inventory For Criteria And Hazardous Air Pollutants, Appendix A, pp. A-89 to A-105.

Prepared for Emissions Inventory and Analysis Group (C339-02) Air Quality Assessment

Division Office of Air Quality Planning and Standards U.S. Environmental Protection Agency

Research Triangle Park, NC 27711, Report No. 05.10.001/9014.401 Jul-06. Available at

ftp://ftp.epa.gov/EmisInventory/2002finalnei/documentation/nonpoint/2002nei_final_nonpoint_d

ocumentation0206version.pdf.

703 Energy Information Administration. 2007. Product Supplied, Petroleum Supply Reporting

System. http://tonto.eia.doe.gov/dnav/pet/pet_cons_psup_dc_nus_mbbl_a.htm

704 EPA. 1994. Final Regulatory Impact Analysis: Refueling Emission Regulations for Light

Duty Vehicles and Trucks and Heavy Duty Vehicles. Office of Mobile Sources, Ann Arbor, MI.

Available at http://www.epa.gov/otaq/orvr.htm.
1078

705 U. S. EPA. 2001. Final Rule: Control of Air Pollution From New Motor Vehicles: HeavyDuty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements,

January 18, 2001.

706 U. S. EPA. 2008. Final Rule: Control of Emissions of Air Pollution from Locomotives and

Marine Compression-Ignition Engines Less than 30 Liters per Cylinder, Regulatory Impact

Analysis, June 30, 2008.

http://www.epa.gov/otaq/locomotv.htm

707 U.S. EPA. 2008. RFS2 Modified version of GREET1.7 Upstream Emissions Spreadsheet,

October 31, 2008.

708 U.S. EPA. 2009. RFS2 FRM modified version of GREET1.8c Upstream Emissions

Spreadsheet, October 30, 2009.

709 Emission Inventories for Ocean-Going Vessels Using Category 3 Propulsion Engines In or

Near the United States, Draft Technical Support Document, EPA420-D-07-007, December 2007.

This is available in Docket OAR-2007-0121at http://www.regulations.gov/ .

710 U. S. EPA. 2009. National-Scale Air Toxics Assessment for 2002.

http://www.epa.gov/ttn/atw/nata2002.

711 U. S. EPA. 2007. Approach for Developing 2002 and Future Emission Summaries.

Memorandum from Madeleine Strum to Docket, November 26, 2007. This document is

available in Docket EPA-HQ-OAR-2007-0491.

712 Federal Register. 2007. National Emission Standards for Hazardous Air Pollutants From

Petroleum Refineries. 72(170): 50715-50742. http://www.epa.gov/EPAAIR/2007/September/Day-04/a17009.htm.

713 Energy Information Administration. 2007. Product Supplied, Petroleum Supply Reporting

System. http://tonto.eia.doe.gov/dnav/pet/pet_cons_psup_dc_nus_mbbl_a.htm

714 Brady, D. and Pratt, G. C. 2007. Volatile Organic Compound Emissions from Dry Mill Fuel

Ethanol Production. J. Air & Waste Manage. Assoc. 57: 1091-1102.

715 Prevention of Significant Deterioration Construction Permit Application. Prepared for

Verasun Fort Dodge, LLC, Fort Dodge Iowa, by Barr, Minneapolis, MN, December, 2007.

716 See Chapter 1.5 of the Draft Regulatory Impact Assessment of this rulemaking.

717U. S. EPA. 2007. Control of Hazardous Air Pollutants from Mobile Sources; Final Rule.

Federal Register, 72: 8428-8570.

718 U. S. EPA. 2007. Regulation of Fuels and Fuel Additives: Renewable Fuel Standard

Program; Final Rule. Federal Register, 72: 23900-24014.

719 U. S. EPA. 2007. Final Regulatory Impact Analysis: Control of Hazardous Air Pollutants

from Mobile Sources; EPA420-R-07-002; Office of Transportation and Air Quality, Ann Arbor,

MI. http://www.epa.gov/otaq/toxics.htm

720 Energy Information Adminstration. 1999. Petroleum Marketing Annual. Report No.

DOE/EIA-0487(99).

http://www.eia.doe.gov/pub/oil_gas/petroleum/data_publications/petroleum_marketing_annual/h

istorical/1999/pma_1999.html

721 Energy Information Administration. 2007. Annual Energy Outlook 2007 with Projections to

2030. U.S. Department of Energy, Report # DOE/EIA-0383.

http://www.eia.doe.gov/oiaf/aeo/index.html

722 U. S. EPA. SPECIATE Version 4.0.

http://www.epa.gov/ttn/chief/software/speciate/index.html
1079

723 723 Auto/Oil Air Quality Improvement Research Program. 1996. Phase I and II Test Data.

Prepared by Systems Applications International, Inc.

724 Diesel Locomotives, Marine Distillate, Residual Boiler -- E. H. Pechan. 2005.

Documentation For Aircraft, Commercial Marine Vessel, Locomotive, And Other Nonroad

Components Of The National Emissions Inventory, Volume I - Methodology.

Prepared for U. S. Environmental Protection Agency, Office of Air Quality Planning and

Standards, Research Triangle Park, NC. http://www.epa.gov/ttn/chief/net/2002inventory.html

725 HDD Trucks -- R. Cook, E. Glover. 2002. Technical Description of the Toxics Module for

MOBILE6.2 and Guidance on Its Use for Emission Inventory Preparation. U. S. Environmental

Protection Agency, Office of Transportation and Air Quality. Report No. EPA420-R-02-011.

http://www.epa.gov/otaq/m6.htm

726 Natural Gas Turbines -- U. S. EPA. 2000. AP-42, 5th Edition, Volume 1. Chapter 3:

Stationary Internal Combustion Sources. http://www.epa.gov/ttn/chief/ap42/ch03/index.html

727 U.S. EPA. 2010. “RFS2 Emissions Inventory for Air Quality Modeling Technical Support

Document,” January 2010.

728 “Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints,”

Oak Ridge National Laboratory, U.S. Department of Energy, March 2009.

729 U.S. EPA. 2009. spreadsheet tool, “2022 Ethanol Vapor Losses during Transport”

12/4/2009.

730 U.S. EPA. 2008. spreadsheet tool, “Ethanol Transport/Distribution VOC Emissions for

RFS2 2022 AQ Modeling: RFS1 Case,” 12/15/2008.

731 U.S. EPA. 2008. spreadsheet tool, “Ethanol Transport/Distribution VOC Emissions for RFS2

2022 AQ Modeling: AEO Case,” 12/11/2008.

732 U.S. EPA. 2008. spreadsheet tool, “Ethanol Transport/Distribution VOC Emissions for

RFS2 2022 AQ Modeling: EISA (RFS2) Case,” 12/15/2008.

733 U. S. EPA. 2008. AP-42, 5th Edition, Volume 1. Chapter 5.2: Transportation And

Marketing Of Petroleum Liquids, July 2008.

734 U.S. EPA. 2009. “Draft Regulatory Impact Analysis: Control of Emissions of Air Pollution

from Category 3 Marine Diesel Engines (Chapter 3: Emission Inventory),” EPA-420-D-09-002,

June 2009.

735 U.S. Army Corps of Engineers Principal Ports file for 2006 (PPORTS06.xls)

736 U.S. EPA. 2008. Spreadsheet file: “RFS2 Ethanol Usage & Constraint Analysis

Inputs.6.23.08.xls”

737 “Annual Energy Outlook 2008,” Table 7 (Transportation Sector Key Indicators and

Delivered Energy Consumption), DOE/EIA-0383(2008), June 2008.

738 The three cases and their corresponding Excel files are as follows:

RFS1: County_To_CountyRefNew_RFS1_1116.xls

AEO: County_To_CountyRefOld_1113R2.xls

EISA: County_To_County2022_Controlled_1114.xls

739 U.S. EPA. 2009. “Ethanol Plant inventory inputs for RFS2 2005 AQ model run,”

Spreadsheet file: “Corn_EtOH_Plant_Inv_2005c.xls”

740 U.S. EPA. 2009. “Ethanol Plant inventory inputs for RFS2 2022 RFS1 reference case AQ

model run,” Spreadsheet file: “Corn_EtOH_Plant_Inv_2022-rfs1-fixC.xls”

741 U.S. EPA. 2009. “Ethanol Plant inventory inputs for RFS2 2022 AEO reference case AQ

model run,” Spreadsheet file: “Corn_EtOH_Plant_Inv_2022-aeo-fixC.xls”
1080

742 U.S. EPA. 2009. “Ethanol Plant inventory inputs for RFS2 2022 EISA reference case AQ

model run,” Spreadsheet file: “Corn_EtOH_Plant_Inv_2022-eisa-fixC.xls”

743 U.S. EPA. 2009. Spreadsheet file: Cellulosic Ethanol Plant inventory inputs for RFS2 2022

EISA control case AQ model run,” 2/25/2009.

744 U.S. EPA. 2009. “Biodiesel Plant inventory inputs for RFS2 2022 RFS1 reference AQ

model run,” Spreadsheet file: “Biodsl_Plant_Inv_2022-rfs1-fixC.xls,” 2/25/2009.

745 U.S. EPA. 2009. “Biodiesel Plant inventory inputs for RFS2 2022 AEO reference AQ model

run,” Spreadsheet file: Biodsl_Plant_Inv_2022-aeo-fixC.xls,” 2/25/2009.

746 U.S. EPA. 2009. “Biodiesel Plant inventory inputs for RFS2 2022 EISA control case AQ

model run,” Spreadsheet file: Biodsl_Plant_Inv_2022-eisa-fixC.xls,” 2/25/2009.

747 U.S. EPA. 2008. “Agricultural SCCs and Emissions Affected by RFS2 in Air Quality

Modeling,” spreadsheet file: “otaq_upstream_2020cc_2020ce_2005ag_tox-docket.xls”

11/5/2008.

748 U.S. EPA. 2008. “Refinery Emission Adjustments for RFS2 Air Quality Modeling,”

spreadsheet file: “RFS2_Refinery_Adjust-docket.xls,” 11/25/2008.

749 U.S. EPA. 2008. “Adjustments to 2022 Platform Gasoline transp & distrib emissions for

RFS2 AQ,” spreadsheet file: “2005ai_tox_SCC_50state_CAPHAP-20081216.xls” 12/16/2008.

750 See http://www.epa.gov/ttn/chief/emch/index.html#2002.

751 Final Rule: Control of Emissions of Air Pollution from Locomotives and Marine

Compression-Ignition Engines Less Than 30 Liters per Cylinder (published May 6, 2008 and

republished June 30, 2008). For details, see

http://www.epa.gov/otaq/locomotives.htm#2008final.

752 U.S. EPA, 2010. NMIM and MOVES Runs for RFS2 Air Quality Modeling: Memorandum,

January 2010.

753 Details concerning the Final Rule: Control of Emissions of Air Pollution from New Nonroad

Spark-Ignition Engines, Equipment, and Vessels may be found at

http://www.epa.gov/otaq/equip-ld.htm#.

754 Sze, C., Whinihan, J., Olson, B., Schenk, C., Sobotowski, R. Impact of Test Cycle and

Biodiesel Concentration of Emissions, SAE report 2007-01-4040 (2007).

755 Byun, D.W., Ching, J. K.S. (1999). Science algorithms of the EPA models-3 community

multiscale air quality (CMAQ) modeling system. Washington, DC: U.S. Environmental

Protection Agency, Office of Research and Development.

756 Byun, D.W., Schere, K.L. (2006). Review of the Governing Equations, Computational

Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality

(CMAQ) Modeling System. Journal of Applied Mechanics Reviews, 59(2), 51-77.

757 Dennis, R.L., Byun, D.W., Novak, J.H., Galluppi, K.J., Coats, C.J., and Vouk, M.A. (1996).

The next generation of integrated air quality modeling: EPA’s Models-3, Atmospheric

Environment, 30, 1925-1938.

758 Hogrefe, C., Biswas, J., Lynn, B., Civerolo, K., Ku, J.Y., Rosenthal, J., et al. (2004).

Simulating regional-scale ozone climatology over the eastern United States: model evaluation

results. Atmospheric Environment, 38(17), 2627-2638.

759 Lin, M., Oki, T., Holloway, T., Streets, D.G., Bengtsson, M., Kanae, S. (2008). Long-range

transport of acidifying substances in East Asia-Part I: Model evaluation and sensitivity studies.

Atmospheric Environment, 42(24), 5939-5955.
1081

760 United States Environmental Protection Agency. (2008). Technical support document for the

final locomotive/marine rule: Air quality modeling analyses. Research Triangle Park, N.C.: U.S.

Environmental Protection Agency, Office of Air Quality Planning and Standards, Air Quality

Assessment Division.

761 Grell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth-Generation Penn

State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for

Atmospheric Research, Boulder CO.

762 Grell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth-Generation Penn

State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for

Atmospheric Research, Boulder CO.

763 U.S. Environmental Protection Agency, Byun, D.W., and Ching, J.K.S., Eds, 1999. Science

algorithms of EPA Models-3 Community Multiscale Air Quality (CMAQ) modeling system,

EPA/600/R-99/030, Office of Research and Development). Please also see:

http://www.cmascenter.org/

764 Yantosca, B. (2004). GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry Modeling

Group, Harvard University, Cambridge, MA, October 15, 2004.

765 U.S. EPA, (2004), Procedures for Estimating Future PM2.5 Values for the CAIR Final Rule

by Application of the (Revised) Speciated Modeled Attainment Test (SMAT)- Updated 11/8/04.

766 U.S. EPA, (2008), Control of Emissions from Nonroad Spark-Ignition Engines and

Equipment, Technical Support Document. EPA 454/R-08-005

767 U.S. EPA (2007) Guidance on the Use of Models and Other Analyses For Demonstrating

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze; EPA-454/B-07-002;

Research Triangle Park, NC; April 2007.

768 Yarwood G, Rao S, Yocke M, Whitten GZ (2005) Updates to the Carbon Bond Chemical

Mechanism: CB05. Final Report to the US EPA, RT-0400675, December 8, 2005.

http://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf.

769 74 FR 24904. Regulation of fuels and Fuel Additives: Changes to Renewable Fuel Standard

Program. Notice of Proposed Rulemaking.

770 Dodge, M.C., 2000. Chemical oxidant mechanisms for air quality modeling: critical review.

Atmospheric Environment 34, 2103-2130.

771 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G.,

Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data

for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for

Atmospheric Chemistry. July 2005 web version. http://www.iupackinetic.ch.cam.ac.uk/index.html.

772 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G.,

Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data

for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for

Atmospheric Chemistry. July 2005 web version. http://www.iupackinetic.ch.cam.ac.uk/index.html.

773 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G.,

Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data

for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for

Atmospheric Chemistry. July 2005 web version. http://www.iupackinetic.ch.cam.ac.uk/index.html.
1082

774 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G.,

Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data

for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for

Atmospheric Chemistry. July 2005 web version. http://www.iupackinetic.ch.cam.ac.uk/index.html.

775 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat,

G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J. (2003) Chemical

Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. NASA

Jet Propulsion Laboratory http://jpldataeval.jpl.nasa.gov/index.html.

776 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat,

G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J. (2003) Chemical

Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. NASA

Jet Propulsion Laboratory http://jpldataeval.jpl.nasa.gov/index.html.

777 Yarwood, G., Rao, S., Yocke, M., Whitten, G.Z., 2005. Updates to the Carbon Bond

Mechanism: CB05. Final Report to the US EPA, RT-0400675. Yocke and Company, Novato,

CA.

778 Luecken, D.J., Phillips, S., Sarwar, G., Jang, C., 2008b. Effects of using the CB05 vs.

SAPRC99 vs. CB4 chemical mechanism on model predictions: Ozone and gas-phase

photochemical precursor concentrations. Atmospheric Environment 42, 5805-5820.

779 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat,

G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J., 2003. Chemical

Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. NASA

Jet Propulsion Laboratory.

780 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat,

G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J., 2003. Chemical

Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. NASA

Jet Propulsion Laboratory.

781 Atkinson R, Arey J (2003) Atmospheric Degradation of Volatile Organic Compounds. Chem

Rev 103: 4605-4638.

782 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G.,

Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data

for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for

Atmospheric Chemistry. July 2005 web version. http://www.iupackinetic.ch.cam.ac.uk/index.html.

783 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G.,

Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data

for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for

Atmospheric Chemistry. July 2005 web version. http://www.iupackinetic.ch.cam.ac.uk/index.html.

784 Edney, E. O., T. E. Kleindienst, M. Lewandowski, and J. H. Offenberg, 2007. Updated SOA

chemical mechanism for the Community Multi-Scale Air Quality model, EPA 600/X-07/025,

U.S. EPA, Research Triangle Park, NC.

785 Carlton, A.G., B. J. Turpin, K. Altieri, S. Seitzinger, R. Mathur, S. Roselle, R. J. Weber,

(2008), CMAQ model performance enhanced when in-cloud SOA is included: comparisons of

OC predictions with measurements, Environ. Sci. Technol. 42 (23), 8798–8802.
1083

786 Lewandowski M, M Jaoui , JH Offenberg , TE Kleindienst, EO Edney, RJ Sheesley, JJ

Schauer (2008) Primary and secondary contributions to ambient PM in the midwestern United

States, Environ Sci Technol 42(9):3303-3309.

http://pubs.acs.org/cgi-bin/article.cgi/esthag/2008/42/i09/html/es0720412.html

787 Kleindienst TE, M Jaoui, M Lewandowski, JH Offenberg, EO Edney (2007) Estimates of the

contributions of biogenic and anthropogenic hydrocarbons to secondary organic aerosol at a

southeastern U.S. location, Atmos Environ 41(37):8288-8300.

788 Offenberg JH, CW Lewis, M Lewandowski, M Jaoui, TE Kleindienst, EO Edney (2007)

Contributions of Toluene and -pinene to SOA Formed in an Irradiated Toluene/-

pinene,NOx/Air Mixture: Comparison of Results Using 14C Content and SOA Organic Tracer

Methods, Environ Sci Technol 41: 3972-3976.

789 Pandis, S.N., Harley, R.A., Cass, G.R., Seinfeld, J.H. (1992) Secondary organic aerosol

formation and transport. Atmos Environ 26, 2269–2282.

790 H Takekawa, H Minoura, S Yamazaki (2003) Temperature dependence of secondary organic

aerosol formation by photo-oxidation of hydrocarbons. Atmos Environ 37: 3413–3424.

791 M J. Kleeman, Q Ying, Jin Lu, M J. Mysliwiec, R J. Griffin, Jianjun Chen, Simon Clegg

(2007) Source apportionment of secondary organic aerosol during a severe photochemical smog

episode. Atmos Environ 41: 576–591.

792 Robinson, A. L.; Donahue, N. M.; Shrivastava, M.; Weitkamp, E. A.; Sage, A. M.;

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802 Offenberg JH, CW Lewis, M Lewandowski, M Jaoui, TE Kleindienst, EO Edney (2007)

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803 Claeys M, R Szmigielski, I Kourtchev, P Van der Veken, R Vermeylen, W Maenhaut, M

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804 Edney EO, TE Kleindienst, M Jaoui, M Lewandowski, JH Offenberg, W Wang, M Claeys

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905 Ryan, P.H.; LeMasters, G.K.; Biswas, P.; Levin, L.; Hu, S.; Lindsey, M.; Bernstein, D.I.;

Lockey, J.; Villareal, M.; Khurana Hershey, G.K.; Grinshpun, S.A. (2007) A comparison of

proximity and land use regression traffic exposure models and wheezing in infants. Environ

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906 Morgenstern, V.; Zutavern, A.; Cyrys, J.; Brockow, I.; Gehring, U.; Koletzko, S.; Bauer, C.P.;

Reinhart, D; Wichmann, H-E.; Heinrich, J. (2007) Respiratory health and individual estimated

expousure to traffic-related air pollutant in a cohort of young children. Occupational and

Environmental Medicine 64: 8-16.

907 Franco Suglia, S.; Gryparis, A.; Wright, R.O.; Schwartz, J.; Wright, R.J. (2007) Association

of black carbon with cognition among children in a prospective birth cohort study. American

Journal of Epidemiology 167: 280-286.

908 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants

(Final). EPA/600/R-05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009

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909 U.S. EPA. (2007). Review of the National Ambient Air Quality Standards for Ozone: Policy

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910 National Research Council (NRC), 2008. Estimating Mortality Risk Reduction and Economic

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911 Bates, D.V., Baker-Anderson, M., Sizto, R. (1990). Asthma attack periodicity: a study of

hospital emergency visits in Vancouver. Environ. Res., 51,51-70.

912 Thurston, G.D., Ito, K., Kinney, P.L., Lippmann, M. (1992). A multi-year study of air

pollution and respiratory hospital admissions in three New York State metropolitan areas:

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913 Thurston, G.D., Ito, K., Hayes, C.G., Bates, D.V., Lippmann, M. (1994) Respiratory hospital

admissions and summertime haze air pollution in Toronto, Ontario: consideration of the role of

acid aerosols. Environ. Res., 65, 271-290.

914 Lipfert, F.W., Hammerstrom, T. (1992). Temporal patterns in air pollution and hospital

admissions. Environ. Res., 59,374-399.

915 Burnett, R.T., Dales, R.E., Raizenne, M.E., Krewski, D., Summers, P.W., Roberts, G.R.,

Raad-Young, M., Dann,T., Brook, J. (1994). Effects of low ambient levels of ozone and sulfates

on the frequency of respiratory admissions to Ontario hospitals. Environ. Res., 65, 172-194.

916 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants

(Final). EPA/600/R-05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009

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917 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants

(Final). EPA/600/R-05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009

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918 Devlin, R. B., McDonnell, W. F., Mann, R., Becker, S., House, D. E., Schreinemachers, D.,

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919 Koren, H. S., Devlin, R. B., Becker, S., Perez, R., McDonnell, W. F. (1991). Time-dependent

changes of markers associated with inflammation in the lungs of humans exposed to ambient

levels of ozone. Toxicol. Pathol., 19, 406-411.

920 Koren, H. S., Devlin, R. B., Graham, D. E., Mann, R., McGee, M. P., Horstman, D. H.,

Kozumbo, W. J., Becker, S., House, D. E., McDonnell, W. F., Bromberg, P. A. (1989). Ozoneinduced inflammation in the lower airways of human subjects. Am. Rev. Respir. Dis., 39, 407-

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921 Schelegle, E.S., Siefkin, A.D., McDonald, R.J. (1991). Time course of ozone-induced

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922 U.S. EPA. (1996). Air Quality Criteria for Ozone and Related Photochemical Oxidants.

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923 Hodgkin, J.E., Abbey, D.E., Euler, G.L., Magie, A.R. (1984). COPD prevalence in

nonsmokers in high and low photochemical air pollution areas. Chest, 86, 830-838.

924 Euler, G.L., Abbey, D.E., Hodgkin, J.E., Magie, A.R. (1988). Chronic obstructive pulmonary

disease symptom effects of long-term cumulative exposure to ambient levels of total oxidants

and nitrogen dioxide in California Seventh-day Adventist residents. Arch. Environ. Health, 43,

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925 Abbey, D.E., Petersen, F., Mills, P.K., Beeson, W.L. (1993). Long-term ambient

concentrations of total suspended particulates, ozone, and sulfur dioxide and respiratory

symptoms in a nonsmoking population. Arch. Environ. Health, 48, 33-46.

926 U.S. EPA. (2007). Review of the National Ambient Air Quality Standards for Ozone: Policy

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927 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants

(Final). EPA/600/R-05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009

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928 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants

(Final). EPA/600/R-05/004aF-cF. Washington, DC: U.S. EPA. Retrieved on March 19, 2009

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929 Avol, E.L., Trim, S. C., Little, D.E., Spier, C.E., Smith, M. N., Peng, R.-C., Linn, W.S.,

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exposure and lung function in children attending a southern California summer camp. Paper no.

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930 Higgins, I. T.T., D'Arcy, J. B., Gibbons, D. I., Avol, E. L., Gross, K.B. (1990). Effect of

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931 Raizenne, M.E., Burnett, R.T., Stern, B., Franklin, C.A., Spengler, J.D. (1989) Acute lung

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932 Raizenne, M.; Stern, B.; Burnett, R.; Spengler, J. (1987 June) Acute respiratory function and

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annual meeting of the Air Pollution Control Association, New York, NY.

933 Spektor, D. M., Lippmann, M. (1991). Health effects of ambient ozone on healthy children at

a summer camp. In: Berglund, R. L.; Lawson, D. R.; McKee, D. J., eds. Tropospheric ozone and

the environment: papers from an international conference; March 1990; Los Angeles, CA.

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934 Spektor, D. M., Thurston, G.D., Mao, J., He, D., Hayes, C., Lippmann, M. (1991). Effects of

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935 Spektor, D. M., Lippman, M., Lioy, P. J., Thurston, G. D., Citak, K., James, D. J., Bock, N.,

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normal children. Am. Rev. Respir. Dis., 137, 313-320.

936 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants

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937 Hazucha, M. J., Folinsbee, L. J., Seal, E., Jr. (1992). Effects of steady-state and variable ozone

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938 Horstman, D.H., Ball, B.A., Folinsbee, L.J., Brown, J., Gerrity, T. (1995) Comparison of

pulmonary responses of asthmatic and nonasthmatic subjects performing light exercise while

exposed to a low level of ozone. Toxicol. Ind. Health., 11(4), 369-85.

939 Horstman, D.H.,; Folinsbee, L.J., Ives, P.J., Abdul-Salaam, S., McDonnell, W.F. (1990).

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940 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1,

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941 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1,

2000. U.S. Environmental Protection Agency, Office of Research and Development, National

Center for Environmental Assessment, Washington, D.C. This document is available online at

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942 Coburn, R.F. (1979) Mechanisms of carbon monoxide toxicity. Prev. Med. 8:310-322.

943 Helfaer, M.A., and Traystman, R.J. (1996) Cerebrovascular effects of carbon monoxide. In:

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944 Benignus, V.A. (1994) Behavioral effects of carbon monoxide: meta analyses and

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945 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1,

2000. U.S. Environmental Protection Agency, Office of Research and Development, National

Center for Environmental Assessment, Washington, D.C. This document is available online at

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946 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1,

2000. U.S. Environmental Protection Agency, Office of Research and Development, National

Center for Environmental Assessment, Washington, D.C. This document is available online at

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947 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1,

2000. U.S. Environmental Protection Agency, Office of Research and Development, National

Center for Environmental Assessment, Washington, D.C. This document is available online at

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948 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1,

2000. U.S. Environmental Protection Agency, Office of Research and Development, National

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949 U. S. EPA. (2009) 2002 National-Scale Air Toxics Assessment.

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950 U.S. EPA (2007) Control of Hazardous Air Pollutants from Mobile Sources. 72 FR 8428;

February 26, 2007.

951 U.S. EPA (2003) Integrated Risk Information System File of Acrolein. National Center for

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material is available electronically at http://www.epa.gov/iris/subst/0364.htm.

952 U.S. EPA (2009) National-Scale Air Toxics Assessment for 2002. This material is available

electronically at http://www.epa.gov/ttn/atw/nata2002/risksum.html.
1094

953 U.S. EPA (2009) National-Scale Air Toxics Assessment for 2002.

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954 U.S. EPA. 2000. Integrated Risk Information System File for Benzene. This material is

available electronically at: http://www.epa.gov/iris/subst/0276.htm.

955 International Agency for Research on Cancer, IARC monographs on the evaluation of

carcinogenic risk of chemicals to humans, Volume 29, Some industrial chemicals and dyestuffs,

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389, 1982.

956 Irons, R.D.; Stillman, W.S.; Colagiovanni, D.B.; Henry, V.A. (1992) Synergistic action of the

benzene metabolite hydroquinone on myelopoietic stimulating activity of

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957 International Agency for Research on Cancer (IARC). 1987. Monographs on the evaluation

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chemicals and dyestuffs, World Health Organization, Lyon, France.

958 U.S. Department of Health and Human Services National Toxicology Program 11th Report

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959 Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene. Environ. Health

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960 Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State of the Art Reviews.

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961 Rothman, N., G.L. Li, M. Dosemeci, W.E. Bechtold, G.E. Marti, Y.Z. Wang, M. Linet, L.Q.

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29: 236-246.

962 U.S. EPA 2002 Toxicological Review of Benzene (Noncancer Effects). Environmental

Protection Agency, Integrated Risk Information System (IRIS), Research and Development,

National Center for Environmental Assessment, Washington DC. This material is available

electronically at http://www.epa.gov/iris/subst/0276.htm.

963 Qu, O.; Shore, R.; Li, G.; Jin, X.; Chen, C.L.; Cohen, B.; Melikian, A.; Eastmond, D.;

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M.; Kwok, E.; Li, Y.; Mu, R.; Xu, B.; Zhang, X.; Li, K. (2003). HEI Report 115, Validation &

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964 Qu, Q., R. Shore, G. Li, X. Jin, L.C. Chen, B. Cohen, et al. (2002). Hematological changes

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965 Lan, Qing, Zhang, L., Li, G., Vermeulen, R., et al. (2004). Hematotoxically in Workers

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966 Turtletaub, K.W. and Mani, C. (2003). Benzene metabolism in rodents at doses relevant to

human exposure from Urban Air. Research Reports Health Effect Inst. Report No.113.

967 U.S. EPA. 2002. Health Assessment of 1,3-Butadiene. Office of Research and Development,

National Center for Environmental Assessment, Washington Office, Washington, DC. Report

No. EPA600-P-98-001F. This document is available electronically at

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968 U.S. EPA. 2002 “Full IRIS Summary for 1,3-butadiene (CASRN 106-99-0)” Environmental

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969 International Agency for Research on Cancer (IARC) (1999) Monographs on the evaluation

of carcinogenic risk of chemicals to humans, Volume 71, Re-evaluation of some organic

chemicals, hydrazine and hydrogen peroxide and Volume 97 (in preparation), World Health

Organization, Lyon, France.

970 U.S. Department of Health and Human Services National Toxicology Program 11th Report

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971 Bevan, C.; Stadler, J.C.; Elliot, G.S.; et al. (1996) Subchronic toxicity of 4-vinylcyclohexene

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975 Goodlett CR, KH Horn, F Zhou. 2005. Alcohol teratogeniesis: mechanisms of damage and

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976 Riley EP, CL McGee. 2005. Fetal alcohol spectrum disorders: an overview with emphasis on

changes in brain and behavior. Exp. Biol. Med. 230:357-365.

977 Zhang X, JH Sliwowska, J Weinberg. 2005. Prenatal alcohol exposure and fetal

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978 Riley EP, CL McGee, ER Sowell. 2004. Teratogenic effects of alcohol: a decade of brain

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979 Gunzerath L, V Faden, S Zakhari, K Warren. 2004. National Institute on Alcohol Abuse and

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980 World Health Organization (WHO). 2004. Global status report on alcohol 2004. Geneva,

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981 Chen W-JA, SE Maier, SE Parnell, FR West. 2003. Alcohol and the developing brain:

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982 Driscoll CD, AP Streissguth, EP Riley. 1990. Prenatal alcohol exposure comparability of

effects in humans and animal models. Neurotoxicol. Teratol. 12:231-238.

983 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and Certain Home

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984 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2003. Mortality from

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985 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004. Mortality from

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986 Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Hoover, R. N.;

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987 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to

formaldehyde: an update. Occup. Environ. Med. 61: 193-200.

988 Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Extended follow-up of a cohort of British

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989 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller.

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990 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller.

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991 Chemical Industry Institute of Toxicology (CIIT).1999. Formaldehyde: Hazard

characterization and dose-response assessment for carcinogenicity by the route of inhalation.

CIIT, September 28, 1999. Research Triangle Park, NC.

992 U.S. EPA. Analysis of the Sensitivity and Uncertainty in 2-Stage Clonal Growth Models for

Formaldehyde with Relevance to Other Biologically-Based Dose Response (BBDR) Models.

U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R-08/103, 2008

993 Subramaniam, R; Chen, C; Crump, K; .et .al. (2008) Uncertainties in biologically-based

modeling of formaldehyde-induced cancer risk: identification of key issues. Risk Anal

28(4):907-923.

994 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in the CIIT 2-stage model

for formaldehyde-induced nasal cancer in the F344 rat: a limited sensitivity analysis-I. Risk Anal

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995 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated

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996 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated model

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997 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in the CIIT 2-stage model

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998 International Agency for Research on Cancer (2006) Formaldehyde, 2-Butoxyethanol and 1-

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999 Agency for Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological profile for

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Service. http://www.atsdr.cdc.gov/toxprofiles/tp111.html

1000 WHO (2002) Concise International Chemical Assessment Document 40: Formaldehyde.

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1001 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. Research and

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1002 U.S. Department of Health and Human Services National Toxicology Program 11th Report

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1003 International Agency for Research on Cancer (IARC). 1999. Re-evaluation of some organic

chemicals, hydrazine, and hydrogen peroxide. IARC Monographs on the Evaluation of

Carcinogenic Risk of Chemical to Humans, Vol 71. Lyon, France.

1004 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. This material

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1005 U.S. EPA. 2003. Integrated Risk Information System File of Acrolein. Research and

Development, National Center for Environmental Assessment, Washington, DC. This material is

available electronically at http://www.epa.gov/iris/subst/0364.htm.

1006 Appleman, L.M., R.A. Woutersen, and V.J. Feron. (1982). Inhalation toxicity of

acetaldehyde in rats. I. Acute and subacute studies. Toxicology. 23: 293-297.

1007 Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda, T. (1993) Aerosolized

acetaldehyde induces histamine-mediated bronchoconstriction in asthmatics. Am. Rev.

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1008 Integrated Risk Information System File of Acrolein. Research and Development, National

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1009 International Agency for Research on Cancer (IARC). 1995. Monographs on the evaluation

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1014 U.S. EPA (U.S. Environmental Protection Agency). (2003) Toxicological review of acrolein in

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Environmental Protection Agency, Washington, D.C., EPA 600/R-05/004aF-cF, 2006. page 5-
1098

63. This document is available in Docket EPA-HQ-OAR-2005-0161. This document may be

accessed electronically at: http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_cd.html

1018 U.S. EPA Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

Environmental Protection Agency, Washington, D.C., EPA 600/R-05/004aF-cF, 2006. page 5-

78. This document is available in Docket EPA-HQ-OAR-2005-0161. This document may be

accessed electronically at: http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_cd.html

1019 U. S. EPA. 2004. Toxicological Review of Naphthalene (Reassessment of the Inhalation

Cancer Risk), Environmental Protection Agency, Integrated Risk Information System, Research

and Development, National Center for Environmental Assessment, Washington, DC. This

material is available electronically at http://www.epa.gov/iris/subst/0436.htm.

1020 Oak Ridge Institute for Science and Education. (2004). External Peer Review for the IRIS

Reassessment of the Inhalation Carcinogenicity of Naphthalene. August 2004.

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=84403

1021 National Toxicology Program (NTP). (2004). 11th Report on Carcinogens. Public Health

Service, U.S. Department of Health and Human Services, Research Triangle Park, NC.

Available from: http://ntp-server.niehs.nih.gov.

1022 International Agency for Research on Cancer (IARC). (2002). Monographs on the

Evaluation of the Carcinogenic Risk of Chemicals for Humans. Vol. 82. Lyon, France.

1023 U. S. EPA. 1998. Toxicological Review of Naphthalene, Environmental Protection Agency,

Integrated Risk Information System, Research and Development, National Center for

Environmental Assessment, Washington, DC. This material is available electronically at

http://www.epa.gov/iris/subst/0436.htm

1024 EPA. 2005. Full IRIS Summary for n-Hexane (CASRN 11054-3). Integrated Risk

Information System (IRIS), Office of Research and Development, National Center for

Environmental Assessment, Washington, DC. Available at www.epa.gov/iris/subst/0486.htm.

1025 Inhalation health effect concerns for volatilized a.i. compounds are discussed for individual

products, e.g., oxydimeton-methyl, carbaryl (and carbamates,generally), diazinon, at

http://www.epa.gov/pesticides/reregistration/status.htm . A.I. vapor pressure and a discussion of

product volatilization can be found at http://www.epa.gov/ttn/chief/ap42/ch09/final/c9s02-2.pdf

1026 U.S. EPA Integrated Risk Information System (IRIS) database is available at:

www.epa.gov/iris

1027 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF

and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR2003-0190 at http://www.regulations.gov/. p. 4-179.

1028 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF

and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR2003-0190 at http://www.regulations.gov/. p. 4-236.

1029 U.S. EPA (2004). Air Quality Criteria for Particulate Matter. Volume I EPA600/P-99/002aF

and Volume II EPA600/P-99/002bF. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR2003-0190 at http://www.regulations.gov/. p. 4-182.

1030 Sisler, J.F. (1996) Spatial and seasonal patterns and long term variability of the composition

of the haze in the United States: an analysis of data from the IMPROVE network. CIRA Report,

ISSN 0737-5352-32, Colorado State University.
1099

1031 U.S. EPA (2004) Air Quality Criteria for Particulate Matter (Oct 2004), Volume I Document

No. EPA600/P-99/002aF and Volume II Document No. EPA600/P-99/002bF. This document is

available in Docket EPA-HQ-OAR-2005-0161.

1032 U.S. EPA (2005) Review of the National Ambient Air Quality Standard for Particulate

Matter: Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper. EPA452/R-05-005. This document is available in Docket EPA-HQ-OAR-2005-0161.

1033 U.S. EPA, 2008. Integrated Science Assessment for Oxides of Nitrogen and SulfurEcological Criteria (Final). U.S. EPA, Washington D.C., EPA/600/R-08/082F.

1034 Environmental Protection Agency (2003). Response Of Surface Water Chemistry to the

Clean Air Act Amendments of 1990. National Health and Environmental Effects Research

Laboratory, Office of Research and Development, U.S. Environmental Protection Agency.

Research Triangle Park, NC. EPA 620/R–03/001.

1035 Fenn, M.E. and Blubaugh, T.J. (2005) Winter Deposition of Nitrogen and Sulfur in the

Eastern Columbia River Gorge National Scenic Area, USDA Forest Service.

1036 Galloway, J. N.; Cowling, E. B. (2002). Reactive nitrogen and the world: 200 years of

change. Ambio 31: 64–71.

1037 Bricker, Suzanne B., et al., National Estuarine Eutrophication Assessment, Effects of

Nutrient Enrichment in the Nation’s Estuaries, National Ocean Service, National Oceanic and

Atmospheric Administration, September, 1999.

1038 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket

EPA-HQ-OAR-2005-0161.

1039 Winner, W.E., and C.J. Atkinson. 1986. “Absorption of air pollution by plants, and

consequences for growth.” Trends in Ecology and Evolution 1:15-18.

1040 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket

EPA-HQ-OAR-2005-0161.

1041 Tingey, D.T., and Taylor, G.E. 1982. “Variation in plant response to ozone: a conceptual

model of physiological events.” In: Effects of Gaseous Air Pollution in Agriculture and

Horticulture (Unsworth, M.H., Omrod, D.P., eds.) London, UK: Butterworth Scientific, pp.113-

138.

1042 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket

EPA-HQ-OAR-2005-0161.

1043 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket

Identification EPA-HQ-OAR-2004-0008-0455 to 0457.

1044 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket

EPA-HQ-OAR-2005-0161.

1045 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket

EPA-HQ-OAR-2005-0161.
1100

1046 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket

EPA-HQ-OAR-2005-0161.

1047 Ollinger, S.V., J.D. Aber and P.B. Reich. 1997. “Simulating ozone effects on forest

productivity: interactions between leaf canopy and stand level processes.” Ecological

Applications 7:1237-1251.

1048 Winner, W.E., 1994. “Mechanistic analysis of plant responses to air pollution.” Ecological

Applications, 4(4):651-661.

1049 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket

EPA-HQ-OAR-2005-0161.

1050 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket

EPA-HQ-OAR-2005-0161.

1051 Fox, S., and R. A. Mickler, eds. 1996. Impact of Air Pollutants on Southern Pine Forests.

Springer-Verlag, NY, Ecol. Studies, Vol. 118, 513 pp.

1052 De Steiguer, J., J. Pye, C. Love. 1990. “Air Pollution Damage to U.S. Forests.” Journal of

Forestry, Vol 88 (8) pp. 17-22.

1053 Pye, J.M. 1988. “Impact of ozone on the growth and yield of trees: A review.” Journal of

Environmental Quality 17 pp.347-360.

1054 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket

EPA-HQ-OAR-2005-0161.

1055 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket

EPA-HQ-OAR-2005-0161.

1056 McBride, J.R., P.R. Miller, and R.D. Laven. 1985. “Effects of oxidant air pollutants on forest

succession in the mixed conifer forest type of southern California.” In: Air Pollutants Effects

On Forest Ecosystems, Symposium Proceedings, St. P, 1985, p. 157-167.

1057 Miller, P.R., O.C. Taylor, R.G. Wilhour. 1982. Oxidant air pollution effects on a western

coniferous forest ecosystem. Corvallis, OR: U.S. Environmental Protection Agency,

Environmental Research Laboratory (EPA600-D-82-276).

1058 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket

EPA-HQ-OAR-2005-0161.

1059 Kopp, R. J.; Vaughn, W. J.; Hazilla, M.; Carson, R. 1985. “Implications of environmental

policy for U.S. agriculture: the case of ambient ozone standards.” J. Environ. Manage. 20:321-

331.

1060 Adams, R. M.; Hamilton, S. A.; McCarl, B. A. 1986. “The benefits of pollution control: the

case of ozone and U.S. agriculture.” Am. J. Agric. Econ. 34: 3-19.

1061 Adams, R. M.; Glyer, J. D.; Johnson, S. L.; McCarl, B. A. 1989. “A reassessment of the

economic effects of ozone on U.S. agriculture.” JAPCA 39:960-968.

1062 Abt Associates, Inc. 1995. Urban ornamental plants: sensitivity to ozone and potential

economic losses. U.S. EPA, Office of Air Quality Planning and Standards, Research Triangle

Park. Under contract to RADIAN Corporation, contract no. 68-D3-0033, WA no. 6. pp. 9-10.
1101

1063 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S.

EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006.This document is available in Docket

EPA-HQ-OAR-2005-0036

1064 Grulke, N.E. (2003). The physiological basis of ozone injury assessment attributes in Sierran

conifers. In A. Bytnerowicz, M.J. Arbaugh, & R. Alonso (Eds.), Ozone air pollution in the Sierra

Nevada: Distribution and effects on forests. (pp. 55-81). New York, NY: Elsevier Science, Ltd.

1065 White, D., Kimerling, A.J., Overton, W.S. (1992). Cartographic and geometric component of

a global sampling design for environmental monitoring. Cartography and Geographic

Information Systems, 19, 5-22.

1066 Smith, G., Coulston, J., Jepsen, E., Prichard, T. (2003). A national ozone biomonitoring

program—results from field surveys of ozone sensitive plants in Northeastern forests (1994-

2000). Environmental Monitoring and Assessment, 87, 271-291.

1067 White, D., Kimerling, A.J., Overton, W.S. (1992). Cartographic and geometric component of

a global sampling design for environmental monitoring. Cartography and Geographic

Information Systems, 19, 5-22.

1068 Smith, G., Coulston, J., Jepsen, E., Prichard, T. (2003). A national ozone biomonitoring

program—results from field surveys of ozone sensitive plants in Northeastern forests (1994-

2000). Environmental Monitoring and Assessment, 87, 271-291.

1069 Coulston, J.W., Riitters, K.H., Smith, G.C. (2004). A preliminary assessment of the Montréal

process indicators of air pollution for the United States. Environmental Monitoring and

Assessment, 95, 57-74.

1070 U.S. EPA (United States Environmental Protection Agency). 2006. Air quality criteria for

ozone and related photochemical oxidants. EPA/600/R-05/004aF-cF. Research Triangle Park,

NC. <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=149923>

1071 Smith, G., Coulston, J., Jepsen, E., Prichard, T. (2003). A national ozone biomonitoring

program—results from field surveys of ozone sensitive plants in Northeastern forests (1994-

2000). Environmental Monitoring and Assessment, 87, 271-291.

1072 U.S. EPA (United States Environmental Protection Agency). 2006. Air quality criteria for

ozone and related photochemical oxidants. EPA/600/R-05/004aF-cF. Research Triangle Park,

NC. <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=149923>

1073 US EPA. (2207) Review of the National Ambient Air Quality Standards for Ozone: Policy

assessment of scientific and technical information. Office of Air Quality Planning and Standards

staff paper. EPA-452/R-07-003.

1074 Chappelka, A.H., Samuelson, L.J. (1998). Ambient ozone effects on forest trees of the

eastern United States: a review. New Phytologist, 139, 91-108.

1075 Nouchi I, S Toyama. 1998. Effects of ozone and peroxyacetyl nitrate on polar lipids and

fatty acids in leaves of morning glory and kidney bean. Plant Physiol. 87:638-646.

1076 Oka E, Y Tagami, T Oohashi, N Kondo. 2004. A physiological and morphological study on

the injury caused by exposure to the air pollutant, peroxyacetyl nitrate (PAN), based on the

quantitative assessment of the injury. J Plant Res. 117:27-36.

1077 Sun E-J, M-H Huang. 1995. Detection of peroxyacetyl nitrate at phytotoxic level and its

effects on vegetation in Taiwan. Atmos. Env. 29:2899-2904.

1078 Koukol J, WM Dugger, Jr., RL Palmer. 1967. Inhibitory effect of peroxyacetyl nitrate on

cyclic photophosphorylation by chloroplasts from black valentine bean leaves. Plant Physiol.

42:1419-1422.
1102

1079 Thompson CR, G Kats. 1975. Effects of ambient concentrations of peroxyacetyl nitrate on

navel orange trees. Env. Sci. Technol. 9:35-38.

1080 Bytnerowicz A, ME Fenn. 1995. Nitrogen deposition in California forests: A Review.

Environ. Pollut. 92:127-146.

1081 US EPA. 1991. Effects of organic chemicals in the atmosphere on terrestrial plants.

EPA/600/3-91/001.

1082 Cape JN, ID Leith, J Binnie, J Content, M Donkin, M Skewes, DN Price AR Brown, AD

Sharpe. 2003. Effects of VOCs on herbaceous plants in an open-top chamber experiment.

Environ. Pollut. 124:341-343.

1083 Cape JN, ID Leith, J Binnie, J Content, M Donkin, M Skewes, DN Price AR Brown, AD

Sharpe. 2003. Effects of VOCs on herbaceous plants in an open-top chamber experiment.

Environ. Pollut. 124:341-343.

1084 Viskari E-L. 2000. Epicuticular wax of Norway spruce needles as indicator of traffic

pollutant deposition. Water, Air, and Soil Pollut. 121:327-337.

1085 Ugrekhelidze D, F Korte, G Kvesitadze. 1997. Uptake and transformation of benzene and

toluene by plant leaves. Ecotox. Environ. Safety 37:24-29.

1086 Kammerbauer H, H Selinger, R Rommelt, A Ziegler-Jons, D Knoppik, B Hock. 1987. Toxic

components of motor vehicle emissions for the spruce Pciea abies. Environ. Pollut. 48:235-243.

1087 Kwaitkowski, J.R., McAloon, A., Taylor, F., Johnston, D.B., Modeling the process and costs

of fuel ethanol production by the corn dry-grind process. Industrial Crops and Products 23

(2006) 288-296. A copy of the current USDA model can be obtained by contacting the

corresponding author.

1088 See Tables 4.1-21 and 4.1-22 of this section for biomass costs. Coal costs taken from EIA

Annual Energy Outlook 2008 Table 15.

1089 Bioenergy Feedstock Characteristics from Jonathan Scurlock, Oak Ridge National

Laboratories, available at http://bioenergy.ornl.gov/papers/misc/biochar_factsheet.html (accessed

October 1, 2008)

1090 Gallagher, P.W., Brubaker, H., and Shapouri, H. “Plant Size: Capital Cost Relationships in

the Dry Mill Ethanol Industry,” Biomass and Bioenergy Vol. 28. 2005. Pp. 565-7.

1091 Isben, Kelly. Opportunities to add cellulosic feedstocks to the corn dry milling industry.

Subcontractor to NREL. Technical Monitors: Mike Cleary and Andy Aden. Received

December 2, 2009.

1092 Corn Stover Collection Project, David A. Glassner, National Renewable Energy Laboratory,

1617 Cole Blvd., Golden, CO 80401, James R. Hettenhaus, Chief Executive Assistance, 3211

Trefoil Ave, Charlotte, NC 28226, and Thomas M. Schechinger, Iron Horse Custom Farming,

816 Iron Horse Rd, Harlan, IA 51537; in BioEnergy ’98: Expanding BioEnergy Partnerships

1093 Abhijith Mukunda, Graduate Student School of Industrial Engineering, Purdue University,

West Lafayette IN 47907, Klein E Ileleji, Assistant Professor, School of Agricultural &

Biological Engineering, Purdue University, West Lafayette IN 47907, Hong Wan, Assistant

Professor, School of Industrial Engineering, Purdue University, West Lafayette IN47906,

Simulation of Corn Stover Logistics from On-farm Storage to an Ethanol Plant, Paper Number:

0661772006, ASABE Annual International Meeting, Portland Convention Center, Portland,

Oregon, 9 - 12 July 2006

1094 Shahab Sokhansanj, , Anthony Turhollow, Oak Ridge National Laboratory, North Logan,

UT 84341-8804, Janet Cushman, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6422,
1103

John Cundiff, Biological Systems Engineering, Virginia Technical University, Blacksburg, VA

24061-0303, Engineering aspects of collecting corn stover for bioenergy, Biomass and

Bioenergy 23 (2002) 347 – 355; Received 15 October 2001; received in revised form 15 April

2002; accepted 3 May 2002; 2002 Published by Elsevier Science Ltd.

1095 David A. Glassner, National Renewable Energy Laboratory, Golden, CO 80401, James R.

Hettenhaus, Chief Executive Assistance, Charlotte, NC 28226, and Thomas M. Schechinger,

Iron Horse Custom Farming, Harlan, IA 51537; Corn Stover Collection Project, in BioEnergy

’98: Expanding BioEnergy Partnerships

1096 J.E. Atchison, Atchison Consultants, Inc., J. R. Hettenhaus, Chief Executive Assistance, Inc.

Charlotte, North Carolina, National Renewable Energy Laboratory Golden, Colorado,

.Innovative Methods for Corn Stover Collecting, Handling, Storing and Transporting, March

2003.

1097 Daniel R. Petrolia, Department of Applied Economics, College of Food, Agriculture, and

Natural Resources Sciences, University of Minnesota, The Economics of Harvesting and

Transporting Corn Stover for Conversion to Fuel Ethanol: A Case Study for Minnesota,

Staff Paper P06-12 August 2006

1098 Renee Sayler & Kenneth Von Bargen, Principal Investigators, Feasibility of Corn Residue

Collection in Kearney, Nebraska Area, for Western Regional Biomass Energy Program, Report

of findings, by University of Nebraska-Lincoln, Industrial Agricultural Products Center, Lincoln,

NE 68583, September 1993.

1099 John S. Cundiff, Professor, Robert D. Grisso, Professor, Biological Systems Engineering

Dept(MS 0303), Virginia Tech, Blacksburg, VA 24061, Hosein Shapouri, Senior Agricultural

Economist, U.S.D.A., Office of Energy Policy and New Uses, Washington DC 20250-3815,

Economic Analysis of two Receiving Facility Designs for a Bioenergy Plant, Paper Number:

076051,2007 ASABE Annual International Meeting, Sponsored by ASABE, Minneapolis

Convention Center, Minneapolis, Minnesota, 17 - 20 June 2007

1100 Kevin J. Shinners – Professor of Agricultural Engineering, Ben N. Binversie – Graduate

Research Assistant, Department of Biological Systems Engineering, University of Wisconsin,

Madison, WI, Philippe Savoie – Research Scientist, Agriculture and Agri-Food Canada, Quebec,

Canada, Whole-Plant Corn Harvesting for Biomass: Comparison of Single-Pass and MultiplePass Harvest Systems, Paper Number: 036089 2003 ASAE Annual International Meeting,

Sponsored by ASAE, Riviera Hotel and Convention Center, Las Vegas, Nevada, USA, 27- 30

July 2003.

1101 S. Sokhansanj1, A. Turhollow, and R. Perlack, Oak Ridge National Laboratory, Tennessee,

USA 37830, Stochastic Modeling of Costs of Corn Stover Costs Delivered to an Intermediate

Storage Facility, Paper Number: 024190, Presented at: 2002 ASAE Annual International

Meeting / CIGR XVth World Congress, Sponsored by ASAE and CIGR, Hyatt Regency

Chicago, Chicago, Illinois, USA, July 28-July 31, 2002

1102 D. Montross, Associate Professor, Scott A. Shearer, Professor, Timothy S. Stombaugh,

Associate Extension Professor, Stephen F. Higgins, Research Specialist, and Samuel G. McNeill,

Associate Extension Professor, Department of Biosystems and Agricultural Engineering,

University of Kentucky, Lexington, Kentucky; and Shahab Sokhansanj, Professor, Oak Ridge

National Laboratory, Environmental Sciences Division, Oak Ridge, Tennessee, Corn Stover

Availability and Collection Efficiency Using Typical Hay Equipment, This article is published

with the approval of the Director of the Kentucky Agricultural Experiment Station and
1104

designated Paper No. 04-05-066. Transactions of the ASABE, Vol. 50(3): 705−711 – 2007

American Society of Agricultural and Biological Engineers, ISSN 0001−2351.

1103 American Society of Agricultural and Biological Engineers (ASABE), ASABE Standards,

2007, 54th Edition, Standards, Engineering Practices, Data, 2950 Niles Road, St. Joseph, MI

49085-9659.

1104 William Edwards, extension economist, Machinery Management: Estimating Farm

Machinery Costs, PM 710 Paper version, Revised November 2001; Electronic version Revised

August 2005, University Extension, Iowa State University.

1105 Mark Hanna, Extension Engineer, University Extension, Iowa State University, Ames Iowa,

Estimating the Field Capacity of Farm Machines, File A3-24, April 2002.

1106 Terry Kastens, Extension Agricultural Economist, Kansas State University Agricultural

Experiment Station and Cooperative Extension Service, Farm Machinery Operation Cost

Calculations, MF-2244 May 1997

1107 Machinery Cost Estimates: Forage Field Operations, University of Illinois Farm Business

Management Handbook, University of Illinois Extension FBM 0203 Department of Agricultural

and Consumer Economics • College of Agricultural, Consumer and Environmental Sciences,

University of Illinois at Urbana-Champaign

1108 Tim Cross, Agricultural Economics, Agriculture Machinery Cost Calculation Methods, E12-

2015-00-032-98, Agricultural Extension Service, The University of Tennessee Institute of

Agriculture.

1109 Agricultural Prices 2006 Summary, July 2007 United States, Department of Agriculture

National Agricultural Statistics Service, Pr 1-3 (07)

1110 National Agricultural Statistics Service, Quick Stats (Agricultural Statistics Data Base, State

and County Data), USDA, http://www.nass.usda.gov/

1111 L. O. Pordesimo, W. C. Edens, The University of Tennessee, Dept. of Biosystems

Engineering and Environmental Science, Knoxville, TN, U.S.A., S. Sokhansanj Oak Ridge

National Laboratory, Environmental Sciences Division, Oak Ridge, TN, U.S.A., Distribution of

Above Ground Biomass in Corn , Stover, Paper Number: 02-6059; 2002 ASAE Annual

International Meeting/CIGR XVth World Congress, Hyatt Regency Chicago, Chicago, Illinois,

USA,; July 29-31, 2002.

1112 Kevin J. Shinners, Professor of Agricultural Engineering, Ben N. Binversie, Graduate

Research Assistant, Department of Biological Systems Engineering, University of Wisconsin,

Madison, WI, Philippe Savoie, Research Scientist, Agriculture and Agri-Food Canada, Quebec,

Canada; Harvest and Storage Of Wet and Dry Corn Stover As A Biomass Feedstock, Paper

Number: 036088, 2003 ASAE Annual International Meeting Riviera Hotel and Convention

Center, Las Vegas, Nevada, USA, 27- 30 July 2003

1113 R.L. Graham, R.D. Perlack, and L.L. Wright, Environ. Sci. Div., Oak Ridge National Lab.,

Oak Ridge, TN 37831-6407; R. Nelson, Engineering Extension Programs, Kansas State Univ.,

Manhattan, KS 66506, and J. Sheehan, National Renewable Energy Lab., Golden, CO 80401.

Current and Potential U.S. Corn Stover Supplies, Agronomy Journal 99:1–11, American Society

of Agronomy, Madison, WI 53711 USA; doi:10.2134/agronj2005.0222 prepared 2005;

published online 1 January 2007

1114 Ron Kotrba, Ethanol Producer Magazine staff writer, The Great Biomass Inquisition,

Ethanol Producer Magazine, January 2007.

http://ethanolproducer.com/article.jsp?article_id=2621&q=&page=all
1105

1115 Kevin J. Shinners, Professor of Agricultural Engineering, Ben N. Binversie, Graduate

Research Assistant, Department of Biological Systems Engineering, University of Wisconsin,

Madison, WI, Philippe Savoie, Research Scientist, Agriculture and Agri-Food Canada, Quebec,

Canada; Harvest and Storage Of Wet and Dry Corn Stover As A Biomass Feedstock, Paper

Number: 036088, 2003 ASAE Annual International Meeting Riviera Hotel and Convention

Center, Las Vegas, Nevada, USA, 27- 30 July 2003.

1116 William Edwards, Extension Economist, Estimating Farm Machinery Costs, PM710,

Machinery Management, University Extension, Iowa State University, revised November 2001

1117 Renee Sayler & Kenneth Von Bargen, Principal Investigators, Feasibility of Corn Residue

Collection in Kearney, Nebraska Area, for Western Regional Biomass Energy Program, Report

of findings, by University of Nebraska-Lincoln, Industrial Agricultural Products Center, Lincoln,

NE 68583, September 1993.

1118 Robert D. Grisso , Professor, Virginia Tech, Department of Biological Systems Engineering,

Blacksburg, Virginia; Paul J. Jasa , Extension Engineer, Biological Systems Engineering

Department, University of Nebraska, Lincoln, Nebraska; and Dale E. Rolofson , Farm Manager,

Agri-Cross Farms, Inc. Greenwood, Nebraska. Corresponding author : Paul J. Jasa, Biological

Systems Engineering Dept., University of Nebraska, Lincoln, Analysis of Traffic Patterns and

Yield Monitor Data for Field Efficiency Determination, Paper No. 011013, Presented at the

2000 ASAE Annual Meeting, Applied Engineering in Agriculture Vol. 18(2): 171-178 ( © 2002

American Society of Agricultural Engineers ). A contribution of the University of Nebraska

Agricultural Research Division, Lincoln, Nebraska. Journal Series No. 13335. Mention of trade

and company names are for the reader and do not infer endorsement or preferential treatment of

the products by the University of Nebraska, Lincoln.

1119 Robert “Bobby” Grisso, Professor, and John C. Cundiff, Professor; and David H. Vaughan,

Professor, Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA.,

Investigating Machinery Management Parameters with Computer Tools, Paper Number: 071030,

Presented at 2007 ASABE Annual International Meeting, Minneapolis Convention Center,

Minneapolis, Minnesota, 17 - 20 June 2007. Mention of trade and company names are for the

reader and do not infer endorsement or preferential treatment of the products by Virginia Tech.

1120 Poorna P. Ravula, Robert D. Grisso and John S. Cundiff, Biological Systems Engineering

Department, Virginia Tech, Blacksburg, VA 24061-0303, Comparison between Two Policy

Strategies for Scheduling in a Biomass Logistic System, Paper Number: 071095, Presented at the

2007 ASABE Annual International Meeting, Sponsored by ASABE, Minneapolis Convention

Center

1121 International Fire Code 2903.4: Agricultural Products. Hay, straw or similar agricultural

products shall not be stored adjacent to structures of combustible materials. Storage shall be

limited to stacks of 100 tons (91 metric tons) each. Stacks shall be separated by a minimum of 20

feet (6096 mm) of clear space. Quantities of hay, straw and other agricultural products shall not

be limited where stored in or near farm structures located outside closely built areas. A permit

shall not be required for agricultural storage.

1122 Daniel R. Petrolia, Department of Applied Economics, College of Food, Agriculture, and

Natural Resources Sciences, University of Minnesota, The Economics of Harvesting and

Transporting Corn Stover for Conversion to Fuel Ethanol: A Case Study for Minnesota,

Staff Paper P06-12 August 2006
1106

1123 Ron Kotrba, staff writer, The Great Biomass Inquisition, Ethanol Producer Magazine.

January 2007 Issue. Reach him at rkotrba@bbibiofuels.com, © 2007 BBI International

http://ethanolproducer.com/article.jsp?article_id=2621&q=&page=all

1124 Ron Kotrba, staff writer, The Great Biomass Inquisition, Ethanol Producer Magazine.

January 2007 Issue. Reach him at rkotrba@bbibiofuels.com, © 2007 BBI International

http://ethanolproducer.com/article.jsp?article_id=2621&q=&page=all

1125 Graeme R Quick, Single-pass Corn and Stover Harvesters: Development and Performance.

Proceedings of the International Conference on Crop Harvesting and Processing, 9-11 February

2003, Louisville, Kentucky USA, ASABE, St. Joseph, Mich.; ASAE Publication Number

701P1103e

1126 A. Aden, M. Ruth, K. Ibsen, J. Jechura, K. Neeves, J. Sheehan, and B. Wallace

National Renewable Energy Laboratory (NREL); L. Montague, A. Slayton, and J. Lukas Harris

Group, Seattle, Washington, Ethanol Process Design and Economics Utilizing Co-Current Dilute

Acid Prehydrolysis and Enzymatic Hydrolysis for Corn Stover; June 2002;NREL is a U.S.

Department of Energy Laboratory Operated by Midwest Research Institute • Battelle • Bechtel;

Contract No. DE-AC36-99-GO10337

1127 Robert M. Prewitt, Graduate Research Assistant, Michael D. Montross, Associate Professor,

Scott A. Shearer, Professor, Timothy S. Stombaugh, Associate Extension Professor, Stephen F.

Higgins, Research Specialist, Samuel G. McNeill, Associate Extension Professor, Department of

Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY; Shahab

Sokhansanj, Professor, Oak Ridge National Laboratory, Environmental Sciences Division, Oak

Ridge, TN, Corn Stover Availability and Collection Efficiency Using Typical Hay Equipment,

Transactions of the American Society of Agricultural and Biological Engineers (ASABE), Vol.

50(3): 705−711, 2007, ISSN 0001−2351; Publication of this article approved by the Director of 

the Kentucky Agricultural Experiment Station and designated Paper No. 04-05-066.

1128 Graeme R Quick, Single-pass Corn and Stover Harvesters: Development and Performance.

Proceedings of the International Conference on Crop Harvesting and Processing, 9-11 February

2003, Louisville, Kentucky USA, ASABE, St. Joseph, Mich.; ASAE Publication Number

701P1103e

1129 Kevin J. Shinners, Professor of Agricultural Engineering, Graham S. Adsit, Graduate

Research Assistant, Benjamin N. Binversie, Graduate Research Assistant, Matthew F. Digman,

Graduate Research Assistant, Department of Biological Systems Engineering, University of

Wisconsin, Richard E. Muck, Research Scientist, Paul. J. Weimer, Research Scientist, US Dairy

Forage Research Center – USDA-ARS, Madison, WI, Characteristic Performance and Yields

using a Single-Pass, Split- Stream Maize Grain and Stover Harvester, Paper Number: 056051,

presented at the 2004 ASAE/CSAE Annual International and the 2005 ASAE Annual

International Meeting, Sponsored by ASAE, Tampa Convention Center, Tampa, Florida 17 - 20

July 2005

1130 Shahab Sokhansanj, , Anthony Turhollow, Oak Ridge National Laboratory, North Logan,

UT 84341-8804, Janet Cushman, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6422,

John Cundiff, Biological Systems Engineering, Virginia Technical University, Blacksburg, VA

24061-0303, Engineering aspects of collecting corn stover for bioenergy, Biomass and

Bioenergy 23 (2002) 347 – 355; Received 15 October 2001; received in revised form 15 April

2002; accepted 3 May 2002; 2002 Published by Elsevier Science Ltd.
1107

1131 Kevin J. Shinners, Professor of Agricultural Engineering, Garrit C. Boettcher, Graduate

Research Assistant, Jesse T. Munk, Graduate Research Assistant, Matthew F. Digman, Graduate

Research Assistant, Department of Biological Systems Engineering, University of Wisconsin;

Richard E. Muck, Research Engineer, Paul. J. Weimer, Research Scientist, US Dairy Forage

Research Center, USDA-ARS, Madison, WI, Single-Pass, Split-Stream of Corn Grain and

Stover: Characteristic Performance of Three Harvester Configurations, Presented at: ASABE

Annual International Meeting, 2006; Oregon Convention Center, Portland, Oregon, 9 – 12 July

2006.

1132 W. C. Edens, L. O. Pordesimo, The University of Tennessee, Dept. of Biosystems

Engineering and Environmental Science, Knoxville, TN, S. Sokhansanj, Oak Ridge National

Laboratory, Environmental Sciences Division, Oak Ridge, TN, Field Drying Characteristics and

Mass Relationships of Corn Stover Fractions, Presented at the ASAE Annual International

Meeting/ CIGR XVth World Congress, 2002, Hyatt Regency Chicago, Chicago, Illinois, USA,

July 28-31, 2002.

1133 Richard Hess, Idaho National Laboratory, Uniform Format Feedstock Supply System Design

for Lignocellulosic Biomass, Western Region Biomass Workshop August 27, 2007

http://sungrant.oregonstate.edu/workshop/HESS_Western_RP_workshop.pdf

1134 Kevin J. Shinners, Professor of Agricultural Engineering, Ben N. Binversie, Graduate

Research Assistant, Department of Biological Systems Engineering, University of Wisconsin,

Madison, WI, Philippe Savoie, Research Scientist, Agriculture and Agri-Food Canada, Quebec,

Canada; Harvest and Storage Of Wet and Dry Corn Stover As A Biomass Feedstock, Paper

Number: 036088, 2003 ASAE Annual International Meeting Riviera Hotel and Convention

Center, Las Vegas, Nevada, USA, 27- 30 July 2003.

1135 Shahab Sokhansanj and Janet Cushman, Bioenergy Feedstock Development Program

Oak Ridge National Laboratory (ORNL), Feedstock Engineering, Presentation, Lincoln, NB,

December 12, 2001 ORNL, Energy Efficiency and Renewable Energy, US DOE, USA

1136 Sudhagar Mani, Ph.D. Candidate, Shahab Sokhansanj, Adjunct Professor, Xiaotao Bi,

Associate Professor, Department of Chemical & Biological Engineering, University of British

Columbia, Vancouver, BC CANADA V6T 1Z4, Modeling of Forage Drying in Single and Triple

Pass Rotary Drum Dryers, Paper Number: 056082, 2005 ASAE Annual International Meeting,

Tampa Convention Center, Tampa, Florida, 17 - 20 July 2005

1137 Xianzhe Zheng, Professor, Yiyuan Jiang, Professor, College of Engineering, North East

Agricultural University, Harbin, China; Yubin Lan, Associate Professor, Agricultural

Engineering Technology, Fort Valley State University, University Systems of Georgia, Fort

Valley, GA 31030, Determination of Parameters for an Alfalfa Rotary Dryer, Paper Number:

056186, 2005 ASAE Annual International Meeting, Tampa Convention Center, Tampa, Florida,

17 - 20 July 2005

1138 This information came in a confidential, personal communication.

1139 Christopher T. Wright , Peter A. Pryfogle, Nathan A. Stevens , J. Richard Hess ,

Corey W. Radtke , Idaho National Laboratory , Idaho Falls, ID, Value of Distributed

Preprocessing of Biomass Feedstocks to a Bioenergy Industry, Paper No: 066151

Presented at the ASABE Annual International Meeting, 2006, Oregon Convention Center

Portland, Oregon, 9 - 12 July 2006
1108

1140 Roger G. Ginder, Professor of Economics, Department of Economics, Iowa State University,

Ames, Iowa 50011, Potential Infrastructure Constraints on Current Corn Based and Future

Biomass Based U.S. Ethanol Production, Working Paper # 07018 , July 2007

1141 E-mailed file from Robert Perlack, U.S. Department of Agriculture, May 2008.

1142 Phillips, S, Thermochemical Ethanol via Indirect Gasification and Mixed Alcohol Synthesis

of Lignocellulosic Biomass; NREL, April, 2007.

1143 Aden, Andy, Mixed Alcohols from Woody Biomass – 2010, 2015, 2022, National

Renewable Energy Laboratory (NREL), September, 23 2009.

1144 OECD, “Biofuels: Linking Support to Performance,” ITF Round Tables No. 138, March

2008.

1145 International Energy Agency (IEA), “Biofuels for Transport: An International Perspective,”

2004.

1146 Goldemberg, J. as sited in Rothkopf, Garten, “A Blueprint for Green Energy in the

Americas,” 2006.

1147 Unicamp, “A Expansāo do Proalcool como Programa de Desenvolvimento Nacional,” 

Powerpoint presentation at Ethanol Seminar in BNDES, 2006. As quoted in OECD, “Biofuels:

Linking Support to Performance,” ITF Round Tables No. 138, March 2008.

1148 Ibid.

1149 Ibid.

1150 Ibid.

1151 Macedo. I.C., “Green house gases emissions in the production and use of ethanol from

sugarcane in Brazil: The 2005/2006 Averages and a Prediction for 2020,” Biomass and

Bioenergy, 2008.

1152 Ibid.

1153 Smeets E, Junginger M, Faaij A, Walter A, Dolzan P, Turkenburg W, “The sustainability of

Brazilian ethanol-An Assessment of the possibilities of certified production,” Biomass and

Bioenergy, 2008.

1154 Official Statistics of the U.S. Department of Commerce, USITC

1155 Davis, Ryan. November 2009. Techno-economic analysis of microalgae-derived biofuel

production. National Renewable Energy Laboratory. Technical Memorandum to EPA.

1156 Sun, A. et al., “Techno-Economic Analysis of Algae Biofuel Deployment.” Presented at the

2009 Algae Biomass Summit, San Diego, CA (October 2009) as noted in report by Davis, Ryan,

op. sit.

1157 Haas, M.J, A process model to estimate biodiesel production costs, Bioresource Technology

97 (2006) 671-678.

1158 Capital cost and other details taken from Syntroleum Corp Investor Presentation materials

dated November 2009, available in the docket or at

http://www.syntroleum.com/Presentations/SyntroleumInvestorPresentation.November%205.200

9.FINAL.pdf

1159 A New Development in Renewable Fuels: Green Diesel, AM-07-10 Annual Meeting NPRA,

March 18-20, 2007.

1160 Per EPA phone discussion July 23, 2007.

1161 Ryan Davis, National Renewable Energy Laboratory, Golden Colorado 80401-3392,

Technical Memo to EPA “ Techno-economic Analysis of Current Technology for Fischer

Tropsch Fuels Production. August 2009.
1109

1162 “Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints”,

prepared for EPA by Oak Ridge National Laboratory, March 2009.

1163 BNSF railway biodiesel freight cost information can be found at

http://www.bnsf.com/markets/agricultural/bnsf4024/cur/itm31001.txt

1164 2007 National Association of Convenience Stores State of the Industry survey data

www.nacsonline.com as reported in the National Petroleum News MarketFacts 2007, October

2007.

1165 National Petroleum News, “2008 marketfacts”, www.npnweb.com

1166 Table 12, Reference and High Price Cases, Annual Energy Outlook 2009; Energy

Information Administration, Department of Energy, April 2009.

1167 Table 2, Reference Case, Annual Energy Outlook 2007; Energy Information Administration,

Department of Energy, February 2007.

1168 Table 2, Reference Case, Annual Energy Outlook 2009; Energy Information Administration,

Department of Energy, April 2009.

1169 D5798-99 (Reapproved 2004), ASTM International.

1170 National Association of Convenience Stores (NACS) online annual price resource kit,

NACS, February 2, 2006.

1171 Combined local, state and federal gasoline taxes, American Petroleum Institute, January

2008.

1172 Greene, David L.; Alternative Fuel Market Share as a Function of Fuel Availability and

Price Advantage, 1997.

1173 Brownstone, D., Small, K., 2005. Valuing time and reliability: assessing the evidence from

road pricing demonstrations. Trans. Res. Part A 39, 279-293.

1174 Brownstone, D., Ghose, D., Golob, T., Kazimi, C., Van Amelsfort, D., 2003. Driver's

willingness-to-pay to reduce travel time: evidence from the San Diego I-15 Congestion Pricing

Project. Trans. Res. Part A 37, 373-387.

1175 Lam, T., Small, K., 2001. The value of time and reliability: measurement from a value

pricing experiment. Trans. Res. Part E 37 (2-3), 231-251.

1176 Steimetz, S., Brownstone, D., 2005. Estimating consumers' 'Value of time' with noisy data: a

multiple imputation approach. Trans. Res. Part B 39, 865-889.

1177 Spot Annual Crude Oil Prices for Western Texas Intermediate (WTI), Cushing Oklahoma;

http://tonto.eia.doe.gov/dnav/pet/hist/rwtca.htm; Energy Information Adminstration, 2008.

1178 National Annual Average Gasoline and Diesel Fuel Prices (all grades);

http://tonto.eia.doe.gov/dnav/pet/pet_pri_gnd_dcus_nus_a.htm; Energy Information

Adminstration, 2008.

1179 Weis, Daniel J., Flex-Fuel Bait and Switch, Center for American Progress, June 18, 2007

available on the Web at: http://www.americanprogress.org/issues/2007/06/flexfuel.html.

1180 U.S. Environmental Protection Agency. (2006). Final Regulatory Impact Analysis (RIA) for

the Proposed National Ambient Air Quality Standards for Particulate Matter. Prepared by:

Office of Air and Radiation. Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html

1181 U.S. Environmental Protection Agency. (2008). Final Ozone NAAQS Regulatory Impact

Analysis. Prepared by: Office of Air and Radiation, Office of Air Quality Planning and

Standards. Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html

1182 U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis:

National Emission Standards for Hazardous Air Pollutants from the Portland Cement
1110

Manufacturing Industry. Office of Air Quality Planning and Standards, Research Triangle Park,

NC. April. Available on the Internet at

<http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf>.

1183 U.S. Environmental Protection Agency (U.S. EPA). 2009b. Proposed NO2 NAAQS

Regulatory Impact Analysis (RIA). Office of Air Quality Planning and Standards, Research

Triangle Park, NC. April. Available on the Internet at

http://www.epa.gov/ttn/ecas/regdata/RIAs/proposedno2ria.pdf. Note: The revised NO2

NAAQS may be final by the publication of this action.

1184 U.S. Environmental Protection Agency (U.S. EPA). 2009c. Draft Regulatory Impact

Analysis: Control of Emissions of Air Pollution from Category 3 Marine Diesel Engines. Office

of Transportation and Air Quality, June. Available on the Internet at

http://www.epa.gov/otaq/regs/nonroad/420d09002.htm. Note: The C3 rule may be final by the

publication of this action.

1185 GeoLytics Inc. (2002). Geolytics CensusCD® 2000 Short Form Blocks. CD-ROM Release

1.0. GeoLytics, Inc. East Brunswick, NJ. Available: http://www.geolytics.com/ [accessed 29

September 2004]

1186 Woods & Poole Economics Inc. 2008. Population by Single Year of Age CD. CD-ROM.

Woods & Poole Economics, Inc. Washington, D.C.

1187 U.S. Environmental Protection Agency. (2006). Air quality criteria for ozone and related

photochemical oxidants (second external review draft). Research Triangle Park, NC: National

Center for Environmental Assessment; report no. EPA/600R-05/004aB-cB, 3v. Available:

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=137307[March 2006]

1188 U.S. Environmental Protection Agency, 2004. Air Quality Criteria for Particulate Matter

Volume II of II. National Center for Environmental Assessment, Office of Research and

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC EPA/600/P99/002bF.

1189 World Health Organization (WHO). (2003). Health Aspects of Air Pollution with

Particulate Matter, Ozone and Nitrogen Dioxide: Report on a WHO Working Group. World

Health Organization. Bonn, Germany. EUR/03/5042688.

1190 Anderson HR, Atkinson RW, Peacock JL, Marston L, Konstantinou K. (2004). Metaanalysis of time-series studies and panel studies of Particulate Matter (PM) and Ozone (O3):

Report of a WHO task group. Copenhagen, Denmark: World Health Organization.

1191 Bell, M.L., et al. (2004). Ozone and short-term mortality in 95 US urban communities,

1987-2000. JAMA, 2004. 292(19): p. 2372-8.

1192 Huang, Y.; Dominici, F.; Bell, M. L. (2005) Bayesian hierarchical distributed lag models

for summer ozone exposure and cardio-respiratory mortality. Environmetrics. 16: 547-562.

1193 Schwartz, J. (2005) How sensitive is the association between ozone and daily deaths to

control for temperature? Am. J. Respir. Crit. Care Med. 171: 627-631.

1194 Bell, M.L., F. Dominici, and J.M. Samet. (2005). A meta-analysis of time-series studies of

ozone and mortality with comparison to the national morbidity, mortality, and air pollution

study. Epidemiology. 16(4): p. 436-45.
1111

1195 Ito, K., S.F. De Leon, and M. Lippmann (2005). Associations between ozone and daily

mortality: analysis and meta-analysis. Epidemiology. 16(4): p. 446-57.

1196 Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an

empiric bayes metaregression analysis. Epidemiology. 16(4): p. 458-68.

1197 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D.

Thurston. (2002). “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine

Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141.

1198 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. (2006). Reduction in Fine

Particulate Air Pollution and Mortality. American Journal of Respiratory and Critical Care

Medicine. 173: 667-672.

1199 Industrial Economics, Incorporated (IEc). (2006). Expanded Expert Judgment Assessment

of the Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Peer

Review Draft. Prepared for: Office of Air Quality Planning and Standards, U.S. Environmental

Protection Agency, Research Triangle Park, NC. August.

1200 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. (1997). The Relationship Between

Selected Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the United

States. Environmental Health Perspectives. 105(6):608-612.

1201 Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. Mills. (1995). Estimated

Long-Term Ambient Concentrations of PM(10) and Development of Respiratory Symptoms in a

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1202 Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittleman. (2001). Increased Particulate

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1203 Schwartz J. (1995). Short term fluctuations in air pollution and hospital admissions of the

elderly for respiratory disease. Thorax. 50(5):531-538.

1204 Schwartz J. (1994a). PM(10) Ozone, and Hospital Admissions For the Elderly in

Minneapolis St Paul, Minnesota. Arch Environ Health. 49(5):366-374.

1205 Schwartz J. (1994b). Air Pollution and Hospital Admissions For the Elderly in Detroit,

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1206 Moolgavkar SH, Luebeck EG, Anderson EL. (1997). Air pollution and hospital admissions

for respiratory causes in Minneapolis St. Paul and Birmingham. Epidemiology. 8(4):364-370.

1207 Burnett RT, Smith-Doiron M, Stieb D, Raizenne ME, Brook JR, Dales RE, et al. (2001).

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2 years of age. Am J Epidemiol. 153(5):444-452.

1208 Moolgavkar, S.H. (2003). “Air Pollution and Daily Deaths and Hospital Admissions in

Los Angeles and Cook Counties.” In Revised Analyses of Time-Series Studies of Air Pollution

and Health. Special Report. Boston, MA: Health Effects Institute.

1209 Ito, K. (2003). “Associations of Particulate Matter Components with Daily Mortality and

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1210 Moolgavkar, S.H. (2000). Air Pollution and Hospital Admissions for Diseases of the

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1211 Sheppard, L. (2003). Ambient Air Pollution and Nonelderly Asthma Hospital Admissions

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1112

1212 Jaffe DH, Singer ME, Rimm AA. (2003). Air pollution and emergency department visits

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1215 Norris, G., S.N. YoungPong, J.Q. Koenig, T.V. Larson, L. Sheppard, and J.W. Stout.

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1216 Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis,

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1218 Schwartz, J., and L.M. Neas. (2000). Fine Particles are More Strongly Associated than

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1114

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1245 U.S. Environmental Protection Agency, (2004a). Final Regulatory Analysis: Control of

Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Prepared by Office of Air and

Radiation. Retrieved on April 10, 2009, from http://www.epa.gov/nonroaddiesel/2004fr/420r04007.pdf

1246 U.S. Environmental Protection Agency, (2005). Regulatory Impact Analysis for the Clean

Air Interstate Rule. EPA 452/-03-001. Prepared by Office of Air and Radiation. Retrieved on

April 10, 2009, from http://www.epa.gov/interstateairquality/tsd0175.pdf

1247 U.S. Environmental Protection Agency, (2006). Regulatory Impact Analysis for the PM

NAAQS. EPA Prepared by Office of Air and Radiation. Retrieved on April 10, 2009, from

http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf

1248 Industrial Economics, Inc. (2006). Expanded Expert Judgment Assessment of the

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared for

EPA Office of Air Quality Planning and Standards, September. Retrieved on April 10, 2009,

from http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf

1249 U.S. Environmental Protection Agency, (2006). Regulatory Impact Analysis for the PM

NAAQS. EPA Prepared by Office of Air and Radiation. Retrieved on April 10, 2009, from

http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf

1250 U.S. Department of Agriculture, National Agricultural Statistics Service, ‘‘Crop

Production’’, August 12, 2009, available online at:

http://usda.mannlib.cornell.edu/usda/current/CropProd/CropProd-08-12-2009.pdf

1251 Committee on Water Implications of Biofuels Production in the United States, National

Research Council, Water Implications of Biofuels Production in the United States, The National

Academies Press, 2008, page 3.

1252 Huang , Wen-yuan, “Tight Supply and Strong Demand May Raise U.S. Nitrogen Fertilizer

Prices”, Amber Waves, November 2007, page 7, available online at:

http://www.ers.usda.gov/AmberWaves/November07/PDF/TightSupply.pdf.

1253 U. S. Environmental Protection Agency, EPA Science Advisory Board, “Hypoxia in the

Northern Gulf of Mexico”, EPA-SAB-08-003, December 2007, available on line at:

http://yosemite.epa.gov/sab/sabproduct.nsf/C3D2F27094E03F90852573B800601D93/$File/EPA

-SAB-08-003complete.unsigned.pdf.
1115

1254 U. S. Department of Agriculture, Economic Research Service, “U.S. Fertilizer Use and

Price”, http://www.ers.usda.gov/Data/FertilizerUse, April 2008.

1255 Inhalation health effect concerns for volatilized a.i. compounds are discussed for individual

products, e.g., oxydimeton-methyl, carbaryl (and carbamates generally), diazinon, at

http://www.epa.gov/pesticides/reregistration/status.htm . A.I. vapor pressure and a discussion of

product volatilization can be found at http://www.epa.gov/ttn/chief/ap42/ch09/final/c9s02-2.pdf

1256 A discussion of acetochlor toxicity can be found in EPA’s Re-registration Eligibility

Document (RED) for this product at http://www.epa.gov/IRIS/subst/0521.htm.

1257 A discussion of carbaryl toxicity can be found in EPA’s Re-registration Eligibility Document

(RED) for this product at http://www.epa.gov/IRIS/subst/0019.htm.

1258 A discussion of alachlor toxicity can be found in EPA’s Re-registration Eligibility

Document (RED) for this product at http://www.epa.gov/IRIS/subst/0129.htm

1259 See http://www.irac-online.org/ , Insecticide Resistance Action Committee, for toxicity

classifications and toxicity mode of action definitions used.

1260 See carcinogen weight of evidence (WoE) determination and description of toxicity

for methomyl at

http://www.epa.gov/pesticides/reregistration/REDs/0028red.pdf

1261 U. S. Department of Agriculture, Economic Research Service, “Farm Income and

Costs: 2009 Farm Sector Income Forecast”,

http://www.ers.usda.gov/Briefing/FarmIncome/nationalestimates.htm, updated

November 24, 2009.

1262 U.S. Environmental Protection Agency, EPA Science Advisory Board, Hypoxia in the

northern Gulf of Mexico, EPA–SAB–08–003, 275 p, available online at:

http://yosemite.epa.gov/sab/sabproduct.nsf/C3D2F27094E03F90852573B800601D93/$File/EP

A–SAB–08–003complete.unsigned.pdf

1263 Secchi, S., J. Tyndall, L. Schulte, and H. Asbjornsen. “Raising the Stakes”, Journal of Soil

and Water Conservation, Volume 63, Number. 3, 2008, pages 68A-73A.

1264 McLachlan, M., M. Carter, C. Rustay, Effects of the Conservation Reserve Program on

Priority Mixed-grass Prairie Birds: A Conservation Effects Assessment Project, 2007, available

online at: ftp://ftpfc.sc.egov.usda.gov/NHQ/nri/ceap/Final%20BCR19%20CRP%20Report.pdf .

1265 Iowa State University Extension, 2007 Iowa Land Value Survey, 2008, available online at:

http://www.extension.iastate.edu/Publications/FM1825.pdf.

1266 U. S. Environmental Protection Agency, EPA Science Advisory Board, op. cit., p. 177.

1267 Wilhelm, W. W., J. M. F. Johnson, D. L. Karlen, and D. T. Lightle, “Corn Stover to Sustain

Soil Organic Carbon Further Constrains Biomass Supply”, Agronomy Journal, Volume 99, 2007,

pages 1665–1667.

1268 U.S. Environmental Protection Agency, Office of Water, Wadeable Streams Assessment: A

Collaborative Survey of the Nation's Streams, EPA 841-B-06-002, 2006, available online at:

http://www.epa.gov/owow/streamsurvey.

1269 U. S. Environmental Protection Agency, Office of Water, National Water Quality Inventory:

Report to Congress 2002 Reporting Cycle, U.S. EPA 841-R-07-001, 2007, available online at

http://www.epa.gov/305b/2002report/.

1270 Paerl, H.W., D. Robin, D. R. Whitall , “Atmospheric Deposition of Nitrogen: Implications

for Nutrient Over-Enrichment of Coastal Waters”, Estuaries, Volume 25, Number 4B, 2002,

pages 677-693.
1116

1271 Chesapeake Bay Program, Sources of Nitrogen Loads to the Bay, available online

at:

http://www.chesapeakebay.net/status_nitrogensources.aspx?menuitem=19797

1272 New York State Department of Environmental Conservation and Connecticut Department of

Environmental Protection, A Total Maximum Daily Load Analysis to Achieve Water Quality

Standards for Dissolved Oxygen in Long Island Sound, December 2000, available online at:

http://www.longislandsoundstudy.net/pubs/reports/tmdl.pdf .

1273 Roberts, D. M., and J. E. Reuter, DRAFT Lake Tahoe Total Maximum Daily Load Technical

Report: California and Nevada, California Regional Water Quality Control Board, Lahontan

Region and Nevada Division of Environmental Protection, September 2007, available online at:

http://www.waterboards.ca.gov/lahontan/water_issues/programs/tmdl/lake_tahoe/docs/laketahoe

_tmdl_techrpt.pdf .

1274 Chesapeake Bay Program, Science and Technical Advisory Committee, Workshop on

Atmospheric Deposition of Nitrogen: Media Brief, May 30, 2007, available online at:

http://www.chesapeake.org/stac/Press/AtmoNmediabrief.pdf

1275 Roberts, D. M., op.cit., page 4-164.

1276 Committee on Water Implications of Biofuels Production in the United States, National

Research Council, op. cit.

1277 Louisiana Universities Marine Consortium, 2009, ‘Gulf of Mexico Dead Zone Surprising

Small, but Severe, available online at:

http://www.gulfhypoxia.net/Research/Shelfwide%20Cruises/2009/Files/Press_Release.pdf

1278 Mississippi River/Gulf of Mexico Watershed Nutrient Task Force, “Gulf Hypoxia Action

Plan 2008 for Reducing, Mitigating, and Controlling Hypoxia in the Northern Gulf of Mexico

and Improving Water Quality in the Mississippi River Basin”, 2008, page 22, available online at:

http://www.epa.gov/msbasin/actionplan.htm.

1279U. S. Environmental Protection Agency, EPA Science Advisory Board, op.cit.

1280 Mississippi River/Gulf of Mexico Watershed Nutrient Task Force, op.cit., page 37.

1281 Alexander, R.B., Smith, R.A., Schwarz, G.E., Boyer, E.W., Nolan, J.V., and Brakebill, J.W.,

“Differences in Phosphorus and Nitrogen Delivery to the Gulf of Mexico from the Mississippi

River Basin”, Environmental Science and Technology, Volume 42, Number 3, 2008, pages 822–

830, available online at: http://pubs.acs.org/cgibin/abstract.cgi/esthag/2008/42/i03/abs/es0716103.html.

1282 Donner, S. D. and Kucharik, C. J., 2008, “Corn-based Ethanol Production Compromises

Goal of Reducing Nitrogen Export by the Mississippi River”, PNAS, Volume 105, Number 11,

2008, pages 4513–4518, available online at: http://www.pnas.org/content/105/11/4513.full.

1283 U. S. Environmental Protection Agency, EPA Science Advisory Board, op.cit. page 205.

1284 Institute for Agriculture and Trade Policy, Staying home: How ethanol will change U.S. corn

exports, 2006, Minneapolis, MN, 26 pp., available online at:

http://www.agobservatory.org/library.cfm?refid=96658 .

1285 Mississippi River/Gulf of Mexico Watershed Nutrient Task Force, op.cit., page 15.

1286 Aulenbach, B.T., Buxton, H.T., Battaglin, W.A., and Coupe, R.H., Streamflow and Nutrient

Fluxes of the Mississippi-Atchafalaya River Basin and Subbasins for the Period of Record

Through 2005: U.S. Geological Survey Open-File Report 2007-1080, 2007, available online at:

http://toxics.usgs.gov/pubs/of-2007-1080/index.html.

1287 U. S. Environmental Protection Agency, EPA Science Advisory Board, op. cit., p. 89.
1117

1288 Mississippi River/Gulf of Mexico Watershed Nutrient Task Force, op. cit., page 16.

1289 Gassman, P.W., Reyes, M. R., Green, C. H. , Arnold, J. G. , “The Soil and Water Assessment

Tool: Historical Development, Applications, and Future Research Directions”, Transactions of

the American Society of Agricultural and Biological Engineers, Volume 50, Number 4, 2007,

pages 1211-1240, available online at:.

http://www.card.iastate.edu/environment/items/asabe_swat.pdf.

1290 Arnold, J. G., R. S. Muttiah, R. Srinivasan, and P. M. Allen, “Regional Estimation of

Base Flow and Groundwater Recharge in the Upper Mississippi Basin”, Journal of

Hydrology, Volume 227, 2000, pages 21-40, available online at:

http://ssl.tamu.edu/media/2900/regional%20estimation%20of%20base%20flow%20and

%20groundwater%20recharge%20in%20the%20upper%20mississippi%20river%20basi

n.pdf

1291 Jha, M., P. W. Gassman, S. Secchi, and J. Arnold, “Upper Mississippi River Basin Modeling

System Part 2: Baseline Simulation Results”, in: Coastal Hydrology and Processes (Eds. V.P.

Singh and Y.J. Xu), Water Resources Publications, Highland Ranch, CO, 2006, pages. 117-126.

1292 U.S. Department of Agriculture, USDA Agricultural Projections to 2018, February 2009,

available on-line at: http://www.ers.usda.gov/Publications/OCE091/

1293 Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan. “Development of a 2001 National

Landcover Database for the United States”. Photogrammetric Engineering and Remote Sensing,

Volume 70, Number 7, 2004, pages 829-840, available online at

http://www.mrlc.gov/pdf/July_PERS.pdf .

1294 U.S. Department of Agriculture, National Agricultural Statistic Service,

http://www.nass.usda.gov/research/Cropland/SARS1a.htm.

1295 U.S. Department of Agriculture, Natural Resources Conservation Service,

http://www.nrcs.usda.gov/techical/NRI.

1296 Conservation Technology Information Center, http://www.ctic.purdue.edu/.

1297 American Society of Agricultural and Biological Engineers, Manure Production and

Characteristics. ASAE D384.2 MAR2005, available online at:

http://asae.frymulti.com/abstract.asp?aid=19432&t=1.

1298 U. S. Department of Energy, Energy Information Administration, Annual Energy Outlook

2007 With Projections to 2030, February 2007, available on-line at:

http://tonto.eia.doe.gov/ftproot/forecasting/0383(2007).pdf

1299 Renewable Fuels Association, “Ethanol Biorefinery Locations,”

http://www.ethanolrfa.org/industry/locations, April 2008.

1300 Chesapeake Bay Commission, Biofuels and the Bay: Getting It Right to Benefit Farms,

Forests and the Chesapeake, Chesapeake Bay Commission, Annapolis, Maryland, 2007, 32

pages, available online at:

http://www.chesbay.state.va.us/Publications/BiofuelsAndTheBay1.pdf

1301 Simpson, T. W., A. N. Sharpley, R.W. Howarth, H.W. Paerl, and K. R. Mankin, “The New

Gold Rush: Fueling Ethanol Production while Protecting Water Quality”, Journal of

Environmental Quality, Volume 37, 2008, pages 318-324.

1302 Chesapeake Bay Foundation, 2007 State of the Bay, 2007, available online at:

http://www.cbf.org/site/DocServer/2007SOTBReport.pdf?docID=10923.
1118

1303 Federal Leadership Committee for the Chesapeake Bay, November 9, 2009, Executive Order

13508: Draft Strategy for Protecting and Restoring the Chesapeake Bay, available on-line at:

http://executiveorder.chesapeakebay.net/

1304 Chesapeake Bay Commission, op. cit., page 11.

1305 U.S. Environmental Protection Agency, Region 7, Environmental Laws Applicable to

Construction and Operation of Ethanol Plants, EPA-907-B-07-001, November 2007,104 pages,

available online at:

http://www.epa.gov/region07/priorities/agriculture/ethanol_plants_manual.pdf

1306 Regassa, T., Koelsch, R., Erickson, G., Impact of Feeding Distillers Grains on Nutrient

Planning for Beef Cattle Systems, University of Nebraska-Lincoln Extension—RP190, 2008, 8

pages, available online at: http://www.ianrpubs.unl.edu/epublic/live/rp190/build/rp190.pdf

1307 Jacob, M. D., Fox, J. T., Drouillard, J. S., Renter, D. G., Nagaraja, T. G., 2008, Effects of

dried distillers’ grain on fecal prevalence and growth of Escherichia coli O157 in batch culture

fermentations from cattle, Applied and Environmental Microbiology, v. 74, no. 1, p. 38–43,

available online at: http://aem.asm.org/cgi/content/abstract/74/1/38

1308 U.S. Department of Agriculture, Natural Resources Conservation Service, “Comprehensive

Nutrient Management Planning Technical Guidance”, National Planning Procedures Handbook,

Part 600.5, available online at: http://policy.nrcs.usda.gov/17088.wba.

1309 Mackay, D.M., de Sieyes, N. R.., Einarson, M.D., Feris, K.P., Pappas, A.A., Wood, I.A.,

Jacobson, L., Justice, L.G., Noske, M.N., Scow, K.M., and Wilson, J.T., 2006, Impact of ethanol

on the natural attenuation of benzene, toluene, and o-Xylene in a normally sulfate-reducing

aquifer, Environmental Science & Technology, v. 40, p. 6123-6130.

1310 Ruiz-Aguilar, G. M. L.; O’Reilly, K.; Alvarez, P. J. J, “Forum: A Comparison of Benzene

and Toluene Plume Lengths for Sites Contaminated with Regular vs. Ethanol-amended

Gasoline”, Ground Water Monitoring and Remediation, Volume 23, 2003, pages 48-53.

1311 Reilly, T.E., Dennehy, K.F., Alley, W.M., and Cunningham, W.L., Ground-Water

Availability in the United States: U.S. Geological Survey Circular 1323, 2008, page 33, available

online at http://pubs.usgs.gov/circ/1323/

1312 McGuire, V.L, Changes in Water Level and Storage in the High Plains Aquifer,

Predevelopment to 2005: U.S. Geological Survey Fact Sheet 2007-3029, 2007, available online

at http://pubs.usgs.gov/fs/2007/3029/ .

1313 U.S. Environmental Protection Agency, Consumer Fact Sheet on Nitrates/Nitrites, available

online at: http://www.epa.gov/safewater/dwh/c-ioc/nitrates.html

1314 U.S. Environmental Protection Agency, Office of Water, FACTOIDS: Drinking Water and

Ground Water Statistics for 2007, EPA 816-K-07-004, 2008, available online at:

http://www.epa.gov/safewater/data/pdfs/data_factoids_2007.pdf

1315 Scribner, E.A., Thurman, E.M., Goolsby, D.A., Meyer, M.T., Battaglin, W.A., and Kolpin,

D.W., 2005, Summary of significant results from studies of triazine herbicides and their

degradation products in surface water, ground water, and precipitation in the Midwestern United

States during the 1990s: U.S. Geological Survey Scientific Investigations Report 2005–5094, 27

p.

1316 U.S. Environmental Protection Agency, Consumer Fact Sheet on Atrazine, available online

at: http://www.epa.gov/safewater/contaminants/dw_contamfs/atrazine.html
1119

1317 See 40 CFR, Part 141, Subpart O, App. A.

1318 U.S. Environmental Protection Agency, A Review of Contaminant Occurrence in Public

Water Systems, EPA 816-R-99-006, 1999, available online at:

http://www.epa.gov/OGWDW/occur/nov99_lo.pdf

1319 U.S. Environmental Protection Agency, of Occurrence Estimation Methodology and

Occurrence Findings Report for the Six-Year Review of Existing National Primary Drinking

Water Regulations, Office of Water, EPA-815-R-03-006, 2003, pages 38 and 40, available

online at

http://epa.gov/OGWDW/standard/review/pdfs/support_6yr_occurancemethods_final.pdf.

1320 U.S. Environmental Protection Agency, Safe Drinking Water Information System (SDWIS)

End of Year data for FY 2007, 2007.

1321 Gilliom, R. J., The Quality of Our Nation's Waters Pesticides in the Nation's Streams and

Ground Water, 1992-2001: U.S. Geological Survey Circular 129, 2006, Tables 7A-1 to 7A-4,

available online at: http://water.usgs.gov/nawqa/pnsp/pubs/circ1291/appendix7/7a.html

1322 Committee on Water Implications of Biofuels Production in the United States, op.cit.

1323 Great Lakes Commission, The Potential Impacts of Increased Corn Production for Ethanol

in the Great Lakes-St. Lawrence River Region, 2007, available online at:

http://www.glc.org/tributary/pubs/documents/EthanolPaper121807FINAL.pdf.