Regulatory Impact Analysis: 

Renewable Fuel Standard Program 
Regulatory Impact Analysis:


Renewable Fuel Standard Program


Assessment and Standards Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency 

NOTICE 

This technical report does not necessarily represent final EPA decisions or 

positions. It is intended to present technical analysis of issues using data 

that are currently available. The purpose in the release of such reports is to 

facilitate an exchange of technical information and to inform the public of 

technical developments. 

EPA420-R-07-004 

April 2007 
Table of Contents 

Statement of Need.........................................................................................................4 

Overview.......................................................................................................................5 

List of Acronyms and Abbreviations......................... .................................................7 

Chapter 1: Industry Characterization..........................................................................11 

Chapter 2: Changes to Motor Vehicle Fuel 

Under the Renewable Fuel Standard Program.................................................50 

Chapter 3: Impacts on Emissions from Vehicles, 

Nonroad Equipment, and Fuel Production Facilities......................................118 

Chapter 4: National Emission Inventory Impacts......................................................173 

Chapter 5: Air Quality Impacts..................................................................................200 

Chapter 6: Lifecycle Impacts on Fossil Energy and Greenhouse Gases....................218 

Chapter 7: Estimated Costs of Renewable Fuels, Gasoline and Diesel......................262 

Chapter 8: Agricultural Sector Impacts.......................................................................323 

Chapter 9: Small-Business Flexibility Analysis..........................................................335 

Endnotes.......................................................................................................................342 

4
Statement of Need 

The United States currently consumes about 190 billion gallons of gasoline and diesel 

fuel annually to meet its transportation fuel needs. Of this volume, about 65 percent, or 124 

billion gallons, is derived from foreign sources. The United States’ dependence on imported 

petroleum to meet its growing demand for transportation fuel exacts a cost on the nation in terms 

of energy security. In addition, petroleum-based fuel exacts a cost on the nation with respect to 

environmental quality. The Renewable Fuel Standard (RFS) program increases national energy 

security by creating a market for renewable fuel as a substitute for petroleum-based fuel. By 

incorporating incentives for investing in research and development of renewable fuels, the RFS 

program also seeks to accelerate the nation’s progress toward energy independence. In addition, 

the RFS program helps to reduce the country’s greenhouse gas emissions, thereby reducing the 

nation's contribution to global climate change and its potential effects on the U.S. economy, 

security, and public health. 

5
Overview 

EPA is finalizing standards which would implement a renewable fuel program as 

required by the Energy Policy Act of 2005 (the Act). The Act specifies the total volume of 

renewable fuel that is required to be used each year, and directs EPA to adjust this amount under 

certain circumstances. The resulting standards represent a level of renewable fuel that each 

refinery or importer must account for relative to its annual volume of gasoline produced or 

imported. In reality, however, renewable fuel use is forecast to exceed the RFS standards due to 

market forces. The analyses of the impacts associated with this increase in renewable fuel use 

are discussed in this Regulatory Impact Analysis (RIA). 

Chapter 1: Industry Characterization

This chapter discusses current gasoline, diesel and renewable fuel production, importation, 

marketing and distribution, as well as likely future changes as a result of increased renewable 

fuel use. 

Chapter 2: Changes to Motor Vehicle Fuel Under the RFS Program 

This chapter discusses our gasoline and renewable fuel consumption predictions (compared to a 

2004 base year), and the expected impacts of various ethanol blends on gasoline properties. 

Chapter 3: Impacts on Emissions from Vehicles, Nonroad Equipment, and Fuel Production 

Facilities 

This chapter evaluates the impacts on vehicle and nonroad equipment emissions under various 

oxygenate assumptions, specifically increasing ethanol and decreasing MTBE, and different 

modeling techniques. The effect of biodiesel use on diesel-powered vehicle emissions is also 

presented. Finally, emissions from ethanol and biodiesel production facilities are discussed. 

Chapter 4: National Emissions Inventory Impacts 

This chapter discusses the methods used to develop the national emissions inventories, and 

quantifies the impact of expanded ethanol and biodiesel use on those inventories. 

Chapter 5: Air Quality Impacts 

This chapter discusses the impacts of expanded renewable fuel use on ozone and particulate 

matter formation. 

Chapter 6: Lifecycle Impacts on Fossil Energy and Greenhouse Gases 

This chapter discusses our fuel lifecycle modeling, that is, analysis which accounts for all energy 

and emissions of the fuel production process. A description of the model we used, how we used 

it, and the results are presented. Impacts on greenhouse gases, including CO2, fossil fuel use, and 

petroleum use are presented. The effects on petroleum imports, import expenditures, and 

domestic energy security are also discussed. 

6 
Chapter 7: Estimated Costs of Renewable Fuels, Gasoline and Diesel 

This chapter contains our analysis of the cost of corn and cellulosic ethanol. We also discuss 

biodiesel and renewable diesel production costs. Costs associated with distributing the volumes 

of ethanol necessary to meet the requirements of the program, and the costs to prepare gasoline 

and diesel blendstocks (for blending with renewable fuels) are also presented. Finally, we 

present the overall fuel cost impacts of expanded renewable fuel use. 

Chapter 8: Agricultural Sector Impacts 

This chapter discusses the likely economic impacts on the agricultural sector that may occur as a 

result of the large expansion of renewable fuel production and use expected in the future. Ongoing work using the FASOM model is also described. 

Chapter 9: Small Business Flexibility Analysis 

This chapter discusses our Small Business Flexibility Analysis (SBFA) which evaluates the rule 

to ensure that concerns regarding small businesses, which would be affected by the rule, are 

sufficiently considered. 

7 
List of Acronyms and Abbreviations 

AAM Alliance of Automobile Manufacturers 

ABT Averaging, Banking, and Trading 

ACE American Coalition for Ethanol 

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

ADM Archer Daniels Midland 

AEO Annual Energy Outlook (an EIA publication) 

ANL Argonne National Laboratory 

AQIRP Auto/Oil Air Quality Improvement Research Program 

ARMS Agricultural Resource Management Survey 

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

bbl Barrel 

BEA Bureau of Economic Analysis 

Bgal, bgal, bilgal, billgal, 

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 

BTU British Thermal Unit 

BU Bushel 

Bu/acre Bushels per acre 

BZ Benzene 

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 

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 

CHP Combined Heat and Power Technology 

CO Carbon Monoxide 

CO2 Carbon Dioxide 

Co-op Cooperative 

CRC Coordinating Research Council 

DDGS Distillers’ Dried Grains with Solubles 

DOE Department of Energy 

DRIA Draft Regulatory Impact Analysis 

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) 

8 
Energy Act Energy Policy Act of 2005 (also the Act) 

EO Executive Order 

EPA Environmental Protection Agency 

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

ETBE Ethyl Tertiary Butyl Ether 

ETOH Ethanol 

ex CA Excluding California 

F, °F Fahrenheit 

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 

GAL Gallon 

g/Btu Grams per Btu 

g/day Grams per day 

GDP Gross Domestic Product 

GHG Greenhouse Gases 

GPA Geographic Phase-in Area 

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

GWP Global warming potentials 

HC Hydrocarbon(s) 

HCO Heavy Cycle Oil (a refinery stream) 

HDN Naphtha Hydrotreater (also Hydro-Denitrogenation Unit) 

HSR Heavy Straight Run (a refinery stream) 

HVGO Heavy Vacuum Gas Oil (a refinery stream) 

IBP Initial Boiling Point 

k Thousand 

kbbl Thousand barrels 

kwh Kilowatt Hour 

Lb Pound 

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) 

mg/m3 Milligrams per cubic meter 

MGY, MMgy Million Gallons per Year 

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 

MOVES2006 EPA’s Next Generation Highway Vehicle Emission Model 

9
MSAT Mobile Source Air Toxics 

MSAT1 2001 Mobile Source Air Toxics Rule 

MSAT2 2006 Proposed Mobile Source Air Toxics Rule 

MTBE Methyl Tertiary-Butyl Ether 

NAAQS National Ambient Air Quality Standards 

NAICS North American Industrial Classification System 

NASS National Agricultural Statistics Service 

NBB National Biodiesel Board 

NCSU North Carolina State University 

NGL Natural gas plant liquids 

NMHC Non-Methane Hydrocarbons 

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 

NOx Oxides of nitrogen 

NPRM Notice of Proposed Rulemaking 

NREL National Renewable Energy Laboratory 

OMB Office of Management and Budget 

OMHCE Organic Material Hydrocarbon Equivalent 

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 

PM Particulate Matter 

PM10 Coarse Particle 

PM2.5 Fine Particle 

PMA Petroleum Marketing Annual (an EIA publication) 

POM Polycyclic Organic Matter 

PONA Paraffin, Olefin, Naphthene, Aromatic 

ppb Parts per billion 

ppm Parts Per million 

PRTP Percentage Reduction Trigger Point 

PSI Pounds per Square Inch 

QBtu Quadrillion btu 

Quadrillion 1015 

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

RBOB Reformulated Blendstock for Oxygenate Blending 

RFA Regulatory Flexibility Act 

RFG Reformulated Gasoline 

RFS Renewable Fuels Standard 

RIA Regulatory Impact Analysis 

RIMS Regional Input-Output Modeling System 

RIN Renewable Identification Number 

RON Research octane number 

RPMG Renewable Products Marketing Group 

RSM Response Surface Model 

RVP Reid Vapor Pressure 

S Sulfur 

SBA Small Business Administration 

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

SBFA Small Business Flexibility Analysis 

SBREFA Small Business Regulatory Enforcement Fairness Act (of 1996) 

10
scf Standard cubic feet 

SOA Secondary Organic Aerosol 

SOx Oxides of Sulfur 

SULEV Super ultra low emission vehicle 

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 

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 

wt% Percent by weight, weight percent 

yr, y Year 

11
Chapter 1: Industry Characterization 

1.1 Transportation Fuel Providers 

1.1.1 Petroleum Refiners 

As of the end of 2005, there were 142 crude oil refineries operating in the United States, 

representing a total of 16.4 million barrels/day of refining capacity. (These refineries produce 

gasoline and other products and are a separate category than “blender refiners” that do not 

process crude oil, but make gasoline from blendstocks.) The greatest number of refineries per 

PADD is in PADD 3 (the Gulf Coast region) which has 52 operating refineries as of the end of 

2005. This PADD also has the greatest refining capacity, at 7.9 million barrels per day. Table 

1.1-1 presents the refineries and their crude oil production capacity, and identifies the PADD 

where the refinery is located. 

Table 1.1-1. 

Refining Capacity by Individual Refinery 

(crude oil processing basis)

Company Capacity 

(MMbbls/cd) 

PADD 

Conoco Phillips 2.2 

Wood River, IL 0.31 2 

Belle Chasse, LA 0.25 3 

Sweeny, TX 0.25 3 

Westlake LA 0.24 3 

Linden, NJ 0.24 1 

Ponca City OK 0.19 2 

Trainer, PA 0.19 1 

Borger TX 0.15 3 

Wilmington CA 0.14 5 

Ferndale WA 0.10 5 

Rodeo CA 0.08 5 

Billings MT 0.06 4 

Valero Energy Corp. 2.0 

 Port Arthur TX 0.26 3 

Memphis TN 0.18 2 

Lima OH 0.15 2 

Texas City TX 0.21 3 

Corpus Christi TX 0.14 3 

Houston TX 0.08 3 

Sunray TX 0.16 3 

Three Rivers TX 0.09 3 

Norco LA 0.19 3 

Paulsboro NJ 0.16 1 

Benecia CA 0.14 5 

Wilmington CA 0.01 5 

 Ardmore OK 0.08 2 

Wilmington CA 0.08 5 

12
Company Capacity 

(MMbbls/cd) 

PADD 

Krotz Springs LA 0.08 3 

Exxon Mobil Corp. 2.0 

Baytown TX 0.56 3 

Baton Rouge LA 0.50 3 

Beaumont TX 0.34 3 

Joliet IL 0.24 2 

Torrance CA 0.15 5 

Billings MT 0.06 4 

Chalmette, LA 0.19 3 

BP PLC 1.5 

Texas City TX 0.44 3 

Whiting IN 0.41 2 

Toledo OH 0.13 2 

Los Angeles CA 0.26 5 

Ferndale WA 0.23 5 

Chevron Corp. 0.9 

Pascagoula MS 0.33 3 

El Segundo CA 0.26 5 

Richmond CA 0.24 5 

Honolulu HI 0.05 5 

 Salt Lake City UT 0.05 4 

Marathon Oil Corp. 1.0 

Garyville LA 0.25 3 

Cattlettsburg KY 0.22 2 

Robinson IL 0.19 2 

Detroit MI 0.10 2 

Canton OH 0.07 2 

Texas City TX 0.07 3 

 Saint Paul Park MN 0.07 2 

Sunoco, Inc. 0.58 

Marcus Hook PA 0.18 2 

Toledo OH 0.16 2 

Westville NJ 0.15 1 

Tulsa OK 0.09 2 

PDV America, Inc. 0.81 

 Citgo; Lake Charles LA 0.43 3 

 Citgo, Lemont IL 0.17 2 

 Citgo; Corpus Christi TX 0.16 3 

Koch Industries 0.57 

Corpus Christi TX 0.29 3 

Saint Paul MN 0.28 2 

 Motiva Enterprises LLC 0.76 

 Port Arthur TX 0.29 3 

Convent LA 0.24 3 

Norco LA 0.23 3 

Tesoro Corp. 0.51 

Anacortes WA 0.12 5 

 Salt Lake City UT 0.06 4 

13
Company Capacity 

(MMbbls/cd) 

PADD 

Martinez CA 0.17 5 

Kapolei HI 0.09 5 

Kenai AK 0.072 5 

Royal Dutch/Shell Group 0.82 

Martinez CA 0.16 5 

Anacortes WA 0.15 5 

Wilmington CA 0.10 5 

Saraland AL 0.08 3 

Deer Park, TX 0.33 3 

Lyondell Chem. Co. 

(Houston) 

0.27 3 

Total SA (Port Arthur, TX) 0.23 3 

Sinclair Oil 0.17 

Tulsa OK 0.07 2 

Sinclair WY 0.07 4 

Evansville WY 0.03 4 

Murphy Oil 0.15 

Meraux LA 0.12 3 

Superior WI 0.03 2 

Frontier Oil 0.15 

El Dorado KS 0.11 2 

Cheyenne WY 0.04 4 

Cenex Harvest States, Inc. 0.14 

McPherson KS 0.08 2 

Laurel MT 0.06 4 

Coffeyville Acquisitions 

(Coffeyville KS) 

0.11 2 

Navajo Refining Corp. 0.11 

Artesia NM 0.07 3 

Woods Cross UT 0.03 4 

 Great Falls MT 0.01 4 

Pasadena Refining Systems 

(Pasadena TX) 

0.10 3 

Giant Industries, Inc. 0.10 

Yorktown VA 0.06 1 

Gallup NM 0.02 3 

Bloomfield NM 0.02 3 

Big West Oil (North Salt 

Lake UT) 

0.10 4 

Source: Table 5 in Energy Information Administration, Refinery Capacity 2006 found at 

http://www.eia.doe.gov/pub/oil_gas/petroleum/data_publications/refinery_capacity_data/current/table5.pdf 

Refining capacity has steadily increased in the U.S. due to increased demand for 

petroleum products, with gasoline representing approximately 45 percent of product demand. 

Refining capacity (crude oil input) was about 14 million bbls/day in 1973 and 17 million 

bbls/day in 2005. While refining capacity has increased, however, the number of refineries has 

14
Capacity (Billion gallons/yr)

N u m b e r o f refin e rie s

C a p a c ity of re fin e rie s

Capacity (Million barrels/day) Capacity (Billion gallons/yr)

N u m b e r o f refin e rie s

C a p a c ity of re fin e rie s

Capacity (Billion gallons/yr)

1973

1974

19751976

19771978

1979

1980

1981

1982

19831984

1985

1986

1987198819891990

19911992

19931994

1995

19961997

1998

1999

2000

20012002

20032004

decreased as less economical refineries have been forced to close. (Many of these came into 

existence for a very short time due to oil price supports in the 1970’s.) In the 1970’s, the number 

of refineries in the U.S. was approximately 270 and has decreased by 47 percent. Figure 1.1-1 

shows the number of refineries and total capacity in the U.S. from 1973 through 2004. 

Figure 1.1-1. 

Number of Refineries and Total Capacity in the U.S. from 1973-2004 

3 5 0 2 0 .0

N um b e r o f r e f i n er i es 

C a p a ci t y of r e f i ne r i e s 

1 8 .0

3 0 0

1 6 .0

1 4 .0

1 2 .0

1 0 .0

8 .0

6 .0

4 .0

2 .0

0 .0

2 5 0

2 0 0

1 5 0

1 0 0

5 0

0

Capacity (Million barrels/day) 

Number of refineries Number of refineries mber of refineries

Source: EIA; Annual Energy Report, 2005 (Table 5.9) 

The increase in capacity combined with the decrease in amount of refineries and the 

increased demand for gasoline and diesel fuels, has resulted in an increase in the average 

utilization rate of refineries. In the 1970’s, the utilization rate ranged from 84 to 94 percent. In 

the last ten years, however, the utilization rate has ranged from 91 to 96 percent. Refineries 

therefore have to produce more with less overall capacity. The amount of gasoline and diesel 

produced by U.S. refiners has steadily increased. Since 1973 through 2004, gasoline and diesel 

production has increased 27 and 36 percent, respectively. Figure 1.1-2 shows the change in 

gasoline and diesel production from 1973 through 2004 

15
1973 

1975 

1977 

1979 

1981 

1983 

1985 

1987 

1989 

1991 

1993 

1995 

1997 

1999 

2001 

2003 

Figure 1.1-2. 

Amount of Gasoline and Diesel Fuels Produced in the U.S. 

Billion gallons/yr 140.0 

120.0 

100.0 

80.0 

Diesel fuel 

Gasoline 

60.0 

40.0 

20.0 

0.0 

Source: EIA Annual Energy Report, 2005; Table 5.8 

1.1.2 Petroleum Imports 

The decrease in U.S. refining capacity discussed in Section 1.1.3, has resulted in 

increases in the amount of gasoline and diesel fuels imported into the U.S. As of 2004, 5.4 and 

11.5 percent of the total respective volumes of gasoline and diesel consumed in the U.S. were 

imported. 

Today, the United States imports approximately 70 percent of all petroleum products used, with 

two-thirds of these products being used for transportation. From 1973 to 2004, the amount of 

crude oil imported has increased from 1.2 to 3.7 billion barrels per year, a tripling of volume, 

representing an average annual increase of about 6 percent. Over the same time period, the 

amount of gasoline imported has increased from 2 to 7.4 billion gallons per year, more than three 

times the amount of volume. The amount of diesel imported in the same time period decreased 

slightly from 6 to 5 percent. Figures 1.1-3 and 1.1-4 show the increase in crude oil and 

gasoline/diesel fuel imports, respectively, from 1973 to 2004. 

16
1973 

1974 

1975 

1976 

1977 

1978 

1979 

1980 

1981 

1982 

1983 

1984 

1985 

1986 

1987 

1988 

1989 

1990 

1991 

1992 

1993 

1994 

1995 

1996 

1997 

1998 

1999 

2000 

2001 

2002 

2003 

Figure 1.1-3. 

Increase in Crude Oil Imports from 1973-2004 

4.00 

3.50 

3.00 

2.50 

2.00 

1.50 

1.00 

0.50 

0.00 

Imported crude (bil bbls) 

Years 

(Source: Annual Energy Outlook, 2005; Energy Information Administration) 

17
1973 

1975 

1977 

1979 

1981 

1983 

1985 

1987 

1989 

1991 

1993 

1995 

1997 

1999 

2001 

2003 

Figure 1.1-4. 

Change in Volumes of Imported Gasoline and Diesel fuels (1973-2004) 

0.00 

2.00 

4.00 

6.00 

8.00 

10.00 

12.00 

14.00 

Imports (bil gal)

Gasoline 

Diesel 

Total 

Years 

Source: Annual Energy Outlook, 2005; Energy Information Administration 

Approximately twenty percent of our trade deficit is from imported petroleum products, a 

deficit which reached $782 billion in 2005. Figure 1.1-5 shows the trade deficits from 1994 

through 2004 (earlier data on petroleum imports is not available from the U.S. Census web site at 

this time). While the overall contribution of petroleum imports to the total deficit is decreasing 

as shown in Figure 1.1-5, this is due to a more rapid growth in the total deficit from other goods 

and services. The portion of the deficit due to petroleum imports by itself is increasing by 

approximately 4 percent per year. Over the last 25 years, the cumulative cost of imported crude 

oil has reached $2.0 trillion in 2005 dollars. 

18
Figure 1.1-5. 

U.S. Trade Deficit and Portions Due to Petroleum Imports 

1994-2004 

(Millions of dollars, chain weighted to 2000) 

0 

100,000 

200,000 

300,000 

400,000 

500,000 

600,000 

700,000 

Millions of dollars

Trade deficit 

Deficit due to petroleum 

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 

Source: U.S. Census Bureau, Foreign Trade Statistics, 2006 

The amount of import facilities in the U.S. has stayed relatively constant since the U.S. 

EPA has been requiring such facilities to register. In 1995 there were a total of 39 such facilities 

in the U.S. The amount has remained relatively constant, in the 50’s since that time and as of 

2004 there were 53 such facilities registered with U.S. EPA. The great majority of such facilities 

are located in PADD 1; as of 2004, 35 facilities were in PADD 1, and a total of 18 in the other 

four PADDs. 

1.2 Renewable Fuel Production 

While the definition of renewable fuel in the Act does not limit compliance with the 

standard to any one particular type of renewable fuel, ethanol is currently the most prevalent 

renewable fuel blended into motor vehicle fuels today. Biodiesel represents another form of 

renewable fuel, which while not as widespread as ethanol use (in terms of volume), has been 

increasing in production capacity and use over the last several years. Ethanol and biodiesel are 

19
expected to continue to dominate renewable fuel use in the timeframe when the RFS rule will be 

phasing in. 

1.2.1 Current U.S. Ethanol Production 

1.2.1.1 Overview 

There are currently 110 ethanol production facilities in the United States with a combined 

production capacity of 5.2 billion gallons per year1

. This baseline, or starting point, for this 

regulatory impact analysis is based on U.S. ethanol production facilities operational as of 

October 2006.2ABCDE 

Approximately 92 percent of today’s ethanol production capacity is produced exclusively 

from corn, mainly from a dry-milling process. The remainder is derived from corn/grain blends, 

cheese whey, and other starches. The majority of ethanol plants are located in Midwest where 

the bulk of corn is produced. PADD 2 accounts for just over 5 billion gallons (or 96 percent) of 

the total U.S. ethanol production. Leading the Midwest in ethanol production are Iowa, Illinois, 

Nebraska, Minnesota, and South Dakota which together represent 76 percent of the total 

domestic product. In addition to the concentration of facilities located in PADD 2, there are also 

a sprinkling of ethanol plants situated outside of the Midwest as far west as California and as far 

south as Georgia. 

1.2.1.2 Ethanol Feedstocks & Processing Technologies 

All of the ethanol currently produced today comes from grain or starch-based feedstocks 

that can easily be broken down into ethanol via traditional fermentation processes. The primary 

feedstock is corn, although grain sorghum (milo), wheat, barley, beverage waste, cheese whey, 

and sugars/starches are also fermented to make fuel-grade ethanol. 

The majority of ethanol (almost 92 percent by volume) is produced exclusively from 

corn. Most of the corn originates from the Midwest and most of the ethanol is produced in 

PADD 2 close to where the corn is grown. However, several corn-ethanol plants are also 

situated outside the traditional “corn belt”. In California, Colorado, New Mexico, and Wyoming 

corn is shipped from the Midwest to supplement locally grown grains or in some cases, serve as 

the sole feedstock. As for the remaining ethanol, almost eight percent is produced from a blend 

of corn and/or similarly processed grains (milo, wheat, or barley) and less than one percent is 

1

 This analysis does not consider ethanol plants that may be located in (or planned for) the Virgin Islands or U.S. 

territories. 

2

 The October 2006 ethanol production capacity baseline was generated based on the June 2006 NPRM plant list and 

updated on October 18, 2006 based on a variety of data sources including: Renewable Fuels Association (RFA), 

Ethanol Producer Magazine (EPM), ICF International, BioFuels Journal, and ethanol producer websites. The 

baseline includes small-scale ethanol production facilities as well as former food-grade ethanol plants that have 

since transitioned into the fuel-grade ethanol market. Where applicable, current ethanol plant production levels have 

been used to represent plant capacity, as nameplate capacities are often underestimated. 

20
produced from waste beverages, cheese whey, and sugars/starches combined. A summary of 

ethanol production by feedstock is presented in Table 1.2-1. 

Table 1.2-1. 

2006 U.S. Ethanol Production by Feedstock 

Plant Feedstock 

Capacity 

MMgy 

% of 

Capacity 

No. of 

Plants 

% of 

Plants 

Cheese Whey 

Corna 

Corn, Barley 

Corn, Milob 

Corn, Wheat 

Milo, Wheat 

Sugars, Starches 

Waste Beveragesc 

8 

4,780 

40 

244 

90 

40 

2 

16 

0.1% 

91.6% 

0.8% 

4.7% 

1.7% 

0.8% 

0.0% 

0.3% 

2 

90 

1 

8 

2 

1 

1 

5 

1.8% 

81.8% 

0.9% 

7.3% 

1.8% 

0.9% 

0.9% 

4.5% 

Total 5,218 100.0% 110 100.0% a

Includes two facilities processing seed corn and another facility 

processing corn which intends to transition to corn stalks, switchgrass, 

and biomass 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. 

There are two primary plant configurations for processing grains into ethanol: dry mill and 

wet mill. A summary of the processing technologies used by today’s ethanol plants is found below 

in Table 1.2-2. 

Dry mill plants simply grind the entire kernel and feed the flour into the fermentation 

process to produce ethanol. At the end, the unfermentable parts are recovered as distillers’ 

grains along with a soluble liquid containing vitamins, minerals, fat and protein. The distillers’ 

grains are concentrated with the solubles stream to make a single co-product, referred to as 

distillers’ grains with solubles (DGS). The co-product is either sold wet (WDGS) or more 

commonly dried (DDGS) to the agricultural market as animal feed. If the feed is going to be 

used by local markets, it’s usually sold wet precluding the need for process dryers. However, if 

the feed is going to be shipped (usually by train) to more distant locations, the product is usually 

dried to facilitate storage and transportation. 

Wet mill plants typically separate the kernel into four products: starch, gluten feed, gluten 

meal, and oil. The starch is used in a fermentation process the same as in dry mill plants, while the 

gluten, oil, and other co-products are sold into food and agricultural markets. Production of these 

multiple streams is more capital-intensive than the dry milling process, and thus wet mill plants are 

generally more expensive to build and tend to be larger in size. 

21
Table 1.2-2. 

2006 U.S. Ethanol Production by Processing Technology 

Processing 

Technology 

Capacity 

MMgy 

% of 

Capacity 

No. of 

Plants 

% of 

Plants 

Dry Milling 

Wet Milling 

Othera 

4,057 

1,137 

25 

77.7% 

21.8% 

0.5% 

92 

10 

8 

83.6% 

9.1% 

7.3% 

Total 5,218 100.0% 110 100.0% 

a

Plants that do not process traditional grain-based crops and thus do not 

require milling. This category includes plants processing cheese whey, 

sugars & starches, or waste beverages. 

As shown above in Table 1.2-2, dry milling is the most predominant production process 

used by today’s ethanol plants. Of the 102 facilities processing corn and/or other similarly 

processed grains, 92 utilize dry milling technologies and the remaining 10 plants rely on wet 

milling processes (refer to Table 1.2-3 below). The remaining “other” eight plants listed above 

process waste beverages, cheese whey, or sugars/starches and operate differently than their 

grain-based counterparts. These facilities do not require milling and instead operate a simpler 

enzymatic fermentation process. 

Table 1.2-3. 

2006 U.S. Grain Ethanol Production - Wet Mill Plants 

Ethanol Plant Location 

Capacity 

MMgy 

Archer Daniels Midlanda 

Archer Daniels Midlanda 

Archer Daniels Midland 

Archer Daniels Midlanda 

Archer Daniels Midland 

Aventine Renewable Energy 

Cargill, Inc. 

Cargill, Inc. 

Grain Processing Corp 

Tate & Lyle 

Cedar Rapids, IA 

Clinton, IA 

Columbus, NE 

Decatur, IL 

Marshall, MN 

Pekin, IL 

Eddyville, IA 

Blair, NE 

Muscatine, IA 

Loudon, TN 

300 

150 

90 

250 

40 

100 

35 

85 

20 

67 

Total 1,137 a

Estimated ADM plant capacities 

In addition to grain and starch-to-ethanol production, another method exists for producing 

ethanol from a more diverse feedstock base. This process involves converting cellulosic 

materials such as bagasse, wood, straw, switchgrass, and other biomass into ethanol. Cellulose 

consists of tightly-linked polymers of starch, and production of ethanol from it requires 

additional steps to convert these polymers into fermentable sugars. Scientists are actively 

pursuing acid and enzyme hydrolysis as well as gasification to achieve this goal, but the 

22 
technologies are still not fully developed for large-scale commercial production. As of October 

2006, the only known cellulose-to-ethanol plant in North America was Iogen in Canada, which 

produces approximately one million gallons of ethanol per year from wood chips. Several 

companies have announced plans to build cellulose-to-ethanol plants in the U.S., but most are 

still in the research and development or pre-construction planning phases. The majority of the 

plans involve converting bagasse, rice hulls, wood, switchgrass, corn stalks, and other 

agricultural waste or biomass into ethanol. For more a more detailed discussion on future 

cellulosic ethanol plants and production technologies, refer to RIA Sections 1.2.3.6 and 7.1.2, 

respectively. 

1.2.1.3 Ethanol Plant Energy Sources 

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 onsite or 

by other dedicated boilers. Of today’s 110 ethanol production facilities, 101 burn natural gas, 7 

burn coal, 1 burns coal and biomass, and 1 burns syrup from the process to produce steam3. Our 

research suggests that 11 plants currently utilize co-generation or combined heat and power 

(CHP) technology, although others may exist. 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. A summary of the energy sources 

and CHP technology utilized by today’s ethanol plants is found below in Table 1.2-4. 

Table 1.2-4. 

2006 U.S. Ethanol Production by Energy Source 

Plant Energy 

Source 

Capacity 

MMgy 

% of 

Capacity 

No. of 

Plants 

% of 

Plants 

CHP 

Tech. 

Coal 

Coal, Biomass 

Natural Gasa 

Syrup 

1,042 

50 

4,077 

49 

20.0% 

1.0% 

78.1% 

0.9% 

7 

1 

101 

1 

6.4% 

0.9% 

91.8% 

0.9% 

2 

0 

9 

0 

Total 5,218 100.0% 110 100.0% 11 a

Includes three facilities burning natural gas which intend to transition to coal or 

biomass in the future. 

1.2.1.4 Ethanol Production Locations 

The majority of domestic ethanol is currently produced in the Midwest within PADD 2 – 

where most of the corn is grown. Of the 110 U.S. ethanol production facilities, 100 are located 

in PADD 2. As a region, PADD 2 accounts for about 96 percent (or over five billion gallons) of 

domestic ethanol production, as shown in Table 1.2-5. 

3

 Facilities were assumed to burn natural gas if the plant fuel type was not mentioned or unavailable. 

23
Table 1.2-5. 

2006 U.S. Ethanol Production by PADD 

PADD 

Capacity 

MMgy 

% of 

Capacity 

No. of 

Plants 

% of 

Plants 

PADD 1 

PADD 2 

PADD 3 

PADD 4 

PADD 5 

0.4 

5,012 

30 

105 

71 

0.0% 

96.0% 

0.6% 

2.0% 

1.4% 

1 

100 

1 

4 

4 

0.9% 

90.9% 

0.9% 

3.6% 

3.6% 

Total 5,218 100.0% 110 100.0% 

Leading the Midwest in ethanol production are Iowa, Illinois, Nebraska, Minnesota, and 

South Dakota with capacities of 1.62, 0.71, 0.61, 0.55, 0.49 billion gallons, respectively. 

Together, these five states’ 70 ethanol plants account for 76 percent of the total domestic ethanol 

production. However, although the majority of ethanol production comes from PADD 2, there 

are a growing number of plants situated outside the traditional corn belt. In addition to the 15 

states comprising PADD 2, ethanol plants are currently located in California, Colorado, Georgia, 

New Mexico, and Wyoming. 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 ethanol production alphabetically by state is found in Table 1.2-6. 

24
Table 1.2-6. 

2006 U.S. Ethanol Production by State 

State 

Capacity 

MMgy 

% of 

Capacity 

No. of 

Plants 

% of 

Plants 

California 

Colorado 

Georgia 

Iowa 

Illinois 

Indiana 

Kansas 

Kentucky 

Michigan 

Minnesota 

Missouri 

North Dakota 

Nebraska 

New Mexico 

Ohio 

Oklahoma 

South Dakota 

Tennessee 

Wisconsin 

Wyoming 

71 

93 

0.4 

1,618 

706 

122 

219 

38 

155 

546 

155 

51 

606 

30 

3 

2 

493 

67 

233 

12 

1.4% 

1.8% 

0.0% 

31.0% 

13.5% 

2.3% 

4.2% 

0.7% 

3.0% 

10.5% 

3.0% 

1.0% 

11.6% 

0.6% 

0.1% 

0.0% 

9.4% 

1.3% 

4.5% 

0.2% 

4 

3 

1 

25 

6 

2 

8 

2 

3 

16 

4 

2 

12 

1 

1 

1 

11 

1 

6 

1 

3.6% 

2.7% 

0.9% 

22.7% 

5.5% 

1.8% 

7.3% 

1.8% 

2.7% 

14.5% 

3.6% 

1.8% 

10.9% 

0.9% 

0.9% 

0.9% 

10.0% 

0.9% 

5.5% 

0.9% 

Total 5,218 100.0% 110 100.0% 

In addition to the domestic ethanol production described above, the U.S. also receives a 

small amount of ethanol from other countries. A discussion on ethanol imports is found in 

Section 1.5 

1.2.1.5 Ethanol Producers and Marketers 

The U.S. ethanol industry is currently comprised of a mixture of corporations and farmerowned cooperatives (co-ops). More than half (or 60) of the plants are owned by corporations 

and the remainder (50 plants) are farmer owned co-ops. On average, a U.S. ethanol production 

facility has a mean plant capacity of about 47 million gallons per year. In general, plants owned 

by corporations (“company-owned”) are above average in size while farmer-owned co-ops are 

below average. Similarly, company-owned plants tend to have a much broader range in ethanol 

production levels than farmer-owned co-ops. A summary of these results is presented in Table 

1.2-7. 

25
Table 1.2-7. 

2006 U.S. Ethanol Production by Plant Ownership 

Plant 

Ownership 

No. of 

Plants 

Production Capacity, MMgy 

Total Avg Min Max 

Company-Owneda 

Farmer-Owned 

60 

50 

3,315 

1,903 

55 

38 

0.4 

3 

300 

60 

Total 110 5,218 47 0.4 300 a

Includes ethanol producers with public offerings. 

Based on the dominating number of company-owned plants and their above-average 

production size, company-owned plants account for nearly 64 percent of the total domestic 

product. Further, more than 50 percent of today’s U.S. ethanol production capacity comes from 

plants owned by just 6 different companies. A list of the top six ethanol producing companies 

and their respective plant capacities is found in Table 1.2-8. 

Table 1.2-8. 

2006 Top Six U.S. Ethanol Producers 

Companya 

Capacity 

MMgy 

No. of 

Plants 

Archer Daniels Midland 

Broin 

VeraSun Energy 

Hawkeye Renewables, LLC 

Global / MGP Ingredients 

Aventine Renewable Energy 

1,070 

838 

230 

200 

190 

150 

7 

18 

2 

2 

3 

2 

Total 2,678 34 a

Includes majority and minority plant ownership. 

Over 80 percent of today’s U.S. ethanol production is sold to the gasoline industry by 

eight marketing companies4. A list of the top eight ethanol marketers and their respective 

marketing capacities based on plant affiliations is found in Table 1.2-9. The remaining ethanol is 

marketed by Kinergy Marketing, The Andersons, Murex International, Noble Americas, and 

other small marketing companies. 

4

 Based on information obtained from ethanol marketer websites, ethanol producer websites, and conversations with 

ethanol marketers/producers. 

26
Table 1.2-9. 

2006 Top Eight U.S. Ethanol Marketers 

Marketing Company 

Capacity 

MMgya 

No. of 

Plants 

Archer Daniels Midland 

Ethanol Products 

Renewable Products Marketing Group 

Aventine Renewable Energy 

Eco-Energy 

Provista (formerly UBE) 

Cargill, Inc. 

Abengoa Bioenergy 

1,172 

991 

612 

666 

325 

217 

120 

110 

9 

22 

15 

14 

5 

5 

2 

3 

Total 4,212 75 a

Volumes based on marketing agreements and respective ethanol 

plant capacities 

1.2.2 Forecasted Growth in Ethanol Production 

1.2.2.1 Overview 

Over the past 25 years, domestic fuel ethanol production has steadily increased due to 

environmental regulation, federal and state tax incentives, and market demand. More recently, 

ethanol production has soared due to the phase out of MTBE, an increasing number of state 

ethanol mandates, and elevated crude oil prices. As shown in Figure 1.2-1, over the past three 

years, domestic ethanol production has nearly doubled from 2.1 billion gallons in 2002 to 4.0 

billion gallons in 2005. For 2006, the Renewable Fuels Association is anticipating about 4.7 

billion gallons of domestic ethanol production5. 

5

 Based on RFA comments received in response to the proposed rulemaking, 71 FR 55552 (September 22, 2006). 

27
1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005 

Figure 1.2-1. U.S. Ethanol Production Over Time 

1,630 

1,770 

2,810 

3,410 

2,130 

4,000 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

3,500 

4,000 

4,500 

Ethanol Production, MMgal 

Year 

Source: Renewable Fuels Association, From Niche to Nation: Ethanol Industry Outlook 2006 

EPA forecasts that domestic ethanol production will continue to grow into the future. In 

addition to the past impacts of federal and state tax incentives, as well as the more recent impacts 

of state ethanol mandates and the removal of MTBE from all U.S. gasoline, crude oil prices are 

expected to continue to drive up demand for ethanol. As a result, the nation is on track to exceed 

the renewable fuel requirements contained in the Act, as explained below. 

1.2.2.2 Expected Increases in Plant Capacity 

Today’s ethanol production capacity (5.2 billion gallons) is already exceeding the 2007 

renewable fuel requirement (4.7 billion gallons). In addition, there is another 3.4 billion gallons 

of production capacity currently under construction.6FGH A summary of the new construction 

and plant expansion projects currently underway (as of October 2006) is found in Table 1.2-10. 

6

 Under construction plant locations, capacities, feedstocks, and energy sources as well as planned/proposed plant 

locations and capacities were derived from a variety of data sources including Renewable Fuels Association (RFA), 

Ethanol Producer Magazine (EPM), ICF International, BioFuels Journal, and ethanol producer websites. 

28
Table 1.2-10. Under Construction U.S. Ethanol Production Capacity 

PADD 

Oct. 2006 Baseline Under Const. Base + Under Const. 

MMgy Plants MMgya Plants MMgya Plants 

PADD 1 

PADD 2 

PADD 3 

PADD 4 

PADD 5 

0.4 

5,012 

30 

105 

71 

1 

100 

1 

4 

4 

115 

2,764 

230 

50 

198 

1 

39 

3 

1 

3 

115 

7,776 

260 

155 

269 

2 

139 

4 

5 

7 

Total 5,218 110 3,357 47 8,575 157 a

Includes plant expansions 

A select group of builders, technology providers, and construction contractors are 

completing the majority of the construction projects described in Table 1.2-10. As such, the 

completion dates of these projects are staggered over approximately 18 months, resulting in the 

gradual phase-in of ethanol production shown in Figure 1.2-27. 

Figure 1.2-2. Estimated Phase-In of Under Construction Plant Capacity 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

7,000 

8,000 

9,000 

10,000 

Oct-06 Dec-06 Mar-07 Jun-07 Sep-07 Dec-07 Mar-08 

Time 

Ethan

ol Capacity (M

Mgy) 

Additional Capacity Total Ethanol Plant Capacity 

7

 Construction timelines based on information obtained from press releases and ethanol producer websites. 

29
As shown in Table 1.2-10 and Figure 1.2-2, once all the construction projects currently 

underway are complete (estimated by March 2008), the resulting U.S. ethanol production 

capacity would be about 8.6 billion gallons. Without even considering forecasted biodiesel 

production (discussed below in 1.2.5), this would be more than enough renewable fuel to satisfy 

the 2012 RFS requirements (7.5 billion gallons). However, ethanol production is expected to 

continue to grow. There are more and more ethanol projects being announced each day. These 

potential projects are at various stages of planning from conducting feasibility studies to gaining 

local approval to applying for permits to financing/fundraising to obtaining contractor 

agreements. Together these potential projects could result in an additional 21 billion gallons of 

ethanol production capacity (as shown in Table 1.2-11). 

Table 1.2-11. 

Other Potential U.S. Ethanol Production Capacity 

PADD 

Base + Under Const. Planned Proposed 

MMgya Plants MMgya Plants MMgya Plants 

PADD 1 

PADD 2 

PADD 3 

PADD 4 

PADD 5 

115 

7,776 

260 

155 

269 

2 

139 

4 

5 

7 

548.0 

4,633 

250 

100 

232 

8 

44 

4 

1 

8 

934 

11,722 

876 

783 

775 

21 

136 

14 

14 

23 

Subtotal 8,575 157 5,763 65 15,090 208 

Total

b 14,339 222 29,428 430 a

Includes plant expansions b

Total including existing plus under construction plants. 

Although there is clearly a great potential for ethanol production growth, it is highly 

unlikely that all the announced projects would actually reach completion in a reasonable amount 

of time, or at all, considering the large number of projects moving forward. Since there is no 

precise way to know exactly which plants will come to fruition in the future, we have chosen to 

focus our subsequent discussion on forecasted ethanol production on plants which are likely to 

be online by 2012.8

 This includes existing plants as well as projects which are under 

construction (refer to Table 1.2-10) or in the final planning stages (denoted as “planned” in Table 

1.2-11). The distinction between “planned” versus “proposed” is that as of October 2006 

planned projects had completed permitting, fundraising/financing, and had builders assigned 

with definitive construction timelines whereas proposed projects did not. 

As shown in Table 1.2-11, once all the under construction and planned projects are 

complete, the resulting U.S. ethanol production capacity would be 14.3 billion gallons. This 

volume, expected to be online by 2012, exceeds the EIA AEO 2006 demand estimate (9.6 billion 

gallons by 2012, discussed more in RIA Section 2.1.4.1). The forecasted growth would nearly 

triple today’s production capacity and greatly exceed the 2012 RFS requirement (7.5 billion 

8

 A more detailed summary of the plants we considered is found in a March 5, 2007 note to the docket titled: RFS 

Industry Characterization – Ethanol Production. 

30
gallons). While our forecast represents ethanol production capacity (actual production could be 

lower), we believe it is still a good indicator or what domestic ethanol production could look like 

in the future. In addition, we predict that domestic ethanol production will continue to be 

supplemented by imports in the future. A more detailed discussion on future ethanol imports is 

found in Section 1.5. 

1.2.2.3 Changes in Feedstocks & Processing Technologies 

Of the 112 forecasted new ethanol plants (47 under construction and 65 planned), 106 

would rely on grain-based feedstocks. More specifically, 89 would rely exclusively on corn, 13 

would process a blend of corn and/or similarly processed grains (milo or wheat), 3 would process 

molasses, and 1 would process a combination of molasses and sweet sorghum (milo). Of the 

remaining six plants (all in the planned stage), four would process cellulosic biomass feedstocks 

and two would start off processing corn and later transition to cellulosic materials. Of the four 

dedicated cellulosic plants, one would process bagasse, one would process a combination of 

bagasse and wood, and two would process biomass. Of the two transitional corn/cellulosic 

plants, one would ultimately process a combination of bagasse, rice hulls, and wood and the 

other would ultimately process wood and other agricultural residues. In addition to the 

forecasted new plants described above, an existing corn ethanol plant plans to expand production 

and transition to corn stalks, switchgrass, and biomass in the future. 

A summary of the resulting overall feedstock usage (including current, under 

construction, and planned projects) is found in Table 1.2-12. A discussion on how the plants 

predicted to process cellulosic feedstocks would help the nation meet the Act’s cellulosic 

biomass ethanol requirement is found in Section 1.2.2.6 

31
Table 1.2-12. Forecasted 2012 U.S. Ethanol Production by Feedstock 

Plant Feedstock 

Capacity 

MMgy 

% of 

Capacity 

No. of 

Plants 

% of 

Plants 

Bagasse 

Bagasse, Wood 

Bagasse, Wood, Rice Hullsa 

Biomass 

Cheese Whey 

Cornb 

Corn, Barley 

Corn, Miloc 

Corn, Wheat 

Corn Stalks, Switchgrass, Biomass

a 

Milo, Wheat 

Molasses

d 

Sugars, Starches 

Waste Beveragese 

Wood Agricultural Residuesa 

7 

2 

108 

55 

8 

12,495 

40 

1,132 

235 

40 

40 

52 

2 

16 

108 

0.1% 

0.0% 

0.8% 

0.4% 

0.1% 

87.1% 

0.3% 

7.9% 

1.6% 

0.3% 

0.3% 

0.4% 

0.0% 

0.1% 

0.8% 

1 

1 

1 

2 

2 

178 

1 

20 

3 

1 

1 

4 

1 

5 

1 

0.5% 

0.5% 

0.5% 

0.9% 

0.9% 

80.2% 

0.5% 

9.0% 

1.4% 

0.5% 

0.5% 

1.8% 

0.5% 

2.3% 

0.5% 

Total 14,339 100.0% 222 100.0% a

Facilities plan to start off processing corn. 

b

Includes two facilities processing seed corn. c

Includes one facility processing small amounts of molasses in addition to corn and milo. d

Includes one facility planning to process sweet sorghum (milo) in addition to molasses. e

Includes two facilities processing brewery waste. 

As shown above, the majority of future plants are predicted to process grains (namely 

corn). Similarly, the vast majority of plants are expected to pursue dry milling technology. Our 

analysis does not foresee any new wet mill facilities, with the exception of a new 100 MMgy wet 

mill plant that is planned for Fort Dodge, IA and a 37 MMgy plant expansion project that is 

underway in Loudon, TN. Further, we do not predict that there will be any new plants 

processing cheese whey, waste beverages, or sugars/starches (which do not require milling). 

The forecasted cellulosic feedstock plants (described in more detail in Section 1.2.2.7) will not 

require milling. However, these facilities will require complex forms of pretreatment (described 

in more detail in Section 7.1.2) to break down the lignocellulosic and hemicellulosic polymers 

into fermentable sugars. A summary of the resulting overall feedstock processing technology 

utilization is found below in Table 1.2-13. 

32
Table 1.2-13. 

Forecasted 2012 U.S. Ethanol Production by Processing Technology 

Processing 

Technology 

Capacity 

MMgy 

% of 

Capacity 

No. of 

Plants 

% of 

Plants 

Dry Milling 

Wet Milling 

Othera 

12,668 

1,274 

397 

88.3% 

8.9% 

2.8% 

192 

11 

19 

86.5% 

5.0% 

8.6% 

Total 14,339 100.0% 222 100.0% 

a

Plants that do not process traditional grain-based crops and thus do not require milling. 

This category includes plants processing cheese whey, sugars & starches, or waste 

beverages as well as plants that plan to process molasses or cellulosic feedstocks. 

1.2.2.4 Changes in Plant Energy Sources 

Of the 112 forecasted new plants, 100 would burn some amount of natural gas - at least 

initially. More specifically, 91 plants would rely exclusively on natural gas; two would rely on a 

combination of natural gas, bran and biomass; one would burn a combination of natural gas, 

distillers’ grains and syrup; and six would start off burning natural gas and later transition to 

coal. As for the remaining 12 plants, three would burn manure-derived methane (biogas); seven 

would rely exclusively on coal; one would burn a combination of coal and biomass; and one 

would burn a combination of coal, tires and biomass. In addition to the new ethanol plants, three 

existing plants currently burning natural gas are predicted to transition to alternate boiler fuels in 

the future. More specifically, two plants plan to transition to biomass and one plans to start 

burning coal. 

Our research suggests that seven of the new plants (mentioned above) would utilize 

combined heat and power (CHP) technology, although others may exist. Three of the new CHP 

plants would burn natural gas, three would burn coal, and one would burn a combination of coal, 

tires, and biomass. Among the existing CHP plants, two are predicted to transition from natural 

gas to coal or biomass at this time. Overall, the net number of CHP ethanol plants would 

increase from 11 to 18. A summary of the resulting overall plant energy source utilization is 

found below in Table 1.2-14. A discussion on how the plants predicted to burn waste materials 

could help the nation meet the Act’s cellulosic biomass ethanol requirement is found in Section 

1.2.2.6. 

33
Table 1.2-14. Forecasted 2012 U.S. Ethanol Production by Energy Source 

Plant Energy Source 

Capacity 

MMgy 

% of 

Capacity 

No. of 

Plants 

% of 

Plants 

CHP 

Tech. 

Biomass

a 

Coalb 

Coal, Biomass 

Coal, Biomass, Tires 

Manure Biogasc 

Natural Gas 

Natural Gas, Bran, Biomass 

Natural Gas, Distillers' Grain, Syrup 

Syrup 

112 

2,095 

75 

275 

144 

11,275 

264 

50 

49 

0.8% 

14.6% 

0.5% 

1.9% 

1.0% 

78.6% 

1.8% 

0.3% 

0.3% 

2 

21 

2 

1 

3 

189 

2 

1 

1 

0.9% 

9.5% 

0.9% 

0.5% 

1.4% 

85.1% 

0.9% 

0.5% 

0.5% 

1 

6 

0 

1 

0 

10 

0 

0 

0 

Total 14,339 100.0% 222 100.0% 18 a

Represents two existing natural gas-fired plants that plan to transition to biomass. b

Includes two plants planning on burning lignite coal or coal fines. Includes one existing plant currently 

burning natural gas that plans to transition to coal. Includes six new plants that will start off burning 

natural gas and later transition to coal. c

Includes one facility planning on burning cotton gin in addition to manure biogas. 

1.2.2.5 Changes in Ethanol Production Locations 

Once all the forecasted ethanol projects are complete, 87 percent of the domestic 

production capacity would originate from PADD 2, followed by PADDs 1, 3, 5, and 4 (all 

contributing less than 5 percent). A summary of the findings is found below in Table 1.2-15. 

Table 1.2-15. 

Forecasted 2012 U.S. Ethanol Production by PADD 

PADD 

Capacity 

MMgy 

% of 

Capacity 

No. of 

Plants 

% of 

Plants 

PADD 1 

PADD 2 

PADD 3 

PADD 4 

PADD 5 

663 

12,409 

510 

255 

501 

4.6% 

86.5% 

3.6% 

1.8% 

3.5% 

10 

183 

8 

6 

15 

4.5% 

82.4% 

3.6% 

2.7% 

6.8% 

Total 14,339 100.0% 222 100.0% 

While PADD 2 ethanol production is expected to more than double (from 5.0 to 12.4 

billion gallons), this represents a decrease in Midwest marketshare (from 96 to 87 percent). This 

predicted shift in marketshare is attributed to the growing number of ethanol plants located 

outside the cornbelt. Arizona, Florida, Hawaii, Louisiana, New York, Oregon, Pennsylvania and 

Texas are scheduled to join the 19 ethanol producing states described in Table 1.2-5. A 

summary of future ethanol production by state is found below in Table 1.2-16. 

34
Table 1.2-16. 

Forecasted 2012 U.S. Ethanol Production by State 

State 

Capacity 

MMgy 

% of 

Capacity 

No. of 

Plants 

% of 

Plants 

Arizona 

California 

Colorado 

Florida 

Georgia 

Hawaii 

Iowa 

Illinois 

Indiana 

Kansas 

Kentucky 

Louisiana 

Michigan 

Minnesota 

Missouri 

New York 

North Dakota 

Nebraska 

New Mexico 

Ohio 

Oklahoma 

Oregon 

Pennsylvania 

South Dakota 

Tennessee 

Texas 

Wisconsin 

Wyoming 

55 

244 

243 

80.0 

150.4 

59.2 

3,016 

1,606 

855 

569 

38 

110 

212 

882 

382 

325 

251 

2,543 

30 

420 

112 

143 

108 

953 

109 

370 

463 

12 

0.4% 

1.7% 

1.7% 

0.6% 

1.0% 

0.4% 

21.0% 

11.2% 

6.0% 

4.0% 

0.3% 

0.8% 

1.5% 

6.2% 

2.7% 

2.3% 

1.7% 

17.7% 

0.2% 

2.9% 

0.8% 

1.0% 

0.8% 

6.6% 

0.8% 

2.6% 

3.2% 

0.1% 

1 

7 

5 

2 

3 

5 

38 

16 

11 

13 

2 

2 

4 

20 

6 

4 

5 

31 

1 

7 

3 

2 

1 

16 

2 

5 

9 

1 

0.5% 

3.2% 

2.3% 

0.9% 

1.4% 

2.3% 

17.1% 

7.2% 

5.0% 

5.9% 

0.9% 

0.9% 

1.8% 

9.0% 

2.7% 

1.8% 

2.3% 

14.0% 

0.5% 

3.2% 

1.4% 

0.9% 

0.5% 

7.2% 

0.9% 

2.3% 

4.1% 

0.5% 

Total 14,339 100.0% 222 100.0% 

1.2.2.6 Meeting the Cellulosic Ethanol Requirement in 2013 

The Energy Policy Act of 2005 (the Energy Act or the Act) requires that 250 million 

gallons of the renewable fuel consumed in 2013 and beyond meet the definition of cellulosic 

biomass ethanol. The Act defines cellulosic biomass ethanol as ethanol derived from any 

lignocellulosic or hemicellulosic matter that is available on a renewable or recurring basis 

including dedicated energy crops and trees, wood and wood residues, plants, grasses, agricultural 

residues, fibers, animal wastes and other waste materials, and municipal solid waste. The term 

also includes any ethanol produced in facilities where animal or other waste materials are 

35
digested or otherwise used to displace 90 percent of more of the fossil fuel normally used in the 

production of ethanol. 

As discussed above in Section 1.2.2.3, there are seven “planned” ethanol plants planning 

on processing cellulosic feedstocks in the future. A summary of these facilities is found below in 

Table 1.2.17. 

Table 1.2-17. Potential Cellulosic Feedstock Plants 

Ethanol Plant Location Plant Feedstock 

Capacity 

MMgy Status 

Worldwide Energy Groupa 

Celunol Corp.

b 

GS Agrifuels Corporationc 

Xethanol Coastal LLC 

Bionol 

Xethanol Corporation 

BioEnergy International 

Kaumakani, HI 

Jennings, LA 

Memphis, TN 

Augusta, GA 

Lake Providence, LA 

Blairstown, IA 

Clearfield County, PA 

Bagasse 

Bagasse, Wood 

Biomass 

Biomass 

Corn then Bagasse, Rice Hulls, Wood 

Corn then Corn Stalks, Switch Grass, Biomass 

Corn then Wood, Agricultural Residues 

7 

2 

5 

50 

108 

40 

108 

Planned 

Planned 

Planned 

Planned 

Planned 

Plannedd 

Planned 

Total Cellulosic Ethanol Potential Based on Plant Feedstocks 320 a

Company also/formerly known as Clearfuels Technology b

Company also/formerly known as BC International c

Project also/formerly known as Mean Green Biofuels d

Includes 5 Mmgy existing plant capacity plus 35 MMgy planned expansion. 

It is unclear whether the above-mentioned cellulosic feedstock plants would be online 

and capable of producing 250 million gallons of ethanol by 2013 to meet the Act’s cellulosic 

biomass ethanol requirement. However, as described above in Section 1.2.2.4 there are 12 

facilities that burn or plan to burn waste materials to power their ethanol plants in the future. 

These facilities, summarized below in Table 1.2.18, could also potentially meet the definition of 

cellulosic biomass ethanol under the Act. 

Table 1.2-18. Potential Waste Energy Plants 

Capacity 

Ethanol Plant Location Plant Energy Source MMgy Status 

Corn LP Goldfield, IA Coal, Biomass 50 Existing 

E Caruso Ethanol Goodland, KS Coal, Biomass 25 Under Construction 

Archer Daniels Midland Columbus, NE Coal, Tires, Biomass 275 Planned 

E3 Biofuels, LLC Mead, NE Manure Biogas 24 Under Construction 

Harrison Ethanol, LLC Cadiz, OH Manure Biogas 20 Planned 

Panda Ethanol Hereford, TX Manure Biogas, Cotton Gin 100 Under Construction 

Central Minnesota Ethanol Co-op Little Falls, MN Natural Gas then Biomass 22 Existing 

Chippewa Valley Ethanol Co. Benson, MN Natural Gas then Biomass 90 Existinga 

Ethanex at SEMO Port Cape Girardeau, MO Natural Gas, Bran, Biomass 132 Planned 

Ethanex Southern Illinois

b Benton, IL Natural Gas, Bran, Biomass 132 Planned 

Green Plains Renewable Energyc Superior, IA Natural Gas, Distillers Grain, Syrup 50 Under Construction 

Corn Plus, LLP Winnebago, MN Syrup 49 Existing 

Total Cellulosic Ethanol Potential Based on Plant Energy Sources 969 a

Includes 45 MMgy existing plant capacity plus 45 MMgy planned expansion. b

Joint venture with Star Ethanol c

Project also/formerly known as Superior Ethanol 

36
Depending on how much fossil fuel is displaced by burning these waste materials (on a 

plant-by-plant basis), a portion or all of the above-mentioned 969 MMgy ethanol production 

capacity could potentially qualify as “cellulosic biomass ethanol” under the Act. Combined with 

the additional 320 MMgy of ethanol production capacity from plants processing cellulosic 

feedstocks, the overall cellulosic ethanol potential could be as high as 1.3 billion gallons. Even 

if only one fifth of this ethanol were to end up qualifying as cellulosic biomass ethanol or come 

to fruition by 2013, it would be more than enough to satisfy the 250 million gallon requirement 

specified in the Act.9 

1.2.3 Current Biodiesel Production 

Biodiesel is defined in several sections of the Act, which we have used in formulating our 

definition for the regulations, which call for meeting ASTM specifications. Biodiesel is 

registered with the EPA for commercial sale and is legal for use at any blend level in both 

highway and nonroad diesel engines although most engine manufacturers will only honor the 

warranty if biodiesel is used in blends of 2, 5 or, in some limited circumstances, 20 percent. 

Biodiesel can be made from almost any vegetable or animal fat, with most of the world’s 

production coming from plants oils, notably soy bean and rapeseed (canola) oil. Biodiesel fuel 

production is rapidly increasing in many regions of the world. The choice of the feedstock oil 

used to make it is dependent upon the vegetable oils and fat supplies that are economically 

available. For the U.S. market, there are many potential plant oil feedstocks that can be used to 

make biodiesel, including soybean, peanut, canola, cottonseed and corn oil. Biodiesel can also 

be made from animal fats such used restaurant grease (yellow grease) and tallow. Though, 

typically for the U.S. market, soybean oil has been the primary major feed stock supply, 

followed by use of yellow grease and animal tallow. 

The resulting biodiesel product can be used as a fuel for diesel engines with minor 

modifications and is commonly blended with refinery produced diesel fuel. Raw vegetable and 

animal oils consist of fatty acids and glycerine products. Though these oils can directly be used 

in engines and give good short term performance, this is highly discouraged as their use can 

cause severe engine problems. This is primarily due to the raw oils forming engines deposits, 

with coking and plugging in engine injectors nozzles, piston rings, lubricating oil, etc. This 

happens due to polymerization of the triglycerides in the raw oils as the fuel is combusted. 

Therefore, it is necessary to convert the raw oils into a form of esters or biodiesel which prevents 

these issues. The biodiesel production process converts the raw vegetable and animal oils into 

esters, though the virgin oils themselves are sometimes (inappropriately) referred to as biodiesel. 

The production process called transesterification consists of adding methanol or ethanol to the 

virgin vegetable oil and animal oil, in the presence of a catalyst such as sodium or potassium 

hydroxide, resulting in esters or biodiesel and a byproduct glycerol. A subsequent step is usually 

9 We anticipate a ramp-up in cellulosic ethanol production in the years to come so that capacity exists to satisfy the 

Act’s 2013 requirement (250 million gallons of cellulosic biomass ethanol). Therefore, for subsequent analysis 

purposes, we have assumed that 250 million gallons of ethanol would come from cellulosic biomass sources by 

2012. 

37
needed, however, to remove glycerin, catalysts and other compounds, to allow the biodiesel to 

meet the required ASTM specifications. 

Biodiesel blends such as B2, B5 and in some cases B20, can be used in existing engines 

without modification, and most engines exhibit no performance problems with the use of 

biodiesel, though this depends on the blend and the season. However, engine fuel filters may 

need to be changed more often, and there may be cold temperature operations due to biodiesel's 

higher cloud point. As a result most engine manufacturers will only recognize their warranties if 

biodiesel is used in low concentrations. Biodiesel produced from vegetable oil has practically 

zero amounts of sulfur and aromatics and a high cetane value, thus making it a good for blending 

into 15 ppm highway and offroad diesel fuel, though biodiesel made from yellow grease and 

animal fat may contain about 24 ppm of sulfurI. Biodiesel also has good lubricity qualities and 

can be used in concentration (~2 vol%) as a lubricity-enhancing additive for conventional diesel. 

1.2.4 Forecasted Biodiesel Production 

Biodiesel production has been increasing rapidly over the past five years and is projected 

to continue at a high rate in part because of the Renewable Fuel Standard (RFS) program. This 

expansion has primarily been driven by better economics, due to the recent large increase in 

diesel prices associated with the run up in crude prices, along with the Biodiesel Blenders Tax 

Credit programs and the Commodity Credit Commission Bio-energy Program, both of which 

subsidize producers and offset production costs. The Act extended the Biodiesel Blenders Tax 

Credit program to year 2008, which provides about one dollar per gallon in the form of a federal 

excise tax credit to biodiesel blenders from virgin vegetable oil feedstocks and 50 cents per 

gallon to biodiesel produced from recycled grease and animal fats. This program was started in 

2004 under the American Jobs Act. The existing Commodity Credit Commission Bio-energy 

Program also pays biodiesel producers grants when the economics to produce biodiesel are poor; 

the program averaged about one dollar per gallon in 2004. Recent payments through the 

Commodity Credit program have been reduced, however, and the program is expiring in fiscal 

year 2006. Historically, the cost to make biodiesel was an inhibiting factor to production. The 

cost to produce biodiesel was high compared to the price of petroleum derived diesel fuel, even 

with consideration of the benefits of subsidies and credits provided by federal and state 

programs. Mandates from states and local municipalities that require the use of biodiesel in 

transport fuels are another factor which is expanding the use of biodiesel. 

In 2005 approximately 91 million gallons of biodiesel were produced in the U.S. based 

on program payments to biodiesel producers under USDA’s Bio-energy Program. This volume 

represents approximately 0.15 percent of all diesel fuel consumed in the domestic market. EIA 

projects the future production volume to expand to 414 million gallons per year in 2007 and then 

decrease to about 303 MM gallons per year in 2012, assuming that the biodiesel blender tax 

credits program expires in 2007 (see Table 1.2-19). 

38
Table 1.2-19. Estimated Biodiesel Productiona 

Year Million Gallons per Year 

2001 5 

2002 15 

2003 20 

2004 25 

2005 91 

2006 150 

2007 414 

2012 303 a

 Historical data from 2001-2004 obtained from estimates from John Baize “ The Outlook and Impact of 

Biodiesel on the Oilseeds Sector” USDA Outlook Conference 06. Year 2005 data from USDA Bioenergy Program. 

Year 2006 data from verbal quote based on projection by NBB in June of 06. Production data for years 2007 and 

higher are from EIA’s AEO 2006. 

With the increase in biodiesel production, there has also been a corresponding rapid 

expansion in biodiesel production capacity. Presently, there are 85 biodiesel plants in operation 

with an annual production capacity of 580 million gallons per yearJ. The majority of the current 

production capacity was built in 2005 and 2006, and was first available to produce fuel in the 

later part of 2005 and in 2006. Though the capacity has grown, historically the biodiesel 

production capacity has far exceeded actual production with only 10-30 percent of this being 

utilized to make biodiesel, see Table 1.2-20. 

Table 1.2-20. U.S. Production Capacity History 

2001 2002 2003 2004 2005 2006 

Plants 9 11 16 22 45 85 

Capacity 

(MM gals/yr) 

50 54 85 157 290 580 

Production, 

(MM gals/yr) 

5 15 20 25 91 150 

Capacity 

Utilization for 

Biodiesel, % 

10 28 24 16 31 26 

 Note: Capacity Data based on surveys conducted. 

Excess production capacity is not easily quantified, though since some of these plants 

may not run at full rate all of the time and may be “idled” for certain days of the week, seasons, 

39 
time of day, etc. The capacity can be classified into two types of producers; capacity dedicated 

to biodiesel production and capacity available from the ole-chemical industry. The plants that 

primarily operate in the ole-chemical industry produce esters for use in the chemical industry. 

These plants are swing producers of biodiesel, which means that when the economics are 

favorable they can shift their operations and make biodiesel esters instead of products for the olechemical market.10

 The capacity from the ole-chemical industry produces mono-alkyl esters 

using a similar transesterification process, with the ester products being sold for to make 

plasticizers, soaps, paints, solvents and other industrial uses. Additionally, the biodiesel 

production capacity volumes may be optimistic, as this is not officially tracked. The capacities 

listed here are those based on each company’s self reported volumes to the National Biodiesel 

Board and may have some inaccuracies due to informal reporting procedures. 

We anticipate that future capacity additions will be geared more towards production of 

biodiesel for use as transportation fuel, rather than serving primarily the oleochemicals markets. 

As of September 2006, there were 65 plants in the construction phase and 13 existing plants that 

are expanding their capacity. All of this new capacity when installed would provide about 1.4 

billion gallons per year of additional throughput capacity. Table 1.2-21 presents the data for the 

biodiesel plant capacities per the categories discussed. 

Table 1.2-21. Biodiesel Plant Capacities 

Existing Plants Construction Phase 

Number of Plants 85 78 

Total Plant Capacity, 

(MM Gallon/year) 

580 1,400 

Considering that it takes 12 to 18 months to construct a biodiesel plant (from the time of 

project feasibility analysis to startup date), a large portion of the capacity in the construction 

phase in late 2006 will be available to produce fuel in 2007.K Data on biodiesel plant 

construction reveals most of the new capacity that is currently being constructed is expected to 

be online and producing fuel in 2006 or by end of 2007. Therefore, the existing capacity plus the 

capacity in the construction phase totals an aggregate amount of about two billion gallons per 

year. Though there is no volume mandate for biodiesel fuel under the RFS program, the total 

capacity available from new and existing plants exceeds EIA’s projected biodiesel volume of 

414 MM in 2007 and 303 MM in 2012 by a wide margin. 

The plants in the construction phase are larger than existing biodiesel plants, with average 

capacity of existing plants at 8.4 MM gallons per year, while plants in construction phase are 

10 Oleochemicals are derived from biological fats and oils using hydrolysis or alcoholysis with products of fatty acid 

esters and glycerol. 

40
averaging 20.9 MM gallons per year, as presented in Table 1.2-22. The distribution of biodiesel 

plants by size and number of companies within each size range are presented in Table 1.2-23. 

Table 1.2-22. Average Plant Capacity by Feedstock (MM gallons per year) 

Feedstock Existing* Construction* 

Canola 57.5 

Multi Feedstock 6.0 16.7 

Other Vegetable 2.0 

Recycled Cooking Oil 0.5 1.0 

Soybean Oil 8.8 19.3 

Tallow 5.0 

Table 1.2-23. Biodiesel Plant Size versus Number of Companies 

Plant Size 

(MM gallons per year)a 

Existing Plants Construction Phase 

<1.00 9 5 

1.0- 5.0 28 9 

5.0-10.0 17 10 

10.0 to 15 .0 9 7 

15.0 to 20.0 2 3 

20.0+ 10 28 

Average Plant Size 8.4 20.9 a

Total capacity of plants in each category; existing plants are 580 MM gal/yr while those in the construction 

phase are 1,400 MM gal/yr. 

Because newer plants are likely to be larger than existing plants, have better technology 

and may have greater alignment with feedstock and feed sources, some of the older plants may 

operate at an economic disadvantage once the new plants come on line. At the moment, it is not 

possible to predict actual biodiesel production based on capacity, since in the past the capacity 

was used at rates less than maximum. Thus, how excess production capacity evolves will be 

dictated by economics, profitability, and fuel demand. 

The majority of existing biodiesel plant capacity is located in the middle and Midwestern 

parts of the country and use soy bean oil as the feedstock. The other plants are scattered with 

locations based on the east and west coasts, with feedstocks based on use of soybean, canola and 

other oils as well as yellow grease as the feedstock. The new plants are being built to process a 

41
wider variety of feedstocks, with multi-feedstock and recycle grease capability. The feedstocks 

for these plants are listed in Table 1.2-24. 

Table 1.2-24. Feedstock Selection for Biodiesel Producers 

Feedstock Existing Construction 

Camelia 

Canola 2 

Cottonseed 1 

Multi Feedstock 29 29 

Palm Oil 

Recycled Cooking Oil 7 3 

Soybean oil 39 36 

Tallow/Poultry Fat 2 

Unknown 7 8 

1.2.5 Baseline and Projected Biodiesel Volumes for Analysis 

For cost and emission analysis purposes, three biodiesel usage cases were considered: a 2004 

base case, a 2012 reference case, and a 2012 control case. The 2004 base case was formed based on 

historical biodiesel usage (25 million gallons as summarized in Table 1.2-16). The reference case 

was computed by taking the 2004 base case and growing it out to 2012 in a manner consistent with 

the growth of diesel fuel (described in Section 2.1.3). The resulting 2012 reference case consisted of 

approximately 30 million gallons of biodiesel. Finally, for the 2012 control case, forecasted 

biodiesel use was assumed to be 300 million gallons based on EIA’s AEO 2006 report (rounded 

value from Table 1.2-16). Unlike forecasted ethanol use (described in 2.1.4), biodiesel use was 

assumed to be constant at 300 million gallons under both the statutory and higher projected 

renewable fuel consumption scenarios. 

1.3 Renewable Fuel Distribution 

1.3.1 Current Renewable Fuel Distribution System 

 Ethanol and biodiesel blended fuels are not currently shipped by petroleum product 

pipeline due to operational issues and additional cost factors.L

 The ability to ship by pipeline is 

also limited because the sources of ethanol and biodiesel are frequently not in the same locations 

as the sources of gasoline and petroleum-based diesel fuel. Hence, a separate distribution system 

is needed for ethanol and biodiesel up to the point where they are blended into petroleum-based 

fuel as it is loaded into tank trucks for delivery to retail and fleet operators. Ethanol and 

42
biodiesel can either be added by “splash blending” where the renewable is added separately to 

the tank truck, or by in-line injection where the renewable is injected into the petroleum fuel 

stream as it is being dispensed into the tank truck. Ethanol and biodiesel are sometimes added to 

petroleum-based fuels downstream of the terminal, but this accounts for little of the total volume 

of used. 

In cases where ethanol and biodiesel are produced within 200 miles of a terminal, 

trucking is often the preferred means of distribution. However, most renewable fuel volumes are 

produced at greater distances from potential centers of demand. For longer shipping distances, 

the preferred method of bringing renewable fuels to terminals is by rail and barge. Dedicated 

pipelines have not been used to distribute renewable fuels to terminals due to the high cost of 

installing new pipelines, the relatively large shipping volumes that would be needed to justify 

such expenditures, and the fact that renewable fuel production facilities tend to be relatively 

numerous and dispersed. 

The relatively low volumes of ethanol used prior to 2002 constrained the ability of the 

distribution system to efficiently move ethanol to distant markets. Ethanol shipments by rail 

were typically made on an individual car basis. Under such an approach, small groups of rail 

cars travel to market as part of trains that carry other goods. This approach results in relatively 

high transportation costs, longer transit times, and potential delays in delivery. Substantial 

improvements in the efficiency of distributing ethanol by rail are being made due the need to 

move large volumes of ethanol over long distances as a consequence of the elimination of MTBE 

in California, New York, and Connecticut beginning in 2004. The use of unit trains, sometimes 

referred to as “virtual pipelines” reduces delivery costs, shortens delivery times, and improves 

reliability. Unit trains are composed entirely of approximately 100 rail cars containing ethanol. 

Ethanol shipped by unit trains is delivered to hub terminals for further distribution to other 

terminals by barge and tank truck. 

Substantial volumes of ethanol can potentially be shipped down the Mississippi river by 

barge for temporary storage in New Orleans.M From New Orleans, ethanol can be loaded onto 

ocean transport for delivery to the East and West Coast. There is also potential to move ethanol 

via the Missouri and Ohio as well as other river systems and the Great Lakes. Marine shipments 

of ethanol require a relatively large minimum shipment size, determined by the minimum size of 

the marine tank compartment.11

 Similar to the case for “unit trains”, there are also efficiencies in 

dedicating whole barges, barge tows, or marine tankers to ethanol distribution. The increased 

demand for ethanol has made it possible to better benefit from these efficiencies of scale. 

The use of inland barges to transport ethanol from production facilities is in large part 

driven by whether there is river access at such facilities. Historically, corn prices tend to be 

higher near river systems that serve as arteries for the export of corn than at inland locations 

distant from these river systems. To take advantage of lower corn prices at inland locations and 

to avoid competing for corn with grain elevators that serve the export market, all of the new 

ethanol production facilities that have been built since 1999 have been built at inland locations. N 

11 River barges typically have a capacity of 10,000 barrels. Ocean barges typically have a capacity of 20,000 

barrels. Barges are sometimes subdivided into 2 or 3 compartments. 

43
Consequently, the majority of the growth in ethanol freight volumes since 1999 has been in the 

rail sector. 

1.3.2 Changes to the Renewable Fuel Distribution System Due to Increased Demand 

This section addresses that changes that we expect will take place in the renewable fuel 

distribution system in response to the anticipated increase in demand for such fuels through 

2012. There may be some limited opportunity to ship renewable fuels by pipeline in the future 

as demand increases. However, because of the constraints discussed previously (see section 

1.3.1), we believe that rail and barge are likely to remain the predominant means of 

transportation. The 2002 DOE Study also reached this conclusion.O While this constraint on the 

ability to ship ethanol and biodiesel by pipeline presents logistical challenges that result in 

additional transportation costs, the need to transport these alternative fuels by other means may 

work to the overall advantage of the fuel distribution system. Petroleum product pipelines are 

nearing capacity. Thus, it seems likely that the pipeline distribution system will find it 

increasingly difficult to keep pace with annual increases in the demand for transportation fuels. 

Displacing some of the volume of transportation fuels from the pipeline distribution system 

through the use of ethanol and biodiesel will relieve some of this strain. 

Small volume rail shipments made on a by-car basis are likely to remain an important 

feature in supplying markets that demand limited volumes. However, as the demand for ethanol 

increases we anticipate that the expansion of the use of unit trains will continue, and that this will 

be a significant means of bringing ethanol to distant markets. There has been some expansion of 

capacity at existing ethanol plants with river access and some new plants are projected to be built 

with river access. However, we anticipate that most new ethanol capacity will not have river 

access. In addition, at least one new ethanol plant slated for production that does have river 

access is planning to move its ethanol to market via rail. Nevertheless, in cases where rail is the 

means to transporting ethanol to hub terminals, marine transport can play an important role in 

further distribution to satellite terminals. 

Substantial improvements to the rail, barge, tank truck, and terminal distribution systems 

will be needed to support the transport of the volumes of renewable fuels necessary to meet the 

requirements of the RFS program. These improvements include the addition of a significant 

number of additional rail cars, and tank trucks. Additional marine barges will also be needed. 

To facilitate the increased use of unit trains, new rail spurs will be needed at terminals. 

Terminals will also need to add facilities to store and blend ethanol. In addition, those terminals 

and retail facilities that had not previously handled ethanol blended fuel will need to make 

certain one-time upgrades to ensure the compatibility of their systems with ethanol. These types 

of changes have been occurring as demand for ethanol and biodiesel has grown rapidly over the 

last several years, and there is no reason to suspect that they would not continue as demand 

continues to warrant it. The costs associated with these changes are discussed in Chapter 7.3 of 

this RIA. 

The most comprehensive study of the infrastructure requirements for an expanded fuel 

ethanol industry was conducted for the Department of Energy (DOE) in 2002 .

P The conclusions 

reached in this study indicate that the changes needed to handle the increased volume of ethanol 

44
required under the RFS will not represent a major obstacle to industry.12

 While some changes 

have taken place since this report was issued (as discussed below), we continue to believe that 

the rail and marine transportation industries can manage the increased growth in an orderly 

fashion. This belief is supported by the demonstrated ability of the industry to handle the rapid 

increases and redistribution of ethanol use across the country over the last several years as 

MTBE was removed. Given that future growth in ethanol use is expected to take place in an 

orderly fashion in response to economic drivers, we anticipate that the distribution system will be 

able to respond appropriately. 

The use of unit trains has accelerated beyond that anticipated in the 2002 DOE report, 

leading to the more efficient distribution of ethanol by rail. As a result, rail has taken a relatively 

greater role in the transportation of new ethanol volumes as compared to shipment by barge than 

was projected in the report. Thus, there is likely to be a relatively greater demand on the rail 

distribution system and somewhat less demand on the marine distribution system than was 

projected in the DOE study. 

The 2002 DOE study estimated that the increase in the volume of ethanol shipped by rail 

needed to facilitate the use of 10 billion gallons of ethanol annually would represent an increase 

in total tank car loadings of 0.33 percent. The increase in tank car loadings for Class I railroads 

was estimated at 4.75 percent. The DOE report concluded that this increase is relatively modest 

by railroad industry standards and could be accommodated given the available lead time. The 

DOE study estimated that the increase in demand on barge movements due to the need to carry 

an increased volume of ethanol would equate to a one percent increase in the total tonnage 

moved by barge. Given that on the one hand relatively few new ethanol plants are projected to 

be cited with river access, and that on the other hand barge is expected to play an important role 

in redistributing ethanol from rail hub terminals, we estimate that the increase in barge 

movements will be 30 percent less than that projected in the 2002 DOE study. This equates to an 

increase in total tank car loadings of 0.44 percent rather than the 0.33 percent projected in the 

DOE study. We believe that this relatively modest potential increase in the demand on the rail 

distribution system can be accommodated without major difficulty given the available lead time. 

Although, the 2002 DOE study generally concluded that the projected one percent 

increase in the demand on the river barge industry could be accommodated without major 

difficulty, it highlighted two potential concerns. The report noted that delays are already being 

experienced at locks on the Mississippi river. The question was raised regarding how the 

projected increase of one percent in river traffic due to increased ethanol shipments might be 

accommodated at these locks. The report also raised concerns regarding the availability of 

sufficient marine vessels capable of traveling between two ports in the United States (Jones Act 

compliant vessels). Given that it appears that there will be less demand placed on the river barge 

industry to transport ethanol than was projected in the 2002 DOE study, the concerns raised in 

the study regarding the capability of the inland waterway system to cope with the increased 

traffic associated with shipping the anticipated new volume of ethanol will be less pronounced. 

12 See section 7.3 of this RIA regarding the projected costs of the necessary infrastructure improvements. 

45
At the present time, the industry is experiencing a shortage of tractor trailers and drivers 

to transport ethanol. The boom in demand for truck transport is due to a number of factors, 

including the precipitous removal of MTBE from gasoline and its replacement by ethanol13 

which has taken place when the demand for truck transport was already growing at a rapid place 

due to the increased imports. The implementation of EPA’s ultra-low sulfur diesel (ULSD) 

program this summer may also cause an increase in the demand for tank trucks if more trucks 

must be dedicated to ULSD service. Given the gradual increase expected from year to year in 

ethanol production, we anticipate that the industry will be able to add sufficient additional tank 

truck service in an orderly fashion without undue burden. 

The necessary facility changes at terminals and at retail stations to dispense ethanol 

containing fuels have been occurring at a record pace due to the removal of MTBE from 

gasoline. The use of ethanol has also become more economically attractive due to higher 

gasoline prices. Now that MTBE has been removed, a more steady increase in the use of ethanol 

is anticipated over time. This will also allow for a smooth transition for terminals and retail 

operators. 

The volumes of biodiesel that are expected to be used by 2012 to comply with the RFS 

will be relatively modest (approximately 300,000,000 gallons). Consequently, we anticipate that 

biodiesel will continue to be distributed to terminals by tank truck and by individual rail car 

shipments. One hundred percent biodiesel (B100)14 forms wax crystals when the temperature 

falls to 35 to 45 degrees Fahrenheit.15 Thus, storage tanks for B100 need to be heated to 

maintain flow-ability during the cold seasons. Shipping vessels used to transport B100 such as 

barges, rail cars, and tank truck containers also typically must either be insulated (and sometimes 

heated) during the cold season or alternatively facilities can be provided at the terminal to reheat 

the vessel prior to delivery. Biodiesel that is blended with diesel fuel and enhanced with cold 

flow additives (if needed) can have comparable cold flow performance to petroleum based diesel 

fuel.

16 

13 MTBE is typically blended with gasoline at the refinery. MTBE production plants are often located nearby to 

refineries allowing transport to the refinery by dedicated pipeline. In cases where, the sources of MTBE are more 

distant from the refinery, barge and rail are the preferred means of transport and relatively little MTBE is transported 

by truck. 

14 The concentration of biodiesel in a biodiesel blend is indicated by the number following the “B” designation. For 

example, B99.9 indicates a biodiesel blend containing 99.9 percent biodiesel, and B80 indicates a blend containing 

80 percent biodiesel. Manufactures of biodiesel sometimes blend in one tenth of one percent diesel fuel into 

biodiesel to create B99.9 prior to shipping the fuel to terminals to create more dilute biodiesel blends so that the 

producer can claim the biodiesel tax credit (pursuant to Internal Revenue Service requirements). 

15 The point at which wax crystals form is referred to as the cloud point. The cloud point of B100 varies depending 

on the feed stock used in its production. 

16 The relatively low concentration biodiesel blends that are typically used in vehicles (up to 20% biodiesel) can be 

formulated to have comparable cold flow performance to petroleum based diesel fuel. Thus, there is no need to heat 

such biodiesel blends in vehicle fuel tanks. 

46
As temperatures fall during the cold seasons, some terminals currently avoid the need for 

heated B100 tanks and facilities to heat shipping vessels by accepting progressively less 

concentrated biodiesel blends (for final blending to produce fuels for use in vehicles). During 

the warm seasons, such terminals typically accept B100 or B99.9. As the weather grows colder, 

the terminal might switch to accepting B80 and during the coldest parts of the year might accept 

B50 (that contains 50 percent number one diesel fuel). The need for insulated tank trucks and 

tank cars is also sometimes avoided if transit times are brief by shipping warmed biodiesel. We 

believe that as the volume of biodiesel grows, most terminals will opt to receive B100 (or B99.9) 

year round for blending into diesel fuel for the consistency in operations which this practice 

offers. A number of terminals are already following this practice. These terminals have installed 

heated storage tanks for biodiesel and insist that biodiesel be delivered in insulated tank trucks 

(or rail cars) so that it may be pumped into the terminal storage tank without concern about the 

potential need for reheating. The cost of the necessary heated and/or insulated equipment is not 

insignificant. However, the modest additional volumes that will need to be shipped via rail and 

tank truck due to the use of biodiesel do not materially affect the conclusions reached above 

regarding the ability of the fuel distribution system to cope with the increased volumes of 

renewable fuels. 

1.4 Blenders 

1.4.1 Ethanol Blending 

Ethanol is miscible with water, and thus can introduce water into the distribution system 

causing corrosion and durability problems as well as fuel quality problems. For this reason, 

ethanol is blended downstream at terminals or into tank trucks. 

The distribution of ethanol is described in more detail in Section 1.3. Briefly, ethanol 

producers provide ethanol either directly to terminals, to marketers or to terminals that are owned 

by refiners. In the first case, ethanol is provided to terminals that are owned entities other than 

refining companies. They receive ethanol from the ethanol producer, and gasoline from any 

number of refiners. The blenders then add ethanol to the gasoline at the terminal. For RFG, the 

terminals receive the blendstock for RFG, called Reformulated Blendstock for Oxygenate 

Blending or RBOB, to which they add the amount of ethanol called for on the Product Transfer 

Document that accompanies such shipments. Once the ethanol is added to the RBOB, the 

product becomes a finished gasoline (RFG) and is sent via truck to retailers. For conventional 

gasoline (CG) ethanol is also added and shipped to retailers. The tracking mechanism for CG is 

not as detailed as it is for RFG, however. The majority of ethanol that is blended into CG has 

historically been “splash-blended” although an increasing volume of ethanol is being blended 

into special blends of conventional gasoline (e.g. sub-octane), or “match blended”. Finally, a 

very small amount is blended as E85. 

1.4.2 Biodiesel Blending 

Biodiesel generally leaves the production facility in its neat form and is shipped by truck 

to locations where it can be blended with conventional diesel fuel. The blending generally 

47
occurs at centralized distribution points such as terminals, although it also sometimes occurs 

within tank trucks themselves. Biodiesel is only rarely used in its neat (unblended) form. 

1.5 Imports/Exports of Renewable Fuel 

Since the early 1980s, the U.S. has maintained a 54 cent per gallon tariff on imported 

ethanol, primarily to offset the blending tax subsidy of the same magnitude that had been put in 

place to support alternative energy production and domestic agriculture. Legislation and 

agreements implemented since then have waived or significantly reduced the tariff on imports 

from Canada, Mexico, and about two dozen Central American and Caribbean nations covered by 

the Caribbean Basin Initiative (CBI). Under the Caribbean Basin Economic Recovery Act, 

which created the CBI, these countries can export ethanol duty free to the U.S. at a rate up to 7% 

of the U.S. fuel alcohol market; quantities above this limit have additional stipulations for 

feedstocks being grown within the supplying country. 

Historically, the CBI nations have had little ethanol production capacity of their own but 

have supplemented it by importing Brazilian ethanol and re-exporting it to the U.S. duty free. 

More recently, with the rapid phase-out of MTBE and the high price of ethanol, it has become 

economically viable to import significant quantities of ethanol directly from other nations despite 

the tariff. Brazil, currently the largest ethanol producing nation in the world, has become the 

largest single country supplier to the U.S. market. As shown in Figure 1.5-1, total imports have 

increased more than 30% in 2004-5 over the previous three-year average. 

48
Figure 1.5-1. Historic U.S. Ethanol Import Volumes and Originsa 

0 

50 

100 

150 

200 

250 

300 

2001 2002 2003 2004 2005 

Millio

n Gallon 

Other 

EU 

Canada 

Brazil 

CBI 

a

 F.O. Licht, “World Ethanol Markets, The Outlook to 2015” (2006). Gross imports (does not account for 

export volumes) including hydrous, dehydrated, and denatured volumes. 

Going forward, as domestic ethanol production capacity increases rapidly, its price is 

expected to fall back into the historic range of 30-40 cents per gallon above gasoline (before 

blending subsidy). This is expected to once again make direct imports from Brazil and other 

full-tariff producers less attractive, and to decrease total imports. According to a current report 

by F.O. Licht, U.S. net import demand is estimated to be around 300 million gallons per year by 

2012, being supplied primarily through the CBI, with some direct imports from Brazil during 

times of shortfall or high price.Q 

Changes in the production and trade climate may influence this however. The Caribbean 

countries with duty free status are seeing both internal and foreign investment to increase ethanol 

production capacity significantly over the next several years, making more cheap imports 

available. It is unclear at this point what volume of ethanol will be supplied through these 

channels. 

On the export side, the U.S. has averaged about 100 million gallons per year since 2000, 

mostly to Canada, Mexico, and the E.U. Figure 1.5-2 shows historical U.S. exports. There is a 

trend over the past five years of exporting larger quantities to fewer countries, with declining 

volumes to Asia and increasing volumes to the E.U. and India. The demand for ethanol in all 

these areas remains strong, and it appears that Asian imports from Brazil and China are making 

up for the decrease in U.S. ethanol moving into the region. 

49
Figure 1.5-2. Historic U.S. Ethanol Export Volumes and Originsa 

0 

20 

40 

60 

80 

100 

120 

140 

2001 2002 2003 2004 2005 

Millio

n Gallon 

Canada Mexico 

Japan EU 

India Others

 a F.O. Licht, “World Ethanol Markets, The Outlook to 2015” (2006). Gross exports (does not account for 

import volumes), includes hydrous, dehydrated, and denatured volumes. 

These numbers are expected to increase modestly as more production comes online, with 

more dramatic increases possible during periods of depressed domestic prices or stock surges. 

Looking out over the next decade, the E.U. has a biofuels directive in place that will bolster 

demand, and Japan and South Korea are expected to increase their use of biofuels steadily as 

well. World ethanol production is projected to grow from the current 10 billion gallons per year 

to more than 25 in 2015, and the international biofuels markets are just beginning to take shape. 

During this period we can expect significant changes in who is supplying and who is demanding 

as the players determine their places and forge agreements on subsidies and tariffs. As of 2005, 

the U.S. became largest ethanol producing nation, eclipsing Brazil, and ample foreign markets 

will be available if conditions are right. 

50
Chapter 2: Changes to Motor Vehicle Fuel Under the 

Renewable Fuel Standard Program 

In this regulatory impact analysis, we begin by describing the renewable fuel 

volume scenarios we used to measure the environmental and economic impacts of 

increased renewable fuel blending. From there we narrow our discussion in on ethanol - 

the predominant renewable fuel expected to be used in the future. We describe historical 

ethanol use, current use and our projections of future ethanol use. The discussion starts 

with an in-depth examination of current ethanol use. More specifically, what factors 

drive ethanol use and where ethanol blending currently occurs - by state, season, and fuel 

type. The discussion then shifts to where ethanol is expected to be used in the future. 

We discuss the ongoing trend in increased ethanol use, the anticipated phase-out of 

MTBE, and ultimately present our LP modeling results which predict where ethanol will 

likely be used in 2012 by PADD, season, fuel type. From there, we describe our 

methodology for allocating ethanol usage by state and in some cases, make distinctions 

on how we think ethanol would fill urban and rural areas. Once we understand how 

ethanol use is expected to change in the future, we measure the anticipated impacts on 

gasoline fuel quality (which later feeds into our emissions and air quality analyses). At 

the end of this chapter, we also provide a brief estimate on how increased biodiesel 

blending will impact diesel fuel properties. 

2.1 Renewable Fuel Volume Scenarios 

The Energy Policy Act of 2005 (the Energy Act or the Act) stipulates that the 

nationwide volumes of renewable fuel required under the Renewable Fuel Standard 

(RFS) program must be at least 4.0 billion gallons in 2006 and increase to 7.5 billion 

gallons by 2012. However, we expect that actual renewable fuel usage will exceed the 

RFS requirement by a significant margin. In Annual Energy Outlook 2006 (AEO 2006), 

the Energy Information Administration (EIA) projects that total renewable fuel demand 

would be 9.9 billion gallons by 2012. More specifically, EIA predicts that 9.6 billion 

gallons of ethanol and 303 million gallons of biodiesel would be consumed in 2012. The 

projected renewable fuel consumption levels were estimated using EIA’s LP refinery 

model which was based on a crude oil price of $48/bbl. This figure is lower than today’s 

crude oil price (tracking around $55/bbl at the time of our analysis).17RS Therefore, 

current market conditions indicate that renewable fuel production could be even more 

favorable and/or prevalent in the future based on economics.18 However, EIA’s AEO 

17 West Texas Intermediate (WTI) crude oil pricing was $59.08/bbl in November, 2006; $61.96/bbl in 

December, 2006; and $54.51/bbl in January 2007 according to EIA spot pricing. 

18 In AEO 2007, EIA forecasted an even higher ethanol consumption of 11.2 billion gallons by 2012. The 

draft report was issued on December 5, 2006, and we were unable to incorporated it into the refinery 

modeling used to conduct our analyses. 

51
2006 analysis also considers the feasibility of building production facilities to 

accommodate for the growing renewable fuel demand. Accordingly, we interpret EIA’s 

ethanol and biodiesel projections to be reasonable estimates considering both economics 

and the rate at which new plants could feasibly come on-line. As a result, in assessing 

the impacts of expanded renewable fuel use, we evaluated two renewable fuel usage 

scenarios (described in more detail below). The first represents the statutorily-required 

minimum and the second reflects the higher levels projected by EIA in AEO 2006. 

Although the actual renewable fuel volumes produced in 2012 may differ from both the 

required and projected volumes, we believe that these two volume scenarios represent a 

reasonable range for analysis purposes. 

The Act also requires that at least 250 million gallons of the total renewable fuel 

use in 2013 and beyond meet the definition of cellulosic biomass ethanol. As described 

in Chapter 1, there are a number of companies planning to produce ethanol from 

cellulosic feedstocks and/or waste-derived energy sources that could potentially meet the 

definition of cellulosic biomass ethanol. Accordingly, we anticipate a ramp-up in 

cellulosic biomass ethanol production in the coming years. Furthermore, for analysis 

purposes, we have assumed that the 250 million gallon requirement would be met by 

2012. 

As discussed in more detail below in Section 2.2.2, we chose 2004 to represent 

current baseline conditions. In 2004, 3.5 billion gallons of ethanol and 25 million gallons 

of biodiesel were consumed in motor vehicle fuels. To compare fuel quality impacts on 

emissions and air quality, we created a 2012 reference case that maintained current fuel 

quality parameters (with the exception of sulfur) but incorporated forecasted increases in 

vehicle miles traveled, changes in fleet demographics, etc. The 2012 fuel reference case 

was developed by growing out the 2004 renewable fuel baseline according to EIA’s 

forecasted energy growth rates. In AEO 2006, EIA predicted that gasoline demand 

would grow by 11.2 percent and diesel fuel demand would grow by 20.5 percent from 

2004 to 2012. As a result, the 2012 reference case is based on 3.9 billion gallons of 

ethanol use and 30 million gallons of biodiesel use in 2012. 

For our analyses, we created two 2012 control cases representing expanded 

renewable use – the “RFS Case” and the “EIA Case”. In both cases, cellulosic biomass 

ethanol use was assumed to be 250 million gallons (statutory required minimum) and 

biodiesel use was assumed to be 303 million gallons (EIA AEO 2006 estimate). The RFS 

Case was designed to exactly meet the RFS program requirements considering the effects 

of higher equivalence values for cellulosic ethanol and biodiesel. Per § 80.1115, one 

gallon of cellulosic ethanol counts 2.5 times towards compliance and one gallon of 

biodiesel counts 1.5 times towards compliance. As a result, in the RFS Case we predict 

that less than 7.5 billion gallons of renewable fuel would actually be consumed in 2012. 

The actual volume of renewable fuel analyzed for the RFS Case was computed to be 

approximately 7.0 billion gallons. The EIA Case represents EIA’s projections of 

renewable fuel use in 2012. Based on AEO 2006, the actual volume of renewable fuel 

52
analyzed for the EIA Case was 9.9 billion gallons. A summary of the renewable fuel 

volume scenarios we evaluated is found below in Table 2.1-1. 

Table 2.1-1 Renewable Fuel Volume Scenarios (MMgal) 

Renewable Fuel 

2004 

Base Casea 

2012 

Ref Caseb RFS Case EIA Casec 

Corn Ethanold 

Cellulosic Ethanole 

Biodiesel 

3,548 

0 

25 

3,947 

0 

30 

6,421 

250 

303 

9,388 

250 

303 

Total Renewable Volume 3,573 3,977 6,974 9,941 

Total Compliance Volumef n/a n/a 7,500 n/a a

Historical ethanol usage derived from EIA’s June 2006 Monthly Energy Review. Biodiesel 

usage derived from “The Outlook and Impact of Biodiesel on the Oilseeds Sector” presented 

by John Baize at the 2006 USDA Outlook Conference. b

The reference case was calculated by applying the 2004-2012 gasoline/diesel energy growth 

rates reported in AEO 2006 to the 2004 Base Case. c

EIA Case based on ethanol and biodiesel energy contributions reported in AEO 2006. d

Includes ethanol imports. e

Ethanol meeting the definition of cellulosic biomass ethanol in The Act. f

Based on applying a 2.5 equivalence value to cellulosic biomass ethanol and a 1.5 

equivalence value to biodiesel. 

2.2 Current Gasoline Oxygenate Use 

2.2.1 Why are oxygenates currently blended into gasoline? 

The blending of oxygenates into gasoline dates back to the 1970’s. However, 

their use greatly expanded in response to the Clean Air Act (CAA) amendments of 1990. 

Areas found to be out of compliance (i.e., in non-attainment) with the National Ambient 

Air Quality Standards (NAAQS) for ozone were required to reformulate their gasoline 

and use oxygenates year-round. In addition, several states began to use oxygenated fuel 

(oxy-fuel) in the wintertime to address carbon monoxide non-attainment. In addition, 

oxygenates (namely ethanol) have historically been used as a gasoline volume extender 

and more recently, to meet state mandates. This section summarizes the current driving 

forces behind gasoline oxygenate use in the U.S. 

2.2.1.1 Federal Reformulated Gasoline Program 

As mentioned above, areas found to be in ozone non-attainment were required to 

use reformulated gasoline (RFG) year-round. The federal RFG program contained a 

minimum oxygenate requirement as well as other fuel quality standards.19 Adding 

19 RFG oxygenate requirement found at 40 CFR 80.41(f). This requirement was effective for 2004 but has 

since been eliminated by the Energy Act Section 1504, promulgated on May 8, 2006 at 71 FR 26691. 

53
oxygen to gasoline and reformulating other gasoline properties has helped to reduce the 

production of smog-forming pollutants that contribute to unhealthy ground-level ozone. 

Besides ozone non-attainment areas, several states/areas also opted into the RFG program 

(otherwise known as “opt-in”). In addition, California and Arizona have state programs 

that promote the use of oxygenated gasoline. 

A list of the 2004 federal RFG areas and their corresponding oxygenate(s) is 

provided in Table 2.2-1. For the purpose of this analysis, only ethanol (ETOH) and 

methyl tertiary-butyl ether (MTBE) have been considered.20 

20Other low-usage oxygenates (e.g. ETBE, TAME, etc.) were assumed to be negligible for the purpose of 

this analysis. 

54
Table 2.2-1. 2004 Federal RFG Areas by StateT 

State City 

No. of 

Countiesa 

Type of 

RFG Area 

Primary 

Oxygenateb 

California Los Angeles 5 Req'd ETOH 

Sacramento 6 Req'd ETOH 

San Diego 1 Req'd ETOH 

San Joaquin Valley 8 Req'd ETOH 

Connecticutc Hartford 6 Req'd ETOH 

Long Island Area 1 Req'd ETOH 

Windham County 1 Opt In ETOH 

Delawarec Sussex County 1 Opt In MTBE 

Wilmington 2 Req'd MTBE 

District of Columbiac Washington DC Area 1 Opt Ind MTBE 

Illinois Chicago Area 8 Req'd ETOH 

Indiana Chicago Area 2 Req'd ETOH 

Kentucky Covington 3 Opt In ETOH 

Louisville 3 Opt In ETOH 

Maryland Baltimore 6 Req'd MTBE 

Philadelphia Area 1 Req'd MTBE 

Queen Anne/Kent Counties 2 Opt In MTBE 

Washington DC Area 5 Opt Ind MTBE 

Massachusettsc Boston Area 10 Opt In MTBE 

Springfield 4 Opt In MTBE 

Missouri St. Louis 5 Opt In ETOH 

New Hampshire Boston Area 4 Opt In MTBE 

New Jersey

c Atlantic City 2 Opt In MTBE 

Long Island Area 12 Req'd Both 

Trenton 6 Req'd MTBE 

Warren County 1 Opt In MTBE 

New York Poughkeepsie 2 Opt In ETOH 

Long Island Area 11 Req'd ETOH 

Pennsylvania Philadelphia Area 5 Req'd MTBE 

Rhode Islandc Providence Area 5 Opt In MTBE 

Texas Dallas/Fort Worth 4 Opt In MTBE 

Houston/Galveston 8 Req'd MTBE 

Virginia Norfolk/Virginia Beach 11 Opt In MTBE 

Richmond 7 Opt In MTBE 

Washington DC Area 10 Opt Ind MTBE 

Wisconsin Milwaukee-Racine 6 Req'd ETOH a

Includes partial counties. b

Oxygenate determination based on 2004 FHWA gasohol data and EPA fuel survey results. c

Entire state/district operates under the Federal RFG program. d

Was "opt-in" in 2004, now a required RFG area. 

As shown above in Table 2.2-1, a little more than half of the Federal RFG areas 

(on a county-by-county basis) used MTBE as opposed to ethanol as an oxygenate in 

2004. However, on a volumetric basis, more ethanol was consumed in RFG than MTBE 

55 
(2.2 billion gallons compared to 1.9 billion gallons as shown in Tables 2.1.5 and 2.1.3, 

respectively). 

2.2.1.2 State Oxygenated Fuel Programs 

In addition to the RFG program, several states require oxygenated fuel (oxy-fuel) 

to be used in the wintertime to address carbon monoxide (CO) non-attainment. CO is 

formed from the incomplete combustion of hydrocarbons (found in all gasoline blends). 

Production of the poisonous gas is more prevalent in oxygen-deficient environments and 

more harmful to human health in the wintertime due to temperature inversions.21 

Together, the winter oxy-fuel program coupled with improving vehicle emissions control 

systems has helped to reduce CO emissions. Many areas have and are continuing to 

come into attainment with the CO national ambient air quality standards (NAAQS). 

However, many former non-attainment areas continue to use the winter oxy-fuel program 

as part of a maintenance plan for remaining in compliance with the CO NAAQS. A list 

of the 2004 oxy-fuel areas is provided in Table 2.2-2. All oxy-fuel areas were assumed to 

use ethanol in 2004 based on information obtained from regional EPA offices. 

Table 2.2-2. 2004 State-Implemented Winter Oxy-Fuel ProgramsU 

Oxy-Fuel Area Location Oxy-Fuel 

Period 

Carbon Monoxide Status Winter Oxy-Fuel Program 

State City Designation Pursuing RDa Required Part of MPb 

Alaska Anchorage 11/1-2/29 Non-attainmentc X X 

Arizona Tucson 10/1-3/31 Attainment X 

Phoenix 11/2-3/15 Non-attainment X X 

California Los Angeles 10/1-2/29 Non-attainment X X 

Colorado Denver/Boulder 11/1-1/31 Attainment X 

Longmont 11/1-1/31 Attainment X 

Montana Missoula 11/1-2/29 Non-attainment X X 

Nevada Las Vegas 10/1-3/31 Non-attainment X 

Reno 10/1-1/31 Non-attainment X X 

New Mexico Albuquerque 11/1-2/29 Attainment X 

Oregon Portland 11/1-2/29 Attainment X 

Texas El Paso 10/1-3/31 Non-attainment X 

Utah Provo/Orem 11/1-2/29 Non-attainment X X 

Washington Spokane 9/1-2/29 Non-attainmentd X X a

Currently pursuing redesignation to CO attainment. b

Area is in currently in CO attainment but oxy-fuel program remains as part of maintenance plan. c

Area was redesignated to attainment effective 7/23/04. d

Area was redesignated to attainment effective 8/29/05. 

2.2.1.3 Other Motivations for Blending Ethanol 

21 Temperature inversions in the lower atmosphere are relatively common, especially during winter months 

in cold climates. A temperature inversion occurs when cold air close to the ground is trapped by a layer of 

warmer air, creating stagnation and trapping pollution close to the ground. 

56 
In addition to the RFG and oxy-fuel programs, gasoline refiners have several 

other motivations for blending oxygenate (namely ethanol) into gasoline. First and 

foremost, the state they provide gasoline to could be operating under a state ethanol 

mandate. In 2004, Hawaii joined Minnesota in approving a state ethanol requirement.22 

Second, blending ethanol into gasoline could help them meet their mobile source air 

toxics (MSAT1) performance standards as determined by the Complex Model.23 Third, 

adding ethanol increases both octane and total fuel volume, thus helping refiners extend 

their gasoline production. Finally, and perhaps most importantly, with record-high crude 

oil prices and the growing availability of grain-based ethanol (especially in PADD 2), 

ethanol use has become increasingly economical. The 1.1 billion gallons of ethanol used 

in PADD 2 conventional gasoline in 2004 (refer to Table 2.2-5 in Section 2.2.2.4) is a 

good indicator of this trend. 

In addition to the increasing availability of ethanol, consumer demand is also 

increasing based on the growing number of ethanol-friendly vehicles on the road. 

Conventional vehicles consume the majority of fuel ethanol and are limited to gasoline 

with 10 volume percent (vol%) ethanol (E10) or less. However, there are currently 

around 6 million flexible fuel vehicles (FFVs) on the road today with more being 

produced and sold each dayV. FFVs are specifically designed to handle a wide range of 

gasoline/ethanol blends up to 85 vol% ethanol (E85). 

2.2.2 Development of the Base Case 

As discussed in 2.1, to evaluate the impacts of increased ethanol blending and 

decreased MTBE blending on gasoline properties (and in turn air quality), we had to 

create a point of comparison. To do so, we assembled a 2004 Base Case to represent 

current baseline conditions, i.e., current gasoline, ethanol, and MTBE use. The 

methodology for assembling the base case, as well as a summary of the results, is 

described below. 

2.2.2.1 Strategy for Establishing the 2004 Base Case 

For the purpose of this regulatory impact analysis, the 2004 calendar year was 

selected to reflect current baseline conditions. This period represented the most current 

year for which gasoline and oxygenate data were available and also captured the 

California, New York, and Connecticut MTBE bans (effective 1/1/04) while avoiding the 

2005 calendar year hurricane upsets. 

22 For analysis purposes, both states were assumed to have ethanol mandates which required 100% of the 

gasoline to contain 10% ethanol. However, in reality, Hawaii’s ethanol mandate only requires that 85% of 

the gasoline contain 10% ethanol. 

23 This RFS proposal is based on MSAT1 conditions. Impacts of the recent MSAT2 rule (72 FR 8428) 

which removes individual refinery toxic performance standards (baselines) in exchange for a nationwide 

benzene standard are reflected in the analysis for that rulemaking. 

57
The approach for assembling the 2004 base case consisted of obtaining gasoline, 

ethanol, and MTBE usage for all 50 states as well as the District of Columbia. As 

mentioned earlier, other low-volume oxygenate use (e.g., ETBE, TAME, etc.) was 

assumed to be negligible and thus ignored for this analysis. All ethanol-blended gasoline 

was assumed to contain 10 vol% ethanol, with the exception of California “RFG” 

(Federal RFG and California Phase 3 RFG (CaRFG3)).24 Current California gasoline 

regulations make it very difficult to meet the NOx emissions performance standard with 

ethanol content higher than about 6 vol%. For our analysis, all California RFG was 

assumed to contain 5.7 vol% ethanol based on discussions with California Air Resources 

Board (CARB). This percentage was also applied to California RFG supplied to the 

Phoenix metropolitan area in the summertime under Arizona’s clean burning gasoline 

(CBG) program.

25

 Finally, all MTBE-blended gasoline was assumed to contain 11 vol% 

MTBE. 

Total gasoline consumption was obtained from the 2004 Petroleum Marketing 

Annual (PMA) report published by the Energy Information Administration (EIA).W The 

reported annual average sales volume for each state was interpreted as total blended 

gasoline (including additives, namely oxygenates). 2004 MTBE usage by state was 

obtained from EIA.26,X The data received was exclusive to states with RFG programs 

(including Arizona’s CBG program). Thus, for the purpose of the 2004 base case 

analysis, MTBE use was assumed to be limited to RFG areas. 2004 ethanol usage by 

state was derived from a compilation of data sources and assumptions. As a starting 

point, total domestic ethanol consumption was acquired from EIA’s Monthly Energy 

Review published in June 2006Y. State ethanol contributions originated from the 2004 

Federal Highway Administration (FHWA) gasohol reportZ. However, there was some 

ambiguity with the 2004 FHWA data. First, the total ethanol consumption did not match 

up with EIA’s reported value (3.7 billion gallons compared to 3.5 billion gallons). 

Second, the gasohol (and thus ethanol) volumes were derived from potentially imprecise 

motor vehicle fuel tax reports.27 And third, not all states using ethanol reported their 

24 The small volumes of E85 (85 percent ethanol) gasoline have been ignored for this analysis. 

25 For the Base Case analysis, all Arizona CBG was classified as “RFG”. In 2004, wintertime Arizona 

RFG was assumed to contain 10% ethanol (governed by the Phoenix oxy-fuel program). Summertime 

RFG was assumed to be comprised of 2/3 California RFG (containing 5.7 percent ethanol) and 1/3 PADD 3 

RFG (containing either 10 percent ethanol or 11 percent MTBE in 2004). 

26 EIA reported 2004 total MTBE usage (in RFG) as 2.0 billion gallons. The reported MTBE usage was 

reduced from 2.0 to 1.9 billion gallons under the assumption that CA, NY, and CT implemented their state 

MTBE bans on time (by 1/1/04). (EIA showed small amounts of MTBE use in these states in 2004). 

EIA’s allocation of MTBE by state was also adjusted based on fuel survey results. Most noteworthy, EIA 

reported MTBE usage in Arizona “RFG” as zero. However, the 2004 Phoenix fuel survey results suggest 

otherwise. As such, an appropriate amount of MTBE was allocated to Arizona based on the assumption 

that 1/3 of all summertime Arizona “RFG” resembles PADD 3 RFG (which contained some level of MTBE 

in 2004). 

27 The U.S. Department of Treasury requires a distinction between gasohol and gasoline on motor vehicle 

fuel tax reports for states with gasohol sales tax exemptions. These financial records are the source of 

FHWA’s gasohol/ethanol data. However, since state gasohol tax exemptions have become virtually 

58
gasohol usage so FHWA had to model-estimate 19 states’ ethanol usage (accounting for 

60% of the total ethanol volume). To improve upon the FHWA data, we used a series of 

oxygenate verification tools including knowledge of state ethanol mandates, state MTBE 

bans, Arizona’s CBG program, and fuel survey results.AABB

 The state-by-state FHWA 

data was adjusted accordingly and allocated by fuel type (RFG, CG, and/or oxy-fuel). 

The summarized oxygenate results are presented throughout this section. 

2.2.2.2 2004 Gasoline/Oxygenate Consumption by PADD 

In 2004, 3.5 billion gallons of ethanol and 1.9 billion gallons of MTBE were 

blended into gasoline to supply the transportation sector with a total of 136 billion gallons 

of gasoline. A breakdown of the 2004 gasoline and oxygenate consumption by PADD is 

found below in Table 2.2-3. 

Table 2.2-3. 2004 Gasoline & Oxygenate Consumption by PADD 

PADD 

Gasoline 

MMgal 

Ethanol MTBEa 

MMgal 

% of 

Gasoline 

% of 

Tot ETOH MMgal 

% of 

Gasoline 

% of 

Tot MTBE 

PADD 1 

PADD 2 

PADD 3 

PADD 4 

PADD 5b 

California 

49,193 

38,789 

20,615 

4,542 

7,918 

14,836 

660 

1,616 

79 

83 

209 

853 

1.3% 

4.2% 

0.4% 

1.8% 

2.6% 

5.8% 

18.9% 

46.2% 

2.3% 

2.4% 

6.0% 

24.4% 

1,360 

1 

498 

0 

19 

0 

2.8% 

0.0% 

2.4% 

0.0% 

0.2% 

0.0% 

72.4% 

0.1% 

26.5% 

0.0% 

1.0% 

0.0% 

Total 135,893 3,500 2.6% 100.0% 1,878 1.4% 100.0% 

a

MTBE blended into RFG b

PADD 5 excluding California 

As shown above, in 2004, almost half (or 46 percent) of the ethanol was 

consumed in PADD 2, where the majority of ethanol was produced. The next highest 

region of use was the State of California which accounted for nearly a quarter (or 24 

percent) of domestic ethanol consumption. This makes sense since California alone 

accounts for over 10 percent of the nation’s total gasoline consumption. And in 2004, 

following their MTBE ban, all fuel (both Federal RFG and CaRFG3) was presumed to 

contain 5.7 vol% ethanol. The next highest region of use was PADD 1 (19 percent) 

which makes sense considering the high concentration of RFG areas (most of which used 

ethanol in 2004 as shown in Table 2.2-1). The remaining 10 percent of ethanol use 

occurred collectively in PADDs 3, 4, and 5/ 

nonexistent over the past several years, gasohol reporting (namely the distinction between gasoline and 

gasohol) has suffered. 

59 
In 2004, total ethanol use exceeded MTBE use. Ethanol’s lead oxygenate role is 

relatively new, however the trend has been a progression over the past few years. From 

2001 to 2004, ethanol consumption more than doubled (from 1.7 to 3.5 billion gallons), 

while MTBE use (in RFG) was virtually cut in half (from 3.7 to 1.9 billion gallons). A 

plot of oxygenate use over the past decade is provided below in Figure 2.2-1. 

Figure 2.2-1. Oxygenate Consumption vs. TimeCC,DD 

0.0 

0.5 

1.0 

1.5 

2.0 

2.5 

3.0 

3.5 

4.0 

4.5 

Oxygenate Use (Bgal) 

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 

Year 

Ethanol Consumption, Total MTBE Consumption, RFG 

The nation’s transition to ethanol is linked to states’ responses to recent 

environmental concerns surrounding MTBE groundwater contamination. Traces of 

MTBE have been found in both surface and ground water in and around RFG areas. The 

MTBE is thought to have made its way into the water from leaking underground storage 

tanks, gasoline spills, and engines. Concerns over drinking water quality prompted 

several states to significantly restrict or completely ban MTBE use in gasoline. At the 

time of our analysis, 19 states had adopted MTBE bans. Ten states had bans that 

impacted the entire 2004 calendar year, four states had bans that impacted a portion of the 

year, and five states had bans that became effective in 2005 and beyond. A list of the 

states with MTBE bans (listed in order of phaseout date) is provided below in Table 2.2­

4. 

60
Table 2.2-4. States MTBE Bans by Phaseout DateEE 

Statea Phaseout Date Type of Banb 

Iowa 

Minnesota 

Nebraska 

South Dakota 

Colorado 

Michigan 

California 

Connecticut 

New York 

Washington 

Kansas 

Illinois 

Indiana 

Wisconsin 

Ohio 

Missouri 

Kentucky 

Maine 

New Hampshire 

07/01/00 

07/02/00; 07/02/05 

07/13/00 

07/01/01 

04/30/02 

06/01/03 

12/31/03 

01/01/04 

01/01/04 

01/01/04 

07/01/04 

07/24/04 

07/24/04 

08/01/04 

07/01/05 

07/31/05 

01/01/06 

01/01/07 

01/01/07 

Partial 

Partial; Complete 

Partial 

Partial 

Complete 

Complete 

Complete 

Complete 

Complete 

Partial 

Partial 

Partial 

Partial 

Partial 

Partial 

Partial 

Partial 

Partial 

Partial a

Arizona is not included because they do not have an official state 

MTBE ban. They adopted legislation on 4/28/00 calling for a 

complete phaseout of MTBE as soon as feasible but no later than 

six months after California's phaseout. The legislation expired on 

June 30, 2001, so it's not official policy. Although the state still 

informally encourages the phaseout of MTBE. b

A partial ban refers to no more than 0.5 vol% MTBE except in the 

case of MN (1/3%), NE (1%), and WA (0.6%) 

As explained above in 2.2.2.1, all MTBE consumption was assumed to occur in 

reformulated gasoline in 2004. As shown in Table 2.2-3, 99 percent of MTBE use (by 

volume) occurred in PADDs 1 and 3. This reflects the high concentration of RFG areas 

in the northeast (PADD 1) and the local production of MTBE in the gulf coast (PADD 3). 

PADD 1 receives a large portion of its gasoline from PADD 3 refineries who either 

produce the fossil-fuel based oxygenate or are closely affiliated with MTBE-producing 

petrochemical facilities in the area. 

2.2.2.3 2004 Gasoline/Oxygenate Consumption by Season 

In 2004, according to EIA Petroleum Marketing Annual (PMA), approximately 

40 percent of gasoline was consumed in the summertime and 60 percent was consumed in 

the wintertime.

FF

 Similarly, according to EIA Monthly Energy Review June 2006, 38 

percent of the ethanol was consumed in the summertime and 62 percent was consumed in 

the wintertime.

28,GG 

61
Total gasoline use is higher in the wintertime because it’s a longer season. The 

RFG regulations define summertime fuel as gasoline produced from May 1st to 

September 15th (4.5 months total).29

 The remaining 7.5 months are considered to be 

wintertime gasoline. Even though on an average per day basis summertime consumption 

is higher, more gasoline is still sold and consumed in the wintertime based on the length 

of the season. 

Seasonal ethanol use follows the same general trend as gasoline. However, 

besides the associated correlation with seasonal gasoline consumption, there are 

additional reasons why 2004 ethanol use may have been higher in the wintertime. First, 

the oxy-fuel program requires oxygenate to be used in certain areas in the wintertime 

only. These same areas, which do not require oxygenate in the summer, were all 

presumed to use ethanol as their oxygenate (as described in 2.2.1.2). Thus, more areas 

use ethanol during the winter months than the summer. Secondly, there is an economic 

penalty associated with blending ethanol into summertime RFG. Refiners supplying 

summertime gasoline to RFG areas have to remove butanes and pentanes from their 

gasoline in order to add ethanol and still comply with the 7 psi Reid vapor pressure 

(RVP) requirement. 

2.2.2.4 2004 Gasoline/Oxygenate Consumption by Fuel Type 

According to fuel survey results, in 2004, approximately 2.2 billion gallons of 

ethanol were blended into reformulated gasoline and the remaining 1.3 billion gallons 

were used in conventional gasoline (including wintertime oxy-fuel).HH,II

 A breakdown of 

the 2004 ethanol consumption by fuel type and PADD is found in Table 2.2-5. 

28 Aforementioned seasonal split for gasoline and ethanol based on RFG production seasons (Summer: May 

1 through September 15th; Winter: January 1st through April 30th and September 16th through December 

31st). 

29 We acknowledge that the aforementioned seasonal split does not exactly match the new summer/winter 

seasons defined in the Energy Act (Summer: April 1st through September 30th; Winter: January 1st through 

March 31st and November 1st through December 31st). 

62 
Table 2.2-5. 

2004 Ethanol Consumption by Fuel Type (MMgal) 

PADD CG OXY

a RFGb Total 

PADD 1 

PADD 2 

PADD 3 

PADD 4 

PADD 5c 

California 

0 

1,072 

31 

0 

45 

0 

0 

0 

21 

83 

89 

0 

660 

544 

26 

0 

75 

853 

660 

1,616 

79 

83 

209 

853 

Total 1,149 193 2,158 3,500 a

Winter oxy-fuel programs b

Federal RFG plus CA Phase 3 RFG and Arizona CBG c

PADD 5 excluding California 

As mentioned above in Section 2.2.2.1, 100 percent of the 1.9 billion gallons of 

MTBE blended into gasoline in 2004, was assumed to be consumed in reformulated 

gasoline. 

2.2.2.5 2004 Gasoline/Oxygenate Consumption by State 

 In 2004, ethanol was blended into gasoline in 34 of the 50 states. No ethanol use 

was observed in the remaining 16 states: Maine, New Hampshire, Vermont, 

Pennsylvania, Delaware, Georgia, North Carolina, South Carolina, West Virginia, 

Tennessee, Oklahoma, Mississippi, Arkansas, Louisiana, Idaho, and West Virginia, nor 

was any ethanol used in Washington DC. A summary of 2004 ethanol usage by state is 

presented in Table 2.2-6. Note that a state ethanol percentage less than 10 indicates that 

only a percentage of the gasoline pool was blended with ethanol, not that ethanol itself 

was blended in less than 10 vol% (E10) proportions, except in the case of California 

gasoline (E5.7). Figure 2.2-2 shows the percentage of E10 by state. 

The states consuming the highest volumes of ethanol in 2004 were California, 

Illinois, New York, Minnesota, and Ohio, respectively. With respect to gasoline use, the 

highest percentage of ethanol use occurred in Minnesota, Hawaii, Connecticut, Illinois, 

and Iowa. Four out of the five states are not surprising. The first two states have ethanol 

mandates and the last two are located in the “corn belt” where ethanol is produced. 

Connecticut’s high percentage of ethanol use may come as a surprise at first glance. 

However, the entire state operates under the RFG program (refer to Table 2.2-1), and 

since they also have a state MTBE ban, ethanol is found in each gallon of gasoline. 

63
Table 2.2-6. 2004 Gasoline/Ethanol Consumption by State 

State 

Gasoline 

MMgal 

Ethanol 

State 

Gasoline 

MMgal 

Ethanol 

MMgal 

% of 

Gasoline MMgal 

% of 

Gasoline 

Alabama 

Alaska 

Arizona 

Arkansas 

California 

Colorado 

Connecticut 

Delaware 

District of Columbia 

Floridab 

Georgia 

Hawaii

a 

Idaho 

Illinois 

Indiana 

Iowa 

Kansas 

Kentucky 

Louisiana 

Maine 

Marylandb 

Massachusetts 

Michigan 

Minnesota 

Mississippi 

Missouri 

2,392 

302 

2,187 

1,406 

14,836 

1,999 

1,522 

449 

119 

8,605 

4,729 

452 

632 

5,177 

3,059 

1,635 

1,396 

2,177 

2,287 

757 

2,480 

2,934 

4,861 

2,684 

1,617 

3,159 

31 

3 

88 

0 

853 

80 

152 

0 

0 

0 

0 

45 

0 

422 

148 

117 

41 

50 

0 

0 

0 

18 

77 

268 

0 

122 

1.3% 

1.1% 

4.0% 

0.0% 

5.8% 

4.0% 

10.0% 

0.0% 

0.0% 

0.0% 

0.0% 

10.0% 

0.0% 

8.1% 

4.8% 

7.1% 

2.9% 

2.3% 

0.0% 

0.0% 

0.0% 

0.6% 

1.6% 

10.0% 

0.0% 

3.9% 

Montana 

Nebraska 

Nevada 

New Hampshire 

New Jersey 

New Mexico 

New York 

North Carolina 

North Dakota 

Ohio 

Oklahoma 

Oregon 

Pennsylvania 

Rhode Islandb 

South Carolina 

South Dakota 

Tennessee 

Texas 

Utah 

Vermont 

Virginiab 

Washington 

West Virginia 

Wisconsin 

Wyoming 

503 

819 

857 

705 

4,235 

966 

5,626 

4,302 

350 

5,156 

2,158 

1,500 

4,786 

490 

2,422 

434 

3,251 

11,948 

1,097 

338 

3,920 

2,621 

772 

2,471 

311 

1 

37 

23 

0 

188 

8 

301 

0 

11 

192 

0 

31 

0 

0 

0 

24 

0 

39 

2 

0 

0 

18 

0 

109 

0 

0.2% 

4.5% 

2.7% 

0.0% 

4.4% 

0.8% 

5.4% 

0.0% 

3.0% 

3.7% 

0.0% 

2.1% 

0.0% 

0.1% 

0.0% 

5.5% 

0.0% 

0.3% 

0.2% 

0.0% 

0.0% 

0.7% 

0.0% 

4.4% 

0.0% 

Total 135,893 3,500 2.6% 

a

Hawaii was assumed to have a 100% E10 mandate in the 2004 Base Case based on RFA's Homegrown for the 

Homeland: Ethanol Industry Outlook 2005. b

Trace amounts of ethanol use (<1 MMGal) in FL, MD, RI and VA. 

64
Figure 2.2-2. 2004 Ethanol Consumption, % E10 by State 

2004 % E10 by State 

0% E10 

<50% E10 

50-99% E10 

100% E10 

Not Pictured 

AK: 13% E10 

HI: 100% E10 

DC: 0% E10 

100% 

E5.7 

2.2.3 Development of the 2012 Reference Case 

To establish the 2012 reference case, we started with the 2004 Base Case 

(presented in Table 2.2-3) and grew out gasoline/oxygenate use according to the EIA 

AEO 2006 motor gasoline energy growth rate from 2004 to 2012.JJ Accordingly, in the 

resulting 2012 reference case, ethanol and MTBE use was proportional to 2004 use by 

both region and fuel type. A summary of the 2012 ethanol reference case is found in 

Table 2.2-7. 

65
Table 2.2-7. 

2012 Reference Case - Gasoline & Oxygenate Consumption by PADD 

(MMgal)30 

Gasoline 

Ethanol MTBEa 

% of % of % of % of 

PADD MMgal MMgal Gasoline Tot ETOH MMgal Gasoline Tot MTBE 

PADD 1 54,743 735 1.3% 18.9% 1,513 2.8% 72.4% 

PADD 2 43,166 1,798 4.2% 46.2% 2 0.0% 0.1% 

PADD 3 22,941 88 0.4% 2.3% 554 2.4% 26.5% 

PADD 4 5,055 93 1.8% 2.4% 0 0.0% 0.0% 

PADD 5b 8,812 232 2.6% 6.0% 21 0.2% 1.0% 

California 16,509 949 5.8% 24.4% 0 0.0% 0.0% 

Total 151,225 3,895 2.6% 100.0% 2,090 1.4% 100.0% a

MTBE blended into RFG b

PADD 5 excluding California 

2.2.4 Development of the 2012 Control Cases 

In Section 2.2.2 we described our methodology behind building the 2004 Base 

Case, which was used to produce the 2012 Reference Case (described above). In this 

section we will describe how we developed the two 2012 control cases representing 

increased ethanol fuel use – the RFS Case and the EIA Case. Both control cases 

incorporate our knowledge of future state ethanol mandates, tax incentives, and 

anticipated winter oxy-fuel usage. Our analysis relied on LP modeling (described in 

more detail below) to determine how much ethanol would be used in each PADD, season, 

and fuel type. From there, we conducted post-processing to determine how much 

ethanol would be used on a state-by-state basis and in some cases and made predictions 

on how ethanol would likely fill urban and rural areas. 

2.2.4.1 Forecasting Ethanol Consumption / LP Modeling Results 

As mentioned earlier in Section 2.2.2.2, groundwater contamination concerns 

have caused many states to ban the use of MTBE in gasoline. In response to the Energy 

Act, all U.S. refiners are expected to eliminate the use of MTBE in gasoline by the end of 

2007, and certainly prior to 2012. Ethanol consumption, on the other hand is expected to 

continue to grow in the future. Not only are the Energy Act’s RFS requirements 

promoting ethanol growth, ethanol is needed to fuel the growing number of ethanolfriendly vehicles being produced as well as satisfy the growing number of state ethanol 

30 The total ethanol volume reported in table 2.2-7 (3.895 Bgal) is slightly lower than the reference case 

value reported in Table 2.1-1 (3.947 Bgal). The reason for the slight discrepancy is because the numbers 

presented here were based off the estimated 2004 base case (3.5 Bgal) whereas the numbers presented in 

Table 2.1-1 were based off a more precise 2004 ethanol use (3.548 Bgal) reported by EIA in July 2006 

Monthly Energy Review. 

66
mandates (Washington, Montana, Louisiana, and Missouri recently joined Minnesota and 

Hawaii)31,KK,LL. 

Based on projections from EIA and others, it’s abundantly clear that renewable 

fuel use (namely ethanol) is growing much faster than the RFS requirement. However 

quantifying future ethanol use is a difficult task. The gasoline refining industry and 

ethanol industry are currently undergoing a variety of changes/expansions and there is no 

precise way to know exactly how things are going to “fall out” in the future. 

Accordingly, as explained in Section 2.1, we have considered two different 2012 

renewable fuel consumption scenarios to represent a reasonable range of ethanol use. For 

the RFS Case we modeled 6.7 billion gallons of ethanol use and for the EIA case we 

modeled 9.6 billion gallons (refer to Table 2.1-1). EPA is not concluding that ethanol 

consumption could not possibly exceed 9.6 billion gallons by 2012, but rather that this 

volume is a reasonable “ceiling” for our analysis. 

To estimate how ethanol use would be allocated in the future, we relied on 

Jacob’s Consultancy LP refinery modeling.MM For the Base Case and Reference Case, 

the LP refinery model was set up to allocate fixed volumes of ethanol/MTBE to regions 

consistent with our analysis of current gasoline oxygenate use (described above in 

Sections 2.2.2 and 2.2.3). This essentially fixed the total ethanol and MTBE use in each 

PADD. From there, the oxygenates were further allocated by season and fuel grade to 

match the oxygen content for RFG, RBOB and CBOB based on 2004 batch report data. 

Any leftover ethanol was allocated to CG. Based on the resulting fuel allocation, the LP 

model generated CG and RFG fuel properties considering the RVP effects and blending 

qualities of ethanol and MTBE (such properties are discussed further and utilized in 

Section 2.3). 

For each of the future control cases, MTBE use was assumed to be zero and the 

amount of ethanol added to gasoline was varied. For the RFS Case, total ethanol use was 

fixed at 6.7 billion gallons and for the EIA Case, ethanol use was fixed at 9.6 billion 

gallons. For each control case, the LP model used gasoline and ethanol blending 

economics (e.g., ethanol distribution costs, seasonal ethanol and gasoline blendstock 

prices, etc.) to determine how much ethanol would be blended into gasoline by PADD, 

season, and fuel type. Again, the results were used to generate CG and RFG fuel 

properties used in Section 2.3. 

Slight adjustments had to be made to the refinery modeling outputs to ensure that 

sufficient ethanol was supplied in the wintertime to meet the oxy-fuel requirements in 

PADDs 4/5. In addition, small corrections were required to ensure that ethanol blending 

in a given region/state did not exceed the maximum blending criteria assumed for the 

31 The Montana state mandate requires all gasoline to contain 10 vol% ethanol once plant production ramps 

up to 40 MMgal/yr. The Washington state mandate requires 20% of all gasoline to contain 10 vol% 

ethanol by 12/1/08. Similarly, the Louisiana state mandate requires 20% of all gasoline to contain 10 vol% 

ethanol once plant production ramps up to 50 MMgal/yr. Finally, the Missouri state mandate requires all 

gasoline to contain 10 vol% ethanol by 1/1/08. At the time of our analysis, these were the only four new 

state ethanol mandates. However, EPA recognizes that as of 7/13/06, several others have new/additional 

biofuel standards pending (California, Colorado, Idaho, Illinois, Indiana, Kansas, Minnesota, New Mexico, 

Pennsylvania, Virginia, and Wisconsin). 

67
analysis - 10 volume percent (vol%) ethanol nationwide, and 5.7 vol% ethanol in 

California. The adjusted LP refinery modeling results for the RFS and EIA control cases 

are summarized below in Tables 2.2-8 and 2.2-9, respectively. 

Table 2.2-8. 

Adjusted LP Modeling Results for the RFS Case (MMgal) 

PADD 

Summer Ethanol Use Winter Ethanol Use Total 

CG Ethanol a RFGb Total CGa RFGb Total 

PADD 1 

PADD 2 

PADD 3 

PADDs 4/5c 

California 

399 

1,667 

161 

135 

0 

679 

59 

47 

0 

414 

1,078 

1,726 

208 

135 

414 

350 

1,082 

146 

138 

0 

706 

288 

0 

0 

398 

1,057 

1,370 

146 

138 

398 

2,134 

3,096 

354 

274 

813 

Total 2,362 1,200 3,562 1,717 1,392 3,109 6,671 

a

Includes Arizona CBG and winter oxy-fuel b

Federal RFG and California Phase 3 RFG c

PADDs 4 and 5 excluding California 

Table 2.2-9. 

Adjusted LP Modeling Results for the EIA Case (MMgal) 

PADD 

Summer Ethanol Use Winter Ethanol Use Total 

CG Ethanol a RFGb Total CGa RFGb Total 

PADD 1 

PADD 2 

PADD 3 

PADDs 4/5c 

California 

610 

1,735 

901 

339 

0 

630 

185 

47 

0 

435 

1,240 

1,919 

949 

339 

435 

267 

1,631 

856 

154 

0 

973 

366 

0 

0 

470 

1,240 

1,998 

856 

154 

470 

2,481 

3,917 

1,805 

492 

905 

Total 3,584 1,298 4,882 2,908 1,809 4,718 9,600 

a

Includes Arizona CBG and winter oxy-fuel b

Federal RFG and California Phase 3 RFG c

PADDs 4 and 5 excluding California 

2.2.4.2 Resulting 2012 Ethanol Consumption by PADD 

Starting with the LP refinery modeling results, we segregated the Rocky 

Mountain (PADD 4) and West Coast (PADD 5) ethanol use (represented as an aggregate 

above in Tables 2.2-8 and 2.2-9) and examined the resulting ethanol allocation by region. 

A summary of the 2012 forecasted ethanol consumption by region (PADDs 1-5 and 

California) for each control case is found below in Table 2.2-10. 

68
Table 2.2-10. 

2012 Forecasted Ethanol Consumption by PADD 

PADD 

6.7 Bgal RFS Case 9.6 Bgal EIA Case 

Gasoline 

MMgal 

ETOH 

MMgal 

% of 

Gasoline 

% of 

Tot ETOH 

Gasoline 

MMgal 

ETOH 

MMgal 

% of 

Gasoline 

% of 

Tot ETOH 

PADD 1 

PADD 2 

PADD 3 

PADD 4 

PADD 5a 

California 

60,468 

48,451 

24,845 

4,869 

8,537 

16,494 

2,134 

3,096 

354 

54 

220 

813 

3.5% 

6.4% 

1.4% 

1.1% 

2.6% 

4.9% 

32.0% 

46.4% 

5.3% 

0.8% 

3.3% 

12.2% 

60,468 

48,451 

25,112 

4,928 

8,626 

16,494 

2,481 

3,917 

1,805 

151 

342 

905 

4.1% 

8.1% 

7.2% 

3.1% 

4.0% 

5.5% 

25.8% 

40.8% 

18.8% 

1.6% 

3.6% 

9.4% 

Total 163,664 6,671 4.1% 100.0% 164,078 9,600 5.9% 100.0% a

PADD 5 excluding California 

As shown above, in 2012 PADD 2 is expected to continue to dominate ethanol 

use. PADD 2 ethanol consumption is expected to double from 1.8 billion gallons (Bgal) 

in the Reference Case (refer to Table 2.2-7) to 3.1 Bgal in the RFS Case and 3.9 Bgal in 

the EIA Case. This represents a slight decrease in Midwest marketshare (from 46% in 

Reference/RFS Case to 40% in the EIA Case). The predicted shift in marketshare is 

attributed to the growing amount of ethanol use outside of the traditional cornbelt. 

The LP modeling suggests that ethanol usage is expected to greatly increase in 

PADDs 1 and 3. In PADD 1, ethanol blending is expected to more than triple from 735 

million gallons in the Reference Case to 2.1 Bgal in the RFS Case and 2.5 Bgal in the 

EIA Case. In PADD 3, ethanol use in expected to sharply increase from 88 million 

gallons in the Reference Case to 354 million gallons in the RFS Case and 1.8 billion 

gallons in the EIA Case. This projected increase in ethanol blending on the East Coast 

and Gulf Coast, reflects the phase out of MTBE (replacement with ethanol) as well as 

ethanol blending economics. 

2.2.4.3 Resulting 2012 Ethanol Consumption by Season 

Furthermore, we examined the resulting ethanol allocation by season. The LP 

refinery modeling assumes equal 182.5-day summer and winter seasons. A summary of 

the resulting 2012 forecasted ethanol consumption by season for each of the control cases 

is found below in Table 2.2-11. 

69
Table 2.2-11. 

2012 Forecasted Ethanol Consumption by Season (MMgal) 

PADD 

6.7 Bgal RFS Case 9.6 Bgal EIA Case 

Summer Winter Total Summer Winter Total 

PADD 1 

PADD 2 

PADD 3 

PADD 4 

PADD 5a 

California 

1,078 

1,726 

208 

29 

106 

414 

1,057 

1,370 

146 

25 

113 

398 

2,134 

3,096 

354 

54 

220 

813 

1,240 

1,919 

949 

125 

213 

435 

1,240 

1,998 

856 

25 

128 

470 

2,481 

3,917 

1,805 

151 

342 

905 

Total 3,562 3,109 6,671 4,882 4,718 9,600 a

PADD 5 excluding California 

As shown above, ethanol usage in 2012 is expected to be slightly more prevalent 

in the summertime than in the wintertime. This is a shift from our 2004 Base Case (38% 

of ethanol use occurred in the summertime and 62% occurred in the wintertime as 

explained in Section 2.2.2.3), mainly because we are changing the way we define the 

seasons. In the Base Case we defined the seasons based on the RFG regulations (4.5 

months of “summer” and 7.5 months of “winter”) whereas in this 2012 forecast we are 

defining them based on 6 months of each season. Since gasoline consumption (gal/day) 

is higher in the summertime, more ethanol-blended gasoline could potentially be 

consumed during the summer months. However, since there is an economic penalty 

associated with blending ethanol into summertime gasoline (refiners have to remove 

butanes and pentanes to comply with the RFG RVP requirements), the result is somewhat 

of a seasonal balance in both the RFS Case and the EIA Case. 

2.2.4.4 Resulting 2012 Ethanol Consumption by Fuel Type 

In addition to providing a PADD and seasonal breakdown, The LP modeling 

determined how much ethanol would be used by fuel type - conventional gasoline (CG) 

versus reformulated gasoline (RFG). The first thing we did was allocate a portion of the 

CG to the required winter oxy-fuel areas. 

Strategy for Allocating Ethanol to Oxy-Fuel Areas 

In the 2004 Base Case, there were 14 state-implemented winter oxy-fuel programs 

in 11 states (summarized previously in Table 2.2-2). Of these programs, 9 were required 

in response to non-attainment with the CO National Ambient Air Quality Standards 

(NAAQS) and 5 were implemented to maintain CO attainment status. However, in the 

future 4 of the 9 required oxy-fuel areas are expected to be reclassified from nonattainment to attainment and discontinue using oxy-fuel in the wintertime32

. These areas 

are: Anchorage, AK; Las Vegas, NV; Provo/Orem, UT; and Spokane, WA. In addition, 

32 Based on conversations with state officials and regional EPA officials. 

70
Colorado is expected to discontinue using winter oxy-fuel in Denver/Boulder and 

Longmont to maintain CO attainment status. The use of oxy-fuel in the above-mentioned 

areas is expected to discontinue by 2012 or sooner. With the removal of these 6 stateimplemented programs, that leaves a total of 8 oxyfuel areas in Tucson and Phoenix, AZ; 

Los Angeles, CA; Missoula, MT; Reno, NV; Albuquerque, NM; Portland, OR; and El 

Paso, TX. We assumed that these areas would continue to blend 10 vol% ethanol into 

their gasoline for their entire winter oxy-fuel period (duration varies by area, six month 

maximum) in the 2012 control cases. 

Once a portion of the conventional gasoline ethanol was allocated to meet winter 

oxy-fuel requirements, this gave use a PADD-by-PADD breakdown of ethanol use by 

conventional gasoline, oxy-fuel, and reformulated gasoline as shown below in Table 2.2­

12. 

Table 2.2-12. 

2012 Forecasted Ethanol Consumption by Fuel Type (MMgal) 

PADD 

6.7 Bgal RFS Case 9.6 Bgal EIA Case 

CGa OXYb RFGc Total CGa OXYb RFGc Total 

PADD 1 

PADD 2 

PADD 3 

PADD 4 

PADD 5d 

California 

750 

2,749 

283 

54 

106 

0 

0 

0 

24 

0 

113 

0 

1,385 

347 

47 

0 

0 

813 

2,134 

3,096 

354 

54 

220 

813 

877 

3,366 

1,733 

151 

228 

0 

0 

0 

24 

0 

113 

0 

1,603 

551 

47 

0 

0 

905 

2,481 

3,917 

1,805 

151 

342 

905 

Total 3,942 137 2,592 6,671 6,356 137 3,107 9,600 a

Conventional gasoline including Arizona CBG b

Winter oxy-fuel programs c

Federal RFG plus CA Phase 3 RFG 

dPADD 5 excluding California 

However, more post-processing was required to determine how much ethanol 

would be used on a state-by-state basis to feed into the emissions and air quality analyses. 

We begin the latter part of this discussion by explaining how we allocated the RFG 

ethanol to specific RFG areas and how we allocated the CG ethanol to specific 

states/regions considering state ethanol mandates and the economic favorability of 

ethanol blending 

Strategy for Allocating Ethanol Among RFG 

In the 2004 Base Case, there were 18 states/districts with RFG programs covering 

a total of 175 counties in 36 areas (summarized previously in Table 2.2-1). For our 

analysis of 2012 ethanol use, we assumed that the number of RFG areas would not 

change and accordingly, that the RFG fuel contribution to the gasoline pool would remain 

the same. However, we considered the amount of ethanol added to RFG to be a variable, 

as discussed below. 

71
In the past, all RFG areas were required to use a minimum amount of oxygenate 

in their reformulated gasoline year-round, as discussed earlier in 2.2.1.1. However, 

effective May 5, 2006, EPA removed the RFG oxygenate requirement in response to the 

Act.NN Although the oxygenate requirement has already been eliminated, many refiners 

are still operating under contracts with ethanol blenders. As such, refiners true response 

to the removal of the oxygenate requirement is relatively unknown at this time. While it 

is difficult to predict exactly how each refinery supplying an RFG area would behave, the 

LP modeling has attempted to do so. 

The modeling suggests that some refineries will continue to blend ethanol into 

RFG (or even increase blending) in 2012 based on octane, volume, and/or toxic 

performance requirements. Some RFG producers may decidedly replace MTBE with 

ethanol while others may pare back or discontinue ethanol use all together. A summary 

of the 2012 forecasted RFG ethanol consumption (by season) for each control case is 

found below in Table 2.2-13. 

Table 2.2-13. 

2012 Forecasted RFG Ethanol Consumption (MMgal)33

PADD/State 

Seasonal 

RFG Use 

MMgal

a 

6.7 Bgal RFS Case 9.6 Bgal EIA Case 

Average 

% ETOH 

in RFG 

Summer Winter Summer Winter 

ETOH 

MMgal 

% of 

Gasoline 

ETOH 

MMgal 

% of 

Gasoline 

ETOH 

MMgal 

% of 

Gasoline 

ETOH 

MMgal 

% of 

Gasoline 

PADD 1 

PADD 2 

PADD 3 / TX 

PADD 5 / CAb 

11,380 

3,661 

2,939 

8,247 

679 

59 

47 

414 

6.0% 

1.6% 

1.6% 

5.0% 

706 

288 

0 

398 

6.2% 

7.9% 

0.0% 

4.8% 

630 

185 

47 

435 

5.5% 

5.0% 

1.6% 

5.3% 

973 

366 

0 

470 

8.5% 

10.0% 

0.0% 

5.7% 

6.6% 

6.1% 

0.8% 

5.2% 

Total 26,227 1,200 4.6% 1,392 5.3% 1,298 4.9% 1,809 6.9% 5.8% 

a

Equal amounts of reformulated gasoline assumed to be used in the summer and winter seasons. b

Includes Federal RFG and CA Phase 3 RFG 

As shown above, the modeling suggests that more ethanol would be consumed in 

RFG in the EIA Case in the presence of more ethanol. The modeling also suggests that 

the greatest ethanol marketshare would occur in California RFG (5.2 vol% ethanol on 

average across both cases/seasons, or 91% E5.7). The next highest areas of RFG use 

would be PADD 1 (6.6 vol% ethanol on average, or 66% E10) followed by PADD 2 (6.1 

vol% ethanol on average, or 61% E10). Little ethanol blending was predicted to occur in 

Texas RFG (0.8% ethanol or 8% E10). 

In both control cases, more ethanol was predicted to be blended into wintertime 

RFG. As discussed earlier, this makes sense because in order to meet the RVP 

requirements pertaining to summertime RFG (7 psi), refiners have to remove butanes and 

33 Gasoline consumed in the greater Phoenix metropolitan area under the Arizona Clean Burning Gasoline 

(CBG) Program, has not been considered “RFG” by the LP refinery modeling and thus discussed in the 

conventional gasoline section. 

72
pentanes to accommodate for ethanol blending (which increases overall gasoline 

volatility). As such, in the absence of an RVP waiver (which exists exclusively for 

summertime CG), refiners are less inclined to blend ethanol into summertime RFG. 

To allocate the RFG ethanol (aggregated by PADD and season in Table 2.2-13) 

by state/RFG area, we assumed that each region would behave uniformly with the 

exception of PADD 1 (discussed in more detail below). For example, consider PADD 2 

summertime RFG. In the RFS Case, RFG in Chicago, Louisville, Milwaukee, etc. would 

all contain 1.6% ethanol on average. Or more accurately, 16% of all the gasoline 

consumed within PADD 2 RFG areas would contain 10% ethanol. 

However, based on our knowledge of the refining industry and distribution 

patterns, we did not assume that PADD 1 RFG would be uniform in ethanol content. The 

LP modeling assumes that the RFG produced in PADD 1 contains ethanol but the RFG 

produced in PADD 3 and shipped to PADD 1 does not. RFG from PADD 3 comes up 

the Colonial Pipeline and passes through Virginia, Washington DC and Maryland on its 

way to Pennsylvania and New York. With the exception of a small Yorktown refinery, 

the southernmost refineries in PADD 1 are located around the Philadelphia area. 

However, there is no cheap way to send fuel south. Therefore, the RFG coming from 

PADD 3 is likely to completely fulfill the RFG demand in Virginia, Washington DC and 

Maryland. Beyond Maryland, the fuel from PADD 1 refineries is sold along with any 

leftover PADD 3 RFG, as distribution costs are roughly the same from Philadelphia 

north. As a result, the Virginia, Washington DC and Maryland RFG areas were assumed 

to receive less ethanol (in most cases zero E10) than the other RFG areas located in 

PADD 1. A summary of the resulting RFG ethanol distribution by state is found below 

in Table 2.2-14. 

Table 2.2-14. 

2012 RFG Ethanol Distribution by State

PADD/State 

6.7 Bgal RFS Case 9.6 Bgal EIA Case 

Summer Winter Summer Winter 

PADD 1 

78% E10 in all states 

except DC, MD, VA 

(0% E10) 

81% E10 in all states 

areas except DC, MD, 

VA (0% E10) 

73% E10 in all states 

except DC, MD, VA 

(0% E10) 

100% E10 in all states 

except DC, MD, VA 

(39% E10) 

PADD 2 16% E10 in all states 78% E10 in all states 51% E10 in all states 100% E10 in all states 

PADD 3/TX 16% E10 in TX 0% E10 in TX 16% E10 in TX 0% E10 in TX 

PADD 5/CA 88% E5.7 in CA 85% E5.7 in CA 93% E5.7 in CA 100% E5.7 in CA 

Strategy for Allocating Ethanol Among CG 

The above-mentioned oxy-fuel requirements combined with state ethanol 

mandates created a “floor” for conventional gasoline ethanol use within each PADD. 

This essentially forced a specific amount of ethanol to be used in wintertime CG in 

PADDs 3 and 5 and a specific amount of ethanol to be added year-round in Minnesota, 

73
Montana, and Missouri (100% E10 mandates); Hawaii (85% E10 mandate); as well as 

Washington and Louisiana (20% E10 mandates). 

To determine how the remaining ethanol would be allocated to the leftover 

conventional gasoline, we devised a systematic way to allocate ethanol by state/area. 

Since the primary motivation to blend (or not blend) ethanol is expected to be economic, 

we devised a way to rank CG areas, on a state-by-state and urban/rural basis, as to the 

economic favorability of ethanol blending. This was done by calculating an ethanol 

margin, which is equal to gasoline price minus ethanol delivered price. Ethanol delivered 

price is equal to ethanol plant gate price plus transportation costs minus any additional 

state plus other adjustments (explained below). The greater the ethanol margin, the 

greater the economic incentive and the more likely ethanol is to be used in that area. 

At the time the analysis was carried out, ethanol plant gate price was taken from 

an older EIA NEMS model. However, since this price was assumed to be the same for 

all ethanol, the actual value is not important when trying to estimate relative allocation 

preferences between areas. All ethanol blending was assumed to be done at 10 volume 

percent. The gasoline prices for each state were the weighted average rack price of all 

conventional grades and all months, taken from EIA Petroleum Marketing Annual 

2004.OO 

Ethanol distribution costs were taken from figures given in the documentation for 

the EIA NEMS model, and are based on a 2002 study by DAI, Inc.PP For the purpose of 

this consumption analysis, all ethanol was assumed to be produced in the Midwest in 

census divisions 3 and 4 (corresponding closely to PADD 2). This is largely consistent 

with the production analysis presented in Chapter 1 of the RIA. While the results of the 

production analysis do not completely coincide with this assumption (as shown in Table 

1.2-15, about 86 percent of the total ethanol plant capacity is expected to originate from 

PADD 2 in 2012 and the rest would originate from other areas throughout the country), 

this simplifying assumption is still very reasonable. 

Ethanol consumed within census divisions 3 and 4 was assumed to be transported 

by truck, while distribution outside of those areas was via rail, ship, and/or barge. A 

single average distribution cost for each destination census division was generated by 

weighting together the 2012 freight costs given for each mode in both census divisions 3 

and 4 according to their volume share. These cent per gallon figures were first adjusted 

upward by 10 percent to reflect higher energy prices, and then additional adjustments 

were applied to some individual states based on their position within the census division. 

In the cases of Alaska and Hawaii, differences in ethanol delivery prices from the 

mainland were inferred from gasoline prices. Table 2.2-15 shows the gasoline price and 

ethanol distribution cost for each state as used in this analysis. 

74
Table 2.2-15. 

Gasoline Price & Ethanol Distribution Costs34

State 

Gasoline Rack 

Price (c/gal) 

ETOH Distribution 

Cost (c/gal) 

Alabama 

Alaska 

Arizona 

Arkansas 

Colorado 

Florida 

Georgia 

Hawaii 

Idaho 

Illinois 

Indiana 

Iowa 

Kansas 

Kentucky 

Louisiana 

Maine 

Maryland 

Michigan 

Minnesota 

Mississippi 

Missouri 

Montana 

Nebraska 

Nevada 

New Hampshire 

New Mexico 

New York 

North Carolina 

North Dakota 

Ohio 

Oklahoma 

Oregon 

Pennsylvania 

South Carolina 

South Dakota 

Tennessee 

Texas 

Utah 

Vermont 

Virginia 

Washington 

West Virginia 

Wisconsin 

Wyoming 

123.2 

157.0 

138.0 

123.3 

129.5 

124.9 

125.8 

151.7 

134.2 

125.7 

125.6 

127.5 

124.3 

125.9 

123.1 

125.5 

124.8 

126.5 

127.4 

123.0 

126.0 

130.5 

126.0 

141.6 

125.3 

128.4 

126.0 

124.4 

127.7 

126.2 

123.4 

133.8 

126.1 

124.9 

127.8 

124.5 

122.5 

132.3 

127.3 

123.4 

132.1 

125.8 

125.2 

130.4 

7.2 

41.5 

15.4 

7.3 

10.4 

8.4 

11.4 

36.5 

15.4 

4.4 

5.4 

3.4 

4.4 

6.2 

7.3 

13.4 

11.4 

6.4 

4.4 

6.2 

4.4 

13.4 

4.4 

16.4 

12.4 

12.4 

11.4 

11.4 

5.4 

5.4 

8.3 

16.5 

8.4 

11.4 

4.4 

6.2 

10.3 

13.4 

12.4 

11.4 

16.5 

11.4 

4.4 

12.4 

75
As the final step in the calculation, subsidies and other adjustments were applied. 

The federal blending credit of 51 cents per gallon was given to all areas, and five state 

retail incentives were included as follows (all cents per gallon of ethanol): Iowa, 29.5; 

Illinois, 20.1, South Dakota, 20; Maine: 7.5; Oklahoma, 1.6.35QQ 

In addition to state subsidies, small penalty adjustments were made for 

distributing ethanol into rural areas in several states (as presented in Table 2.2-16). The 

reasoning behind this is that when large shipments of ethanol come from the Midwest by 

barge, ship, or rail, they will be unloaded initially at large terminals near metropolitan 

areas. Further storage and handling will be required to allow smaller quantities to be 

distributed via truck into rural areas. Several states have gasoline pipelines that traverse 

them with connections at various points, helping to reduce distribution burdens, but 

ethanol is not expected to be shipped via pipeline. Overall, the largest adjustments were 

applied to the Rocky Mountain states since they are generally larger in area and 

additional expense is required to transport freight through higher elevations and rugged 

terrain. Smaller adjustments were applied to states that are smaller, flatter, or have 

navigable water access on one or more sides. The states that do not appear on this list are 

either located in the Midwest (where ethanol is produced and readily available to 

virtually all areas at similar costs) or are small northeast states not believed to have 

significant differences between rural and urban distribution costs. 

Table 2.2-16. 

Adjustment for Ethanol Distribution into Rural Areas 

States 

Rural Area 

Adjustment (c/gal) 

OH 2 

AL, AR, FL, GA, KY, LA, ME, 

MS, NC, NY, OK, OR, PA, SC, 

TN, VA, WA, WV 

4 

AK, AZ, CO, ID, NM, NV, UT, 

WY, TX 5 

To determine which in-use areas/counties would receive urban versus rural 

ethanol distribution pricing based on the economies of scale described above, we looked 

to the U.S. Census Bureau which considers population density and other factors. 

34 The following states have intentionally been excluded from this CG gasoline/ethanol cost table because 

they do not consume any CG (100% RFG): CA, CT, DC, DE, MA, NJ, RI. 

35 EPA acknowledges that other states are considering (or may have even approved) retail pump incentives 

for gasohol. However, at the time this consumption analysis was completed, these were the only five states 

offering retail pump incentives that were likely to be applicable in 2012. 

76 
Metropolitan Statistical Areas (MSAs) served as the starting point for determining 

the areas within a state that would be considered “urban”. MSAs are geographic entities 

defined by the U.S. Office of Management and Budget (OMB) for use by federal 

statistical agencies in collecting, tabulating, and publishing federal statistics. An MSA is 

defined as having a core urban area of 50,000 or more people. Each MSA consists of one 

or more counties including the counties contained the core urban area, as well as any 

adjacent counties that have a high degree of social and economic integration with the 

urban core. For the purposes of this analysis, we only considered MSAs with populations 

greater than 1 million people, or other areas having special qualifications. Such 

qualifications include MSAs with less than 1 million people that happen to be the largest 

MSA in a less-populated state (i.e. Montana and Wyoming), or other MSAs deemed 

likely to receive ethanol by rail based on proximity to major rail lines. 

Once the urban counties for each state were determined, county-level vehicle 

miles traveled (VMT) from 2002 were used (as a surrogate for fuel consumption) to 

weight the urban counties’ approximate fuel demand. Expressing the urban VMT as a 

function of statewide VMT gave us the percentage of ethanol demand that would be 

considered eligible for an urban ethanol distribution cost (values presented in Table 2.2­

15). The remaining percentage of ethanol demand was considered to be “rural” and 

subject to the ethanol blending penalty adjustments found in Table 2.2-16. 

Considering the urban/rural split for each state and the resulting ethanol margin 

(ethanol delivered price minus gasoline production cost), we came up with the resulting 

ranking system for distributing ethanol into conventional gasoline. For PADD 1, refer to 

Table 2.2-17; PADD 2, refer to Table 2.2-18, PADD 2, Table 2.2-19; and PADDs 4/5, 

Table 2.2-20. The summer and winter percentages are the same for each urban/rural area 

with the exception of states containing winter oxy-fuel areas. For these states, winter 

oxy-fuel was deducted from the winter urban fuel since this volume of gasoline was 

already accounted for (refer to Table 2.2-12). 

77
Table 2.2-17. 

Precedence for Adding ETOH to PADD 1 CG 

State 

Rural / 

Urban 

Ethanol 

Margin (c/gal) 

PADD 1 

Precedence 

% of CG Volume 

Summer Winter 

ME 

PA 

FL 

ME 

VT 

NY 

GA 

WV 

PA 

SC 

MD 

NC 

NH 

FL 

VA 

NY 

WV 

GA 

SC 

NC 

VA 

u 

u 

u 

r 

-

u 

u 

u 

r 

u 

-

u 

-

r 

u 

r 

r 

r 

r 

r 

r 

50.6 

48.7 

47.5 

46.6 

45.9 

45.6 

45.4 

45.4 

44.7 

44.5 

44.4 

44.0 

43.9 

43.5 

43.0 

41.6 

41.4 

41.4 

40.5 

40.0 

39.0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

35.7% 

44.7% 

59.9% 

64.3% 

100.0% 

67.8% 

51.2% 

21.1% 

55.3% 

20.2% 

100.0% 

14.7% 

100.0% 

40.1% 

63.9% 

32.3% 

78.9% 

48.8% 

79.8% 

85.3% 

36.1% 

35.7% 

44.7% 

59.9% 

65.3% 

100.0% 

67.8% 

51.2% 

21.1% 

55.3% 

20.2% 

100.0% 

14.7% 

100.0% 

40.1% 

63.9% 

32.3% 

78.9% 

48.8% 

79.8% 

85.3% 

36.1% 

78
1

2

3

4

5

6

7

8

9

10

11

12

Table 2.2-18. 

Precedence for Adding ETOH to PADD 2 CG 

State 

Rural / 

Urban 

Ethanol 

Margin (c/gal) 

PADD 2 

Precedence 

% of CG Volume 

Summer Winter 

IA 

SD 

IL 

ND 

NE 

OH 

WI 

IN 

MI 

KS 

KY 

OH 

TN 

OK 

KY 

TN 

OK 

-

-

-

-

-

u 

-

-

-

-

u 

r 

u 

u 

r 

r 

r 

84.6 1 

74.4 2 

72.4 3 

53.3 4 

52.6 5 

51.8 6 

51.8 7 

51.2 8 

51.1 9 

50.9 10 

50.7 11 

49.8 12 

49.3 13 

47.7 14 

46.7 15 

45.3 16 

43.7 17 

100.0% 

100.0% 

100.0% 

100.0% 

100.0% 

37.7% 

100.0% 

100.0% 

100.0% 

100.0% 

10.2% 

62.3% 

38.4% 

29.9% 

89.8% 

61.6% 

70.1% 

100.0% 

100.0% 

100.0% 

100.0% 

100.0% 

37.7% 

100.0% 

100.0% 

100.0% 

100.0% 

10.2% 

62.3% 

38.4% 

29.9% 

89.8% 

61.6% 

70.1% 

Table 2.2-19. 

Precedence for Adding ETOH to PADD 3 CG 

State 

Rural / 

Urban 

Ethanol 

Margin (c/gal) 

PADD 3 

Precedence 

% of CG Volume 

Summer Winter 

MS 

NM 

AR 

AL 

LA 

MS 

TX 

AR 

AL 

LA 

NM 

TX 

u 

u 

u 

u 

u 

r 

u 

r 

r 

r 

r 

r 

47.8 

47.0 

47.0 

47.0 

46.8 

43.8 

43.2 

43.0 

43.0 

42.8 

42.0 

38.2 

6.8% 

31.3% 

26.4% 

31.2% 

16.7% 

93.2% 

61.8% 

73.7% 

68.8% 

63.3% 

68.7% 

38.2% 

6.8% 

16.0% 

26.4% 

31.2% 

16.7% 

93.2% 

58.2% 

73.7% 

68.8% 

63.3% 

68.7% 

38.2% 

79
Table 2.2-20. 

Precedence for Adding ETOH to PADDs 4/5 CG 

State 

Rural / 

Urban 

Ethanol 

Margin (c/gal) 

PADDs 4/5 

Precedence 

% of CG Volume 

Summer Winter 

NV 

AZ 

NV 

CO 

UT 

ID 

WY 

AZ 

OR 

WA 

AK 

HI 

CO 

UT 

ID 

OR 

WY 

WA 

AK 

u 56.2 

u 53.6 

r 51.2 

u 50.1 

u 49.9 

u 49.8 

u 49.0 

r 48.6 

u 48.3 

u 46.6 

u 46.5 

- 46.2 

r 45.1 

r 44.9 

r 44.8 

r 44.3 

r 44.0 

r 42.6 

r 41.5 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

57.8% 

55.2% 

42.2% 

48.6% 

38.7% 

32.3% 

11.6% 

44.8% 

38.2% 

43.6% 

36.4% 

15.0% 

51.4% 

61.3% 

67.7% 

61.8% 

88.5% 

36.4% 

63.6% 

49.2% 

5.4% 

42.2% 

48.6% 

38.7% 

32.3% 

11.6% 

44.8% 

1.4% 

43.6% 

36.4% 

15.0% 

51.4% 

61.3% 

67.7% 

61.8% 

88.5% 

36.4% 

63.6% 

2.2.4.5 Resulting 2012 Gasoline/Oxygenate Consumption by State 

Applying the CG order of precedence tables to the remaining conventional 

gasoline ethanol (less state mandated and winter oxy-fuel volumes) and factoring in the 

RFG ethanol distribution (described above in 2.2.4.4), we came up with an ethanol 

distribution by state for each control case. The resulting state-by-state ethanol 

distribution is summarized below in Table 2.2-21 and a graphical representation for each 

control case is provided in Figures 2.2-3 and 2.2-4 below. 

Table 2.2-21. 

2012 Forecasted Ethanol Consumption by State 

(continued on next page) 

80
6.7 Bgal RFS Case 9.6 Bgal EIA Case 

State Abbrv 

Gasoline 

MMgal 

ETOH 

MMgal 

% of 

Gasoline 

Gasoline 

MMgal 

ETOH 

MMgal 

% of 

Gasoline 

Alabama 

Alaska 

Arizona 

Arkansas 

California 

Colorado 

Connecticut 

Delaware 

District of Columbia 

Florida 

Georgia 

Hawaii 

Idaho 

Illinois 

Indiana 

Iowa 

Kansas 

Kentucky 

Louisiana 

Maine 

Maryland 

Massachusetts 

Michigan 

Minnesota 

Mississippi 

Missouri 

Montana 

Nebraska 

Nevada 

New Hampshire 

New Jersey 

New Mexico 

New York 

North Carolina 

North Dakota 

Ohio 

Oklahoma 

Oregon 

Pennsylvania 

Rhode Island 

South Carolina 

South Dakota 

Tennessee 

Texas 

Utah 

Vermont 

Virginia 

Washington 

West Virginia 

Wisconsin 

Wyoming 

AL 

AK 

AZ 

AR 

CA 

CO 

CT 

DE 

DC 

FL 

GA 

HI 

ID 

IL 

IN 

IA 

KS 

KY 

LA 

ME 

MD 

MA 

MI 

MN 

MS 

MO 

MT 

NE 

NV 

NH 

NJ 

NM 

NY 

NC 

ND 

OH 

OK 

OR 

PA 

RI 

SC 

SD 

TN 

TX 

UT 

VT 

VA 

WA 

WV 

WI 

WY 

2,869 

324 

2,377 

1,686 

16,494 

2,143 

1,827 

538 

143 

10,734 

5,899 

484 

677 

6,737 

3,820 

2,017 

1,722 

2,742 

2,743 

944 

2,990 

3,522 

5,995 

3,310 

1,940 

3,986 

539 

1,010 

920 

855 

5,083 

1,152 

6,876 

5,366 

432 

6,358 

2,661 

1,623 

5,909 

588 

3,022 

535 

4,010 

14,454 

1,176 

422 

4,786 

2,810 

964 

3,117 

333 

90 

0 

123 

44 

813 

0 

146 

43 

0 

521 

0 

0 

0 

454 

352 

202 

86 

42 

99 

34 

0 

281 

346 

331 

25 

343 

54 

101 

6 

50 

406 

35 

289 

0 

43 

438 

0 

34 

318 

47 

0 

54 

37 

62 

0 

0 

0 

56 

0 

268 

0 

3.1% 

0.0% 

5.2% 

0.0% 

4.9% 

0.0% 

8.0% 

0.0% 

0.0% 

4.8% 

0.0% 

0.0% 

0.0% 

6.7% 

9.2% 

10.0% 

5.0% 

1.5% 

0.0% 

0.0% 

0.0% 

8.0% 

5.8% 

10.0% 

0.0% 

8.6% 

10.0% 

10.0% 

0.7% 

0.0% 

8.0% 

3.0% 

4.2% 

0.0% 

10.0% 

6.9% 

0.0% 

2.1% 

0.0% 

8.0% 

0.0% 

10.0% 

0.0% 

0.4% 

0.0% 

0.0% 

0.0% 

2.0% 

0.0% 

8.6% 

0.0% 

2,910 

327 

2,397 

1,710 

16,494 

2,169 

1,827 

538 

143 

10,734 

5,899 

490 

686 

6,737 

3,820 

2,017 

1,722 

2,742 

2,782 

944 

2,990 

3,522 

5,995 

3,310 

1,968 

3,986 

546 

1,010 

931 

855 

5,083 

1,167 

6,876 

5,366 

432 

6,358 

2,661 

1,638 

5,909 

588 

3,022 

535 

4,010 

14,574 

1,191 

422 

4,786 

2,843 

964 

3,117 

337 

291 

0 

149 

171 

905 

57 

158 

46 

3 

474 

12 

42 

12 

570 

368 

202 

172 

72 

278 

64 

52 

304 

599 

331 

197 

372 

55 

101 

52 

54 

439 

109 

423 

0 

43 

636 

28 

34 

328 

51 

0 

54 

78 

759 

25 

21 

52 

64 

0 

291 

2 

10.0% 

0.0% 

6.2% 

10.0% 

5.5% 

2.6% 

8.6% 

8.6% 

2.0% 

4.4% 

0.2% 

8.5% 

1.7% 

8.5% 

9.6% 

10.0% 

10.0% 

2.6% 

10.0% 

6.8% 

1.7% 

8.6% 

10.0% 

10.0% 

10.0% 

9.3% 

10.0% 

10.0% 

5.6% 

6.3% 

8.6% 

9.3% 

6.1% 

0.0% 

10.0% 

10.0% 

1.0% 

2.1% 

5.6% 

8.6% 

0.0% 

10.0% 

2.0% 

5.2% 

2.1% 

5.0% 

1.1% 

2.3% 

0.0% 

9.3% 

0.6% 

Total 163,664 6,671 4.1% 164,078 9,600 5.9% 

81
Figure 2.2-3. 2012 Forecasted Ethanol Consumption 

6.7 Bgal RFS Case, % E10 by State 

2012 % E10 by State 

0% E10 

<50% E10 

50-99% E10 

100% E10 

Not Pictured 

AK: 0% E10 

HI: 0% E10 

DC: 0% E10 

86% 

E5.7 

Figure 2.2-4. 2012 Forecasted Ethanol Consumption 

9.6 Bgal EIA Case, % E10 by State 

2012 % E10 by State 

0% E10 

<50% E10 

50-99% E10 

100% E10 

Not Pictured 

AK: 0% E10 

HI: 85% E10 

DC: 20% E10 

96% 

E5.7 

82
2.3 Effects of Ethanol and MTBE on Gasoline Fuel Properties 

For the final rulemaking, we estimate the impact of increased ethanol use and 

decreased MTBE use on gasoline quality using refinery modeling conducted specifically 

for the RFS rulemaking.36 The methods, analyses, and results of the refinery modeling 

are discussed in more detail in Chapter 7. In general, adding ethanol to gasoline reduces 

the aromatic content of conventional gasoline (CG) and the mid and high distillation 

temperatures (e.g., T50 and T90). RVP increases except in areas where ethanol blends 

are not provided a 1.0 RVP waiver of the applicable RVP standards in the summer. With 

the exception of RVP, adding MTBE directionally produces the same impacts. Thus, the 

effect of removing MTBE results in essentially the opposite impacts. Neither oxygenate 

is expected to affect sulfur levels, as refiners control sulfur independently in order to 

meet the Tier 2 sulfur standards. 

The impacts of oxygenate use are smaller with respect to RFG. This is due to the 

applicability of VOC and toxics emission performance specifications, which limit the 

range of feasible fuel quality values. Thus, RVP and aromatic and benzene contents are 

not consistently affected by oxygenate type or level. 

Table 2.3-1 shows the fuel quality of a typical summertime, non-oxygenated 

conventional gasoline and how these qualities change with the additional of 10 volume 

percent ethanol (10 vol%). Similarly, the table shows the fuel quality of a typical MTBE 

RFG blend and how fuel quality might change with either ethanol use or simply MTBE 

removal. Note that the table does not reflect county-specific fuel properties. 

Table 2.3-1. CG and RFG Summer Fuel Quality With and Without Oxygenates 

 Conventional Gasoline Reformulated Gasoline a 

Fuel Parameter Typical 

9 RVP 

Ethanol 

Blend 

MTBE 

Blend 

Ethanol 

Blend 

Non-Oxygenated 

Blend 

RVP (psi) 8.7 9.7 7.0 7.0 7.0 

T50 218 205 179 184 175 

T90 332 329 303 335 309 

E200 41 50 60 58 52 

E300 82 82 89 82 88 

Aromatics (vol%) 32 27 20 20 20 

Olefins (vol%) 7.7 7.7 4 14 15 

Oxygen (wt%) 0 3.5 2.1 3.5 0 

Benzene (vol%) 1.0 1.0 0.74 0.70 0.72 a

 MTBE blend –Reference Case PADD 1 South, Ethanol blend – RFS Case PADD 1 North, Non-oxy 

blend – RFS Case PADD 1 South 

 Refinery modeling performed in support of the original RFG rulemaking is also used to help separate the 

effects of the two oxygenates. 

83

36
2.3.1 Effect of Ethanol on Conventional Gasoline Fuel Properties 

To estimate effects of ethanol on conventional gasoline, we used the refinery 

model output shown below in Table 2.3-2. These values represent average properties 

across the five PADDs and winter and summer seasons. 

Table 2.3-2. Properties of Conventional Gasoline Per Refinery Modeling 

Case Aromatic 

s (vol%) 

Olefins 

(vol%) 

E200 

(vol%) 

E300 

(vol%) 

T50 

(°F) 

T90 

(°F) 

MTBE 

(vol%) 

Ethanol 

(vol%) 

2012 Reference 29.18 12.39 49.96 81.64 190.3 335.3 0.65 1.66 

2012 RFS 29.02 12.12 51.89 83.68 187.4 326.2 0.00 3.87 

2012 EIA 27.97 12.39 53.04 82.20 185.9 332.8 0.00 5.38 

Using this output, we estimated an average change in each fuel property per vol% 

change in ethanol content. To derive these estimations, we first adjusted the Reference 

case properties to isolate the effects of ethanol by mathematically removing the effects of 

MTBE using factors derived from the RFG RIARR. Then, we calculated the change in 

each fuel property per change in ethanol vol% for each combination of cases. That is, we 

compared Reference Case to RFS Case, Reference to EIA, and EIA to RFS. Finally, we 

averaged the three results from the case-to-case comparisons to derive a useful factor for 

adjusting county-level fuel properties for a change in county-level ethanol content. These 

ethanol effects are shown below in Table 2.3-3. 

Table 2.3-3. Change in Conventional Gasoline Properties Per Vol% Increase in 

Ethanol 

Aromatics 

(vol%) Olefins (vol%) E200 (vol%) E300 (vol%) T50 (°F) T90 (°F) 

-0.46 0.02 0.91 0.06 -1.33 -0.28 

2.3.2 Effects of MTBE on Conventional Gasoline Fuel Properties 

In support of the final rule implementing the RFG program in 1993, refinery 

modeling was performed which estimated the impact of MTBE blending on the various 

gasoline properties.SS While this modeling was performed in the context of projecting 

the cost of producing RFG, it is applicable to the use of MTBE in CG, as well. The 

refinery modeling examined a number of incremental steps involved in the production of 

RFG. Because RFG was mandated to contain oxygen and MTBE was expected to be the 

oxygenate of choice, MTBE was added in the first step of the analysis, before the fuel 

met the rest of the RFG requirements. Table 2.3-4 shows the results of adding MTBE 

based on this refinery modeling. 

This modeling of MTBE effects is somewhat dated (circa 1993). However, since 

removing MTBE does not involve any predictions of its total usage level nor the location 

of its use, economics (such as crude oil price) are not a factor. It is primarily an issue of 

chemical properties and general refinery operation, such as octane management. Also, 

84
MTBE is always match-blended, since gasoline can be shipped with MTBE through 

pipelines. Thus, MTBE is always added at the refinery, allowing the refiner to take full 

advantage of its properties. 

Table 2.3-4. Effect of MTBE on Gasoline Properties: RFG Final Rule 

Fuel Parameter Base 9 RVP Gasoline MTBE Blend Difference 

RVP (psi) 8.7 8.7 0 

T50a 218 207 -11 

T90a 329 321 -8 

E200 (vol%) 41 46.7 5.7 

E300 (vol%) 83 84.9 1.9 

Aromatics (vol%) 32.0 25.5 -6.5 

Olefins (vol%) 13.1 13.1 0 

Oxygen (wt%) 0 2.1 2.1 

Benzene (vol%) 1.53 0.95 -0.58 a

 Estimated using correlations developed in support of EPA RFG final rule, Docket A-92-12, February 

1994. 

T50 = 302 – E200 / 0.49 and T90 = 707 - E300 / 0.22 

As with ethanol blending, MTBE blending reduces aromatic content significantly 

as refiners take advantage of MTBE’s high octane level. Like ethanol, MTBE also tends 

to increase E200 and E200 and decrease T50 and T90. Unlike ethanol, MTBE does not 

increase RVP. 

MTBE blending is shown to modestly reduce sulfur and benzene levels, as well. 

This refinery modeling was performed prior to the development of the Tier 2 sulfur 

standards for gasoline. With these standards, gasoline must meet a 30 ppm sulfur 

standard on average with or without MTBE blending. As refiners can adjust the severity 

of their hydrotreating processes to account for various changes in feedstocks and 

oxygenate use, we do not expect that the removal of MTBE will result in any increase in 

sulfur content. Otherwise, the reversal of the differences shown in Table 2.3-4 are 

expected to occur when MTBE is removed from gasoline (when the MTBE content was 

11 vol%). 

2.3.3 Effects of Ethanol and MTBE on Reformulated Gasoline Fuel Properties 

RFG has historically contained oxygenate due to the applicable 2.0 weight percent 

oxygen content requirement. RFG has contained 11 vol% MTBE or ten vol% ethanol, 

except in California, where 6 vol% ethanol blends have been common. As discussed in 

Section 2.2, the use of MTBE in RFG has ceased. It has been replaced by either 10 vol% 

or ethanol or high octane hydrocarbon blending components, such as alkylate or 

reformate. In either case, as discussed in Section 2.3.2, RFG will continue to have to 

meet stringent VOC, NOx, and toxic emission performance standards, though compliance 

85
with the NOx standard is essentially assured with compliance with the Tier 2 sulfur 

standards applicable to all gasoline37. 

For the NPRM, we assumed that the properties of RFG other than oxygenate 

would not be affected by changes in oxygenate use. For the FRM, we are utilizing the 

recent refinery modeling to estimate RFG properties by PADD and season for the three 

ethanol use scenarios. 

As described above, five refinery models were developed, each representing one 

PADD. These models produced fuel for use in its own PADD, as well as for use in other 

PADDs in the several cases of PADD-to-PADD distribution of gasoline. For RFG, 

refinery modeling projected that a significant volume of RFG used in PADD 1 would be 

produced by PADD 3 refineries. Because this PADD 3 RFG is shipped to PADD 1 via 

pipeline, this fuel tends to be used in the southernmost RFG areas of PADD 1, namely 

those in Virginia, the District of Columbia and Maryland. In order to reflect this, we 

assumed that the RFG produced in PADD 3 will be used preferentially in the RFG areas 

of these three states and the RFG produced in PADD 1 will be used to fulfill the 

remaining demand for RFG in PADD 1. For refinery modeling, it was estimated that a 

small volume of RFG would be produced in PADD 3 and shipped to PADD 2. Because 

of the small volume involved, we did not assign this volume to a specific RFG area 

within PADD 2. 

As part of their work, the refinery modeling contractors calibrated their model to 

match EPA’s estimate of fuel quality existing in 2004 (i.e., the base case in this analysis). 

Therefore, estimates of the properties of RFG for the base case comprise an accurate 

estimate of actual 2004 RFG, at least on a PADD-average basis. We also have available 

the results of the RFG fuel survey for each RFG area. This survey data sometimes 

reflects significant differences in the properties of RFG for specific RFG areas within a 

PADD. Good examples of this would be RFG areas in New York and Connecticut, 

which implemented MTBE bans starting in 2004. We considered using the more precise 

RFG survey data to represent RFG fuel quality in the base case, but rejected this 

approach for two reasons. One, this would introduce an extraneous difference in RFG 

fuel quality between the base case and the RFS/EIA cases. While refinery model base 

case projections reasonably match EPA’s estimate of 2004 fuel quality, they do not match 

exactly. Comparisons between the base case and the RFS and EIA cases would therefore 

include the difference between the RFG survey data and the refinery modeling 

contractor’s estimate of this data, plus the effect of additional ethanol use and reduced 

MTBE use. Two, we primarily present the emission impacts of the RFS rule on a 

nationwide basis. On a nationwide basis, reflecting differences between RFG fuel quality 

within a PADD would have little impact. Also, the Ozone RSM can only reflect a single 

change in VOC and NOx emissions in non-attainment areas (e.g., RFG areas). Thus, 

differences between specific RFG areas would be eliminated by the limitation that only 

37 Though the MSAT2 final rulemaking (72 FR 8428, February 26, 2007) eliminates these air toxics and 

NOx requirements beginning in 2011. 

86
the average emission effect can be modeled. Thus, we used refinery modeling 

projections of RFG fuel quality for all three fuel scenarios. 

Tables 2.3-5, 2.3-6, and 2.3-7 present the fuel properties of summertime RFG 

under the base, RFS and EIA fuel scenarios, respectively. Under the RFS and EIA cases, 

there is no MTBE or TAME in the fuel, so these rows are not shown (i.e., total oxygen 

content is the same as ethanol content in terms of weight percent). 

Table 2.3-5. RFG Fuel Properties: Base Case – Summer 

PADD 1 South PADD 1 North PADD 2 PADD 3 PADD 5 All U.S. 

RVP 7.0 7.1 7.0 7.0 6.8 6.9 

Sulfur ppm 6.7 22.6 4.5 6.9 10.0 13.0 

Aromatics 21.0 23.9 21.6 20.0 22.0 22.3 

Benzene 0.74 0.70 0.76 0.70 0.57 0.71 

Olefins 4.3 13.7 8.0 4.4 5.7 8.5 

E200 59.9 55.0 58.4 59.8 54.6 56.3 

E300 88.9 80.3 86.0 88.9 86.2 84.7 

T50 179.2 189.6 182.5 179.8 190.5 186.9 

T90 302.7 341.7 316.0 302.7 315.0 321.9 

Oxygen (wt%) 2.1 2.3 2.3 2.1 1.9 2.1 

MTBE (wt%O) 1.9 0.5 1.3 1.9 0.0 0.7 

TAME (wt%O) 0.2 0.0 0.1 0.2 0.0 0.1 

Ethanol (wt%O) 0.0 1.8 0.9 0.1 1.9 1.3 

Table 2.3-6. RFG Fuel Properties: RFS Case – Summer 

PADD 1 South PADD 1 North PADD 2 PADD 3 PADD 5 All U.S. 

RVP 7.0 7.0 7.1 7.0 6.8 6.9 

Sulfur ppm 20.5 7.6 21.3 19.8 9.0 13.6 

Aromatics 20.1 20.0 17.9 20.0 22.5 20.5 

Benzene 0.72 0.70 0.67 0.70 0.57 0.66 

Olefins 14.6 13.6 17.3 14.1 5.7 11.9 

E200 52.0 57.6 54.1 52.0 54.5 54.5 

E300 87.5 81.9 81.8 87.5 86.2 84.9 

T50 174.7 184.2 185.3 195.7 190.5 185.9 

T90 308.8 334.6 334.8 308.8 315.0 321.0 

Ethanol (wt%O) 0.0 3.7 0.6 0.2 1.8 1.6 

87
Table 2.3-7. RFG Fuel Properties: EIA Case – Summer 

PADD 1 South PADD 1 North PADD 2 PADD 3 PADD 5 All U.S. 

RVP 7.0 7.0 7.1 7.0 6.8 6.9 

Sulfur ppm 23.1 10.0 19.3 22.3 8.9 14.9 

Aromatics 20.2 19.7 17.9 20.0 22.6 20.5 

Benzene 0.70 0.74 0.60 0.67 0.57 0.65 

Olefins 18.9 10.3 14.7 18.3 5.7 12.1 

E200 52.0 57.7 55.8 52.0 54.6 54.7 

E300 84.3 81.8 80.9 84.3 86.2 83.8 

T50 179.4 184.0 183.4 195.7 190.5 186.4 

T90 323.6 334.7 339.2 323.6 315.0 325.7 

Ethanol (wt%O) 0.0 3.7 1.8 0.2 1.9 1.8 

As shown in Tables 2.3-6 and 2.3-7, summer RFG produced in PADD 1 under the 

two increased ethanol use cases contains 10 vol% ethanol (i.e., 3.7 wt% oxygen). 

However, RFG produced in the other PADDs contains less than the maximum 10 vol% 

ethanol. For other parameters the results generally support the proposed assumption that 

they would remain constant, although there were some small changes. The biggest 

changes in the RFS and EIA fuel scenarios include higher levels of olefins, T50 and T90 

and lower aromatic levels. 

Tables 2.3-8, 2.3-9, and 2.3-10 present the fuel properties of wintertime RFG 

under the base, RFS and EIA fuel scenarios, respectively. 

Table 2.3-8. RFG Fuel Properties: Base Case – Winter 

PADD 1 South PADD 1 North PADD 2 PADD 3 PADD 5 All U.S. 

RVP 11.2 12.9 12.8 11.2 11.5 12.0 

Sulfur ppm 28.0 25.5 26.2 26.9 9.5 21.1 

Aromatics 21.1 19.0 13.9 20.0 20.8 19.3 

Benzene 0.74 0.70 0.64 0.70 0.47 0.63 

Olefins 12.6 16.0 11.6 12.2 5.7 11.1 

E200 63.6 57.4 66.9 63.1 59.3 61.0 

E300 88.9 80.3 79.5 88.9 86.2 84.5 

T50 179.2 184.7 165.4 173.1 180.8 178.5 

T90 302.7 341.7 345.2 302.7 315.0 322.7 

Oxygen (wt%) 2.1 2.5 3.3 2.1 2.2 2.4 

MTBE (wt%O) 2.1 0.5 0.0 2.0 0.0 0.7 

TAME (wt%O) 0.0 0.0 0.0 0.1 0.0 0.0 

Ethanol (wt%O) 0.0 2.0 3.3 0.1 2.2 1.7 

88
Table 2.3-9. RFG Fuel Properties: RFS Case – Winter 

PADD 1 South PADD 1 North PADD 2 PADD 3 PADD 5 All U.S. 

RVP 11.8 13.1 13.0 11.8 11.5 12.3 

Sulfur ppm 28.0 25.0 25.4 26.7 9.4 20.8 

Aromatics 21.4 19.0 17.8 21.2 23.7 20.9 

Benzene 0.75 0.70 0.89 0.73 0.43 0.65 

Olefins 13.3 16.0 12.3 12.6 5.7 11.4 

E200 53.5 63.1 63.9 53.4 58.2 59.0 

E300 87.5 81.9 79.5 87.5 86.2 84.5 

T50 174.7 173.0 171.4 192.9 183.0 178.4 

T90 308.8 334.6 345.2 308.8 315.0 322.7 

Ethanol (wt%O) 0.0 3.7 3.0 0.0 1.8 2.0 

Table 2.3-10. RFG Fuel Properties: EIA Case – Winter 

PADD 1 South PADD 1 North PADD 2 PADD 3 PADD 5 All U.S. 

RVP 11.9 12.8 12.9 11.9 11.5 12.2 

Sulfur ppm 27.5 25.2 23.8 25.6 8.5 19.9 

Aromatics 22.2 19.0 20.7 21.7 23.7 21.2 

Benzene 0.61 0.70 0.95 0.58 0.43 0.63 

Olefins 14.8 16.0 12.3 13.6 5.7 11.9 

E200 52.5 63.6 64.5 52.4 59.4 60.4 

E300 84.3 81.9 79.5 84.3 86.2 83.3 

T50 179.4 172.1 170.1 194.9 180.6 177.5 

T90 323.6 334.3 345.2 323.6 315.0 327.9 

Ethanol (wt%O) 0.0 3.7 3.7 0.0 2.2 2.6 

As shown in Tables 2.3-9 and 2.3-10, winter RFG produced in PADD 1 under the 

two ethanol use cases contains 10 vol% ethanol (i..e., 3.7 wt% oxygen). PADD 2 winter 

RFG contains 10 vol% ethanol in the EIA case. However, RFG produced in the other 

PADDs and cases contains less than the maximum 10 vol% ethanol. On an annual 

average basis, RFG produced for use in California contains about 5.7 vol% ethanol (2.1 

wt% oxygen). This is to be expected given the increase in NOx emissions assigned by 

CARB’s Phase 3 Predictive Model to blends with more than 2.1 wt% oxygen. As for the 

summer cases, changes in other fuel parameters were small and mixed. 

2.3.4 Estimation of County-Specific Gasoline Properties 

In order to estimate the impact of increased ethanol use and reduced MTBE use 

on national emissions and air quality (described in Chapters 4 and 5), we need to estimate 

gasoline properties on a county-specific basis throughout the U.S. In support of previous 

analyses of national impacts of various rules, EPA has developed a set of gasoline 

89
specifications for each county in the U.S. for various months and calendar years.

TT

 We 

based our analysis on the fuel quality specifications for January and July of 2008, since 

2008 is the first year of full implementation of the Tier 2 sulfur standard of 30 ppm. 

Some of the EPA county-level gasoline specifications were based on old data, so we 

reviewed the estimates and made several modifications before applying the changes 

expected due to ethanol addition and MTBE removal. 

First, we adjusted RVP values using more recent information on local RVP 

programs and to reflect commingling. Second, we revised the oxygenate content and 

type in each county to match the levels estimated in Section 2.2 to be sold there under 

each of the three ethanol use scenarios evaluated. Third, we adjusted the other properties 

of gasoline which are affected by the oxygenate use determined in step three. These 

modifications are described in more detail below. 

2.3.4.1 Adjustments to RVP Levels Prior to Oxygenate Use 

Our review of the NMIM database of county-specific RVP levels for July 

indicated that the same RVP level was often applied to all the counties of a specific state. 

In many cases, this appeared reasonable, since the same RVP standard applied throughout 

the entire state. However, in other cases, for example, Florida, most counties have a 9.0 

RVP standard, while those comprising several large urban areas have a 7.8 RVP standard. 

The RVP levels in the NMIM database were consistent with the 7.8 RVP control 

programs, implying that the 7.8 RVP fuel was sold throughout the entire state. This was 

true for much of the south. 

As mentioned above, the NMIM fuel quality database was based primarily on fuel 

survey data from 1999. Fuel surveys tend to focus on large urban areas, as opposed to 

smaller urban or rural areas. Thus, the only available fuel survey data was likely from the 

areas with the tighter local RVP controls. RVP control reduces gasoline supply, since 

lighter hydrocarbons must be removed in order to reduce RVP. Some, but not all of these 

hydrocarbon components can be moved to higher RVP fuel sold elsewhere. Obviously 

gasoline prices are now much higher than they were in 1999. So the incentive to increase 

supply is greater now than in 1999. As discussed in Chapter 7, high gasoline prices are 

projected for the foreseeable future, at least relative to those existing in 1999. Thus, we 

believe that it is reasonable to project that refiners will market gasoline blends with as 

high a level of RVP as practical given the applicable standards. For example, in Florida, 

two fuels will be marketed: one to meet the 7.8 RVP standard in several urban areas and 

another to meet the 9.0 RVP standard applicable elsewhere. There certainly could be 

some spillover of the 7.8 RVP fuel into adjacent 9.0 RVP counties. However, we lack 

data indicating the degree to which this is occurring and might occur in the future. 

Lacking this data, it seems more reasonable to project only that level of RVP control 

which is guaranteed by the applicable standards than to assume that refiners will overcomply with RVP standards and reduce the volume of gasoline which they can produce. 

90
Past studies have shown that a typical compliance margin for RVP is about 0.3 

psi. Thus, for those counties where the standard 9.0 RVP standard applies, we set the 

July RVP level to 8.7 psi. 

EPA maintains a list of counties where its 7.8 RVP standard applies, as well as 

any local standards more stringent than 9.0 RVP.UU Using this list, we assigned RVP 

values in each county equal to 0.3 psi less than the standard applicable in July. We also 

reduced the RVP levels of two sets of counties which had voluntary local RVP control 

programs (and therefore not listed the above Guide). These two areas were Seattle and 

Tulsa. Based on a review of annual fuel survey data collected by the Alliance of 

Automobile Manufacturers (AAM)VV, the fuel being sold in these areas was very similar 

to that for an area with a 7.8 RVP standard. Thus, we assigned a value of 7.5 psi RVP to 

Tulsa County, Oklahoma, and to King, Pierce, and Snohomish Counties, Washington. 

We then assigned an RVP value of 6.8 psi to counties subject to the Federal RFG 

program, again based on an EPA list of the counties subject to this program.WW The EPA 

list of RFG counties includes the Baton Rouge, Louisiana, area. However, litigation has 

held up implementation of this program, so these counties were assigned RVP values 

consistent with the currently applicable 7.8 RVP standard instead. The RVP value of 6.8 

psi was typical for the RFG areas included in the AAM fuel surveys. 

For the purposes of our analysis, we also assigned the entire State of California an 

RVP of 6.8 psi, since California fuel must meet a similar VOC performance standard to 

RFG. Likewise, RVP in Maricopa and Pinal counties in Arizona were assigned a level of 

6.8 psi. These two counties are subject to Arizona’s unique reformulated gasoline 

program. This program basically requires that gasoline sold in these two counties meet 

either the California RFG or Federal RFG standards. Thus, RVP in these two counties 

will be the same as in those other two areas, similar to national RFG fuel. 

These RVP levels for 9.0 RVP and low RVP areas are appropriate when no 

ethanol is being blended into gasoline. However, most of these areas increase the 

applicable standard by 1.0 psi for ethanol blends, which is the typical impact of ethanol 

blending. Therefore, these levels need to be adjusted for the expected level of ethanol 

use, which is discussed below. 

2.3.4.2 County-Specific Oxygenate Type and Content 

The three ethanol use scenarios developed in Section 2.2 assign ethanol and 

MTBE use by state and fuel type (i.e., conventional gasoline, RFG, oxyfuel). In order to 

develop county level estimates of ethanol and MTBE use, we simply assume that ethanol 

and MTBE use within a state and fuel type is uniform. For example, if the E10 market 

share in conventional gasoline Iowa is 34%, then ethanol use in every county receiving 

conventional gasoline in Iowa was assigned an E10 market share of 34%. 

As described above, we nearly always assume that ethanol use is in the form of a 

10 vol% blend with gasoline. The two exceptions are California fuel and Arizona RFG. 

91
California fuel containing ethanol is assumed to contain 5.7 vol% ethanol. Arizona RFG 

is assumed to be a mix of 67% California fuel and 33% Federal RFG produced in PADD 

3. Therefore, its ethanol content is a 2/1 mix of the ethanol contents of California RFG 

and PADD 3 Federal RFG. 

Similarly, we assume that MTBE is used at an 11 vol% level in RFG, since this 

meets the previously mandated oxygen content of 2.1 wt%. MTBE in conventional 

gasoline was assumed to be used at a 3 vol% level. This was somewhat arbitrary, but 

does not affect the outcome of the analysis. The effect of MTBE blending on emissions 

is very linear. Therefore, whether the fuel pool in a particular area consists of 10% of a 

10 vol% MTBE blend or 33% of a 3 vol% MTBE blend is immaterial. Though MTBE is 

present in the 2012 Reference Case, it is assumed to be completely phased-out in the RFS 

and EIA ethanol use cases. 

EPA’s NMIM model (described in more detail in Chapter 4) will only accept a 

single composite fuel for each county. Therefore, we could not use the mix of fuels often 

projected to be supplied to counties developed in Section 2.2. In order to produce a 

single, composite fuel, we simply multiplied the ethanol and MTBE contents of each 

blend by their market share in that county in order to determine the average ethanol and 

MTBE contents of each county’s fuel pool, respectively. For example, if the E10 market 

share in a specific county was 50%, the ethanol content for that fuel was set to 5 vol%. 

We then adjusted the other fuel properties to account for these oxygenates, which is 

discussed below. 

2.3.4.3 Adjustments to Other Gasoline Properties for Oxygenate Use 

We next adjusted other gasoline properties to account for the level of countyspecific oxygenate use projected to occur under the three ethanol use scenarios. Our 

review of the NMIM fuel database indicated that properties, such as aromatics, reflected 

the level of oxygenate use existing in 1999. Therefore, we used the oxygenate levels in 

the NMIM database, which differ from those developed in Section 2.2 for 2004, as the 

basis for our adjustments of the other fuel properties. For example, if the NMIM 

database indicated an ethanol content of 3 vol% for fuel sold in Wayne County, 

Michigan, and the 2004 projection for this county was 5 vol%, we adjusted the NMIM 

fuel properties for this county to reflect the addition of 2 vol% ethanol. 

The bases for these adjustments were those developed in Sections 2.3.1 through 

2.3.4 above. As described there, these adjustments apply primarily to conventional 

gasoline. These adjustments are summarized in Table 2.3-11 below. 

92
Table 2.3-11 

Change in Property per 1 Vol% Increase in Ethanol and MTBE Content 

E200 (%) E300 (%) Aromatics 

(Vol%) Olefins (Vol%) RVP (psi) 

Conventional Gasoline 

Ethanol +1.0 +0.24 -0.5 -0.16 +0.1 

MTBE +0.52 +0.17 -0.59 0 0 

Reformulated Gasoline 

Ethanol 0 0 0 0 0 

MTBE 0.1 0.1 0 0 0 

To calculate new fuel properties for each county, we applied the ethanol and 

MTBE factors to the change in county-level ethanol and MTBE content. The overall 

adjustment to the fuel property was the addition of the ethanol effect and the MTBE 

effect to the baseline fuel property. 

For the impact of ethanol blending on aromatic and olefin contents, we followed a 

slightly different approach. We assumed that the ethanol present in 1999 had been 

splash-blended, while that being used in the future will be match-blended. This 

difference doesn’t affect the adjustment of RVP, E200, or E300, since we assume that 

these parameters are affected in the same way regardless of whether the ethanol is splashor match-blended. However, the change in aromatics does depend on which blending 

approach is used. The situation is similar for olefins, though to a lesser extent. Thus, we 

employed what can be thought of as a two step process in adjusting aromatic and olefin 

contents for the change in ethanol content between the NMIM estimate and those for the 

three ethanol use scenarios developed in Section 2.2. 

The first step is to account for any splash-blended ethanol in the NMIM database. 

With splash-blending, aromatic and olefin contents are reduced simply by dilution, since 

ethanol contains is neither an aromatic nor an olefin. The following equation shows how 

the NMIM level of aromatics was adjusted: 

NMIM Intermediate NMIM ( 1 --

( NMIM Ethanol Aromatic = Aromatic ÷ Ethanol )( )÷ 100 

Content Content Content Market ) Share 

Then, the effect of any ethanol projected to be sold in that county in the three 

ethanol use scenarios developed in Section 2.2 was applied using the approach described 

above for RVP, E200 and E300 (and for the effect of MTBE on aromatics and olefins). 

In this case, the NMIM ethanol content and market share is zero, since we already 

adjusted the NMIM aromatic and olefin contents to represent those existing for a zero 

ethanol content. For example, the equation for the ethanol effect is as follows: 

93
New Fuel Intermediate RFS RFS Fuel Property 

Property = Fuel Property + ( Ethanol )( Market ) × ( Change per 1 

Level Level Content Share vol% Ethanol ) Increase 

We make one final adjustment to RVP to add a commingling effect to account for 

areas where vehicles may be fueled by a mix of ethanol-blend gasoline. Commingling of 

ethanol and non-ethanol blends can increase the average RVP of gasoline in vehicle fuel 

tanks by 0.1-0.3 psi. Appendix 2-A presents a detailed analysis of the impact of 

commingling on the RVP of gasoline in vehicle fuel tanks. Table 2.3-12 presents our 

estimate of the net impact of commingling on in-use RVP as a function of the market 

share of ethanol blends. 

Table 2.3-12. Impact of Ethanol Blends on In-Use RVP (psi) 

E10 market share Commingling Impact 

0% 0 

2% 0 

5% 0.116 

10% 0.116 

20% 0.202 

30% 0.238 

40% 0.264 

50% 0.273 

60% 0.263 

70% 0.226 

80% 0.172 

90% 0.102 

97% 0.102 

100% 0.000 

EPA’s MOBILE6.2 model normally accounts for this effect automatically. 

However, when NMIM is used to run MOBILE6.2, the commingling effect in 

MOBILE6.2 is by-passed. Therefore, any effect of commingling needs to be accounted 

for in the average fuel specified to be sold in each county. To roughly account for this 

effect, we increased RVP by 0.1-0.27 psi in all states where the E10 market share was 

significant (i.e., more than 5%) but less than 95%. The states which fell into this 

category, for CG and RFG, are shown in Table 2.3-13. The specific RVP increase 

depended on the ethanol market share in that county, as indicated in Table 2.3-12. 

94
Table 2.3-13. States Where RVP was Increased Due to Commingling 

Fuel Case Conventional Gasoline 

Reference 

ILLINOIS SOUTH DAKOTA INDIANA NEBRASKA 

OHIO IOWA KANSAS NORTH 

DAKOTA WISCONSIN ALABAMA MICHIGAN 

MISSOURI 

RFS 

ALABAMA ARIZONA ARKANSAS 

COLORADO FLORIDA KENTUCKY 

LOUISIANA MAINE MISSISSIPPI NEVADA 

NEW MEXICO PENNSYLVANIA TENNESSEE 

WASHINGTON WYOMING 

EIA 

COLORADO FLORIDA KENTUCKY 

LOUISIANA PENNSYLVANIA TENNESSEE 

WASHINGTON WYOMING IDAHO NEW 

HAMPSHIRE NEW JERSEY NEW YORK OKLAHOMA 

OREGON TEXAS UTAH 

Reformulated Gasoline 

Reference ARIZONA MASSACHUSETTS NEW JERSEY TEXAS 

RFS 

KENTUCKY PENNSYLVANIA NEW HAMPSHIRE 

NEW JERSEY NEW YORK TEXAS 

MAINE CONNECTICUT DELAWARE ILLINOIS 

INDIANA MASSACHUSETTS MISSOURI RHODE 

ISLAND VERMONT WISCONSIN 

EIA 

KENTUCKY PENNSYLVANIA NEW HAMPSHIRE 

NEW JERSEY NEW YORK TEXAS 

MAINE CONNECTICUT DELAWARE 

ILLINOIS INDIANA MASSACHUSETTS 

MISSOURI RHODE ISLAND VERMONT 

WISCONSIN 

2.4 Effects of Biodiesel on Diesel Fuel Properties 

Our assessment of the effects of biodiesel on diesel fuel properties is found in the 

2002 EPA report “A Comprehensive Analysis of Biodiesel Impacts on Exhaust 

Emissions”XX. Table 2.4-1 below displays the difference in fuel properties between 

biodiesel (B100) and conventional diesel. Note that by 2010, all highway and nonroad 

diesel fuel will meet a 15 ppm cap on sulfur. 

The data in the table below were derived from a wide-range of biodiesels, 

primarily plant- and animal-based. The 2002 EPA report did not provide properties for 

soy-only based biodiesel. 

95
Table 2.4-1. Comparison Between Biodiesel and Conventional Diesel Fuela 

Average Biodiesel Average Diesel 

Natural cetane number 55 44 

Sulfur, ppm 54 333 

Nitrogen, ppm 18 114 

Aromatics, vol% 0 34 

T10, deg F 628 422 

T50, deg F 649 505 

T90, deg F 666 603 

Specific gravity 0.88 0.85 

Viscosity, cSt at 40 deg F 6.0 206 a

Conventional diesel fuel sold outside of California. 

96
Chapter 2: Appendix 

Comprehensive Vehicle Refueling Model

 Vehicle refueling patterns affect non-exhaust emissions in a number of ways, 

including the distribution of vehicle fuel tank fill levels existing at any given time and the 

quality of fuel in the tank. Given the interaction between these parameters, we have 

developed a single model which represents vehicle refueling patterns. We then use this 

model to estimate the distribution of vehicle fuel tank fill levels and the quality of fuel in 

the vehicle fuel tanks. 

Vehicle fuel tank fill levels are primarily a function of the level at which people 

refuel their vehicles and the volume of fuel which they add. In-use, vehicle fuel tanks 

will slowly empty until the point of refueling again. The California Air Resources Board 

(CARB) recently conducted a survey of vehicle refueling patterns in three California 

cities. We will base our estimates of refueling patterns primarily on these data. 

Most fuel parameters remain unchanged as the fuel is burned. One except is 

volatility, particularly RVP, which decreases due to evaporation of the fuel as the tank 

heats up either due to rising ambient temperatures or vehicle operation (e.g., heat transfer 

from the exhaust system, engine cooling air flowing under the vehicle, and fuel 

recirculation from the engine compartment). 

While ethanol content doesn’t change significantly while the vehicle is being 

operated, the ethanol content of gasoline in vehicle fuel tanks can be a function of vehicle 

refueling patterns if some of the specific gasolines being marketed in an area contain 

ethanol and some do not. The effect of ethanol on RVP is not linear. Thus, knowledge 

of the distribution of ethanol content in vehicle fuel tanks is important in estimating the 

RVP of gasoline in vehicle fuel tanks and non-exhaust emissions. We use the vehicle 

refueling model to estimate ethanol content, fuel RVP, and average fill level. 

There are four main aspects of the vehicle refueling model. The first two aspects 

affect all types of gasoline, ethanol containing or not. The first aspect is a description of 

the refueling patterns of vehicle operators. How low is the tank when they refuel? How 

much fuel do they add? Does the volume of fuel added depend on how low the tank was 

when they stopped to refuel? The second aspect is the weathering of the fuel as the 

vehicle is operated. In general, the degree of weathering, or RVP reduction, depends on 

both the ambient temperature and initial RVP of the fuel. 

The third aspect of the model is the effect of ethanol on RVP. While the ethanol 

content of gasoline tends to be either 5.7 or 10 percent by volume (vol%) at the service 

station, the ethanol content of gasoline in a vehicle’s fuel tank can vary from zero to 10 

vol%. The fourth aspect of the model is a description of the probability that a vehicle 

operator will purchase fuel at the same service station as the last refueling or at another 

outlet selling the same brand fuel (i.e., gasoline brand loyalty). Brand loyalty is relevant, 

because service stations carrying the same brand of gasoline almost always sell either 

gasoline with ethanol or gasoline without ethanol, but not both. It is the mixing of 

97
gasoline with and without ethanol is vehicles’ fuel tanks that can cause the RVP of fuel in 

the tank to differ from that dispensed at the service station. This is referred to as the 

commingling effect. 

Each of these four aspects of the vehicle refueling model is described below. 

2A.1 Vehicle Refueling Patterns 

During August and September, 2001, the CARB surveyed consumers’ refueling 

habits at 19 service stations in three local areas (Lake Tahoe, the Bay Area and Los 

Angeles).38 Basic refueling information was obtained for 396 vehicle refuelings (i.e., 

initial fuel tank level and volume of fuel added). Fuel samples were also obtained from 

254 vehicles, though we are most interested in the volumetric data here. CARB also 

asked those refueling whether they refueled with the same brand gasoline the last time the 

vehicle was refueled. 

We obtained and analyzed the raw volumetric fuel data obtained by CARB. Of 

the 396 sets of data, 391 included both initial fuel tank level and volume of fuel added. 

One of the two pieces of information was missing for five vehicles, so we discarded these 

partial data sets from the analysis. The tank fill level prior to refueling was recorded in 

terms of eighths of a fraction of a full tank, as this is usually how the tank fill level is 

indicated on the vehicle dash board. Table 2A-1 shows the probability of a vehicle being 

refueled at various fuel tank fill levels. 

Table 2A-1. Fill Level Prior to Refueling 

Fraction of fill level Probability 

0.000 0.414 

0.125 0.133 

0.250 0.253 

0.375 0.054 

0.500 0.095 

0.625 0.020 

0.750 0.020 

0.875 0.010 

As can be seen, over 40% of the vehicles surveyed came in with an “empty” tank. 

CARB also recorded whether the vehicle operator “filled up” the tank or not. We 

observed that there was a trend towards a greater probability of a “fill up” as the level of 

the tank prior to refueling increased. Table 2A-2 shows the probability of a fill-up as a 

function of tank fill level prior to refueling. 

 “Draft Assessment of the Real-World Impacts of Commingling California Phase 3 Reformulated 

Gasoline, California Environmental Protection Agency, Air Resources Board, August 2003. 

98

38
Table 2A-2. 

Probability of Fill-Up as a Function of Initial Tank Fill Level 

Initial Tank Fill Level Likelihood of a Fill-up 

0.000 0.117 

0.125 0.500 

0.250 0.586 

0.375 0.619 

0.500 0.784 

0.625 0.875 

0.750 0.750 

0.875 0.750 

Overall, when the tank was at least half full, the tank was filled up 79% of the 

time. 

In those cases where the fuel tank was not filled to capacity, the volume of fuel 

added was recorded in terms of gallons. Therefore, some processing was required to 

estimate the final fill level in terms of fraction of tank capacity. To do so, we had to 

estimate the volume of each vehicle’s fuel tank. CARB recorded the basic model type of 

each vehicle in the survey. Based on this model type, we placed each vehicle into one of 

six possible categories. First, each vehicle was identified as either a car or light truck. 

Then, we estimated whether it would have a relatively small, medium, or large fuel tank 

for that vehicle class. The fuel tank sizes assumed for each class are shown in Table 2A3 below. 

Table 2A-3. Estimated Fuel Tank Volumes (gallons) 

Relative Size Car Light Truck 

Small 12 16 

Medium 16 20 

Large 20 24 

Using these tank volumes, we converted the volume of fuel added during partial 

fill ups to an equivalent fraction of tank volume and added this to the observed initial fill 

level to estimate the final fill level. In five cases (out of the 176 partial fills), the 

estimated final fill level exceeded 100%. Either the initial gauge reading was off or 

rounded up, or more likely, our estimate of the total tank volume was too small. In these 

cases, we reduced the final fill level to 95%. (Given that this was a partial fill-up, the 

final fuel tank level had to be less than 100%.) 

For all of the partial fill-ups, we converted the volume of fuel added from gallons 

of fuel to fractional tank volume. Both the mean and standard deviation of these volumes 

were determined as a function of initial fill level. These figures are shown in Table 2A-4. 

99
Table 2A-4. Volume of Fuel Added During Partial Fills (% of Fill Level) 

Initial Fill Level 

Volume of Fuel Added 

Mean Standard Deviation 

0.000 0.406 0.200 

0.125 0.434 0.157 

0.250 0.382 0.167 

0.375 0.451 0.185 

0.500 0.314 0.120 

0.625 0.325 ---

0.750 0.225 0.04 

0.875 0.030 ---

As can be seen from Table 2A-4, the final fill level from partial fills is very close 

to a full tank when the initial fuel tank level was 0.625 or higher. The actual number of 

cases when vehicles initiated refueling when the tank fill level was 0.625 or higher was 

quite small (20). The number of partial fills surveyed was even smaller (4). Given this 

small number and the fact that the fraction of fill-ups for half full tanks exceeded that 

found for 0.75 and 0.875 full tanks (from Table 2A-3), we assumed that all tanks which 

were at least half full when refueling was initiated were filled-up. When the initial fill 

level was less than 0.5, we assumed that the mean volume of fuel added were those 

shown in Table 2A-4. 

As also can be seen from the figures in Table 2A-4, the estimates of the standard 

deviation in the volume of fuel added are substantial relative to the mean volumes of fuel 

added. We desired to reflect this variability in the volume of fuel added during partial 

fills. Thus, we utilized both the estimates of the mean and standard deviation in the 

volume of fuel during refueling. We accomplished this by multiplying the standard 

deviation by a randomly generated standard normal deviate and adding this to the mean 

volume of fuel added to estimate the volume of fuel added during each partial refueling. 

2A.2 Weathering 

Fuel weathering is the result of the evaporation of the lighter components of 

gasoline when the temperature in the fuel tank rises. This temperature rise can be the 

result of diurnal swings in ambient temperature or from vehicle operation. In the latter 

case, the heat can be transferred either convectively from the exhaust system and engine 

cooling air flowing under the vehicle or conductively from recirculated fuel from the 

engine’s fuel system or both. Gasoline is a mixture of many different chemicals. Some 

of these chemicals, such as butane, evaporate more quickly than other chemicals with a 

higher molecular weight, such as octane. The loss of lighter chemicals can be sufficient 

to reduce the concentration of these lighter chemicals in the liquid gasoline. This reduces 

the RVP of the fuel and its tendency to evaporate as the current tank of fuel is consumed. 

We base our estimate of weathering on RVP on the methodology currently in 

MOBILE6.2. This estimate was first developed for MOBILE4 and was also used in 

100
MOBILE5. This methodology first calculates an effective in-use tank temperature (Tevap) 

which drives fuel evaporation. This temperature is a function of the daily minimum 

temperature (Tmin) and maximum temperature (Tmax), as indicated in the following 

equation: 

Tevap = -1.7474 + 1.029*Tmin + 0.99202*(Tmax – Tmin) - 0.0025173 * Tmin*(Tmax – Tmin) 

The loss in RVP is a function of both Tevap and dispensed RVP, as indicated by 

the following equation: 

RVP reduction (psi) due to weathering = -2.4908 + 0.026196 * Tevap 

+ 0.00076898 * Tevap * Dispensed Fuel RVP 

This RVP loss is that occurring when the vehicle fuel tank is 54.57% full, which 

is the effective in-use average tank level for estimating non-exhaust emissions in 

MOBILE6.2. For a typical high ozone day where the ambient temperature might range 

from 72 F to 96 F, and for a dispensed RVP of 9 psi, the RVP loss due to weathering is 

0.54 psi. In order to estimate weathering at other tank fill levels, we assume that 

weathering is linear with tank fill level. 

2A.3 Effect of ethanol on RVP as a Function of Ethanol Content 

In general, the chemicals comprising gasoline blend ideally. That is, the property 

of the finished gasoline is the sum of the property of each component weighted by its 

molar, volume or mass fraction, whichever is technically appropriate. Each component 

of gasoline has its own RVP level. Adding a component to gasoline at the level of 10 

volume percent (vol%), which is the typical ethanol concentration would increase the 

blend’s RVP by 10% of the component’s RVP and decrease the blend’s RVP by 10% of 

the original RVP. For example, normal butane with an RVP of 42 psi can be added to 

gasoline with an RVP of 8 psi. If the butane is added to a final level of 5 vol%, then the 

final RVP is 0.05 times 42 plus 0.95 times 8, or 9.7 psi. 

Ethanol blending affects the RVP of the finished gasoline quite differently. 

Ethanol is a highly polar compound, due to the presence of the hydroxyl radical. In pure 

liquid ethanol, these hydroxyl radicals interact with each other, increasing the degree of 

attraction between ethanol molecules and lowering their tendency to evaporate. This 

phenomena is commonly known as hydrogen bonding and is most commonly associated 

with water. When added to non-polar hydrocarbons at low concentrations, such as those 

comprising gasoline, the evaporative tendency of ethanol increases dramatically. This 

increase in vapor pressure is indicated by what is referred to as the activity coefficient. 

The activity coefficient is the ratio of a compound’s actual vapor pressure in a mixture to 

that predicted by ideal blending. Table 2A-5 shows ethanol’s activity coefficient at 

various levels of concentration in a typical gasoline. 

101
Table 2A-5. Activity Coefficient of Ethanol in Gasoline Blends 39 

Ethanol Concentration (vol%) * Activity Coefficient 

3% 7.5-8.0 

6% 3.8-4.1 

10% 2.3-2.5 

14% 1.9-2.0 

18% 1.6-1.8 

As can be seen, these activity coefficients are substantially greater than 1.0, 

indicating a significant increase in the vapor pressure of ethanol beyond that predicted by 

ideal blending. 

Adding ethanol to gasoline can also increase the vapor pressure of the 

hydrocarbon components. In general, instead of the hydrocarbons’ vapor pressures 

decreasing with the addition of another component (e.g., by 10% with the addition of 10 

vol% ethanol), they remain constant or even increase. This could be due to a tendency of 

the hydrocarbons in the vapor phase to “bounce” off of the ethanol molecules at the 

surface of the liquid phase. 

A number of studies have shown that the full effect of ethanol’s impact on RVP is 

reached at very low concentrations. For example, a study performed by the Energy and 

Environmental Research Center at the University of North Dakota indicates that 90% of 

the full impact of ethanol on RVP is reached when the ethanol concentration is only 2 

vol%.40 Researchers at the University of Delaware found the same relationship.41 Below 

2 vol% ethanol, we have assumed that the effect is essentially linear. 

The full effect of ethanol on gasoline RVP is a function of the RVP of the base 

hydrocarbon gasoline. In general, the increase in RVP caused by ethanol blending 

increases as the base RVP decreases. The actual RVP of specific commercial ethanol and 

non-ethanol gasoline blends are generally known for a specific area being modeled. 

Thus, they are not a primary concern here. However, in order to develop realistic 

estimates of weathering and commingling across a range of ethanol blend market shares, 

39 Harley, Robert A. and Shannon C. Coulter-Burke, “Relating Liquid Fuel and Headspace Vapor 

Composition for California Reformulated Gasoline Samples Containing Ethanol,” Environmental Science 

and Technology, Volume 34, Number 19, 2000. pp 4088+4094, Figure 3. It should be noted that the 

ethanol concentrations shown in the reference are in terms of mole fraction, which are essentially a factor 

of 2 higher than volume fraction. 

40 Aulich, Ted and John Richter, “Addition of Nonethanol Gasoline to E10 – Effect on Volatility,” Energy 

and Environmental Research Center at the University of North Dakota, July 15, 1999. 

41 Bennett, Alison, Stephan Lamm, Hasan Orbey and Stanley I. Sandler, “Vapor-Liquid Equilibria of 

Hydrocarbons and Fuel Oxygenates. 2,” Jounal of Chemical Engineering Data, Volume 38, 1993, pp. 263­

269, Figure 7. This reference shows the ethanol concentration in terms of mass fraction, which is nearly 

identical to volume fraction. 

102
it would be helpful to use realistic RVP levels for commercial ethanol and non-ethanol 

gasoline blends. 

Ethanol blending generally occurs under two types of RVP standards. One type 

of standard requires that both ethanol and non-ethanol blends meet the same RVP 

standard. This is the case in reformulated gasoline (RFG) areas. In most other areas, 

ethanol blends are allowed to meet an RVP standard 1.0 psi higher than that applicable to 

non-ethanol blends. 

We will estimate the impact of weathering and commingling for both situations. 

For RFG-like situations, we will assume that both ethanol and non-ethanol blends have 

the same RVP. In order to estimate the impact of ethanol blending in areas where ethanol 

blends are allowed to have a higher RVP level, we evaluated recent fuel quality data 

collected by the Alliance of Automobile Manufacturers. We combined the data collected 

from 2001-2003 and found six cities where significant numbers of both ethanol and nonethanol blends were sampled and analyzed. These six cities were: Albuquerque, 

Cleveland, Denver, Detroit, Minneapolis, and Seattle. The average RVP levels of the 

non-ethanol gasoline samples in each city ranged from 7.46-9.00 psi, while that of the 

ethanol blends ranged from 8.50-9.93 psi. We observed a relationship between the RVP 

of the non-ethanol gasolines and the difference between the RVP of the ethanol and nonethanol blends. In general, as the RVP of the non-ethanol gasoline increased, the 

difference between the RVP of the ethanol and non-ethanol blends decreased. Figure 2A1 shows this relationship for the six cities, along with a best-fit line based on least squares 

regression. The r-squared value for the best-fit line was 0.25. 

Figure 2A-1. Effect of Ethanol Blending on RVP in Six U.S. Cities 

y = -0.1538x + 2.3606 

R2

 = 0.2508 

0.6 

0.8 

1.0 

1.2 

1.4 

7.0 7.5 8.0 8.5 9.0 9.5 

Non-Ethanol Blend RVP 

Et

h

anol Bl

e

nd RVP 

Difference 

On average, ethanol blending led to a 1.0 psi RVP higher RVP level when the 

RVP level of the non-ethanol gasoline was 9.0 psi. Ethanol’s effect increased to 1.2 psi 

103
when the RVP level of the non-ethanol gasoline was 7.5 psi. When evaluating the effect 

of weathering and commingling, we will evaluate non-ethanol gasoline RVP levels of 

6.8, 7.8 and 9.0 psi, as these are common RVP standards in use today. Using the 

relationship indicated in Figure 1, the RVP levels of the ethanol blends associated with 

these base RVP levels are 8.1, 9.0, and 10.0 psi, respectively. 

2A.4 Brand Loyalty 

CARB recently conducted a fairly extensive direct survey of vehicle refueling 

patterns. This study is both more recent and more extensive than those made in the 

past.YY During their refueling survey, CARB asked vehicle operators whether the vehicle 

was refueled with the same brand of fuel the previous time the vehicle was refueled. The 

resulting responses are summarized in Table 2A-6 below. 

Table 2A-6. Brand Loyalty in the CARB Refueling Survey 

Response to Question: Did you refuel 

with the 

Los Angeles Bay Area Lake Tahoe 

“Yes” 62% 59% 31% 

“No” 31% 38% 67% 

“Don’t Know” 7% 3% 2% 

Breakdown of Retail Outlets Surveyed 

Major Brands (Shell, Chevron, 

Texaco, Mobil, ARCO) 

4 (100%) 5 (84%) 3 (33%) 

Intermediate (Valero) 0 (0%) 1 (16%) 0 (0%) 

Local Brands (USA, Fox, United) 0 (0%) 0 (0%) 6 (67%) 

CARB thought that the relatively low level of brand loyalty in Lake Tahoe was 

due to a high rate of rental car usage in that city. However, they did not present any data 

regarding the fraction of total VMT by rental vehicles to justify their rationale. Rental 

vehicle VMT would have to represent roughly half of all VMT in the Tahoe area 

(assuming such use was negligible in the other two areas) for this factor to explain the 

large difference seen in Table 2A-6. This seems quite unlikely, despite Lake Tahoe being 

a resort area. 

We believe that there is a more likely explanation for the difference. A review of 

the service stations surveyed in the three areas shows significant differences in the types 

of brands surveyed. The service stations surveyed in Los Angeles and the Bay Area were 

dominated by major, nationally recognized brands. Those in Lake Tahoe were dominated 

by more local brands. We believe that brand loyalty could easily be stronger for 

nationally known brands which advertise and which offer their own credit cards. A few 

major brands offer a significant discount when their credit card is used to buy their 

gasoline (e.g., Shell, BP). 

The breakdown of service stations into major and local brands in the three areas is 

shown in the lower half of Table 2A-6. We have defined major brands to include 

104
vertically integrated oil companies which have been in the retail business for several 

decades, market in several regions across the U.S. and are known to widely advertise. As 

shown in Table 2A-6, all four retail fuel outlets surveyed in Los Angeles fell into this 

category, while five out of six stations in the Bay Area reflected major brands. The sixth 

outlet in the Bay Area was a Valero outlet. Valero is a newcomer in the retail market 

relative to Chevron, Shell, etc. However, it is currently the largest refiner in the U.S. and 

offers its own credit card. Thus, Valero appears to fall into an intermediate category 

somewhere between the major brands and the local brands. 

The situation is essentially reversed in Lake Tahoe. Two-thirds of the retail 

outlets surveyed were local brands. Only three out of nine outlets represented major 

brands. 

In order to investigate the potential difference between brand loyalty between 

major and non-major brands, we assumed that each type of fuel brand had its own level 

of loyalty across the three areas. We then estimated these two levels of brand loyalty 

identified to best predict the overall brand loyalty in each area. Overall, loyalty levels of 

62% for major brands and 15% for non-major brands (including Valero) fit the survey 

data reasonably well. 

The U.S. Energy Information Administration (EIA) tracks a broad range of 

proprietary physical and financial data from large energy producers through their 

Financial Reporting System. This information includes the volume of gasoline sold 

through retail outlets owned by or leased from these companies and selling fuel with the 

company’s brand name. Up to 1997, these retail outlets sold about 45% of all gasoline 

sold in the U.S. In 1998, EIA expanded the number of companies included in the 

Financial Reporting System by 50% (from 22 to 33 companies). The percentage of 

gasoline sold by these firms’ retail outlets increased to 62% of all gasoline sold in the 

U.S. 

The nature of the firms included in the Financial Reporting System changed with 

the eleven companies added in 1998. Prior to 1998, this system included 22 companies. 

A few of these firms were not oil companies, (e.g., Burlington Resources, Enron, 

Sonat/El Paso Energy, Union Pacific Resources, USX). Several others were not major 

gasoline retailers, at least under their corporate names (e.g., Anadarko Petroleum, KerrMcGee, Occidental Petroleum). 

In 1998, EIA added an additional 11 companies to the Financial Reporting 

System. Most of these were gasoline refiners (e.g., Citgo, Clark, Equilon, LyondellCitgo, Motiva, Sunoco, Tesoro, Ultramar Diamond Shamrock (UDS), Valero and 

Williams). At the same time, the volume of gasoline sold by the original 22 companies 

decreased to 31% of all gasoline sold in the U.S. This drop is likely due to the spin-off of 

refineries by companies like Shell and Texaco to partnerships like Motiva and Equilon. 

The actual retailing of this 14% of gasoline sales likely didn’t change significantly (e.g., 

the retail brand name continued to be Shell). The net increase of 17% of U.S. gasoline 

sales represented the other refiners, such as Tesoro, Valero, Citgo, USD, etc. These latter 

105
companies have a much more regional footprint and have not established brand name 

familiarity coupled with a perception of higher quality gasoline. With the exception of 

the single Valero outlet in the Bay Area, none of the stations surveyed by CARB offered 

gasoline from these companies. Thus, we believe that the major brands included in the 

CARB survey are more similar to the fuel suppliers included in EIA’s Financial 

Reporting System prior to 1998 than to those included after 1998. 

Given this, we estimate that 45% of U.S. gasoline sales are sold through stations 

carrying a major brand. Weighting the loyalty levels of 62% for major brands by 45% 

and the loyalty level of 15% for other brands by 55% yields an overall national average 

loyalty level of 36%. For non-loyal consumers, the probability of brand selection is 

assumed to be random. Practically, this means that the probability of choosing either a 

non-ethanol or ethanol blend depends on each fuel’s market share. 

The exact question asked by the CARB surveyors was whether the vehicle had 

been refueled the last time with the same brand of fuel. CARB assumed that this meant 

that the vehicle was always refueled with the same brand. However, the question was 

limited to only the refueling immediately preceding the current one. Our primary 

estimate of brand loyalty is that directly addressed by the CARB survey: the likelihood 

that the previous refueling was with the same brand of gasoline. We also estimate the 

sensitivity of our estimate of commingling to the assumption made by CARB below. 

2A.5 Procedures for Modeling Vehicle Refueling and Resultant Fuel Quality 

We developed a model to predict the fuel tank level and fuel quality existing in a 

typical onroad vehicle through 500 refuelings. The vehicle is assumed to begin its life 

with a full tank of non-ethanol fuel. The fuel tank level at which the vehicle is refueled 

is based on the probabilities shown in Table 2A-1 above. First, a cumulative distribution 

of refueling probabilities was generated by adding the probabilities shown in the second 

column of Table 2A-1. Then, a random number valued between 0.0 and 1.0 is generated. 

If the random number is less than the cumulative probability of the tank being empty at 

refueling (0.414), the tank is assumed to be empty. If the random number is between this 

figure and the cumulative probability of the tank being 1/8 full at refueling (0.547), the 

tank is assumed to be 1/8 full at refueling, etc. 

The RVP level of this fuel is reduced using the weathering equation shown in 

Section 2A.2. The level of RVP loss is assumed to be proportional to the volume of fuel 

used since the last refueling. For example, a vehicle might be driven from a full fuel tank 

down to a tank which is 20% full. Fuel usage is 80% of a tank. The above RVP 

weathering equation represents the RVP drop for a vehicle being driven from a full tank 

down to a 60% full tank, or a fuel usage of 40%. Therefore, the RVP decrease due to 

weathering in this case would be twice that indicated by the weathering equation in 

Section 2A.2. Fuel composition (i.e., ethanol content) is assumed to be unaffected by 

driving. 

106
Only the hydrocarbon portion of the fuel is weathered, since the effect of ethanol 

on RVP is essentially independent of its concentration. Thus, the value of RVP used in 

the weathering equation is that for the hydrocarbon portion of the fuel, not the total RVP 

of the blend. This means that we need to track the RVP of the hydrocarbon portion of the 

fuel separately. 

The probability of the fuel tank being completely filled during refueling is 

determined from the estimates shown in Table 2A-2. Again an independent random 

number is generated with a value between 0.0 and 1.0. If the value is less than the 

probability shown in Table 2A-2 for that initial fill level, the tank is assumed to be filled 

up. When a partial fill is indicated, another random number between 0.0 and 1.0 is 

generated. This random number is used in conjunction with the NORMINV function in 

Excel to generate a random level of the standard normal deviate, or the number of 

standard deviations to add to the mean estimate for the volume of fuel added during a 

partial fill-up. The values for the mean and standard deviations for volume of fuel added 

are shown in Table 2A-4. As discussed in Section 2A.2, whenever the initial fuel tank 

level is 0.5 or greater, we assume that the tank is filled up. 

Occasionally, the volume of fuel added during a partial fill will exceed the 

capacity of the tank. This occurs when the random number generator produces a large 

positive number of standard deviations to be added to the mean fuel volume typically 

added during a partial fill. In these cases, we set the final tank level after refueling to 

100%. 

The type of fuel added, ethanol or non-ethanol blend, is determined both by the 

level of brand loyalty and the mix of fuels available in the local area. As discussed in 

Section 2A.4, brand loyalty is estimated to be 36%. Again, an independent random 

number is generated with a value between 0.0 and 1.0. If the value is less than 0.36, the 

type of fuel added is assumed to be the same as that added during the last refueling. 

Otherwise, the probability of refueling with any particular fuel is assumed to be 

independent of the previous fuel used. The probabilities of refueling with a non-ethanol 

blend and an ethanol blend are the market shares of the respective fuels. This is selection 

is made by choosing a new random number. 

We then determine the quality of the fuel in the tank after the refueling event. 

The ethanol concentration of the tank fuel is simply the ethanol concentration of the fuel 

prior to refueling plus that of the fuel added during refueling, each weighted by their 

respective volumes. We assume that the fuel tank contains some volume of fuel, even 

when it indicates empty. Consistent with CARB in their assessment of commingling, we 

assume that this tank “heel” is 10% of the tank capacity. The “ethanol” portion of the 

ethanol blend is assumed to be 95% pure ethanol (i.e., it contains 5% denaturant). This 

denaturant is assumed to have the same RVP as the non-oxygenated gasoline. 

Calculating the RVP of the gasoline blend after refueling is more complicated. 

We first calculate the RVP of the hydrocarbon portion of the fuel after refueling in the 

same way as described above for the ethanol concentration. The RVP of hydrocarbons 

blend linearly or ideally, so we simply weight the RVP of the hydrocarbon portion of the 

107
fuel left in the tank just prior to refueling (adjusted downward for weathering as indicated 

above) with that of the new fuel by their respective contributions to the total volume of 

fuel in the tank after refueling. We then increase this RVP based on the concentration of 

ethanol in the tank. As described in Section 2A.3, we assume that ethanol’s impact on 

RVP is constant between 2 and 10 vol% and a function of the RVP of the hydrocarbon 

blendstock, as discussed above. Between zero and 2 vol%, we assume that its RVP effect 

increases linearly up to its full effect. 

At this point, the vehicle has been refueled and we have determined the quality of 

the fuel currently in the tank. The next step is to repeat the entire process described 

above, starting with a new level at which the tank is refueled once again. 

Once a vehicle has been refueled 500 times, we determine the RVP and ethanol 

concentration of the fuel in the tank over a range of fuel tank fill levels. We split the full 

range of possible tank fill levels into 10 discrete segments, each representing a 10% range 

of tank fill level (e.g., 20-30% full). We then determine which tank fill levels the vehicle 

will be driven through prior to the next refueling. For example, if a vehicle is refueled to 

100% of tank capacity and is then driven down to 1/8 full before its next refueling, its 

tank moves from 100% full to 90% full to 80% full, etc. until it reaches 12.5% full. 

Thus, in this example, the tank was never in the range of 0-10% full. We assume that the 

vehicle spends the same amount of time and accumulates the same amount of VMT at 

each tank fill level between its starting and ending points. (This is simply equivalent to 

assuming that vehicles are driven differently depending on their level of fuel tank fill 

level; a safe assumption.) The RVP of the fuel in the tank is adjusted at each fill level as 

the vehicle is being driven, including the effect of weathering. The same is done for 

ethanol concentration. For each segment of fuel tank fill level, the RVP and ethanol 

concentration occurring between each set of refueling events is averaged. 

The entire process is then repeated 50 times. Overall, both RVP and ethanol 

concentration versus tank fuel fill level is tracked for 25,000 refuelings (500 refuelings 

per model pass-through times 50 model pass-throughs). Overall averages are then 

determined and retained for analysis. 

One output of the model which is independent of the RVP levels of the fuels is the 

distribution of the fuel tank levels of vehicle on the road at any one time. This 

distribution is shown in Table 2A-7. 

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Table 2A-7. Distribution of Fuel Tank Fill Levels for the In-Use Fleet 

Range of Fuel Tank Fill Level 

Lower Limit Upper Limit % of Vehicles

0% 10% 7.6% 

10% 20% 9.4% 

20% 30% 12.9% 

30% 40% 12.5% 

40% 50% 12.2% 

50% 60% 10.6% 

60% 70% 9.4% 

70% 80% 8.7% 

80% 90% 8.4% 

90% 100% 8.2% 

As can be seen, the most frequent onroad fuel tank fill levels are between 20% to 

50%. This distribution will be used to weight the effect of commingling which occurs for 

each range of fuel tank fill level. 

2A.6 Modeling Results 

We performed the procedure described in Section 2A.5. for a set of base gasoline 

RVP levels ranging from roughly 7 RVP to 9 RVP and for the two types of ethanol 

blending (matched RVP and increased RVP). An example of the sequence of 

calculations is as follows: 

1) Select the RVP of non-oxygenated gasoline (E0), the RVP of the ethanol 

blend (E10), and the market share of E10, 

2) Begin with a tank full of non-oxygenated gasoline, 

3) Choose a random number which is used to probabilistically determine: a) the 

level at which the tank is being refueled, b) the level to which the tank is 

filled, c) the volume of fuel thus being added, and d) the type of fuel used to 

fill the tank (E0 or E10), 

4) Determine which tank fill levels the vehicle passed through between the prior 

fill level and the point at which it was refilled and determine the fuel RVP at 

each 10% increment in tank fill level using the weathering equation, 

5) Determine the RVP of the hydrocarbon portion of the fuel at the time of refill 

using the weathering equation, 

6) Determine the concentration of ethanol in the refilled fuel tank using the 

ethanol concentration of the fuel after the prior fill-up, the volume of fuel in 

the tank at the time of refill, the ethanol concentration of the fuel used to refill 

the tank currently and the volume of fuel added during this refill, 

7) Determine the RVP of the hydrocarbon portion of the fuel after refill by 

weighting the RVP of the hydrocarbon portion of the fuel in the tank at the 

time of refueling and the RVP of hydrocarbon portion of the fuel being added 

by the volume of the hydrocarbon portion of the fuel in the tank at the time of 

refill and the volume of the hydrocarbon portion of the fuel being added 

during refueling, respectively, 

109
8) Determine the RVP of the total fuel in the tank after refueling from the RVP 

of the hydrocarbon portion of the fuel from step 6 and the effect of ethanol on 

RVP using its concentration from step 5. 

9) Return to step 2) and proceed through 500 refuelings. 

Once the model has been applied to 500 refuelings (essentially the life of the 

vehicle), the results are compiled. The average RVP level for each interval of tank fill 

level is determined. For example, over 500 refuelings, approximately 200 have the tank 

being refilled when it was less than one-eighth full, so that the vehicle was driven when 

the tank was 10% full. For these 200 occurrances, the tank RVP level is averaged. This 

becomes the average RVP at a fuel tank fill level of 10%. Approximately 260 refueling 

involve the vehicle being driven when the fuel tank fill level is 20%. Fuel RVP is again 

averaged for these 260 situations. The process is repeated for a 30% fuel tank fill level, 

40%, and so on through 100% full (which occurs every time the tank is completely filled 

up). The RVP predictions of the model for various mixes of 9 RVP non-oxygenated 

gasoline and a 10 RVP ethanol blend are shown in Tables 2A-8 through 2A-13. The last 

line of each table shows the weighted average RVP level using the distribution of in-use 

fuel tank fill levels shown above in Table 2A-7. 

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Table 2A-8. In-Use Fuel Tank RVP Levels – 9 RVP CG with Ethanol Waiver (p

Fuel Tank Fill Level 

Ethanol Blend Market Share 

0% 2% 5% 10% 20% 30% 40% 50% 60% 70% 80% 90% 

5% 8.15 8.19 8.26 8.35 8.52 8.67 8.79 8.90 8.98 9.05 9.11 9.15 

15% 8.23 8.27 8.33 8.42 8.58 8.74 8.87 8.97 9.05 9.12 9.18 9.22 25% 8.29 8.33 8.40 8.49 8.65 8.81 8.93 9.04 9.11 9.19 9.24 9.29 35% 8.35 8.39 8.45 8.55 8.71 8.87 8.99 9.09 9.17 9.25 9.30 9.35 45% 8.40 8.44 8.50 8.60 8.77 8.93 9.05 9.15 9.23 9.31 9.35 9.40 55% 8.44 8.48 8.56 8.65 8.82 8.98 9.11 9.21 9.29 9.36 9.40 9.45 65% 8.50 8.55 8.62 8.71 8.89 9.05 9.17 9.27 9.35 9.43 9.46 9.51 75% 8.58 8.62 8.70 8.79 8.97 9.13 9.25 9.35 9.43 9.50 9.54 9.58 85% 8.66 8.71 8.78 8.87 9.05 9.22 9.34 9.44 9.52 9.59 9.63 9.67 95% 8.75 8.80 8.87 8.97 9.15 9.31 9.43 9.53 9.61 9.68 9.72 9.76 

Wtd.Avg. 8.42 8.47 8.53 8.63 8.80 8.96 9.08 9.18 9.26 9.34 9.38 9.42 

Table 2A-9. In-Use Fuel Tank RVP Levels – 7.8 RVP CG with Ethanol Waiver (

Fuel Tank Fill Level 

Ethanol Blend Market Share 

0% 2% 5% 10% 20% 30% 40% 50% 60% 70% 80% 90% 

5% 7.08 7.13 7.20 7.30 7.50 7.64 7.79 7.91 8.01 8.10 8.15 8.19 

15% 7.14 7.19 7.26 7.36 7.56 7.71 7.85 7.97 8.07 8.15 8.21 8.25 25% 7.20 7.25 7.31 7.41 7.62 7.76 7.91 8.03 8.12 8.21 8.27 8.30 35% 7.24 7.30 7.36 7.46 7.67 7.81 7.96 8.08 8.17 8.26 8.31 8.35 45% 7.29 7.34 7.41 7.51 7.71 7.86 8.01 8.13 8.22 8.30 8.35 8.40 55% 7.33 7.38 7.45 7.55 7.76 7.92 8.06 8.18 8.27 8.35 8.40 8.44 65% 7.38 7.43 7.50 7.61 7.82 7.97 8.12 8.23 8.33 8.40 8.45 8.49 75% 7.44 7.49 7.57 7.67 7.88 8.04 8.19 8.30 8.40 8.47 8.52 8.55 85% 7.51 7.56 7.64 7.75 7.95 8.12 8.27 8.38 8.47 8.54 8.59 8.62 95% 7.59 7.64 7.72 7.82 8.03 8.19 8.35 8.45 8.55 8.61 8.67 8.70 

Wtd.Avg. 7.31 7.36 7.43 7.53 7.74 7.89 8.04 8.15 8.25 8.33 8.38 8.42 

112
Table 2A-10. In-Use Fuel Tank RVP Levels – 6.8 RVP CG with Ethanol Waiver (

Fuel Tank Fill Level 

Ethanol Blend Market Share 

0% 2% 5% 10% 20% 30% 40% 50% 60% 70% 80% 90% 

5% 6.19 6.24 6.31 6.43 6.63 6.82 6.96 7.09 7.19 7.29 7.35 7.40 

15% 6.24 6.29 6.37 6.48 6.68 6.86 7.02 7.13 7.24 7.33 7.40 7.45 25% 6.29 6.34 6.41 6.53 6.73 6.91 7.07 7.18 7.29 7.38 7.45 7.50 35% 6.33 6.38 6.46 6.57 6.77 6.95 7.11 7.23 7.34 7.42 7.49 7.53 45% 6.36 6.42 6.49 6.61 6.81 7.00 7.16 7.27 7.38 7.47 7.53 7.57 55% 6.40 6.45 6.53 6.65 6.86 7.04 7.20 7.31 7.42 7.51 7.57 7.61 65% 6.44 6.50 6.57 6.69 6.90 7.09 7.25 7.36 7.47 7.56 7.61 7.65 75% 6.50 6.55 6.63 6.75 6.96 7.15 7.31 7.42 7.53 7.61 7.67 7.71 85% 6.56 6.61 6.69 6.81 7.02 7.21 7.37 7.49 7.59 7.67 7.73 7.77 95% 6.63 6.68 6.76 6.88 7.09 7.28 7.44 7.55 7.66 7.74 7.79 7.84 

Wtd.Avg. 6.39 6.44 6.51 6.63 6.84 7.02 7.18 7.30 7.40 7.49 7.55 7.59 

Table 2A-11. In-Use Fuel Tank RVP Levels as – 9 RVP Gasoline with No Ethanol W

Fuel Tank Fill Level 

Ethanol Blend Market Share 

0% 2% 5% 10% 20% 30% 40% 50% 60% 70% 80% 90% 

5% 8.15 8.17 8.20 8.26 8.33 8.38 8.40 8.41 8.40 8.36 8.32 8.26 

15% 8.23 8.24 8.28 8.33 8.40 8.45 8.47 8.47 8.46 8.43 8.39 8.33 25% 8.29 8.31 8.34 8.39 8.47 8.51 8.54 8.54 8.53 8.50 8.45 8.40 35% 8.35 8.37 8.40 8.45 8.52 8.58 8.60 8.60 8.59 8.56 8.51 8.45 45% 8.39 8.42 8.45 8.50 8.58 8.63 8.65 8.66 8.65 8.61 8.56 8.50 55% 8.44 8.47 8.50 8.55 8.63 8.69 8.71 8.72 8.71 8.67 8.62 8.55 65% 8.50 8.53 8.56 8.62 8.70 8.75 8.78 8.78 8.77 8.73 8.68 8.62 75% 8.58 8.60 8.64 8.69 8.77 8.83 8.86 8.86 8.85 8.81 8.76 8.69 85% 8.67 8.69 8.72 8.78 8.86 8.92 8.95 8.95 8.94 8.90 8.85 8.78 95% 8.76 8.78 8.81 8.87 8.95 9.01 9.04 9.04 9.03 8.99 8.94 8.87 

Wtd.Avg. 8.43 8.45 8.48 8.53 8.61 8.66 8.69 8.69 8.68 8.64 8.60 8.53 

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Table 2A-12. In-Use Fuel Tank RVP Levels – 8 RVP Gasoline with No Ethanol Waiv

Ethanol Blend Market Share 

Fuel Tank Fill Level 0% 2% 5% 10% 20% 30% 40% 50% 60% 70% 80% 90% 

5% 7.26 7.28 7.32 7.36 7.44 7.50 7.53 7.53 7.52 7.48 7.43 7.36 

15% 7.33 7.35 7.38 7.43 7.50 7.57 7.59 7.60 7.57 7.55 7.49 7.42 25% 7.39 7.41 7.44 7.49 7.56 7.62 7.64 7.66 7.64 7.60 7.56 7.48 35% 7.43 7.45 7.49 7.54 7.62 7.68 7.70 7.71 7.69 7.66 7.60 7.53 45% 7.47 7.50 7.53 7.59 7.67 7.73 7.75 7.76 7.74 7.71 7.65 7.57 55% 7.52 7.54 7.57 7.63 7.72 7.78 7.80 7.81 7.79 7.76 7.70 7.62 65% 7.57 7.59 7.63 7.69 7.77 7.84 7.86 7.87 7.85 7.81 7.75 7.67 75% 7.63 7.66 7.69 7.75 7.84 7.91 7.93 7.94 7.92 7.88 7.82 7.74 85% 7.71 7.73 7.77 7.83 7.92 7.98 8.01 8.02 7.99 7.96 7.90 7.82 95% 7.79 7.81 7.85 7.91 8.00 8.06 8.09 8.10 8.07 8.04 7.98 7.90 

Wtd.Avg. 7.50 7.52 7.56 7.61 7.69 7.76 7.78 7.79 7.77 7.73 7.68 7.60 

Table 2A-13. In-Use Fuel Tank RVP Levels – 7 RVP Gasoline with No Ethanol Waiv

Ethanol Blend Market Share 

Fuel Tank Fill Level 0% 2% 5% 10% 20% 30% 40% 50% 60% 70% 80% 90% 

5% 6.37 6.40 6.43 6.49 6.57 6.63 6.66 6.66 6.65 6.61 6.56 6.48 

15% 6.43 6.45 6.48 6.54 6.62 6.68 6.71 6.71 6.70 6.66 6.61 6.54 25% 6.47 6.50 6.53 6.59 6.68 6.73 6.76 6.76 6.75 6.71 6.66 6.59 35% 6.51 6.54 6.58 6.64 6.72 6.78 6.81 6.81 6.79 6.76 6.70 6.63 45% 6.55 6.58 6.62 6.68 6.77 6.82 6.85 6.85 6.84 6.80 6.74 6.67 55% 6.59 6.61 6.66 6.72 6.81 6.87 6.90 6.90 6.89 6.84 6.78 6.71 65% 6.63 6.66 6.70 6.77 6.86 6.92 6.96 6.95 6.94 6.89 6.83 6.75 75% 6.69 6.72 6.76 6.82 6.92 6.98 7.02 7.01 7.00 6.95 6.89 6.81 85% 6.75 6.78 6.82 6.89 6.98 7.05 7.08 7.08 7.06 7.02 6.95 6.88 95% 6.82 6.85 6.89 6.96 7.05 7.11 7.15 7.15 7.13 7.08 7.02 6.94 

Wtd.Avg. 6.57 6.60 6.64 6.70 6.79 6.85 6.88 6.88 6.86 6.82 6.76 6.69 

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The next step is to estimate the impact of commingling on in-use RVP. We use the in-use tank RVP levels sh

for ethanol blend market shares of zero and 100% to represent situations where no commingling occurs. In the absenintermediate levels of ethanol blend market share, the RVP should vary linearly between those found at zero and 100is then the difference between the actual level of RVP estimated by the model and the RVP estimated from the zero ashare RVP levels. Table 2A-14 shows the impact of commingling for the case where gasoline RVP is 9 psi and ethaRVP waiver. 

Table 2A-14. 

Commingling as a Function of Fuel Tank Fill Level and Ethanol Blend Market Share – 9 RVP CG 

Fuel Tank Fill Level 

Ethanol Blend Market Share 

2% 5% 10% 20% 30% 40% 50% 60% 70% 80% 90

5% 0.016 0.049 0.101 0.167 0.213 0.235 0.234 0.220 0.183 0.132 0.07

15% 0.017 0.047 0.096 0.164 0.210 0.229 0.228 0.214 0.179 0.129 0.0725% 0.019 0.049 0.098 0.166 0.210 0.228 0.230 0.217 0.180 0.130 0.0735% 0.021 0.049 0.097 0.170 0.217 0.234 0.235 0.222 0.184 0.134 0.0745% 0.023 0.051 0.101 0.175 0.224 0.243 0.243 0.229 0.192 0.138 0.0755% 0.023 0.052 0.105 0.181 0.232 0.251 0.252 0.240 0.197 0.143 0.0765% 0.025 0.054 0.110 0.185 0.238 0.259 0.260 0.246 0.201 0.147 0.0775% 0.025 0.055 0.112 0.187 0.240 0.263 0.263 0.250 0.204 0.150 0.0785% 0.026 0.055 0.112 0.186 0.242 0.265 0.266 0.251 0.206 0.150 0.0795% 0.025 0.055 0.112 0.186 0.242 0.265 0.266 0.252 0.206 0.151 0.07

Wtd.Avg. 0.022 0.051 0.104 0.176 0.226 0.246 0.246 0.233 0.192 0.140 0.07

As can be seen, the impact of commingling increases slightly moving from low levels of fuel tank fill level to 

previous studies of commingling, the impact of commingling is lowest when either E0 or E10 fuels predominate the of E0 and E10 is approximately 50/50. Again, the weighted average of the commingling impact is determined by apcommingling impact at each fuel tank fill level by the distribution of fill levels in-use shown in Table 2A-7. 

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Table 2A-15 shows the weighted average commingling impacts for the six fuel cases. 

Table 2A-15. Weighted Average Commingling Impact for Various Sets of E0 and E10 F

E0/E10 Ethanol Blend Market Share RVP Level 2% 5% 10% 20% 30% 40% 50% 60% 70% 80% 90

9/10 0.022 0.060 0.102 0.171 0.227 0.249 0.249 0.227 0.199 0.143 0.08

7.8/8.9 0.029 0.065 0.110 0.201 0.239 0.273 0.274 0.255 0.218 0.160 0.08

7/8.2 0.028 0.067 0.122 0.203 0.265 0.299 0.290 0.275 0.236 0.173 0.09

9/9 0.022 0.051 0.104 0.176 0.226 0.246 0.246 0.233 0.192 0.140 0.078/8 0.021 0.055 0.110 0.189 0.249 0.269 0.278 0.253 0.218 0.160 0.087/7 0.027 0.065 0.125 0.212 0.267 0.298 0.294 0.277 0.233 0.172 0.09

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Ethanol use in the three RFS rule fuel cases (Reference, RFS, and EIA) occurs 

predominately under three situations: 1) 9 RVP CG with an RVP waiver for ethanol, 2) 7.8 RVP 

CG with an RVP waiver for ethanol, and 3) 7 RVP RFG. In order to simplify application of the 

impact of commingling to our emission modeling, we averaged the commingling impacts for 

these situations from Table 2A-15 and applied that to the entire U.S. as a function of ethanol 

blend market share. This average set of commingling impacts is shown in Table 2A-16. 

Table2A-16. 

Commingling Impact Applied in RFS Rule Emission Modeling 

Ethanol Blend Market Share Commingling Impact (psi) 

0% 0 

2% 0.026 

5% 0.064 

10% 0.113 

20% 0.194 

30% 0.244 

40% 0.273 

50% 0.272 

60% 0.253 

70% 0.217 

80% 0.159 

90% 0.087 

95% 0.045 

98% 0.019 

100% 0.000 

118 
Chapter 3: Impacts on Emissions from Vehicles, Nonroad 

Equipment, and Fuel Production Facilities 

As described in Chapter 2, there are a large number of potential fuels that qualify as 

renewable. However, only two are expected to be used in significant volumes by 2012: ethanol 

and biodiesel. Of these, ethanol use is expected to predominate. In particular, ethanol is 

expected to dominate the “growth” in renewable fuel use between now and 2012. Thus, our 

primary focus here will be on the impact of the use of ethanol on emissions in spark-ignited 

vehicles and equipment. We will more briefly touch on the impact of biodiesel fuel use on 

emissions. 

Similarly, we expect that the bulk of the impact of ethanol use on emissions and air 

quality will be associated with emissions from spark-ignited vehicles and equipment using low 

level ethanol-gasoline blends. We expect the use of high level ethanol-gasoline blends, like E85 

to be relatively small in comparison. Thus, the discussion here will focus on emissions from the 

use of low level ethanol blends. We will more briefly discuss the per vehicle impacts of use of 

high level ethanol-gasoline blends relative to gasoline. 

Finally, we present estimates of the emissions related to the production and distribution 

of both ethanol and biodiesel. The emissions related to the production and use of ethanol can be 

significant relative to the emission impacts of the use of ethanol blends, due to the significantly 

increase in the volume of ethanol expected to be produced in the future. 

3.1 Effect of Fuel Quality on Onroad Spark-Ignited Vehicle Emissions 

Ethanol belongs to a group of gasoline additives commonly referred to as oxygenates. 

The two most commonly used oxygenates are ethanol and MTBE, though TAME has been used 

in significant volumes, as well. All oxygenates have relatively high levels of octane (i.e., greater 

than 100 R+M/2). Both ethanol and MTBE have been used historically to meet the gasoline 

oxygen requirements for oxyfuel and RFG. Historically, MTBE was the predominant oxygenate 

used in gasoline in the U.S. Over time, MTBE use has decreased in the U.S, while ethanol use 

has increased, to the point where ethanol use now predominates. This trend appears to be 

accelerating, to the point where it appears that essentially all MTBE use will cease in the U.S 

sometime in 2007. 

The impact of oxygenate use on emissions from motor vehicles has been evaluated since 

the mid-1980’s. Several models of the impact of gasoline quality on motor vehicle emissions 

were developed in the early 1990’s and updated periodically since that time. We use the most 

up-to-date versions of these models here to estimate the impact of changes in oxygenate use on 

emissions. Still, as will be described below, significant uncertainty exists as to the effect of these 

gasoline components on emissions from both motor vehicle and nonroad equipment, particularly 

from the latest models equipped with the most advanced emission controls. Assuming adequate 

funding, we plan to conduct significant vehicle and equipment testing over the next several years 

119
to improve our estimates of the impact of these additives and other gasoline properties on 

emissions. The results of this testing are not available for inclusion in this analysis. We hope 

that the results from these test programs will be available for reference in the comprehensive 

evaluation of the emission and air quality impacts of all the fuel-related requirements of the 

Energy Act required by Section 1506. A draft of this study is required to be completed in 2009. 

As we discuss the emission impacts of increased ethanol use below, we identify the areas where 

current estimates appear to be the most uncertain and where we hope to obtain additional data 

prior to the 2009 study. 

3.1.1 Low Level Ethanol and MTBE-Gasoline Blends 

EPA has developed a number of emission models relating the impact of gasoline quality 

on emissions from motor vehicles. In 1993, EPA published the Complex Model, which predicts 

the effect of gasoline quality on VOC, NOx and air toxic emissions from 1990 model year lightduty motor vehicles (i.e., Tier 0 vehicles). This model is used to determine refiners’ compliance 

with RFG and anti-dumping standards. The Complex Model also contains estimates of the 

impact of gasoline RVP on non-exhaust VOC emissions. These estimates were taken from the 

then-current version of the MOBILE emissions model, MOBILE5. 

In 2001, in responding to California’s request for a waiver of the RFG oxygen mandate, 

EPA performed a new analysis of the impact of gasoline quality on exhaust VOC and NOx 

emissions from Tier 0 vehicles. This analysis included essentially all of the data used to develop 

the Complex Model, as well as some additional data developed since 1993. It also used more 

advanced statistical tools, such as a mixed statistical model, which were not available in 1993. 

These VOC and NOx models are referred to here as the EPA Predictive Models. Thus, in terms 

of both supporting data and modeling tools, the EPA Predictive Models represent an 

improvement over the Complex Model, at least for exhaust VOC and NOx emissions. Because 

the criteria for granting California a waiver of the oxygen requirement focused on ozone and PM 

impacts, EPA did not develop a similar model for toxics or CO emissions. 

In roughly the same timeframe, EPA developed its latest motor vehicle emission 

inventory model, MOBILE6. Some of the fuel-emission relationships from the Complex Model 

were incorporated into MOBILE6. These included the effect of selected gasoline properties on 

exhaust VOC and NOx emissions and the fraction of VOC emissions represented by several air 

toxics (benzene, formaldehyde, acetaldehyde, and 1,3-butadiene). The EPA Predictive Models 

were not available in time for their incorporation into MOBILE6. MOBILE6 also contains 

estimates of the effect of certain gasoline parameters on CO emissions, namely RVP and oxygen 

content. The effect of RVP on non-exhaust VOC emissions contained in MOBILE6.2 represents 

an update of the MOBILE5 and Complex Model estimates. 

We desire in this RFS analysis to utilize the most up to date estimates of the impact of 

gasoline quality on emissions currently available. No one model currently contains the most up 

to date estimates for all the pollutants of interest. Therefore, we have broken up the remainder of 

this sub-section into six parts. The first discusses emissions of VOC and NOx, as the EPA 

Predictive Models address these pollutants. The second discusses CO emissions, as neither the 

Complex Model nor the EPA or CARB Predictive Models address this pollutant. The third 

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section addresses emissions of air toxics, as a combination of models represents the best estimate 

of the impact of fuel quality on these emissions. The fourth section addresses non-exhaust VOC 

emissions. The fifth section addresses PM emissions. The sixth section addresses emissions of 

aromatic hydrocarbons. The seventh and final section presents the impact of ethanol and MTBE 

blending on per mile emissions from gasoline-fueled motor vehicles. 

3.1.1.1 Exhaust VOC, CO and NOx Emissions 

In this section we evaluate the various models available which predict the impact of 

gasoline quality on exhaust VOC, CO and NOx emissions. Several such models have been 

developed over the past 15 years. We first discuss the EPA Complex Model, the EPA Predictive 

Models and the CARB Predictive Models due to the wide range of fuel parameters which they 

address and their similar form. We next describe the fuel effects contained in EPA’s 

MOBILE6.2 emission inventory model, as this model addresses CO emissions, which the other 

models do not. These models best predict emissions from Tier 0 vehicles, as most of the 

emission data upon which they were based were from these vehicles. A number of fuel effects 

test programs which tested later model year vehicles have been performed since the above 

emission models were developed. We summarize these studies below and develop emission 

projections based on consistent statistical procedures. Finally, we select which model best 

predicts the effect of fuel quality for each pollutant. Due to the uncertainty involved with 

predicting the impact of fuel quality on emissions from Tier 1 and later vehicles, we develop two 

alternative approaches to making such predictions for the purpose of this rule. 

3.1.1.1.1 EPA Complex Model and CARB and EPA Predictive Models 

In 1993, EPA published the Complex model to investigate the effects of changing 

gasoline fuel parameters on the exhaust emissions of Tier 0 and older vehicles. This model 

predicts the effect of gasoline quality on exhaust VOC, toxics and NOx emissions and nonexhaust emissions of VOC and benzene. The Complex Model is used to determine compliance 

with the emissions performance requirements for federal RFG by comparing the predicted 

emissions of a candidate fuel to that of a baseline fuel for common baseline vehicle technology. 

The baseline fuel and the baseline vehicle technology represent those fuels and vehicles included 

in the 1990 US light duty vehicle fleet (Tier 0 technology). 

In 1999, the state of California petitioned EPA for a waiver of the oxygen requirement in 

RFG. The reasoning behind the waiver request centered on the California Air Resources Board’s 

(CARB) analysis which showed that reducing the amount of oxygen in RFG would lead to 

reduced NOx exhaust emissions. The model that CARB developed to support their claim was 

called the Phase 3 predictive modelZZ

. This model differed from the previous version of 

CARB’s predictive model (the Phase 2 model) in a number of ways. The most significant 

difference included a substantially expanded database, mainly for model year 1986 and newer 

vehicles, as well as an improved version of the statistical analysis software package used to 

develop the model (SAS® PROC MIXED). According to CARB, the Phase 3 predictive model 

displays a steeper NOx/Oxygen response than the Phase 2 Predictive model as a result of 

eliminating the RVP by Oxygen term which the previous model had erroneously included. This 

caused an increase in the NOx exhaust emissions predicted, and for many areas this increase 

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would lead to NOx levels exceeding those set by National Ambient Air Quality Standards 

(NAAQS). 

According to the 1990 Clean Air Act, EPA can waive the RFG oxygen requirement if it 

prevents compliance with a NAAQS. In order to properly perform an environmental impact 

analysis in response to this waiver request, EPA considered using both its Complex Model, as 

well as CARB’s Phase 3 Predictive Model to estimate the impact of gasoline quality on 

emissions. The EPA Complex model, while considered statistically robust due to the large 

number of vehicles comprising the dataset, was not considered to be adequate for a number of 

reasons. First, the Complex Model was based on an emissions database which did not include 

several studies that have since been published. Second, the EPA Complex Model was developed 

using a fixed effects statistical modeling approach42. In contrast, both the CARB Phase 2 and 3 

models were mixed models, employing a more sophisticated statistical approach than was 

available at the time of development of the Complex model. 

EPA also rejected using CARB’s Phase 3 Predictive Model in its analysis of the waiver 

request. While CARB had developed a very detailed protocol for developing the Phase 3 model, 

it rejected the results of this protocol because the resulting model differed too substantially from 

the Phase 2 model. Thus, EPA decided to create its own “predictive models” for exhaust VOC 

and NOx emissions which combined the protocols used to develop the Complex Model with the 

expanded database and improved statistical tools which were now available. EPA relied on 

existing EPA models for evaporative VOC emission effects. However, these latter estimates 

were augmented with recent data indicating that ethanol increased permeation emissions, as well 

as the consideration of several commingling models and associated assumptions about drivers’ 

refueling behavior. 

One main conclusion drawn by EPA in the California Oxygen waiver analysis was that 

insufficient data existed at that time to conclusively determine the response of Tier 1 and newer 

vehicles to fuel parameters other than sulfur.43 Some data indicated that oxygen increased NOx 

emissions from Tier 1 and later vehicles, while other data contradicted this. Due to this 

inconsistency, EPA assumed that oxygen did not affect exhaust VOC, NOx or CO emissions 

from Tier 1 and later vehicles in its analysis of CARB’s request for a waiver of the RFG oxygen 

mandate. 

42 A "fixed effects" model of this kind makes no attempt to estimate the error introduced by sampling from some 

larger population of vehicles or fuels. The model just describes quantitatively the relationships among variables that 

are present in the dataset that was analyzed. A "mixed" model, as was used by CARB in both the Phase 2 and Phase 

3 predictive models’ construction, attempts to go beyond description of the available data to make statistical 

inference to some larger population from which the available data were sampled. In this case CARB treated the 

vehicle effects as random (assuming that the test vehicles were sampled from some larger fleet) while fuel effects 

were treated as "fixed" (assuming that all fuels of interest were represented in the data). Such a modeling approach 

makes it possible to estimate the probable error in modeled effects in a way that is not possible with a fixed effects 

model. The approach, moreover, improves the accuracy of the significance measures used to decide which terms to 

include in the model. 

43At the time of that 1999 analysis, sufficient data existed on the emissions effects of Sulfur on Tier 1 vehicles to be 

modeled. However, sulfur levels were not expected to change as a result of the removal of oxygen from RFG in 

California, so the effect of sulfur was moot in this situation. 

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None of the three models discussed above (i.e., the Complex Model, the CARB 

Predictive Model, and the EPA Predictive Models) address CO emissions. Historically, this is 

because the RFG program did not mandate a specific reduction in CO emissions and the lesser 

role of CO emissions in forming ambient ozone. The CARB Predictive Model considers the 

impact of fuel oxygen content beyond 2 wt%, but does not address the full range of oxygen 

levels on CO emissions, nor the impact of other fuel parameters. The only EPA model which 

predicts the impact of fuel quality on CO emissions is MOBILE6.2, which is discussed in the 

next section. 

3.1.1.1.2 MOBILE6.2 

The exhaust emission effects contained in MOBILE6.2 often differ for normal and high 

emitting vehicles. They can also vary by model year. As it is difficult to determine the fraction 

of emissions coming from each model year’s vehicles in MOBILE6.2, as well as normal and 

high emitters, it is not feasible to predict outside of the model how a specific fuel is going to 

affect in-use emissions. In addition, the split between normal and high emitters varies depending 

on the presence and type of inspection and maintenance (I/M) program applicable in a particular 

local area. Thus, the effect of a specific fuel on emissions can vary to some degree from one 

county to another. 

In order to quantify the effect of various fuel parameters on exhaust emissions in 

MOBLIE6.2 under the conditions existing in the 2012-2020 timeframe, we compared the 

changes in emissions predicted by the NMIM modeling described in Chapter 4 of the Draft RIA 

with the changes in fuel quality occurring in the ethanol use scenarios. Specifically, we first 

determined the percentage change in exhaust VOC, CO and NOx emissions by county for the 

base and 7.2 Minimum RFG Use scenarios evaluated for the NPRM. We then performed a series 

of linear regressions of these ratios against the change in fuel RVP, ethanol content and MTBE 

content. We did this for the 2012, 2015 and 2020 emission projections separately. For each 

combination of county and calendar year, the only property that changed was fuel quality. All 

other parameters relevant to emissions (e.g., the distribution of vehicles by age and class, VMT, 

ambient temperature, etc.) were otherwise identical. The results are summarized in Table 3.1-1. 

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Table 3.1-1. Fuel-Exhaust Emission Effects in MOBILE6.2 

RVP (% / psi) Ethanol (% / Vol %) MTBE (% / Vol%) Adjusted r-Square 

2012 (fleet average) 

VOC 7.1% -1.1% -0.7% 0.83 

NOx 0.6% 0.0% 0.0% 0.95 

CO 12.7% -0.75% -0.4% 0.36 

2015 (fleet average) 

VOC 7.0% -1.2% -0.7% 0.85 

NOx 0.6% 0.0% 0.0% 0.95 

CO 12.7% -0.73% -0.4% 0.36 

2020 (fleet average) 

VOC 6.7% -1.2% -0.7% 0.87 

NOx 0.6% 0.0% 0.0% 0.95 

CO 12.6% -0.67% -0.4% 0.39 

For comparative purposes, the effect of RVP, ethanol and MTBE on exhaust VOC and 

NOx emissions from the EPA Predictive Models are shown in Table 3.1-2. The base fuel is a 

typical non-oxygenated, summertime, conventional gasoline, with 8.7 RVP, 30 ppm sulfur, 32 

vol% aromatics, 13 vol% olefins, T50 of 218 F, T90 of 329, and no oxygen. 

Table 3.1-2. 

Fuel-Exhaust Emission Effects per the EPA Predictive Models 

RVP (% / psi) Ethanol (% / Vol %) MTBE (% / Vol%) 

VOC 1.1% -0.16% -0.17% 

NOx 1.1% 0.75% 0.36% 

As can be seen, the exhaust emission effects contained in the EPA Predictive Models 

differ quite dramatically from those in MOBILE6.2. Regarding the effect of RVP, both models 

predict that an increase in RVP will increase both exhaust VOC and NOx emissions. However, 

MOBILE6.2 predicts that an increase of 1.0 psi will increase exhaust VOC by roughly 7%, while 

the EPA Predictive Models predict only a 1% increase. Regarding NOx emissions, the EPA 

Predictive Models predict the larger effect (1%), while the effect in MOBILE6.2 is smaller 

(0.6%). While the ratio of these two effects is significant, the absolute difference (0.4%) is very 

small. 

Regarding the addition of ethanol, the two models again predict very different results. 

MOBILE6.2 predicts roughly 7 times the exhaust VOC reduction per volume percent of ethanol 

added, with no increase in NOx. The EPA Predictive Models project a significant increase in 

NOx emissions. The relative differences are similar for the addition of MTBE to gasoline, 

though the difference between the two estimates of exhaust VOC reduction is smaller. 

Regarding CO Emissions from Tier 0 vehicles, MOBILE6.2 projects that a 3.5 wt% 

oxygen fuel (i.e., E10) will reduce CO emissions from normal emitters by 11% and those from 

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high emitters by 19%.44

 For Tier 1 and later vehicles, MOBILE6.2 projects that a 3.5 wt% 

oxygen fuel (i.e., E10) will have no effect on CO emissions from normal emitters and will reduce 

CO emissions from high emitters by the same 19% estimated for Tier 0 vehicles. This latter 

projection was based on a similar assumption to those made during EPA’s review of California’s 

request for a RFG oxygen waiver due to the same lack of relevant emission data. We estimate 

that the fraction of CO emissions coming from high emitters is 64.8% based on the overall effect 

of 6.7% in 2020 shown in Table 3.1-1 for 2020, when the fleet is entirely Tier 1 and later 

vehicles. This produces a fleet wide CO emission reduction for an E10 blend of 13.8% for Tier 0 

vehicles and 6.7% for Tier 1 and later vehicles. MOBILE6.2 does not project any impact of the 

other relevant fuel parameters (aromatics, olefins, T50, and T90) on CO emissions for either Tier 

0 or Tier 1 vehicles. 

Since that time, the results of several test programs have been published. Given the 

dwindling numbers of Tier 1 vehicles on the road, these more recent studies have focused on 

vehicles certified to California’s low emission vehicle (LEV), ultra low emission vehicle 

(ULEV), and super ultra low emission vehicle (SULEV) standards, as well as federal LEV and 

Tier 2 standards. The results of these more recent studies, as well as the few available in 2001, 

are discussed in the following section. 

3.1.1.1.3. CRC E-67 Study 

3.1.1.1.3.1 Overview 

In early 2006, the Coordinating Research Council (CRC) published the results of their E67 study investigating the effects of three fuel parameters, ethanol, T50 and T90, on exhaust 

emissions from recent model year vehicles.2 The twelve vehicles tested included both cars and 

light trucks, certified to California LEV, ULEV and SULEV standards, with model years ranging 

from 2001 to 2003. A matrix of twelve (12) fuels was tested in this program, with varying levels 

of ethanol, T50, and T90. Each fuel parameter (ethanol, T50, and T90) was tested at each of 

three levels. However, a full factorial matrix of 27 test fuels was deemed unnecessarily large due 

to subtle differences between fuels that may not have yielded statistically significant results, or 

due to practical considerations regarding the fuels that could be blended using existing refinery 

streams. 

The E-67 report presents the results of emission testing for each fuel, as well as a mixed 

statistical model created from the emission data. The model indicates that each of the three fuel 

parameters always has a statistically significant effect on both NMHC45 and NOx emissions. In 

addition, significant interactions between the three fuel parameters are also often present. 

The first step in our analysis of the CRC E-67 model was to compare the emissions 

changes predicted by the CRC mixed model to the actual emissions changes observed for each 

44 A normal emitter is generally a vehicle whose emissions are no more than twice its certification emission 

standards. A high emitter is a vehicle whose emissions exceed this level. 

 NMHC is essentially equivalent to VOC for our purposes in this study. 

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45
fuel in the test program. We calculated average NMHC and NOx emissions over the Federal 

Test Procedure for all twelve vehicles on each fuel. The CRC mixed model predicts the 

percentage change in emissions for each fuel relative to another fuel. These changes in predicted 

exhaust emissions for each fuel were applied to the measured emissions for fuel “H” in order to 

create a set of absolute emission levels for each fuel. We then compared the emissions predicted 

by the CRC E-67 model to the measured emission levels to observe how well the model 

predicted the effects of each fuel. The fuel properties of the CRC E-67 test fuels are listed in 

Table 3.1-3AAA, below, and in greater detail in Table 3A-1 of Appendix 3A. We selected CRC 

fuel “H” as the “base” fuel since its properties are the closest to a national average nonoxygenated conventional gasoline (0% Ethanol, 215oF T50, 330 oF T90). (See Table 2.2-6 in 

Chapter 2 for data pertaining to gasoline survey results across the U.S.) 

Table 3.1-3. CRC E-67 Test Program Fuels Propertiesa 

Target Properties for Design Variables Actual Values 

Fuel T50 (oF) T90 (oF) Ethanol (%) T50 (oF) T90 (oF) Ethanol 

A 195 295 0 195 294 0 

B 195 295 5.7 191 290 5.6 

C 195 330 10 193 329 10.4 

D 195 355 0 199 355 0 

E 195 355 10 198 352 10.3 

F 215 295 0 217 295 0 

G 215 295 10 212 291 10.1 

H 215 330 0 216 327 0.1 

I 215 355 5.7 216 354 5.9 

J 235 330 5.7 237 329 5.9 

K 235 355 0 236 355 0 

L 235 355 10 233 349 10.5 a

See Table 3A-1 in Appendix 3A for detailed properties of all E-67 test fuels 

The comparison between predicted and measured NOx emissions is shown in Figure 3.1­

1. The fuels are shown to indicate a trend in ethanol content, from the lowest levels on the left to 

the highest levels on the right. Within a constant level of ethanol content, the fuels are then 

shown in order of their level of T50 (lowest again on the left and highest on the right). The yaxis scale in this figure is set to match that for NMHC emissions, which will be presented and 

discussed next. 

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Figure 3.1-1. CRC E-67 Predicted vs. Actual Emissions 

Relative NOx Emissions Change from Base (CRC H: 215 T50, 330 T90, 0% EtOH) 

0% Ethanol 5.7% Ethanol 10% Ethanol 

-10.0% 

0.0% 

10.0% 

20.0% 

30.0% 

40.0% 

50.0% 

A F H D K B J I G C E L 

CRC Fuels (order of increasing O2, T50, T90) 

% Change NOx 

CRC E-67 NOx Predicted 

Actual CRC Emissions 

As shown in Figure 3.1-1, the CRC model for NOx emissions predicts the general trend 

in the emission data, which roughly indicates an increase in NOx emissions with increasing 

ethanol content. However, the model clearly does not reflect many of the fuel to fuel differences 

indicated by the actual emissions data. One example of this is the change between fuels G and C 

– two 10% ethanol blends with relatively low distillation temperatures. In changing from fuel G 

to fuel C, the CRC E-67 model predicts a 4.3% increase in NOx emissions whereas the actual 

test data clearly shows a 9.2% decrease. This likely indicates the existence of interactions 

between the fuel parameters which are more complex than those which could be included in the 

model. While fuel parameters other than ethanol, T50, and T90 were held as constant as possible 

among all the test fuels, the level of specific compounds (such as toluene or the various xylenes) 

could not be held constant. It is possible that some of these compounds are affecting NOx 

emissions and confounding the ability of the model based just on ethanol, T50, and T90 to 

predict the observed changes. 

Figure 3.1-2 repeats this comparison for NMHC. 

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Figure 3.1-2. CRC E-67 Predicted vs. Actual Emissions 

Relative NMHC Emissions Change from Base (CRC H: 215 T50, 330 T90, 0% EtOH) 

-10.0% 

0.0% 

10.0% 

20.0% 

30.0% 

40.0% 

50.0% 

% Change NMHC 

CRC E-67 NMHC Predicted 

Actual CRC Emissions 

0% Ethanol 5.7% Ethanol 10% Ethanol 

A F H D K B J I G C E L 

CRC Fuels (order of increasing O2, T50, T90) 

First, it is very apparent that NMHC emissions are much more sensitive to fuel quality 

than NOx emissions. The largest increase in NMHC emissions relative to Fuel H is three times 

that for NOx emissions. Except for Fuels A and F (and of course Fuel H), the CRC model 

generally under-predicts the measured NMHC data. Directionally, however, the predicted 

emissions changes are very consistent with those observed in the test results. For this dataset at 

least, the effect of fuel quality on NMHC emissions are much more predictable than NOx 

emissions. 

The fuels studied in this test program were varied independently at low, medium, and 

high levels of T50, T90, and Ethanol. If you include all the possible linear, quadratic, and 

interactive terms, there are a total of possible 10 combinations. The CRC E-67 models included 

8 out of the10 possible fixed effects for the NOx, NMHC, and CO models. These terms were: 

T50, T90, ethanol (EtOH), T50 squared, T90 squared, EtOH squared, T50 by EtOH, and T90 by 

EtOH. The excluded terms were T50 by T90, and T50 by T90 by EtOH, which CRC excluded 

from consideration since previous studies had indicated that these terms had little effect on 

emissions. 

Also, several of the terms that were included in the CRC model had p-values greater than 

0.1, indicating that those terms are less than marginally significant.46 Specifically, the EtOH by 

EtOH term in the NMHC model and the T90, T90 by T90 and T90 by EtOH terms in the NOx 

model all had p-values above 0.10. In developing both the Complex Model and the EPA 

46 Using the widely accepted criteria of a 95% confidence interval, p ≤ 0.05 is considered to be statistically 

significant, 0.05 < p ≤ 0.10 is marginally significant, and p > 0.10 is not considered statistically significant. 

128
Predictive Models, our procedures would normally exclude the least significant term. A new 

regression would then be performed without this term being included in the model. This process 

would be repeated until all the remaining terms were statistically significant.47 

We desired to determine how these statistically insignificant terms might be affecting the 

predicted emission changes. This, plus the discrepancies between the CRC E-67 model and the 

actual emissions data, especially for the NOx model, prompted us to create our own NOx and 

NMHC models using the CRC E-67 dataset. Conducting our own modeling also provides us 

with the opportunity to apply a wide range of statistical tests in order to better understand the 

role of various fuel parameters in affecting emissions from these vehicles. The following 

sections provide details pertaining to the verification of the CRC model and the motivation for 

constructing a new model from this data. 

3.1.1.1.3.2. Development of a New Mixed Model: The EPA E-67 Model 

Using the E-67 dataset provided by the CRC, EPA first verified the coefficients and pvalues of the CRC E-67 model using the full E-67 dataset (no outliers were removed) with the 

same 8 fixed fuel effects that were included by CRC. This was successful and the coefficient 

and p-values resulting from this modeling are shown in Table 3.1-4. 

Table 3.1-4. CRC E-67 Model P-Values and Coefficients 

CRC E-67 NMHC CO NOx 

Effect P-Value Coefficient P-Value Coefficient P-Value Coefficient 

Intercept <.0001 -3.2942 0.0001 -0.7966 <.0001 -2.6183 

T50 <.0001 0.0063 0.3099 0.001227 0.8939 -0.00013 

T50*T50 <.0001 0.000176 0.0428 0.000099 0.2182 -0.00006 

T90 0.0541 0.001685 0.0051 -0.0045 0.762 0.00024 

T90*T90 0.0035 0.000058 0.0815 0.000045 0.1163 0.000043 

EtOH 0.1124 0.005679 0.0174 -0.01581 0.0504 0.00571 

EtOH*EtOH 0.2816 0.000722 0.0005 0.003118 0.0861 0.001622 

T50*EtOH 0.084 0.000195 0.0182 0.000355 0.0414 -0.00032 

T90*EtOH 0.0004 0.000244 0.0534 0.000174 0.99 -1.19E-06 

EPA then created a new model starting with all combinations of T50, T90, and EtOH 

along with their squares, cross products, and the interactive terms T50 by T90 by EtOH for a 

total of 10 fixed effects. From this “full model”, variables were eliminated in order to improve 

the fit statistics between the model and the test data until a “final model” was created that 

contained 7 fixed fuel effects for NMHC and CO, and 6 fixed fuel effects for NOx. Table 3.1-5, 

on the following page, shows the p-values and coefficients for the fixed effect terms of each 

model. 

 One exception to this process is that the linear form of a variable, such as ethanol, would always be retained in 

the model if a second order term included ethanol (e.g., the ethanol by T90 term). 

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47
Table 3.1-5. EPA E-67 Model P-Values and Coefficients 

EPA E-67 NMHC CO NOx 

Effect P-Value Coefficient P-Value Coefficient P-Value Coefficient 

Intercept <.0001 -3.2773 0.0002 -0.7684 <.0001 -2.6418 

T50 <.0001 0.006272 0.5815 0.00066 0.687 -0.00037 

T50*T50 <.0001 0.000168 

T90 0.0498 0.00172 0.0059 -0.00437 0.7761 0.000224 

T90*T90 0.0039 0.000057 0.0735 0.000047 

EtOH 0.101 0.005892 0.0111 -0.01726 0.062 0.005393 

T50*EtOH 0.0987 0.000186 <.0001 0.003843 0.0446 0.001854 

T90*EtOH 0.0002 0.00025 0.0299 0.000178 0.0426 -0.00031 

T50*T90 <.0001 0.000126 

T50*T90*EtOH 0.0003 0.000023 

As shown in Table 3.1-5, the EPA E-67 Model does not include terms with p-values 

greater than 0.10 (except for linear terms included in statistically significant higher order terms). 

Statistical tests show that these two models are not significantly different from one another. The 

null hypothesis in this case is that EPA E-67 fits the data just as well as the original CRC E-67 

model based on a chi-squared test. However, based on several fit statistics (AIC, AICC, and 

BIC) the EPA E-67 model provides a slightly better fit to the test data than either the original 

CRC E-67 model or the full model with all 10 terms included. The next step is to compare the 

EPA E-67 model predictions to both the E-67 data and the predictions of the EPA Predictive 

Models, which reflect the emission effects for older vehicles. 

Both the EPA E-67 and EPA Predictive models are mixed models that predict the relative 

changes in exhaust emissions due to carefully controlled changes in gasoline quality, including 

the addition of an oxygenate such as ethanol. The models are not intended to be accurate at 

predicting absolute emission levels, but rather the difference in emissions when fuel properties 

are varied. The goal of this analysis is to determine if the EPA Predictive models, which were 

developed using data from Tier 0 and earlier vehicles, predict the same relative changes in 

emissions as the Tier 1 vehicles used for the EPA E-67 model. 

A key difference between the models is that there are only three fuel parameters used as 

inputs for the EPA E-67 model: T50, T90, and ethanol content. The EPA Predictive Models use 

these three properties along with RVP, aromatic content, olefin content, and sulfur content as 

fuel parameter inputs to the model. 

We ran the EPA E-67 and EPA predictive models with the 12 fuels used in the CRC test 

program, inputting the applicable fuel properties used in each model. Following the same 

procedure as outlined above, CRC test fuel H was selected as a “base” fuel in order to compare 

relative changes between this fuel and others with varying amounts of ethanol, T50, and T90. 

The NOx emissions predicted by the EPA E-67 and EPA Predictive models, together with the 

actual E-67 study data, are shown graphically in Figure 3.1-3, below. 

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Figure 3.1-3. EPA E-67 and EPA Predictive vs. Actual Emissions 

% Change NOx 

Relative NOx Emissions Change from Base (CRC H: 215 T50, 330 T90, 0% EtOH) 

50.0% 

40.0% 

30.0% 

20.0% 

10.0% 

A F H D K B J I G C E L 

EPA E-67 

EPA Predictive 

actual 

0% Ethanol 5.7% Ethanol 10% Ethanol 

0.0% 

-10.0% 

-20.0% 

CRC Test Fuels (in order of increasing O2, T50, T90) 

As shown in Figure 3.1-3, neither model predicts the actual test data with complete 

accuracy. The EPA E-67 shows the same general relationship to the emission data as did the 

CRC E-67 NOx model. Thus, removing the statistically insignificant terms had little impact on 

the relative fit of the model to the data. The EPA Predictive NOx models, on the other hand, 

appear to be primarily sensitive to ethanol, with T50 and T90 playing very limited roles in 

affecting NOx emissions. In contrast, the E-67 model shows sensitivities to all three parameters. 

Overall, the E-67 study indicates that NOx emissions from recent model year vehicles 

(LEVs, ULEVs and SULEVs) are still sensitive to at least several fuel parameters. As indicated 

by the inability of either the EPA E-67 model or the EPA Predictive Models to accurately predict 

all of the changes seen in the E-67 data, this study is very valuable in identifying the continued 

sensitivity of LEV and cleaner vehicles to changes in fuel quality. 

Figure 3.1-4, below, shows the comparison of NMHC emissions predicted by the EPA E67 and Predictive models together with the E-67 study data. 

131
Figure 3.1-4. EPA E-67 and EPA Predictive vs. Actual Emissions 

Relative NMHC Emissions Change from Base (CRC H: 215 T50, 330 T90, 0% EtOH) 

EPA E-67 

EPA Predictive 

Actual 

0% Ethanol 5.7% Ethanol 10% Ethanol 

A F H D K B J I G C E L 

CRC Test Fuels (in order of increasing O2, T50, T90) 

-20.0% 

-10.0% 

0.0% 

10.0% 

20.0% 

30.0% 

40.0% 

50.0% 

% Change NMHC 

From Figure 3.1-4, it is apparent that both models do a better job at predicting changes in 

NMHC emissions than was the case for NOx emissions. The EPA E-67 model is clearly the 

more accurate of the two models. However, this is to be expected given it was based on the data 

being depicted. The ability of the EPA Predictive Model to predict the general trend of nearly all 

the CRC E-67 test fuels is impressive, given it is based on data from Tier 0 vehicles with 5-10 

times the NMHC emission levels of the vehicles in the E-67 test program. Overall, it appears 

that NMHC emissions from LEVs and cleaner vehicles are even more sensitive to changes in 

fuel quality than NMHC emissions from Tier 0 vehicles. 

The preceding figures illustrate the differences between the models for all 12 fuels 

included in the E-67 test program. Some of these fuels are more practical, or likely to be 

commercially produced, than others. Based on the results of AAM fuel surveys presented in 

Chapter 2, summertime E10 blends will generally have levels of T50 and T90 that are about 29 

oF and 7 oF lower than non-ethanol blends. Thus, it could be useful to focus on sets of fuels in 

the CRC E-67 study which reflect these differences. 

The fuel pair which most closely reflects these differences are CRC fuel “C”, a 10 vol% 

ethanol blend, and CRC fuel “H”, a non-oxygenated fuel. Both fuels have a mid-range level of 

T90. A second, more complex set of fuels involve those with higher levels of T90. The CRC 

“E” fuel contains 10 vol% ethanol and has the high level of T90. However, there is not a good 

match to this fuel which is non-oxygenated. Yet two non-oxygenated fuels (“D” and “K”), when 

considered together, represent a reasonable match to fuel “E. Fuel D reflects no change in T50 

relative to fuel E, while fuel K reflects slightly more than a typical drop in T50. Thus, by 

averaging the emissions for fuels K and D and then comparing this to the emissions with fuel E, 

132
we are able to generate a second direct indication of the impact of ethanol blending on emissions 

from these low emitting vehicles. The general properties of these five fuels and the emissions 

changes predicted by the two models are shown in Table 3.1-6 below. 

Table 3.1-6. 

Predicted NOx and NMHC Emissions Changes 

for EPA E-67and Predictive Models48

Fuel Changes H to C K to E D to E 

T50 (oF) Mid - Low High – Low Low - Low 

T90 (oF) Mid - Mid High – High High - High 

Oxygen (vol%) 0 - 10 0 – 10 0 - 10 

Change in Emissions 

EPA Predictive Model NOx 9.5% 8.4% 9.4% 

EPA E-67 NOx 11.0% 5.8% 10.4% 

Actual E-67 Data 3.3% 1.6% -1.8% 

EPA Predictive Model NMHC -3.7% -11.1% 7.3% 

EPA E-67 NMHC -3.8% -17.3% 8.8% 

Actual E-67 Data -6.3% -21.0% 9.2% 

As shown in Table 3.1-6, the two models agree quite closely on the effect of fuel C 

relative to fuel H on both NMHC and NOx emissions. However, that said, both models tend to 

overestimate the impact of fuels C and E on NOx emissions and underestimate the impact of 

these fuels on NMHC emissions. 

Regarding the comparison of fuel E to fuels K and D, the two models tend to agree on the 

effect of fuel E to fuel D, but differ more with respect to the effect of fuel E to fuel K, 

particularly for NMHC emissions. One reason for the difference in the latter comparison is that 

the EPA E-67 NMHC model is more sensitive to very high levels of T50 than the EPA 

Predictive Model for NMHC. 

Overall, the results of the E-67 study suggest that our assumption that Tier 1 and later 

vehicles would not be sensitive to fuel parameters such as ethanol, T50 ad T90 (made in our 

consideration of California’s request for a waiver of the RFG oxygen requirement) may not be 

valid. The observation that NMHC emissions from LEVs, et. al. could actually be more 

sensitive than Tier 0 vehicles (on a percentage basis), particularly challenges our assumption. 

While the effect of fuel quality on NOx emissions from low emitting vehicles is still not clear 

from the recent test data, these emissions do appear to be sensitive to fuel quality. 

48 For an additional comparison between the models with an expanded set of fuels to be used later in this analysis, 

refer to Table 3A-2 in Appendix 3A. 

133
3.1.1.1.3.3. Effect of Oxygen Content Alone on Emissions: CRC E-67 Study 

In order to provide an estimate of fuel-emission effects comparable to those of the other 

studies of the effect of fuel quality on emissions from LEV and later vehicles, we selected three 

pairs of fuels tested in the CRC E67 study where the only change in fuel properties was oxygen 

content. One pair of fuels compared non-oxygenated fuel versus E6 (fuels A and B). Two pairs 

of fuel compared non-oxygenated fuel versus E10 (fuels D and E and fuels F and G). We 

applied mixed univariate statistical models to the logarithm of the emission data from fuels A 

and B and to fuels D, E, F and G, with vehicle as a random variable and fuel type as a fixed 

variable. Table 3.1-7 presents the results in terms of the percentage change in emissions between 

the non-oxygenated fuel and the ethanol blend in each pair. 

Table 3.1-7. 

Matched Fuel Pair Results from the CRC E-67 Study: Effect of Oxygen Alone 

NMHC CO NOx 

% Change % Change % Change 

6 vol% ethanol vs. no oxygenate 

(Fuels A and B) 

-4.5%* -7.5% 2.6% 

10 vol% ethanol vs. no oxygenate 

(Fuels D, E, F and G) 

4.7% -18.1% 9.5% 

* Bold type indicates the difference was significant at a 90% confidence level. 

As can be seen, the addition of 6 vol% ethanol increased NOx emissions, while 

decreasing NMHC and CO emissions. None of the effects were statistically significant at a 90% 

confidence level. The addition of 10 vol% ethanol decreased CO emissions, while increasing 

NMHC and NOx emissions. All three pollutant effects were statistically significant at a 90% 

confidence level. 

3.1.1.1.4. AAM-AIAM Sulfur and Oxygenate Study 

AAM, together with AIAM and Honda, performed a test program in 2001 to evaluate the 

effect of fuel sulfur and oxygen on emissions of CARB Tech 5 vehicles (i.e., LEV and later 

vehicles)BBB. This program was performed at the request of the CARB in conjunction with the 

MTBE ban and new Phase 3 Cleaner Burning Gasoline regulations. The first of the program’s 

two distinct goals was to evaluate the emissions of very low sulfur fuels at 1, 30, and 100 ppm 

sulfur, with other fuel parameters held constant. Part two was to compare the emissions effects 

of MTBE and ethanol blended fuels to non-oxygenated fuel, again with other parameters held as 

constant as possible. The fuel specifications for the oxygenated fuel matrix are shown in Table 

3.1-8. (The sulfur-related testing is not relevant here, so the fuels which only reflect a change in 

sulfur are not shown.) 

134
Table 3.1-8. 

AAM Fuel Properties for Oxygenated Test Fuels 

Test Fuel Non-Oxy MTBE E10 

RVP (psi) 7.1 6.9 7.1 

Arom (vol.%) 24.6 22 21.6 

Olefins (vol.%) 4.6 3.4 4.1 

T50 (oF) 210 202 205 

T90 (oF) 297 294 291 

MTBE/EtOH (vol.%) 0 11.3 11.5 

Sulfur (ppm) 35 31 28 

The sulfur effects portion of this study tested 13 TLEV, LEV, and ULEV vehicles while 

the oxygenate phase of the study used 8 and 2 of phase 1’s LEV and ULEV vehicles, 

respectively. These vehicles were a mixture of light and medium duty passenger cars and trucks 

of undisclosed make and model year. The FTP 75 was selected as the drive cycle, with regulated 

data collected both at the tailpipe and engine out for a subset of vehicles to evaluate catalyst 

efficiency. 

The sulfur related testing found a clear relationship between the level of sulfur in the fuel 

and the natural log of emissions (CO, NOx and NMHC). Bag weighted emissions of NOx, CO, 

and HC emissions were reduced by 16%, 12%, and 11% (respectively) when fuel sulfur levels 

were reduced from 30 to 1 ppm. The effects found in the oxygenate portion of the study were 

less clear, since some results were not statistically significant. Average bag weighted emissions 

of HC and CO tended to decrease with increasing oxygen content. Both oxygenated fuels 

showed a decrease in NOx emissions, with MTBE having slightly lower emissions than ethanol. 

The details of the statistical procedures applied to the data were not described in the 

documentation. 

In order to provide a consistent basis for comparing the results of this study to the other 

studies of the effect of oxygenate on LEV and later vehicle emissions, we applied a mixed 

univariate statistical model to the logarithm of the emission data from the AAM-AIAM 

oxygenate study, with vehicle as a random variable and fuel as a fixed variable. Table 3.1-9 

presents the results in terms of the percentage change in emissions between the 11 vol% MTBE 

blend, the 11 vol% ethanol blend and the non-oxygenated fuel. 

Table 3.1-9. 

Fuel Effects from the AAM-AIAM Oxygenate Study: EPA Mixed Model 

NMHC CO NOx 

% Change % Change % Change 

11 vol% MTBE vs. no oxygenatea -15.3%b -23.7% -12.7% 

11 vol% ethanol vs. no oxygenate -12.6% -24.6% -6.6% 

11 vol% ethanol vs. 11 vol% MTBE -1.4% -5.7% 25.2% a

 Fewer vehicles were tested on the MTBE blend. 

b Bold type indicates the difference was significant at a 90% confidence level. 

135 
As can be seen in Table 3.1-9, the addition of 11 vol% MTBE was found to significantly 

reduce the emissions of all three pollutants by 10% or more relative to the non-oxygenated 

California RFG. The addition of 11 vol% ethanol was also found to significantly reduce the 

emissions of all three pollutants relative to the non-oxygenated California RFG. However, the 

reductions were smaller than those of the MTBE blend for all three pollutants. Also, the 

reduction in NOx emissions was not statistically significant at a 90% confidence level. When we 

compared the emissions with the ethanol blend to those with the MTBE blend, we found that the 

increases were all statistically significant at a 90% confidence level. 

Based solely on this single study of seven vehicles and three fuels, it appears that adding 

ethanol to a severely reformulated gasoline while holding other properties constant tends to 

reduce NMHC, CO and NOx emissions. However, replacing MTBE with the same volume of 

ethanol tends to increase these emissions. Focusing on just this one study, the effect of increased 

ethanol use could differ in RFG areas, where MTBE has traditionally been used, and 

conventional fuel areas, where no oxygenate has traditionally been used. However, this one 

study is not a sufficient basis to draw a firm conclusion regarding the effect of ethanol blending 

on exhaust emissions. 

3.1.1.1.5. ExxonMobil Study of Oxygenate Type and Content 

In the fall of 1999, ExxonMobil (Mobil Oil at that time) conducted a test program to 

investigate the emissions effects of MTBE and ethanol on LEV and ULEV vehicles. The 

information which follows was made publicly available on the CARB websiteCCC and is taken 

directly from a presentation posted on that webpage. The vehicles tested are listed in Table 3.1­

10. 

Table 3.1-10. Vehicles Tested in 1999 ExxonMobil Study 

Emissions Fuel 

Make/Model Calibration Engine System 

1999 Dodge Stratus LEV 2.4L MFI 

1999 Chevrolet Malibu LEV 3.1L PFI 

1999 Mazda Protégé ULEV 1.6L MPF 

1999 Ford Crown Victoria LEV 4.6L SFI 

1998 Honda Accord ULEV 2.3L MFI 

Four test fuels were developed with varying MTBE and ethanol content, with other 

parameters controlled as tightly as possible. The fuel specifications are listed below in Table 

3.1-11. 

136
Table 3.1-11. ExxonMobil Test Fuel Specifications 

Test Fuel M1 M2 M3 M4 

RVP, psi 6.5 6.4 7.3 7.2 

T50 (oF) 192 191 198 203 

T90 (oF) 279 272 274 282 

MTBE (vol%) 0 10 0 0 

Ethanol (vol%) 0 0 7 10 

Aromatics (vol%) 21 21 21 21 

Olefins (vol%) <1 <1 <1 <1 

Oxygen (wt%) 0 1.82 2.436 3.48 

Vehicles were tested in duplicate over the FTP 75 drive cycle, and composite weighted 

emissions for regulated pollutants reported. The average results for all vehicles, based on least 

squares means from the analysis of variance, is shown in Table 3.1-12., below. The oxygenate 

effect is the percent change in emissions between a given fuel and the base fuel M1. 

Table 3.1-12. Average Exhaust Emissions: ExxonMobil Study (all vehicles) 

Exhaust Emissions, g/mi 

Oxygenate 

Effect (a) 

Fuel Oxygenate HC CO NOx HC CO NOx 

M1 None 0.058 0.70 0.187 

M2 10% MTBE 0.058 0.72 0.198 0% 3% 6% 

M3 7% EtOH 0.059 0.66 0.213 1% -6% 14% 

M4 10% EtOH 0.061 0.66 0.239 5% -6% 28% 

(a) Bold font represents statistically significant (α=0.1) 

The bold numbers in the above table represent statistically significant differences in 

emissions at the 90% confidence level. Only the effect of ethanol on NOx emissions was found 

to be statistically significant while the effects on other criteria pollutants were not. As was the 

case for the AAM-AIAM study, the presentation to CARB did not describe the statistical 

analysis applied to the detail in sufficient detail to replicate the results. Thus, we applied a 

mixed univariate statistical model to the logarithm of the emission data from the ExxonMobil 

oxygenate study, with vehicle as a random variable and fuel type as a fixed variable. Because of 

the greater number of fuels in this study, we applied two different types of mixed models. One 

set of models compared emissions between various pairs of fuels (e.g., M1 and M2). We applied 

five models of this type. A sixth model used oxygen content as the fuel variable and considered 

the significance of the square of oxygen content, as well. The upper half of Table 3.1-13 

presents the results in terms of the percentage change in emissions between the various fuel 

pairs. The bottom half of Table 3.1-13 presents the estimated emission effects for 2.1 wt% and 

3.5 wt% oxygen fuels using the results of the fuel oxygen content based model. 

137
Table 3.1-13. Fuel Effects from the ExxonMobil Oxygenate Study 

THC CO NOx 

% Change % Change % Change 

Two Fuel Comparisons 

10 vol% MTBE vs. no oxygenate -2.5%a -5.1% -0.4% 

7 vol% ethanol vs. no oxygenate -0.9% -11.3% 9.3% 

10 vol% ethanol vs. no oxygenate 3.6% -11.6% 20.3% 

10 vol% ethanol vs. 10 vol% MTBE 6.3% -7.6% 21.1% 

Oxygen Content model 

2.1 vol% Oxygen -2.0% -9.1% 2.9% 

3.5 vol% Oxygen 3.5% -15.2% 17.3% a

 Bold type indicates the difference was significant at a 90% confidence level. 

As can be seen, the results of our statistical analysis differ from those performed by 

ExxonMobil. This may have been due to our focus on the logarithm of emissions or the use of a 

mixed model. As shown in Table 3.1-13, the addition of 10 vol% MTBE was found to reduce 

the emissions of all three pollutants relative to the non-oxygenated California RFG. While the 

differences tended to be substantial in magnitude on average, they were not statistically 

significant at a 90% confidence level. The addition of 7 vol% ethanol to the non-oxygenated 

California RFG (with an increase of roughly 1.0 psi RVP) was also found to reduce the 

emissions of all three pollutants. Again, the reductions in NMHC and NOx emissions were not 

statistically significant. While the reduction in CO emissions was slightly smaller on average 

than that for MTBE, the effect was more consistent across vehicles and statistically significant at 

a 90% confidence level. The addition of 10 vol% ethanol to the non-oxygenated California RFG 

(again with an increase of roughly 1.0 psi RVP) was also found to reduce emissions of NMHC 

and CO, but increased NOx emissions slightly. Like that for the 7 vol% ethanol blend, only the 

CO emission effect was statistically significant at a 90% confidence level. Finally, the 

substitution of 10 vol% ethanol for 10 vol% MTBE was found to increase emissions of THC and 

NOx substantially, while reducing CO emissions slightly. None of the effects were significant at 

a 90% confidence level, though the NOx increase was nearly so (e.g., 89% confidence). 

With respect to the model using oxygen content to describe the four fuels in the study, the 

effect of oxygen on emissions was found to be statistically significant for all three pollutants. In 

addition, the square of the oxygen content was statistically significant at the 90% confidence 

level for THC and NOx emissions. The predictions shown in Table 3.1-13 utilize the square of 

oxygen content for these two pollutants, but not for CO emissions. As can be seen, a 2.1 wt% 

oxygen fuel (e.g., an 11 vol% MTBE or 6% ethanol blend) is predicted to decrease THC and CO 

emissions, but increase NOx emissions. A 3.5 wt% oxygen fuel (e.g., a 10% ethanol blend) is 

predicted to decrease CO emissions, but increase THC and NOx emissions. 

The two modeling approaches produce markedly different predictions for a 10 vol% 

ethanol blend, especially for THC and NOx. The direct comparison of the clear fuel and the E10 

blend shows the E10 blend to reduce THC and NOx slightly. In contrast, the oxygen content 

approach predicts that the E10 blend will increase both THC and NOx emissions. This 

138
underlines the need for more data to accurately predict the emission effect of various types and 

levels of oxygenate on modern vehicles. 

3.1.1.1.6. Toyota Study of MTBE and Ethanol Blends 

Toyota Motor Company of Japan performed a small study, presented in 2000, evaluating 

the effect of MTBE and ethanol blended gasoline on exhaust emissions of nine LEV, TLEV, 

ULEV vehicles.DDD The model years for these vehicles were not presented.. Three test fuels 

were evaluated in this study: Phase 2 California RFG containing MTBE, a matched RVP E10 

and a higher RVP E10. However, exhaust emission testing was only performed on the MTBE 

fuel and the higher RVP ethanol fuel. The fuel properties for these test fuels are listed in Table 

3.1-14, below: 

Table 3.1-14. Fuels Tested in the Toyota Oxygenate Test Program 

Fuel 

Parameter MTBE Blend 

Matched RVP Ethanol 

Blend Higher RVP Ethanol Blend 

RVP (psi) 6.8 7 7.6 

Arom (vol.%) 24 23 24.1 

Olefins (vol.%) 5 5 3.4 

T50 (oF) 156 208 212 

T90 (oF) 290 294 297 

MTBE (vol.%) 11.1 0 0 

EtOH (vol.%) 0 11.2 8.9 

Sulfur (ppm) 30 29 30 

As can be seen in the above table, aromatics, olefins, and sulfur were held relatively 

constant while other parameters varied. The emission test cycle used was not stated. We assume 

it was the FTP 75 test. Only regulated emissions results are provided. 

The study found that, on average across all vehicles and tests, NOx emissions increased 

by 5% for E10-B relative to MTBE. Correspondingly, CO emissions were reduced by 6% and 

NMHC emissions were decreased by 0.6% for E10-B relative to MTBE. 

Again for comparison purposes, we applied a mixed univariate statistical model to the 

logarithm of the emission data from the Toyota oxygenate study, with vehicle as a random 

variable and fuel type as a fixed variable. Table 3.1-15 presents the results in terms of the 

percentage change in emissions between the 11 vol% MTBE and the 9 vol% ethanol blends. 

Table 3.1-15. Fuel Effects from the Toyota Oxygenate Study 

NMHC CO NOx 

% Change % Change % Change 

9 vol% ethanol vs. 11 vol% MTBE -0.8% a -6.1% 3.8% a Bold type indicates the difference was significant at a 90% confidence level. 

139
As can be seen, the substitution of 9 vol% ethanol for 11 vol% MTBE in a RFG-type 

blend was found to reduce the emissions of NMHC and CO, but increase NOx emissions. None 

of the emission effects was found to be statistically significant at a 90% confidence level. 

Based solely on this single study of nine vehicles and three fuels, it appears that replacing 

MTBE with roughly the same volume of ethanol in reformulated gasoline while holding other 

properties constant (except RVP) tends to reduce NMHC and CO, and increases NOx emissions. 

It is not possible to separate the effect of ethanol from RVP in this study. 

3.1.1.1.7. Mexican Petroleum Institute Fuel-Emission Effects Study 

In 2006, the Instituto Mexicano del Patroleo (hereafter referred to as Mexican Petroleum 

Institute) published a paper on a recently conducted research program investigating the effects of 

gasoline properties on exhaust and evaporative emissions on 30 light duty cars and trucks, 

ranging in model year from 1993 to 2002.EEE The fuel quality parameters investigated include 

RVP, oxygen, Sulfur, olefins, aromatics, and distillation parameters. The results of this study 

were used to develop a statistical model for predicting emissions based on fuel quality for use in 

guiding national air quality improvement program policy. In their analysis, a comparison was 

made between the “predictive model,” developed based in this test data, and EPA’s Complex 

model. The end result is a general qualitative agreement with the EPA Complex model, with 

some quantitative differences pertaining to the vehicles and fuels used in each model’s 

development. Selected properties of the test fuels are shown below in Table 3.1-16. 

Table 3.1-16. Fuel Tested in the Mexican Petroleum Institute Fuel Effects Study 

Fuel 

Aromatics 

(vol%) 

Olefins 

(vol%) 

Oxygen a 

(wt%) 

Benzene 

(vol%) 

RVP 

(psi) 

T50 

(oF) 

T90 

(oF) 

Sulfur 

(ppm) 

1 19.1 6.6 0 0.86 6.8 224 325 411 

2 19.3 6.6 0.98 1.09 6.8 222 324 412 

3 18.8 6.5 2.03 0.9 6.9 216 324 406 

4 20.6 6.7 2.1 1.13 7 227 326 386 

5 19 6.6 1 1.15 8.6 213 323 423 

6 18.7 6.9 1.03 0.98 10.7 199 325 387 

7 40.2 6.9 0.98 2.26 6.6 233 324 402 

8 20.7 15 1.07 1.31 10.9 197 322 415 

9 35.8 15.5 1.06 1.25 10.8 209 326 403 

10 19.8 5 1.03 0.6 6.6 220 335 89 

11 20 7.4 0.98 0.8 6.7 223 321 209 

12 19.8 6.1 1.05 0.75 6.6 222 321 817 

L-S 40.3 4.8 1.14 1.06 8.1 232 324 34 

Ref 28 13.5 0.34 1.14 8.9 207 332 724 

ZM 24.1 9 1.21 1 7.7 213 326 403 a Oxygenate is MTBE, except fuel 4 (Ethanol) 

140
The base fuel from which other fuel parameters were varied to form additional fuels is 

indicated as fuel 2 in the above table, and is a low-level MTBE fuel blended to represent a 

composite average of all brands of commercially available Mexican gasoline. The reference fuel 

for comparative fuel effects purposes, fuel “Ref”, is a fuel blended to have refinery average 

levels of sulfur, benzene, RVP, aromatics, olefins, and distillation properties for the year 2000. 

The technology groups investigated were referred to as either “Tier 0” and “Tier 1” 

vehicles, which is a bit of a misnomer as these vehicles were not equipped with on-board 

diagnostic equipment (OBD), nor were they subject to emissions durability standards49

. Rather, 

each technology class acts as a surrogate for emission control technologies with the certification 

standards shown in Table 3.1-17. 

Table 3.1-17. Certification Standards of Test Vehicles by Technology Class 

Technology class # of Vehicles Model Years CO (g/km) NMHC (g/km) NOx (g/km) 

"Tier 0" 12 1993-1998 2.1 0.25 (THC) 0.62 

"Tier 1" 14 1999-2002 2.1 0.156 0.25 

Vehicles were tested on a chassis dynamometer over the FTP-75 test. Regulated 

emissions, as well as speciated hydrocarbons and carbonyls, were collected for each test. The 

procedures and statistical methods employed to develop the predictive model for this test 

program were similar to those used to construct the complex models for exhaust VOC and NOx 

emissions. 

The emissions test results were separated into “Tier 0” and “Tier 1” categories, and 

reported as mean emissions rates for all vehicles of that type on each test fuel. The natural 

logarithm of emissions was then regressed to develop a predictive model, using a statistical 

approach that is “similar to the techniques used to construct the complex model for exhaust VOC 

and NOx emissions” (Schifter et al, 1275). The report then goes into details on the experimental 

vs. model predicted results, as well as a validation of their model which will not be discussed 

here. The model predicted percent changes in emissions (for both vehicle fleets) for the Mexican 

Petroleum Institute predictive model and EPA Complex model are shown in Figures 3.1-5 and 

3.1-6, below. 

49 The Mexican Environmental Agency currently mandates emissions durability certificates to be issued on new 

vehicles 

141
Figure 3.1-5. Emissions Effects Predicted by EPA Complex Model 

Figure 3.1-6. Emissions Effects Predicted by Mexican Petroleum Institute 

“Predictive” Model 

The values above represent average emissions across both vehicle technology fleets 

considered together, and with the industry average fuel (“Ref”) as a baseline for predicted 

changes in emissions. As you can see, their model predicts an even greater reduction in exhaust 

THC and NOx emissions with ethanol (and MTBE) than the complex model, along with 

directionally inconsistent results for fuels with high aromatics, RVP, and olefins. This is partly 

due to differences in the fuels and vehicles used to develop each model (along with the properties 

of the base fuel selected) and speaks to the fact that there is a high degree of uncertainty and 

sensitivity to consider when extrapolating the results of a fuel effects model to the larger vehicle 

and fuel population. 

As with the other studies, we applied a mixed univariate statistical model to the logarithm 

of the emission data from the Mexico fuel study, with vehicle as a random variable and fuel type 

as a fixed variable. We restricted the vehicles to those which had NOx emissions of 0.20 g/mi or 

142
less on the low sulfur fuel (30 ppm) included in the test matrix. This resulted in the inclusion of 

seven vehicles (numbers 9, 15, 25, 27, 28, 29, and 31). Table 3.1-18 presents the results in terms 

of the percentage change in emissions between the non-oxygenated base fuel, the 5.5 vol% and 

11 vol% MTBE blends and the 6 vol% ethanol blend. 

Table 3.1-18. Fuel Effects from the Mexico Fuels Study 

NMHC CO NOx 

% Change % Change % Change 

5.5 vol% MTBE vs. no oxygenate 11.1% a -5.5% -3.1% 

11 vol% MTBE vs. no oxygenate -11.1% -9.7% 10.0% 

6 vol% ethanol vs. no oxygenate 3.9% a -6.3% 27.2% 

6 vol% ethanol vs. 6 vol% MTBE -5.8% -0.9% 32.8% 

6 vol% ethanol vs. 11 vol% MTBE 19.5% 4.0% 14.2% a Bold type indicates the difference was significant at a 90% confidence level. 

As can be seen, the addition of 5.5 vol% MTBE to a low-RVP type fuel at constant RVP 

was found to increase the emissions of NMHC and CO, and decrease NOx emissions. The 

addition of 11 vol% MTBE to a low-RVP type fuel at constant RVP was found to increase the 

emissions of NOx and CO, and decrease NMHC emissions. Only the NMHC reduction was 

found to be statistically significant at a 90% confidence level. The addition of 6 vol% ethanol to 

a low-RVP type fuel at constant RVP was found to reduce the CO emissions, and increase 

NMHC and NOx emissions. Only the NOx emission increase was statistically significant at a 

90% confidence level. 

The substitution of 6 vol% ethanol for 11 vol% MTBE at constant RVP and oxygen 

content was found to decrease NMHC emissions and increase NOx emissions. CO emissions 

were essentially unchanged. Only the NOx emission increase was statistically significant at a 

90% confidence level. The substitution of 10 vol% ethanol for 11 vol% MTBE was found to 

increase emissions of all three pollutants. Only the NMHC emission increase was statistically 

significant at a 90% confidence level. 

Based solely on this single study of six vehicles and three fuels, it appears that either 

MTBE or ethanol blends with roughly 2 wt% oxygen increase NOx emissions, while the effect 

on NMHC and CO emissions are inconsistent. Substituting ethanol for MTBE at the same 

oxygen content appears to increase NMHC, CO and NOx emissions. 

3.1.1.1.8. Overview of LEV and Later Vehicle Studies 

The differences in the details of the various studies prevent a simple quantitative 

comparison of their results. However, we have performed a qualitative comparison by simply 

determining whether the study found an increase or a decrease in emissions of 2% or more and 

whether the effect was statistically significant at 90% confidence or not. The results of this 

determination are shown in Table 3.1-19 below for a number of fuel pairs. 

143
Table 3.1-19. Summary of Oxygenate Emission Effects: LEV and Later Vehicles 

 THC/NHMC CO NOx 

Lower Higher Lower Higher Lower Higher 

10/11 

vol% 

MTBE 

AAM-AIAM 

Mexico 

ExxonMobil* 

AAM-AIAM 

Mexico 

ExxonMobil 

AAMAIAM 

Mexico 

E6/E7 Mexico 

CRC E67 

Mexico 

ExxonMobil 

CRC E67 

Mexico 

ExxonMobil 

CRC E67 

E10/E11 AAM-AIAM CRC E67 

ExxonMobil 

AAM-AIAM 

ExxonMobil 

CRC E67 

AAMAIAM ExxonMobil 

CRC E67 

E10/11 

vs. 

MTBE 

Toyota 

AAM-AIAM 

ExxonMobil ExxonMobil 

AAM-AIAM 

Toyota AAM-AIAM 

ExxonMobil 

Starting with MTBE, none of the three studies which tested both non-oxygenated and 

MTBE fuels found MTBE to increase the emissions of any of the pollutants. One to two studies 

found statistically significant reductions in THC/NMHC, CO and NOx emissions with the use of 

MTBE. 

Three studies tested a non-oxygenated fuel and a 6-7 vol% ethanol blend. Ethanol 

blending at this level likely reduces CO emissions and increases NOx emissions. Two of the 

three studies showed an increase in NMHC emissions, but neither result was statistically 

significant at a 90% confidence level. Thus, the effect of E6 on NMHC emissions is particularly 

unclear. 

Three studies tested a non-oxygenated fuel and a 10-11 vol% ethanol blend. Ethanol 

blending at this level likely reduces CO emissions, as all three studies showed a statistically 

significant reduction. Two of three studies found an increase in NOx emissions, while one found 

a decrease, all statistically significant at a 90% confidence level. Again, the effect of ethanol 

blending on exhaust NMHC emissions is not clear. Two of three studies found an increase in 

THC/NMHC emissions, while one found an increase. The results of one of the two studies 

finding an increase and those of the study finding a decrease were statistically significant at a 

90% confidence level. 

Finally, three studies tested both MTBE and 10-11 vol% ethanol blends. Ethanol 

blending at this level appears to reduce CO emissions relative to MTBE. The effect on the other 

two pollutants is less clear. Two of three studies found a decrease in THC/NMHC emissions, 

though the one study finding an increase was the only one where the result was statistically 

significant at a 90% confidence level. Two of three studies found an increase in NOx emissions 

and both of these were statistically significant at a 90% confidence level. 

3.1.1.1.9 Selection of Models for Exhaust NMHC, CO and NOx Emissions 

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3.1.1.1.9.1 Exhaust NMHC and NOx Emissions 

For Tier 0 vehicles, the EPA Predictive Models are based on more data and the most 

advanced statistical tools. Therefore, we will use these models here to project the effect of 

increased ethanol use and decreased MTBE use on exhaust VOC and NOx emissions. 

For Tier 1 and later vehicles, the choice is much less clear. In our analysis of California’s 

request for an RFG oxygen waiver, we assumed that there was too little data upon which to 

project the effect of fuel quality on the emissions of these vehicles. Substantially more data exist 

on the effect of oxygenates on LEV and later vehicle exhaust emissions than did in 2001. 

However, as indicated by the summary of these results shown in Table 3.1-19, there does not 

appear to be sufficiently consistency to confidently predict the impact of oxygenate type and 

content on exhaust NMHC and NOx emissions. 

For the NPRM, we developed two separate sets of predictions: a primary analysis 

assuming no effect of oxygen on NMHC and NOx emissions from Tier 1 and later vehicles, and 

a sensitivity analysis which applied the Predictive Model effects to Tier 1 and later vehicles. The 

qualitative summary shown in Table 3.1-19 supports a continuation of this approach. The effect 

of ethanol blending on NMHC emissions is unclear in Table 3.1-19. This is reasonable 

bracketed by the primary and analysis, which assumes no effect and the sensitivity analysis 

which assumes a reduction. Table 3.1-19 also indicates that five out of six studies found that 6­

10 vol% ethanol blends increased NOx emissions from LEV and later vehicles. This is also 

reasonable bracketed by the primary and analysis, which assumes no effect and the sensitivity 

analysis which assumes an increase. Given the uncertainty in the fuel-emissions effects for Tier 

1 and later vehicles, there is also some value in maintaining consistency with our analysis 

conducted in response to California’s request for an RFG oxygen waiver. The primary analysis 

does this. 

The varied results across these studies indicate the need for additional test data. It may 

also be possible in the future to combine the emission data from all such studies (as was done for 

the Complex and Predictive Models) in order to develop a more robust estimate of the impacts of 

oxygenate blending on emission from late model year vehicles. 

3.1.1.1.9.1 Exhaust CO Emissions 

For Tier 0 vehicles, MOBILE6.2 is the standard modeling tool for estimating the effect of 

fuel quality on CO emissions. Therefore, we will use that model here to project the effect of 

increased ethanol use and decreased MTBE use on CO emissions. 

Regarding later vehicles, the five studies of LEV and later vehicles all found that 

increasing oxygen content in terms of MTBE or ethanol reduces CO emissions. This is 

consistent with both the primary and sensitivity analyses, as both approaches include a reduction 

in CO emissions. 

145
The five studies of LEV and later vehicles all tested normally emitting vehicles. 

MOBILE6.2 estimates that an E10 blend will reduce CO emissions from Tier 0 vehicles by 11%. 

The quantitative results of the five studies generally support this degree of reduction (Toyota, 

ExxonMobil, Mexican Petroleum Institute) or perhaps a larger degree of reduction (CRC E67, 

AAM-AIAM). Due to the absence of an EPA Predictive Model for CO emissions, for the 

NPRM, we did not develop a sensitivity case for CO emissions. However, given the possibility 

that the CO emission reduction is larger than that currently estimated by MOBILE6.2 for these 

vehicles, it appears reasonable to include a sensitivity analysis for CO emissions, as well as 

NMHC and NOx. An approach analogous to that taken for NMHC and NOx emissions appears 

reasonable. That is, for the primary analysis, we will continue to use MOBILE6.2 to project the 

effect of fuel properties on CO emissions. This means essentially a 6.7% reduction in CO 

emissions from Tier 1 and later vehicles for an E10 blend. For the sensitivity analysis, we will 

apply the MOBILE6.2 CO emission reduction of 13.8% for Tier 0 vehicles to Tier 1 and later 

vehicles. 

As discussed above, the five studies of LEV and later vehicles are not sufficient for use in 

quantitatively projecting the impact of fuel quality on emissions from these vehicles. Additional 

data must still be collected over a broader set of vehicles, fuel changes, and conditions. 

3.1.1.2 Exhaust Toxic Emissions 

Two EPA models project the impact of fuel quality on exhaust toxic emissions: the 

Complex Model and MOBILE6.2. The Complex Model projects the impact of fuel quality on 

toxic emissions directly. That is, any impact of fuel quality on total exhaust VOC emissions 

(which includes the air toxics) is implicitly included in the model’s predictions. MOBILE6.2 

separates the process into two steps. Total exhaust VOC emissions are projected first, in part 

based on fuel quality. Then, the fraction of VOC represented by each air toxic is estimated, in 

part based on fuel quality. 

The effect of fuel quality on exhaust VOC emissions in MOBILE6.2 was already 

discussed above. The effect of fuel quality on the fraction of exhaust VOC emissions 

represented by each air toxic in MOBILE6.2 is based on the projections contained in the 

Complex Model. These Complex Model’s effects of fuel quality on exhaust toxic emissions 

were used with the effect of fuel quality on exhaust VOC emissions backed out. Thus, with 

respect to the effect of fuel quality on the fraction of exhaust VOC emissions represented by each 

air toxic, the Complex Model is the basis of both the Complex Model and MOBILE6.2 

predictions. 

With respect to exhaust VOC emissions, we already decided above that the EPA 

Predictive Models represent the best estimate for Tier 0 vehicles. For Tier 1 and later vehicles, 

we assume in our primary analysis that these vehicles’ exhaust VOC emissions are unaffected by 

fuel quality. As a sensitivity analysis, we decided to extend the impacts indicated by the EPA 

Predictive Models to all vehicles. 

We follow the two-step process taken in MOBILE6.2 here in modeling the impact of fuel 

quality on exhaust toxic emissions. We will use the EPA Predictive Models to project the impact 

146
of fuel quality on exhaust VOC emissions, as discussed above. We will use the effects of fuel 

quality on the toxic fractions of exhaust VOC emissions contained in MOBILE6.2. 

3.1.1.3 Non-Exhaust Emissions 

Both the Complex Model and MOBILE6.2 evaluate the effect of gasoline quality on nonexhaust VOC emissions. However, the effects in the Complex Model were taken from an older 

version of MOBILE, as was mentioned above. Therefore, MOBILE6.2 represents the better of 

the two estimates of the effect of gasoline quality on non-exhaust VOC emissions. The EPA 

Predictive Models do not address non-exhaust emissions, so they are not applicable here. 

In EPA’s second analysis of California’s request for a waiver of the RFG oxygen 

requirement, we enhanced the estimate of non-exhaust emissions in MOBILE6.2 by adding 

additional permeation emissions related to the use of ethanol.FFF Recent testing at that time 

indicated that ethanol increases the rate of permeation of hydrocarbons through plastic fuel tanks 

and elastomers used in fuel line connections, as well as permeating ethanol itself. Subsequent 

testing has confirmed this effect. Therefore, we have added the effect of ethanol on permeation 

emissions to MOBILE6.2’s estimate of non-exhaust VOC emissions in assessing the impact of 

gasoline quality on emissions here. 

Air Improvement Resource, Inc. for the American Petroleum Institute, recently 

summarized the available test data on the effect of ethanol on permeation emissions and 

developed a methodology for estimating in-use permeation emissions in several U.S. cities.GGG 

This study provides a useful starting point for incorporating these emissions into this RFS 

analysis. 

Before examining this study, it is useful to point out that the non-exhaust emission 

estimates in MOBILE6.2 include permeation emissions for non-oxygenated gasoline. Typical 

extended diurnal emission tests (e.g., those lasting 2-3 days) automatically include any emissions 

permeating through plastic and elastomeric fuel system components. However, since the 

emission tests used as the basis of the MOBILE6.2 estimates of non-exhaust emissions primarily 

were performed with non-oxygenated gasoline. Those tests that did include ethanol blends only 

exposed the vehicle to this fuel for a few days. The CRC study of ethanol-related permeation 

indicates that it takes at least a week or two for the effect of ethanol to fully develop. Therefore, 

it is very unlikely that the tests performed by EPA and others to assess the impact of ethanol and 

other fuel components on non-exhaust emissions included the effect of ethanol on permeation 

emissions. In those cases where a vehicle may have been exposed to an ethanol blend for some 

time prior to testing, the increased permeation emissions likely were still present when the 

vehicle was tested on a non-oxygenated gasoline, still masking the effect. Therefore, our task 

here is to develop an estimate of the incremental impact of ethanol use on permeation emissions, 

and not an estimate of total permeation emissions with and without ethanol. 

The primary source of ethanol permeation emission data is the CRC E-65 study.HHH This 

study tested 10 vehicles, 6 cars and 4 light trucks, ranging in model year from 1989 to 2001. 

Permeation emissions were measured using two fuels, a non-oxygenated gasoline and a 6 vol% 

ethanol blend like that commonly sold in California. AIR placed these vehicles into three 

147
groups, based on a combination of model year and applicable evaporative emission standards. 

The vehicles in the test program were certified to two distinct evaporative emission 

requirements. The older vehicles were certified to EPA’s or California’s 2 gram hot soak plus 

diurnal emission standard based on an accelerated one-hour diurnal test. The three newest 

vehicles were certified to the enhanced evaporative emission requirements first implemented in 

the 1996 model year, which included an extended two or three day diurnal test. In addition, the 

data indicated that the three pre-1990 model year vehicles had much larger incremental ethanol 

permeation emissions than the later pre-enhanced evaporative emission vehicles. Therefore, AIR 

split the pre-enhanced evaporative emission category into two groups, pre-1990 model year 

vehicles and 1990 and later model year vehicles. We believe that this is appropriate and apply 

this split here, as well. 

Since the earliest calendar year during which emissions are assessed in the RFS analysis 

is 2012 and MOBILE6.2 only considers vehicles which are 24 years old or newer, at most only 

two model years of pre-1990 vehicles are present in our analysis. Due to accumulated 

scrappage, these vehicles comprise a very small percentage of the on-road fleet in 2012 and 

disappear from our analysis by 2015. Therefore, we decided to ignore the pre-1990 model year 

data here. 

AIR estimated the average incremental ethanol permeation emission rates for the 1990 

and later model year pre-enhanced evaporative emission vehicles to be 0.86 gram per day 

(g/day), while that enhanced evaporative emission vehicles was 0.80 g/day. Given the small 

number of vehicles tested and the variability in the rates measured for individual vehicles, for the 

purposes of this analysis, we consider these two levels to be generally equivalent. Therefore, we 

use an average incremental ethanol permeation emission rate of 0.8 g/day for all vehicles. A 

follow-on study performed by CRC indicates that the permeation emissions associated with a 10 

vol% ethanol blend could not be distinguished statistically from those of the 6 vol% blend.III 

Therefore, we use the 0.8 g/day incremental permeation emission rate for both 6 vol% and 10 

vol% ethanol blends here. 

Beginning with the 2004 model year, EPA and California implemented further 

enhancements to their evaporative emission standards. The EPA “Tier 2” requirements include 

accumulating mileage on durability data vehicles with an ethanol blend. However, actual 

emission testing is still performed using non-oxygenated gasoline. We believed that this 

combination of requirements would incorporate any effects of ethanol on emissions, including 

potential permeation effects. Because of these and other aspects of the 2004 and later standards, 

AIR estimated that the permeation emissions due to ethanol would be reduced to 0.43 g/day for 

these vehicles. 

We believe that it is likely that permeation emissions for non-oxygenated blends will be 

lower for these vehicles, due to the fact that the diurnal emission standard was reduced from 2 

g/day to 0.95 g/day and lower in some cases. However, as mentioned above, the effect of 

ethanol on permeation emissions takes about 2 weeks to fully develop and to fully disappear. 

Therefore, it is possible to accumulate mileage on a certification vehicle using an ethanol blend, 

change the fuel to the emission test fuel, wait two weeks and then test the vehicle. In this case, 

the effect of ethanol on permeation will have disappeared during the certification testing. Thus, 

148
until EPA requires certification emission testing with ethanol blends, we have no assurance that 

manufacturers will modify their vehicle designs to address the effect of ethanol on permeation. 

Thus, for the purposes of this analysis, we maintain the estimate of 0.8 g/day for Tier 2, and 

earlier vehicles. 

Permeation emissions vary significantly with ambient temperature, with emissions 

increasing with increases in temperature. The 0.8 g/day emission estimate applies at an average 

temperature of 95 F. The literature indicates that permeation varies exponentially with 

temperature. The CRC testing indicates that permeation emissions double with every increase in 

temperature of 18 F. Vice versa, permeation emissions drop 50% with every decrease in 

temperature of 18 F. We apply this relationship in Chapter 4 in developing incremental ethanol 

permeation emissions for each hour of the day in each county in the U.S. 

We plan to update our projections of the effect of gasoline quality on non-exhaust VOC 

emissions from Tier 1 and later model year vehicles based on additional testing which is 

expected to begin in 2007. Additional testing of permeation emissions is already underway with 

the CRC E-77 test program. These updated projections will be used in the comprehensive 

assessment of the impact of the fuel-related provisions of the Energy Act which due in 2009. 

Non-exhaust emissions are a function of ambient temperature and temperatures vary 

across the nation. Therefore, it is not as simple to determine the effect of RVP and other fuel 

qualities on non-exhaust emissions on a per vehicle basis as it is for exhaust emissions. 

Therefore, we performed a regression of the non-exhaust VOC and benzene emissions developed 

in Chapter 4 as a function of fuel properties in order to estimate these effects on a per vehicle 

basis. Specifically, we regressed the ratio of non-exhaust VOC and benzene emissions in each 

county in July between two fuel scenarios (the RFS case and the base case) against the change in 

RVP, ethanol content and MTBE content. The results are summarized in Table 3.1-20. 

Table 3.1-20. Fuel-Non-Exhaust Emission Effects in MOBILE6.2: 2012 

RVP (%/psi) Ethanol (%/Vol %) MTBE (%/Vol%) Adjusted r-Square 

VOC 11.8% 0.3% 0.0% 0.53 

Benzene 2.0% 1.0% -0.5% 0.50 

Non-exhaust emissions of benzene are estimated in MOBILE6.2. MOBILE6.2 adjusts 

these emissions for RVP, benzene fuel content, MTBE fuel content, temperature and the total 

non-exhaust VOC emissions produced by the vehicle fleet in question. We will use MOBILE6.2 

here to project the impact of decreased MTBE use and increased ethanol use on non-exhaust 

benzene emissions. 

Benzene is also emitted via permeation. Just as MOBILE6.2 does not include the effect 

of ethanol on VOC emissions via permeation, it does not include the effect of ethanol on benzene 

emissions via permeation. Thus, we will add this effect outside of the MOBILE6.2 model in the 

same way as was just described for VOC emissions via permeation. 

149
Both the CRC E-65 study and the E-65 Phase 3 follow-on study referenced above 

measured the benzene content of permeation emissions during some of the tests performed. 

Table 3.1-21 presents the available data by fuel type for both studies. 

Table 3.1-21. Benzene Permeation Emissions: CRC E-65 Studies 

E-65 Phase No. of 

Vehicles E0 

E6 (Average 

Aromatics) 

E6 (High 

Aromatics) E10 

MTBE 

Blend 

Fuel Benzene Content (vol%) 

Phase 1 --- 0.73 0.72 --- --- 0.53 

Phase 3 --- 0.41 0.55 0.43 0.51 --- 

Benzene Emissions (% of total permeation emissions) 

Phase 1 10 2.5-2.6% 2.2% --- --- 2.2% 

Phase 3 4-5 1.4% 1.4% 1.4% 1.7% --- 

Benzene Emissions Adjusted to 0.88 vol% Fuel Benzene (% of total permeation emissions) 

Phase 1 10 3.0-3.1% 2.7% --- --- 3.6% 

Phase 3 4-5 3.0% 2.2% 2.8% 2.9% --- 

As can be seen from Table 3.1-21, the benzene content of permeation emissions is 

slightly higher for the Phase 1 study than Phase 3. This is consistent with the higher benzene 

contents of the fuels tested in Phase 1. The Phase 1 fuels have particularly low benzene contents 

compared to levels typical across the U.S. Therefore, assuming a linear relationship between 

benzene fuel content and benzene permeation emissions, we adjusted the benzene permeation 

emissions to those for a fuel benzene content of 0.87 vol%, which is the average of the benzene 

fuel content of summertime gasoline produced nationwide in the base, RFS and EIA cases from 

the recent refinery modeling described in Chapter 2. We focused on summer benzene content 

since permeation emissions are a strong function of temperature. These figures are shown in the 

bottom third of Table 3.1-21. As can be seen, the benzene content of permeation emissions is 

much more consistent across the two Phases of E-65 after being adjusted to consistent fuel 

benzene content than before this adjustment. 

There appears to be no definite trend in the benzene content of permeation emissions with 

increasing ethanol fuel content. Both CRC studies found that the addition of ethanol does not 

simply increase permeation emissions via increased emissions of ethanol, but also through 

increased emissions of other fuel components. Thus, we will assume for this analysis that the 

benzene fraction of permeation emissions is independent of ethanol fuel content (i.e., benzene 

emissions increase at the same rate as total permeation emissions). This is also consistent with 

our conclusion in Chapter 2 that ethanol blending does not affect benzene fuel content. Given 

this, we determined the average benzene fraction of permeation emissions by averaging the 

benzene fraction across all the fuels and vehicles tested, excluding the MTBE blend. This 

average was 3%. This figure will be used here to estimate the benzene portion of the increase in 

permeation emissions resulting from ethanol blending. 

Given this, we determined the average benzene fraction of permeation emissions by 

averaging the benzene fraction across vehicles. Thus, the average benzene fraction of 

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permeation emissions is the average of the figures in the rightmost column of Table 3.1-21, or 

2%. 

3.1.1.4 PM Emissions 

The amount of data evaluating the impact of ethanol and MTBE blending on direct 

emissions of PM from gasoline-fueled vehicles is extremely limited. Three fairly limited studies 

have evaluated the impact of ethanol blending on PM emissions from gasoline vehicles. These 

studies are summarized below. 

The Colorado Department of Public Health and Environment tested 24 vehicles on two 

winter grade commercial fuels at 35oF in Denver (i.e., at high altitude).JJJ Both fuels were 

obtained from a local refinery. One fuel was non-oxygenated and represented fuel sold outside 

of the Denver area. The other contained 10 vol% ethanol and represented fuel sold in the Denver 

area, which has an oxygenated fuel mandate. As would be expected, the fuels differed in other 

qualities besides ethanol content. The ethanol blend had a 2 vol% lower aromatic content, which 

is somewhat less than expected. However, it also had a 53 F lower T50 level, which is a much 

greater difference than is typical. The two fuels used during this testing appear to have been 

used in random order (i.e., sometimes the non-oxygenated fuel was tested first, other times the 

E10 fuel was tested first). 

Half of the 24 vehicles were certified to Tier 0 emission standards, while the other half 

were certified to Tier 1 standards. Each group of 12 vehicles included 8 cars and 4 light trucks. 

The study found that PM emissions for the 24 vehicles over the FTP decreased from 

about 9 mg/mi to about 6 mg/mi with the ethanol blend, for a reduction of 36%. In addition, the 

vehicles with the highest base PM emission rates showed by far the largest reductions, both in 

absolute terms and in terms of percentage. PM emissions from Tier 1 vehicles decreased from 

roughly 5.5 mg/mi to 4 mg/mi with the ethanol blend, for a reduction of 27%. Essentially all of 

the emission reduction occurred during Bag 1 of the test (i.e., related to the cold start). 

PM emissions were also measured over a warmed up California Unified Cycle (i.e., no 

cold start). PM emissions for the 24 vehicles over this cycle for the two fuels were not 

statistically different. The ethanol blend increased PM emissions from the Tier 0 vehicles 

slightly and decreased those from the Tier 1 vehicles slightly. 

Finally, PM emissions were also measured over an EPA REP05 Cycle, again with no 

cold start. PM emissions over this cycle were 4-5 times those over the California Unified Cycle, 

indicating the impact of high speed, aggressive driving on PM emissions. However, despite this 

general increase in PM emissions, for the 24 vehicles PM emissions over the REP05 cycle were 

again very similar for the two fuels. This time, however, the ethanol blend decreased PM 

emissions from the Tier 0 vehicles slightly and increased those from the Tier 1 vehicles slightly. 

Overall, this testing indicates that the effect of ethanol (together with lower aromatics and 

T50 levels) may reduce PM emissions due to cold starting at 35 F under high altitude conditions. 

151
However, PM emissions during warmed up driving are very low and an effect of fuel quality was 

indiscernible. 

 The State of Alaska, in conjunction with General Motors Corp. and EPA, measured PM 

emissions from ten vehicles ranging in model year from 1977 to 1994 using two fuels.KKK The 

non-oxygenated fuel was a commercial wintertime fuel from the Fairbanks areas. The ethanol 

blend in this study was created from the non-oxygenated fuel via splash blending. Testing was 

performed in Alaska using a portable dynamometer. Three of the vehicles were also tested at 

EPA’s laboratory in Research Triangle Park, N.C, ranging in model year from 1987 to 1994. 

The testing in Alaska was performed at -20oF, 0oF, and 20oF. The EPA testing was performed at 

these same temperatures plus 75 F. Both sets of testing began with the non-oxygenated fuel, 

followed by testing with the E10 fuel. This could introduce a bias into the results, but the degree 

of this is unknown. 

The cold conditions led to difficulties in measuring PM emissions in Alaska. Therefore, 

few acceptable measurements of PM were made and the results were not presented in the paper. 

The fact that the EPA testing was conducted in a laboratory made vehicle conditioning and 

operation and particulate collection more feasible. The PM emissions from the three vehicles 

tested by EPA on the two fuels are presented in the paper. 

Only one measurement of PM emissions was made for each combination of vehicle, fuel 

and temperature. Thus, no direct measurement of test to test variability is available. We 

calculated the percentage difference in PM emissions between the E10 and E0 fuel for each of 

the eleven combinations tested. PM emissions with the ethanol blend ranged from 81% lower to 

84% higher than those with the E0 fuel. Thus, there appears to be considerable variability in the 

test results. Taken together, the average of the percentage changes for each condition showed 

the ethanol blend reducing PM emissions by 21%. However, this decrease was not statistically 

significant at the 90% confidence level. The ethanol blend more consistently decreased PM 

emissions at -20 F and 0F, but not at 20 F or 75 F. The paper states that PM emissions at the 

higher two temperatures were very low and the differences tended to be within measurement 

accuracy. It is important to note, however, that the lower end of this range is 20 F. Only a small 

percentage of driving in the U.S. occurs below this temperature. 

The third and final study was performed by EPA’s laboratory in Research Triangle Park, 

N.C.LLL This study was conducted in three phases; the last two of which are relevant here. In 

Phase II, PM emissions from two 1993-1995 model year vehicles were tested at -20 F, 0 F, and 

20 F. In Phase III, PM emissions from an additional five 1987-2001 model year vehicles were 

tested at -20 F, 0 F, 20 F and 40 F. Both phases utilized two fuels, one a wintertime nonoxygenated fuel and the other a 10 vol% ethanol fuel created from the non-oxygenated fuel via 

splash blending. Both phases measured PM emissions over the FTP and over a series of four 

back-to-back IM240 tests.50 It is not clear whether the fuels were always tested in the same 

order or tested randomly. Some testing was performed with various malfunctions induced on the 

 The IM240 test is a warmed up test consisting of a portion of the FTP driving cycle. It was designed as a short 

transient test cycle for use in vehicle inspection and maintenance programs. 

152

50
vehicles, like disconnecting the oxygen sensor. We focus on the emissions from the properly 

operating vehicles here. 

Of the 26 combinations of vehicles and temperatures tested, valid PM measurements over 

the FTP were successfully obtained for both fuels in 21 of them. The average percent change in 

PM emissions due to ethanol blending was +1%, in other words a very slightly increase. In 

contrast to the results of the two test programs discussed previously, the ethanol blend did not 

show a benefit at -20 F, and showed only a very slight 1% reduction in PM emissions at 0 F. 

The data show some tendency for the ethanol blend to produce a greater PM emission reduction 

for the highest emitting vehicles. However, this trend is not as clear as in the Colorado study. 

Thus, this study indicates no clear effect of ethanol on PM emissions. 

The IM240 testing showed much lower PM emission levels due to the warmed up nature 

of the test. There was also no clear trend in the effect of ethanol on PM emissions in this testing. 

The available data indicate that ethanol blending might reduce exhaust PM emissions 

under very cold weather conditions (i.e., 0 F or less), particularly at high altitude. There is no 

indication of PM emission reductions at higher temperatures and under warmed up conditions. 

The data are certainly too limited to support a quantitative estimate of the effect of ethanol on 

PM emissions. 

Fine particles can also be formed through a series of chemical reactions in the atmosphere 

from gasses such as sulfate (SO4

2-), nitrate (NO3-

), ammonia (NH3), and volatile organic 

compounds (VOC) emitted from motor vehicles. This aerosol formed secondarily in the 

atmosphere through these gas to particle conversions will be discussed in greater detail in 

Chapter 5 of this document. Emerging science is indicating that gaseous aromatic compounds 

are likely among the most important VOCs which are precursors of carbonaceous PM which is 

formed in the atmosphere. Therefore, we discuss the effect of fuel quality on aromatic 

hydrocarbon emissions in the next section. 

3.1.1.5 Aromatic Emissions 

The Auto/Oil Air Quality Improvement Research Program tested over 100 vehicles from 

model years 1983 - 1989 on a fuel matrix of over 80 fuel blends to determine the exhaust 

emission effects of varying fuel parameters - including ethanol and aromatics.MMM Phase 1 of 

this study tested two fleets of vehicles: twenty (20) 1989 model year vehicles, and fourteen (14) 

1983-1985 model year vehicles. A matrix of 16 fuels (Matrix A) was developed in the first 

portion of the study with half the fuels containing 20% aromatics by volume and half with 45% 

aromatics by volume. This data was used to investigate the impact of changing aromatic levels 

in the fuel on the aromatics emitted in the exhaust as a function of total hydrocarbon emissions. 

Linear regression of the test data indicates that there is a linear relationship between the level of 

aromatics by volume in the fuel and the mass of aromatics emitted in the exhaust. Based on the 

results of this regression, aromatics have a tendency to be emitted less than proportionally to 

their percent volume in the fuel, as shown in the following equation: 

AromaticsExhaust (wt%) = 0.64× AromaticsFuel (vol%) 

153
The coefficient in the above equation was statistically significant at the 90% confidence 

level (0.64 ± 0.02). However, when we considered the presence of an intercept, it was not 

statistically significant (-0.35 ± 2.26). Therefore, we forced the regression line through zero and 

repeated the regression. 

The Auto/Oil program also produced data which allows the effect of ethanol on aromatic 

hydrocarbon emissions to be assessed. As discussed in Section 2.2, ethanol blending tends to 

reduce the aromatic content of gasoline. Of interest here is whether ethanol has any other effect 

on aromatic hydrocarbon emissions beyond that associated with reducing the aromatic content of 

gasoline. 

The Auto/Oil data contained a subset of fuels designed specifically for this analysis. A 

total of 4 ethanol blends were produced by splash blending ethanol into four non-oxygenated 

fuels. Two of the non-oxygenated fuels came from the original group of sixteen tested during 

Phase 1 of the research study. Base fuel A (industry average fuel) was a 9 RVP fuel with 32% 

aromatics. Base fuel F was also a 9 RVP gasoline with 20% aromatics. Two new nonoxygenated fuels were created from base fuels A and F. In both cases, and butane was removed 

to lower the RVP level by 1 psi resulting in fuels V and S, respectively. To each of these 4 nonoxygenated fuels, 10% ethanol was splash blended resulting in the final 8 fuel test matrix. A 

summary of the differences between the expected and actual aromatic content in the fuel and in 

the exhaust as a function of THC is shown in Table 3.1-22.NNN 

Table 3.1-22. 

Expected vs. Predicted Non-Oxy and E10 Fuel Properties and 

 Exhaust Aromatics Reductions for Auto/Oil AQIRP “Fuel Matrix B” 

% Reduction in % Reduction 

Non- Fuel Measured Exhaust in Exhaust 

Oxy Fuel E10 Aromatics Fuel Aromatics Aromatics 

Fuel Aromatics Fuel Expecteda Aromatics Expected Actual 

A 32 X 28.8 27.2 2.05 2.39 

V 33.5 W 30.15 29.0 2.14 1.54 

F 20 U 18 19.1 1.28 0.93 

S 21.2 T 19.08 18.1 1.36 0.63 a

Based on dilution as a result of splash blending 10% ethanol 

The aromatic contents of the ethanol blends, as listed in Table 3.1-22, do not reflect the 

10% reduction from the aromatic contents of their non-oxygenated base fuels which would be 

expected from splash blending with 10 vol% ethanol. The discrepancies between the measured 

and estimated fuel aromatic contents are small, on the order of 1-2 vol% aromatics. However, 

when the total difference is on the order of 3 vol% ethanol, these discrepancies are significant. 

The discrepancies are likely the result of measurement uncertainty of both base and ethanol 

fuels. 

Using the relationship between fuel aromatic content and aromatic hydrocarbon 

emissions developed above, we can predict the reduction in exhaust aromatic emissions 

154
associated with the differences in the aromatic contents of the non-oxygenated fuels and their 

ethanol containing counterparts. We believe that it is most accurate to use the expected aromatic 

contents for the ethanol blends rather than the measured levels, since it is likely that the volume 

of ethanol added was very close to 10 vol%. 

The expected reduction in the percentage of VOC emissions represented by aromatic 

hydrocarbons based on the expected reduction in fuel aromatic content is shown in the second to 

the last column in Table 3.1-22. The measured reduction in the percentage of VOC emissions 

represented by aromatic hydrocarbons is shown in the last column. In three out of four cases (all 

but fuels A and X), the actual reduction in aromatic emissions is less than the predicted reduction 

based on dilution. Had we used the measured aromatic contents for the ethanol blends, the 

outcome would have been the same: In three out of four cases (in this case all but fuels F and U), 

the actual reduction in aromatic emissions is less than the predicted reduction based on dilution. 

Qualitatively, this indicates that there does not appear to be any additional benefit in 

reducing aromatic hydrocarbon emissions associated with the use of ethanol beyond that 

expected from the reduction in the aromatic content of gasoline portion associated with ethanol 

blending. 

Based on our analysis in Section 2.2, increased ethanol blending will significantly reduce 

gasoline aromatic content. This could cause a corresponding reduction in the aromatic fraction 

of exhaust VOC emissions relative to non-oxygenated conventional gasoline. In addition, 

ethanol also reduces total exhaust VOC emissions from older vehicles and may do so from newer 

vehicles, based on the CRC E-67 study. This would further reduce emissions of aromatic 

hydrocarbons. As will be discussed further in Chapter 5, this reduction in aromatic hydrocarbon 

emissions could reduce ambient levels of secondary organic PM. 

3.1.1.6 Emission Effects Associated with Specific Fuel Blends 

3.1.1.6.1 Conventional Gasoline Analysis 

In Section 2.2 of Chapter 2, we estimated the effect of blending ethanol and MTBE on 

the quality of conventional gasoline (see Tables 2.2-3 and 2.2-4). Here, we present the effect of 

these changes in fuel quality on emissions from motor vehicles in percentage terms, relative to 

those of a typical non-oxygenated U.S. gasoline blend. Because of the Tier 2 sulfur standards, 

sulfur is held constant at 30 ppm. Also, due to the MSAT standards, we assume that benzene 

levels are not affected, as well. Table 3.1-23 presents the gasoline qualities of a typical 9 RVP 

CG, as well as MTBE and ethanol blends which reflect the effect of adding these two oxygenates 

to gasoline. 

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Table 3.1-23. CG Fuel Quality With and Without Oxygenatesa 

Fuel Parameter Typical 9 RVP CG MTBE CG Blend Ethanol CG Blend 

RVP (psi) 8.7 8.7 9.7 

T50 218 207 205 

T90 332 321 329 

Aromatics (vol%) 32 25.5 27.4 

Olefins (vol%) 7.7 7.7 7.5 

Oxygen (wt%) 0 2 3.5 

Sulfur (ppm) 30 30 30 

Benzene (vol%) 1.0 1.0 1.0 a

Assumes summer (July) conditions 

Table 3.1-24 presents the differences in emissions of the MTBE and ethanol blends 

relative to that of non-oxygenated conventional gasoline. 

Table 3.1-24. Effect of Oxygenates on Conventional Gasoline Emissionsa 

Pollutant Source 11 Vol% MTBE 10 Vol% Ethanol 9.7 RVP 

CG 

Exhaust VOC EPA Predictive 

Models 

-9.2% -7.4% +1.1% 

NOx 2.6% 7.7% +1.1% 

COb MOBILE6.2 -6% / -11% -11% / -19% +12.7% 

Exhaust Benzene EPA Predictive 

and Complex 

Models 

-22.8% -24.9% -2.6% 

Formaldehyde +21.3% +6.7% -3.7% 

Acetaldehyde +0.8% +156.8% -2.0% 

1,3-Butadiene -3.7% -13.2% -2.6% 

Non-Exhaust VOC MOBILE6.2 Zero +30% +30% 

Non-Exhaust 

Benzene 

MOBILE6.2 & 

Complex Models 

-9.5% +15.8% +15.8% 

a

Assumes summer (July) conditions 

b

The first figure shown applies to normal emitters; the second applies to high emitters. 

The two oxygenated blends both reduce exhaust VOC and CO emissions, but increase 

NOx emissions. The MTBE blend does not increase non-exhaust VOC emissions due to the fact 

that non-oxygenated and MTBE blends have to meet the RVP standard. Ethanol blending 

increases non-exhaust VOC emissions in two ways. First, ethanol blends are allowed 1.0 psi 

higher RVP levels in most areas with CG. Second, ethanol increases permeation emissions. The 

most notable effect on toxic emissions in percentage terms is the increase in acetaldehyde with 

the use of ethanol. Acetaldehyde emissions more than double. However, as will be seen below, 

base acetaldehyde emissions are low relative to the other toxics. Thus, the absolute increase in 

emissions is relatively low. 

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3.1.1.6.2 Reformulated Gasoline (RFG) Analysis 

The previous section discussed the relative emission changes to expect when adding 

ethanol to the conventional non-oxygenated gasoline pool. A second scenario to consider is the 

case where RFG areas change from MTBE, a commonly used oxygenate in RFG areas, to either 

ethanol RFG or a non-oxygenated RFG. 

Table 3.1-25 presents the gasoline qualities of three types of RFG: non-oxygenated, a 

typical MTBE RFG as has been marketed in the Gulf Coast and a typical ethanol RFG which has 

been marketed in the Midwest. The fuel specifications shown are based on specific RFGs 

predicted by the refinery modeling discussed in Chapter 2 for the cases and PADDs shown. 

These specific fuels were selected as they represented PADD-wide RFGs which contained 

primarily one oxygenate at the desired volumetric concentration. 

Table 3.1-25. Summer RFG Fuel Quality With and Without Oxygenatesa 

Fuel Parameter Non-Oxygenated 

RFG 

MTBE RFG Ethanol RFG 

Case PADD 3 Reference PADD 1 Reference PADD 2 RFS 

OXYGEN (wt%) 0.0 2.1 3.7 

SULFUR (ppm) 30.0 30.0 30.0 

RVP (psi) 7.0 7.0 7.0 

E200 (%) 52.0 59.9 57.6 

E300 (%) 87.5 88.9 81.9 

T50 (F) 184 190 185 

T90 (F) 335 342 335 

AROMATICS (vol%) 20.1 21.0 20.0 

OLEFINS (vol%) 14.6 4.3 13.6 

BENZENE (vol%) 0.7 0.7 0.7 a

Assumes summer (July) conditions 

Table 3.1-26 presents the emission impacts of these three types of RFG relative to the 9 

RVP CG described in Table 3.1-23. 

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Table 3.1-26. 

Effect of RFG on Per Mile Emissions from Tier 0 Vehicles 

Relative to a Typical Conventional Gasolinea

Pollutant Source Non-Oxy 

RFG 

11 Volume 

Percent MTBE 

10 Volume 

Percent Ethanol 

Exhaust Emissions 

VOC EPA Predictive 

Models 

-13.4% -15.3% -9.7% 

NOx -2.4% -1.7% 7.3% 

CO MOBILE6.2 -22% -31% -36% 

Exhaust Benzene EPA Predictive 

and Complex 

Models 

-21.2% -29.7% -38.9% 

Formaldehyde -5.9% 19.4% 2.3% 

Acetaldehyde -0.2% -9.5% 173.7% 

1,3-Butadiene 20.9% -29.2% 6.1% 

Non-Exhaust Emissions 

VOC MOBILE6.2 & 

CRC E-65 

-30% -30% -18% 

Benzene MOBILE6.2 & 

Complex Models 

-40% -43% -32% 

a

Assumes summer (July) conditions 

As can be seen, the oxygenated RFG blends are predicted to produce a greater reduction 

in CO emissions, but increase NOx emissions. Exhaust VOC emission effects are mixed. Nonexhaust VOC emissions with the exception of permeation are roughly the same due to the fact 

that the RVP level of the three blends is the same. However, the increased permeation emissions 

associated with ethanol reduces the overall effectiveness of ethanol RFG. The most notable 

effect on toxic emissions in percentage terms is the increase in acetaldehyde with the use of 

ethanol. Acetaldehyde emissions more than double. However, as will be seen below, base 

acetaldehyde emissions are low relative to the other toxics. Thus, the absolute increase in 

emissions is relatively low. The ethanol RFG also produces a greater reduction in exhaust 

benzene emissions and somewhat lower reduction in non-exhaust benzene emissions. 

The exhaust emission effects shown for VOC and NOx emissions only apply to Tier 0 

vehicles in our primary analysis. In the sensitivity analysis, these effects are extended to Tier 1 

and later vehicles. The effect of RVP on non-exhaust VOC emissions is temperature dependent. 

The figures shown above represent the distribution of temperatures occurring across the U.S. 

under summer conditions (average July fuel specifications). 

3.1.2 High-Level Ethanol Blends 

The vast majority of ethanol blended into gasoline as a result of the RFS is expected to be 

used in a 10 vol% ethanol blend (E10) rather than an 85 vol% ethanol blend (E85), as discussed 

in Chapter 1. At the same time, some ethanol is likely to be used as E85, and its use is growing. 

Current estimates indicate that roughly 6 million FFVs are on the road today, with US 

automakers projecting an additional 3 to 5 million FFVs produced annually over the next several 

years. The analysis to follow relies upon the limited amount of data available on both older and 

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current technology (Tier 2) FFVs, the later of which will dominate the FFV fleet in future years. 

Based on this data of Tier 2 vehicles, we believe that with the increased use of E85 (which is 

inherently a low-sulfur fuel) emissions should be neutral or better than operation on E0 or E10 

fuel blends for CO and NOx. NMOG emissions may be higher primarily due to emissions of 

unburned ethanol at cold starts, while running NMOG emissions are lower with E85 based on 

certification data. The sections to follow examine these issues in greater detail. 

3.1.2.1 Exhaust emissions 

3.1.2.1.1 Regulated Gaseous Pollutants 

Relatively little data is available for investigating the effects of high level ethanol blends 

on exhaust emissions. Part of the 1993 Auto/Oil Air Quality Improvement Research Program 

(AQIRP) investigated the emissions associated with the use of E85 blends. Emissions over the 

Federal Test Procedure (FTP) were measured from three Tier 0 and Tier 1 certified flexible-fuel 

vehicles with three test fuels. A source of emission data for Tier 2 FFVs is EPA’s Certification 

and Fuel Economy Information System (CFEIS) database, which contains certification data for 

five model year 2006 FFVs certified to Tier 2 standards (bins 5-8).OOO However, certification 

data, composed of regulated emissions while operating on E85, represents very limited operating 

conditions. It does not include aggressive driving or cooler ambient temperature starts or 

operation. 

The Auto/Oil Study found that E85 reduced FTP composite NOx emissions by 49% 

compared to conventional gasoline with 1988 industry average fuel properties. This is likely the 

result of improved catalyst efficiency due to the low sulfur concentration in E85 (only 5 ppm vs. 

339 ppm in the industry average fuel). The 2006 CFEIS data from Tier 2 FFVs, on the other 

hand, shows only a 3% decrease in NOx emissions with E85 from a cold start test but a 

significant 45% decrease in hot running NOx emissions. CO emissions are reduced at least 33% 

on a cold start according to CFEIS, while the Auto/Oil study did not find statistically significant 

changes in CO emissions. Emissions of Non-Methane Organic Gases (NMOG) increased 10% 

for the Tier 2 CFEIS vehicles. The Auto/Oil data showed NMOG increased by 26%, but this 

change was not statistically significant (p-value of 0.28). However, CFEIS data indicated a 50% 

reduction in CO and HC emissions during hot operation. The measurement used to determine 

NMOG in both CFEIS and Auto/Oil data include the mass of oxygen in all measured organic 

species except methane. 

While the emissions of NMOG appear to increase with E85 compared to E0 for Tier 2 

certified vehicles, the majority (~55%) of E85 NMOG emissions are direct emissions of ethanol, 

which has a relatively low reactivity compared to other NMOG species. Thus there may still be 

a slight NMOG benefit based on ozone reactivity despite a potential net increase in total NMOG 

emissions. An important point worth noting is that the cold start emissions with E85 represent a 

greater % of bag weighted emissions than with E0. This manifests itself primarily in the form of 

unburned ethanol emissions during cold start, before the combustion chamber has reached a high 

enough temperature to promote complete ethanol vaporization. Thus NMOG emissions with 

E85 at colder temperatures could be much greater (2 to 3 times higher) than with E0 due to 

prolonged periods at low temperature. Because of this unique start behavior and the lower 

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emissions observed during hot operation, emissions from E85 may be better quantified if 

separated between starts and hot driving operation. 

3.1.2.1.2 Air Toxics 

With increasing use of E85, some air toxics may increase while others decrease relative 

to E0. Emissions of Benzene and 1,3-Butadiene decrease while acetaldehyde, formaldehyde, and 

emissions of ethanol increase. The net result is an increase in total air toxics, but this is largely 

driven by increase ethanol and acetaldehyde emissions. Table 3.1-27, below, shows the percent 

change in FTP composite g/mile emissions of several air toxics for the three FFVs tested on three 

fuels as part of the 1993 Auto/Oil study. The fuels tested were AQIRP gasoline with1988 

Industry average qualities (CG), a 1996 California phase 2 reformulated gasoline (RFG blended 

with MTBE), and an E85 blend with identical gasoline specs as RFG.PPP 

Table 3.1-27. 

Percent Difference in Toxic Emissions Between E0 and E85 

% Difference Between Fuels 

RFG vs CG E85 vs CG E85 vs RFG 

Formaldehyde -2 93 97 

Benzene -55 -87 -72 

1,3-Butadiene -31 -85 -79 

Acetaldehyde -18 2620 3220 

Total Toxics -42 108 255 

The increase in acetaldehyde emissions is substantial, on the order of 20 to 30 times that 

of E0. This is substantially higher than the15 to 20 fold increase shown with Tier 2 FFVs in the 

CFEIS data. Emissions of benzene, 1,3-butadiene, ethylbenzene, hexane, styrene, toluene, mxylene, p-xylene, o-xylene, and naphthalene are all expected to decrease significantly (50-80%) 

with the use of E85 vs. E0 according to CFEIS, which is consistent with the Auto/Oil results 

presented in Table 3.1-27. Regardless of vehicle technology, the increased emissions of 

acetaldehyde could be a potential concern due to its strong odor, as well as its respiratory system 

irritating and potentially carcinogenic properties. 

3.1.2.1.3 Particulate Matter 

Even less data exists to draw firm conclusions on direct particulate matter emissions due 

to increased E85 use. Theoretically, E85 use has the potential to increase direct emissions of PM 

under modes of rich engine operation. This is especially important at cold start, before the 

catalyst has reached its operating temperature and when an E85 fueled vehicle runs substantially 

richer than if it were fueled with E0. In this situation, the low temperatures in the combustion 

chamber, compounded by the evaporative cooling effect of ethanol, makes fuel vaporization 

difficult and may increases exhaust emissions of raw fuel and PM at cold start. Sustained 

periods of high load may also have increased emissions of PM with E85 than with E0 due to 

richer operation with E85. Results from a 2003 SAE paper showed a negligible increase in 

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direct PM emissions from E85 vs. E0 fueled vehicles over the European Test Cycle (Directive 

70/220/EEC and its amendments).QQQ Tests conducted at 23oC and 16oC showed an increase in 

PM emissions with decreasing temperature for both E0 and E85, with slightly higher PM 

emissions at cold temperature with E85. This study only used one E85 blend and one model year 

2002 FFV, however, so these results cannot be considered entirely representative of the on-road 

FFV vehicle fleet. Again, since the projected use of ethanol as E85 is very small compared to 

its use as E10, the emissions impacts associated with E85 will be also be quite small on an 

absolute scale. 

As discussed above, emerging science is beginning to identify gaseous aromatics as an 

important precursor to secondary organic aerosol. Exhaust aromatic emissions should be 

reduced with E85 since the fuel aromatics content of E85 is much lower than that for E0 or E10 

blends. This reduction in exhaust aromatics should reduce the formation of secondary organic 

aerosol. However, as mentioned earlier, no specifications currently exist for the 15% gasoline 

portion of E85. Thus, the degree to which the aromatic content of E85 will be lower than 

gasoline is not known with any confidence. Lack of data regarding the speciation of VOC 

emissions also prevents any quantitative estimate of any benefit in this area. 

3.1.2.2 Non-Exhaust Emissions 

We currently have very little data regarding non-exhaust emissions from E85 vehicles. 

Theoretically, evaporative emissions of E85 fueled vehicles have the potential to be lower than 

with E0 or E10. This is because ethanol blended with a given gasoline at the 85% level is likely 

to be less volatile than E0 or E10 (with the same gasoline fuel quality). This is not entirely 

certain, however, since there is no fuel specification for the hydrocarbon composition of the 15% 

of E85 that is gasoline. Thus, the RVP of the final E85 blend could be closer to that of E0 or 

E10 fuels than commonly thought to be the case. Moreover, since the volatility of ethanol blends 

peaks between 6 and 30 vol% ethanol, the fuel in the tank of drivers of flex-fuel vehicles who 

alternate between E85 and gasoline will experience a wide range of ethanol concentrations in the 

fuel at any given time, and therefore a wide variation in the corresponding evaporative 

emissions. 

Similarly, we have very little data with which quantitative predictions of the impact of 

E85 use on non-exhaust emissions of air toxics (e.g., benzene) can be drawn. The Auto/Oil 

study mentioned in the previous section tested the same three Tier 0 and Tier 1 vehicles for hotsoak evaporative emissions. They found no statistically significant change in NMOG or 

OMHCE51 evaporative emissions, yet found a statistically significant 60% reduction in benzene 

emissions. Directionally, you would expect both hydrocarbon and air toxic evaporative 

emissions to decrease due to the dilution of the hydrocarbon portion of the fuel with ethanol. 

However, again, it is highly dependent on the volatility of the gasoline component of the specific 

E85 used and its benzene content, neither of which is regulated. 

51 Organic Material Hydrocarbon Equivalent 

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3.2 Effect of Fuel Quality on Spark-Ignited Nonroad Equipment Emissions 

We use EPA’s NONROAD emission model to estimate the effect of gasoline quality on 

emissions from nonroad equipment. We use the 2005 version of this model, NONROAD2005, 

which includes the effect of ethanol on permeation emissions from several types of equipment: 

all small spark-ignition equipment (including handheld and non-handheld equipment less than or 

equal to 25 hp), all spark-ignition recreational marine watercraft (includes all outboard, sterndrive inboard, and personal watercraft). Note that these categories do not include recreational 

vehicles (motorcycles, ATVs, and snowmobiles) or large spark-ignition equipment. 

Only a limited number of fuel parameters affect emissions in NONROAD. Exhaust 

VOC, CO and NOx emissions are a function of sulfur and oxygen. Here, only the latter fuel 

parameter is of interest. Emissions of all three pollutants are assumed to change proportionally 

with fuel oxygen content. Table 3.2-1 shows the effect of moving to a 10 volume percent 

ethanol blend (3.5 wt% oxygen) on these emissions, either from a non-oxygenated fuel or from 

an 11 volume percent MTBE blend (2.0 wt% oxygen).RRR 

Table 3.2-1. 

Effect of a 10 Volume Percent Ethanol Fuel on Nonroad Exhaust Emissions 

4-Stroke Engines 2-Stroke Engines 

Base Fuel NonOxygenated 11 Volume 

Percent MTBE 

NonOxygenated 11 Volume 

Percent MTBE 

VOC -16% -7% -2% -1% 

CO -22% -9% -23% -10% 

NOx +40% +17% +65% +28% 

As can be seen, the higher oxygen content of ethanol blends reduces exhaust VOC and 

CO emissions. However, it also increases NOx emissions quite substantially, especially from 4­

stroke engines. However, it should be noted that NOx emissions from these engines tend to be 

fairly low to start with, given the fact that these engines run richer than stoichiometric. Thus, a 

large percentage increase of a relative low base value can be a relatively small increase in 

absolute terms. This will be seen below in Chapter 4, when we evaluate the impact of increased 

ethanol use on the local and national emission inventories. 

Non-exhaust VOC emissions (other than permeation) are a function of gasoline RVP and 

ethanol content in NONROAD2005. Ethanol content only affects permeation emissions. Both 

of these emissions are temperature dependent, so the effect of ethanol and RVP is also 

temperature dependent. Based on the results of modeling national emissions in July, a 10 

volume percent ethanol blend increases non-exhaust VOC emissions by 15 percent. This 

assumes a 1.0 psi increase in RVP. 

Hose permeation emissions in the public version of NONROAD2005 are independent of 

fuel quality. In support of the development of new emission standards for small nonroad 

engines, EPA has been testing small nonroad engines for hose permeation emissions using fuels 

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with and without ethanol. Based on this testing, as well as discussions with nonroad equipment 

manufacturers, we developed new hose permeation emission rates for NONROAD2005 for both 

gasoline and E10 fuels. Roughly, these revised permeation rates indicate that emissions with 

E10 are 2-3 times higher than those for gasoline. This increase is similar to that found for the 

three oldest onroad vehicles in the CRC E-65 study, discussed in section 3.1.1.1.4 above.52 The 

NONROAD2005 hose permeation factorsSSS were adjusted as follows: 

1) Permeation emissions in the public version of NONROAD2005 were 450 grams per 

meter-squared per day (g/m2/day) for both small spark-ignition engines and for the supply 

hoses on portable fuel tanks in recreational marine watercraft, applicable to all fuels. For 

both types of equipment, the permeation emission rates were changed to 122 g/m2/day for 

gasoline and 222 g/m2/day for E10. 

2) Permeation emissions in the public version of NONROAD2005 were 100 g/m2/day 

for supply hoses on outboard recreational marine watercraft (> 25 hp), 300 g/m2/day for 

supply hoses on personal watercraft (PWC), and 110 g/m2/day for fill neck hoses on both 

outboards and PWC. These three permeation emission rates were changed to 42 

g/m2/day for gasoline and 125 g/m2/day for E10. 

3) Permeation emissions from sterndrive/inboard recreational marine watercraft in the 

public version of NONROAD2005 were 100 g/m2/day for supply hoses and 110 g/m2/day 

for fill neck hoses. Both of these permeation emission rates were changed to 22 g/m2/day 

for gasoline and 40 g/m2/day for E10. 

4) Permeation emissions in the public version of NONROAD2005 were 0 g/m2/day for 

vent hoses on all recreation marine watercraft. This permeation emission rate was 

changed to 2.5 g/m2/day for gasoline and 4.9 g/m2/day for E10. 

5) One final adjustment was to double the vent hose length for all gasoline-fueled 

outboards, personal watercraft, and sterndrive/inboard watercraft. 

The NONROAD emissions model does not estimate emissions of toxic air pollutants 

from nonroad equipment. However, the National Mobile Inventory Model (NMIM) does make 

such estimates. NMIM utilizes the MOBILE and NONROAD models to develop national 

emission estimates for motor vehicles and nonroad equipment. For the most part, NMIM 

provides the relevant inputs to MOBILE6.2 and NONROAD and processes the results. 

However, with respect to nonroad toxic emissions, NMIM takes exhaust and non-exhaust VOC 

emission estimates from NONROAD and applies a set of toxic fractions of VOC emissions 

based on fuel quality.TTT NMIM contains estimates of the toxic fractions of VOC emissions for 

three fuels: a non-oxygenated gasoline, an MTBE blend and an ethanol blend. NMIM applies 

the fraction of VOC emissions represented by each of the air toxics to either the exhaust or non52 Permeation emissions from nonroad equipment are not regulated. Thus, the elastomers used in the fuel systems of 

nonroad equipment are likely to be more similar to those of older onroad vehicles than those of later onroad vehicles 

which were subject to stringent non-exhaust VOC emission standards. 

163
exhaust VOC emissions estimated by NONROAD. The toxic fractions of VOC were derived 

from motor vehicle testing. Thus, they are considered approximate. 

We hope to update our projections of the effect of gasoline quality on exhaust and nonexhaust emissions from nonroad equipment based on additional testing to be conducted over the 

next several years if funding allows. These updated projections could be used in the 

comprehensive assessment of the impact of the fuel-related provisions of the Energy Act which 

is due in 2009. 

3.3 Effect of Fuel Quality on Compression-Ignited Vehicle and Equipment 

Emissions – Biodiesel 

Biodiesel is expected to be one of two renewable fuels to be used in significant volumes 

through 2020. While ethanol will dominate the market, biodiesel use is likely to grow 

considerably reaching 300 million gallons by 2012, according to EIA estimates. It is produced 

domestically from vegetable oils, animal fats and recycled cooking oils, with the majority of this 

product coming from soybean oil. It is typically used in 2%, 5% and 20% blends with diesel fuel 

which have been assigned B2, B5 and B20 designations, respectively. 

In 2002, EPA issued a report entitled “A Comprehensive Analysis of Biodiesel Impacts 

on Exhaust Emissions” based on existing data from various test programs. This report included a 

technical analysis of biodiesel effects on regulated and unregulated pollutants from diesel 

powered vehicles and concluded that biodiesel fuels improved PM, HC and CO emissions of 

diesel engines while slightly increasing their NOx emissions. 

While the conclusions reached in the 2002 EPA report relative to biodiesel effects on 

VOC, CO and PM emissions have been generally accepted, the magnitude of the B20 effect on 

NOx remains controversial due to conflicting results from different studies. Significant new 

testing is being planned with broad stakeholder participation and support in order to better 

estimate the impact of biodiesel on NOx and other exhaust emissions from the in-use fleet of 

diesel engines. We hope to incorporate the data from such additional testing into the analyses for 

other studies required by the Energy Act in 2008 and 2009, and into a subsequent rule to set the 

RFS program standard for 2013 and later. 

3.4 Emissions from Fuel Production Facilities 

3.4.1 Ethanol 

The primary impact of renewable fuel production and distribution regards ethanol, since 

it is expected to be the predominant renewable fuel used in the foreseeable future. For the 

NPRM, we estimated the impact of increased ethanol production, including corn farming, on 

emissions based on DOE’s GREET model, version 1.6. This estimate also included emissions 

related to distributing the ethanol and take credit for reduced emissions related to distributing 

displaced gasoline. Since the time of the NPRM analysis, DOE has published the next version of 

164
GREET, version 1.7. The emission estimates related to ethanol production and distribution in 

GREET1.7 differ significantly from those in GREET1.6. In addition, through EPA’s regional 

offices, we contacted a number of States to obtain the latest emission estimates for ethanol plants 

currently in production. These plant-specific estimates provide a useful comparison to the 

inherently generic emission factors used by a nationwide-average model, such as GREET. 

In Section 3.4.1.1, we describe and compare the emission estimates from the GREET 

model, both versions 1.6 and 1.7. In Section 3.4.1.2, we describe the data obtained from the 

States and consolidate it into two sets of emission factors; one for wet mills and one for dry 

mills. Finally, in Section 3.4.1.3, we describe how we will use both the GREET and State 

estimates in estimating national emissions from new ethanol plants in Chapter 4. 

3.4.1.1 GREET Emission Estimates 

The emissions related to producing and distributing ethanol for use in gasoline blends 

from both GREET1.6 and 1.7 are summarized in Table 3.4-1. GREET presents emission factors 

in a variety of units, such as per bushel of corn harvested, gallons of ethanol produced, etc. All 

the emission factors shown in this table have been converted to a per gallon of ethanol produced 

or distributed basis using the default conversion factors contained in GREET. One of GREET’s 

default assumptions is that 80% of ethanol plants are associated with dry corn milling, while the 

other 20% are associated with wet milling. Nearly all future ethanol plants are planned to be dry 

mill facilities. Therefore, we only show the emission factors for dry mill ethanol plants below. 

165
Table 3.4-1. 

Well-to-Pump Emissions for Producing and Distributing Ethanol from Corn 

Dry Mill Facility (grams per gallon ethanol) 

Pollutant 

Corn Farming 

and 

Transportation 

Ethanol 

Production 

CoProduct 

Credits 

Ethanol 

Transportation 

Gasoline 

Transportation 

Credit 

Total 

Emissions 

Dry Mills - GREET1.6 

VOC 0.8 6.8 -4.1 0.5 -0.9 3.1 

CO 4.3 2.9 -3.3 0.2 -0.1 4.1 

NOx 11.3 4.6 -6.9 1.5 -0.4 10.1 

PM10 8.1 0.4 -2.5 0.0 0.0 6.1 

SOx 1.2 6.1 -0.9 0.2 -0.1 6.5 

Dry Mills - GREET1.7 

VOC 1.6 2.6 -2.5 1.6 -1.5 1.8 

CO 4.0 1.7 -1.7 0.2 -0.1 4.1 

NOx 10.7 3.9 -4.1 1.3 -0.4 11.4 

PM10 1.1 4.2 -0.5 0.0 0.0 4.9 

SOx 5.0 3.7 -2.4 0.2 -0.1 6.4 

Wet Mills - GREET1.6 

VOC 0.8 6.8 -1.8 0.5 -0.9 5.5 

CO 4.3 3.3 -2.1 0.2 -0.1 5.7 

NOx 11.3 6.2 -4.8 1.5 -0.4 13.8 

PM10 8.1 0.5 -2.3 0.0 0.0 6.4 

SOx 1.2 9.2 -0.6 0.2 -0.1 9.9 

Wet Mills - GREET1.7 

VOC 1.6 2.6 -3.2 1.6 -1.5 1.1 

CO 4.0 2.3 -2.1 0.2 -0.1 4.3 

NOx 10.7 4.9 -4.6 1.3 -0.4 11.9 

PM10 1.1 5.9 -0.6 0.0 0.0 6.5 

SOx 5.0 4.1 -2.7 0.2 -0.1 6.5 

As can be seen, the emission estimates from the two versions of GREET differ 

significantly. In particular, VOC emissions in GREET1.7 are about 60% as large as those 

estimated in GREET1.6 for dry milss and even lower for wet mills. The other differences are in 

the +20% range, and differ in direction depending on pollutant. 

The default mix of dry and wet mill ethanol plants is 80/20 in both versions of GREET. 

This is a reasonable estimate for current production. However, the vast majority of new plants 

are expected to be of the dry mill variety. Therefore, we will use the above emission factors for 

a 80/20 mix of dry and wet mill plants to estimate the emissions from current ethanol plants and 

the dry mill emission factors to estimate the emissions from future plants. 

While emissions related to ethanol production and distribution will increase,,areas with 

refineries might experience reduced emissions, not necessarily relative to current emission 

166
levels, but relative to those which would have occurred in the future had ethanol use not risen. 

However, to the degree that increased ethanol use reduces imports of gasoline, as opposed to the 

domestic production of gasoline, these reduced refinery emissions will occur overseas and not in 

the U.S. Therefore, we will not take any credit for reduced refinery emissions here. 

Similarly, areas with MTBE production facilities might experience reduced emissions 

from these plants as they cease producing MTBE. However, some of these plants are likely to be 

converted to produce other gasoline blendstocks, such as iso-octane or alkylate. In this case, 

their emissions are not likely to change substantially. 

The emission factors shown in Table 3.4-1 do include a credit for reduced emissions 

related to a reduction in the volume of non-oxygenated gasoline being distributed. These are 

taken directly from the GREET emission estimates for conventional gasoline production and 

distribution. We assumed that ethanol use will reduce gasoline use one for one on an energy 

basis. 

3.4.1.2 Ethanol Production Emissions Received from States 

The emissions from most of the steps involved in ethanol production and distributions are 

very diffuse (e.g., tractors plowing corn fields). However, the emissions from ethanol production 

plants are point sources whose emissions are often measured and tracked by local or state 

governments. We contacted over a dozen States in an attempt to improve our estimate of 

emissions from ethanol production. The results of this process are summarized below.UUU 

We received emission estimates from 13 States for current ethanol plants with a 

combined capacity of 3 billion gallons per year. The emission data involved annual emission 

estimates for one or more years between 2001-2005. Overall, these plants represent roughly 

three-fourths of current ethanol capacity. The emission data cover a very wide range of plant 

capacities, ranging from 0.4 to 274 million gallons of ethanol per year. 

The capacity-weighted average emissions of wet and dry mill ethanol plants are 

summarized in Table 3.4-2. 

Table 3.4-2. Emissions From Ethanol Plants: State Data (g/gal ethanol) 

Wet Mills Dry Mills 

VOC 17.5 4.0 

CO 15.0 1.9 

NOx 18.3 5.5 

PM10 8.8 2.2 

SOx 24.6 7.0 

As can be seen, the emissions from wet mills are much higher than those from dry mills. 

In general, the wet mill plants are older than the dry mills. They also involved a different set of 

processes and produce a different set of by-products. 

167 
The ethanol plant emissions from GREET1.6 and GREET1.7 are shown in Table 3.4-3 

below. 

Table 3.4-3. Emissions From Ethanol Plants: GREET (g/gal ethanol) 

Wet Mills Dry Mills 

GREET1.6 GREET1.7 GREET1.6 GREET1.7 

VOC 6.8 2.6 6.8 2.6 

CO 3.3 2.3 2.9 1.7 

NOx 6.2 4.9 4.6 3.9 

PM10 0.5 5.9 0.4 4.2 

Sox 9.2 4.1 6.1 3.7 

As can be seen from comparing the emission estimates in Tables 3.4-2 and 3.4-3, the 

State data indicate that the emission from wet mills are much higher than those estimated in 

either version of GREET. In contrast, the emission data obtained from the States for dry mills is 

generally consistent with the estimates in GREET1.6 and higher than those in GREET1.7. An 

exception to the latter are emissions of PM10, which the state data indicate are lower than the 

estimate in GREET1.7 and higher than that in GREET1.6. 

The reasons for the differences in the State data and the estimates in GREET1.6 and 

GREET1.7 are not known. It is possible, particularly for wet mills, that the State estimates 

include the emissions from an entire geographically-defined facility which may include more 

operations than just corn milling. It is also possible that the estimates in GREET represent 

emissions from plants which would be designed today or in the future and are less representative 

of plants which were built over 20 years ago. This deserves further investigation. At this time, 

we will use the average of the State emission data as a second estimate of ethanol plant 

emissions, along with GREET1.7, in order to better indicate the range of possible emissions from 

these plants. 

3.4.1.3 Selection of Ethanol Production and Distribution Emission Estimates 

We have available three estimates of the emissions from ethanol plants and two estimates 

of the emissions from the other steps in the process of growing corn through ethanol distribution. 

The estimates contained in GREET1.7 represent an update of those in GREET1.6. Therefore, we 

will use the emission factors from GREET1.7 in lieu of those in GREET 1.6 in Chapter 4 where 

we estimate national emissions from ethanol production and distribution. 

In addition, we develop a second estimate of these emissions by substituting the average 

emission factors based on the State data for the dry mill ethanol plant emissions contained in 

GREET1.7. While the State data represents emissions from current plants and our primary focus 

is future plants, it is not certain that the emissions of the two sets of ethanol plants will differ. 

Therefore, the use of the State data will provide a useful indication of the potential uncertainty in 

the GREET1.7 estimates. We chose not to use the state data for wet mills, as these emission 

factors are often a factor of 10 higher than those from GREET1.6 or GREET1.7. More 

understanding of the processes producing these emissions is needed before they can be all 

168
assigned to ethanol production. Thus, we use the GREET1.7 emission factors for wet mill 

ethanol production, while using the state data for dry mill ethanol production. 

The two sets of estimates are shown in Table 3.4-4. In both sets of estimates, the 

emission factors for current ethanol plants assume an 80/20 mix of dry and wet mills, while those 

for future ethanol plants assume 100% dry mills. 

Table 3.4-4. 

Selected Emission Factors for Ethanol Production and Distribution 

(g/gal ethanol) 

GREET1.7 GREET1.7 + State Data 

Current Plants Future Plants Current Plants Future Plants 

VOC 1.8 1.8 3.6 3.2 

CO 4.0 4.1 4.4 4.3 

NOx 11.4 11.4 10.8 13.0 

PM10 4.9 4.9 6.1 2.8 

Sox 6.4 6.4 7.2 9.7 

3.4.2 Biodiesel 

Like ethanol, we base our emission factors for biodiesel production distribution on the 

estimates contained in the GREET model, version 1.7. Table 3.4-6 shows the emission factors 

associated with soybean farming, soy oil production and esterification, and biodiesel distribution. 

We also include emissions related to distributing the biodiesel and take credit for reduced 

emissions related to distributing displaced diesel fuel. 

Table 3.4-5. 

Well-to-Pump Emissions for Producing and Distributing Biodiesel from Soybeans 

 (grams per gallon biodiesel) 

Pollutant 

Total: 

Soybean 

Farming and 

Transportation 

Biodiesel 

Production 

Biodiesel 

Transportation 

Diesel Fuel 

Transportation 

Credit 

Total 

Emissions 

VOC 2.7 34.8 0.2 -0.2 37.6 

CO 10.6 2.1 0.2 -0.1 12.7 

NOx 19.3 5.5 0.9 -0.6 25.1 

PM10 2.6 2.2 0.0 0.0 4.8 

SOx 17.6 4.1 0.1 -0.1 21.8 

At the same time, areas with refineries might experience reduced emissions, not 

necessarily relative to current emission levels, but relative to those which would have occurred in 

the future had biodiesel use not risen. However, to the degree that increased biodiesel use 

reduces imports of diesel fuel, as opposed to the domestic production of diesel fuel, these 

reduced refinery emissions will occur overseas and not in the U.S. 

169 
Chapter 3: Appendix 

Fuel Property Tables and Summary of Predicted Emissions Chang

170
Table 3A-1. CRC E-67 Study Test Fuel Properties 

171
Table 3A-2. Summary of EPA E-67 vs. EPA Predictive Model Effects of E10 and MTBE Use Relative 

Base Fuel: AAM Summer avg. non-oxy fuel 

E0: AAM CG RVP -1 psi delta 

E0: AAM CG RVP -2 psi delta 

E10: AAM Summer Avg 

E10: AAM Summer avg, T50 limited to 195oF 

E10: AAM Summer avg, T50 T90 O2 only for EPA model (T50=195oF) 

11% MTBE: Fuel props are deltas from AAM CG 

Phase 2 RFG: Nonoxy, from 1993 region class C Dalow RVP 

Fuel Parameters 

RVP (psi) 8.7 7.8 6.8 9.7 9.7 8.7 8.7 6.7 

T50 (oF) 218 218 218 186 195 195 206 214 T90 (oF) 332 332 332 325 325 325 324 325 

Aromatics (vol %) 32 32 32 27 27 32 25.5 25.48 

Olefins (vol %) 7.7 7.7 7.7 6.1 6.1 7.7 7.7 13.1 

Oxygen (wt%) 0 0 0 3.5 3.5 3.5 2.1 0 

Sulfur (ppm) 30 30 30 30 30 30 30 30 

Predicted Emissions Changes 

EPA Predictive Models (% change) 

NOx 0.0 -1.0 -2.1 7.7 7.3 7.7 2.6 -1.7 

NMHC 0.0 -1.0 -2.0 -7.4 -7.0 -7.5 -9.2 -7.7 

EPA E-67 Model (% change) 

NOx 0.0 0.0 0.0 9.2 6.7 6.7 -1.9 -0.7 

NMHC 0.0 0.0 0.0 -5.7 -6.5 -6.5 -4.7 -3.2 

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173
Chapter 4: National Emission Inventory Impacts 

This chapter describes the methods used to develop national emissions inventories under 

the Renewable Fuel Standard (RFS) program. These inventories account for impacts from 

ethanol use, the removal of MTBE, and the resulting changes to gasoline. These inventories also 

account for the impacts of ethanol and biodiesel production and distribution. This chapter also 

presents and discusses these inventories. 

4.1 Impact of Ethanol Use 

This section describes the methods used to develop national emissions inventories with 

respect to ethanol consumption. This section also presents and discusses these inventories. 

These inventories reflect only emissions from vehicles and equipment operating on ethanolblend gasoline, from both onroad and off-road sources. The off-road sources do not include 

nonroad diesel, locomotive, or marine applications. 

4.1.1 Overview of Cases 

As described in Section 2.1, we consider three cases for the future use of ethanol-blend 

gasoline: a Reference Case, an RFS Case, and an EIA Case. The main difference between the 

cases is our assumption about how much ethanol will be used and where it will go. The 

Reference case represents our estimate of fuel quality by county which existed in 2004 when 

approximately 3.5 billion gallons of ethanol were consumed nationwide. In terms of 2012 fuel 

consumption, about 4.0 billion gallons of ethanol is consumed nationwide in the Reference case. 

The RFS case assumes 6.7 billion gallons of ethanol consumption in 2012, in accordance with 

the requirements of the RFS mandate. The EIA case assumes 9.6 billion gallons of ethanol is 

used nationwide in 2012, based on projections made in the Energy Information Agency’s 2006 

Annual Energy Outlook. We evaluate each case by predicting fuel quality in each county of the 

U.S. in 2012. This 2012 fuel matrix is then used for all inventory and air quality assessments. 

While Chapter 2 discusses our methods for determining how much ethanol will go to 

each state in each case and how fuel properties will be affected, this section of the RIA uses 

those distributions to derive estimates of the impact on national emissions inventories. 

4.1.2 National Emissions Inventory Estimation Procedure 

Having approximated the effects of adding ethanol and removing MTBE on fuel 

properties (see Chapter 2), the next step was to use the EPA’s National Mobile Inventory Model 

(NMIM)VVV to calculate emissions inventories for gasoline fueled motor vehicles and nonroad 

equipment in years 2012, 2015, and 2020. For all three years, we ran NMIM for January and 

July, assuming that each was representative of winter and summer conditions, respectively. We 

estimate annual emission inventories by summing the two monthly inventories and multiplying 

by six. This was done in order to reduce the amount of time needed to actually run the model. 

174
One additional simplification was made to shorten the time required to run NMIM for the 

three years, three fuel cases, two months and roughly 3100 counties in the U.S. Counties within 

a state with identical fuels and inspection-maintenance programs and similar temperatures were 

grouped together and run through NMIM as a single geographical area. The temperatures used 

for this area were those of the county with the highest VMT in the group. As the specific 

counties within a state with identical fuels sometimes changed across the three fuel cases, the 

groupings of counties sometimes changed across the NMIM runs of the three fuel cases. This 

occasionally introduced a change in the temperatures estimated for a county between fuel cases. 

This in turn produced a change in emissions independent of changes in fuel quality. 

We evaluated the potential for this simplification to bias the projected emission impacts 

of the various fuel cases. Counties where RFG is sold were always modeled consistently across 

all three fuel cases and so are unaffected by this simplification. Counties with low RVP and 9 

RVP fuel were sometimes affected. On average, the changes in emissions occurring due to a 

change in temperature appear to be unbiased (i.e., emissions increase as often as they decrease). 

Also, many of the emission impacts of changing fuel quality (e.g., exhaust VOC and NOx 

impact) were applied outside of the NMIM model and so are unaffected by this simplification. 

Since we do not present or use the emission impacts for individual counties, we believe that this 

simplification does not significantly impact the emission impacts presented below. 

We chose 2012 as the first projection year, because it is the year of full RFS program 

implementation. We also chose 2015 and 2020 to illustrate how the emissions will change over 

time as the fleet changes. We increased ethanol consumption beyond 2012 only by volumes 

required to maintain the same proportion to gasoline that existed in 2012, and not by growth 

predicted in EIA estimates. By restricting ethanol growth in this way, the same fuel quality that 

existed in 2012 would apply to 2015 and 2020, which would better highlight the effects of fleet 

turnover. 

NMIM’s estimates of both onroad and nonroad emissions were “post-processed” to 

reflect factors not yet included in the model. For onroad emissions, the effect of fuel quality on 

exhaust VOC and NOx emissions contained in the model (i.e., those in MOBILE6.2) were 

replaced with those from the EPA Predictive Model. We further adjusted the NMIM estimates 

of exhaust VOC, CO and NOx emissions from onroad vehicles in a “sensitivity” analysis in 

order to reflect the significant degree of uncertainty which currently exists with respect to these 

effects. Air toxic emissions were adjusted in order to reflect changes in total exhaust VOC 

emissions,. Finally, the effect of ethanol on permeation VOC and benzene emissions also were 

added to the onroad emission estimates. This series of post-processing steps are further 

described in the sections below. 

For nonroad emissions, the only adjustment to the NMIM estimates was to adjust air 

toxic emissions in the two control cases to reflect the change in the toxic fraction of VOC 

emissions to that estimated for onroad vehicles, as opposed to that estimated for nonroad 

equipment. These steps for calculating emissions inventories are described in the following 

sections. A summary of the models used and fundamental post-processing steps are shown in 

Table 4.1-1 below. 

175
Table 4.1-1. Estimation of National Emissions Inventories: 

Models Used and Fundamental Post-Processing Steps 

 Exhaust Emissions Non-Exhaust Emissions 

Onroad 

Model: NMIM which runs MOBILE6.2. 

Post-processing: 

1. Replace VOC and NOx fuel effects for Tier 0 

vehicles from MOBILE6.2 with fuel effects from 

EPA Predictive Model; 

2. Conduct sensitivity analysis by applying fuel 

effects for Tier 0 vehicles to all vehicles. 

3. Adjust exhaust air toxics emissions to reflect 

adjustment to exhaust VOC emissions. 

Model: NMIM which runs MOBILE6.2. 

Post-processing: 

1. Add effect of ethanol on permeation emissions 

of VOC and benzene. 

Nonroad 

Model: NMIM which runs NONROAD2005 

(modified to account for hose permeation). 

Post-processing: 

1. Changes in toxic fraction of VOC emissions in 

two fuel control cases based on onroad estimates 

instead of nonroad estimates. 

Model: NMIM which runs NONROAD2005 

(modified to account for hose permeation). 

Post-processing: 

1. Changes in toxic fraction of VOC emissions in 

two fuel control cases based on onroad estimates 

instead of nonroad estimates. 

4.1.2.1 Onroad Emission Estimation Procedures 

We ran NMIM to estimate county-specific emissions from gasoline motor vehicles for 

January and July in years 2012, 2015, and 2020. For each month and year combination, we ran 

the three onroad cases (Reference, RFS, and EIA). The NMIM model utilizes the 

MOBILE6.2WWW model to estimate motor vehicle emissions, as well as the effect of fuel quality 

on emissions. As discussed in Chapter 3, the EPA Predictive Model contains more recent 

estimates of the impact of fuel quality on exhaust VOC and NOx emissions. Therefore, we 

removed the impact of fuel quality on exhaust VOC and NOx emissions as estimated by 

MOBILE6.2 and replaced these impacts with those of the EPA Predictive Model. As also 

discussed in Chapter 3, MOBILE6.2 does not include the impact of ethanol on permeation 

emissions. Therefore, we added these emissions to those estimated by NMIM. Finally, we 

arrived at annual emissions estimates by summing the January and July results, then multiplying 

by six. The procedures for making these changes are discussed below. 

4.1.2.1.1 Onroad Exhaust Emissions 

MOBILE6.2 performs most of its emission estimation procedures for a non-oxygenated 

8.7 RVP gasoline. The effect of differing fuel quality is represented by a set of adjustment 

factors, which can vary by vehicle type, model year, and whether the vehicle is properly 

operating or not (i.e., is a low or high emitter). Because the mix of vehicle types, model years, 

and low and high emitters varies by county and calendar year, it is infeasible to estimate the net 

impact of each fuel parameter on emissions outside of the model. In Section 3.1.1.1.2 of Chapter 

3, we describe a process whereby we performed linear regressions on the exhaust emissions 

176
estimated by NMIM in order to determine the average effect of RVP, ethanol content and MTBE 

content on exhaust VOC and NOx emissions. Also in Section 3.1.1.1.2, we describe these same 

impacts using the EPA Predictive Model. We combined these fuel-emission effects with the fuel 

quality expected to exist in each county under each ethanol use case to estimate the adjustment 

which NMIM had applied to exhaust VOC and NOx emissions. This NMIM adjustment for fuel 

quality was removed and replaced by one based on the EPA Predictive Models. In our primary 

analysis, the fuel-emission effects from the EPA Predictive Models were only applied to the 

fraction of exhaust VOC and NOx emissions which are emitted by Tier 0 vehicles. In our 

sensitivity analysis, the fuel-emission effects from the EPA Predictive Models were applied to all 

exhaust VOC and NOx emissions. 

Table 4.1-2 shows the values for “Tier 0 Fraction”; i.e., the fraction of VOC and NOx 

emissions from vehicles with Tier 0 emissions characteristics. Note that the fraction drops as 

time progress, reflecting the attrition of such vehicles in the national fleet. In the sensitivity 

analysis, the Tier 0 vehicle emission fraction is 1.0 for all years and pollutants. 

Table 4.1-2. Fraction of In-Use Exhaust Emissions Attributable to 

Vehicles with Tier 0 Emissions Characteristics 

Calendar Year VOC NOx 

2012 0.339 0.162 

2015 0.183 0.065 

2020 0 0 

After adjusting exhaust VOC and NOx according to the methods described above, we 

adjusted the four exhaust toxic emissions: benzene, 1,3-butadiene, formaldehyde, and 

acetaldehyde. MOBILE6.2 estimates exhaust toxic emissions by first estimating the fraction of 

exhaust VOC emissions represented by each toxic based on fuel quality. The model then applies 

this fraction to exhaust VOC emissions to estimate absolute emissions of air toxics. Since we 

adjusted exhaust VOC emissions, it was necessary to adjust exhaust toxic emissions, as well, by 

the ratio of the change in exhaust VOC emissions. 

As described in Section 3.1.1.1.2 of Chapter 3, carbon monoxide emissions were also 

adjusted. The following equation illustrates the CO adjustment: 

Adj. = NMIM 

× (1+(Etoh Vol% x Etoh Mkt Shr + MTBE Vol% x MTBE Mkt Shr x 0.5454) × CO Adj. Factor ) CO CO 

4.1.2.1.2 Onroad Non-Exhaust Emissions 

The only adjustment to the non-exhaust emission estimates from NMIM was to add 

county-specific estimates of the increase in permeation emissions due to ethanol use. In Section 

3.1.1.3 of Chapter 3, we determined that a 10 vol% ethanol blend increased permeation 

emissions by 0.8 grams per day at 95 F. We also concluded there that permeation emissions 

double with every increase in temperature of 18 °F. Because of this temperature relationship, 

177
permeation effects were only accounted for in the July emission estimate since emissions during 

the winter months could be at least four times lower, and thus negligible. 

Permeation emissions occur whether a vehicle is being used or is parked. Therefore, the 

average hourly emission factor in each county in July is determined by adjusting the 0.8 gram 

per day emission rate for the average fuel tank temperature occurring in that hour of the day in 

each county in July and multiplying by the market share of E10 fuel in that county. Total 

monthly emissions in each county were determined by summing across hours of the day, 

multiplying by 31 days and multiplying by the number of vehicles estimated to reside in that 

county. 

The average fuel tank temperature is a function of the average ambient temperature at 

that hour of the day, adjusted to account for the increase in fuel tank temperature for those 

vehicles which are operating or which are still cooling down from operating. We obtain 

estimates of these latter two factors from EPA’s Draft MOVES2006 model.XXX These are 

shown in Table 4.1-3. The fuel tank temperature of vehicles which have been parked some time 

tend to lag the ambient temperature both when the latter is rising and falling. We assume here 

that the fuel tank temperature of these parked vehicles is equal to the ambient temperature, which 

is true on average for the day. 

Table 4.1-3. Increase in Fuel Tank Temperature Relative to Ambient 

Hour of the Day Vehicles Operating or in Hot Soak Average Tank Temperature Rise (F) 

Midnight 2.6% 10.0 

1:00 AM 2.8% 6.9 

2:00 AM 1.2% 6.1 

3:00 AM 0.9% 4.9 

4:00 AM 0.8% 3.1 

5:00 AM 2.6% 3.0 

6:00 AM 6.6% 3.7 

7:00 AM 12.3% 4.6 

8:00 AM 14.0% 3.5 

9:00 AM 10.0% 3.8 

10:00 AM 11.1% 3.8 

11:00 AM 12.5% 4.9 

Noon 15.6% 4.8 

1:00 PM 16.0% 5.5 

2:00 PM 17.2% 6.6 

3:00 PM 21.0% 7.7 

4:00 PM 23.7% 8.6 

5:00 PM 28.5% 8.3 

6:00 PM 30.0% 8.8 

7:00 PM 25.7% 9.2 

8:00 PM 18.7% 8.3 

9:00 PM 13.5% 7.6 

10:00 PM 10.6% 8.0 

11:00 PM 7.8% 8.4 

178
The total number of gasoline vehicles in the U.S. in 2004 is estimated to be 228 

million.YYY We increased this figure by 1.9% per year53 to derive estimates of the gasoline 

vehicle fleet in 2012, 2015 and 2020. This produced estimates for the fleet of gasoline vehicles 

in the U.S. of 265, 281 and 308 million vehicles in 2012, 2015 and 2020, respectively. These 

vehicles were allocated to each county based on the county-specific distribution of national VMT 

by gasoline vehicles contained in NMIM. 

As described in Section 3.1.1.3 of Chapter 3, we estimate that benzene represents 3% of 

the increased VOC permeation emissions due to ethanol use. Thus, we added this 3% to the nonexhaust emissions of benzene estimated by NMIM. 

4.1.2.2 Nonroad Emissions 

NMIM is capable of utilizing any one of a series of EPA’s NONROAD emission models. 

We chose to use the NONROAD2005ZZZ model to estimate emissions from nonroad equipment 

here, as it reflects EPA’s latest estimates of emission factors for nonroad equipment. EPA has 

also recently developed a set of emission factor inputs for the NONROAD model which include 

the effect of ethanol on permeation emissions from a number of types of nonroad equipment (see 

Chapter 3). 

For the proposed rule inventories, the NONROAD model was not able to select ethanol 

related emission factors based on the fuel quality inputs to the model. It was therefore necessary 

to run NMIM for two extreme ethanol use cases (no ethanol use and 100% ethanol use) and use 

those results to estimate emissions for the five ethanol use cases which were the focus of the 

proposed rule. 

For the final rule, NONROAD model capabilities were updated to account for oxygenate 

effects. Therefore, we were able to run NMIM (which runs NONROAD) using the same fuel 

property inputs that were used for onroad emissions inventories. This eliminated the need to 

interpolate between the “No Oxygen” and “All Oxygen” NONROAD runs that were needed for 

the proposal. 

For nonroad toxic exhaust emissions, the toxic emissions factors for nonroad equipment 

are based on very limited data. In EPA’s recent final rule which implemented new Mobile 

Source Air Toxic (MSAT) standards, we adjusted the fraction of nonroad VOC emissions 

represented by the various air toxics contained in NMIM for a reduction in fuel benzene content 

with those estimated for the same fuel change by MOBILE6.2 for onroad motor vehicles. This 

was done because of the very limited amount of nonroad emission test data which both varied 

fuel quality and measured toxics emissions. We take the same approach here. We begin with 

the estimate of nonroad toxic emissions from NMIM for the Reference Case. Then, any change 

in the toxics fraction of nonroad VOC emissions due to a change in fuel quality predicted by 

NMIM is replaced by the change in the toxics fraction of onroad VOC emissions due to the same 

 Annual growth rate in gasoline consumption on an energy basis per EIA Annual Energy Outlook, 2006 (therefore 

it applies regardless of future ethanol use scenario). Assumes constant annual mileage per vehicle over this 

timeframe. 

179

53
change in fuel quality predicted by MOBILE6.2. This adjustment is illustrated in the following 

equation: 

NMIM VOC MOBILE6.2 Toxic Emissions (RFS or EIA Case) Adjusted Emissions (RFS MOBILE6.2 VOC Emissions (RFS or EIA Case 

Nonroad NMIM or EIA case) = Toxic × × Toxic Emissions NMIM VOC MOBILE6.2 Toxic Emissions (Reference Case) 

Emissions Emissions MOBILE6.2 VOC Emissions (Reference Case) 

(Reference case) 

4.1.3 National Emissions Inventory Projections 

4.1.3.1 Emission Inventories: Primary Analysis 

This section provides the national emissions inventories for the primary case analyses. 

Criteria pollutant inventories are included, along with a brief discussion of the trends. A short 

discussion of air toxics inventories is also included. See Tables 4A-1 through 4A-7 in the 

Chapter 4 Appendix for complete primary-case inventories on air toxics and criteria pollutants, 

as well as the percent changes in inventories from the Reference case. 

Table 4.1-4 shows ethanol impacts on VOC inventories for each of the three cases of 

renewable fuel use in years 2012, 2015, and 2020. In any given year, the data suggest that total 

VOC emissions will increase as ethanol use increases. The largest increase is seen in the EIA 

case, where the increase is about 1% of the Reference case inventory. 

Our analysis indicates that this increase is a result of VOC non-exhaust emissions, such 

as those from evaporation or permeation. While VOC exhaust emissions decrease, they do not 

decrease enough to counteract the increase from non-exhaust emissions. 

Table 4.1-4. 

National VOC Emissions from Gasoline Vehicles and Equipment: 

Reference Case Inventory and Change in Inventory for Control Cases (Tons/Year) 

Primary Case Tons/Year 

Total 2012 2015 2020 

Reference 5,882,000 5,569,000 5,356,000 

RFS Case (Change) 18,000 25,000 34,000 

EIA Case (Change) 43,000 49,000 58,000 

On-Road 2012 2015 2020 

Reference 3,417,000 3,269,000 3,244,000 

RFS Case (Change) 10,000 16,000 23,000 

EIA Case (Change) 32,000 36,000 42,000 

Non-Road 2012 2015 2020 

Reference 2,465,000 2,300,000 2,112,000 

RFS Case (Change) 8,000 9,000 11,000 

EIA Case (Change) 11,000 13,000 16,000 

180
Table 4.1-5 shows ethanol impacts on CO inventories for each of the three cases of 

renewable fuel use in years 2012, 2015, and 2020. In any given year, data suggest that total CO 

emissions will decrease as ethanol use increases. The largest reduction is seen in the EIA case; 

this decrease is still less than 3% of the Reference inventory. 

Table 4.1-5. 

National CO Emissions from Gasoline Vehicles and Equipment: 

Reference Case Inventory and Change in Inventory for Control Cases (Tons/Year) 

Primary Case Tons/Year 

Total 2012 2015 2020 

Reference 55,022,000 53,702,000 53,949,000 

RFS Case (Change) -483,000 -473,000 -460,000 

EIA Case (Change) -1,366,000 -1,329,000 -1,286,000 

On-Road 2012 2015 2020 

Reference 37,656,000 36,171,000 35,723,000 

RFS Case (Change) -45,000 -39,000 -19,000 

EIA Case (Change) -359,000 -321,000 -252,000 

Non-Road 2012 2015 2020 

Reference 17,366,000 17,531,000 18,226,000 

RFS Case (Change) -438,000 -434,000 -441,000 

EIA Case (Change) -1,007,000 -1,008,000 -1,034,000 

Table 4.1-6 shows ethanol impacts on NOx inventories for each of the three cases of 

renewable fuel use in years 2012, 2015, and 2020. In any given year, the data suggest that total 

NOx emissions will increase as ethanol use increases. The largest increase is seen in the EIA 

case, which is around 2% of the Reference inventory. 

Our analysis also indicates that nonroad NOx emissions increase much greater than 

onroad emissions. While onroad inventories increase less than one percent in control cases, 

nonroad inventories increase up to 11% in the EIA case. 

181
Table 4.1-6. 

National NOx Emissions from Gasoline Vehicles and Equipment: 

Reference Case Inventory and Change in Inventory for Control Cases (Tons/Year) 

Primary Case Tons/Year 

Total 2012 2015 2020 

Reference 2,487,000 2,059,000 1,695,000 

RFS Case (Change) 23,000 18,000 17,000 

EIA Case (Change) 40,000 33,000 32,000 

On-Road 2012 2015 2020 

Reference 2,240,000 1,797,000 1,407,000 

RFS Case (Change) 9,000 3,000 0 

EIA Case (Change) 13,000 4,000 0 

Non-Road 2012 2015 2020 

Reference 247,000 262,000 288,000 

RFS Case (Change) 14,000 15,000 17,000 

EIA Case (Change) 27,000 29,000 32,000 

Table 4.1-7 shows ethanol impacts on air toxic emissions for each of the three cases of 

renewable fuel use in 2012. 

For all air toxics shown, the most extreme changes occur in the EIA case. The data 

suggest that, in 2012, total benzene emissions will decrease by about 4% due to decreases in both 

onroad and nonroad emissions. Total 1,3-butadiene emissions decrease by less than 2% due to 

decreases in both onroad and nonroad emissions. Total formaldehyde emissions decrease by up 

to 1.5%. Total acetaldehyde emissions increase by as much as 36% due to increases in both 

onroad and nonroad emissions. 

Generally, the trends in 2015 and 2020 parallel those of 2012 and are shown in the 

appendix to this chapter. Benzene maintains a drop of up to about 6% with increased ethanol 

use. Formaldehyde remains fairly flat, ranging from a 0.5% increase to a 1.2% decrease. 

Acetaldehyde maintains an increase of as much as 36.5%. Finally, 1,3-butadiene remains fairly 

flat, ranging from no change to a 0.5% increase. 

Again, we emphasize that the toxics inventories are based on very limited data, especially 

when it comes to emissions from nonroad equipment. 

182
Table 4.1-7. 

National Toxic Emissions from Gasoline Vehicles and Equipment in 2012: 

Reference Case Inventory and Change in Inventory for Control (Tons/Year) 

Primary Case Benzene 1,3-Butadiene Formaldehyde Acetaldehyde 

Total 

Reference 178,000 18,900 40,400 19,900 

RFS Case (Change) -3,200 -200 -600 3,400 

EIA Case (Change) -7,200 -300 -200 7,100 

Onroad 

Reference 124,100 12,000 29,900 15,500 

RFS Case (Change) -2,300 -200 -600 2,400 

EIA Case (Change) -5,400 -200 -300 5,400 

Nonroad 

Reference 53,900 6,900 10,500 4,400 

RFS Case (Change) -900 0 0 1,000 

EIA Case (Change) -1,800 -100 100 1,700 

4.1.3.2 Emission Inventories: Sensitivity Analyses 

This section provides the national emissions inventories for the sensitivity case analyses. 

Criteria pollutant inventories are included, along with a brief discussion of the trends. See 

Tables 4A-1 through 4A-7 in the Chapter 4 Appendix for complete sensitivity-case inventories 

on air toxics and criteria pollutants, as well as the percent changes in inventories from the 

reference case. 

Table 4.1-8 shows ethanol impacts on VOC inventories for each of the three cases of 

renewable fuel use in years 2012, 2015, and 2020. Where the primary analysis showed total 

VOC emissions increasing with ethanol use in all cases, the sensitivity analysis shows that total 

VOC emissions decrease. Onroad emissions decrease in all cases, while nonroad emissions 

increase to the same extent as under the primary analysis. 

183
Table 4.1-8. 

National VOC Emissions from Gasoline Vehicles and Equipment: 

Reference Case Inventory and Change in Inventory for Control Cases (Tons/Year) 

Sensitivity Case Tons/Year 

Total 2012 2015 2020 

Reference 5,834,000 5,510,000 5,281,000 

RFS Case (Change) -20,000 -23,000 -27,000 

EIA Case (Change) -4,000 -10,000 -17,000 

On-Road 2012 2015 2020 

Reference 3,369,000 3,210,000 3,169,000 

RFS Case (Change) -28,000 -32,000 -38,000 

EIA Case (Change) -15,000 -23,000 -33,000 

Non-Road 2012 2015 2020 

Reference 2,465,000 2,300,000 2,112,000 

RFS Case (Change) 8,000 9,000 11,000 

EIA Case (Change) 11,000 13,000 16,000 

Table 4.1-9 shows ethanol impacts on CO inventories for each of the three cases of 

renewable fuel use in years 2012, 2015, and 2020. In any given year, the data suggest that total 

CO emissions will decrease as ethanol use increases. The onroad vehicle CO emission 

reductions increase by roughly a factor of three compared to the primary analysis. This increases 

the overall CO emissions reduction from about 3% in the primary case to 4% in the sensitivity 

case. 

Table 4.1-9. 

National CO Emissions from Gasoline Vehicles and Equipment: 

Reference Case Inventory and Change in Inventory for Control Cases (Tons/Year) 

Sensitivity Case Tons/Year 

Total 2012 2015 2020 

Reference 54,315,000 52,998,000 53,183,000 

RFS Case (Change) -692,000 -676,000 -676,000 

EIA Case (Change) -1,975,000 -1,929,000 -1,937,000 

On-Road 2012 2015 2020 

Reference 36,949,000 35,467,000 34,957,000 

RFS Case (Change) -254,000 -242,000 -235,000 

EIA Case (Change) -968,000 -921,000 -903,000 

Non-Road 2012 2015 2020 

Reference 17,366,000 17,531,000 18,226,000 

RFS Case (Change) -438,000 -434,000 -441,000 

EIA Case (Change) -1,007,000 -1,008,000 -1,034,000 

Table 4.1-10 shows ethanol impacts on NOx inventories for each of the three cases of 

renewable fuel use in years 2012, 2015, and 2020. In any given year, the data suggest that total 

NOx emissions will increase as ethanol use increases. The largest increase is seen in the EIA 

case, where the increase in total emissions is as high as 4.6% of the reference inventory. As in 

184 
the primary analysis, nonroad NOx emissions increase much greater than onroad emissions. 

While onroad inventories increase up to 3.5%, nonroad inventories increase upwards of 11.1% in 

the EIA case. 

Table 4.1-10. 

National NOx Emissions from Gasoline Vehicles and Equipment: 

Reference Case Inventory and Change in Inventory for Control Cases (Tons/Year) 

Sensitivity Case Tons/Year 

Total 2012 2015 2020 

Reference 2,519,000 2,087,000 1,717,000 

RFS Case (Change) 68,000 57,000 48,000 

EIA Case (Change) 106,000 91,000 79,000 

On-Road 2012 2015 2020 

Reference 2,272,000 1,825,000 1,429,000 

RFS Case (Change) 54,000 42,000 31,000 

EIA Case (Change) 79,000 62,000 47,000 

Non-Road 2012 2015 2020 

Reference 247,000 262,000 288,000 

RFS Case (Change) 14,000 15,000 17,000 

EIA Case (Change) 27,000 29,000 32,000 

Table 4.1-11 shows ethanol impacts on air toxic emissions for each of the five cases of 

renewable fuel use in 2012. The impacts in 2015 and 2020 are shown in the Appendix to this 

chapter. 

Table 4.1-11. 

National Toxic Emissions from Gasoline Vehicles and Equipment in 2012: 

Reference Case Inventory and Change in Inventory for Control (Tons/Year) 

Sensitivity Case Benzene 1,3-Butadiene Formaldehyde Acetaldehyde 

Total 

Reference Case 175,700 18,600 39,600 19,500 

RFS Case (Change) -5,000 -400 -1,100 3,000 

EIA Case (Change) -9,400 -600 -700 6,600 

Onroad 

Reference Case 121,800 11,700 29,100 15,100 

RFS Case (Change) -4,100 -400 -1,100 2,000 

EIA Case (Change) -7,600 -500 -800 4,900 

Nonroad 

Reference Case 53,900 6,900 10,500 4,400 

RFS Case (Change) -900 0 0 1,000 

EIA Case (Change) -1,800 -100 100 1,700 

As in the primary analysis, the most extreme changes in the sensitivity analysis tend to 

occur in the EIA case. 

185 
The data suggest that, in 2012, total benzene emissions will decrease by as much as 5.4% 

due to decreases in both onroad and nonroad emissions. Total formaldehyde emissions decrease 

by up to 2.8%. Nonroad formaldehyde emissions tend to remain relatively flat, while onroad 

emissions decrease. Total acetaldehyde emissions increase by as much as 34% due to increases 

in both onroad and nonroad emissions. Total 1,3-butadiene emissions decrease by about 3%. 

4.1.3.3 Local and Regional VOC and NOx Emissions (Summer 2015) 

We also estimate the percentage change in VOC, NOx, and CO emissions from gasoline 

fueled motor vehicles and equipment in those areas which actually experienced a significant 

change in ethanol use. Specifically, we focused on areas where the market share of ethanol 

blends was projected to change by 50 percent or more. We also focused on summertime 

emissions, as these are most relevant to ozone formation as discussed in Chapter 5. We modeled 

2015 because the ozone Response Surface Model (RSM) used for air quality modeling (also 

discussed in Chapter 5) is based upon a 2015 emissions inventory, though we would expect 

similar results in 2012. Finally, we developed separately estimates for: 1) RFG areas, including 

the state of California and the portions of Arizona where their CBG fuel programs apply, 2) low 

RVP areas (i.e., RVP standards less than 9.0 RVP, and 3) areas with a 9.0 RVP standard. This 

set of groupings helps to highlight the emissions impact of increased ethanol use in those areas 

where emission control is most important. 

Table 4.1-12 presents our primary analysis estimates of the percentage change in VOC, 

NOx, and CO emission inventories for these three types of areas when compared to the 2015 

reference case. Note that the analyses here is very similar to that described in Section 5.1, with 

the exception that Table 4.1-12 below reflects 50-state emissions (instead of 37 eastern states) 

and excludes diesel emissions. 

Table 4.1-12. 

Change in July 2015 Emissions from Gasoline Vehicles and Equipment in Counties Where 

Ethanol Use Changed Significantly – Primary Analysis 

Ethanol Use RFS Case EIA Case 

RFG Areas 

Ethanol Use Down Up 

VOC 0.8% 2.3% 

NOx -3.4% 1.6% 

CO 6.1% -2.6% 

Low RVP Areas 

Ethanol Use Up Up 

VOC 4.2% 4.6% 

NOx 6.2% 5.7% 

CO -12.5% -13.7% 

Other Areas (9.0 RVP) 

Ethanol Use Up Up 

VOC 3.6% 4.6% 

NOx 7.3% 7.0% 

CO -6.4% -6.0% 

186
As expected, increased ethanol use tends to increase NOx emissions. The increase in low 

RVP and other areas is greater than in RFG areas, since the RFG in the RFG areas included in 

this analysis all contained MTBE. Also, increased ethanol use tends to increase VOC emissions, 

indicating that the increase in non-exhaust VOC emissions exceeds the reduction in exhaust 

VOC emissions. This effect is muted with RFG due to the absence of an RVP waiver for ethanol 

blends. See Chapter 2 for a discussion of how ethanol levels will change at the state-level. 

Table 4.1-13 presents the percentage change in VOC, NOx, and CO emission inventories 

under our sensitivity analysis (i.e., when we apply the emission effects of the EPA Predictive 

Models to all motor vehicles). 

Table 4.1-13. 

Change in July 2015 Emissions from Gasoline Vehicles and Equipment in Counties Where 

Ethanol Use Changed Significantly – Sensitivity Analysis 

Ethanol Use RFS Case EIA Case 

RFG Areas 

Ethanol Use Down Up 

VOC -1.0% 1.0% 

NOx -0.9% 5.6% 

CO 7.3% -3.0% 

Low RVP Areas 

Ethanol Use Up Up 

VOC 3.4% 3.7% 

NOx 10.4% 10.8% 

CO -15.0% -16.4% 

Other Areas (9.0 RVP) 

Ethanol Use Up Up 

VOC 3.0% 3.9% 

NOx 10.8% 11.0% 

CO -9.0% -8.9% 

Directionally, the changes in VOC and NOx emissions in the various areas are consistent 

with those from our primary analysis. The main difference is that the increases in VOC 

emissions are smaller, due to more vehicles experiencing a reduction in exhaust VOC emissions, 

and the increases in NOx emissions are larger. 

4.2 Impact of Biodiesel Use 

As discussed in Chapter 1, biodiesel use totaled 25 million gallons in 2004 and is 

projected to increase to 300 million gallons in 2012. Total diesel fuel use in onroad diesels in 

2004 was roughly 39.4 billion gallons and is expected to grow to 47.5 billion gallons per year by 

2012.54

 The volumes of biodiesel produced thus represent 0.06% and 0.6% of onroad diesel fuel 

54 Based on linear interpolation between estimate for 2001 from Table 7.1.2-1 and that for 2014 from Table 7.1.3-4, 

both from the 2010 Nonroad FRM Final RIA, EPA420-R-04-007, May 2004, available in EPA Docket OAR-2003­

0012. 

187
consumption in 2004 and 2012, respectively. Given the very small contribution of biodiesel to 

the pool of diesel fuel, the nationwide emission impacts of biodiesel are expected to be similarly 

small for the foreseeable future. As a result, we have not included biodiesel emission impacts in 

our emission inventory estimates for this rule. 

We do intend to investigate these impacts in the future, however. As stated in Chapter 3, 

the 2002 EPA report entitled “A Comprehensive Analysis of Biodiesel Impacts on Exhaust 

Emissions” concluded that biodiesel fuels improved PM, CO and HC emissions of diesel engines 

while slightly increasing their NOx emissions. Nevertheless, these conclusions remain 

controversial due to conflicting results from different studies. As a result, preparations are being 

made to launch a test program with stakeholder participation to further investigate the emission 

impacts of biodiesel. 

4.3 Impact of Renewable Fuel Production and Distribution 

4.3.1 Ethanol 

In Chapter 2, we estimated that 3.5 billion gallons of ethanol was produced for use in 

motor fuel in 2004, which comprises our estimate of fuel quality for the base case . Maintaining 

fuel quality, but increasing fuel volume to that expected in 2012,55 ethanol use would increase to 

3.9 billion gallons. The increases in emissions associated with ethanol production and 

distribution under the RFS and EIA cases are, thus, determined relative to the emissions 

associated with producing and distributing 3.9 billion gallons of ethanol. 

We describe the emissions associated with producing and distributing ethanol on a per 

gallon basis in Chapter 3.4.1. There, we compare emissions factors from DOE’s GREET model, 

versions 1.6 and 1.7, as well as estimates of ethanol plant emissions obtained from the States. 

We decided there to use two emission estimates here, one from GREET1.7, and the other from 

GREET1.7 augmented by the State estimates for ethanol plant emissions. Here, we simply 

multiply those emission factors by the volume of ethanol being used in each scenario. Table 4.3­

1 shows estimates of annual emissions expected to occur nationwide due to increased production 

of ethanol. It should be noted that emissions in the base case assume a 80/20 mix of dry mill 

and wet mill facilities. New plants (and thus, the emission increases) assume 100% dry mil 

facilities. 

EIA projects gasoline demand of 16.93 and 18.84 quadrillion Btu in 2004 and 2012, respectively. This represents 

overall growth between these two years of 11.3%. 

188

55 
Table 4.3-1. 

Annual Emissions Nationwide from Ethanol Production and Transportation: 2012 

(tons per year) 

GREET1.7 GREET1.7 + State Data 

Base Case RFS Case EIA Case Base Case RFS Case EIA Case 

Emissions Increase in Emissions Emissions Increase in Emissions 

VOC 8,000 5,000 11,000 14,000 10,000 20,000 

NOx 17,000 13,000 26,000 18,000 14,000 27,000 

CO 49,000 35,000 72,000 56,000 40,000 81,000 

PM10 21,000 15,000 30,000 12,000 9,000 18,000 

SOx 27,000 20,000 41,000 42,000 30,000 61,000 

As can be seen, the potential increases in VOC and NOx emissions from ethanol 

production and transportation are of the same order of magnitude as those from ethanol use. 

Generally, ethanol plants are not located in ozone non-attainment areas, so the ozone impact of 

the increased VOC and NOx emissions should be minimal. 

According to our estimates, almost 120 counties throughout the nation are constructing 

new ethanol plants, expanding existing plants, or planning construction for future plants. The 

increases in ethanol production across these counties range from as low as 2 million gallons per 

year for modest expansions, to over 270 million gallons per year due to the construction of 

entirely new facilities. To estimate the potential increase in VOC and NOx emissions associated 

with these plants, whether construction is planned or underway, we apply the ethanol production 

emission factors (EFs) derived from state data as well as those found in GREET 1.7. See 

Chapter 3.4 for a discussion of the emission factors related to ethanol production and plant 

emissions. 

The ethanol production emission factors are applied to the increase in the volume of 

ethanol production expected in each of the counties. Figures 4.3-1 and 4.3-2 illustrate potential 

increases in future monthly VOC and NOx emissions, respectively, in counties that can expect a 

growth in ethanol production. The emissions reflect plants operating for one month at 90% 

capacity. In each figure, the distribution of counties is presented in order from the lowest-tohighest increase in ethanol production volume. The figures show results based upon both statebased emission factors and GREET 1.7 emission factors. 

189
VOC (tons/mo

NOx (tons/mo

Figure 4.3-1. Figure 4.3-2. 

Monthly VOC Emissions in Counties Expecting an Monthly NOx Emissions in Counties Expecting an 

Increase in Ethanol Production Increase in Ethanol Production 140 140 

120 120 

VOC (State data EFs) NOx (State data EFs) )

100 

) 

100 

VOC (GREET 1.7 EFs) NOx (GREET 1.7 EFs) 

80 

60 

40 

80 

60 

40 

20 20 

0 

0 20 40 60 80 100 

Distribution of Counties 

(in order of increasing ethanol production) 

120 

0 

0 20 40 60 80 100 

Distribution of Counties 

(in order of increasing ethanol production) 

120 

As the figures indicate, most counties will see an increase of less than 40 tons/month 

VOC and less than 60 tons/month NOx, according to the distribution based upon the state data 

emission factors. The average emissions are about 26 tons/month VOC and 35 tons/month NOx 

using state data, and about 17 tons/month VOC and 25 tons/month NOx using GREET 1.7 

emission factors. However, average VOC and NOx emissions increase to about 61 tons/month 

and 83 tons/month, respectively, in the 10% of counties expecting largest increases in ethanol 

production. The average emissions for the remaining 90% of counties is about 21 tons/month 

VOC and 29 tons/month NOx. For both VOC and NOx, emissions estimates are about 35% less 

when using the GREET 1.7 emission factors. 

4.3.2 Biodiesel 

In Chapter 1, we estimated that 25 million gallons of biodiesel were produced for use in 

motor fuel in 2004. Based on growth in overall diesel fuel demand between 2004 and 2012,56 

this would represent the equivalent of 30 million gallons of biodiesel in 2012 for our reference 

case. Here, we estimate the increase in emissions which will occur with an increase in biodiesel 

production and distribution from 30 million gallons to 300 million gallons per year. 

We describe the emissions associated with producing and distributing biodiesel on a per 

gallon basis in Chapter 3. Here, we simply multiply those emission factors by the volume of 

biodiesel being used in each scenario. Table 4.3-2 shows estimates of annual emissions expected 

to occur nationwide due to increased production of biodiesel. 

EIA projects gasoline demand of 16.93 and 18.84 quadrillion Btu in 2004 and 2012, respectively. This represents 

overall growth between these two years of 11.3%. Source: Annual Energy Outlook 2006, DOE/EIA-0383(2006), 

Reference Case Table 2, available in docket EPA-HQ-OAR-2005-0161. 

190

56 
Table 4.3-2. 

Annual Emissions Nationwide from Biodiesel Production and Transportation: 2012 

(tons per year) 

Reference Inventory: 

28 mill gal biodiesel per year 

Increase in Emissions: 

300 mill gal biodiesel per year 

VOC 1,400 14,000 

NOx 1,500 15,000 

CO 800 8,000 

PM10 50 500 

SOx 250 2,500 

As can be seen, the potential increases in emissions from biodiesel production and 

transportation are of the same order of magnitude as those from biodiesel use, with the exception 

of CO emissions. Generally, biodiesel plants are not located in ozone non-attainment areas, so 

the ozone impact of the increased VOC and NOx emissions should be minimal. 

4.4 Total Emission Impacts of Renewable Fuel Production and Use 

Tables 4.4-1 and 4.4-2 combine the VOC, CO and NOx emission impacts for ethanol use 

from Section 4.1 and renewable fuel production and distribution from Section 4.3. Table 4.4-1 

includes the emission impacts from gasoline vehicles and equipment under our primary analysis 

and renewable fuel production and distribution from GREET1.7. Table 4.4-2 includes the 

emission impacts from gasoline vehicles and equipment under our sensitivity analysis and 

renewable fuel production and distribution from GREET1.7 augmented with the State data for 

ethanol production plants. Emissions from renewable fuel production and distribution in 2012 

were increased by 1.9% per year to account for growth in gasoline and diesel fuel demand. 

191
Table 4.4-1. 

National Emissions from Gasoline Vehicles and Equipment and Renewable Fuel 

Production and Distribution: Primary Case and GREET1.7 (Tons/Year) 

Tons/Year 

VOC Emissions 2012 2015 2020 

Reference 5,891,000 5,578,513 5,366,368 

RFS Case (Change) 33,000 41,969 51,584 

EIA Case (Change) 63,000 71,311 83,496 

CO Emissions 2012 2015 2020 

Reference 3,467,000 3,321,850 3,301,600 

RFS Case (Change) 50,000 58,337 69,232 

EIA Case (Change) 108,000 116,446 130,856 

NOx Emissions 2012 2015 2020 

Reference 2,483,000 2,319,026 2,132,736 

RFS Case (Change) 33,000 36,482 39,952 

EIA Case (Change) 38,000 42,596 48,256 

Table 4.4-2. 

National Emissions from Gasoline Vehicles and Equipment and Renewable Fuel 

Production and Distribution: Sensitivity Case and GREET1.7/State Data (Tons/Year) 

Tons/Year 

VOC Emissions 2012 2015 2020 

Reference 5,849,000 5,525,855 5,298,280 

RFS Case (Change) -1,000 -746 -3,656 

EIA Case (Change) 25,000 22,824 18,864 

CO Emissions 2012 2015 2020 

Reference 3,426,000 3,270,249 3,234,664 

RFS Case (Change) 16,000 15,622 13,992 

EIA Case (Change) 70,000 67,959 66,224 

NOx Emissions 2012 2015 2020 

Reference 2,484,000 2,320,083 2,133,888 

RFS Case (Change) 34,000 37,539 41,104 

EIA Case (Change) 50,000 55,280 62,080 

192
Chapter 4: Appendix 

193
Table 4A-1. VOC Emission Inventories under Various Ethanol Use Cases 

Primary Tons/Year Change from Reference (tons) % Change from Reference 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 5,882,000 5,569,000 5,356,000 -­ -­ -­ -­ -­ -­

RFS Case 5,900,000 5,594,000 5,390,000 18,000 25,000 34,000 0.3% 0.4% 0.6% 

EIA Case 5,925,000 5,618,000 5,414,000 43,000 49,000 58,000 0.7% 0.9% 1.1% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 3,417,000 3,269,000 3,244,000 -­ -­ -­ -­ -­ -­

RFS Case 3,427,000 3,285,000 3,267,000 10,000 16,000 23,000 0.3% 0.5% 0.7% 

EIA Case 3,449,000 3,305,000 3,286,000 32,000 36,000 42,000 0.9% 1.1% 1.3% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 2,465,000 2,300,000 2,112,000 -­ -­ -­ -­ -­ -­

RFS Case 2,473,000 2,309,000 2,123,000 8,000 9,000 11,000 0.3% 0.4% 0.5% 

EIA Case 2,476,000 2,313,000 2,128,000 11,000 13,000 16,000 0.4% 0.6% 0.8% 

Sensitivity Tons/Year 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 5,834,000 5,510,000 5,281,000 -­ -­ -­ -­ -­ -­

RFS Case 5,814,000 5,487,000 5,254,000 -20,000 -23,000 -27,000 -0.3% -0.4% -0.5% 

EIA Case 5,830,000 5,500,000 5,264,000 -4,000 -10,000 -17,000 -0.1% -0.2% -0.3% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 3,369,000 3,210,000 3,169,000 -­ -­ -­ -­ -­ -­

RFS Case 3,341,000 3,178,000 3,131,000 -28,000 -32,000 -38,000 -0.8% -1.0% -1.2% 

EIA Case 3,354,000 3,187,000 3,136,000 -15,000 -23,000 -33,000 -0.4% -0.7% -1.0% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 2,465,000 2,300,000 2,112,000 -­ -­ -­ -­ -­ -­

RFS Case 2,473,000 2,309,000 2,123,000 8,000 9,000 11,000 0.3% 0.4% 0.5% 

EIA Case 2,476,000 2,313,000 2,128,000 11,000 13,000 16,000 0.4% 0.6% 0.8% 

194
Table 4A-2. CO Emission Inventories under Various Ethanol Use Cases 

Primary Tons/Year Change from Reference (tons) % Change from Reference 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 55,022,000 53,702,000 53,949,000 -­ -­ -­ -­ -­ -­

RFS Case 54,539,000 53,229,000 53,489,000 -483,000 -473,000 -460,000 -0.9% -0.9% -0.9% 

EIA Case 53,656,000 52,373,000 52,663,000 -1,366,000 -1,329,000 -1,286,000 -2.5% -2.5% -2.4% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 37,656,000 36,171,000 35,723,000 -­ -­ -­ -­ -­ -­

RFS Case 37,611,000 36,132,000 35,704,000 -45,000 -39,000 -19,000 -0.1% -0.1% -0.1% 

EIA Case 37,297,000 35,850,000 35,471,000 -359,000 -321,000 -252,000 -1.0% -0.9% -0.7% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 17,366,000 17,531,000 18,226,000 -­ -­ -­ -­ -­ -­

RFS Case 16,928,000 17,097,000 17,785,000 -438,000 -434,000 -441,000 -2.5% -2.5% -2.4% 

EIA Case 16,359,000 16,523,000 17,192,000 -1,007,000 -1,008,000 -1,034,000 -5.8% -5.7% -5.7% 

Sensitivity Tons/Year 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 54,315,000 52,998,000 53,183,000 -­ -­ -­ -­ -­ -­

RFS Case 53,623,000 52,322,000 52,507,000 -692,000 -676,000 -676,000 -1.3% -1.3% -1.3% 

EIA Case 52,340,000 51,069,000 51,246,000 -1,975,000 -1,929,000 -1,937,000 -3.6% -3.6% -3.6% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 36,949,000 35,467,000 34,957,000 -­ -­ -­ -­ -­ -­

RFS Case 36,695,000 35,225,000 34,722,000 -254,000 -242,000 -235,000 -0.7% -0.7% -0.7% 

EIA Case 35,981,000 34,546,000 34,054,000 -968,000 -921,000 -903,000 -2.6% -2.6% -2.6% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 17,366,000 17,531,000 18,226,000 -­ -­ -­ -­ -­ -­

RFS Case 16,928,000 17,097,000 17,785,000 -438,000 -434,000 -441,000 -2.5% -2.5% -2.4% 

EIA Case 16,359,000 16,523,000 17,192,000 -1,007,000 -1,008,000 -1,034,000 -5.8% -5.7% -5.7% 

195
Table 4A-3. NOx Emission Inventories under Various Ethanol Use Cases 

Primary Tons/Year Change from Reference (tons) % Change from Reference 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 2,487,000 2,059,000 1,695,000 -­ -­ -­ -­ -­ -­

RFS Case 2,510,000 2,077,000 1,712,000 23,000 18,000 17,000 0.9% 0.9% 1.0% 

EIA Case 2,527,000 2,092,000 1,727,000 40,000 33,000 32,000 1.6% 1.6% 1.9% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 2,240,000 1,797,000 1,407,000 -­ -­ -­ -­ -­ -­

RFS Case 2,249,000 1,800,000 1,407,000 9,000 3,000 0 0.4% 0.2% 0.0% 

EIA Case 2,253,000 1,801,000 1,407,000 13,000 4,000 0 0.6% 0.2% 0.0% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 247,000 262,000 288,000 -­ -­ -­ -­ -­ -­

RFS Case 261,000 277,000 305,000 14,000 15,000 17,000 5.7% 5.7% 5.9% 

EIA Case 274,000 291,000 320,000 27,000 29,000 32,000 10.9% 11.1% 11.1% 

Sensitivity Tons/Year Change from Reference (tons) % Change from Reference 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 2,519,000 2,087,000 1,717,000 -­ -­ -­ -­ -­ -­

RFS Case 2,587,000 2,144,000 1,765,000 68,000 57,000 48,000 2.7% 2.7% 2.8% 

EIA Case 2,625,000 2,178,000 1,796,000 106,000 91,000 79,000 4.2% 4.4% 4.6% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 2,272,000 1,825,000 1,429,000 -­ -­ -­ -­ -­ -­

RFS Case 2,326,000 1,867,000 1,460,000 54,000 42,000 31,000 2.4% 2.3% 2.2% 

EIA Case 2,351,000 1,887,000 1,476,000 79,000 62,000 47,000 3.5% 3.4% 3.3% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 247,000 262,000 288,000 -­ -­ -­ -­ -­ -­

RFS Case 261,000 277,000 305,000 14,000 15,000 17,000 5.7% 5.7% 5.9% 

EIA Case 274,000 291,000 320,000 27,000 29,000 32,000 10.9% 11.1% 11.1% 

196
Table 4A-4. Benzene Emission Inventories under Various Ethanol Use Cases 

Primary Tons/Year Change from Reference (tons) % Change from Reference 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 178,000 175,400 179,900 -­ -­ -­ -­ -­ -­

RFS Case 174,800 164,800 178,200 -3,200 -10,600 -1,700 -1.8% -6.0% -0.9% 

EIA Case 170,800 169,100 174,200 -7,200 -6,300 -5,700 -4.0% -3.6% -3.2% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 124,100 124,200 130,600 -­ -­ -­ -­ -­ -­

RFS Case 121,800 122,600 129,400 -2,300 -1,600 -1,200 -1.9% -1.3% -0.9% 

EIA Case 118,700 119,400 126,100 -5,400 -4,800 -4,500 -4.4% -3.9% -3.4% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 53,900 51,200 49,300 -­ -­ -­ -­ -­ -­

RFS Case 53,000 42,200 48,800 -900 -9,000 -500 -1.7% -17.6% -1.0% 

EIA Case 52,100 49,700 48,100 -1,800 -1,500 -1,200 -3.3% -2.9% -2.4% 

Sensitivity Tons/Year 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 175,700 172,700 176,500 -­ -­ -­ -­ -­ -­

RFS Case 170,700 168,000 171,900 -5,000 -4,700 -4,600 -2.8% -2.7% -2.6% 

EIA Case 166,300 163,600 167,200 -9,400 -9,100 -9,300 -5.4% -5.3% -5.3% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 121,800 121,500 127,200 -­ -­ -­ -­ -­ -­

RFS Case 117,700 117,500 123,100 -4,100 -4,000 -4,100 -3.4% -3.3% -3.2% 

EIA Case 114,200 113,900 119,100 -7,600 -7,600 -8,100 -6.2% -6.3% -6.4% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 53,900 51,200 49,300 -­ -­ -­ -­ -­ -­

RFS Case 53,000 50,500 48,800 -900 -700 -500 -1.7% -1.4% -1.0% 

EIA Case 52,100 49,700 48,100 -1,800 -1,500 -1,200 -3.3% -2.9% -2.4% 

197
Table 4A-5. Acetaldehyde Emission Inventories under Various Ethanol Use Cases 

Primary Tons/Year Change from Reference (tons) % Change from Reference 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 19,900 20,000 21,100 -­ -­ -­ -­ -­ -­

RFS Case 23,300 23,400 24,700 3,400 3,400 3,600 17.1% 17.0% 17.1% 

EIA Case 27,000 27,300 28,800 7,100 7,300 7,700 35.7% 36.5% 36.5% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 15,500 15,800 17,000 -­ -­ -­ -­ -­ -­

RFS Case 17,900 18,300 19,800 2,400 2,500 2,800 15.5% 15.8% 16.5% 

EIA Case 20,900 21,500 23,300 5,400 5,700 6,300 34.8% 36.1% 37.1% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 4,400 4,200 4,100 -­ -­ -­ -­ -­ -­

RFS Case 5,400 5,100 4,900 1,000 900 800 22.7% 21.4% 19.5% 

EIA Case 6,100 5,800 5,500 1,700 1,600 1,400 38.6% 38.1% 34.1% 

Sensitivity Tons/Year 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 19,500 19,500 20,400 -­ -­ -­ -­ -­ -­

RFS Case 22,500 22,400 23,400 3,000 2,900 3,000 15.4% 14.9% 14.7% 

EIA Case 26,100 26,100 27,200 6,600 6,600 6,800 33.8% 33.8% 33.3% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 15,100 15,300 16,300 -­ -­ -­ -­ -­ -­

RFS Case 17,100 17,300 18,500 2,000 2,000 2,200 13.2% 13.1% 13.5% 

EIA Case 20,000 20,300 21,700 4,900 5,000 5,400 32.5% 32.7% 33.1% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 4,400 4,200 4,100 -­ -­ -­ -­ -­ -­

RFS Case 5,400 5,100 4,900 1,000 900 800 22.7% 21.4% 19.5% 

EIA Case 6,100 5,800 5,500 1,700 1,600 1,400 38.6% 38.1% 34.1% 

198
Table 4A-6. Formaldehyde Emission Inventories under Various Ethanol Use Cases 

Primary Tons/Year Change from Reference (tons) % Change from Reference 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 40,400 40,100 41,400 -­ -­ -­ -­ -­ -­

RFS Case 39,800 39,600 41,200 -600 -500 -200 -1.5% -1.2% -0.5% 

EIA Case 40,200 40,100 41,600 -200 0 200 -0.5% 0.0% 0.5% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 29,900 30,100 32,000 -­ -­ -­ -­ -­ -­

RFS Case 29,300 29,700 31,800 -600 -400 -200 -2.0% -1.3% -0.6% 

EIA Case 29,600 30,100 32,200 -300 0 200 -1.0% 0.0% 0.6% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 10,500 10,000 9,400 -­ -­ -­ -­ -­ -­

RFS Case 10,500 9,900 9,400 0 -100 0 0.0% -1.0% 0.0% 

EIA Case 10,600 10,000 9,400 100 0 0 1.0% 0.0% 0.0% 

Sensitivity Tons/Year 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 39,600 39,200 40,300 -­ -­ -­ -­ -­ -­

RFS Case 38,500 38,100 39,200 -1,100 -1,100 -1,100 -2.8% -2.8% -2.7% 

EIA Case 38,900 38,400 39,500 -700 -800 -800 -1.8% -2.0% -2.0% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 29,100 29,200 30,900 -­ -­ -­ -­ -­ -­

RFS Case 28,000 28,200 29,800 -1,100 -1,000 -1,100 -3.8% -3.4% -3.6% 

EIA Case 28,300 28,400 30,100 -800 -800 -800 -2.7% -2.7% -2.6% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 10,500 10,000 9,400 -­ -­ -­ -­ -­ -­

RFS Case 10,500 9,900 9,400 0 -100 0 0.0% -1.0% 0.0% 

EIA Case 10,600 10,000 9,400 100 0 0 1.0% 0.0% 0.0% 

199
Table 4A-7. 1,3-Butadiene Emission Inventories under Various Ethanol Use Cases 

Primary Tons/Year Change from Reference (tons) % Change from Reference 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 18,900 18,500 19,100 -­ -­ -­ -­ -­ -­

RFS Case 18,700 18,600 19,100 -200 100 0 -1.1% 0.5% 0.0% 

EIA Case 18,600 18,500 19,100 -300 0 0 -1.6% 0.0% 0.0% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 12,000 12,000 12,800 -­ -­ -­ -­ -­ -­

RFS Case 11,800 12,000 12,800 -200 0 0 -1.7% 0.0% 0.0% 

EIA Case 11,800 12,000 12,800 -200 0 0 -1.7% 0.0% 0.0% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 6,900 6,500 6,300 -­ -­ -­ -­ -­ -­

RFS Case 6,900 6,600 6,300 0 100 0 0.0% 1.5% 0.0% 

EIA Case 6,800 6,500 6,300 -100 0 0 -1.4% 0.0% 0.0% 

Sensitivity Tons/Year 

Total 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 18,600 18,200 18,700 -­ -­ -­ -­ -­ -­

RFS Case 18,200 18,000 18,300 -400 -200 -400 -2.2% -1.1% -2.1% 

EIA Case 18,000 17,800 18,200 -600 -400 -500 -3.2% -2.2% -2.7% 

On-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 11,700 11,700 12,400 -­ -­ -­ -­ -­ -­

RFS Case 11,300 11,400 12,000 -400 -300 -400 -3.4% -2.6% -3.2% 

EIA Case 11,200 11,300 11,900 -500 -400 -500 -4.3% -3.4% -4.0% 

Non-Road 2012 2015 2020 2012 2015 2020 2012 2015 2020 

Reference 6,900 6,500 6,300 -­ -­ -­ -­ -­ -­

RFS Case 6,900 6,600 6,300 0 100 0 0.0% 1.5% 0.0% 

EIA Case 6,800 6,500 6,300 -100 0 0 -1.4% 0.0% 0.0% 

200
Chapter 5: Air Quality Impacts 

5.1 Ozone 

We performed ozone air quality modeling simulations for the eastern United States using 

the ozone Response Surface Model (RSM) to estimate the effects of the projected changes in 

emissions from gasoline vehicles and equipment. The ozone RSM is a screening-level air 

quality modeling tool that allows users to quickly assess the estimated air quality changes over 

the modeling domain. The ozone RSM is a model of a full-scale air quality model and is based 

on statistical relationships between model inputs and outputs obtained from the full-scale air 

quality model. In other words, the ozone RSM uses statistical techniques to relate a response 

variable to a set of factors that are of interest, e.g., emissions of precursor pollutants from 

particular sources and locations. The following section describes the modeling methodology, 

including the development of the multi-dimensional experimental design for control strategies 

and implementation and verification of the RSM technique. Additional detail is available in the 

Air Quality Modeling Technical Support Document (AQMTSD) that was drafted for the Mobile 

Source Air Toxics Rule Proposal (published March 29, 2006).AAAA 

The foundation for the ozone response surface metamodeling analyses was the CAMx 

modeling done in support of the final Clean Air Interstate Rule (CAIR). The CAIR modeling is 

fully described in the CAIR Air Quality Modeling Technical Support Document, but a brief 

description is provided below.BBBB The modeling procedures used in the CAIR analysis (e.g., 

domain, episodes, meteorology) have been used for several EPA rulemaking analyses over the 

past five years and are well-established at this point. 

The ozone RSM uses the 2015 controlled CAIR emissions inventory as its baseline, 

assuming future fuel quality remains unchanged from pre-Act levels, which serves as the 

baseline for the analysis of the final RFS standards.CCCC We then compare these baseline 

emissions to the emissions which would have occurred in the future if fuel quality had remained 

unchanged from pre-Act levels to those which will occur with fuel quality reflecting the 

increased renewable fuel use projected in the future. This approach differs from that 

traditionally taken in EPA regulatory impact analyses. Traditionally, we would have compared 

future emissions with and without the requirement of the Act. However, as described in Chapter 

1, we expect that total renewable fuel use in the U.S. in 2012 to exceed 7.5 billion gallons even 

in the absence of the Renewable Fuel Standard (RFS). Thus, a traditional regulatory impact 

analysis would have shown no impact on emissions or air quality. 

The modeling simulations that comprised the metamodeling were conducted using 

CAMx version 3.10. It should be noted that because the ozone RSM is built from CAMx air 

quality model runs, it therefore has the same strengths and limitations of the underlying model 

and its inputs. CAMx is a non-proprietary computer model that simulates the formation and fate 

of photochemical oxidants including ozone for given input sets of meteorological conditions and 

emissions. The gridded meteorological data for three historical episodes were developed using 

the Regional Atmospheric Modeling System (RAMS), version 3b.DDDD In all, 30 episode days 

were modeled using frequently-occurring, ozone-conducive, meteorological conditions from the 

201 
summer of 1995. Emissions estimates were developed for the evaluation year (1995) as well as a 

future year (2015). 

The CAMx model applications were performed for a domain covering all, or portions of, 

37 States (and the District of Columbia) in the Eastern U.S., as shown in Figure 5.1-1. The 

domain has nested horizontal grids of 36 km and 12 km. However, the output data from the 

metamodeling is provided at a 12 km resolution (i.e., cells from the outer 36 km cells populate 

the nine finer scale cells, as appropriate). Although the domain of the ozone RSM is the 37 

Eastern states, the expanded use of ethanol in fuel is expected to occur nationwide. Chapter 4 

describes the nationwide inventory impacts associated with the F standards. 

Figure 5.1-1. Map of the CAMx Domain Used for RFS Ozone Metamodeling 

The ozone RSM used for assessing the air quality impacts of expanded ethanol use in fuel 

was developed broadly to look at various control strategies with respect to attaining the 8-hour 

ozone NAAQS. The experimental design for the ozone RSM covered three key areas: type of 

precursor emission (NOx or VOC), emission source type (i.e., onroad vehicles, nonroad vehicles, 

area sources, electrical generating utility (EGU) sources, and non-utility point sources), and 

location in or out of a 2015 model-projected residual ozone nonattainment area. This resulted in 

a set of 14 emissions factors. 

The 14 emission factors were randomly varied and used as inputs to CAMx. The 

experimental design for these 14 factors was developed using a Maximin Latin Hypercube 

method. Based on a rule of thumb of 10 runs per factor, we developed an overall design with 

202 
140 runs (a base case plus 139 control runs). The range of emissions reductions considered 

within the metamodel ranged from 0 to 120 percent of the 2015 CAIR emissions. This 

experimental design resulted in a set of CAMx simulations that serve as the inputs to the ozone 

response surface metamodel. 

To develop a response surface approximation to CAMx, we used a multidimensional 

kriging approach, implemented through the MIXED procedure in SAS. We modeled the 

predicted changes in ozone in each CAMx grid cell as a function of the weighted average of the 

modeled responses in the experimental design. A response-surface was then fit for several ozone 

metrics, namely the ozone design value, the 1-hour maximum value, the 24-hour average value 

and the average ozone level between 9 am and 5 pm. The effect of changes in VOC and NOx 

emissions on ozone was estimated in each grid cell covered by the model for each ozone metric 

except the ozone design value. The ozone design value is the mathematically determined 

pollutant concentration at a particular monitoring site that must be reduced to, or maintained at or 

below the National Ambient Air Quality Standard to assume attainment. The 8-hour ozone 

design value is the 3-year average of the fourth-highest daily maximum 8-hour average ozone 

concentrations measured at each monitor within an area over each year, which must not exceed 

0.08 ppm (85 ppb, considering round-off). Thus, ozone design values only exist for grid cells 

which contain ozone monitoring stations and where ozone attainment has been an issue. Ozone 

design values have been developed for 525 of the 2696 counties in the 37 state region. of the 31 

these The specific ozone design values used in this analysis are those for 2001, which represent 

the average of the ozone design values determined for three, three-year periods (1999-2001, 

2000-2002, and 2001-2003). Validation was performed and is summarized in the Mobile Source 

Air Toxics rule Air Quality Modeling Technical Support Document. The validation exercises 

indicated that the ozone RSM replicates CAMx response to emissions changes very well for 

most emissions combinations and in most locations. 

The ozone RSM limits the number of geographically distinct changes in VOC and NOx 

emissions which can be simulated. Emissions from motor vehicles and nonroad equipment can 

be varied separately. Distinct percentage changes in either the motor vehicle or nonroad 

inventories can also be applied in ozone nonattainment and attainment areas. However, distinct 

emission impacts cannot be simulated in various ozone nonattainment areas (e.g., Chicago and 

Houston or New York and Kansas City). This limits our ability to simulate the impact of 

increased ethanol use in a couple of ways. First, ethanol use is not geographically uniform 

across the U.S., either currently or in the future. Thus, the emission impacts resulting from 

changes in ethanol use also varies geographically. Second, the emission impacts of ethanol use 

are not uniform. Ethanol use in RFG and other areas which do not grant ethanol blends a 1.0 psi 

RVP waiver will not experience as much of an increase in VOC emissions with increased 

ethanol use as areas which grant ethanol blends an RVP waiver. Third, the impacts of new 

ethanol plants will be even more geographically focused. The Ozone RSM cannot generally be 

applied to model the emission impacts from such local sources for a couple of reasons. One, the 

location of new ethanol plants is difficult to predict in many cases. Two, the impact of these 

plants on local emissions can be very large in percentage terms given the absence of a lot of 

other industrial activity. The Ozone RSM was designed to represent the ozone impact of the 

same change in VOC or NOx emissions across a broad region (e.g., all attainment areas). 

Therefore, it cannot be used to model the impact of a large change in one county’s emissions 

203 
without also assuming the same change in the upwind county’s emissions. As not every county 

will contain a new ethanol plant, the assumptions inherent in the Ozone RSM do not match the 

situation of a new individual point source, such as an ethanol plant. 

We developed a methodology which would best approximate the impact of changes in 

local emissions on the ozone level in each local area, while maintaining as much of the impact of 

ozone transport from other areas as possible given the above mentioned limitations. We do this 

by running the ozone RSM twice for each scenario and drawing the resultant ozone impact from 

the run which best matched the emission impact expected in a particular local area, considering 

both the change in emissions modeled for that particular local area, as well as that occurring in 

upwind areas. 

First, as mentioned above, ethanol use is expected to change dramatically in some areas, 

but not at all in others. Averaging the emission impacts across these two types of areas and 

estimating the associated ozone impact would be very misleading. No area would be likely to 

experience the ozone impact predicted. Some areas would experience a much greater impact, 

while others would experience no impact. Therefore, the first step in using the Ozone RSM to 

predict the ozone impacts related to the RFS is to estimate the change in VOC and NOx 

emissions in those areas ethanol blend market share changed significantly. As was done in the 

analysis of local emission impacts presented in Section 4.1.3.3 above, we defined a significant 

change in ethanol blend market share as a change of 50% or more. This focuses the change in 

emissions in those areas where the change is likely to occur. 

As discussed in Chapters 3 and 4, the effect of ethanol use on emissions differs 

depending on the baseline fuel quality and the applicable RVP standards. In particular, ethanol 

use has significantly different impacts on emissions in RFG, low RVP and 9 RVP areas. 

Therefore, in order to better predict the ozone impact likely to occur in specific areas, we 

estimate the change in VOC and NOx emissions separately for RFG, low RVP and 9 RVP areas 

(per above, only for those areas in each case where ethanol blend market share changed by 50% 

or more). 

The Ozone RSM only covers the 37 easternmost states in the U.S. Therefore, we limited 

the calculation of VOC and NOx emission impacts to only those states. The Ozone RSM was 

developed with the year 2015 as the default year. Since we develop most of our impacts of the 

RFS for the year 2012 and 2015, we chose to run the Ozone RSM for, 2015. The Ozone RSM is 

designed to accept emission changes in terms of total onroad and total nonroad sources, 

respectively, and both emission categories include diesels. Therefore, we included estimates of 

VOC and NOx emissions from diesel vehicles and equipment in 2015 in our calculation of the 

emission impacts. These diesel emissions do not change between the various RFS scenarios. 

However, they do reduce the effective percentage change in VOC and NOx emissions which is 

projected to occur. Overall, these analyses are very similar to those described in Section 4.1.3.3 

above, with the exceptions of the limitation to 37 states and the inclusion of diesel emissions. 

The results of these calculations are shown in Table 5.1-1. 

204 
Table 5.1-1. 

Emission Inputs to Ozone Modeling: Change in Total Mobile Sources Emissions in 37 

Eastern States where Ethanol Use Changes Significantly, July 2015 (percent change) 

VOC NOx 

On Road Non Road On Road Non Road 

Attain. 

(9 RVP) 

NonAttain. Attain. 

(9 RVP) 

NonAttain. Attain. 

(9 RVP) 

NonAttain. Attain. 

(9 RVP) 

NonAttain. 

Primary Analysis 

RFS 

RFG 7.5% -1.2% 1.5% 1.8% 0.2% 0.1% 3.0% -1.9% 

LRVP 7.5% 8.9% 1.5% 1.7% 0.2% 0.2% 3.0% 3.3% 

EIA 

RFG 8.2% 1.7% 2.3% 2.4% 0.2% 0.1% 3.2% 0.7% 

LRVP 8.2% 9.3% 2.3% 2.0% 0.2% 0.2% 3.2% 3.3% 

Sensitivity Analysis 

RFS 

RFG 6.0% -5.3% 1.5% 1.8% 2.9% 1.7% 3.0% -1.9% 

LRVP 6.0% 7.0% 1.5% 1.7% 2.9% 3.5% 3.0% 3.3% 

EIA 

RFG 6.6% -1.2% 2.3% 2.4% 3.0% 2.2% 3.2% 0.7% 

LRVP 6.6% 7.4% 2.3% 2.0% 3.0% 3.6% 3.2% 3.3% 

Our category of 9 RVP areas is very similar to the set of attainment areas in the Ozone 

RSM. Therefore, the application of the emission impacts expected in 9 RVP areas in the Ozone 

RSM was straightforward. However, both RFG and low RVP areas together generally comprise 

the set of nonattainment areas in the Ozone RSM. As seen in Table 5.1-1, the expected emission 

impacts of the various RFS scenarios differ significantly depending on whether the area has RFG 

or low RVP fuel. Both sets of emission impacts could not be run in the Ozone RSM at the same 

time. Therefore, we ran the Ozone RSM twice. The first run applied the emission impacts 

estimated for RFG areas to the ozone nonattainment areas in the Ozone RSM and applied the 

emission impacts for 9.0 RVP areas to the ozone attainment areas in the Ozone RSM. This run 

should produce satisfactory projections of ozone impacts for all areas except those areas with 

low RVP, as well as those areas where ethanol use is not expected to change. 

The second run applied the emission impacts estimated for Low RVP areas to the ozone 

nonattainment areas in the Ozone RSM and applied the emission impacts for 9.0 RVP areas to 

the ozone attainment areas in the Ozone RSM. This run should produce satisfactory projections 

of ozone impacts for all areas except those areas with RFG, as well as those areas where ethanol 

use is not expected to change. 

For both runs of the Ozone RSM, we set the predicted change in ozone to zero in those 

counties not expected to experience a significant change in ethanol use. This ignores any impact 

from ozone transport from other areas where ethanol use did change. However, we believe that 

the ozone impacts due to transport are much smaller than those associated with changes in local 

emissions. This is particularly true in this case, where the percentage change in emissions would 

be the same in both the local and upwind areas. 

205 
We merged the results of the two runs by attributing ozone impacts from each county 

according to their nonattainment designation and fuel type. For non-attainment areas, this choice 

was obvious. Non-attainment counties with RFG programs were assigned the ozone impacts 

from the first run (i.e., the run where the changes in VOC and NOx emissions were the average 

of those observed for RFG areas). Non-attainment counties with Low RVP programs were 

assigned the ozone impacts from the second run (i.e., the run where the changes in VOC and 

NOx emissions were the average of those observed for Low RVP areas). For attainment areas 

(i.e., 9 RVP areas), the results of either run could be used, as both runs of the Ozone RSM 

applied the same emission changes to attainment areas. Thus, the local emission impacts would 

be identical in the two Ozone RSM runs. Ozone transport is also likely identical for the vast 

majority of these counties, given that they are likely downwind from other attainment area 

counties. The only difference occurs if an attainment area is downwind of a RFG or Low RVP 

area. For a nationwide analysis such as this one, we were not able to determine for each 

attainment area whether a potential upwind area was more likely to be an RFG or Low RVP area. 

Therefore, we chose to use the ozone impacts results from the first Ozone RSM run of the model 

(i.e., where the emission impacts for RFG areas were applied to ozone nonattainment areas) for 

all attainment areas. We chose this run because RFG areas tend to have the higher ozone levels 

than Low RVP areas and thus, would be more likely to affect areas downwind. We present the 

ozone impacts of increased ethanol use resulting from this methodology in the following section. 

5.1.1 Ozone Response Surface Metamodel Results 

This section summarizes the results of our modeling of ozone air quality impacts in the 

future with and without the expanded use of ethanol in fuel. The impact of increased ethanol use 

on the 8-hour ozone design values in 2015 are presented in Table 5.1-2. The changes presented 

in Table 5.1-2 are for those counties with 2001 modeled design values.57 The Chapter 5 

Appendix presents the impacts of increased ethanol use on a number of alternative measures of 

ambient ozone concentration. 

57 2001 design values were calculated as an average of the 1999-2001, 2000-2002 and 2001-2003 design values at 

each monitoring site. Please see the Air Quality Modeling Technical Support Document for the final Clean Air 

Interstate Rule for additional information. 

206 
Table 5.1-2. 

Impact of Increased Ethanol Use on 8-hour Ozone Design Values in 2015 (ppb) 

RFS Case EIA Case 

Primary Analysis 

Minimum Change -0.015 0.000 

Maximum Change 0.329 0.337 

Average Change Across 37 States 0.057 0.079 

Population-Weighted Change Across 37 States 0.052 0.056 

Average Change Where Ethanol Use Changed Significantly States 0.153 0.181 

Population-Weighted Change Where Ethanol Use Changed Significantly 

States 

0.154 0.183 

Sensitivity Analysis 

Minimum Change -0.115 0.000 

Maximum Change 0.624 0.549 

Average Change Across 37 States 0.111 0.142 

Population-Weighted Change Across 37 States 0.092 0.096 

Average Change Where Ethanol Use Changed Significantly States 0.300 0.325 

Population-Weighted Change Where Ethanol Use Changed Significantly 

States 

0.272 0.315 

As can be seen, ozone levels generally increase with increased ethanol use. This is likely 

due to the projected increases in both VOC and NOx emissions. Some areas do see a small 

decrease in ozone levels. In our primary analysis, where exhaust emissions from Tier 1 and later 

onroad vehicles are assumed to be unaffected by ethanol use, the population-weighted increase in 

ambient ozone levels is 0.052-0.056 ppb. Since the 8-hour ambient ozone standard is 0.08 ppm 

(85 ppb), this increase represents about 0.06 percent of the standard, a very small percentage58. 

While small, this figure includes essentially zero changes in ozone in areas where ethanol use did 

not change. When we focus just on those areas where the market share of ethanol blends 

changed by 50 percent or more, the population-weighted increase in ambient ozone levels rises 

to 0.154-0.183 ppb. This increase represents about 0.2 percent of the standard. 

In our sensitivity analysis, where exhaust emissions from Tier 1 and later onroad vehicles 

are assumed to respond to ethanol like Tier 0 vehicles, the population-weighted increase in 

ambient ozone levels across the entire 37 state area is slightly less than twice as high, or 0.092­

0.096 ppb. This increase represents about 0.11 percent of the standard. When we focus just on 

those areas where the market share of ethanol blends changed by 50 percent or more, the 

population-weighted increase in ambient ozone levels rises to 0.272-0.315 ppb. This increase 

represents about 0.35 percent of the standard. 

For the primary analysis, we also present the counties with the largest increases in the 

ozone design value. Table 5.1-3 presents the county level ozone design value impacts of the 

RFS case, while Table 5.1-4 presents the same information for the EIA case. It is important to 

note that the results of this ozone response surface metamodeling exercise is meant for 

screening-level purposes only and does not represent the results that would be obtained from 

58 Appendix I of 40 CFR Part 50. 

207 
full-scale photochemical ozone modeling. It is also important to note that the ozone RSM results 

indicate that the counties which are projected to experience the greatest increase in ozone design 

values are generally counties that are projected to have ambient concentrations well below the 

0.08 ppm ozone standard in the 2015 baseline. 

Table 5.1-3. RFS Case, Primary Analysis: 

2015 Ozone Response Surface Metamodeling Resultsa for Counties with 

Largest Increases in Ozone 8hr Design Value (ppb) Due to Increased Use of Ethanol 

State Name County Name 2015 Baseline 

(Post-CAIR)

b 2015 RFS Case 

Effect of Expanded 

Ethanol Use (ppb) 

2015 

Population 

Arkansas Crittenden Co 78 78.3289 0.3 53,852 

Ohio Geauga Co 82.5 82.7972 0.3 108,600 

Ohio Mahoning Co 74.7 74.9943 0.3 248,545 

Indiana Lake Co 80.7 80.9929 0.3 490,796 

Ohio Medina Co 72 72.2909 0.3 187,686 

Ohio Summit Co 77.4 77.6901 0.3 557,892 

Maine York Co 77.6 77.8825 0.3 210,006 

Ohio Stark Co 71.7 71.9707 0.3 384,672 

Ohio Clinton Co 75.7 75.9705 0.3 50,635 

Louisiana 

West Baton 

Rouge Parish 77.4 77.6685 0.3 23,202 

Louisiana 

Livingston 

Parish 76.6 76.8656 0.3 141,807 

Illinois Cook Co 81.1 81.3605 0.3 5,362,932 

Indiana Shelby Co 76.2 76.4587 0.3 47,904 

Ohio Knox Co 71.4 71.6541 0.3 62,138 

Alabama Mobile Co 68 68.2514 0.3 430,341 

Louisiana Jefferson Parish 77.1 77.351 0.3 512,963 a

 The Ozone RSM is meant for screening-level purposes only and does not represent results that would be 

obtained from full-scale photochemical ozone modeling. In particular, the model does not account for changes in 

CO emissions or VOC reactivity, both of which should decrease with increased ethanol use and directionally 

reduce ozone. in areas where ozone formation is VOC-limited. b The Clean Air Interstate Rule (CAIR) modeling is fully described in the CAIR Air Quality Modeling Technical 

Support Document (Docket EPA-HQ-OAR-2005-0036). 

208 
Table 5.1-4. EIA Case, Primary Analysis: 

2015 Ozone Response Surface Metamodeling Results for Counties with 

Largest Increases in Ozone 8hr Design Value (ppb) Due to Increased Use of Ethanol 

State Name County Name 2015 Baseline 

(Post-CAIR)

c 2015 EIA Case 

Effect of Expanded 

Ethanol Use (ppb) 

2015 

Population 

Ohio Geauga Co 82.5 82.8369 0.3 108,600 

Ohio Clinton Co 75.7 76.0218 0.3 50,635 

Ohio Mahoning Co 74.7 75.0213 0.3 248,545 

Arkansas Crittenden Co 78 78.3204 0.3 53,852 

Ohio Summit Co 77.4 77.7175 0.3 557,892 

Mississippi Adams Co 67.2 67.5164 0.3 33,495 

Ohio Stark Co 71.7 72.0153 0.3 384,672 

Indiana Shelby Co 76.2 76.5115 0.3 47,904 

Maine York Co 77.6 77.902 0.3 210,006 

New York Wayne Co 71.6 71.8926 0.3 103,846 

Texas Travis Co 69.4 69.6912 0.3 1,022,772 

Ohio Medina Co 72 72.2909 0.3 187,686 

Maine Hancock Co 76.8 77.0904 0.3 55,606 

Maine Kennebec Co 64.9 65.1903 0.3 122,363 

Louisiana Livingston Parish 76.6 76.8883 0.3 141,807 

Louisiana 

West Baton 

Rouge Parish 77.4 77.6869 0.3 23,202 

Louisiana Lafourche Parish 72.7 72.984 0.3 95,881 

Mississippi Warren Co 56.2 56.4827 0.3 52,075 

Michigan Huron Co 71.9 72.1766 0.3 37,530 

Ohio Franklin Co 77 77.2716 0.3 1,181,578 

Ohio Trumbull Co 80 80.2713 0.3 227,546 

Louisiana Jefferson Parish 77.1 77.3707 0.3 512,963 

Florida Pinellas Co 62.3 62.5639 0.3 998,864 

Ohio Delaware Co 72.1 72.3606 0.3 149,341 

Ohio Knox Co 71.4 71.6579 0.3 62,138 

Florida Duval Co 50.6 50.8568 0.3 895,678 

Indiana Marion Co 74.6 74.8565 0.3 889,645 

Indiana Madison Co 72.9 73.1564 0.3 140,575 

Massachusett 

s Middlesex Co 75.8 76.0564 0.3 1,498,849 

Michigan Oakland Co 79.2 79.4542 0.3 1,355,671 

Pennsylvania Beaver Co 70.5 70.7528 0.3 184,649 

New York Monroe Co 74.3 74.5521 0.3 754,405 

Mississippi Harrison Co 69.3 69.5517 0.3 216,926 

There are a number of important caveats concerning our estimated ozone impacts using 

the Ozone RSM. The Ozone RSM does not account for changes in CO emissions. As shown in 

Chapter 4, ethanol use should reduce CO emissions significantly, directionally reducing ambient 

ozone levels in areas where ozone formation is VOC-limited. Accounting for the reduction in 

CO emissions in NOx-limited areas, however, may have little impact on the ozone impact of 

ethanol use. 

209 
The Ozone RSM also does not account for changes in VOC reactivity. With additional 

ethanol use, the ethanol content of VOC should increase. Ethanol is less reactive than the 

average VOC. Therefore, this change should also reduce ambient ozone levels in a way not 

addressed by the Ozone RSM. Again, like the impact of reduced CO emissions, this effect 

applies to those areas where ozone formation is VOC-limited. Another limitation is the RSM’s 

inability to simulate the spatial distribution of emission impacts associated with the standard. 

Instead, we are forced to make simplifying assumptions about the geographic uniformity of RFS 

emissions impacts, explained above. The caveats and limitations associated with the RSM 

highlight the fact that it should only be used as a screening-level tool to characterize broad trends 

associated with changes in different source categories of ozone precursors. 

Finally, our application of the Ozone RSM here does not include the impact of emissions 

from new ethanol plants. Directionally, this will increase ozone levels in the vicinity of the new 

plant. As discussed in Chapter 4, the overall VOC and NOx emission impacts of new ethanol 

plants are only slightly lower than the emission impacts resulting from increased use of ethanol 

in vehicles and equipment. Given the concentrated nature of these impacts, the ozone impacts of 

these new plants should be a focus of further study in the future. 

Keeping these limitations in mind, the expanded use of ethanol will impact the national 

emissions inventory of precursors to ozone, such as VOCs and NOx, as described in Chapter 4. 

Exposure to ozone has been linked to a variety of respiratory effects including premature 

mortality, hospital admissions and illnesses resulting in school absences. Ozone can also 

adversely affect the agricultural and forestry sectors by decreasing yields of crops and forests. 

The Wisconsin Department of Natural Resources (DNR) recently performed a similar 

study of the impact of increased ethanol use on ozone.EEEE They estimated that the conversion of 

gasoline outside of RFG areas in Wisconsin to E10 blends would increase ozone in these areas 

on the order of 1 ppb to as much as 2 ppb. (RFG areas in Wisconsin already contain 10 vol% 

ethanol.) This ozone increase was due to the predicted increase in NOx emissions associated 

with ethanol use, since the non-RFG areas in Wisconsin are generally NOx limited for ozone 

formation. 

The Wisconsin DNR estimated the ozone impact for calendar year 2003 and assumed that 

all vehicles experience the increase in NOx emissions. Thus, their results are more comparable 

to our sensitivity analysis, than our primary analysis. For the two increased ethanol use 

scenarios, our sensitivity analysis projects increased ozone levels for several Wisconsin counties 

of 0.35-40 ppb. Because the Wisconsin DNR analyzed calendar year 2003 emissions and air 

quality, their base emission levels are much higher than those estimated here for the year 2015. 

Emission standards applicable to new vehicles and equipment are continually reducing emissions 

over time. Per the emission models used here and by the State of Wisconsin (NONROAD and 

MOBILE6), the effect of fuel quality is generally estimated in terms of a percentage change in 

the base emission level. As emissions from vehicles and equipment decrease over time, the 

absolute impact of fuel quality changes decreases at the same rate. Thus, the absolute emission 

changes predicted here for 2015 could easily be a factor of two lower than those predicted by 

210 
Wisconsin for 2003. This is likely the primary cause of the difference in the two sets of 

projected ozone impacts. 

We received a comment from the Missouri Department of Natural Resources on the 

ozone impact analysis performed for the NPRM which expressed a concern that our approach of 

zeroing out the ozone impact in areas which did not experience a significant change in ethanol 

use had the effect of ignoring the impact of ozone transport due to increased ethanol use in 

upwind areas. This comment is correct. In a national analysis such as this one, it is not practical 

to go through over 3100 counties to determine which counties might have not experienced a 

change in ethanol use in a particular ethanol use case, but is downwind of an area which did. 

Still, the issue is a potentially relevant one and of reasonable interest particularly to those tasked 

with air quality management. 

In an attempt to approximate the impact of ozone transport from areas which did 

experience a change in ethanol use on ozone in areas which did not, we performed one additional 

run of the Ozone RSM. This additional run applied the changes in VOC and NOx emissions 

estimated above for attainment areas from our sensitivity analysis for the EIA case to emissions 

in attainment areas, and applied no change in emissions in non-attainment areas. We then 

compared the resulting ozone levels to those from the base case, focusing on the difference in 

ozone levels in non-attainment areas. Emissions in non-attainment areas were the same in both 

cases (no change from the base case). Thus, the difference in ozone levels in non-attainment 

areas should only be due to changes in emissions and ozone levels in upwind attainment areas. 

The results of this comparison indicated that, in terms of the 8-hour ozone design value, 

ozone levels in non-attainment areas (i.e., RFG or Low RVP areas) decreased by 0.03 ppb. 

Thus, the average impact due to ozone transport is a reduction in ozone in downwind areas. 

However, the standard deviation in the ozone impact was 0.05 ppb, indicating that a significant 

number of areas experienced an increase, though most experienced a decrease. This is not 

surprising given that ozone in some attainments areas is VOC limited and may be decreasing in 

this fuel case, while others are NOx limited and may be increasing. The maximum ozone 

reduction was 0.17 ppb, while the maximum increase was 0.12. More precise local atmospheric 

dispersion modeling will be needed in order to estimate this type of impact for specific nonattainment areas. 

In summary, we estimate that the measurable changes in VOC and NOx which are a 

result of increased ethanol use will, on average, result in small increases in ambient ozone 

formation. As we discussed above, the ozone modeling results in a net increase in the average 

population weighted ozone design value metric measured within the modeled domain (37 

Eastern states and the District of Columbia). In Appendix A, we also present the impacts of 

increased ethanol use on a number of alternative measures of ambient ozone concentration. We 

acknowledge, however, that to the extent it occurs, increased future levels of ambient 

concentrations of ozone related to the increased use of ethanol may result in detrimental health 

and welfare effects due to ozone. 

211 
5.2 Particulate Matter 

5.2.1 Impact of Changes in Direct PM Emissions 

The amount of data evaluating the impact of ethanol and MTBE blending on direct 

emissions of PM from gasoline-fueled vehicles is extremely limited, as discussed in Chapter 3. 

Most studies do not test PM emissions from vehicles fueled with unleaded gasoline, because the 

level of PM emissions from properly operating vehicles is usually very low, less than 0.1 g/mi. 

Two studies indicate that the addition of ethanol might reduce direct PM emissions from 

gasoline vehiclesFFFF ,GGGG. However, both studies were performed under wintertime conditions 

and one at high altitude. One of the studies only consisted of three vehicles. The available data 

indicate that ethanol blending might reduce exhaust PM emissions under very cold weather 

conditions (i.e., 0 F or less), particularly at high altitude. There is no indication of PM emission 

reductions at higher temperatures or under warmed up conditions. Thus, the data are certainly 

too limited to support a quantitative estimate of the effect of ethanol on PM emissions. 

5.2.2 Potential Impact of Changes in Secondary PM Formation 

In addition to being emitted directly from a combustion source, fine particles can be 

formed through a series of chemical reactions in the atmosphere when SO2, NOx, and VOC 

oxidize or otherwise react to form a wide variety of secondary PM. For example, SO2 oxidizes 

to SO3 and sulfuric acid and NOx oxidizes to NO3 and nitric acid which, in turn, react with 

ammonia in the atmosphere to form ammonium sulfate and ammonium nitrate. Particles 

generated through this gas to particle conversion are referred to as secondary aerosols (SA) and 

represent a significant portion of ambient fine particulate matter. Studies have shown that as 

much as 70% of the total organic carbon in urban particulate matter can be attributed to 

secondary organic aerosol (SOA) formation although the amount can also be less.HHHH 

Secondary PM tends to form more in the summer with higher temperatures and more intense 

sunlight. 

Source-receptor modeling studies conducted in the Los Angeles area is 1993 by Schauer 

et alIIII indicate that as much as 67% of the fine particulate matter collected could not be 

attributed to primary sources. The authors concluded that much of this unidentifiable organic 

matter is secondary organic aerosol formed in the atmosphere. This is consistent with previous 

studies conducted by Turpin and Huntzicker in 1991 who concluded that 70% of the total 

organic carbon in urban PM measurements made in southern California can be attributed to 

SOA. 

Gas phase VOCs are oxidized by OH, NO2, peroxyacetylnitrate (PAN), and ozone in the 

atmosphere, but their propensity to condense in the particle phase is a function of two factors: 

volatility and reactivity. To accumulate as an aerosol, a reaction product must first be formed in 

the gas phase at a concentration equal to its saturation concentration. This requirement will not 

be met if the relevant gas-phase reactions of the VOC are too slow or if the vapor pressure of the 

reaction product is higher than the initial concentration of its VOC precursor.JJJJ Limited data 

for reaction rate constants determined both experimentally and estimated by structural 

212 
relationships are available in the published literature. However, the atmospheric chemistry 

behind SOA reaction rates and the estimated aerosol yield is highly complex and carries with it a 

great deal of uncertainty. Research in this area is ongoing and thus the capacity to quantitatively 

model SOA formation is not yet a straightforward process. 

In general, all reactive VOC are oxidized by OH or other compounds. Additionally, 

alkenes, cycloalkenes, and other olefinic compounds can react with ozone and NO2 to form 

secondary aerosols. In fact, ozone is responsible for nearly all the SOA formation from olefins, 

while OH plays little or no role at all (Grosjean and Seinfeld, 1989; Izumi and Fukuyama, 1990). 

Many VOC, however, will never form secondary organic aerosol under atmospheric conditions 

regardless of their reactivity. This is because the products of reactions of these compounds have 

vapor pressures that are too high to form aerosols at atmospheric temperatures and pressures. 

These include all alkanes and alkenes with up to 6 carbon atoms, benzene and many lowmolecular weight carbonyls, chlorinated compounds and oxygenated solvents (Grosjean, 1992). 

The VOC that have the greatest propensity to form SOA include aromatic hydrocarbons 

(such as toluene but even including benzene), higher molecular weight olefins and cyclic olefins, 

and higher molecular weight paraffins. Kleindienst et al suggest that a high fraction of SOA is 

due to aromatic hydrocarbon precursors. Furthermore, “aromatic products having a single alkyl 

group on the aromatic ring were found to represent a ‘high-yield’ family (e.g., toluene, 

ethylbenzene); compounds having multiple methyl groups (e.g., m-xylene, 1,2,4­

trimethylbenzene) were found to represent a ‘low-yield’ family” (Kleindienst, 269). All of the 

above mentioned VOC precursors are important either because there are large amounts of these 

particular VOC emitted per day, or because a large fraction of the VOC reacts, or a combination 

of the two. Based on VOC emissions inventory data collected in the Los Angeles area, the most 

important aerosol precursors (in the LA area using 1982 VOC emissions inventories) are listed in 

Table 5.2-1 below: 

Table 5.2-1. 

Predicted In Situ SOA Formation 

During a Smog Episode in Los Angeles

VOC Functional Group kg emitted daily* Secondary PM % 

Produced (kg)* yield 

Aromatics 223985 3061 1.37 

Olefins 

Alkenes 31163 608 1.95 

Cyclic Olefins 3220 144 4.47 

Terpenes 6000 626 10.43 

Paraffins 

Alkanes 140493 368 0.26 

Cycloalkanes 37996 96 0.25 

*Source: Grosjean et al, 1992 

These predictions are a function of input data collected in the Los Angeles area, and 

assume ambient levels of [ozone] = 100 ppb, [OH]=1.0x106

 molecules/cm3, and [NO3]=0 with 6 

hours of reaction time. Aromatics are the largest functional group in terms of the absolute 

213 
quantities of VOC emitted daily, and thus they eventually form the most SOA. Likewise, many 

high molecular weight paraffins (alkanes) form SOA on a significant scale simply because their 

emissions are high. However, the relative fraction of paraffins that react is less than that of 

aromatics in smog chamber experiments simulating SOA formation in the atmosphere. For 

olefins, the alkenes exhibit a combination of both relatively high emissions, and a high fraction 

of VOC reacted to form SOA. Cyclic olefins, in contrast, are emitted in relatively low levels, but 

a high fraction of these VOC react and the end result is a proportionally higher SOA yield than 

with the alkenes. Lastly, there are several “miscellaneous” compounds and terpenes that are 

emitted on a relatively small scale (in southern California), but that produce a substantial amount 

of secondary organic aerosol. 

Researchers at EPA recently completed a field study in the Raleigh/Durham area of 

North Carolina that investigated the contribution of various sources to ambient PM 2.5 

concentrations.

KKKK In the study they identified toluene as an SOA precursor. They estimate 

that mobile sources contribute nearly 90% of the total toluene emissions in that region based on a 

chemical mass balance approach. At the same time, however, SOA attributable to non-fuelrelated VOC (i.e., biogenic emissions) was found to be an even larger contributor to SOA (i.e., 

toluene was not likely the dominant source of SOA in this area). This study is currently 

undergoing peer review and will be published shortly. Qualitatively, however, this information 

is still quite useful since the study identifies a contributing source of SOA that is attributable 

almost entirely due to mobile sources. 

VOC reaction rates increase with increasing ambient temperature and sunlight intensity, 

so the level of SOA formed is much higher in summer than in winter. Even in the more 

temperate coastal climates of southern CA, studies have found the summertime concentration of 

SOA calculated through Chemical Mass Balance models show SOA formation to be anywhere 

from 2 – 5 times higher in summer than winter. In a study conducted at both urban and rural 

locations in the southeastern United Sates, the concentration of SOA in the summer and early fall 

was roughly 2-3 times that of colder monthsLLLL. 

As mentioned in Chapters 2 and 3, the addition of ethanol should reduce aromatics in 

gasoline, which will in turn reduce the aromatics emitted in the exhaust. However, quantifying 

the emission reduction is not possible at this time due to a lack of speciated exhaust data for 

newer vehicles running on ethanol blends. In addition, increased NOx emissions resulting from 

the increased use of ethanol could increase the formation of nitrate PM. 

Based on the following, we believe that it is likely that the decrease in secondary PM 

from organic aromatic hydrocarbons is likely to exceed the increase in secondary nitrate PM. In 

1999, NOx emissions from gasoline-fueled vehicles and equipment comprised about 20% of 

national NOx emissions from all sourcesMMMM. In areas where ethanol use increases 

dramatically, NOx emissions from gasoline-fueled vehicles and equipment increases roughly 5­

10%. This is roughly equivalent to a 1-2% increase in NOx emissions nationwide. 

In contrast, gasoline-fueled vehicles and equipment comprised over 60% of all national 

gaseous aromatic VOC emissions59

. In areas where ethanol use increases dramatically, fuel 

214 
aromatic content decreases by about 4 vol% in the summertime, averaged across conventional 

gasoline and RFG. This represents about a 15% reduction from a base level of around 27 vol%. 

Assuming a proportional relationship between fuel aromatics and aromatic emissions, this 

represents about a 24% reduction in aromatic emissions nationwide. 

In most urban areas, ambient levels of excess summer carbonaceous PM (a reasonable 

estimate of secondary organic PM) tend to exceed those of secondary nitrate PM. Thus, 

directionally, it appears likely that a net reduction in ambient PM levels will result from 

increased ethanol use. However, this should be considered a rough comparison at this time. A 

more precise comparison will have to await the incorporation of secondary organic aerosol 

formation into models, such as CMAx. 

The research to facilitate this incorporation is currently underway. EPA ORD scientists 

are currently carrying out a wide variety of laboratory studies to refine the SOA chemistry 

mechanisms for use in the next version of the CMAQ model, which is expected to be completed 

in 2007 and submitted for peer review. This information should be available in time for the 

comprehensive study of the Act’s fuel requirements which is due in 2009.60 

59 Based on internal analyses of emissions inventories. 

60 Subject to funding. 

215 
Chapter 5: Appendix 

216 
Table 5A-1. 2015 Ozone Response Surface Metamodeling Summary Statistics for the RFS 

Rulea,; Primary Scenario 

8hour Design Value (ppb) 

Statistic RFS Scenario EIA Scenario 

Minimum Change -0.015 0.000 

Maximum Change 0.329 0.337 

Average Change 0.057 0.079 

Standard Deviation 0.086 0.100 

Population-Weighted Change 0.052 0.056 

24hr Average (ppb) 

Statistic RFS Scenario EIA Scenario 

Minimum Change -0.168 -0.162 

Maximum Change 0.197 0.074 

Average Change 0.008 0.013 

Standard Deviation 0.018 0.021 

Population-Weighted Change 0.014 0.013 

1hr Maximum (ppb) 

Statistic RFS Scenario EIA Scenario 

Minimum Change -0.104 -0.094 

Maximum Change 0.213 0.180 

Average Change 0.015 0.024 

Standard Deviation 0.033 0.040 

Population-Weighted Change 0.029 0.033 

Average 9-to-5 (ppb) 

Statistic RFS Scenario EIA Scenario 

Minimum Change -0.107 -0.097 

Maximum Change 0.203 0.141 

Average Change 0.012 0.019 

Standard Deviation 0.027 0.031 

Population-Weighted Change 0.022 0.024 

Average 10-to-3 (ppb) 

Statistic RFS Scenario EIA Scenario 

Minimum Change -0.108 -0.107 

Maximum Change 0.207 0.149 

Average Change 0.012 0.019 

Standard Deviation 0.027 0.032 

Population-Weighted Change 0.023 0.024 

a Note that the statistics presented here represent ethanol use changes across the entire 37-state 

ozone RSM domain. 

217 
Table 5A-2. 2015 Ozone Response Surface Metamodeling Summary Statistics for the RFS 

Rulea; Sensitivity Scenario 

8hour Design Value (ppb) 

Statistic RFS Scenario EIA Scenario 

Minimum Change -0.115 0.000 

Maximum Change 0.624 0.549 

Average Change 0.111 0.142 

Standard Deviation 0.158 0.170 

Population-Weighted Change 0.092 0.096 

24hr Average (ppb) 

Statistic RFS Scenario EIA Scenario 

Minimum Change -0.181 -0.173 

Maximum Change 0.184 0.142 

Average Change 0.015 0.024 

Standard Deviation 0.034 0.041 

Population-Weighted Change 0.025 0.028 

1hr Maximum (ppb) 

Statistic RFS Scenario EIA Scenario 

Minimum Change -0.150 -0.133 

Maximum Change 0.498 0.346 

Average Change 0.027 0.043 

Standard Deviation 0.060 0.072 

Population-Weighted Change 0.052 0.062 

Average 9-to-5 (ppb) 

Statistic RFS Scenario EIA Scenario 

Minimum Change -0.177 -0.163 

Maximum Change 0.400 0.260 

Average Change 0.022 0.034 

Standard Deviation 0.049 0.057 

Population-Weighted Change 0.040 0.046 

Average 10-to-3 (ppb) 

Statistic RFS Scenario EIA Scenario 

Minimum Change -0.182 -0.167 

Maximum Change 0.431 0.273 

Average Change 0.022 0.035 

Standard Deviation 0.050 0.058 

Population-Weighted Change 0.041 0.047 

a Note that the statistics presented here reflect the impact of ethanol use changes across the 

entire eastern U.S. 37-state ozone RSM domain. 

218 
Chapter 6: Lifecycle Impacts on Fossil Energy and Greenhouse 

Gases 

6.1 Lifecycle Modeling 

Lifecycle modeling accounts for the energy and emissions from a production process. It 

incorporates the material aspects, input and output, of each step in a product system. This 

method helps to identify key processes and emission sources and facilitates comparisons between 

processes, consumption of natural resources, pollutant generation and environmental burden. It 

is important to note that lifecycle modeling typically provides only general comparisons, based 

on industry-wide estimates and assumptions; it does not reflect general equilibrium impacts, such 

as effects on input markets. The results of this type of analysis are highly dependent upon the 

input data used, the variables considered, and the assumptions made. Nevertheless, within these 

limitations, it can be an extremely useful tool for evaluating some of the environmental impacts 

of products and processes. 

For transportation fuels, lifecycle modeling considers all steps in the production of the 

fuel. This includes production of the fuel feedstock, transportation of the fuel feedstock to a 

processing facility, fuel processing, and distribution of the fuel to the retail outlet. If the analysis 

considers only the finished product, it is sometimes called a ‘well-to-pump’ analysis; if the fuel 

combustion emissions are included, it can be called a ‘well-to-wheel’ analysis. While both 

approaches have advantages, in this work we have considered ‘well-to-wheel’ impacts. 

However, we are not addressing the issues of vehicle technology and energy efficiency, since we 

are making the assumption that the vehicle issues will not be affected by the presence of 

renewable fuels (i.e., efficiency of combusting one Btu of renewable fuel is equal to the 

efficiency of combusting one Btu of conventional fuel). 

To put this type of analysis into perspective, consider the example of gasoline. The fuel 

feedstock is crude oil. The lifecycle analysis accounts for the energy used to extract the oil from 

the ground and any associated emissions, such as the natural gas that is flared at the well head. 

Next you evaluate transportation of the crude oil to the refinery. If it is domestic crude oil, it 

may be delivered by pipeline and/or barge. The analysis takes into account national trends for 

domestic oil transportation, and apportions energy used and emissions generated to each type of 

transportation. For foreign crude oil, the energy and emissions from ocean tankers is included, 

with an estimate of the average distance traveled by these tankers. Next is an estimation of the 

energy use and emissions from the refinery. Because gasoline is not the only product produced 

at the refinery, only a portion of the energy and emissions is allocated to gasoline production. 

There are different methods for making this allocation, based on the value of the co-products or 

an engineering assessment of the energy use and emissions from the various units in the refinery. 

You then evaluate the energy use and emissions from transporting the gasoline to market, via 

pipeline and truck, based on national average distances. Finally, vehicle energy use and 

emissions are estimated. Figure 6.1-1 illustrates this process. 

219 
Figure 6.1-1: Lifecycle Production Process, ‘Well-to-Wheel’, for Gasoline 

Crude

Refueling

C Fuel rude Crude Refining Fuel Productio

T

n ransport Transport

via pipeline via pipeline Use

Lifecycle modeling has been a useful tool in evaluating the environmental benefits of 

various alternative transportation fuels. It allows the replacement fuel to be fairly compared 

against the conventional transportation fuels – gasoline and diesel fuel. There have been several 

significant lifecycle analyses of transportation fuels done in the last decade. The lifecycle 

analysis done for this Renewable Fuel Standard (RFS) program uses a model developed by the 

Department of Energy (DOE) Argonne National Laboratory (ANL) called the Greenhouse 

Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. EPA has 

reviewed and modified GREET somewhat to reflect the data and assumptions appropriate for the 

RFS. These modifications are discussed further in section 6.1.2. 

6.1.1 Scope of the Lifecycle Analysis 

An important step in conducting a lifecycle analysis is to define the scope of the study. 

Varying results can be obtained depending on the scope identified. The scope of the analysis 

includes (1) the goal (2) the system boundaries (3) what flows are considered (4) temporal 

considerations and (5) modeling tools used. Each of these components is examined in the 

following sections. 

6.1.1.1 Goal 

The goal of this analysis is to determine the GHG emission and fossil fuel impact of the 

increased use of renewable fuels. This analysis is based on comparing future scenarios 

representing an increased percentage of the overall transportation sector fuel pool coming from 

renewable fuels compared to a reference case with the percentage of renewable fuels use at 

current levels. This implies that our future scenarios assume renewable fuels are displacing their 

petroleum based counterparts and causing less to be used. This RIA reflects increases in ethanol 

production of 85% and 150% respectively from the baseline. As this analysis is compared to a 

reference case we are only interested in the savings of the new or marginal renewable fuels used. 

220 
We have evaluated the absolute savings (e.g., tons of GHG emissions) as well as 

determining what percentage these absolute savings are in terms of overall transportation sector 

and economy wide emissions and energy use. 

6.1.1.2 System Boundaries 

The lifecycle analysis for the relevant activities identified in the GREET model is 

conducted without any regard to the geographic attributes of where emissions or energy use 

occurs. While the primary emphasis of a rulemaking analysis is typically to examine the 

domestic implications of a rulemaking, the lifecycle analysis of this final rule represent global 

reductions in GHG emissions and energy use, not just those occurring in the U.S. For example, 

under a full lifecycle assessment approach, the savings associated with reducing overseas crude 

oil extraction and refining are included here, as are the international emissions associated with 

producing imported ethanol. This assumes that for every gallon of gasoline that’s not imported 

into the US, the corresponding quantity of crude oil is not extracted or processed to make this 

gasoline regardless where the extraction or production takes place. This type of modeling does 

not allow for behavioral changes that may be occur, called “rebounding effect,” discussed later. 

There are two important caveats to this analysis, both dealing with secondary impacts that 

may result internationally due to the expanded use of renewable fuels within the United States. 

The first caveat is the emissions associated with international land use change. Due to 

decreasing corn exports some changes to international land use may occur, for example, as more 

crops are planted in other regions to compensate for the decrease in crop exports from the U.S. 

While the emissions associated with domestic land use change are well understood and are 

included in our lifecycle analysis, we did not include the potential impact on international land 

use and any emissions that might directly result. Our currently modeling capability does not 

allow us to assess what international land use changes would occur or how these changes would 

affect greenhouse gas emissions. For example, we would need to know how international 

cropping patterns would change as well as farming inputs and practices that might affect 

emissions assessment. The second caveat results from the assumption of reduced petroleum 

imports. It is commonly presumed in economic analyses that demand for a normal good (i.e., 

oil) will increase as price decreases. A world wide reduction of oil price that could result from 

reduced U.S. imports can reduce the cost of producing transportation fuel which in turn would 

tend to reduce the price consumers would have to pay for this fuel. To the extent fuel prices are 

decreased, demand and consumption would tend to increase; this impact of reduced cost of 

driving is sometimes referred to as a “rebound effect.” Such a greater consumption would 

presumably result in an increase in greenhouse gas emissions as consumers would drive more. 

These increased emissions would in part offset the emission benefits otherwise accounted for this 

rule61

. It is important to note that GREET does not model behavioral changes that may affect 

prices of relevant commodities and goods which through various feedback loops ultimately 

energy use. The model does not include a general equilibrium approach that examines how a 

shock (whether economic, technical or legal) affects not only the sector of interest but also other 

sectors and the economy as a whole.62

 While such impacts of U.S. actions are important to 

61 The extent to which this offset would occur would depend on sensitivity of demand to price. 

221 
understand, we have not have fully considered and quantified the rebound effects of this 

renewable fuel standard. Nevertheless, such impacts remain an important consideration for 

future analysis. 

The system boundaries for this study encompass both the renewable fuels lifecycle stages 

as well as their petroleum based counterparts. Table 6.1-1 shows the lifecycle stages considered 

for each fuel. 

Table 6.1-1. Lifecycle Stages Included in Analysis 

Corn Ethanol Cellulosic Ethanol Biodiesel Petroleum-Based 

Gasoline 

Petroleum-Based 

Diesel Fuel 

Corn Farming Biomass Farming Soybean Farming Crude Oil 

Extraction 

Crude Oil 

Extraction 

Corn Transport Biomass Transport Soybean Transport Crude Oil Transport Crude Oil Transport 

Soybean Crushing 

Ethanol Production Ethanol Production Biodiesel 

Production 

Refining Refining 

Ethanol T&D Ethanol T&D Biodiesel T&D Gasoline T&D Diesel Fuel T&D 

Ethanol Tailpipe 

Emissions 

Ethanol Tailpipe 

Emissions 

Biodiesel Tailpipe 

Emissions 

Gasoline Tailpipe 

Emissions 

Diesel Fuel 

Tailpipe Emissions 

The boundaries around each lifecycle stage include the emissions and energy use 

associated with that operation as well as upstream components that feed into it. For example, the 

corn farming 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. These components typically have the biggest impact on the 

results. We did not include for example, energy and emissions associated with producing the 

steel and concrete used to construct the ethanol plants or petroleum refineries. 

As other lifecycle studies of renewable fuels have included an expanded set of system 

boundaries, a sensitivity analysis was performed that includes the energy use and the emissions 

associated with producing farm equipment, and is described in section 6.1.2.7. 

A potentially important system boundary affect, however, could be changes in land use. 

This is particularly the case for GHGs if new land (e.g., rainforest land) must first be cleared in 

order to grow the biofuel feedstocks. This lifecycle analysis is conducted without any regard to 

the geographic attributes of where emissions or energy use occurs. The benefits of this final 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. One exception to this is the emissions associated with international land use change. 

Due to decreasing corn exports and modest decreases in soybean exports, there may be some 

62 Since GREET is not a behavioral model, it cannot assess any economic efficiency implications associated with 

increased ethanol production. Analyzing these implications would be important for future ethanol rulemakings. 

222 
additional corn and soybean acres planted internationally to meet world demand. The emissions 

associated with domestic land use change are included in our lifecycle analysis but international 

land use change was not as it was outside the scope of our agriculture sector analysis. However, 

if emissions from international land use change were included it would lower the overall benefits 

of this rule. This is an area we will continue to examine for future analysis. 

6.1.1.3 Environmental Flows Considered 

One issue that has come to the forefront in the assessment of the environmental impacts 

of transportation fuels relates to the effect that the use of such fuels could have on emissions of 

greenhouse gases (GHGs). The combustion of fossil fuels has been identified as a major 

contributor to the increase in concentrations of atmospheric carbon dioxide (CO2) since the 

beginning of the industrialized era, as well as the build-up of trace GHGs such as methane (CH4) 

and nitrous oxide (N2O). This lifecycle analysis evaluates the impacts of increased renewable 

fuel use on greenhouse gas emissions. 

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 Third Assessment Report63, and are shown in Table 6.1-2. 

Table 6.1-2. 

Global Warming Potentials for Greenhouse Gases 

Greenhouse Gas GWP 

CO2 1 

CH4 23 

N2O 296 

Greenhouse gases are measured in terms of CO2-equivalent emissions, which result from 

multiplying the GWP for each of the three pollutants shown in the above table by the mass of 

emissions for each pollutant. The sum of impacts for CH4, N2O, and CO2, yields the total 

effective GHG impact. 

The impact increased volumes of renewable fuels use has on GHG emissions (in terms of 

CO2-eq.) as well as for only CO2 emissions which represent a subset of the overall GHG 

emissions, is considered in this analysis. The impact increased volumes of renewable fuels use 

has on fossil energy (in terms of Btus) is also considered. Fossil energy use includes energy 

63 IPCC “Climate Change 2001: The Scientific Basis”, Chapter 6; Intergovernmental Panel on Climate Change; J.T. 

Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, C.A. Johnson, and K. Maskell, eds.; 

Cambridge University Press. Cambridge, U.K. 2001. http://www.grida.no/climate/ipcc_tar/wg1/index.htm 

223 
associated with coal, natural gas, and petroleum products. Fossil energy use is strongly linked 

with CO2 and GHG emissions and is an important consideration when looking at overall 

sustainability. 

Petroleum energy use is a subset of fossil energy use and is the major contributor to 

overall transportation sector energy use. Petroleum energy use is also linked to CO2 and GHG 

emissions but also has impacts on national energy concerns such as dependence on foreign 

sources of petroleum. Therefore, petroleum energy was also considered separately in this 

analysis and examined in terms of overall energy use, as well as in terms of petroleum imports 

avoided through the increased use of renewable fuels. 

6.1.1.4 Time Frame and Volumes Considered 

The results presented in this analysis represent a snapshot in time. They represent annual 

GHG and fossil fuel savings in the year considered, in this case 2012. 

Consistent with the renewable fuel volume scenarios described in Chapter 2, our analysis 

of the GHG and fossil fuel consumption impacts of renewable fuel use was conducted using 

three volume scenarios. The first scenario was a reference case representing 2004 renewable 

fuel production levels, projected to 2012. This scenario provided the point of comparison for the 

other two scenarios. The other two renewable fuel scenarios for 2012 represented the RFS 

program requirements and the volume projected by EIA. 

In both the RFS and EIA scenarios, we assumed that the biodiesel production volume 

would be 0.303 billion gallons based on an EIA projection. Furthermore, the Energy Act 

requires that 250 million gallons of cellulosic ethanol be produced starting in the year 2013, for 

both scenarios we assume that 250 million gallons of ethanol that qualify for cellulosic credit 

will be produced in 2012. The remaining renewable fuel volumes in each scenario would be 

ethanol made from corn and imports. The import volume is based on EIA’s projections for the 

percent of total ethanol volume supplied by imports in 2012. The total volumes for all three 

scenarios are shown in Table 6.1-3. 

Table 6.1-3. Volume Scenarios in 2012 (billion gallons) 

Reference 

Case 

RFS Case EIA Case 

Corn-ethanol 3.947 5.985 8.758 

Cellulosic ethanol 0.0 0.25 0.25 

Biodiesel 0.030 0.303 0.303 

Ethanol imports 0.0 0.436 0.630 

Total volume 3.977 6.974 9.941 

As we are comparing against a reference case, we are only interested in the emissions and 

energy savings associated with new or marginal renewable fuels production that comes on-line 

after 2004 (the baseline assumed for the reference case). 

224 
6.1.1.5 Model Used 

The lifecycle model used in the evaluation of the impacts of the RFS program is the fuelcycle model developed by DOE’s Argonne National Laboratory. For this work, EPA used the 

most recent version of this model, GREET 1.7 (November 10, 2006 release). GREET, a multidimensional spreadsheet model, is one of the most widely used model of this type for 

transportation fuels. It has been reviewed, used, and referenced by a wide variety of analysts, 

including General Motors, National Corn Growers Association, several fuel industry 

organizations, and a wide variety of academic institutions. It is the most comprehensive and 

user-friendly model of its type. It has been under development for over 10 years, with input 

from EPA, USDA, DOE laboratories, and industry representatives. The model addresses the full 

lifecycle for an exhaustive number of alternative transportation fuels and automotive 

technologies. For these reasons, EPA felt it was the best tool for evaluating the energy and 

emission impacts of the RFS program. 

The GREET model has been developed to calculate per-mile energy use and emission 

rates of various combinations of vehicle technologies and fuels for both fuel cycles and total 

energy cycles. The model actually consists of three components: GREET 1.x, which calculates 

fuel cycle energy use and emissions, GREET 2.x, which calculates light-duty vehicle cycle 

energy use and emissions, and GREET 3.x, which calculates heavy-duty vehicle cycle energy 

use and emissions. All discussion here refers to GREET 1.7, the most recent version of the fuel 

component of GREET. 

To estimate fuel cycle energy use and emissions, GREET first estimates energy use and 

emissions for a given upstream stage. The model then combines the energy use and emissions 

from all upstream stages for a fuel cycle, to estimate total upstream fuel cycle energy use and 

emissions. Inputs are national-average energy usage rates, efficiencies and emission factors for 

each stage. The model calculates total energy use, fossil energy use, and emission rates for the 

regulated pollutants and greenhouse gases, reported as grams per mile or grams per million Btu. 

These results allow comparison of transportation fuels, based on energy use and/or emissions. 

One of the main comments we received on our lifecycle approach was that our sole 

reliance on the GREET model should be avoided, given other models are available. There are 

several other models that have been developed for conducting renewable fuels lifecycle analysis. 

For example, researchers at the Energy and Resources Group (ERG) of the University of 

California Berkeley have developed the ERG Biofuel Analysis Meta-Model (EBAMM) and 

Mark Delucchi at the Institute of Transportation Studies of the University of California Davis has 

developed the Lifecycle Emissions Model (LEM). There are also other non-fuel specific 

lifecycle modeling tools that can be used to perform renewable fuel lifecycle analysis. The main 

differences in these models are with input assumptions used as described below. 

Several studies have been released recently making use of these other models and 

showing different results than we find in the analysis done for this rule. For example, whereas 

GREET estimates a net GHG reduction of about 22% for corn ethanol compared to gasoline, the 

previously cited works by Farrell et al. utilizing the EBAMM show around a 13% reduction. 

While there may be small differences in the models in terms of emissions and energy uses 

225 
associated with ancillaries (e.g., emissions to produce fertilizer, electricity, etc.) the main 

difference in results is not due to model used but assumptions on scope and input data used. 

For example, most studies focus on average or current ethanol production which uses a 

current mix of wet and dry mill ethanol production and use of coal and natural gas as process 

energy. In contrast, we consider new or marginal ethanol production which implies a higher 

portion of more efficient dry mill production and mix of process fuels. Other studies also 

typically base ethanol and farm energy use on historic data while we are assuming a state of the 

art dry milling plant and most current farming energy use data. Assumptions concerning land 

use change CO2 emissions and agriculture related GHG emissions could also have an impact on 

overall results. Other studies also differ in the environmental flows considered. For example, 

DelucchiNNNN uses different types of greenhouse gases and GWPs compared to those used in this 

analysis as shown in Table 6.1-2 to determine GHG emissions. 

Other researchers have performed lifecycle analysis of renewable fuels not specifically 

focused on GHG emissions. One result that has been debated recently is the net energy balance 

of corn-based ethanol fuel. Some analysts have suggested that there is actually a negative energy 

balance for corn ethanol, meaning that it takes more fossil energy to produce the ethanol than is 

contained in the resulting fuel, making it an unattractive transportation fuel. While we do not 

believe this is an appropriate metric to use when examining renewable fuels, as discussed in 

Section 6.2.3, it is still useful in examining the range of lifecycle results. Two studies Pimental 

(2003)64 and Patzek (2005), 65 concluded that the energy balance is negative. . Many other 

researchers, however, have criticized that work as being based on out-dated farming and ethanol 

production data, including data not normally considered in lifecycle analysis for fuels, and not 

following the standard methodology for lifecycle analysis in terms of valuing co-products. 

Furthermore, several recent surveys have concluded that the energy balance is positive, although 

they differ in their numerical estimates.66,67,68 Authors of the GREET model have also 

concluded that the lifecycle amount of fossil energy used to produce ethanol is less than the 

amount of energy in the ethanol itself. Based on our review of all the available information, and 

the results of our own analysis, we also believe that the energy balance is positive. 

64 Pimentel, David “Ethanol Fuel: Energy Balance, Economics, and Environmental Impacts are Negative”, Vol. 12, 

No 2, 2003 International Association for Mathematical Geology, Natural Resources Research 

65 Pimentel, D.; Patzek, T. "Ethanol production using corn, switchgrass, and wood; biodiesel production using 

soybean and sunflower." Nat. Resour. Res. 2005, 14 (1), 65-76. 

66 Hammerschlag, R. "Ethanol's Energy Return on Investment: A Survey of the Literature 1990 - Present." Environ. 

Sci. Technol. 2006, 40, 1744 - 1750. 

67 Farrell, A., Pelvin, R., Turner, B., Joenes, A., O’Hare, M., Kammen, D., “Ethanol Can Contribute to Energy and 

Environmental Goals”, Science, 1/27/2006, Vol 311, 506-508. 

68 Hill, J., Nelson, E., Tilman, D., Polasky, S., Tiffany, D., “Environmental, economic, and energetic costs and 

benefits of biodiesel and ethanol biofuels”, Proceedings of the National Academy of Sciences, 7/25/2006, Vol. 103, 

No. 30, 11206-11210. 

226 
The differences found by different studies and models used emphasize the importance of 

the input data and methodology when using lifecycle analysis. It also shows how dependent this 

type of analysis is on the assumptions made throughout the model. Based on differences in 

scopes and input data considered between these other studies and what we defined in this 

analysis, we believe the differences in results that are seen are reasonable and the values we are 

obtaining from our use of the GREET model are acceptable for this analysis. 

6.1.2 Modifications to GREET 

EPA chose to use GREET 1.7 to evaluate the lifecycle impacts of the RFS program. 

GREET 1.7 is the most recently released version of the GREET model. However, this version of 

the model does not reflect the potential impacts on transportation fuel industries as a result of the 

RFS program. In addition, for this regulation our intent was to evaluate the impact of 

incremental renewable fuel production resulting from the RFS program and not a current 

industry average. Therefore, EPA has modified some of the input variables and assumptions 

made in the GREET model. The renewable fuels considered in this analysis were modeled as 

being produced from the following feedstocks and processes: 

− Corn Ethanol: 

o Wet Milling 

 Mix of coal and natural gas as process fuel 

o Dry Milling 

 Natural gas as process fuel 

 Coal as process fuel 

 Biomass as process fuel 

− Cellulosic Ethanol: 

o Hybrid Poplar Feedstock 

 Fermentation route 

o Switchgrass Feedstock 

 Fermentation route 

o Corn Stover Feedstock 

 Fermentation route 

o Forest Waste Feedstock 

 Gasification route 

− Biodiesel: 

o Soybean Oil Feedstock 

 Transesterification route 

o Yellow Grease Feedstock 

 Transesterification route 

These feedstocks and processes were primarily based on what was available in the 

GREET model with some minor modifications as described below. However, there are other 

pathways for producing renewable fuels not covered here, for example different feedstocks for 

cellulosic ethanol production (e.g., MSW) as well as different process for the feedstocks 

227 
considered, like gasification of switchgrass and production of soybean oil diesel fuel through 

hydrotreating. 

Furthermore, the lifecycle analysis used for this rulemaking is based on averages of the 

different renewable fuels modeled. For example, the GHG emission and fossil energy savings 

associated with increased use of corn ethanol are calculated based on a mix of process fuels, 

assuming a certain projected mix of each process fuel as outlined below. While this method may 

not exactly represent the reductions associated with a given gallon of renewable fuel, it is 

reasonable for the purpose of this analysis which is to determine the impact of the total increased 

volume of renewable fuels used. 

We recognize that different feedstocks and processes will each have unique 

characteristics when it comes to lifecycle GHG emissions and energy use. However, we 

understand that other feedstocks and processes as well as differences in other parts of the 

renewable fuel lifecycle will impact the savings associated with their use and this is the focus of 

ongoing work at the agency. 

GREET is subject to periodic updates by ANL, each of which results in some changes to 

the inputs and assumptions that form the basis for the lifecycle estimates of emissions generated 

and energy consumed. These updates generally focus on those input values for those fuels or 

vehicle technologies that are the focus of ANL at the time. As a result there are a variety of 

other inputs related to ethanol and biodiesel that may not have been updated in some time. In the 

context of the analysis of the RFS and EIA scenarios, we determined that some of the GREET 

input values that were either based on outdated information or did not appropriately reflect 

market conditions under a renewable fuels mandate should be examined more closely, and 

updated if necessary. 

Since the analysis done for the NPRM, several changes have been made to the GREET 

model, some as part of periodic updates ANL had planned and some as part of an interagency 

agreement between ANL and EPA to investigate a variety of GREET input values. A summary 

of the changes is as follows: 

− Included CO2 emissions from corn farming lime use 

− Updated the corn farming fertilizer use inputs 

− Added cellulosic ethanol production from corn stover and forest waste 

− Modeled biomass as a process fuel source in corn ethanol dry milling 

In addition to the changes above we also examined and updated other GREET input 

assumptions for corn ethanol and biodiesel production. A summary of the GREET input values 

we investigated and modified is given below. We also examined several other GREET input 

values, but determined that the default GREET values should not be changed for a variety of 

reasons as discussed in the following sections. These included corn and ethanol transport 

distances and modes and byproduct allocation methods. Our investigation of these other GREET 

input values are discussed more fully below. The current GREET default factors for these other 

inputs were included in the analysis for this final rule. 

228 
We did not investigate the input values associated with the production of petroleumbased gasoline or diesel fuel in the GREET model for this final rule. However, the refinery 

modeling discussed in Chapter 7 will provide some additional information on the process energy 

requirements associated with the production of gasoline and diesel under a renewable fuels 

mandate. We will use information from this refinery modeling in future analysis to determine if 

any GREET input values should be changed. 

A summary of the GREET corn ethanol input values we investigated for this final rule is 

given below. 

6.1.2.1 Wet Mill versus Dry Mill Ethanol Plants 

As described in Chapter 1, 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 dried for animal 

feed (dried distillers grains with solubles, or DDGS). 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. 

While other lifecycle models often base the mix of wet and dry milling on existing plants, 

for this analysis, we are only interested in marginal ethanol production. We expect most new 

ethanol plants will be dry mill operations. That has been the trend in the last few years as the 

demand for ethanol has grown, and our analysis of ethanol plants under construction and planned 

for the near future has verified this. Our analysis of production plans, as outlined in Chapter 1, 

indicates that essentially all new ethanol production will be from dry mill plants (99%). 

6.1.2.2 Coal versus Natural Gas in Ethanol Plants 

The type of fuel used within the ethanol plant for process energy to power the various 

components that are used in ethanol production (dryers, grinders, heating, etc.) can vary among 

ethanol plants. The type of fuel used has an impact on the energy usage, efficiency, and 

emissions of the plant, and is primarily determined by economics. Most new dry mill plants built 

in the last few years have used natural gas. However, some new plants are using coal. For these 

cases, EPA is promoting the use of combined heat and power, or cogeneration, in ethanol plants 

to improve plant energy-efficiency and to reduce air emissions. This technology, in the face of 

increasing natural gas prices, may make coal a more attractive energy source for new ethanol 

plants. 

GREET default factors represent the average percentage of fuel use for the entire 

industry, and may not reflect the recent growth in the industry. Therefore, we based our fuel mix 

assumptions on the review of plants under construction and those planned for the near future 

outlined in Chapter 1. Our analysis indicates that coal will be used as process fuel for 

approximately 14% of the new dry mill under construction and planned ethanol production 

229 
volume capacity. This is the value we used in GREET for our analysis of dry milling ethanol 

production fuel mix. 

As opposed to typical dry mill plants, corn wet mill ethanol plants can use a mix of 

process fuel sources at the same plant. For the 1% of additional ethanol production from wet 

mills, the GREET model defaults of 40% coal and 60% natural gas process fuel was used in this 

analysis. 

As described below, the ethanol production stage of the lifecycle typically represents the 

stage where the largest amount of fossil fuel energy is consumed and where the impact on 

lifecycle emissions is the greatest. Therefore, the type of process fuel used in ethanol production 

will have a significant impact on the fuel’s lifecycle GHG results. For example, our analysis 

indicates that ethanol produced in a coal fired dry mill plant would not have any GHG benefits as 

compared to petroleum gasoline. Given that the relative prices of natural gas and coal could 

change over time, and thus change the percentage of each used in ethanol production, our 

analysis of fuels used in plants under construction and those planned for the future would need to 

be reevaluated for future work. 

6.1.2.3 Ethanol Plant Process Efficiency 

For the corn-to-ethanol fuel cycle, the largest amount of fossil fuel energy consumed 

occurs at the ethanol production plant. The energy use at a dry mill plant using natural gas was 

based on the model developed by USDA which was documented in a peer-reviewed journal 

paper on cost modeling of the dry-grind corn ethanol process.OOOO This model was modified by 

EPA for use in the cost analysis of this rulemaking described in Chapter 7. GREET inputs are 

total energy use per gallon of ethanol produced. The USDA model predicts the annual thermal 

(natural gas) and electricity demand shown in Table 6.1-4. 

Table 6.1-4. 

Annual Energy Use at Dry Mill Ethanol Plant 

Energy Input Value 

Purchased Electricity (MWh/yr.) 41,308 

Natural Gas (mmBtu/yr.) 1,617,094 

Output 

Ethanol (mmgal/yr.) 50 

Electricity energy use was converted from MWh to Btu based on a conversion of 3,410 

btu/kWh. The primary energy used to produce electricity is accounted for in the GREET model. 

Table 6.1-5 shows the GREET input used for natural gas process fuel dry milling plants in this 

analysis. 

230 
Table 6.1-5. 

GREET Inputs for Corn Ethanol 

Natural Gas Dry Mill Energy Use 

Total Energy Use (mmBtu/gal.) 35,159 

% electricity 8.0% 

Energy requirements for a coal fired ethanol plant are different from a natural gas fired 

plant. Typically coal boilers are slightly less efficient than natural gas boilers. Furthermore 

additional electricity is required for coal storage and handling as compared to natural gas. 

Additionally a large portion of the energy at an ethanol plant is due to drying the DDGS. A 

natural gas plant utilizes natural gas driers for this process while a coal fired plant would use 

steam dryers, the efficiency loss of converting coal to steam represents additional thermal energy 

required at a coal fired plant vs. a natural gas one. 

Most other lifecycle models assume the same energy efficiency for both coal and natural 

gas ethanol plants, however, for this analysis, it was assumed that a coal plant would require 

15%69 more electricity demand due to coal handling and have a 13% increase in thermal demand 

for steam dryers as compared to the natural gas fueled plant. The increase in thermal demand 

was based on breaking out the drying energy in the USDA process model and assuming the same 

amount of energy would be produced by 78% efficient coal boilers. Table 6.1-6 shows the 

GREET input used for coal process fuel dry milling plants in this analysis. 

Table 6.1-6. 

GREET Inputs for Corn Ethanol 

Coal Dry Mill Energy Use 

Total Energy Use (mmBtu/gal.) 40,079 

% electricity 8.1% 

The Energy Act also allows ethanol made from non-cellulosic feedstocks to receive 

cellulosic ethanol production volume credit if 90 percent of the process energy used to operate 

the facility is derived from a renewable source. In the context of our cost analysis, we have 

assumed that 250 million gallons of corn ethanol will be produced using 90 percent or more 

biomass energy and receive the cellulosic ethanol volume credit. Further discussion of this issue 

can be found in Chapter 1. 

For the lifecycle analysis we considered the case where a corn ethanol dry mill plant 

utilized biomass as a fuel source. For this case the same amount of fuel and purchased electricity 

energy per gallon as a coal powered plant was assumed. This assumption is based on the 

biomass plant having more fuel handling than a natural gas plant and producing steam for DDGS 

drying. 

69 Baseline Energy Consumption Estimates for Natural Gas and Coal-based Ethanol Plants - The Potential Impact of 

Combined Heat and Power (CHP), Prepared for: U.S. Environmental Protection Agency Combined Heat & Power 

Partnership, Prepared by: Energy and Environmental Analysis, Inc., July 2006. 

231 
As discussed in section 6.2.3, CO2 emissions from combustion of biomass 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 

ethanol plant. The fossil energy use and GHG emissions from producing the electricity used at 

the plant are included. 

For the 1% of corn ethanol produced from wet milling, the GREET process energy use 

default of 49,950 Btu/gallon of ethanol produced by the wet milling process was used in the 

analysis. 

6.1.2.4 Corn Transport Distances 

Corn transport distances selected for use in this analysis are 100 miles round trip. Corn 

used in the ethanol production process is assumed to travel from corn fields to ethanol 

production facilities in a two-step process; first, corn is transported from outlaying farms to 

centrally-located collection facilities, such as county elevators. Second, this corn is transported 

from the collection facilities to the ethanol production facilities. The first leg of the corn 

transport process is assumed to be a 20-mile round trip and the second leg is assumed to be an 

80-mile round trip. These assumptions coincide with those used in GREETPPPP Version 1.7 and 

GREET Version 1.5. 

Corn transport data is limited, however; Graboski70 found that the average one-way 

hauling distance for corn from fields to county elevators was 7.5 miles and from county elevators 

to ethanol processing facilities was 49.7 miles for an effective average round-trip corn transport 

distance of 74.6 miles. Similarly, Gervais and BaumelQQQQ found that average one-way corn 

transport distances for the 1994-1995 Iowa growing season was 37.2 miles for semi-trucks 

(35.8%), 4.9 miles for wagons (33.3%), and 9.1 miles for single and tandem axel vehicles 

(30.9%). Several Minnesota corn mills indicated that the maximum radius of supply for their 

mills was 65 to 80 miles (values apparently cited in the same study). 

The available data on corn transport distances does not provide a clear indication that the 

default values in GREET are unreasonable. Therefore, we retained the GREET default values 

for our analysis. This assumes that the land use pattern (where corn is planted) and the plant 

location decisions by ethanol plants will not change significantly. We believe this is reasonable 

for the fuel volumes considered. This is an area we will continue to examine for future analysis. 

70 The authors assume that the corn payload weight is equal to the transport vehicle weight, that the vehicle returns 

empty, and the effective average round-trip vehicle distance can be estimated as being one and a half times the oneway travel distance (1.5 times 49.7 miles = 74.6 miles); Graboski, 2002, Fossil Energy Use in the Manufacture of 

Corn Ethanol, Colorado School of Mines, (Prepared for the National Corn Growers Association). 

232 
6.1.2.5 Ethanol Transportation Distances and Modes 

The default values in GREET for ethanol transportation and modes are shown in Table 

6.1-7. These values correspond to numbers in a USDA study on the energy balance of corn 

ethanol.RRRR 

Table 6.1-7. GREET Ethanol Transportation Input Data 

Mode Plant to Terminal Terminal to Station 

% Distance (miles) % Distance (miles) 

Rail 40% 800 0% 

Barge 40% 520 0% 

Truck 20% 80 100% 30 

The GREET default values are consistent with the analysis we performed on ethanol 

distribution infrastructure. Chapter 1 of this document discusses current ethanol transportation 

and distribution and indicates that if ethanol facilities are located within 100-200 miles of a 

terminal, trucking is preferred. Rail and barge are used for longer distances. Pipelines are not 

currently used to transport ethanol and are not projected to play a role in ethanol transport in the 

future time frame considered. 

We also discuss in Chapter 1 future ethanol transportation and distribution needs based 

on the increased amounts of renewable fuels used as a result of this rule. We concluded that 

most new ethanol capacity will not have river access. In addition, at least one new ethanol plant 

slated for production that does have river access is planning to move its ethanol to market via rail 

so most new ethanol freight volumes will be handled by rail and that ethanol transport by inland 

waterway will remain constant. 

A recent USDA Cost of Ethanol Production report also provides information on ethanol 

distribution distances and modes.SSSS The report includes 2002 data from a survey of 21 dry mill 

ethanol plants. The survey collected data on modes and distances traveled for ethanol transport 

from the facilities. The report concluded that 46 percent of the ethanol produced at the surveyed 

plants in 2002 was shipped by truck an average one way distance of 93 miles, with a range of 30 

to 250 miles. The remaining 54 percent of ethanol produced was shipped by rail an average one 

way distance of 1,163 miles, with a range of 800 to 2,500 miles. However, this data is for a 

subset of existing plants where, for example, there is no barge transportation listed, and also does 

not take into account the increased demand for ethanol projected by this rule. 

233 
Comparing the GREET default values to these other sources indicates that the GREET 

defaults values for percent of ethanol transported by rail may be low. However, due to lack of 

precise data on future ethanol transportation by mode, we concluded that the current GREET 

default values for percent of ethanol transported by mode are appropriate for the RFS analysis. 

The GREET default values for miles shipped by mode fall within the range of values 

listed in the USDA survey data of existing plants. The USDA survey data indicate higher than 

average transportation distances; however the data is not comprehensive enough, only 

representing a small fraction of total and projected ethanol production capacity, thus not 

warranting a change to the default GREET values. Therefore, the default values shown in Table 

6.1-7 were used in this analysis. This is an area we will continue to examine for future analysis. 

6.1.2.6 Biodiesel Transportation Distances and Modes 

The default values in GREET for biodiesel transportation and modes are shown in Table 

6.1-8. 

Table 6.1-8. GREET Biodiesel Transportation Input Data 

Mode Plant to Terminal Terminal to Station 

% Distance (miles) % Distance (miles) 

Barge 8% 520 0% 

Pipeline 63% 400 0% 

Rail 29% 800 0% 

Truck 0% 100% 30 

The GREET default assumptions for mode of biodiesel transportation are not consistent 

with the analysis we performed on biodiesel distribution infrastructure. The distribution 

infrastructure discussion in Chapter 1 of this document indicates pipelines are not currently used 

to transport biodiesel and are not projected to play a role in biodiesel transport in the future time 

frame considered. 

Therefore, GREET default factors for biodiesel transportation from plant to terminal were 

modified to remove pipeline transport. The percent of biodiesel shipped by barge and rail were 

increased in the same proportion as the current percentage split. The result was 22% of biodiesel 

shipped by barge and 78% shipped by rail. The GREET default distances for biodiesel rail and 

234 
barge transport as well as terminal to station assumptions are consistent with ethanol 

transportation and distribution assumptions and were used in this analysis. 

6.1.2.7 Corn Yield and Related Inputs 

GREET includes a collection of energy use and material inputs to corn farming per 

bushel (bu) of corn produced. Several corn farming input data parameters and default values 

were updated from the version of GREET used for the NPRM to the version used in the FRM 

analysis. The current GREET corn farming input data default values are shown in Table 6.1-9. 

Table 6.1-9. GREET Corn Farming Input Data 

Input Parameter Default Value 

Energy Use for Corn Farming 22,500 Btu/bu 

 - Energy use from diesel fuel 38.3% 

 - Energy use from gasoline 12.3% 

 - Energy use from natural gas 21.5% 

 - Energy use from LPG 18.8% 

 - Energy use from purchased electricity 9.0% 

Nitrogen Fertilizer (as N) 420 g/bu 

Phosphate Fertilizer (as P2O5) 149 g/bu 

Potash Fertilizer (as K2O) 174 g/bu 

Lime (as CaCO3) 1,202 g/bu 

Herbicide Use: 8.1 g/bu 

Insecticide Use: 0.68 g/bu 

The default GREET input values for corn farming shown in Table 6.1-9 are based in part 

on farm energy use and material inputs per acre divided by an assumed corn yield in bu/acre. 

Therefore, while corn yield is not a direct input in GREET, it is a critical part of the calculation 

of corn energy and material input requirements. Although corn yields have been generally rising 

over time, see Figure 6.1-2, the annual variation is volatile. 

235 
Figure 6.1-2. U.S. Average Corn YieldTTTT 

60 

80 

100 

120 

140 

160 

180 

1990 1995 2000 2005 2010 

bu/acre 

We examined data on farm energy use, material input, and yield data to determine if the 

GREET default values needed to be updated. The lifecycle modeling conducted for the RFS 

program is based on future predictions. Unfortunately, no good projections of future energy use 

associated with corn farming are available. USDA does list projections for corn yield. The 2012 

projected U.S. average corn yield is 158.5 bu/acre.UUUU Historic data on corn farming energy 

use is available from the following USDA information sources. 

• The USDA Agricultural Resource Management Survey (ARMS) provides data from 

selected States on fuel, electricity, natural gas, and seed corn used per acre on the farm 

and activities of moving farm products to initial storage facilities. 

• The USDA National Agricultural Statistics Service (NASS) produces annual data on crop 

production including yields per acre and total production of corn by state. 

USDA NASS data on corn yields and production values are provided annually. 

However, the three most recent years of the ARMS data and specifically the costs-of-production 

portion of the survey dedicated to corn are 1991, 1996, and 200171

. Table 6.1-10 lists corn 

farming energy input data for the three years of the ARMS study. 

71 Use of historic farming energy use may not be representative of current practice. Higher energy prices relative to 

the years considered here could lead to farmers adopting practices that lower overall energy use. 

236 
Table 6.1-10. Farm Energy Use Data per Acre 

Input Units 9-State Weighted Average Values 

1991 1996a 2001 3 Yr. Avg. 

Seed bu/acre 1.51 1.50 1.69 1.57 

Energy: 

- Diesel Gallons/acre 7.81 9.80 6.40 8.01 

- Gasoline Gallons/acre 3.42 3.07 1.65 2.71 

- LPG Gallons/acre 3.86 7.25 5.10 5.41 

- Electricity kWh/acre 32.72 79.38 38.22 50.11 

- Natural Gas Cubic ft/acre 284.73 208.12 207.09 233.31 

Total Energy Use mmBtu/acre 2.12 2.71 1.78 2.20 a High energy use in the 1996 survey is due to increased corn drying requirements. See the discussion below. 

Although USDA corn data is available for every state that produces corn, the data 

documented in Table 6.1-10 is for nine major corn producing States: Illinois, Indiana, Iowa, 

Minnesota, Nebraska, Ohio, Michigan, South Dakota, and Wisconsin. In 2005, these nine States 

accounted for 80 percent of U.S. corn production. In 2001 these nine States represented 92 

percent of U.S. ethanol production, and based on our analysis outlined in Chapter 1 are projected 

to represent 82 percent of ethanol production in 2012. The data in Table 6.1-10 are weighted 

based on corn production data for each of the nine States from the NASS. The total energy use 

values listed in Table 6.1-10 were calculated by converting fuel use to Btu based on the lower 

heating values of the fuels as listed in the GREET model. These estimates may be biased 

downward if the corn production attributable to the incremental increase in ethanol production 

will occur on less productive land than was used in the 1991-2001 period, when corn prices were 

lower than they are projected to be in this analysis. Also, as corn production expands due to 

expanded ethanol production, it may increasingly take place in dryer climates that may increase 

irrigation demand and result in different yields. This is an area we will continue to examine for 

future analysis. 

The ARMS surveys include information on energy use and also on dollars spent by 

farmers on custom work. This custom work includes farmers contracting outside services for 

corn drying, planting, fertilizing and harvesting. The cost of custom work includes machine 

overhead, fuel charges, and labor costs. Therefore, there is some energy use associated with the 

dollars spent on fuel used in custom work. It was assumed that 10% of custom work cost was 

spent on fuelVVVV. This fuel cost was assumed to be split between LPG and diesel fuel in the 

same percentage as reported energy use for each state. Cost was converted to gallons based on 

price paid by farmers for LPG and diesel fuel in each of the survey yearsWWWW. Custom work 

energy use is included in Table 6.1-10. 

It can be seen from Table 6.1-10 that there is substantial variation in the three years of 

energy use survey data. Several factors can influence corn farming energy use. For example, it 

was reported that 1991 was a dry year, lowering the moisture content of the corn crop and thus 

requiring less energy to dry the corn, whereas the 1996 crop was reported to have a higher 

moisture content and thus require more energy to dry resulting in the high energy use values for 

237 
1996. Farm diesel use is also dependent on tillage type and soil conditions, wetter soil requiring 

more diesel use, and decreased tillage requirements (e.g., no till) reducing diesel use.XXXX 

To project corn farming fuel use in 2012, the average energy use from the three years of 

survey data were taken, in terms of energy per acre. As energy use is somewhat weather related 

and it is impossible to confidently predict future conditions, it was felt that the three years of 

historic data represented a good mix of high and low energy use years. The average energy use 

in terms of Btu/acre was divided by the projected corn yield in 2012 of 156.9 bu/acre. This is the 

USDA projected corn yield adjusted to account for seed corn energy use as shown in Table 6.1­

10. The seed use shown in Table 6.1-10 accounts for seed corn energy use. We assumed that 

growing seed corn requires 4.7 times the energy and material inputs to grow than cornYYYY,ZZZZ. 

The result was 14,036 Btu of energy needed to produce a bushel of corn, which was used in 

GREET for this analysis. 

The GREET default values for corn farming material inputs were updated from the 

values in the NPRM version. GREET defaults were based on historic data provided from the 

following USDA sources. 

• The USDA National Agricultural Statistics Service (NASS) produces annual reports 

listing quantities of fertilizers and chemicals used per acre of corn. 

• The USDA Economic Research Service (ERS) produces an Agricultural Resources and 

Environmental Indicators report that has data on lime used per acre of corn. 

The USDA sources provide average material use data per harvested acre of corn. The 

GREET defaults are based on the assumption that material input use per acre will be flat from 

2005 into the future. The 2005 values are based on a three year average of 2003 through 2005 

data. Data on inputs per acre are divided by projected corn yields to get GREET defaults in 

terms of g/bushel of corn. While these values are felt to be reasonable to be used in this analysis, 

the agency cautions that these estimates are based on the historical record while the incremental 

corn production attributable to expanded ethanol production may occur on less productive land 

than was used historically. As a result, these estimates may be biased downward, resulting in 

over-estimates of ethanol displacement indices. 

Another potential input to corn farming is the energy and emissions associated with 

producing farm equipment. As described in Section 6.1.1.2, this input is considered outside the 

system boundaries of our lifecycle analysis. However, the latest version of GREET has an 

option to include energy use and emissions associated with producing farm equipment in the 

corn ethanol lifecycle results. We performed a sensitivity analysis on expanding the corn 

production system to include farm equipment production to determine the impact it has on the 

overall results of our analysis. 

It was found that including farm equipment production energy use and emissions 

increases ethanol lifecycle energy use and GHG emissions and decreases the corn ethanol 

displacement index by approximately 1 percent. Furthermore, to be consistent in the modeling if 

system boundaries are expanded to include production of farming equipment they should also be 

expanded to include producing other material inputs to both the ethanol and petroleum lifecycles. 

238 
For example, this expansion of system boundaries would include the energy use and emissions 

associated with producing concrete and steel used in the petroleum refinery72. The net effect of 

this would be a slight increase in both the ethanol and petroleum fuel lifecycle results and a 

smaller or negligible effect on the comparison of the two. 

The corn farming material and energy use used in the lifecycle analysis is based on 

producing and average bushel of corn. There are differences associated with variations in corn 

yield, inputs required for existing land vs. land converted to crops, etc. Furthermore, there are 

ripple effects associated with increased corn used for ethanol that could have GHG emission 

implications, ranging from changes in manure management to the acres of rice grown. One such 

effect is CO2 associated with land use change which is examined in the following section. Other 

effects and variations in corn farming will be examined as part of future analysis. 

6.1.2.8 CO2 from Land Use Change 

Farming practices could potentially release carbon stored in soil as CO2 emissions. If 

non-cropland (e.g., pastureland, Conservation Reserve Program (CRP) land) is converted to crop 

production, carbon sequestered in the soil and existing cover could be released. The agricultural 

sector modeling work done for this rulemaking examined the issue of land use change due to 

increases in renewable fuel production and use. The agricultural sector modeling results indicate 

that, compared to the 2012 Reference Case, approximately two and a half million acres will 

come out of CRP land as a result of increased renewable fuel production. Not all of these two 

million acres will go directly into corn production used to produce ethanol. However, the entire 

amount of CO2 emissions from the CRP land use change is attributable to the increased amount 

of ethanol produced, as without the increased demand for corn there would be no change in CRP 

land. The agricultural modeling results also indicated a reduction in U.S. corn exports and a 

modest decline in U.S. soybean exports which could impact crop production in other countries. 

However, we did not consider impacts on non-U.S. land use that might result from decrease in 

U.S. exports of corn and soybeans. 

The GREET model has a default factor for CO2 from land use change that was included 

in the NPRM analysis. This factor was updated based on the results of the agricultural sector 

modeling mentioned above and included in the final rulemaking lifecycle analysis. The CO2 

emissions from land use change used in the final rulemaking represent approximately 1% of total 

corn ethanol lifecycle GHG emissions. However, this value could be more significant if 

increased amounts of renewable fuels are used. 

The issue of CO2 emissions from land use change associated with converting forest or 

CRP land into crop production for use in producing renewable fuels is an important factor to 

consider when determining the overall sustainability of renewable fuel use. While the analysis 

described above is indicating that this rulemaking will not cause a significant change in land use, 

this is an area we will continue to research for any future analysis. 

72 The expansion of system boundaries would apply to existing refineries as ethanol is assumed to replace gasoline 

from existing production. 

239 
6.1.2.9 Ethanol Production Yield 

Modern ethanol plants are now able to produce more than 2.7 gallons of ethanol per 

bushel of corn compared with less than 2.4 gallons of ethanol per bushel of corn in 1980. The 

development of new enzymes continues to increase the potential ethanol yield. We used a value 

of 2.7173 gal/bu in our analysis, which may underestimate actual future yields. However, this 

value is consistent with the ethanol model developed by USDA described in Section 6.1.2.2 and 

was used in the cost modeling of corn ethanol discussed in Chapter 7. 

6.1.2.10 Byproduct Allocation 

There are a number of by-products made during the production of ethanol. In lifecycle 

analyses, the energy consumed and emissions generated by an ethanol plant must be allocated 

not only to ethanol, but also to each of the by-products. There are a number of methods that can 

be used to estimate by-product allocations. These include methods based on the economic value 

of each by-product, or on energy usage, based on engineering analysis of the actual processes 

related to each product. The method preferred by EPA is called the displacement method. This 

method most accurately accounts for these by-products by calculating the lifecycle emissions of 

the products that will be displaced by them. In this method the lifecycle emissions of the 

displaced product are calculated and subtracted from the ethanol lifecycle. The ethanol receives 

a credit for the lifecycle emissions of whatever product is displaced, since a quantity of that 

product is no longer needed and is displaced by the ethanol by-products. 

For example, the DDGS produced by an ethanol dry mill plant is a replacement for corn 

and soybean animal feed. We based the amount of DDGS produced by an ethanol dry mill plant 

on the USDA model used in the cost analysis work of this rulemaking. That model predicted 

6.21 dry lb. of DDGS per gallon of ethanol produced. As per the agricultural sector modeling 

done for this rulemaking, we assumed that this DDGS displaces 50% corn and 50% soybean 

meal on a mass basis. So the lifecycle emissions of producing 3.1 lb. of corn and 3.1 lb. of 

soybean meal were calculated and subtracted from the lifecycle emissions associated with 

producing a gallon of ethanol. 

By-products from the ethanol wet milling process include corn gluten meal and corn 

gluten feed that are assumed to displace corn production, as well as corn oil that is assumed to 

displace soybean oil. Ethanol produced from cellulosic feedstock through the fermentation route 

is assumed to produce excess electricity as a by-product, from onsite combustion of lignin. This 

excess electricity is assumed to displace electricity from the grid. The fermentation process used 

to produce ethanol in corn wet and dry milling and cellulosic ethanol production also produces 

CO2 as a by-product. This CO2 could be sold to an organization that specializes in cleaning and 

pressurizing it for use in the food industry for example to carbonate beverages, to manufacture 

dry ice, and to flash freeze meat. While CO2 could potentially displace other sources of CO2 

production, this was not considered in our analysis and no value was associated with this CO2 

co-product. 

73 All yield values presented represent pure ethanol production (i.e. no denaturant). 

240 
The displacement method for by-product allocation is the default for the GREET model. 

EPA supports that approach and continues to use that method in this analysis. However, other 

researchers have used different allocation methods in their ethanol fuel cycle studies. We 

evaluated one of these other methods used by USDA in a recent ethanol energy balance 

reportAAAAA to determine the impact this assumption has on the overall results of the analysis. 

The method used by USDA was to split the energy use and emissions of corn agriculture and 

ethanol production between the ethanol and co-products. The lifecycle analysis results were then 

based on only the ethanol portion. A process simulation was used to allocate the energy used in 

the ethanol plant to ethanol and by-products. Using this approach they determined that on 

average 59 and 64 percent of the energy used in dry and wet mills respectively is used to produce 

ethanol. The remaining energy is used for the production of by-products. Therefore, for dry mill 

ethanol production only 59 percent of the plant energy use and associated emissions were 

allocated to the ethanol lifecycle. Corn production energy use and emissions were allocated 

based on the starch content of the corn, assumed to be 66 percent of corn kernel weight. So, only 

66 percent of the energy and emissions used to produce corn were allocated to the ethanol 

lifecycle. 

Use of the process energy based allocation method reduces ethanol lifecycle energy use 

and GHG emissions by approximately 30 percent compared to the displacement allocation 

approach. This indicates that ethanol lifecycle analysis results are extremely sensitive to the 

choice of allocation method used. However, as mentioned above, EPA feels that the 

displacement allocation method is the most reasonable and is the preferred method to use. This 

decision is supported by international lifecycle assessment standards which indicate that 

whenever possible the product system should be expanded to include the additional functions 

related to the co-productsBBBBB. 

6.1.2.11 Biodiesel Production 

Two scenarios for biodiesel production were considered, one utilizing soybean oil as a 

feedstock and one using yellow grease. 

For the soybean oil scenario, the energy use and inputs for the biodiesel production 

process were based on a model developed by NREL and used by EPA in the cost modeling of 

soybean oil biodiesel, as discussed in Chapter 7. 

The GREET model does not have a specific case of biodiesel production from yellow 

grease. Therefore, as a surrogate we used the soybean oil based model with several adjustments. 

For the yellow grease case, no soybean agriculture emissions or energy use was included. 

Soybean crushing was still 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. 

As per the cost modeling of yellow grease biodiesel discussed in Chapter 7, the energy use for 

yellow grease biodiesel production was assumed to be 1.72 times the energy used for soybean oil 

biodiesel. 

241 
The biodiesel lifecycle results were based on a 50% / 50% split between soybean oil and 

yellow grease biodiesel production based on EIA’s AEO 2006 projections for biodiesel produced 

from the different feedstocks. 

6.2 Methodology 

As outlined in the scoping discussion, the goals of this analysis are to both examine the 

total GHG and fossil fuel reductions of increased renewable fuel use in absolute tons and gallons 

and to compare these reductions to the U.S. transportation sector and nationwide GHG emissions 

and fossil fuel use. The output of the GREET model can be used directly to calculate tons of 

GHG and gallons of petroleum reduced. However, these results are not entirely consistent with 

transportation sector and nationwide emissions inventories which are based on slightly different 

assumptions concerning fuel heating values and carbon content. As a result we could not use 

GREET directly to estimate the nationwide impacts of replacing some gasoline and diesel with 

renewable fuels. 

To be consistent between our modeling of savings and overall sector inventories, we used 

GREET instead to generate comparisons between renewable fuels and the petroleum-based fuels 

that they displace. These comparisons allowed us to develop displacement indexes which 

represent the percent of lifecycle GHGs or fossil fuel reduced when a Btu of renewable fuel 

replaces a Btu of gasoline or diesel. In this way GREET was used to generate percent reductions 

and not absolute values. These percent reductions or displacement values were then applied to 

the same gasoline and diesel fuel inventories used to generate transportation sector and 

nationwide inventories. This ensured that savings and sector wide inventories in terms of 

absolute values were calculated in a consistent manner. 

In order to estimate the impacts of increased use of renewable fuels on fossil energy and 

greenhouse gases, we first determined how much gasoline and diesel would be replaced as a 

result of this rule. We then combined lifecycle percent reductions from GREET with lifecycle 

inventories and petroleum consumption values for gasoline and diesel fuel use to get the amounts 

of fossil energy and greenhouse gases reduced. For example, to estimate the impact of cornethanol use on GHGs, these factors were combined in the following way: 

242 
_______________________________________________________ 

SGHG,corn ethanol = Rcorn ethanol x LCgasoline x DIGHG,corn ethanol 

where: 

SGHG,corn ethanol = Lifecycle GHG emission reduction over the reference case associated with 

use of corn ethanol (million metric tons of GHG) 

Rcorn ethanol = Amount of gasoline replaced by corn ethanol on an energy basis (Btu) 

LCgasoline = Lifecycle emissions associated with gasoline use (million metric tons of 

GHG per Btu of gasoline) 

DIGHG,corn ethanol = Displacement Index for GHGs and corn ethanol, representing the percent 

reduction in gasoline lifecycle GHG emissions which occurs when a Btu 

of gasoline is replaced by a Btu of corn ethanol 

Variations of the above equation were also generated for impacts on all four endpoints of 

interest (fossil fuel consumption, petroleum consumption emissions of CO2, and emissions of 

GHGs) as well as all three renewable fuels examined (corn-ethanol, cellulosic ethanol, and 

biodiesel). These values are then compared to the total U.S. transportation sector and nationwide 

inventories of fossil energy and greenhouse gases to get the overall impacts of the rule. 

In this regard, the impact on overall transportation sector GHG emissions due to the 

increased use of renewable fuels can be described mathematically as follows: 

TSector%,GHG = SGHG,corn ethanol + SGHG,cell ethanol + SGHG,biodiesel 

 TSectorGHG 

where: 

TSector%,GHG = Percent reduction in overall transportation sector GHG emissions resulting 

from the use of renewable fuels (%) 

SGHG,corn ethanol = Lifecycle GHG emission reduction over the reference case associated with 

use of corn ethanol (million metric tons of GHG) 

SGHG,cell ethanol = Lifecycle GHG emission reduction over the reference case associated with 

use of cellulosic ethanol (million metric tons of GHG) 

SGHG,biodiesel = Lifecycle GHG emission reduction over the reference case associated with 

use of biodiesel (million metric tons of GHG) 

TSectorGHG = Overall transportation sector GHG emissions in 2012 (million metric tons 

of GHG) 

243 
We used the same approach to estimate fossil energy, petroleum energy, and CO2 

reductions in the transportation sector. We also used the same approach to estimate nationwide 

reductions. 

Section 6.2.1 describes how we estimated the amount of gasoline and diesel fuel replaced 

as modeled for this rule. Section 6.2.2 describes the lifecycle emissions and energy associated 

with gasoline and diesel fuel use. In Section 6.2.3 below, we outline how we generated 

displacement indexes using GREET. Section 6.2.4 outlines how we developed the overall 

transportation sector and nationwide fossil energy and greenhouse gas emissions. 

6.2.1 Modeling Scenarios 

In general, the volume fraction (R) in the equation above represents the amount of 

conventional fuel no longer consumed – that is, displaced – as a result of the use of the 

replacement renewable fuel. Thus R represents the incremental amount of renewable fuel used 

under each of our renewable fuel volume scenarios, in units of Btu. We make the assumption 

that vehicle energy efficiency will not be affected by the presence of renewable fuels (i.e., 

efficiency of combusting one Btu of ethanol is equal to the efficiency of combusting one Btu of 

gasoline). 

As described in Section 6.1.1.4, our analysis of the GHG and fossil fuel consumption 

impacts of renewable fuel use was conducted using three volume scenarios. The total volumes 

for all three scenarios are shown in Table 6.1-3. For the purposes of calculating the R values, we 

assumed the ethanol volumes shown in Table 6.1-3 are 5% denatured, and the ethanol volumes 

were adjusted down to represent pure (100%) ethanol. The adjusted volumes were then 

converted to total Btu using the appropriate volumetric energy content values (76,000 Btu/gal for 

ethanol, and 118,000 Btu/gal for biodiesel). 

Since the impacts of increased renewable fuel use were measured relative to the 2012 

reference case, the value of R actually represented the incremental amount of renewable fuel 

between the reference case and each of the two other scenarios. The results are shown in Table 

6.2-1. The results shown in Table 6.2-1 are direct reductions in fuel use and do not represent 

lifecycle savings. 

244 
Table 6.2-1. 

Direct Conventional Fuel Replaced in 2012 (quadrillion Btu) 

RFS Case EIA Case 

Gasoline Replaced by Corn Ethanol 0.147 0.347 

Gasoline Replaced by Cellulosic Ethanol 0.018 0.018 

Diesel Fuel Replaced by Biodiesel 0.032 0.032 

Gasoline Replaced by Ethanol Imports 0.031 0.045 

Total Energy 0.229 0.443 

6.2.2 Lifecycle Impacts of Conventional Fuel Use 

In order to determine the lifecycle impact that increased renewable fuel volumes may 

have on any particular endpoint (fossil fuel consumption or emissions of GHGs), we also needed 

to know the conventional fuel inventory on a lifecycle basis. Since available sources of GHG 

emissions are provided on a direct rather than a lifecycle basis, we converted these direct 

emission and energy estimates into their lifecycle counterparts. 

To do this, we used GREET to develop multiplicative factors for converting direct 

(vehicle-based) emissions of GHGs, or direct (vehicle-based) consumption of petroleum, into 

full lifecycle factors. GREET output was used to generate the conversion factors shown in Table 

6.2-2. 

Table 6.2-2. 

Direct (wheel only) Conversion Factors to Well-to-Wheel (lifecycle) 

Emissions or Energy Use 

Gasoline Diesel 

Petroleum 1.11 1.10 

Fossil fuel 1.22 1.21 

GHG 1.26 1.25 

CO2 1.23 1.21 

245 
The factors in Table 6.2-2 were applied to gasoline and diesel fuel inventories of 

emissions or energy consumption at the consumer level (i.e. direct emissions or energy) to 

convert them into alternative inventories representing full lifecycle contributions. 

The direct petroleum energy for gasoline and diesel fuel is just the energy content of the 

fuels used. Consistent with U.S. EPA National Inventory calculationsCCCCC, we converted 

energy use values for gasoline and diesel fuel to direct CO2 emissions by multiplying by a carbon 

content coefficient, a carbon oxidation factor, and converting the resulting carbon emissions into 

CO2. The CO2 emissions were then scaled up by assuming a fraction increase to the CO2 

emissions to account for non-CO2 GHGs (CH4 and N2O). The fraction increase was based on the 

U.S. EPA National Inventory 2004 values for both CO2 and total GHG emissions. Table 6.2-3 

shows the total lifecycle petroleum and GHG emissions associated with direct use of a Btu value 

of gasoline or diesel fuel. These values represent factor LC in the equation described above. 

Table 6.2-3. 

Lifecycle Emissions and Energy (LC Values) 

Gasoline Diesel 

Petroleum (Btu/Btu) 1.11 1.10 

Fossil fuel (Btu/Btu) 1.22 1.21 

GHG (Tg-CO2-eq/QBtu) 99.4 94.5 

CO2 (Tg-CO2/QBtu) 94.2 91.9 

6.2.3 Displacement Indexes 

In order to permit a quantitative evaluation of the degree to which a renewable fuel 

reduces lifecycle fossil fuel consumption or GHG emissions, several metrics have been 

developed. Three of the most prominent metrics are shown in Table 6.2-4. 

Table 6.2-4. Metrics Used to Measure Lifecycle Impacts of Renewable Fuels 

Metric Calculation 

Net energy balance Renewable energy out - fossil energy in 

Energy efficiency Fossil energy in ÷ renewable energy out 

(or alternatively renewable energy out ÷ fossil energy in) 

Displacement index % reduction in emissions or energy compared to the fuel 

that it replaces 

Of these metrics, we believe the displacement index is the most appropriate to use as it 

compares the renewable fuel to the petroleum fuel it is displacing. The net energy balance and 

energy efficiency approaches only consider the renewable fuel itself and do not account for the 

246 
fact that the use of renewable fuels result in decreased use of petroleum fuels and thus provide 

misleading results. 

As an example, if 81,000 Btu of fossil fuels were required to make, transport, and store 

one gallon of ethanol, then the energy efficiency would be calculated as follows: 

Energy efficiency = 81,000 Btu/gal ÷ 76,000 Btu/gal = 1.07 

This result would imply that ethanol cannot be labeled "renewable," since one gallon of 

ethanol contains less energy than was required to make that one gallon. However, the use of 

ethanol may still reduce overall lifecycle fossil fuel use even in this case. If, for example, 18,000 

Btu of fossil fuels were required to make one ethanol-equivalent gallon of gasoline (i.e. 76,000 

Btu of gasoline), then a total of 94,000 Btu of fossil fuel energy would be consumed whenever 

76,000 Btu of gasoline energy was combusted in a conventional vehicle. Since 81,000 Btu is 

less than 94,000 Btu, the use of ethanol would result in less fossil fuel consumption than the use 

of gasoline, even though the energy efficiency is greater than 1.0. The 81,000 Btu of fossil 

energy required to produce the ethanol includes lifecycle energy. The energy content of the 

ethanol (76,000 Btu) itself is not considered fossil energy and therefore not included in the 

comparison with gasoline calculation above. Thus, even in cases where the net energy balance 

of a renewable fuel is negative or has energy efficiency less than 1.074, there may still be an 

overall reduction in lifecycle fossil fuel use (and associated GHG emissions) due to decreased 

petroleum fuel use. 

Therefore, studies that rely on the energy balance metric and conclude for example that 

the net energy balance of corn ethanol is negative, or the energy efficiency is less than 1.0, 

making it an unattractive transportation fuel, are not capturing the full implications of the use of 

the fuel and are providing misleading results. 

Because of this potential for the net energy balance and energy efficiency metrics to 

provide misleading information, for our analysis of this rule we have chosen to use the 

displacement index. The displacement index provides the most direct measure of the impacts of 

replacing conventional gasoline or diesel with a renewable fuel, and is also better suited to 

describing impacts of renewable fuel use on fossil fuel consumption and GHGs. 

The displacement index (DI) represents the percent reduction in GHG emissions or fossil 

fuel energy brought about by the use of a renewable fuel in comparison to the conventional 

gasoline or diesel that the renewable fuel replaces. The formula for calculating the displacement 

index depends on which fuel is being displaced (i.e. gasoline or diesel), and which endpoint is of 

interest (e.g. petroleum energy, GHG). For instance, when investigating the CO2 impacts of 

ethanol used in gasoline, the displacement index is calculated as follows: 

 A net energy balance of zero, or an energy efficiency of 1.0, would indicate that the full lifecycle fossil fuels used 

in the production and transportation of ethanol are exactly equal to the energy in the ethanol itself. 

247 

74
 DICO2 = 1 - lifecycle CO2 emitted for ethanol in g/Btu

lifecycle CO2 emitted for gasoline in g/Btu 

The units of g/Btu ensure that the comparison between the renewable fuel and the 

conventional fuel is made on a common basis, and that differences in the volumetric energy 

content of the fuels is taken into account. The denominator includes the CO2 emitted through 

combustion of the gasoline itself in addition to all the CO2 emitted during its manufacturer and 

distribution. The numerator, in contrast, includes only the CO2 emitted during the manufacturer 

and distribution of ethanol, not the CO2 emitted during combustion of the ethanol. 

The combustion of biomass-based fuels, such as ethanol from corn and woody crops, 

generates CO2. However, in the long run 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

DDDDD. As a result, CO2 

emissions from biomass-based fuels combustion are not included in their lifecycle emissions 

results and are not used in the CO2 displacement index calculations shown above. Net carbon 

fluxes from changes in biogenic carbon reservoirs in wooded or crop lands are accounted for 

separately in the GREET model. 

When calculating the GHG displacement index, however, the CH4 and N2O emitted 

during biomass-based fuels combustion are included in the numerator. Unlike CO2 emissions, 

the combustion of biomass-based fuels does result in net additions of CH4 and N2O to the 

atmosphere. We assume that combustion CH4 and N2O emissions are not offset by carbon 

uptake of renewable biomass production. As shown in Table 6.1-2, CH4 and N2O emissions 

contribute to the total GHG impact. Therefore, combustion CH4 and N2O emissions are included 

in the lifecycle GHG emissions results for biomass-based fuels and are used in the GHG 

displacement index calculations. 

Using GREET, we calculated the lifecycle values for energy consumed and GHGs 

produced for corn-ethanol, cellulosic ethanol, and soybean-based biodiesel, as well as the 

gasoline and diesel fuel that would be displaced. For both renewable and conventional fuels, we 

summed the lifecycle results for both the feedstock and the fuel. The results are shown in Table 

6.2-5. 

248 
Table 6.2-5. Output from GREET Used to Develop Displacement Indexes 

Units Gasolinea Corn 

ethanol 

Corn ethanol 

(biomass fuel) 

Cellulosic 

ethanol 

L S 

Diesel 

Biodiesel 

Well-to-Pump 

Fossil 

energy 

Btu/mmBtu 224,133 742,411 290,324 88,973 207,008 464,594 

Petroleum 

energy Btu/mmBtu 107,298 90,771 88,896 91,977 98,656 96,539 

CO2 g/mmBtu 17,893 56,275 26,089 -71 16,629 28,468 

CO2-eq g/mmBtu 20,435 75,219 43,043 6,427 19,134 31,193 

End point combustion 

Fossil energy Btu/mmBtu 1,000,000 0 0 0 1,000,000 0 

Petroleum 

energy Btu/mmBtu 1,000,000 0 0 0 1,000,000 0 

CO2 

combustionb g/mmBtu 76,419 74,755 74,755 74,755 77,570 79,388 

Fossil CO2 

combustion 76,419 0 0 0 77,570 0 

CO2-eq 

combustionc g/mmBtu 79,015 2,596 2,596 2,596 77,669 99 

a Volume-weighted average of conventional gasoline (65%), RFG blendstock (25%), and CaRFG blendstock (10%). 

b

 Based on carbon content of the fuel. c

 Includes Fossil CO2, CH4, and N2O tailpipe emissions. CH4 and N2O emissions based on assuming an increase over CO2

emissions, the percent increase is from the U.S. EPA National Inventory for CO2 and GHG emissions from on-road sources.

We used the values from the table above to calculate the displacement indexes. The 

results are shown in Table 6.2-6. 

249 
Table 6.2-6. Displacement Indexes Derived from GREET 

Corn ethanol Corn ethanol 

(biomass fuel) 

Cellulosic ethanol Imported 

ethanol 

Biodiesel 

DIFossil Fuel 39.3% 76.3% 92.7% 69.0% 61.5% 

DIPetroleum 91.8% 92.0% 91.7% 92.0% 91.2% 

DIGHG 21.8% 54.1% 90.9% 56.0% 67.7% 

DICO2 40.3% 72.3% 100.1% 71.0% 69.8% 

The displacement indexes in this table represent the impact of replacing a Btu of gasoline 

or diesel with a Btu of renewable fuel. Thus, for instance, for every Btu of gasoline which is 

replaced by corn ethanol, the total lifecycle GHG emissions that would have been produced from 

that Btu of gasoline would be reduced by 21.8 percent. For every Btu of diesel which is replaced 

by biodiesel, the total lifecycle petroleum energy that would have been consumed as a result of 

burning that Btu of diesel fuel would be reduced by 91.2 percent. 

Consistent with the cost modeling done for this rule, for the 2012 cases we assume the 

“cellulosic” ethanol volume is actually produced from corn utilizing a biomass fuel source at the 

ethanol production plant. The displacement index for that fuel as shown in Table 6.2-6, is used 

in the calculation of reductions. 

The displacement index for imported ethanol in all cases is based on an average of corn 

and cellulosic ethanol. While not exclusively, we anticipate much imported ethanol to be 

primarily sugarcane based ethanol. There currently is no sugarcane ethanol lifecycle values 

included in GREET. The GHG emissions when producing sugarcane ethanol differs from corn 

ethanol in that the GHG emissions from growing sugarcane is likely different than for growing a 

equivalent amount of corn to make a gallon of ethanol, the process of turning sugar into ethanol 

is easier and therefore less energy intensive (which typically translates into lower GHG) and, 

importantly, we understand that at least some of the ethanol produced in Brazil uses the bagasse 

from the sugarcane itself as a process fuel source. We know from our analysis that using a 

biomass source for process energy greatly improves the GHG benefit of the renewable fuel. 

These factors would result in sugarcane ethanol having a greater GHG benefit per gallon than 

corn ethanol, certainly where natural gas or coal is the typical process fuel source used. 

Conversely, sugarcane ethanol production does not result in a co-product such as distillers grain 

as in the case of corn ethanol. In our analyses, accounting for co-products significantly 

improved the GHG displacement index for corn ethanol. Furthermore, there would be additional 

transportation emissions associated with transporting the imported ethanol to the U.S. as 

compared to domestically produced ethanol. Developing a technically rigorous lifecycle 

estimate for energy needs and GHG impacts for sugarcane ethanol is not a simple task and was 

not available in the timeframe of this rulemaking. Considering all of the differences between 

imported and domestic ethanol, for this rulemaking, we assumed imported ethanol would be 

predominately from sugarcane and have estimated DI’s approximately mid-way between the 

DI’s for corn ethanol and DI’s for cellulosic ethanol. We are continuing to develop a better 

250 
understanding of the lifecycle energy and GHG impacts of producing ethanol from sugarcane 

and other likely feedstocks of imported ethanol for any future analysis. 

6.2.4 Transportation Sector and Nationwide Inventories 

For our analysis described above, we need estimates of transportation sector and 

nationwide fossil energy and GHG emissions to determine the percent reduction impacts of the 

program (e.g., TSectorGHG factor in the equation above). These inventories are direct not 

lifecycle and are needed for 2012 to compare to the projected renewable fuel savings in 2012. 

6.2.4.1 Fossil Fuel Inventory 

The transportation sector and nationwide fossil fuel inventory is just the energy content 

of the fuels used. Fossil fuel use in the transportation sector includes gasoline and diesel as well 

as other petroleum fuels, such as residual oil and LPG. It also includes other fossil energy use in 

the form of natural gas and the fossil portion of electricity used. Inherent with the assumptions 

on the amounts of renewable fuels use projected to 2012, there are also assumed values for 

gasoline and diesel fuel use. Values for energy use of the different transportation fuels other 

than gasoline and diesel (e.g., jet fuel, natural gas, etc.) were taken directly from the 2006 

Annual Energy Outlook. 

The nationwide fossil fuel inventory includes petroleum, natural gas, and coal energy use. 

The direct fossil fuel inventory values are shown in Table 6.2-7. 

Table 6.2-7. Direct Fossil Fuel Inventories (QBtu) 

2012 

Nationwide 94.53 

Transportation Sector 31.41 

6.2.4.2 Petroleum Inventory 

As with fossil energy, the transportation sector and nationwide petroleum inventory is 

just the energy content of the fuels used. The transportation sector petroleum inventory includes 

gasoline and diesel as well as other petroleum fuels, such as residual oil and LPG. 

The nationwide petroleum inventory includes petroleum use in the transportation sector 

as well as other sectors. The direct petroleum inventory values are shown in Table 6.2-8. 

251 
Table 6.2-8. Direct Petroleum Inventories (QBtu) 

2012 

Nationwide 43.87 

Transportation Sector 30.47 

6.2.4.3 CO2 Inventories 

We calculated direct CO2 emissions for the transportation sector in 2012 by applying 

carbon emissions factors to the projected amount of fuels used in those years. 

Direct CO2 emissions from the transportation sector as a whole are calculated in the same 

way as direct gasoline and diesel emissions are calculated as described in Section 6.2.2. We 

converted energy use values for transportation sector fuels to direct CO2 emissions by 

multiplying by a carbon content coefficient, a carbon oxidation factor, and converting the 

resulting carbon emissions into CO2. Emissions from electricity use in the transportation sector 

(rail) are calculated based on the U.S. average mix of fossil fuels used to generate electricity. 

Consistent with the EPA inventory report we made an adjustment to diesel fuel, jet fuel 

and residual oil use to subtract out the emissions associated with bunker fuel. The AEO values 

include the energy use of bunker fuels, but the emissions of these fuels are not considered part of 

the U.S. transportation sector emissions. This adjustment was done by decreasing emissions of 

diesel fuel, jet fuel, and residual oil by the portion of emissions associated with bunker fuels as 

determined in the EPA inventory report. 

Direct nationwide CO2 emissions are also calculated in the same way applying factors for 

all fossil fuels used as reported by the 2006 Annual Energy Outlook. This type of analysis 

results in a small understatement of total Nationwide CO2 emissions as it does not capture other 

industrial sources of CO2 emissions for example CO2 emissions from calcinations of limestone in 

the cement industry. However, there are no projections of these other emissions sources for 

2012, and they are a relatively small part of total Nationwide CO2 emissions, representing only 

6% of total CO2 emissions in 2004 according to the EPA National Inventory values. Therefore, 

while impacts of increased renewable fuel use as a percent of nationwide CO2 emissions may be 

slightly overestimated the impacts on results are not thought to be significant. The results of 

direct CO2 emission calculations are shown in Table 6.2-9. 

252 
Table 6.2-9. CO2 Direct Inventories (Tg CO2) 

2012 

Nationwide 6,406 

Transportation Sector 2,108 

6.2.4.4 GHG Inventories 

Projections for direct GHG emissions can not be calculated directly from the energy 

projections as was done for CO2. The approach to estimating CO2 emissions from mobile 

combustion sources varies significantly from the approach to estimating non-CO2 GHG 

emissions (CH4 and N2O emissions). While CO2 can be reasonably estimated by applying an 

appropriate carbon content and fraction of carbon oxidized factor to the fuel quantity consumed, 

CH4 and N2O emissions depend largely on the emissions control equipment used (e.g., type of 

catalytic converter) and vehicle miles traveled. Emissions of these gases also vary with the 

efficiency and vintage of the combustion technology, as well as maintenance and operational 

practices. Due to this complexity, a much higher level of uncertainty exists in the estimation of 

CH4 and N2O emissions from mobile combustion sources, compared to the estimation of CO2 

emissions. 

Projections for direct transportation sector and nationwide GHG emission are done by 

assuming a fraction increase to the CO2 emissions to account for non-CO2 GHGs. The fraction 

increase was based on the U.S. EPA National Inventory 2004EEEEE values for both CO2 and total 

GHG emissions. This same increase is applied to 2012 CO2 values. Table 6.2-10 shows the 

fraction increase values for GHGs over CO2 emissions calculated from the U.S. EPA National 

Inventory report. 

Table 6.2-10. U.S. National Inventory 2004 CO2 and GHG Inventories 

CO2 (Tg-CO2) GHG (Tg-CO2–eq.) Fraction Increase 

Nationwide 5,988 7,074 1.1807 

Transportation 

Sector 1,860 1,960 1.0538 

The results of direct GHG emission calculations are shown in Table 6.2-11. 

253 
Table 6.2-11. GHG Direct Inventories (Tg CO2-eq.) 

2012 

Nationwide 7,564 

Transportation Sector 2,222 

6.3 Impacts of Increased Renewable Fuel Use 

We used the methodology described above to estimate impacts of increased use of 

renewable fuels on consumption of petroleum and fossil fuels and also emissions of CO2 and 

GHGs. This section describes our results. 

6.3.1 Fossil Fuels and Petroleum 

We used the S equation in Section 6.2 to estimate the reduction associated with the 

increased use of renewable fuels on lifecycle fossil fuel and petroleum consumption. These 

values are then compared to the total U.S. transportation sector and nationwide inventories to get 

a percent reduction. The estimates are presented in Tables 6.3-1 and 6.3-2. 

Table 6.3-1. 

Estimated Fossil Fuel Impacts of Increased Use of Renewable Fuels in 2012, 

In Comparison to the Reference Case 

RFS Case EIA Case 

Reduction (quadrillion Btu) 0.15 0.27 

Percent reduction in 

Transportation Sector Energy Use 

0.48 % 0.85 % 

Percent reduction in Nationwide 

Energy Use 

0.16 % 0.28 % 

254 
Table 6.3-2. 

Estimated Petroleum Impacts of Increased Use of Renewable Fuels in 2012, 

In Comparison to the Reference Case 

RFS Case EIA Case 

Reduction (billion gal) 2.0 3.9 

Percent reduction in 

Transportation Sector Energy Use 

0.82 % 1.60 % 

Percent reduction in Nationwide 

Energy Use 

0.57 % 1.11 % 

6.3.2 Greenhouse Gases and Carbon Dioxide 

We used the S equation in Section 6.2 to estimate the reduction associated with the 

increased use of renewable fuels on lifecycle emissions of CO2. These values are then compared 

to the total U.S. transportation sector and nationwide emissions to get a percent reduction. The 

estimates are presented in Table 6.3-3. 

Table 6.3-3. 

Estimated CO2 Emission Impacts of Increased Use of Renewable Fuels in 2012,

In Comparison to the Reference Case 

RFS Case EIA Case 

Reduction (million metric tons CO2) 11.0 19.5 

Percent reduction in Transportation 

Sector Emissions 

0.52 % 0.93 % 

Percent reduction in Nationwide 

Emissions 

0.17 % 0.30 % 

Carbon dioxide is a subset of GHGs, along with CH4 and N2O as discussed above. It can 

be seen from Table 6.2-6 that the displacement index of CO2 is greater than for GHGs for each 

255 
renewable fuel. This indicates that lifecycle emissions of CH4 and N2O are higher for renewable 

fuels that for the conventional fuels replaced. Therefore, reductions associated with the 

increased use of renewable fuels on lifecycle emissions of GHGs are lower than the values for 

CO2. The estimates for GHGs are presented in Table 6.3-4. 

Table 6.3-4. 

Estimated GHG Emission Impacts of Increased Use of Renewable Fuels in 2012, 

In Comparison to the Reference Case

RFS Case EIA Case 

Reduction 

(million metric tons CO2-eq.) 

8.0 13.1 

Percent reduction in Transportation 

Sector Emissions 

0.36 % 0.59 % 

Percent reduction in Nationwide 

Emissions 

0.11 % 0.17 % 

6.4 Implications of Reduced Imports of Petroleum Products 

6.4.1 Impacts on Imports of Petroleum Products 

To assess the impact of the RFS program on petroleum imports, the fraction of domestic 

consumption derived from foreign sources was estimated using results from the AEO 2006. We 

describe in this section how fuel producers might change their levels and mix of imports in 

response to a decrease in fuel demand. 

We compared the levels and mix of imports in the AEO reference case with the AEO low 

macroeconomic growth case and AEO high oil price case. The latter two cases reflect different 

assumptions by EIA regarding economic growth and world oil prices, respectively. The net 

effect for both cases is a reduction in domestic petroleum consumption compared to the AEO 

reference case. The changes in the level and mix of imports were examined, given a reduction in 

petroleum consumption similar to the amount estimated in the RFS for 2012 (0.25 to 0.49 

Quads). Note that the EIA has conducted three separate analyses of Congressional bills which 

included earlier forms of the renewable fuel standard. These separate analyses however were 

based on earlier AEO versions and, in some instances, considered numerous provisions in 

addition to an RFS which collectively affected world oil prices and domestic oil consumption. 

256 
Thus, we did not directly use these earlier analyses, rather opting to use only the results in the 

AEO 2006 cases, as discussed above, to assess the RFS impacts on imports. 

Comparison of the AEO 2006 reference case against the low macroeconomic growth case 

allowed us to evaluate how a decrease in domestic petroleum demand might affect the mix of 

imported finished products, imported crude oil, and domestic production. Note that the world 

price of crude oil remains the same between the AEO low macroeconomic growth and reference 

cases. Comparison of the two cases show that with an initial decrease in petroleum consumption 

(approximately 300,000 barrels per day or 0.61 Quads, higher than 2012 values), net imports will 

account for approximately 95% of the reductions on an energy basis.FFFFF These net imports 

include imports of crude oil or petroleum products minus exports of crude oil or petroleum 

products.GGGGG Both reduced domestic crude production and natural gas plant liquids account for 

most of the remainder. Note that for all levels of reduced petroleum demand, domestic crude 

production appears to account for less than 5% of the change. In addition, the reductions shown 

here do not reflect any rebound effect that may occur. Out of the initial reductions in net 

petroleum imports, imported finished products account for almost all the reductions. As domestic 

petroleum demand is reduced even further (over 860,000 barrels per day), approximately 50% of 

the reductions come from imported finished products, 44% from imported crude oil, and the 

remainder from reduced domestic, natural gas plant liquid (NGL) production, and exports. 

Under the low macroeconomic growth case assumptions, imported finished products are 

initially reduced presumably because they represent the higher marginal cost source for refineries 

versus imported crude oil. Refineries may prefer to refine crude oil as opposed to importing 

finished products because of the higher margins involved with the former and the potentially 

more optimum use of refining capacity. Crude oil, as an international commodity, will be 

purchased at the market price by refineries. Thus, while crude oil from abroad may be produced 

more cheaply than domestic production sources, refineries that purchase from either source will 

pay the international market price for that specific grade of crude oil based on specific gravity 

and sulfur content plus the cost of transport to the U.S. 

Note that there is uncertainty in quantifying how refineries will change their mix of 

sources with a decrease in petroleum demand, particularly at the levels estimated for the RFS. 

Changes in world oil price from the reference case could also significantly alter the mix of 

sources from which refineries choose. For example, a comparison between the AEO high price 

case and the reference case (under a decrease in petroleum consumption of 0.64 Quads) shows 

that 80% of the reductions (on an energy basis) come from reductions in net petroleum imports, 

while the remaining 20% comes from reductions in domestic production. As petroleum 

consumption is reduced even further, reductions in net petroleum imports make up an even 

greater percentage. For the reductions in net petroleum imports, imports of finished products are 

observed to actually increase while imports of crude oil decrease even more. 

We believe that the actual refinery response might range between these two AEO cases, 

so that net import reductions could compose 80-95% of the reductions in petroleum demand for 

2012. The split between the changes in imports of finished products versus crude oil are more 

uncertain. Discussions with EIA suggest the split could be close to 50-50. Thus, we believe the 

range could be between these two estimates (nearly all to 50% finished product). For the 

257 
purposes of this RIA, we show values for the case where net import reductions come entirely 

from imports of petroleum products, with an example shown below. 

By using the petroleum reduction levels as discussed in 6.3.2 of the RIA and comparing 

these to the AEO 2006 results, we estimate that 95% of the lifecycle petroleum reductions will 

be met through reductions in net petroleum imports. Table 6.4-1 shows the reductions in net 

petroleum imports estimated for the RFS program. We expect that these import reductions will 

be met almost exclusively from finished petroleum products rather than from crude oil, for the 

reasons given above and consistent with the results of the AEO 2006 low macroeconomic growth 

case. As an example calculation, we apportioned 95% of the total reductions in gasoline and 

diesel to displaced finished product imports. By 2012, imports of finished products are estimated 

to be reduced by 123,000 and 240,000 barrels per day, respectively, for the RFS and EIA cases. 

We compare these reductions in imports against the AEO projected levels of net petroleum 

imports. The range of reductions in net petroleum imports are estimated to be between 0.9 to 

1.7%. 

Table 6.4-1. Net reductions in Imports in 2012 

RFS Case EIA Case 

Reduction in finished productsa 

(barrels per day) 

123,000 240,000 

Percent reductionb 0.89% 1.73% a Net reductions relative to 2012 reference case 

b Compared to AEO2006 projections for 2012 reference case 

6.4.2 Impacts on Import Expenditures 

The reductions in petroleum imports were discussed in Section IX.D of the preamble. As 

noted in the preamble, we calculate the change in expenditures on petroleum imports and ethanol 

imports assuming this would not result in any other changes in consumer behavior that would be 

reflected in fuel use. 95% of all reductions in petroleum imports were calculated to be from 

finished petroleum products rather than crude oil, as discussed in the prior section. The economic 

savings in petroleum product imports was calculated by multiplying the reductions in gasoline 

and diesel imports by their corresponding price. According to the EIA, the price of imported 

finished products is the market price minus domestic local transportation from refineries and 

minus taxes.HHHHH An estimate was made by using the AEO 2006 wholesale gasoline, distillate, 

and ethanol price forecasts for the specific analysis years. The current ethanol import tariff of 

$0.54/gallon placed on countries outside the Caribbean and Central America is not included in 

the import expenditures, since the tariff revenue collected would remain in the U.S. 

As an example calculation, the RFS case is expected to yield a reduction of 2.0 billion 

gallons of gasoline in the year 2012. 95% of these reductions, or 1.9 billion gallons, are 

expected to come from imports of finished gasoline. Thus, the domestic refining sector would 

avoid purchases of 1.9 billion gallons of gasoline and diesel at the wholesale price. According to 

the AEO 2006, the end-user prices of gasoline and diesel are forecasted to be $2.01 per gallon 

and $1.98 per gallon respectively. Minus federal taxes, state taxes, and distribution costs, the 

258 
wholesale prices of gasoline and diesel forecasted in the AEO 2006 are $1.376 and $1.382 per 

gallon, respectively (2004$). Note that the AEO wholesale prices were used for this calculation, 

as opposed to the gasoline and diesel production costs in Chapter 7 of the RIA, to stay consistent 

with the other AEO results used herein. The avoided petroleum payments abroad thus total $2.6 

billion in 2012 as shown in Table 6.4-2. The additional ethanol import expenditures, using the 

same approach, is estimated to be $0.7 billion in 2012. The net avoided expenditures in imports 

is thus the difference, or $1.9 billion in 2012 as shown in Table 6.4-2. 

We compare these avoided petroleum import expenditures against the projected value of 

total U.S. net exports of all goods and services economy-wide. Net exports is a measure of the 

difference between the value of exports of goods and services by the U.S. and the value of U.S. 

imports of goods and services from the rest of the world. For example, according to the AEO 

2006, the value of total import expenditures of goods and services exceeds the value of U.S. 

exports of goods and services to the rest of the world by $695 billion for 2006 (for a net export 

level of minus $695 billion) and by $383 billion for 2012 (for a net export level of minus $383 

billion).75 In Table 6.4-2, we compare the avoided expenditures in imports versus the total value 

of U.S. net exports of goods and services for the whole economy for 2012. Note that changes to 

corn exports, discussed in Chapter 8 of the RIA, are also included in the calculation of net 

exports. Relative to the 2012 projection, the avoided import expenditures due to the RFS would 

represent 0.4 to 0.7% of economy-wide net exports. 

Table 6.4-2. 

Avoided Import Expenditures ($2004 billion) 

Cases AEO Total 

Net Exports 

Expenditures 

on Petroleum 

Imports 

Expenditures 

on Ethanol 

Imports 

Decreased 

Corn Exports 

Net 

Expenditure 

s on Imports 

Percent of 

Total Net 

Exports 

RFS Case - $383 - $2.6 + $0.7 + $0.6 - $1.4 0.4% 

EIA Case (year 2012) - $5.1 + $1.0 + $1.3 - $2.8 0.7% 

6.5 Energy Security Implications of RFS 

6.5.1 Background 

One of the effects of increased use of renewable fuels in the U.S. from the RFS is that it 

diversifies the energy sources in making transportation fuel. A potential disruption in supply 

reflected in the price volatility of a particular energy source carries with it both financial as well 

as strategic risks. These risks can be reduced to the extent that diverse sources of fuel energy 

reduce the dependence on any one source. This reduction in risks is a measure of improved 

energy security. 

75 For reference, the U.S. Bureau of Economic Analysis (BEA) reports that the 2005 import expenditures on energyrelated petroleum products totaled $235.5 billion (2004$) while petroleum exports totaled $13.6 billion – for a net of 

$221.9 billion in expenditures. Net petroleum expenditures made up a significant fraction of the $591.3 billion 

current account deficit in goods and services for 2005 (2004$). (http://www.bea.gov/) 

259 
At the time of the proposal, EPA stated that an analysis would be completed and 

estimates provided in support of this rule. In order to understand the energy security implications 

of the RFS, 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 new study produced for the RFS, entitled "The Energy Security Benefits of Reduced Oil Use, 

2006-2015," ORNL has updated and applied the method used in the 1997 report "Oil Imports: 

An Assessment of Benefits and Costs", by Leiby, Jones, Curlee and Lee.76,

77

 While the 1997 

report including a description of methodology and results at that time has been used or cited on a 

number of occasions, this updated analysis and results have not been available for full public 

consideration. Since energy security will be a key consideration in future actions aimed at 

reducing our dependence on oil, it is important to assure estimates of energy security impacts 

have been thoroughly examined in a full and open public forum. Since the updated analysis was 

only recently available, such a thorough analysis has not been possible. Therefore, EPA has 

decided not to rely on the results of this report for the purposes of this rulemaking. Rather, we 

are including it as part of the record of this rulemaking and are inviting further public analysis 

and consideration of both this particular report and other perspectives on how to best quantify 

energy security benefits. To facilitate that additional consideration, we highlight below some of 

the key aspects of this particular analysis. 

The approach developed by ORNL estimates the incremental benefits to society, in 

dollars per barrel, of reducing U.S. oil imports, called “oil premium.” Since the 1997 publication 

of this report, changes in oil market conditions, both current and projected, suggest that the 

magnitude of the oil premium has changed. Significant driving factors that have been revised 

include: oil prices, current and anticipated levels of OPEC production, U.S. 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 from the EIA's AEO 2006 were used. Using the 

"oil premium" approach, estimates of benefits of improved energy security from reduced U.S. oil 

imports from increased use of renewable fuels are calculated. 

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 so called "demand" or "monoposony" 

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). 

76 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. 

77 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. 

260 
1. Effect of Oil Use on Long-Run Oil Price, U.S. Import Costs, and Economic 

Output 

The first component of the full economic costs of importing petroleum into 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 reduced U.S. petroleum demand can reduce 

the world price of crude oil. Thus, one consequence of decreasing U.S. oil purchases due to 

increased use of renewable fuel is the potential decrease in the crude oil price paid for all crude 

oil purchased. 

2. Short-Run Disruption Premium From Expected Costs of Sudden Supply 

Disruptions 

The second component of the external economic costs resulting from U.S. oil imports 

arises from the vulnerability of the U.S. economy to oil shocks. The cost of shocks depends on 

their likelihood, size, and length, the capabilities of the market and U.S. Strategic Petroleum 

Reserve (SPR), the largest stockpile of government-owned emergency crude oil in the world, to 

respond, and the sensitivity of the U.S. economy to sudden price increases. While the total 

vulnerability of the U.S. economy to oil price shocks depends on the levels of both U.S. 

petroleum consumption and imports, variation in import levels or demand flexibility can affect 

the magnitude of potential increases in oil price due to supply disruptions. Disruptions are 

uncertain events, so the costs of alternative possible disruptions are weighted by disruption 

probabilities. The probabilities used by the ORNL study are based on a 2005 Energy Modeling 

Forum78 synthesis of expert judgment and are used to determine an expected value of disruption 

costs, and the change in those expected costs given reduced U.S. oil imports. 

3. Costs of Existing U.S. Energy Security Policies 

The last often-identified component of the full economic costs of U.S. oil imports is the 

costs to the U.S. taxpayers of existing U.S. energy security policies. The two primary examples 

are maintaining a military presence to help secure stable oil supply from potentially vulnerable 

regions of the world and maintaining the SPR to provide buffer supplies and help protect the 

U.S. economy from the consequences of global oil supply disruptions. 

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 imports. Similarly, while the costs for building and 

maintaining the SPR are more clearly related to U.S. oil use and imports, historically these costs 

78 Stanford Energy Modeling Forum, Phillip C. Beccue and Hillard G. Huntington, “An Assessment of Oil Market 

Disruption Risks,” Final Report, EMF SR 8, October, 2005. 

261 
have not varied in response to changes in U.S. oil import levels. Thus, while SPR is factored into 

the ORNL analysis, the cost of maintaining the SPR is excluded. 

As stated earlier, we have placed the report in the docket of this rulemaking for the 

purposes of inviting further consideration. However, the results of that report have not been used 

in quantifying the impacts of this rule. 

262 
Chapter 7: Estimated Costs of Renewable Fuels, Gasoline and 

Diesel 

This section describes our methodology for estimating the cost impacts of increased 

production, distribution, and blending of renewable fuels, including corn and cellulosic ethanol 

and biodiesel. Detailed information is given on expected changes to the nation’s fuel distribution 

system, as well as changes in refining processes that will likely occur as larger volumes of 

ethanol are blended into gasoline. The impact of subsidies is also addressed. 

7.1 Ethanol 

This subsection provides a description of the analysis we conducted for estimating the 

cost of corn and cellulosic ethanol. Our analysis indicates that corn ethanol will cost $1.32 per 

gallon to produce (2004 dollars) in 2012. We also estimated that using cellulosic feedstock, the 

production costs for ethanol would be approximately $1.65 per gallon (2004 dollars). By 2012 

this cellulosic cost may decline with breakthroughs and advances in technology. Based on 

reports from a variety of sources and discussions we held with members of academia as well as 

those directly involved in the industry, we believe several hurdles still have to be overcome in 

the production of large volumes of cellulosic derived ethanol. However, it appears that good 

progress continues to be made and we remain optimistic that cellulosic ethanol will become 

increasingly important in the future. 

7.1.1 Corn Ethanol 

Of the new ethanol production capacity expected to be built, according to Section 1.2.2 of 

this RIA, less than three percent combined is expected to be produced from cellulosic feedstocks 

or in plants that differ significantly from dry mill corn ethanol plants. Several plants will be able 

to utilize other starchy feedstocks besides corn, such as milo, barley, wheat, and sorghum. 

However, corn is the primary feedstock, and therefore, the following analyses will focus on dry 

mill starch ethanol production. 

7.1.1.1 Engineering and Construction Requirements for Corn Ethanol Plants 

To meet the RFS Case goal of 6.7 billion gallons per year (Bgal/y) of ethanol in 2012 

from the October 2006 capacity of 5.2 Bgal/yr, 1.5 Bgal/yr of additional capacity will have to be 

constructed.79 If we consider that it is likely that at least 9.6 Bgal/yr of actual ethanol capacity 

will come on-line by 2012 (EIA Case), the annual capacity increase is 4.4 Bgal/yr. Our industry 

characterization work considering plants that are either under construction or are planned to be 

constructed in the next 2-3 years suggests the average new plant size will be 81 million gallons 

per year (MMgal/yr) (including a small number of expansions). 

79 For details on current and expected ethanol capacity, refer to Section 1.2 of this RIA. Note that volumes 

considered cellulosic are also included here, since we believe that virtually all near-term cellulosic ethanol 

production will be from starch-based feedstocks that meet the alternative definition in the Act (discussed further in 

Section 7.1.1.2). 

263 
Based on conversations with representatives from design-build firms working in this 

field, as well as material from public sources, each new plant requires design engineering work 

lasting about six months followed by construction lasting 12-14 months before plant startup is 

possible, resulting in a total project timeline of 18-20 months. The design phase for a basic 50 

MMgal/yr plant is expected to require the attention of about 12 engineers full time, and the 

construction phase will employ an average of about 125-150 workers each day. To correlate 

these figures with requirements for an 81 MMgal/yr plant, the number of construction personnel 

(150) were scaled proportionally, while the number of engineering personnel were assumed to be 

constant. 

These figures provide a basis for estimating the personnel requirements of the total 

volume needed to meet the expected volumes. Over the six-year build-up period, a maximum of 

970 construction workers and 30 engineers would be required on a monthly basis for the RFS 

case, while for the EIA case, these numbers increase to 2,328 and 75, respectively. 

These figures simply estimate the number of workers required at the final assembly stage 

of the plant, and do not capture many more personnel hours that will go into designing and 

constructing vessels, pipe fittings, control systems, and other pieces of equipment that will be 

installed and brought online by the plant construction crews. A report produced by one 

consultant suggested that expansion of the ethanol industry was responsible for more than 65,000 

construction jobs in 2005.IIIII 

7.1.1.2 Corn Ethanol Production Costs 

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.JJJJJ It produces results that compare well with cost information found in surveys of 

existing plants.KKKKK 

The USDA model is for a 40 MMgal/yr corn plant producing ethanol with a primary coproduct of distillers dried grains with solubles (DDGS). The ethanol yield used in the model is 

2.87 gallons per bushel with 5.0% 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 were then put together with feedstock, energy, and 

equipment cost information in a spreadsheet format to arrive at a final per-gallon cost estimate. 

Although the model is current in terms of technology, yields, and capital estimates, we made 

some modifications to allow estimation of costs for ethanol plants of different sizes and 

operating under different energy and feedstock prices. We believe that these updates, in 

combination with the industry and supplier surveys done by USDA in developing the model, 

result a reasonable estimate for projected ethanol production costs. 

We estimate an average corn ethanol production cost of $1.26 per gallon in 2012 (2004 

dollars) for the RFS case and $1.32 per gallon for the EIA case. The cost of ethanol production 

is most sensitive to the prices of corn and the primary co-product, DDGS. Utilities, capital, and 

labor expenses also have an impact, although to a lesser extent. Corn feedstock minus DDGS 

264 
sale credit represents about 48% of the final per-gallon cost, while utilities, capital and labor 

comprise about 19%, 9%, and 6%, respectively. For this work, we used corn and DDGS price 

projections generated by the Forestry and Agricultural Sector Optimization Model, which is 

described in Chapter 8.1.1 of this RIA. Corn and DDGS prices are given there in Table 8.1-1. 

Figure 7.1-1 shows the cost breakdown for production of a gallon of ethanol. Note that this 

production model does not account for the cost to pelletize or ship the DDGS. Those costs are 

external and are expected to increase the price of DDGS an end user located far from the plant. 

More details are given in Section 8.1.1 where the FASOM model is discussed. 

Figure 7.1-1. 

Cost Breakdown of Corn Ethanol Production (2004$). 

-$0.40 

-$0.20 

$0.00 

$0.20 

$0.40 

$0.60 

$0.80 

$1.00 

$1.20 

$1.40 

$ per gallon 

DDGS Sales Credit 

Denaturant 

Other Raw Materials 

Corn Feedstock Hauling 

Labor, Supplies, Overheads 

Depreciation 

Utilities 

Shelled Corn 

Assumes 81 MMgal/yr plant size, 

5% denaturant, $2.50/bu corn, 

$83.35/ton DDGS. 

Cost of corn

as feedstock 

The ability to address plant scaling in the model was accomplished 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. However, there is information suggesting that a general factor may be considerably 

higher for ethanol plants. Based on a recent journal publication, a factor of 0.84 was used in this 

work.LLLLL With this factor, the model indicates 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. In this analysis we used an average new plant size of 81 

MMgal/yr, derived from our industry characterization work in Chapter 1. 

265 
We also added functions to estimate the per-gallon cost impact of coal combustion as a 

process energy source rather than natural gas. Our industry characterization work suggests that 

about 14% of ethanol production from new plants being constructed will use coal for the process 

energy source, so the effect on average costs is relatively small. Capital cost used for an 81 

MMgal/yr gas-fired plant was $99 million (2004$). For the coal system versus natural gas, 

additional requirements were estimated at $45 million in capital for the same size plant, as well 

as one additional operator per shift and 10% additional electric utility use. These figures should 

be considered conservative estimates, and were based on information from press releases as well 

as a conversation with staff of a company that designs and builds ethanol plants. Additionally, 

we adjusted the thermal efficiency of coal combustion processes downward by 13% relative to 

natural gas, and electricity consumption upward by 10% to reflect operational differences in the 

processes.

MMMMM Using this information in the model, the cost savings is about $0.04 per gallon 

of ethanol for a coal-fired plant compared to natural gas firing. The results presented here are a 

weighted average of coal and gas production costs (using 14% coal). 

Under the Energy Act, starch ethanol can be counted as cellulosic if at least 90% of the 

process energy is derived from animal wastes or other waste materials.NNNNN It is expected that 

the vast majority of the 250 million gallons per year of cellulosic ethanol production required by 

2013 will be made using this provision. While we have been unable to develop a detailed 

production cost estimate for ethanol from corn which meets cellulosic criteria, we assume that 

the costs will not be significantly different from conventionally produced corn ethanol. We 

believe this is reasonable because to the extent that these processes are utilized, we expect them 

to be in locations where the very low or zero cost of the feedstock or biogas itself will likely 

offset the costs of hauling the material and the additional capital for handling and combusting it. 

In addition, because the quantity of ethanol produced using these processes is still expected to be 

a relatively small fraction of the total ethanol demand, the sensitivity of the overall analysis to 

this assumption is also very small. 

In general, energy prices used in the model were taken from historical EIA data for 2004 

and scaled according the ratios of 2004-2012 price forecasts published in the Annual Energy 

Outlook 2006. OOOOO,PPPPP The prices used in the modeling are shown in Table 7.1-2. Several 

sensitivity cases were run using the model, and the results are shown in Table 7.1-3. Input 

values in this table were chosen to give a significant margin around current and anticipated 

future prices. 

Table 7.1-2. 

Energy Prices Used for Ethanol Cost Modeling for 2012 (2004$) 

Natural Gas

a

 Coala Electricitya Natural Gasolineb 

$/MMBtu $/MMBtu $/kWh $/gal 

6.16 1.94 0.044 1.36 a Historical data based on averages for Iowa, Illinois, Minnesota, and Nebraska b Natural gasoline (or natural gas liquids) is the typical denaturant used in ethanol production, since it is cheaper than 

finished gasoline. The price used was based on its value being 20 cents per gallon below wholesale gasoline. 

266 
Table 7.1-3. 

Energy and Feedstock Price Sensitivities (2004$) 

Natural Gas = $6.00/MMBtu Natural Gas = $12.00/MMBtu

Corn DDGS Ethanol Corn DDGS Ethanol

$/bu $/ton $/gal $/bu $/ton $/gal

$2.00 $50.00 $1.18 $2.00 $50.00 $1.35 

$100.00 $1.02 $100.00 $1.19 

$150.00 $0.86 $150.00 $1.03 

$2.50 $50.00 $1.36 $2.50 $50.00 $1.52 

$100.00 $1.20 $100.00 $1.36 

$150.00 $1.04 $150.00 $1.20 

$3.00 $50.00 $1.53 $3.00 $50.00 $1.70 

$100.00 $1.37 $100.00 $1.54 

$150.00 $1.21 $150.00 $1.38 

$3.50 $50.00 $1.71 $3.50 $50.00 $1.87 

$100.00 $1.55 $100.00 $1.71 

$150.00 $1.39 $150.00 $1.55 

$4.00 $50.00 $1.88 $4.00 $50.00 $2.04 

$100.00 $1.72 $100.00 $1.88 

$150.00 $1.56 $150.00 $1.72 

$5.00 $50.00 $2.23 $5.00 $50.00 $2.39 

$100.00 $2.07 $100.00 $2.23 

$150.00 $1.91 $150.00 $2.07 

$6.00 $50.00 $2.58 $6.00 $50.00 $2.74 

$100.00 $2.42 $100.00 $2.58 

$150.00 $2.26 $150.00 $2.42 

7.1.2 Cellulosic Ethanol 

7.1.2.1 How Ethanol Is Made from Cellulosic Feedstocks 

It is not clear when the first processes to produce ethanol from cellulosic biomass were 

discovered. While ethanol produced from starch can be traced historically to ancient times, 

cellulosic derived ethanol appears to have been investigated in the 1800’s. Until recently, the 

demand for fuel ethanol has been somewhat limited, and not sufficient to support a costcompetitive, commercial process to convert cellulose into ethanol. 

With the increasing demand for fuel ethanol during the past few years, good progress has 

been made toward producing ethanol from cellulosic feedstocks. Interest in ethanol has 

continued to grow, initially fostered in part from EPA’s reformulated gasoline (RFG) regulations 

that required such gasoline to contain a minimum of 2 percent oxygen by weight in the fuel. 

This minimum oxygen requirement has recently been revoked by EPA in response to the Energy 

Act, which revised the Clean Air Act requirement for oxygen in RFG. The Renewable Fuel 

Standard (RFS) continues to create a demand for ethanol. Likewise, there is an additional 

incentive to produce cellulosic ethanol because the Energy Act mandates that, starting in 2013, 

renewable fuels used in gasoline must include. 

267 
There is a wide variety of government and renewable fuels industry research and 

development programs dedicated to improving our ability to produce renewable fuels from 

cellulosic feedstocks. There are at least three completely different approaches to producing 

ethanol from cellulosic biomass, sometimes referred to as “platforms”. The first is based on 

what NREL refers to as the “sugar platform,”80 which refers to pretreating the biomass, then 

hydrolyzing the cellulosic and hemicellulosic components into sugars, and then fermenting the 

sugars into ethanol. Corn grain is a nearly ideal feedstock for producing ethanol by 

fermentation, especially when compared with cellulosic biomass feedstocks. Corn grain is easily 

ground into small particles, following which the exposed starch which has α-linked saccharide 

polymers is easily hydrolyzed into simple, single component sugar which can then be easily 

fermented into ethanol. By comparison, the biomass lignin structure must be either mechanically 

or chemically broken down to permit hydrolyzing chemicals and enzymes access to the 

saccharide polymers. The central problem is that the cellulose/hemicellulose saccharide 

polymers are β-linked which makes hydrolysis much more difficult. Simple microbial 

fermentation used in corn sugar fermentation is also not possible, since the cellulose and 

hemicellulose (6 & 5 carbon molecules, respectively) have not been able to be fermented by the 

same microbe. We discuss various pretreatment, hydrolysis and fermentation technologies, 

below. The second and third approaches have nothing to do with pretreatment, acids, enzymes, 

or fermentation. The second is sometimes referred to as the “syngas” or “gas-to-liquid” 

approach; we will call it the “Syngas Platform.” Briefly, the cellulosic biomass feedstock is 

steam-reformed to produce syngas which is then converted to ethanol over a Fischer-Tropsch 

catalyst. The third approach uses plasma technology. 

Technologies that are currently being developed may solve some of the problems 

associated with producing cellulosic ethanol. Specifically, one problem, mentioned previously, 

with cellulosic feedstocks is that the hydrolysis reactions produce both glucose, a six-carbon 

sugar, and xylose, a five-carbon sugar (pentose sugar, C5H10O5; sometimes called “wood sugar”). 

Early conversion technology required different microbes to ferment each sugar. Recent research 

has developed better cellulose hydrolysis enzymes and ethanol-fermenting organisms.81 Now, 

glucose and xylose can be co-fermented—hence, the terminology, weak-acid enzymatic 

hydrolysis and co-fermentation. In addition, at least one group is researching the use of recently 

developed genome modifying technology to produce a variety of new or modified enzymes and 

microbes that show promise for use in a process known as weak-acid, enzymatic-prehydrolysis 82 

80 Enzyme Sugar Platform (ESP), Project Next Steps National Renewable Energy, Dan Schell, FY03 Review 

Meeting; Laboratory Operated for the U.S. Department of Energy by Midwest Research Institute • B NREL, 

Golden, Colorado, May 1-2, 2003; U.S. Department of Energy by Midwest Research Institute • Battelle • Bechtel 

 “Purdue yeast makes ethanol from agricultural waste more effectively.” Purdue News, June 28, 2004; Writer: 

Emil Venere, (765) 494-4709, venere@purdue.edu; source: Dr. Nancy Ho, (765) 494-7046, 

nwyho@ecn.purdue.edu. 

82 DOE Genomics: GTL Roadmap, Systems Biology for Energy and Environment, U.S. Department of Energy, 

Office of Science, Office of Biological and Environmental Research, Office of Advanced Scientific Computing 

Research, Germantown, MD 20874-1290, August 2005; DOEGenomesToLife.org/roadmap; downloadable as whole 

or in sections. 

268 

81
7.1.2.2 Difficulties in Estimating Capital and Operating Costs for New or Pioneer 

Process Plants 

Many years ago the petroleum and chemical process industries learned that it can be 

financially problematic to scale-up a bench or laboratory scale process to a full commercial sized 

operation. There are simply too many process variables that act one way at a batch rate of oneor two-gallons per day, or even 100-gallons per day, but then act a completely different way in a 

continuous, 70,000 gallon per day operation. Under these, admittedly somewhat extreme 

expectations, there is also absolutely no reasonable way to optimize a process. We expect that at 

least pilot or demonstration size projects will be necessary before a fully commercial sized, 

reasonably optimized plant can be constructed. 

The petroleum and chemical process industries have also learned that if a different 

feedstock, with similar, but at the same time sufficiently different characteristics, becomes 

available, it is nearly always necessary to make several pilot plant runs before the feedstock in 

introduced into the process. There are a wide variety of potential cellulosic feedstocks, such as 

switch grass, forest thinnings, municipal waste, wood chips, and corn stover (corn stalks). The 

physical characteristics of these materials, such as size, composition, and density vary widely. 

As a result, there could be significant differences in the process configurations, as well as 

differences in the enzyme “cocktails” required to hydrolyze and convert each of them into 

ethanol. Compositional and density variations may require different reactor residence times for 

each feedstock, which will impact throughput. Many of the process streams will actually be 

slurries of the feedstock. It is also quite likely that each slurry stream will have its own flow and 

compositional characteristics. The flow characteristics of any slurry, under real operating 

conditions, must be well understood in order to properly design an optimum system. 

Additionally, valve and pump types, sizes, and materials of construction, as well as line sizes and 

configurations, may vary. Apart from the various process issues, questions also remain 

regarding which of the feedstocks is actually the best in terms of ethanol yield per dollar. 

Consequently, we believe a good deal more process data is necessary before a reasonably 

accurate cost to design, engineer, and build a commercial scale cellulosic based ethanol plant can 

be expected. At the present time, there is only one cellulosic ethanol plant in North America 

(IogenQQQQQ a privately held company, based in Ottawa, Ontario, Canada). On February 28, 

2007, however, the Department of Energy (DOE) announced that it will provide grants of up to 

$385 billion for six biorefinery projects over the next four years. These facilities are expected to 

produce more than 130 million gallons of celluloic ethanol per year. As additional information 

on these future facilities are made available, EPA will have more information on process design 

from which we will better be able to project production costs for cellulosic ethanol. 

Although the industry seems to be moving down several different pathways, one of the 

more mature process being tested and improved uses dilute acid enzymatic prehydrolysis with 

simultaneous saccharification (enzymatic) and co-fermentation. Because there is more publicly 

available information about this process, the model we used incorporates this type of process to 

estimate the cost of producing ethanol from corn stover. We chose corn stover because it is 

ubiquitous and because of the likelihood it will eventually be used as a feedstock. 

269 
In 1999, the National Renewable Energy Laboratory (NREL) published a report outlining 

its work with the USDA to design a computer model of a plant to produce ethanol from hardwood chips.RRRRR Although the cellulosic model was originally prepared for hardwood chips, it 

was meant to serve as a modifiable-platform for ongoing research using cellulosic biomass as 

feedstock to produce ethanol. Their long-term plan was that various indices, costs, technologies, 

and other factors would be regularly updated. 

NREL modified the model in order to compare the cost of using corn-grain with the cost 

of using corn stover to produce ethanol. We used the corn stover model from the second 

NREL/USDA study for this analysis. Because there are no operating plants that could 

potentially provide real world process design, construction, and operating data for processing 

cellulosic ethanol, NREL had originally considered modeling the plant based on assumptions 

associated with a pioneer plant. Such assumptions would likely result in costs significantly 

higher than corn ethanol plants due to the higher level of uncertainty in both the design and 

engineering as well as the final construction and operating costs. The literature indicates that 

such models often underestimate actual costs since the high performance assumed for pioneer 

process plants is generally unrealistic. 

The NREL analysis assumed that the corn stover plant was an Nth generation plant, built 

after the industry had been well enough established to provide verified costs. The corn stover 

plant was normalized to the corn kernel plant, e.g., placed on a similar basis. Additional costs 

for risk financing, longer start-ups, and other costs associated with first-of-a-kind or pioneer 

plants were not included in the study.SSSSS It is also reasonable to expect the cost of cellulosic 

ethanol will be higher than corn ethanol because of the complexity of the cellulose conversion 

process. During the recent past, process improvements and other advancements in corn 

production have considerably reduced the cost of producing corn ethanol. We also believe it is 

realistic to assume that cellulose-derived ethanol process improvements will be made and that 

one can likewise reasonably expect that as the industry matures, the cost of producing ethanol 

from cellulose will also decrease. 

7.1.2.3 Methods, Data Sources, and Assumptions 

For our analysis, we used the spreadsheet model that NREL developed for its comparison 

of the costs of producing ethanol from corn grain and corn stover.83

 We believe NREL’s 

approach was reasonable and well thought out. They also had an outside engineering firm 

validate their work, to the extent possible. The Delta-T Corporation (Delta-T) assisted in 

preparing, reviewing, and estimating costs for the process design. Delta-T worked with NREL 

process engineers to review all the process design and equipment costs (with the exclusion of 

wastewater treatment and the burner-boiler system, which were reviewed by Merrick 

Engineering and Reaction Engineering, Inc., respectively). For the plant areas that are actively 

83 The first, woodchip-plant study was designed to produce 52.2 million gallons of ethanol per year from about 

2,200 tons per day (350 operating days per year; 15 days for downtime, including turn-around) of woodchips. The 

second study normalized the original woodchip plant into the corn stover plant to produce 25 million gallons of 

ethanol per year (about 1,235 wet tons per day), in 1999 dollars. The adjustments included feedrate and feedstock 

volume and cost adjustments; equipment sizes with adjustments to capital and installation costs, and the cost of 

capital, labor, and process chemicals, including denaturant. 

270 
being investigated under DOE programs (e.g., prehydrolysis, cellulase enzyme production, and 

simultaneous saccharification and co-fermentation), Delta-T used the results of the DOE 

sponsored research to identify process design criteria and equipment requirements. These were 

used as a basis for sizing and costing major equipment components in the facility. The results of 

Merrick Engineering’s work on wastewater treatment and REI’s work on the burner/boiler were 

also included.TTTTT The NREL model used the Aspen PlusTM process simulator to calculate the 

flows and the heat and material balances for the process. We decided to use the NREL 

spreadsheet corn stover model, as is, since we did not have access to the Aspen PlusTM model nor 

to all the input. Rather, we left the feedrate, yields, and streams flows as they were, but adjusted 

equipment capital and installation costs, and utility, chemical, and labor costs to 2004 dollars. 

We used the same indices used by NREL to update their corn stover study; however, we used 

actual costs and indices for 2004 where possible. For example, in their 2000 calculations, NREL 

had extrapolated the Chemical Engineering Plant Cost Index and the Chemical Cost Index 

through 2012. However, we used actual 2004 data rather than the extrapolated data. 

We did not change the corn stover cost. Several issues remain to be settled regarding the 

amount of stover that should be left in place and how it should be gathered, baled, and shipped. 

We found cost ranges of from $25 per dry ton to $45 per dry ton. For purposes of this analysis 

we used the $35 per dry ton that NREL assumed in its analysis. 

For the analysis, we calculated the annual production cost in dollars per gallon of fuel 

ethanol. The annual production cost includes equipment straight-line depreciation for the life of 

the plant (10 years), and variable costs, labor, supplies and overhead, minus any by-product 

credits. Gasoline for denaturant and diesel for bulldozers to move the stover were projected into 

2012 prices using IEA’s AEO 2006UUUUU report. The market selling price minus the annual 

production cost is the before-tax profit. We calculated variable operating costs using NREL’s 

best estimate of quantities of chemicals and additives based on their laboratory work. NREL 

calculated fixed costs using industry standards for percentages of direct labor (indirect labor was 

40% of direct labor and overhead was 60% of total labor); other operating supplies, insurance, 

etc. totaled 3.25% of total installed cost. According to the analysis three major cost categories 

made up the majority of the total production cost: feeds stock – 31.2%; fixed costs – 23.8%; and 

depreciation (reflects installed capital cost of equipment) – 33.8%. 

As previously stated, several feedstock issues remain to be settled, not least being which 

of the many available feedstocks will be the best or most efficient. We chose an average cost of 

$35 per dry ton; we don’t believe the cost will rise and as a result of the research that is currently 

under way, there is reason to expect it to come down a little. On the other hand, several 

researchers have indicated switch grass may be better than corn stover; others point to forest 

wastes, etc. In the end, the best feedstock will likely be the one that is readily available and close 

to the plant; gathering, baling, and hauling continue to be important issues that will definitely 

impact the viability of a feedstock. Equipment cost reductions may have a significant impact on 

future costs. For example, there appear to be reasons to expect significant savings from 

purchasing enzymes rather than growing them onsite. Another issue that remains to be 

investigated is whether a particular kind of feedstock can be processed using one type of 

technology while a different kind may require the use of a completely different technology. 

271 
7.1.2.4 Results and Discussion 

Given the limitations we’ve already discussed, and perhaps others, we determined that it 

would have cost approximately $1.65 in 2012 (2004 dollars) to produce a gallon of ethanol using 

corn stover as a cellulosic feedstock. 

The provisions offering grants and shared financing included in Title XV of the Energy 

ActVVVVV will likely encourage process development work to generate the necessary 

construction and operating cost estimates. We assume the results produced by the above 

referenced NREL study are accurate and reasonable given the state of our current knowledge. 

7.2 Biodiesel and Renewable Diesel Production Costs 

7.2.1 Overview of Analysis 

We based our estimate for the cost to produce biodiesel on the use of USDA’s, NREL’s 

and EIA’s biodiesel computer models, along with estimates from engineering vendors that 

design biodiesel plants. Biodiesel fuel can be made from a wide variety of virgin vegetable oils 

such as canola, corn oil, cottonseed, etc. though, the operating costs (minus the costs of the 

feedstock oils) for these virgin vegetable oils are similar to the costs based on using soy oil as a 

feedstock, according to an analysis by NREL84. Biodiesel costs are therefore determined based 

on the use of soy oil, since this is the most commonly used virgin vegetable feedstock oil, and 

the use of recycled cooking oil (yellow grease) as a feedstock. Production costs are based on the 

process of continuous transesterification, which converts these feedstock oils to esters, along 

with the ester finishing processes and glycerol recovery. The models and vendors data are used 

to estimate the capital, fixed and operating costs associated with the production of biodiesel fuel, 

considering utility, labor, land and any other process and operating requirements, along with the 

prices for feedstock oils, methanol, chemicals and the byproduct glycerol. 

 The USDA, NREL and EIA models are based on a medium sized biodiesel plant that 

was designed to process raw degummed virgin soy oil as the feedstock. Additionally, the EIA 

model also contains a representation to estimate the biodiesel production cost for a plant that 

uses yellow grease as a feedstock. In the USDA model, the equipment needs and operating 

requirements for their biodiesel plant were estimated through the use of process simulation 

software. This software determines the biodiesel process requirements based on the use of 

established engineering relationships, process operating conditions and reagent needs. To 

substantiate the validity and accuracy of their model, USDA solicited feedback from major 

biodiesel producers. Based on responses, they then made adjustments to their model and updated 

their input prices to year 2005. The NREL model is also based on process simulation software, 

though the results are adjusted to reflect NREL’s modeling methods, using prices based on year 

2002. The origin of the EIA model is not known, though it is based on 2004 prices. The output 

for all of these models was provided in spreadsheet format. We also use engineering vendor 

84 NREL Presentation “U.S. Biodiesel Feedstock Supply” June 2004. 

272 
estimates as another source to generate soy oil and yellow grease biodiesel production costs. 

These firms are primarily engaged in the business of designing biodiesel plants. 

The production costs are based on a 10 million gallon per year biodiesel plant located in 

the Midwest using feedstock oils and methanol, which are catalyzed into esters and glycerol by 

use of sodium hydroxide. Because local feedstock costs, distribution costs, and biodiesel plant 

type introduce some variability into cost estimates, we believe that using an average plant to 

estimate production costs provides a reasonable approach. Therefore, we simplified our analysis 

and used costs based on an average plant and average feedstock prices since the total biodiesel 

volumes forecasted are not large and represent a small fraction of the total projected renewable 

volumes. 

The models and vendor estimates are further modified to use input prices for feedstocks, 

byproducts and energy that reflect the effects of the fuels provisions in the Energy Act. In order 

to capture a range of production costs, we generated cost projections from all of the models and 

vendors. We present the details on these estimates later in this section. 

For soy oil biodiesel production, based on the USDA model, we estimate a production 

cost of $2.06 per gallon in 2004 and $1.89 per gal in 2012 (in 2004 dollars). With the NREL 

model, we estimate soy oil biodiesel production costs of $2.28 and $2.11 per gallon in 2004 and 

2012, respectively, which is slightly higher than the USDA results. The EIA model generated 

soy oil based costs of $2.33 and $ 2.15/gal, while the engineering vendor’s costs averaged $2.27 

and $2.09/gal, in years 2004 and 2012, respectively. 

For yellow grease derived biodiesel, we used the EIA and vendor estimates and generated 

a range of costs, as discussed later. The total production costs ranged from $1.24 to $1.60/gal in 

2004, and from $1.11 to $1.56 for year 2012. 

Table 7.2-1. 

Summary of Production Costs for Biodiesel made from Soy Oil, per Gallon 

(2004 cents) 

Total 

Production 

Cost 

Subsidized 

Production 

Cost 

Feed Capital Reagent 

and 

Chemicals 

Labor Energy/Utilities 

USDA 189 89 156 11.3 12.7 5.0 4.8 

NREL 211 111 165 17.0 17.0 6.0 7.4 

EIA 215 115 161 14.4 NA NA 16.0 

PSI-Lurgi 220 120 174 18.8 12.6 8.2 5.5 

Superior 

Process 

Technologies 

224 124 175 11.7 16.5 7.6 5.0 

273 
Table 7.2-2. 

Summary of Production Costs for Biodiesel made from Yellow Grease, per Gallon

(2004 cents) 

 Total 

Production 

Cost 

Subsidized 

Production 

Cost 

Feed Capital Reagent 

and 

Chemicals 

Labor Energy/Utilities 

EIA 138 88 80 14.4 NA NA 16.9 

Superior 

Process 

Technologies 

167 117 114 14.7 18.3 8.7 9.0 

With the current Biodiesel Blender Tax Credit Program, producers using virgin vegetable 

oil stocks receive a one dollar per gallon tax subsidy while yellow grease producers receive 50 

cents per gallon, reducing the net production cost to a range of 89 to 115 c/gal for soy oil and 61 

to 106 c/gal for yellow greased derived biodiesel fuel in 2012. This compares favorably to the 

projected wholesale diesel fuel prices of 138 cents per gallon in 2012, signifying that the 

economics for biodiesel are positive under the effects of the blender credit program, though the 

tax credit program will expire in 2008 if it is not extended. Congress may later elect to extend 

the blender credit program, though, following the precedence used for extending the ethanol 

blending subsidies. Additionally, the Small Biodiesel Blenders Tax credit program and state tax 

and credit programs offer some additional subsidies and credits, though the benefits are modest 

in comparison to the Blender’s Tax credit. 

7.2.2 Inputs to and Results of USDA’s Model 

We used USDA’s biodiesel model as a source to generate an estimate for the cost to 

produce biodiesel fuel. The model is in spreadsheet format with inputs in 2005 dollars, and 

contains all of the capital and operating costs for a plant to produce 10 million gallons per year of 

biodiesel fuel. 

7.2.2.1 Feedstock Costs 

Feedstock prices are the largest component in generating production costs for biodiesel 

fuel. For soy oil prices, we used prices based on USDA=s 2006 Outlook, which has forecasted 

soy oil prices considering production of biodiesel under EPAct 2005. USDA=s Outlook is a 

national forecasting analysis that models the effects of demand for farm products and farm 

product prices for soy beans, soy bean oil, corn and other farm commodities. The 2006 Outlook 

estimated soy oil prices considering the demand of soy oil derived biodiesel fuel at 

approximately 160 MM gallons per year in 2006 and 312 MM gallons a year in 200785. This is 

in close proximity to EIA=s soy oil derived biodiesel volume projection of 135 MM gals and 265 

MM gals in 2006 and 2007, respectively. We therefore used the soy oil prices from USDA=s 

Outlook to determine biodiesel production costs. The USDA does not forecast yellow grease 

prices, so we assumed that yellow grease feedstocks costs would maintain the same relative 

historical pricing differential to virgin soy oil. In the past, some analysis has shown that yellow 

85 Per USDA phone discussion 6/22/06 

274 
grease has sold for about half the price of soy oil86

. The resulting feedstock costs to make a 

gallon of biodiesel under projected volumes for RFS are in Table 7.2-3. 

Table7.2-3. 

Projected Prices of Feedstock (2004 Dollars per Gallon) 

Marketing Year Soy Oila Yellow Grease 

2004 1.71 0.86 

2012 1.56 0.78 a

Production of Biodiesel assumed to consume 7.42 lbs of soy oil per gallon. USDA prices in 2012 are adjusted to 

2004 dollars to account for inflation, using GDP index of 109.7 in year 2004 and 130.8 in year 2012. 

7.2.2.2 Capital Costs 

For capital costs we used USDA=s total installed capital cost of $10.66 MM for a 10 MM gallon 

per year plant. This estimate was determined by the USDA, using a detailed analysis to 

generate costs for equipment needs, installation, land, engineering and construction work, 

buildings, utility needs, contingencies, startup costs etc. The USDA model is based on 2005 

dollars, so we adjusted the numbers to 2004 values using the GDP index. Per the USDA method, 

the total installed capital costs on a per gallon basis was amortized on a 10 year straight line 

depreciation rate using a facility dependent cost of 10 percent times the capital costs. 

Maintenance charges, insurances and facility supply costs were also calculated as percentages of 

the capital. The total of all of these are equal to 16 cents per gallon. 

7.2.2.3 Operating Costs 

The total operating expenses were 20 and 18 c/gal for a soy based biodiesel plant in 2004 

and 2012, respectively. The operating cost included a 4 cent per gallon offset from sale of the 

glycerol product at a price of 5 cents/lb. The operating costs include values for utilities, feed 

reagents, manpower and were based on the USDA=s model. The components of the operating 

costs are discussed below. 

7.2.2.4 Utility and Labor Costs 

We estimated utility costs using energy requirements from USDA=s model and adjusted 

the inputs to match the energy and electricity prices for the Midwest, using prices from EIA=s 

AEO. The cost for steam was estimated using the price of natural gas. Each pound of steam 

was produced from heating water, which required 810 British Thermal Units (BTUs) per pound 

of steam. Additionally, the steam costs are estimated assuming that the BTUs to make steam are 

increased by a factor of two, to account for steam distribution efficiency losses, treatment of 

boiler water to prevent fouling, maintenance and other miscellaneous costs. The utility 

requirements per gallon of biodiesel and energy prices are presented in Tables 7.2-4 and 7.2-5 

86Energy Information Administration NEMS Petroleum Marketing Model Documentation page J-2 

275 
Table 7.2-4. 

Utility Requirements per Gallon Biodiesela

Medium Pressure Steam, lbs 4.0 

Electricity, kWh 0.10 

Cooling Tower Water, lbs 96.1 

a

Utilities per USDA model from the production of biodiesel from soy oil. 

Table 7.2-5. 

Midwest Energy Prices per Year (in 2004 $) 

Year 2004 2012 

Electricity, $/kWh 0.046 0.044 

Natural gas, $/MM BTU 7.16 6.16 

Labor costs include the salaries and benefits for personnel to operate a biodiesel plant. 

This was estimated in the USDA model, though the labor costs were in 2005 dollars, which we 

adjusted to 2004 dollars using the GDP price index. The resulting labor costs are 5 cents per 

gallon of produced biodiesel fuel. 

7.2.2.5 Chemical Reagents 

Another operating expense, the production of biodiesel also requires the use of chemicals 

and chemical reagents, as these act as a catalyst in the transesterification process. Additionally, 

methanol is required as it is the feedstock that is chemically combined with soy oil and yellow 

grease during the transesterification process, yielding the biodiesel product. The amount of 

chemicals and methanol required to make a gallon of biodiesel are listed in Table 7.2-6. 

Table 7.2-6. 

Reagent Requirements 

Reagent Annual Requirement, lbs per gallon 

Water 0.0323 

Hydrochloric acid 0.0185 

Methanol 0.8006 

Sodium Methoxide 0.0231 

Sodium Hydroxide 0.0031 

For the prices of chemical reagents, we used prices that were supplied in USDA=s 2005 

model and adjusted them to 2004 dollars. Additionally, since we have no forecasting 

mechanism we assumed that the chemical reagent prices remained unchanged in 2012. 

276 
However, we estimated methanol prices, as the cost for this feedstock is a significant component 

of the total operating costs. For our analysis, we generated values by use of a correlation that 

calculates methanol’s price as a function of the price of natural gas87. In 2004 and 2012, using 

Midwest natural gas prices, we estimated methanol prices of 13.1 and 11.6 cents per pound, 

respectively. All other chemical prices, we assumed were constant over time and are in Table 

7.2-7. 

Table 7.2-7. 

Reagent Prices (in 2004 $) 

Reagent Prices, $/lb 

Hydrochloric acid 0.167 

Sodium Methoxide 1.358 

Sodium Hydroxide 0.273 

7.2.2.6 Glycerol Byproduct 

The feedstock cost credit for the glycerin by product in our modeling work was 5 cents 

per pound, based on recent pricing trends, assuming that additional glycerol generated from 

expansion of biodiesel production will continue to keep prices low. The model, like many 

biodiesel plants, produces a crude 80% glycerin stream, which is usually sold to glycerin refiners 

for purification. In the past, crude glycerin has sold for around $0.15 / pound. Because of the 

increase in biodiesel production around the world, however, the crude glycerin market has 

become saturated and the price is now around $0.05 / pound. As more biodiesel capacity comes 

on line, this price may very well drop further, though other markets for the use of glycerol are 

likely to develop because glycerol is a platform chemical used throughout industry. We assumed 

that the current glycerin pricing environment will continue in the future. For our cost estimation, 

the byproduct glycerin was sold at 5 cents per pound, reflecting current saturated market and low 

pricing conditions. The income from sale of the byproduct glycerin lowered biodiesel production 

costs by 2 percent and 4 percent for soy oil and yellow grease derived biodiesel fuel, 

respectively. 

The total biodiesel production costs derived using the USDA’s model are summarized in 

Table 7.2-8 

87 Per EIA paper “MTBE Production Economics” Tancred C. M. Lidderdale, methanol price cents per gallon = 

15.79 + 0.099 *natural gas price ($ per million BTU) 

277
Table7.2-8. 

Projected Production Costs for Biodiesel by Feedstock per Gallon 

(2004 Dollars) 

Marketing Year Soy Oil 

2004 2.06 

2012 1.89 

7.2.3 Inputs to and Results of NREL’s Biodiesel Model 

We used NREL’s biodiesel model as another source to generate an estimate for the cost 

to produce biodiesel fuel. Similar to the USDA’s model, the NREL biodiesel model also 

represents a continuous transesterification process that uses sodium hydroxide and methanol to 

convert soy oil to biodiesel and which has the finishing processes for biodiesel and glycerol. The 

model is in spreadsheet format, and contains all of the capital and operating costs for a plant to 

produce 10 million gallons per year of biodiesel fuel. We again simplified our analysis, and used 

the NREL model to estimate production costs for an average biodiesel plant that makes 10 MM 

gallon per year. To make the results directly comparable to USDA’s model, we used energy 

costs in the Midwest, and based the analysis on production of soy oil derived biodiesel. 

Based on the results of the NREL model, we estimate that the total production costs to 

make soy oil derived biodiesel fuel are $2.28 and $2.11 per gallon for years 2004 and 2012, 

respectively. This is 22 cents more per gallon than the estimate derived from USDA’s model. 

The components that make up our NREL estimate are discussed in the sections that follow. 

7.2.3.1 Feedstock Costs 

The feedstock costs increase because the NREL model assumes 7.87 pounds of soy oil 

are required to make a gallon of biodiesel fuel. This is slightly higher than the pounds required 

by the USDA model, though the difference may be due to each model being based on soy oils 

with differing chemical structures, i.e. more esters, differing densities. The higher amount of soy 

oil required by the NREL model raises the production costs for biodiesel by about 10 cents per 

gallon for feedstock costs alone, versus the USDA model. The feedstock costs are summarized in 

Table7.2-9. 

Table7.2-9. 

Projected Prices of Feedstock (2004 Dollars per Gallon) 

Marketing Year Soy Oil 

2004 181.0 

2012 165.2 

278
7.2.3.2 Capital Costs 

The total capital cost in the NREL model account for all of the costs for building a plant, 

including but not limited to the expenses for equipment, tanks, installation costs, engineering, 

tanks, construction, land and site development, start up and permitting charges. These costs do 

not account for expenses incurred from maintenance, insurance and taxes, however. The total 

capital coats for a plant are $14.8 million in 2002 dollars, which we adjusted to 2004 dollars 

using the GDP price index. The capital costs were amortized assuming a seven percent return on 

investment, resulting in a cost of 17 cents per gallon. All of the economic factors used for 

amortizing the capital costs are summarized in Table 7.2-10. 

Table 7.2-10. 

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) 

Resulting 

Capital 

Amortization 

Factor 

Societal Cost 10 Years 15 Years 0% 7% 0.11 

7.2.3.3 Operating Costs 

The total operating costs are 31 and 30 cents per gallon for years 2004 and 2012, 

respectively. These costs are not directly comparable to those from the USDA model, as fixed 

operating cost are included in the operating costs for the NREL model, while the USDA model 

accounts for fixed costs in the capital estimate. The operating cost for the NREL analysis 

includes items for utilities, reagents, manpower, insurance, taxes, general administration and 

maintenance costs, though do not account for capital costs. Additionally, the sale of the glycerol 

byproduct (80% strength) generated income of 4 cents per gallon of produced biodiesel, using 

glycerol price of 5 cents per pound. The cost associated with insurance, taxes, general 

administration and supplies incur a cost of 2.4 cents per gallon of biodiesel. The remaining 

components of operating costs for the NREL modeling analysis are discussed below. 

7.2.3.4 Utility and Labor 

The utility costs were estimated using the energy requirements in the NREL model 

along with the same prices for energy, steam and electricity, as those used in our USDA analysis. 

The utility requirements per gallon of biodiesel fuel are listed in Table 7.2-11 

279
Table 7.2-11. 

Utility Requirements per Gallon of Biodiesel 

Natural Gas, SCF 2.0 

Medium Pressure Steam, lbs 3.2 

Electricity, kWh 0.1 

Cooling Tower Water, lbs 8.3 

The NREL model accounts for the salaries of 4 employees per shift to run and maintain 

the plant. In addition to salaries for these personnel, the labor expenses also accounted for 

employee fringe benefits and the cost for a plant supervisor. The resulting labor costs are 6 cents 

for each gallon of biodiesel. 

7.2.3.5 Chemical Reagents 

The NREL model also requires the use of the same chemicals and chemical reagents that 

are used in the USDA model. The amount of chemical reagents in the NREL model, however, 

reflect the use of diluted hydrochloric acid (HCl) and sodium methoxide for the biodiesel 

production process. Hydrochloric acid is listed as being at 33 percent strength, which we 

assumed also applied to the strength of sodium methoxide, since the amount of HCl in the model 

is reflective of about one third the value of the USDA’s model. For the chemical and reagents 

prices, we used the same pricing values as those in our USDA modeling analysis. The resulting 

total chemical and reagent costs on a per gallon basis are about 17 cents for each gallon of 

biodiesel fuel produced. All of the required chemicals and reagents for the production of 

biodiesel are presented on an undiluted basis in Table 7.2-12. 

Table 7.2-12. 

Reagent Requirements 

Reagent Annual Requirement, lbs per gallon 

Water 3.4646 

HCla 0.0098 

Methanol 0.6037 

NAOCH3 

a 0.0338 

Sodium Hydroxide 0.1901 

a

HCl is Hydrochloric acid, NAOCH3 is sodium methoxide. 

The total biodiesel production costs derived from the NREL model are summarized in Table 

7.2-13. 

280 
Table 7.2-13. 

Projected Production Costs for Biodiesel by Feedstock per Gallon 

(2004 dollars) 

Marketing Year Soy Oil

a 

2004 2.28 

2012 2.11 

a

Production consumes 7.87 lbs of soy oil per gallon of biodiesel. USDA prices in 2012 are adjusted to 2004 

dollars to account for inflation, using GDP index of 109.7 in year 2004 and 130.8 in year 2012. 

7.2.4 EIA NEMS Model for Biodiesel 

We also estimated production costs using the biodiesel plant representation in EIA’s 

NEMS model. This biodiesel model is in spreadsheet format and has the aggregated cost 

components for an average soy oil and yellow grease based biodiesel plant. We could not locate 

written documentation that describes the basis for these models, though we will assume for our 

analysis that it represents an average biodiesel plant. 

 EIA’s model requires 7.65 lbs/gal of soy oil and yellow grease feedstock to produce a 

gallon of biodiesel fuel. Using the oil feedstock costs as discussed in section 7.2.2.1, feed stock 

costs for soy oil are $1.76/gal and $1.61/gal for 2004 and year 2012, respectively, while yellow 

grease feedstock costs are $0.88/gal and $0.80/gal for years 2004 and 2012, respectively. 

The EIA model does not provided specific individual cost components for biodiesel 

production, though it does have an estimate for total energy, operating and capital costs for both 

plant types. Capital costs are estimated at 14.4 cents/gal in 2004 for both plant types, which we 

assumed contains all of costs associated with building a plant, along with the depreciation and 

capital payoff costs. The energy costs are provided in the model on an aggregated basis and do 

not contain the individual amounts of natural gas, electricity and steam used by a plant. The 

model, though, has the total energy needs in year 2004, which are 13.7 c/gal for soy oil and 14.5 

cents/gal for yellow grease. For 2012, we determined the energy costs by adjusting the 2004 

aggregate energy cost by the EIA projected price change of natural gas in the Midwest from 

2004 to 2012, resulting in an energy cost of 16.0 and 16.9 cents/gal for soy oil and yellow grease, 

respectively. All of the other operating costs are represented by an aggregate number, which in 

year 2004 is 32.6 and 34.5 cent/gal for soy oil and yellow grease, respectively. This cost 

represents all of the operating costs not associated with energy and capital requirements. For our 

analysis, we assume that this cost does not change in 2012. We used a glycerin price of 5 c/lb, 

which generates income and offsets operating cost by 4 c/gal. 

The net production cost for yellow greases (minus feedstock costs) is about 3 c/gal more 

than the net production cost for soy based biodiesel, indicating the extra cost incurred for the 

yellow grease process. The resulting total production cost are presented in Table 7.2-14 

281
Table7.2-14. 

Projected Production Costs for Biodiesel by Feedstock per Gallon 

(2004 Dollars) 

Marketing Year Soy Oil

a

 Yellow Grease 

2004 2.33 1.47 

2012 2.15 1.38 

a

Production consumes 7.65 lbs of soy oil per gallon of biodiesel. USDA prices in 2012 are adjusted to 2004

dollars to account for inflation, using GDP index of 109.7 in year 2004 and 130.8 in year 2012. 

7.2.5 Vendor Production Estimates for Biodiesel 

We used engineering vendor estimates as another source to generate the cost to produce 

biodiesel fuel. For this, we used engineering details from two firms, Superior Technologies and 

PSI-Lurgi Engineering Inc. These engineering vendors are engaged in the business of designing, 

constructing and building biodiesel plants. The biodiesel production processes provided by these 

firms are also based on the continuous transesterification process, using sodium hydroxide and 

methanol to convert soy oil and yellow grease to biodiesel, along with the finishing processes for 

biodiesel and glycerol, similar to the other models. The vendors generated estimates of the total 

cost to build and operate a biodiesel plant, providing the requirements for the equipment, energy, 

capital and operating. We adjusted these estimates to a 2004 year costs basis for comparative 

purposes, and used energy costs in the Midwest. 

The vendor estimates we used for PSI-Lurgi are those listed in the report “Economic 

Feasibility of Producing Biodiesel in Tennessee” 88. The biodiesel plant in this analysis was 

sized for soy oil feedstock, based on a 13 MM gallon per year plant, which we assumed is directly 

comparable to the 10 MM gallon plant used in the USDA, NREL and EIA models. In making 

this comparison, we relied on a report89 from Superior Process Technologies to generate the 

production costs to make biodiesel from soy oil and yellow grease. This report has the various 

cost components for a 10 MM gallon per year plant. 

The total soy oil based biodiesel production cost estimate for year 2004 is $2.20/gal and 

$2.34/gal for PSI-Lurgi and Superior Technology, respectively with an average cost of $2.27/gal. 

For 2012, we project the soy oil production cost from both vendors would average approximately 

$2.09 gal. The Superior Technology’s yellow grease biodiesel production cost is $1.66/gal in 

2004. 

88 “Economic Feasibility of Producing Bio-diesel in Tennessee” AIM-AG Agri-Industry Modeling & Analysis 

Group, 2002 

89 Superior Process Technologies, “ Biodiesel Plant Economics and Process Description”, 8/18/06 

282
7.2.5.1 PSI-Lurgi estimate of Biodiesel Costs 

The feedstock costs assume that 7.569 pounds of soy oil are required to make a gallon of 

biodiesel fuel. The resulting feedstock cost is 174.1 cents/gal for year 2004 . We adjusted all 

other cost items in the PSI estimate from a 2002 year to 2004 basis, scaling by the relative change 

of GDP, though the costs for reagent needs and utilities are adjusted using the methods as 

discussed in the following sections. 

7.2.5.2 Capital Costs 

The total capital cost provided in the estimate account for all of the costs for building a 

plant, though excluding maintenance costs, similar to the capital requirements in the NREL 

model. The total capital coats for a plant are $19.7 million in 2004 dollars, which we adjusted 

from 2002 dollars. The capital costs were amortized assuming a seven percent return on 

investment, resulting in an annualized cost of 16.7 cents per gallon. The economic factors used 

for amortizing the capital costs are the same as those listed in Table 7.2-8. 

7.2.5.3 Operating Costs 

The total operating costs are 29 cents per gallon for year 2004, excluding capital charges. 

The cost associated with insurance, taxes and general administrations is 7.3 cents per gallon, 

while the cost for maintenance is 2 cents/gal. The sale of the glycerol byproduct at 80% 

strength generates incomes of 4 cents per gallon of produced biodiesel, assuming a glycerol price 

of 5 cents per pound. The remaining components of operating costs are discussed below. 

7.2.5.4 Utility and Labor 

The utility costs were estimated using the energy requirements presented in Table 3.1 

of the report, along with the same prices for energy, steam and electricity, as those used in our 

2004 model analysis. The total utility requirements are 5.5 cents per gallon. The utility 

requirements per gallon of biodiesel fuel are listed in Table 7.2-15. 

Table 7.2-15. 

Utility Requirements per Gallon of Biodiesel 

Natural Gas, SCF 0 

Medium Pressure Steam, lbs 3.92 

Electricity, kWh 0.093 

Cooling Tower Water, lbs 200.7 

The PSI-Lurgi estimate accounts for the salaries of 4 employees per shift to run and 

maintain the plant. In addition to salaries for these personnel, the labor expenses also accounted 

for employee fringe benefits. The resulting labor costs are 6.3 cents for each gallon of biodiesel. 

In addition to these costs, the SG&A expenses are estimated at 6.3 c/gal. 

283 
7.2.5.5 Chemical Reagents 

The PSI-Lurgi estimate also requires the same chemicals and chemical reagents in the 

USDA/NREL models. We assumed that the hydrochloric acid and the sodium methoxide used 

in the PSI estimate is 33% strength, as the prices listed in the study are reflective of being on a 

diluted basis. We also assumed that the price for the amount of caustic soda required is on an 

undiluted basis. We adjusted the prices for the chemical and reagents using the 2004 year 

pricing values used in our USDA/NREL modeling analysis, though the price of phosphoric acid 

was adjusted using the GDP index. The resulting total chemical and reagent costs on a per 

gallon basis are about 12.6 cents for each gallon of biodiesel fuel produced. All of the required 

chemicals and reagents for the production of biodiesel are presented on an undiluted basis in 

Table 7.2-16. 

Table 7.2-16. 

Reagent Requirements 

Reagent Annual Requirement, lbs per gallon 

Water 1.666 

HCla 0.026 

Methanol 0.700 

NAOCH3 

a

 0.038 

Caustic Soda / Sodium Hydroxide 0.04 

Phosphoric Acid 0.013 a

HCl is Hydrochloric acid, NAOCH3 is sodium methoxide. 

The total soy oil based biodiesel production costs derived from the PSI-Lurgi are $2.20 

dollars in year 2004.

7.2.6 Superior Process Technologies Estimate 

The Superior feedstock costs assume that 7.60 pounds of soy oil and yellow grease are 

required to make a gallon of biodiesel fuel, which results in a feedstock cost of 175 cents/gal for 

year 2004 for soy oil and 87.5 c/gal for yellow grease, using the feedstock costs in section 7.2.2.1. 

We adjusted all other cost items in the Superior estimate from a 2006 year to 2004 basis, 

adjusting the cost by the relative change of GDP, though the costs for reagent needs and utilities 

are adjusted using the methods as discussed below. 

7.2.6.1 Capital Costs 

 The total capital coats for a soy oil based plant are $10.7 million, while the costs for a 

yellow grease plant are $13.4 million, in 2004 dollars. These costs are inclusive of the amount 

needed for a new plant, which is similar to the other biodiesel estimates. The capital costs were 

amortized assuming a seven percent return on investment, resulting in a cost of 11.7 cents per 

gallon and 14.7 cents per gallon for a soy oil and yellow grease based plant, respectively. 

7.2.6.2 Operating Costs 

284
The total operating costs, excluding capital charges are 47 and 64 cents per gallon for soy 

oil and yellow grease plants in year 2004, respectively. Insurance, taxes, rent and local taxes 

incur a cost of about 6 and 7 cents per gallon, for soy oil and yellow grease, respectively. The 

costs for maintenance, plant overhead costs and supplies account for about 16.0 and 21.5 

cents/gal, for soy oil and yellow grease. The sale of the glycerol byproduct at 80% strength 

generates incomes of 4 cents per gallon of produced biodiesel, assuming glycerol price of 5 cents 

per pound. The remaining components of operating costs are discussed below. 

7.2.6.3 Utility and Labor 

The overall utility costs were provided, though the specific amounts of natural gas, 

electricity were not provided for the 10 MM gallon plant. We adjusted the Superior utility cost 

estimate from a 2006 year to a 2004 year basis, using the relative price change of natural gas and 

electricity, assuming that natural gas supplies 90 percent of the energy, and electricity supplies 

the remaining 10 percent. The resulting energy requirement is 5.0 and 8.6 cents per gallon, for 

soy oil and yellow grease, respectively. 

The estimate accounts for personnel cost to run and maintain the plant, including 

laboratory, plant supervisory and administration costs. The overall labor costs on a 2004 year 

basis are 7.6 and 12.8 cents for each gallon of biodiesel, for soy oil and yellow grease, 

respectively. 

7.2.6.4 Chemical Reagents 

The Superior vendor estimate also requires the same chemicals and chemical reagents as 

used in the USDA/NREL models and Tennessee study. The total chemical reagent cost from 

Superior on a 2006 year reagent pricing basis is 18.1 and 20.1 c/gal, respectively for soy and 

yellow grease plants. Superior provided the prices for each of the chemicals, though the specific 

amounts of each chemical were not provided for the soy oil based estimate. We therefore, 

adjusted the total chemical reagent cost to a 2004 year basis, assuming the demands for reagent 

as documented in the PSI-Lurgi estimate. The resulting reagent costs on a 2004 year basis are 

16.5 and 18.3 cents/gal, for soy oil and yellow grease. The Superior prices for the required 

chemicals and reagents are presented in Table 7.2-17. 

Table 7.2-17. 

Superiors Reagent Prices 

Reagent Dollar / lb 

NAOCH3 

a

 (25% solution) 0.50 

HCl a (32%) 0.091 

Methanol 0.146 

Phosphoric Acid (75% ) 0.42 

Caustic Soda / Sodium Hydroxide (50%) 0.14 a

HCl is Hydrochloric acid, NAOCH3 is sodium methoxide. 

The total resulting biodiesel production costs derived from Superior Process description 

and engineering estimate is $2.34 and $ 1.66 per gallon for soy oil and yellow grease derived 

285 
biodiesel respectively, in year 2004. Yellow greases net production cost (minus feedstock costs) 

are about 20 c/gal more than the net production cost for soy based biodiesel. 

7.2.7 Yellow Grease Production Costs 

Yellow grease’s production cost is higher than soy oil produced biodiesel fuel, due to the 

extra capital and operating costs required to remove contaminants in the grease feedstock. In the 

prior sections, the EIA and Superior analysis indicated that the yellow grease production cost for 

biodiesel is higher than the production cost based on use of soy oil, excluding the feedstock 

costs. The EIA analysis showed that yellow grease’s production cost is 3 c/gal higher, while the 

Superior results showed that the yellow grease’s production cost is about 20 c/gal higher than 

soy oil production costs. Both of these provide a measurement of the extra production costs 

(excluding feedstock) associated with making biodiesel from yellow grease versus soy oil. 

In this section, we use the EIA and Superior results to generate yellow grease costs as 

inputs to the models, adjusting the soy oil production costs to reflect the extra cost for producing 

yellow grease. We assume the same feedstock costs for yellow grease as those listed in section 

7.2.2.1, and that it takes 7.6 lbs of yellow grease to produce a gallon biodiesel fuel. Table 7.2­

18, contains the resulting yellow grease production costs based on the EIA and Superior 

analyses. 

Table 7.2-18. 

Yellow Grease Costs Based on EIA and Superior Results 

EIA 2004 Superior 2004 EIA 2012 Superior 2012 

USDA, c/gal 124 141 111 128 

NREL, c/gal 138 155 131 148 

EIA, c/gal 143 160 132 149 

Vendor avg, c/gal 142 159 139 156 

Average 136.8 153.8 128.3 145.2 

We averaged all of the yellow grease results, and generated an average production cost of 

$1.45/gal in 2004 and $1.37/gal in 2012 for yellow grease derived biodiesel. 

7.2.8 Biodiesel Blending Credit Programs 

There are numerous credit and incentive programs that encourage the blending of 

biodiesel. These programs reimburse blenders and producers for adding biodiesel to transport 

diesel fuel, which acts to lower the production costs and makes the production of biodiesel more 

economically competitive with petroleum derived diesel fuel. There are several 

federal/nationwide biodiesel credit programs that offer subsidies for blending or use of biodiesel 

as a transport diesel fuel which are discussed below. 

The Commodity Credit Commission Bio-energy Program is an existing program that 

expires at the end of fiscal year 2006, though due to a funding shortfall the program will 

terminate on July 31, 2006. This program was administered by the USDA and pays biodiesel 

producers grants when the economics to produce biodiesel are poor. The stipend is determined 

286
based on available funding and the volume of renewable fuel that can receive the credit. For 

historical purposes, the payments in 2004 and 2005 averaged about 107 and 50 c/gal of fuel 

produced, respectively. For the first half of 2006, the credit on a per gallon basis is reduced 

further, as the payment is diluted by increased production volume of fuels available to receive 

the credit. 

The Energy Act extended the Biodiesel Blenders Tax Credit program to the end of year 

2008. This program was created under the American Jobs Creation Act of 2004 which created an 

excise tax credit that can be claimed by anyone who blends biodiesel into transport diesel fuel. 

Under this program, blenders may claim a credit against the applicable federal motor fuels excise 

tax for blends containing biodiesel. According to IRS guidelines, the credit may be claimed by 

anyone who adds biodiesel into diesel fuel at a level greater than 0.1 percent in the final blend. 

The full credit for biodiesel made from virgin vegetable oils and animal fats is $1.0 per gallon, 

while biodiesel derived from recycled grease receives 50 cents/gallon. A blender with more 

excise tax credits than taxes owed can receive a refund from the IRS. Additionally, under the 

current program, imported biodiesel and fuel made from imported feedstocks can also receive the 

credit. 

The Income Tax Credit Alternative is a program that is also available. This program does 

not require any blending of biodiesel, though it does offers allow a similar excise tax credit as in 

the blenders tax credit program. The excise tax can only be taken against actual income, 

however, which makes the program less economically attractive than the blenders’ credit 

program. 

The Energy Act also created the Small Biodiesel Blenders Tax credit program. Under this 

program, a credit of 10 c/gal is available to small producers who make biodiesel fuel from virgin 

vegetable oils. This stipend is limited to companies with annual production volumes less than 60 

MM gallons per year, using the aggregated capacity from all production sites for an individual 

company. The maximum payment per company is capped at $15 MM per year and the program 

is set to expire at the end of year 2008. 

In addition to the federal programs, there are state and local programs that offer state fuel 

tax exemptions, tax credits, and incentives that are more modest. 

7.3 Distribution Costs 

7.3.1. Ethanol Distribution Costs 

There are two components to the costs associated with distributing the volumes of 

ethanol necessary to meet the requirements of the Renewable Fuels Standard (RFS): 1) the 

capital cost of making the necessary upgrades to the fuel distribution infrastructure system, and 

2) the ongoing additional freight costs associated with shipping ethanol to terminals. The most 

comprehensive study of the infrastructure requirements for an expanded fuel ethanol industry 

was conducted for the Department of Energy (DOE) in 2002 .

WWWWW This study provides the 

foundation for our estimates of the capital costs associated with upgrading the distribution 

287
infrastructure system as well as the freight costs to handle the increased volume of ethanol 

needed in 2012. Distribution costs are evaluated here for the RFS and EIA cases. 

7.3.1.1 Capital Costs to Upgrade the Ethanol Distribution System 

The 2002 DOE study examined two cases regarding the use of renewable fuels. The first 

case assumed that 5.1 Bgal/yr of ethanol would be used in 2010, and the second case assumed 

that 10 Bgal/yr of ethanol would be used in 2015. We interpolated between these two cases to 

provide the foundation for our estimate of the capital costs to support the use of 6.67 Bgal/yr of 

ethanol in 2012 (the RFS case). The 10 Bgal/yr case from the DOE study was used as the 

foundation to estimate the capital costs under the EIA case. For both the 6.67 Bgal/yr and 9.64 

Bgal/yr cases, we adjusted the results from the DOE study to reflect a 3.9 Bgal/yr 2012 ethanol 

use baseline. Table 7.3-1 contains our estimates of the infrastructure changes and associated 

capital costs for the two ethanol use scenarios examined in today’s rule.90 

90 These capital costs will be incurred incrementally during the period of 2007-2012 as ethanol volumes increase. 

For the purpose of this analysis, we assumed that all capital costs will be incurred in 2007. 

288
Table 7.3-1. 

Ethanol Distribution Infrastructure Capital Costs 

Relative to a 3.9 Billion Gallon per Year Reference Case 

 RFS Case 

(6.67 Bgal/yr) 

EIA Case 

(9.64 Bgal/yr) 

New Terminal Blending Systems for Ethanol 

Number of terminals 243 515 

 Capital cost $73,044,000 $154,530,000 

New Ethanol Storage Tanks at Terminals 

Number of tanks 168 370 

 Capacity 1,526,000 barrels 3,415,000 barrels 

 Capital cost $21,939,000 $48,803,000 

Terminal Storage Tanks Converted to Ethanol 

Number of tanks 44 83 

 Capacity 319,000 barrels 592,000 barrels

 Capital cost $931,000 $1,739,000 

Terminals Using Ethanol for the First Timea

 Number of terminals 212 453 

 Capital cost $4,238,000 $9,065,000 

New Rail Delivery Facilities at Terminals 

 Number of terminals 42 76 

 Capital cost $14,869,000 $27,127,000 

Retail Facilities Using Ethanol for First Time

a 

Number of retail facilities 33,600 74,820

 Capital cost $19,824,000 $44,146,000 

New Tractor Trailer Transport Trucks 

Number of Trucks 209 435 

 Capital Costs $24,027,000 $50,075,000 

New Barges 

Number of new barges 11 23 

 Capital cost $21,475,000 $43,204,000 

New Rail Cars 

 Number of new rail cares 2,024 3,491 

 Capital cost $172,012,000 $296,729,000 

Total Capital Costs $352,361,000 $675,418,000 

Capital Costs Attributed to Terminal and Retail (i.e. fixed) 

Facilities 

$134,847,000 $285,410,000 

Capital Costs Attributed to Mobile Facilities (tank trucks, 

rail cars, & barges) 

$217,514,000 $390,008,000 

a

 Terminal and retail facilities using ethanol for the first time will need to make various modifications to ensure the 

compatibility of their systems with ethanol. 

Our estimated capital costs in this final rule differ from those in the proposal for several 

reasons. First, the volume for the RFS case was updated to reflect the fuel rule provisions. 

Second, we adjusted our estimate of capital costs from those in the proposal to reflect an increase 

in the cost of rail tank cars and barges since the DOE study was conducted. Third, we are 

assuming a 30 percent increase in the reliance on rail versus marine transport over that projected 

in the DOE study. The 2002 DOE study estimated that 53 percent of the increase in ethanol 

volume shipped between PADDs would be carried by barge and 47 percent by rail. For the 

purposes of this analysis, we assumed that 30 percent of the increased volume in ethanol 

289
shipments that were projected to be carried by barge in the DOE study would instead be carried 

by rail. This equates to 37 percent of the increase in ethanol shipments being carried by barge 

and 63 percent by rail. To provide a conservatively high estimate of the potential economic 

impact, we assumed that this shift translates into a 30 percent increase in rail infrastructure costs. 

This incorporates the increased cost to prepare additional terminals to receive ethanol by rail, and 

to provide a sufficient number of additional rail tank cars for ethanol transport. The actual 

increase in rail infrastructure costs may be somewhat lower given improvements in the efficiency 

of ethanol transport by rail. 

Amortized over 15 years at a 7 percent cost of capital, the total capital costs (of 

$352,361,000 under the RFS case and 675,418,000 under the EIA case) equates to an annual cost 

of approximately $38,687,000 under the RFS case and $74,157,000 under the EIA case. This 

translates to approximately 1.4 cents per gallon of new ethanol volume under the RFS case and 

1.2 cents per gallon under the EIA case. Under both cases, approximately 0.5 cents per gallon is 

attributed to mobile facilities and the remainder to fixed facilities. 

7.3.1.2 Ethanol Freight Costs 

The 2002 DOE study contains estimated ethanol freight costs for each of the 5 PADDs. 

These estimated costs are summarized in the following Table 7.3-2.XXXXX A map of the PADDs 

is contained in Figure 7.3-1. 

Table 7.3-2. 

Estimated Ethanol Freight Costs from the 2002 DOE Study 

PADD 

5.1 billion gallons per year 

(cents per gallon) 

10.0 billion gallons per year 

(cents per gallon) 

1 11.1 7.2 

2 4.3 2.4 

3 6.6 5.8 

4 4.7 7.4 

5 12.7 10.7 

National Average 7.7 5.7 

290
Figure 7.3-1. 

PAD District Definitions. 

The Energy Information Administration (EIA) translated the cost estimates from the 2002 

DOE study to a census division basis.YYYYY A summary of the resulting (EIA) ethanol 

distribution cost estimates are contained in the following Table 7.3-3. A map of the census 

divisions is contained in Figure 7.3-2. 

291
Table 7.3-3. 

EIA Estimated Ethanol Freight Costs 

(derived from the 2002 DOE Study) 

Census Division Freight Cost 

From To (cents per gallon) 

East North Central New England 9.8 

East North Central Middle Atlantic 9.8 

East North Central East North Central 4 

East North Central South Atlantic 9.8 

East North Central East South Central 4.7 

East North Central Pacific 14.0 

West North Central New England 11.4 

West North Central Middle Atlantic 11.4 

West North Central East North Central 4 

West North Central West North Central 4 

West North Central South Atlantic 11.4 

West North Central East South Central 4.7 

West North Central West South Central 4.7 

West North Central Mountain 4.5 

West North Central Pacific 13.0 

292
Figure 7.3-2. 

Census Divisions 

We took the EIA projections and translated them into State-by-State ethanol freight costs. 

For the purposes of this analysis, all ethanol was assumed to be produced in the East and West 

North Central Census Divisions (corresponding closely to PADD 2). We believe that this is a 

reasonable approach because the cost of shipping corn feedstock from PADD 2 to ethanol plants 

located outside of PADD 2 will typically negate any potential reduction in freight cost from 

reduced shipping distances for ethanol or dried distiller grains. The vast majority of ethanol 

plants planned for outside of PADD 2 are projected to begin operation using corn supplied from 

PADD 2.91 Many have stated plans to transition to local feedstocks. However, we believe that 

such a transition will typically not be accomplished within the timeframe considered by this 

91 Hawaii is a special case because plants potentially located there will use local feedstocks from their initial start up 

date. 

293
analysis (i.e. 2012). Other local considerations may provide a unique cost advantage to locating 

a plant outside of PADD 2. One such consideration might be that if by locating a plant outside 

of PADD 2 the ethanol producer could avoid the need to dry the distiller grains it produces and 

sell the wet distiller grains to a local market. Although this might result in a significant cost 

savings, it is unclear the extent to which this will be possible given the short shelf life of wet 

distiller grain (~3 days). Also, any potential cost savings might be offset by the relatively lower 

price that can be negotiated for wet versus dry distiller grains. In any event, there is insufficient 

data at this time to evaluate the extent to which such local conditions may result in an advantage 

in lower freight costs for ethanol plants outside of PADD 2. Further, our projection of where 

new ethanol production plants might be located indicates that only 10 percent of production 

capacity could be located outside of PADD 2. Thus, any potential freight cost advantage that 

might be enjoyed by such plants would not likely have a significant impact on our national 

analysis. Furthermore, to the extent that the location of ethanol plants outside of PADD 2 

imparts a savings in ethanol distribution costs, this would suggest that our estimates of ethanol 

freight costs in this rule are conservatively high. 

Ethanol consumed within census divisions belonging to PADD 2 was assumed to be 

transported by truck, while distribution outside of these areas was assumed to be by rail, ship, 

and/or barge. A single average distribution cost for each destination census division was 

generated by weighting together the 2012 freight costs given for each mode in both source 

census divisions according to their volume share. These cents per gallon figures were first 

adjusted upward by 10 percent to reflect the increased cost of transportation fuels used to ship 

ethanol since the 2002 DOE study, and then additional adjustments were applied to some 

individual states based on their position within the census division. In the case of Alaska and 

Hawaii, differences in ethanol delivery prices from the mainland were inferred from gasoline 

prices. 

For some states, different freight costs for ethanol supplied to large hub terminals versus 

small satellite terminals was estimated. The reasoning behind this is that large shipments of 

ethanol shipped from the Midwest by barge, ship, and/or unit train will often be initially 

unloaded at hub terminals for further distribution to satellite terminals. In cases where 

redistribution from a hub to a satellite terminal doesn’t take place, the volume of ethanol shipped 

directly from the producer to a lesser volume (“satellite”) terminal will also incur a higher freight 

rate than ethanol shipped to a larger-volume “hub” terminal. The largest adjustment was applied 

to the Rocky Mountain States since they are generally large in area and additional expense is 

required to transport freight through higher elevations and rugged terrain. Smaller adjustments 

were applied to states that are smaller, flatter, or have access by navigable waterways. The states 

to which an adjustment was not applied were generally in the Midwest or were so small as to not 

warrant different distribution costs. Given the large number of ethanol plants in the Midwest, we 

do not believe that there are substantial differences in the cost of distributing ethanol with that 

area. 

We made several adjustments in our estimates of ethanol freight costs from those in the 

proposal. First, the differential cost of shipping ethanol to satellite terminals versus hub 

terminals was increased to better reflect the additional costs incurred in either redistributing the 

ethanol from a hub to a satellite terminal, or of shipping ethanol directly from the producer to the 

294
satellite terminal in a lesser volume. The estimated additional freight cost of shipping ethanol to 

satellite terminals versus hub terminals is contained in the following Table 7.3-4. 

Table 7.3-4. 

Additional Freight Cost to Deliver Ethanol to a Satellite Terminal

Compared to a Hub Terminal 

States cents per gallon 

OH 2 

AL, AR, FL, GA, KY, LA, MD, ME, MS, NC, NH, NY, 

OK, OR, PA, SC, TN, VA, VT, WA, WV 

4 

AK, AZ, CO, ID, NM, NV, TX, UT, WY 5 

Another change that we made from the proposal was with respect the volume of ethanol 

we estimated would be delivered to hub versus satellite terminals. The proposal assumed a 50/50 

spit. For this final rule, we project that all of the ethanol volume blended into reformulated 

gasoline would be used in urban areas served by hub terminals. The percentage of ethanol 

blended into conventional gasoline that is used in an urban area (and hence delivered to a hub 

terminal) versus that used in a rural area (and hence delivered to a satellite terminal) was based 

on our analysis of the percentage of vehicle miles traveled in urban versus rural areas.92 

The final change from the proposal pertains to our consideration of the cost of shipping 

ethanol from the production plant to the rail head / marine terminal either for large volume 

shipment by unit train or marine shipment to hub terminals, or for shipment at single car rates via 

multiple-product trains directly to satellite terminals. Our review of current ethanol freight rates 

conducted in response to a comment on the proposed rule indicates that we did not adequately 

account for this added cost in the proposal. Chicago is a primary ethanol gathering point from 

producers for further distribution. A 4 cent per gallon conveyance fee is charged to account for 

delivery of ethanol from the production plant gate to the Chicago Board of Trade delivery point 

for taking ethanol. This includes train shipments, loading costs, and other miscellaneous fees. 

Based on this information, we have added 4 cents per gallon to our ethanol freight estimates. 

Our estimates of the State-by-State ethanol freight costs under the RFS and EIA cases are 

contained in Tables 7.3-5 and 7.3-6. National and PADD average freight costs under both the 

RFS and EIA cases are contained in Table 7.3-7. We are assuming that these freight costs do not 

include the costs associated with the recovery of capital for the distribution facility changes that 

are necessary to accommodate the increased volume of ethanol. This may tend to overstate 

distribution costs to some extent because some capital recovery may be incorporated into the 4 

cent per gallon conveyance fee. The inclusion of rail tank car lease fees also suggests that these 

estimated freight costs may be conservatively high given that rail car lease fees incorporate a 

capital recovery and profit margin. 

92 See Chapter 2 of this RIA for additional discussion of our estimate of the percentage of ethanol that will be used 

in urban versus a rural areas 

295
Table 7.3-5. 

State-by-State Ethanol Freight Costs

State PADD 

Ethanol Freight Cost*: (cents per gallon) 

Hub Satellite Average Freight Cost

** 

Terminal Terminal RFS Case EIA Case 

Connecticut 1 15.4 15.4 15.4 15.4 

Maine 1 17.4 21.4 20.0 20.0 

Massachusetts 1 15.4 15.4 15.4 15.4 

New Hampshire 1 16.4 16.4 16.4 16.4 

Rhode Island 1 15.4 15.4 15.4 15.4 

Vermont 1 16.4 16.4 NA*** 16.4 

New Jersey 1 15.4 15.4 15.4 15.4 

New York 1 15.4 19.4 15.4 15.7 

Pennsylvania 1 12.4 16.4 13.8 13.7 

Delaware 1 15.4 15.4 15.4 15.4 

District of Columbia 1 15.4 15.4 NA*** 15.4 

Florida 1 12.4 16.4 14.0 14.0 

Georgia 1 15.4 19.4 NA*** 17.4 

Maryland 1 15.4 15.4 NA*** 15.4 

North Carolina 1 15.4 19.4 NA*** NA*** 

South Carolina 1 15.4 19.4 NA*** NA*** 

Virginia 1 15.4 19.4 NA*** 15.4 

West Virginia 1 15.4 19.4 NA*** NA*** 

Illinois 2 4.4 4.4 4.4 4.4 

Indiana 2 5.4 5.4 5.4 5.4 

Michigan 2 6.4 6.4 6.4 6.4 

Ohio 2 5.4 7.4 6.6 6.6 

Wisconsin 2 4.4 4.4 4.4 4.4 

Iowa 2 3.4 3.4 3.4 3.4 

Kansas 2 4.4 4.4 4.4 4.4 

Minnesota 2 4.4 4.4 4.4 4.4 

Missouri 2 4.4 4.4 4.4 4.4 

Nebraska 2 4.4 4.4 4.4 4.4 

North Dakota 2 5.4 5.4 5.4 5.4 

South Dakota 2 4.4 4.4 4.4 4.4 

Kentucky 2 6.2 10.2 7.1 7.3 

Tennessee 2 6.2 10.2 8.7 8.7 

Oklahoma 2 8.3 12.3 NA*** 11.1 

* Freight rates from PADD 2 production facilities 

** Hub and satellite freight rates were volume weighted to arrive at an average freight rate. 

*** No significant ethanol use. See Chapter 2 of this RIA regarding our estimates of where ethanol will be 

used. 

296
Table 7.3-5. 

State-by-State Ethanol Freight Costs (continued)

State PADD Ethanol Freight Cost*: (cents per gallon) 

Hub Satellite Average Freight Cost

** 

Terminal Terminal RFS Case EIA Case 

Alabama 3 11.2 15.2 14.0 14.0 

Mississippi 3 10.2 14.2 13.9 13.9 

Arkansas 3 11.3 16.3 15.0 15.0 

Louisiana 3 11.3 15.3 13.8 13.8 

Texas 3 14.3 19.3 14.3 16.0 

New Mexico 3 16.4 21.4 19.0 19.6 

Colorado 4 14.4 19.4 NA*** 17.0 

Idaho 4 19.4 24.4 NA*** 22.8 

Montana 4 17.4 22.4 21.8 21.8 

Utah 4 17.4 22.4 NA*** 20.5 

Wyoming 4 16.4 21.4 NA*** 20.8 

Arizona 5 19.4 24.4 20.3 20.5 

Nevada 5 20.4 25.4 21.1 22.3 

Alaska 5 45.5 50.5 NA*** NA*** 

Hawaii 5 40.5 40.5 NA*** 40.5 

Oregon 5 20.5 24.5 20.5 20.5 

Washington 5 20.5 24.5 21.9 21.9 

California 5 20.5 20.5 20.5 20.5 

* Freight rates from PADD 2 production facilities. 

** Hub and satellite freight rates were volume weighted to arrive at the average freight rate. 

*** No significant ethanol use. See Chapter 2 of this RIA regarding our estimates of where ethanol will be used. 

Table 7.3-6. 

National and PADD Average Ethanol Freight Costs

Ethanol Freight Cost* (cents per gallon) 

RFS Case EIA Case 

National Average 11.3 11.9 

PADD 1 14.9 15.1 

PADD 2 5.1 5.3 

PADD 3 14.6 15.2 

PADD 4 21.8 19.8 

PADD 5 excluding AK & HI 20.6 20.7 

PADD 5 including AK & HI 20.6 22.1 

* Freight rates from PADD 2 production facilities. 

The national average ethanol freight cost of 11.3 cents per gallon under the RFS case and 

11.9 cents per gallon under the EIA case translates to an annual freight cost for the additional 

volume of ethanol used in 2012 of $313,123,000 and $678,300,000 respectively. Adding in the 

annualized capital costs, results in a total annual ethanol distribution cost of 351,810,000 or 12.7 

cents per gallon under the RFS case and $752,457,000 or 13.1 cents per gallon under the EIA 

93 case.

297
7.3.2 Biodiesel Distribution Costs 

The volume of biodiesel used by 2012 under the RFS is estimated at 300 million gallons 

per year. The 2012 reference case against which we are estimating the cost of distributing the 

additional volume of biodiesel needed to meet the requirements of the RFS is 30 million 

gallons.94 

The capital costs associated with distribution of biodiesel are higher per gallon than those 

associated with the distribution of ethanol due to the need for storage tanks, blending systems, 

barges, tanker trucks and rail cars to be insulated and in many cases heated during the winter 

months.95 In the proposal, we estimated that that these capital costs would be approximately 

$50,000,000. We adjusted our estimate of these capital costs for this final rule based on 

additional information regarding the cost to install the necessary storage and blending equipment 

at terminals and the need for additional rail tank cars for biodiesel.96 We now estimate that 

handling the increased biodiesel volume will require a total capital cost investment of 

$145,500,000 which equates to about 6 cents per gallon of new biodiesel volume.97 

In the proposal, we estimated that the freight costs for ethanol adequately reflect those for 

biodiesel as well. In response to comments, we sought additional information regarding the 

freight costs for biodiesel. This information indicates that freight costs for biodiesel are typically 

30 percent higher than those for ethanol which translates into an estimate of 15.5 cents per gallon 

for biodiesel freight costs.98 

Including the cost of capital recovery for the necessary distribution facility changes, we 

estimate the cost of distributing biodiesel to be 21.5 cents per gallon. 

93 All capital costs were assumed to be incurred in 2007 and were amortized over 15 years at a 7 percent cost of 

capital. 

94 See Chapter 1 of this RIA regarding the 2012 reference case. 

95 See Chapter 1.3 of the Regulatory Impact Analysis associated with today’s rule for a discussion of the special 

handling requirements for biodiesel under cold conditions. 

96 Information on biodiesel facility costs was obtained from a number of biodiesel blenders on the condition that the 

specific source of such information would not be identified. Biodiesel rail tank cars typically have a capacity of 

25,500 gallons as opposed to 30,000 gallons for an ethanol tank car. Thus, additional tank cars are need to transport 

a given volume of biodiesel relative to the same volume of ethanol. 

97 Capital costs will be incurred incrementally over the period of 2007-2012 as biodiesel volumes increase. For the 

purpose of this analysis, all capital costs were assumed to be incurred in 2007 and were amortized over 15 years at a 

7 percent cost of capital. 

 This is based on our review of publicly available biodiesel and ethanol freight rates from CSX and BNSF rail at 

www.csx.com and www.bnsf.com , on information regarding the lease rates for biodiesel versus ethanol freight cars 

considering the smaller size of biodiesel tank cars, and on discussions with biodiesel distributors. The estimated 

ethanol freight costs were increased by 30 percent to arrive at the estimate of biodiesel freight costs. 

298

98
7.4 Gasoline and Diesel Blendstock Costs 

The previous sections of this chapter have presented estimates of the cost of producing 

and distributing ethanol and biodiesel. In this section, we summarize the results of refinery 

modeling conducted by Jacobs Consultancy under contract to EPA. Jacobs’s used the Haverly 

Linear Programming (LP) model to conduct the 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. The Haverly model uses Jacobs’s Refining Process Technology 

Database to represent refining operations 

The modeling was conducted to analyze the effect of the increased renewable fuel use on 

the production costs and composition of the nation’s gasoline and diesel fuel. The refinery 

modeling output described in this section includes the changes in volumes and capital 

investments as well as the resulting capital and fixed operating costs, the variable costs, and the 

total of all these costs. The costs are expressed in 2006 dollars and capital costs are amortized at 

7% before tax return on investment (ROI). The costs for the RFS and EIA cases are expressed 

incremental to the reference case. We first report the results of the RFS case, followed by the 

results of the EIA case. 

7.4.1 Description of Refinery Modeling Cases Modeled 

The modeling cases were set up to analyze the RFS and EIA cases described in Chapter 

2. The primary renewable fuel modeled was ethanol in gasoline, while considering a fixed 

production amount of biodiesel as projected by EIA in 2012. Along with the increased use of 

renewable fuels, the analyses for the RFS and EIA cases both include the elimination of the RFG 

oxygen content standard and the resulting removal of MTBE from the U.S. gasoline market. 

These scenarios both assume the current Mobile Source Air Toxics standard (MSAT1) is in 

place. The effects of the MSAT2 standard are modeled in that rulemaking which has just recently 

been made final. 

Jacobs conducted a Linear Programming (LP) modeling analysis of the refining industry 

for the various RFS scenarios using a model developed by Haverly’s LP technology. The 

modeling was set up to analyze the extent to which ethanol will be used in CG versus RFG by 

region and the resulting effects on gasoline composition. The refining industry was modeled 

based on five aggregate complex refining regions, representing PADD’s 1, 2, 3, 4 & 5 together 

minus California and California separately. All of the PADDS were modeled simultaneously 

together in the LP model, in order to balance and meet the national gasoline and fuel demands. 

7.4.1.1 RVP 

The analysis modeled summer and winter seasons, with all gasoline types including 

California RFG, Federal RFG, 7.0, 7.8 RVP controlled areas and 9.0 CG. The control cases 

consisted of the minimum renewable fuel volume as specified by EPAct and discussed in 

Chapter 2 and the 2006 AEO projection of 9.6 billion gallons of ethanol per year in 2012. 

299
Winter gasoline RVP levels were adjusted higher than EPA data, to account for refiner RVP 

reporting inaccuracies from use of the complex model in the winter season. (Some refiners 

reported lower RVP levels than actually produced, as the complex model has a fixed upper 

reporting limit of 8.8 in the winter season.) 

7.4.1.2 Base case (2004) 

The base case was established by modeling fuel volumes for 2004. Information was 

based on process capacities from Oil and Gas Journal, EIA data and gasoline emissions and 

property data from EPA. Fuel property data for this base case was built off of 2004 refinery 

batch reports provided to EPA; however, the base case assumed sulfur standards based on 

gasoline data in 2004, not with fully phased in of Tier 2 gasoline standards at the 30 ppm level. 

In addition we assumed the phase-in of 15 ppm sulfur standards for highway, nonroad, 

locomotive and marine diesel fuel. The supply/demand balance for the U.S. was based on 

gasoline volumes from EIA and the California Air Resources Board (CARB). Our decision to 

use 2004 rather than 2005 as the baseline year was because of the refinery upset conditions 

associated with the Gulf Coast hurricanes in 2005. 

7.4.1.3 MSAT1 Provisions for Refinery Cases in 2012 

For CG and RFG, gasoline qualities were modeled to assure Complex model Phase 2 

calculations seasonal and annual compliance, taking into account the elimination of the oxygen 

requirement for RFG; (by PADD and California), and under MSAT1 gasoline standards. 

Incremental gasoline volumes above the 2004 base case volumes for each PADD were allowed 

to conform to less stringent toxics performance standards as allowed by the MSAT1 provisions. 

For this, the MSAT 1 PADD constraints were calculated using gasoline data from 1998-2000 

EPA batch reports, considering that new incremental volumes of gasoline above the 1998-2000 

annual average would comply with MSAT1 provisions, as predicated by EPact 2005. The 

following tables show the resulting conventional and RFG gasoline MSAT1 baseline constraints, 

which was applied to gasoline produced for the cases modeled in year 2012. 

Table 7.4-1. 

Conventional Gasoline MSAT 1 2012 Baseline Data 

Exhaust Toxics mg/mi* NOx mg/mi* 

PADD 1 88.33 1,440.84 

PADD 2 92.79 1,432.57 

PADD 3 88.79 1,438.76 

PADD 4 & 5, excluding 

California 

99.85 1,414.00 

*mg/mi is milligram per mile. 

300
Table 7.4-2. 

RFG Gasoline MSAT 1 2012 Baseline Data 

Total Toxics mg/mi* Toxics Percent Reduction 

PADD 1 75.11 27.39 

PADD 2 80.11 22.56 

PADD 3 74.74 27.75 

PADD 4 & 5, excluding 

California 

NA NA 

*mg/mi is milligram per mile. 

7.4.1.4 Reference case (2012) 

The reference case was based on modeling the base case, using 2012 fuel prices, and 

scaling the 2004 fuel volumes to 2012 based on growth in fuel demand. In addition, we scaled 

MTBE and ethanol upward, in proportion to gasoline growth, and assumed the RFS program 

would not be in effect. For example, if the PADD 1 gasoline pool MTBE oxygen was 0.5 wt% 

in 2004, the reference case assumed it should remain at 0.5 wt%. Finally, we assumed the 

MSAT 1 standards would remain in place as would the RFG oxygen mandate. We assumed the 

crude slate quality in 2012 is the same as the baseline case. 

7.4.1.5 Control cases (2012) 

Two control cases were run for 2012. The assumptions for the control cases are 

summarized below: 

• Control Case 1 (RFS case): 6.7 billion gallons/yr (BGY) of ethanol in gasoline; it 

reflects the renewable fuel mandate. In addition, it is assumed that no MTBE is in 

gasoline, MSAT1 is in place, the 1 psi waiver for CG containing 10 volume 

percent ethanol remains in effect for all states where it currently applies, the RFS 

is in effect, and there is no RFG oxygenate mandate. 

• Control Case 2 (EIA case): Same as Control Case 1, except that the ethanol 

volume in gasoline is 9.6 BGY. 

7.4.2 Assumptions made for Refinery Modeling 

7.4.2.2 Fuels Production and Demand 

The production of and demand for gasoline and other refinery fuels in the reference and 

control cases were based on EIA’s AEO 2006 projections for year 2012 . The modeling also was 

set up to meet demand based on terminals’ sales in each refining area, using EIA fuel sales data. 

The LP modeling accounted for inter-PADD transfers of finished products and gasoline 

blendstocks from refiners, to meet demand at terminals, based on historical transfer data from 

EIA, including CBOB and RBOB. Both the RFS and EIA control cases did not model any 

production of biodiesel fuel in fulfilling transportation diesel fuel demand. 

301
7.4.2.3 Ethanol 

The control cases were based on fixed national ethanol volumes as specified in Chapter 

2. For the control cases, however, the LP modeling analysis used ethanol blending economics 

and ethanol distribution costs to allocate ethanol to each PADD, and to allocate ethanol use in 

CG and RFG grades of gasoline. Additionally, the modeling assumed that all ethanol added to 

gasoline is match-blended for octane by refiners in the reference and control cases, while splash 

blending of ethanol was assumed as appropriate for the base case using EPA gasoline data. 

The price of ethanol was based on the 2004 yearly average price spread between 

regular conventional gasoline sold on spot market in Houston and ethanol sold on spot market on 

Chicago Board of Trade (CBOT). This was used to determine a Midwest ethanol production 

price. To derive ethanol prices for all other PADDs outside the Midwest, the Midwest ethanol 

production price was then adjusted for transportation costs to deliver ethanol from the Midwest 

to end use terminals (see section 7.3 for additional details). The price of ethanol was also 

adjusted to account for the 51 cent/gal rebate from the Federal subsidies, but did not account for 

the impact of state subsides. 

The reference and control cases where modeled assuming that ethanol CG blends are 

entitled to the 1.0 psi RVP waiver during the summer (i.e., for all 9.0 RVP and low RVP control 

programs) so as to assess the impact on summertime butane removal. 

7.4.2.4 Processes and Capital 

All changes in refining capital was assessed at a 15% Return on Investment (ROI) after 

taxes, which was adjusted to 7 % ROI before taxes. Crude and other input prices were based on 

Jacobs’ projection of refinery margins and crude prices in 2012 cases, which was also based on 

the historical price spreads of fuels between PADDS, using information from EIA’s 2004 price 

information tables, Platts, and AEO 2006, see the Jacob’s report for the petroleum fuel prices 

used in the modeling analysis. 

7.4.3 Results of Refinery Modeling 

7.4.3.1 Summary of Changes in Refinery Inputs and Outputs to the RFS Case 

There are a number of changes in individual and overall volume for specific gasoline 

blendstocks between the RFS case and reference case based on the refinery model results. The 

changes include the increased blending of ethanol, the removal of MTBE, and the increased 

volumes of isooctane, isooctene and alkylate from the reuse of isobutylene formerly used to 

produce MTBE. The isooctane and isooctene are produced by merchant MTBE plants that 

formerly produced MTBE from mixed butanes, ethylene crackers, and propylene oxide plants as 

determined by a survey of how those plants are being converted to produce other gasoline 

blendstocks. The alkylate is produced from the isobutylene previously used to produce MTBE at 

captive (refinery-based) MTBE plants. The total volume of these gasoline blend stocks is 

302
summarized in Table 7.4-3 for both the reference case and RFS case adjusting the volume of 

ethanol and MTBE to reflect their gasoline energy-equivalent volumes. 

Table 7.4-3. 

Comparison of Ethanol, MTBE, Isooctane, Isooctene, and Alkylate Volumes 

by PADD for the RFS Case and Reference Case (barrels per day)

Case Gasoline Blendstock PADD 1 PADD 2 PADD 3 PADD 4/5 CA USA 

Ethanol 57,620 114,900 5,242 20,676 58,934 257,372 

Reference 

Case 

MTBE 54,887 0 122,474 0 0 177,360 

Isooctane/Isooctene 200 200 200 200 200 1,000 

Gasoline-equivalent 

Volume 82,687 76,034 102,864 13,846 39,096 314,527 

Ethanol 139,224 201,989 23,091 17,853 53,004 435,160 

MTBE 0 0 0 0 0 0 

RFS Case 

Isooctane/Isooctene 

Alkylate from MTBE 11,042 200 212,177 200 21,484 245,103 

Gasoline-equivalent 

Volume 102,930 133,513 227,417 11,983 56,466 532,308 

Change in Gasoline 

Equivalent Volume 20,243 57,479 124,553 -1,863 17,370 217,781 

As the bottom row in Table 7.4-3 shows, the gasoline-equivalent volumes for the 

aggregated volume of these gasoline blendstocks are expected to increase as we compare the 

RFS case to the reference case. It is this net increase in gasoline blendstock volume that is 

expected to result in a net reduction in petroleum consumption. 

The addition of ethanol to wintertime gasoline, and to summertime RFG, will cause an 

increase of approximately 1 psi in RVP that needs to be offset to maintain constant RVP levels. 

An obvious means that refiners could choose to offset the increase in RVP is to reduce the 

butane levels in their gasoline. To some extent, the modeling results showed some occurrences 

of that, but it also did not report an overall increase in butane sales as a result of the increased 

use of ethanol. 

To convert the captive MTBE over to alkylate, after the rejection of methanol, refiners 

will need to combine one molecule of refinery produced isobutane with the isobutylene that was 

the feedstock for MTBE. The use of the isobutane will reduce the RVP of the gasoline pool 

from which it comes, helping to offset the RVP impacts of ethanol. Also, the increased 

production of alkylate provides a low RVP gasoline blendstock that offsets a portion of the 

cracked stocks produced by the fluidized catalytic cracker unit. Other means that the refinery 

model used to offset the high blending RVP of ethanol includes purchasing gasoline components 

with lower RVP, producing more poly gasoline which has low RVP and selling more high-RVP 

naphtha to petrochemical sales. 

In Table 7.4-4, we summarize the inputs into and gasoline outputs from the refinery 

model separate from the ethanol and converted MTBE blendstocks summarized above. The 

summary shows that crude oil and vacuum gas oil and residual fuel purchases are expected to 

decrease about 1 percent averaged over all the PADDs. The refinery model also estimates that 

the volume of purchased gasoline components will increase in most PADDs. These gasoline 

components include renewable blendstocks for ethanol blending (RBOB), which is a very low 

303
RVP gasoline blendstock. A likely reason for the increased gasoline blendstock volumes over 

purchased crude oil purchases is that the refinery model is seeking to purchase low RVP 

blendstocks to offset the volatility impacts of ethanol, as opposed to having to crack crude oil 

which produces more volatile four carbon compounds. Table 7.4-4 also shows the volume of 

gasoline projected to be produced for both the RFS case and reference case. We adjusted the 

gasoline volume for the RFS case to reflect the same energy density of the gasoline reported for 

the reference case. While the national energy-adjusted gasoline production levels for RFS case 

are about the same as the reference case, the energy adjusted gasoline production levels vary 

significantly by PADD. The refineries in PADDs 1 and 2 are projected to produce more gasoline 

in the RFS case compared to the reference case, while the refineries in PADD 3 are projected to 

produce less gasoline in the RFS case. 

Table 7.4-4. 

Summary of Refinery Model Input and Output Volumes by 

PADD for the RFS Case and Reference Case (barrels per day) 

Case Crude Oil and Gasoline PADD 1 PADD 2 PADD 3 PADD 4/5 CA USA 

Crude Oil 1,823,008 3,650,044 9,071,056 1,529,442 1,952,560 18,026,111 

Reference 

Case 

VGO and Residual Fuel 152,467 59,552 680,329 0 27,400 919,748 

Gasoline Component Inputs 144,293 69,233 144,782 49,247 51,475 459,030 

Gasoline Volume 1,378,811 2,398,179 4,004,675 778,262 1,184,533 9,744,461 

Gasoline Energy Content 5.012 4.997 5.093 5.019 5.024 5.044 

Crude Oil 1,762,018 3,579,232 9,071,056 1,520,709 1,952,560 17,885,576 

VGO and Residual Fuel 75,044 59,552 680,329 0 40,707 855,631 

Change in Crude oil and 

VGO/resid p 

Inputs 

-138,413 

200,272 

-70,812 

69,233 

0 

178,080 

-8,733 

49,247 

13,307 

67,146 

-204,651 

563,979 

RFS Case 

Change in Gasoline 

Component Inputs 55,979 0 33,298 0 15,672 104,949 

Gasoline 1,483,535 2,584,977 3,753,849 765,880 1,184,533 9,772,775 

Gasoline Energy Content 4.951 4.957 5.073 5.015 5.046 5.016 

Total Gasoline at Constant 

Energy 1,465,385 2,564,330 3,739,352 765,181 1,189,801 9,719,422 

Volume at Constant 

Energy 86,573 166,151 -265,323 -13,081 5,268 -25,039 

The addition of ethanol, the phase out of MTBE and the reuse of former MTBE 

feedstocks to make other gasoline blendstocks is expected to change the capital investments that 

would otherwise occur if these changes were not made. Table 7.4-5 summarizes the change in 

refinery unit throughputs by PADD comparing the RFS case to the reference case. 

304
Table 7.4-5. 

Change in the Refinery Unit Capacities by PADD between the RFS Case and Reference 

Case (thousand barrels per day) 

Unit or Category PADD 1 PADD 2 PADD 3 PADD 4/5 CA USA 

Crude Tower -61 -71 0 -9 0 -141 

Vacuum Tower -22 -32 0 -2 0 -56 

Sats Gas Plant -13 -12 6 -2 0 -21 

Unsats Gas Plant -38 -19 0 -4 3 -59 

FCC DeC5 Tower -8 -3 56 -4 -9 33 

FCC -130 -61 0 -16 2 -205 

FCC Splitter -97 -5 116 -8 3 9 

Hydrocracker 0 0 0 0 -6 -6 

Delayed Coker -13 -16 0 0 3 -27 

Visbreaker 0 0 0 0 -3 -3 

Thermal Naphtha Splitter -2 -2 0 0 0 -3 

CRU Reformer 0 8 48 0 0 56 

SRU Reformer 0 0 0 0 -4 -4 

BTX Reformer 0 0 0 0 0 0 

C4 Isomerization -13 0 1 0 -2 -14 

C5/C6 Isomerization 0 3 -57 15 -26 -65 

HF Alkylation 0 -1 1 0 0 0 

H2SO4 Alkylation -22 0 59 -1 -3 33 

Dimersol 0 0 0 -2 0 -2 

Cat Poly 1 0 18 0 0 19 

Isooctane 0 0 0 0 0 0 

DHT - Total 0 1 22 5 -1 28 

DHT 2nd RCT - Total 0 3 2 5 0 10 

DHT Arom Saturation 0 0 0 0 -4 -4 

NHT - Total Fd -9 3 -13 -1 -4 -23 

NHT - Isom/Thermal Fd -9 -5 -61 0 0 -75 

NHT - Reformer Fd 0 8 48 0 -4 52 

CGH - Generic -81 -34 -42 -4 0 -161 

CGH - Olefin Sat'n 0 0 0 0 28 28 

FCCU Fd HDT -39 0 0 0 0 -39 

LSR Splitter -12 -32 0 0 -6 -50 

LSR Bz Saturator -6 -15 0 0 -2 -22 

Reformate Saturator -2 -24 0 0 -4 -30 

SDA 0 0 0 0 0 0 

MTBE -5 0 -114 0 0 -119 

TAME 0 0 0 0 0 0 

Hydrogen Plant - Total BSCF -11 1 -75 3 -27 -109 

Lube Unit 0 0 0 0 0 0 

Sulfur Plant -276 -298 138 -13 81 -368 

Fuel System - Fuel Oil 0 0 0 0 0 0 

Fuel System - CO2 (BLb/Day) -18 -9 -5 -1 -2 -35 

Utilities - Steam (Blbs) -22 -12 -17 0 -6 -58 

Utilities - Steam Vent (Blbs) 0 0 0 0 0 0 

Utilities - Power (Mwh) -715 -226 749 1 -133 -324 

Utilities - Cooling H2O (Bgal) -213 -93 -156 -14 -27 -502 

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. Of the negative throughput changes, the large reduced volume of the fluidized 

catalytic cracker is important. As discussed above, the refinery model likely chose to decrease 

the fluidized catalytic cracker throughput to crack less heavy hydrocarbons to light 

hydrocarbons, producing less four-carbon compounds to offset the volatility impacts of ethanol. 

There are several units which show throughput capacity increases, primarily in PADD 3. PADD 

3 refineries will have a substantial loss in octane because of the removal of a substantial volume 

305
of MTBE, but the refinery model did not choose to blend much ethanol in PADD 3. Instead, the 

refinery model chose to make additional alkylate from the captive MTBE plants formerly 

operating in PADD 3, and blend in isooctene from the conversion of merchant MTBE plants. 

The refinery model also added some reformer capacity to make up the balance of octane loss. 

The refinery model added depentanizer capacity mostly in PADD 3 to enable the blending of 

ethanol into RFG. 

Refiners can also control the gasoline production and quality by adjustments they can 

make to several of their refinery conversion units. Refiners can adjust the conversion of their 

FCC and hydrocracker units, and change the severity of their reformers. Table 7.4-6 contains the 

percent conversions and severities of these units. 

Table 7.4-6. 

Comparison of Key Refinery Unit Operations by PADD between 

the RFS Case and Reference Case (percent)

Case Refinery Unit Operations PADD 1 PADD 2 PADD 3 PADD 4/5 CA 

FCCU Conversion 73 74 74 71 75 

Reference 

Continuous Reformer 

Severity 99 99 97 0 0 

Case Semi-Regen Severity 0 0 0 94 95 

Hydrocracker 

Conversion 80 80 85 85 85 

FCCU Conversion 72 74 74 71 75 

RFS Case 

Continuous Reformer 

Severity 100 96 97 0 0 

Semi-Regen Severity 0 0 0 93 96 

Hydrocracker 

Conversion 80 80 85 85 85 

The refinery model maintains the same FCC unit conversion percentage for the RFS case 

compared to the reference case, except for PADD 1 which showed a small decrease in FCC unit 

conversion. For all PADDs, hydrocracker conversion percentage remains the same. Continuous 

reformer severity is projected to increase slightly in PADD 1 likely because of the octane loss 

caused by the removal of MTBE from the RFG pool which is not completely made up by the 

increased ethanol volume there. In PADD 2 where a lot of ethanol is being blended, reformer 

severity decreases significantly from 99 RON to 96 RON. Reformer severity remains the same 

in PADD 3. Reformer severity is projected to increase slightly in California due to an 

anticipated small decrease in ethanol. Finally, reformer severity is projected to decrease slightly 

in PADDs 4 and 5 despite the small decrease in ethanol there. 

These changes in refinery unit throughputs are associated with changes in capital 

investments. Table 7.4-7 summarizes the projected change in capital investments between the 

reference case and the RFS control case. Table 7.4-7 shows that incremental to the reference 

case, refiners are expected to reduce their capital investments by $5.8 billion compared to 

business as usual. Most of the reduction occurs in PADDs 1 and 2 where large volumes of 

ethanol, and other gasoline blendstocks, are expected to enter the gasoline pool. Of course, this 

capital cost decrease is countered by the $2.3 billion in capital costs being incurred to build new 

ethanol plants and put into place the distribution system required to distribute the new ethanol. 

306
Table 7.4-7. 

Comparison of Capital Expenditures by PADD 

between the RFS Case and Reference Case (million dollars) 

PADD 1 PADD 2 PADD 3 PADD 4/5 CA U.S. Total 

Unit 

CAPEX vs 

Reference 

Case 

CAPEX vs 

Reference 

Case 

CAPEX vs 

Reference 

Case 

CAPEX vs 

Reference 

Case 

CAPEX vs 

Reference 

Case 

CAPEX vs 

Reference 

Case 

Crude Tower -228.6 0.0 0.0 0.0 0.0 -228.6 

Vacuum Tower -141.4 0.0 0.0 2.6 0.0 -138.7 

Sats Gas Plant -101.2 -13.6 -1.8 2.6 -22.7 -136.8 

Unsats Gas Plant -280.1 -225.5 -2.5 -29.5 -1.0 -538.6 

FCC DeC5 Tower 17.4 -52.0 54.3 -16.6 0.0 3.1 

FCC -1426.9 -1160.4 0.0 -103.8 0.0 -2691.0 

FCC Splitter -144.2 -37.0 49.5 -6.6 0.0 -138.3 

Hydrocracker 0.0 0.0 0.0 0.0 0.0 0.0 

H-Oil Unit 0.0 0.0 0.0 0.0 0.0 0.0 

Delayed Coker 0.0 0.0 0.4 0.0 0.0 0.4 

Visbreaker -0.1 -0.1 0.0 0.0 0.0 -0.2 

Thermal Naphtha Splitter 0.0 0.0 0.0 0.0 0.0 0.0 

CRU Reformer 0.0 0.0 0.0 0.0 0.0 0.0 

SRU Reformer 0.0 0.0 0.0 23.9 0.0 23.9 

BTX Reformer 0.0 0.0 2.4 0.0 0.0 2.4 

C4 Isomerization 0.0 0.0 28.9 0.0 -60.6 -31.7 

C5/C6 Isomerization 0.0 0.0 0.0 153.7 0.0 153.7 

HF Alkylation 0.0 0.0 0.0 0.0 0.0 0.0 

H2SO4 Alkylation -698.3 0.0 607.5 0.0 0.0 -90.9 

Dimersol 0.0 0.0 0.0 -23.3 -25.0 -48.3 

Cat Poly 29.0 0.0 100.3 0.0 0.0 129.3 

Isooctane 0.0 0.0 0.0 0.0 0.0 0.0 

DHT - Total 1.6 0.0 217.8 32.0 11.2 262.6 

DHT 2nd RCT - Total 0.0 11.0 6.1 21.1 0.0 38.2 

DHT Arom Saturation 0.0 0.0 0.0 0.0 0.0 0.0 

NHT - Total Fd -39.7 0.0 0.0 69.3 0.0 29.6 

CGH - Generic -472.8 -154.5 -139.5 -70.3 0.0 -837.2 

CGH - Olefin Sat'n 0.0 0.0 0.0 0.0 160.5 160.5 

FCCU Fd HDT -525.0 0.0 0.0 0.0 0.0 -525.0 

LSR Splitter 0.0 -47.2 0.0 0.0 0.0 -47.2 

LSR Bz Saturator -44.7 -151.7 0.0 0.0 0.4 -196.0 

Reformate Saturator -8.2 -272.4 0.0 0.0 -47.3 -328.0 

Reformate Splitter -4.4 -142.7 0.0 0.0 -8.8 -155.9 

SDA 0.0 0.0 0.0 0.0 0.0 0.0 

MTBE 0.0 0.0 -175.4 0.0 0.0 -175.4 

TAME 0.0 0.0 0.0 0.0 0.0 0.0 

Hydrogen Plant -109.6 2.2 -196.1 -7.4 -58.3 -369.2 

Lube Unit 0.0 0.0 0.0 0.0 0.0 0.0 

Sulfur Plant -1.9 -2.8 0.0 -0.1 0.1 -4.7 

Merox Jet 0.0 0.0 0.0 0.0 0.0 0.0 

Merox Diesel 0.0 0.0 0.0 0.0 0.0 0.0 

BTX Reformer - Tower feed 0.0 0.0 0.0 0.0 0.0 0.0 

BTX Reformer - Extract feed 0.0 0.0 0.0 0.0 0.0 0.0 

Total Capital Costs $MM -4,179 -2,247 552 48 -51 -5,878 

307
7.4.3.2 Summary of Changes in Refinery Inputs and Outputs to the EIA Case 

The EIA case has some similarities to the RFS case. The MTBE is still estimated to no 

longer be blended into gasoline, and the former MTBE feedstocks are converted over to other 

low-RVP gasoline blendstocks. The annual volume of ethanol blended into gasoline, however, is 

almost 3 billion gallons higher. This increased volume of ethanol is expected to be spread over 

all the PADDs, although PADD 3 is projected to absorb the most. The much increased volume 

of very high octane ethanol is expected to slightly reduce the consumption of the gasoline 

blendstocks produced from former MTBE feedstocks. The net gasoline-equivalent volume 

increase by ethanol and other gasoline blendstock changes is expected to be over 100 thousand 

barrels per day. Table 7.4-8 contains the volumes of these gasoline blendstocks by PADD. 

Table 7.4-8. 

Comparison of Ethanol, MTBE, Isooctane, Isooctene, and Alkylate Volumes 

by PADD for the EIA Case and Reference Case (barrels per day) 

Case Gasoline Blendstock PADD 1 PADD 2 PADD 3 PADD 4/5 CA USA 

Ethanol 57,620 114,900 5,242 20,676 58,934 257,372 

Reference 

Case 

MTBE 54,887 0 122,474 0 0 177,360 

Isooctane/Isooctene 200 200 200 200 200 1,000 

Gasoline-equivalent 

Volume 82,687 76,034 102,864 13,846 39,096 314,527 

Ethanol 161,821 255,512 117,722 32,113 59,055 626,223 

MTBE 0 0 0 0 0 0 

EIA Case 

Isooctane/Isooctene 

Alkylate from MTBE 11,042 200 200,119 200 17,010 228,571 

Gasoline-equivalent 

Volume 117,844 168,838 277,816 21,395 55,986 641,878 

Change in Gasoline 

Equivalent Volume 35,157 92,804 174,952 7,548 16,889 327,351 

Table 7.4-9 summarizes the inputs into and gasoline outputs from the refinery model 

separate from the ethanol and converted MTBE blendstocks summarized in Table 7.4-12 above. 

Crude oil and vacuum gas oil and residual fuel purchases are expected to decrease about 1.7 

percent averaged over all the PADDs. The refinery model also estimates that the volume of 

purchased gasoline components will increase incrementally over the RFS case. It seems that a 

likely reason for the increased gasoline blendstock volumes over purchased crude oil purchases 

is that the refinery model is seeking to purchase low RVP blendstocks to offset the volatility 

impacts of ethanol, as opposed to having to crack crude oil which produces more volatile four 

carbon compounds. Table 7.4-15 also shows the energy-adjusted volume of gasoline projected 

to be produced for both the RFS case and reference case. The national energy-adjusted gasoline 

production levels for EIA case is somewhat lower than the reference case which suggests that the 

crude oil savings described above are somewhat overstated. The refineries in PADDs 1 and 2 

are projected to produce much more gasoline in the EIA case compared to the reference case, 

while the refineries in PADD 3 are projected to produce much less gasoline in the EIA case. 

308
Table 7.4-9. 

Summary of Refinery Model Input and Output Volumes by PADD 

for the EIA Case and Reference Case (barrels per day) 

Case Crude Oil and Gasoline PADD 1 PADD 2 PADD 3 PADD 4/5 CA USA 

Crude Oil 1,823,008 3,650,044 9,071,056 1,529,442 1,952,560 18,026,111 

Reference 

Case 

VGO and Residual Fuel 152,467 59,552 680,329 0 27,400 919,748 

Gasoline Component Inputs 144,293 69,233 144,782 49,247 51,475 459,030 

Gasoline Volume 1,378,811 2,398,179 4,004,675 778,262 1,184,533 9,744,461 

Gasoline Energy Content 5.012 4.997 5.093 5.019 5.024 5.044 

Crude Oil 1,667,893 3,539,369 9,058,059 1,528,255 1,952,560 17,746,134 

VGO and Residual Fuel 97,621 59,552 675,959 0 41,194 874,325 

Change in Crude oil and 

VGO/resid -209,962 -110,675 -17,368 -1,188 13,794 -325,398 

Gasoline Component 

Inputs 208,809 69,233 166,023 49,247 62,913 556,224 

EIA Case 

Change in Gasoline 

Component Inputs 64,516 0 21,240 0 11,438 97,194 

Gasoline 1,602,258 2,584,977 3,657,519 775,512 1,184,533 9,804,799 

Gasoline Energy Content 4.924 4.922 5.042 5.000 5.042 4.988 

Total Gasoline at Constant 

Energy 1,574,007 2,546,302 3,620,530 772,501 1,188,742 9,695,751 

Change in Total Gasoline 

Volume at Constant 

Energy 195,196 148,123 -384,145 -5,761 4,208 -48,710 

The addition of ethanol, the phase out of MTBE and the reuse of former MTBE 

feedstocks to make other gasoline blendstocks is expected to change the capital investments that 

would otherwise occur if these changes were not made. 

Table 7.4-10 summarizes the change in refinery unit throughputs by PADD comparing 

the EIA case to the reference case. 

309
Table 7.4-10. 

Change in the Refinery Unit Capacities by PADD between the EIA Case and Reference 

Case (thousand barrels per day) 

Unit or Category PADD 1 PADD 2 PADD 3 PADD 4/5 CA USA 

Crude Tower -155 -111 -13 -1 0 -280 

Vacuum Tower -63 -50 12 4 0 -97 

Sats Gas Plant -17 -20 -20 0 1 -56 

Unsats Gas Plant -37 -28 -6 -7 3 -74 

FCC DeC5 Tower -7 0 40 -6 -9 17 

FCC -130 -90 -18 -24 3 -260 

FCC Splitter -99 0 82 -12 3 -27 

Hydrocracker 0 0 0 0 -7 -7 

Delayed Coker -10 -25 -7 0 3 -39 

Visbreaker 3 3 1 0 -3 3 

Thermal Naphtha Splitter -1 -3 -1 0 0 -5 

CRU Reformer -15 -19 -109 0 0 -143 

SRU Reformer 0 0 0 -1 -3 -4 

BTX Reformer 0 0 0 0 0 0 

C4 Isomerization -13 3 0 0 -2 -12 

C5/C6 Isomerization 0 -14 -93 -3 -30 -140 

HF Alkylation 0 -3 1 0 0 -2 

H2SO4 Alkylation -22 0 49 -1 -5 22 

Dimersol 0 0 0 -2 3 1 

Cat Poly 0 0 18 0 0 19 

Isooctane 0 0 0 0 0 0 

DHT - Total -24 -3 -66 16 0 -78 

DHT 2nd RCT - Total 0 -15 17 15 0 17 

DHT Arom Saturation 0 0 0 0 -4 -4 

NHT - Total Fd -32 -27 -145 -1 -3 -208 

NHT - Isom/Thermal Fd -17 -7 -36 0 0 -60 

NHT - Reformer Fd -15 -19 -109 -1 -3 -147 

CGH - Generic -81 -53 26 -6 0 -114 

CGH - Olefin Sat'n 0 0 0 0 29 29 

FCCU Fd HDT -39 0 0 0 0 -39 

LSR Splitter -12 -32 0 0 -6 -50 

LSR Bz Saturator -6 -15 0 1 -2 -21 

Reformate Saturator -3 -24 0 0 -5 -32 

SDA -3 0 0 0 0 -3 

MTBE -5 0 -114 0 0 -119 

TAME 0 0 0 0 0 0 

Hydrogen Plant - Total BSCF -34 7 4 4 -30 -49 

Lube Unit 0 23 0 0 0 23 

Sulfur Plant -340 -551 -261 -4 86 -1,070 

Fuel System - Fuel Oil 0 0 0 0 0 0 

Fuel System - CO2 (BLb/Day) -20 -13 -10 -2 -2 -47 

Utilities - Steam (Blbs) -25 -12 -28 -3 -7 -75 

Utilities - Steam Vent (Blbs) 0 0 0 0 0 0 

Utilities - Power (Mwh) -1,029 -582 -98 40 -168 -1,837 

Utilities - Cooling H2O (Bgal) -246 -128 -256 -24 -28 -682 

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. Of the negative throughput changes, the reduced volume of the fluidized 

catalytic cracker is important. As discussed above, the refinery model likely chose to decrease 

the fluidized catalytic cracker throughput to crack less heavy hydrocarbons to light 

310 
hydrocarbons, producing less four-carbon compounds to offset the volatility impacts of ethanol. 

The reduction in FCC unit throughput is relatively less for the EIA case than it was for the RFS 

case. There are several units which show throughput capacity increases, primarily in PADD 3. 

PADD 3 refineries will have a substantial loss in octane because of the removal of a substantial 

volume of MTBE. Unlike the RFS case, however, much of the ethanol is projected to be blended 

into PADD 3’s gasoline pool making up for the octane loss. This can be seen in Table 7.4-15 as 

reformer and alkylation throughputs volumes are projected to be lower for the EIA case. The 

refinery model added depentanizer capacity mostly in PADD 3 to enable the blending of ethanol 

into RFG. 

Refiners can also control the gasoline production and quality by adjustments they can 

make to several of their refinery conversion units. Refiners can adjust the conversion of their 

FCC and hydrocracker units, and change the severity of their reformers. Table 7.4-11 contains 

the percent conversions and severities of these units. 

Table 7.4-11. 

Comparison of Key Refinery Unit Operations by PADD between 

The EIA Case and Reference Case (percent)

Case Refinery Unit Operations PADD 1 PADD 2 PADD 3 PADD 4/5 CA 

FCCU Conversion 73 74 74 71 75 

Reference 

Continuous Reformer 

Severity 99 99 97 0 0 

Case Semi-Regen Severity 0 0 0 94 95 

Hydrocracker 

Conversion 80 80 85 85 85 

FCCU Conversion 72 74 74 71 75 

EIA Case 

Continuous Reformer 

Severity 100 94 96 0 0 

Semi-Regen Severity 0 0 0 93 97 

Hydrocracker 

Conversion 80 80 85 85 85 

The refinery model maintains the same FCC unit conversion percentage for the RFS case 

compared to the reference case, except for PADD 1 which showed a small decrease in FCC unit 

conversion. For all PADDs, hydrocracker conversion percentage remains the same. Continuous 

reformer severity is projected to increase slightly in PADD 1 despite the ethanol blended into 

that PADD’s gasoline. In PADD 2 where a lot of ethanol is being blended, reformer severity 

decreases significantly from 99 RON to 94 RON. Reformer severity is projected to decrease 

slightly in PADD 3 due to the large volume of ethanol being blended into the gasoline in that 

PADD. Reformer severity is projected to decrease slightly in California due to an anticipated 

small increase in ethanol. Finally, reformer severity is projected to decrease slightly in PADDs 4 

and 5 due to the increase in ethanol there. 

These changes in refinery unit throughputs are associated with changes in capital 

investments. Table 7.4-12 summarizes the projected change in capital investments between the 

reference case and the EIA control case. Table 7.4-12 shows that incremental to the reference 

case, refiners are expected to reduce their capital investments by $7.3 billion compared to 

business as usual. Most of the reduction occurs in PADDs 1 and 2 where large volumes of 

311
ethanol, and other gasoline blendstocks are expected to enter the gasoline pool. Of course, this 

capital cost decrease is countered by the estimated $6.5 billion in capital costs incurred to build 

new ethanol plants and put into place the distribution system that the new ethanol requires. 

312
Table 7.4-12. 

Comparison of Capital Expenditures by PADD between 

the EIA Case and Reference Case (million dollars) 

PADD 1 PADD 2 PADD 3 PADD 4/5 CA U.S. Total 

Unit 

CAPEX vs 

Reference 

Case 

CAPEX vs 

Reference 

Case 

CAPEX vs 

Reference 

Case 

CAPEX vs 

Reference 

Case 

CAPEX vs 

Reference 

Case 

CAPEX vs 

Reference 

Case 

Crude Tower -453.8 0.0 0.0 0.0 0.0 -453.8 

Vacuum Tower -295.0 0.0 103.7 8.9 0.0 -182.4 

Sats Gas Plant -115.9 -13.6 -55.8 0.1 -23.4 -208.7 

Unsats Gas Plant -275.6 -261.5 -20.1 -49.6 2.5 -604.4 

FCC DeC5 Tower 17.7 -58.9 50.9 -18.5 0.0 -8.8 

FCC -1426.9 -1160.4 -68.1 -331.7 0.0 -2987.1 

FCC Splitter -147.0 -48.7 46.6 -9.6 0.0 -158.7 

Hydrocracker 0.0 0.0 0.0 0.0 0.0 0.0 

H-Oil Unit 0.0 0.0 0.0 0.0 0.0 0.0 

Delayed Coker 0.0 0.0 -185.9 0.0 0.0 -185.9 

Visbreaker 7.2 6.2 0.0 0.0 0.0 13.4 

Thermal Naphtha Splitter 0.0 0.0 -0.3 0.0 0.0 -0.3 

CRU Reformer 0.0 0.0 0.0 0.0 0.0 0.0 

SRU Reformer 0.0 0.0 0.0 -2.6 0.0 -2.6 

BTX Reformer 0.0 0.0 1.8 0.0 0.0 1.8 

C4 Isomerization 0.0 0.0 0.0 0.0 -60.6 -60.6 

C5/C6 Isomerization 0.0 0.0 0.0 -56.6 0.0 -56.6 

HF Alkylation 0.0 0.0 0.0 0.0 0.0 0.0 

H2SO4 Alkylation -715.4 0.0 497.6 0.0 0.0 -217.8 

Dimersol 0.0 0.0 0.0 -17.9 7.8 -10.1 

Cat Poly 4.3 0.0 114.9 0.0 0.0 119.2 

Isooctane 0.0 0.0 0.0 0.0 0.0 0.0 

DHT - Total -169.8 0.0 -219.5 93.2 21.6 -274.6 

DHT 2nd RCT - Total 0.0 -165.3 105.6 138.0 0.0 78.3 

DHT Arom Saturation 0.0 0.0 0.0 0.0 0.0 0.0 

NHT - Total Fd -39.7 0.0 0.0 -1.9 0.0 -41.7 

CGH - Generic -471.6 -179.5 102.0 -77.1 0.0 -626.1 

CGH - Olefin Sat'n 0.0 0.0 0.0 0.0 161.0 161.0 

FCCU Fd HDT -525.0 0.0 0.0 0.0 0.0 -525.0 

LSR Splitter 0.0 -47.2 0.0 0.0 0.0 -47.2 

LSR Bz Saturator -44.7 -151.7 0.0 21.9 0.4 -174.0 

Reformate Saturator -19.2 -272.4 0.0 0.0 -49.9 -341.6 

Reformate Splitter -20.1 -142.7 0.0 0.0 -10.1 -172.9 

SDA 0.0 0.0 0.0 0.0 0.0 0.0 

MTBE 0.0 0.0 -175.4 0.0 0.0 -175.4 

TAME 0.0 0.0 0.0 0.0 0.0 0.0 

Hydrogen Plant -188.5 22.9 6.6 54.2 -58.3 -163.2 

Lube Unit 0.0 0.0 0.0 0.0 0.0 0.0 

Sulfur Plant -2.5 -2.8 -0.2 0.0 0.1 -5.4 

Merox Jet 0.0 0.0 0.0 0.0 0.0 0.0 

Merox Diesel 0.0 0.0 0.0 0.0 0.0 0.0 

BTX Reformer - Tower feed 0.0 0.0 0.0 0.0 0.0 0.0 

BTX Reformer - Extract feed 0.0 0.0 -0.1 

Total -4,882 -2,476 304 -249 -9 -7,311 

313
7.4.3.3 Adjustments to the LP Refinery Model’s Cost Estimate 

We made several adjustments to the costs directly estimated by the LP refinery cost 

model for the RFS and EIA cases which are included in the costs reported below. One 

adjustment made was to adjust the costs based on the ethanol prices used in the LP cost model to 

reflect the ethanol production costs estimated and reported above in section 7.1.1. This 

adjustment resulted in much lower ethanol costs to refiners because Jacobs largely based its 

ethanol prices on ethanol’s octane costs instead of its historical price relationship to gasoline, 

which is much lower. We also adjusted the ethanol distribution costs from those used in the LP 

refinery cost study, which roughly corresponded to those used for the proposed rule cost 

analysis, to those estimated for the final rule as discussed above in section 7.3.1. In Table 7.4-13 

we summarize the ethanol production and distribution costs used in the LP refinery cost model 

and those we estimated for the final rule. 

Table 7.4-13 

Ethanol Price and Distribution Costs used in the LP Refinery Model versus 

Those used for the Final Rule Cost Analysis (cents per gallon) 

Case PADD 1 PADD 2 PADD 3 PADD 

4/5 ex CA 

CA 

Prices 

used in 

Ethanol 

Price in 

Midwest 

RFS and 

EIA 

Case 

158 158 158 158 158 

LP 

Refinery 

Cost 

Ethanol 

Distribution 

Cost 

RFS and 

EIA 

Case 

12 0 10 17 18 

Model Ethanol 

Price in 

PADD 

RFS and 

EIA 

Case 

170 158 168 175 176 

Ethanol 

Production 

RFS 

Case 

126 126 126 126 126 

Costs 

used in 

Cost EIA 

Case 

131 131 131 131 131 

Final 

Cost 

Analysis 

Ethanol 

Distribution 

Cost 

RFS and 

EIA 

Case 

16 6.5 16 23 22 

Ethanol Cost 

in PADD 

RFS 

Case 

142 132.5 142 149 148 

EIA 

Case 

147 137.5 147 154 153 

314
Another adjustment we made to the costs directly estimated by the LP refinery cost 

model was to add a cost for distributing gasoline. The refinery cost model did not include 

distribution costs for gasoline for moving the gasoline from the refinery to the terminal. We 

assigned gasoline distribution costs to be 4 cents per gallon applied as a cost savings to the 

gasoline-equivalent volume of ethanol blended into each PADD’s gasoline, since this roughly 

corresponded to the volume of gasoline displaced by the ethanol. 

7.4.3.4 Estimated Costs 

7.4.3.4.1 Estimated Costs for the RFS Case 

Table 7.4-14 summarizes the costs for the RFS case excluding federal and state ethanol 

consumption subsidies. The costs are reported by different cost component as well as aggregated 

total and the per-gallon costs.99

 This estimate of costs reflects the changes in gasoline that are 

occurring with the expanded use of ethanol, including the corresponding removal of MTBE and 

reuse of MTBE feedstocks. The operating costs include the labor, utility and other operating 

costs and are a direct output from the refinery model. These costs are adjusted to reflect 

ethanol’s production cost plus distribution costs instead of the ethanol prices used in the refinery 

cost model. The fixed costs are 3 percent of the capital costs. The costs associated with lower 

energy density gasoline are accounted for using the fractional change in energy density shown in 

Table 7.4-4, multiplied times the wholesale price of gasoline. By excluding the federal and state 

ethanol consumption subsidies in the table, we avoid the transfer payments caused by these 

subsidies that would hide a portion of the program’s costs. 

Table 7.4-14. 

Summary of RFS Case Costs without Ethanol Consumption Subsidies 

(million dollars per year and c/gal, except as noted; 2004 dollars, 7% ROI before taxes ) 

RFS Case 

6.7 Billion Gals 

Incremental to Reference Case 

Capital Costs ($MM) -5,878 

Amortized Capital Costs ($MM/yr) -647 

Fixed Operating Cost ($MM/yr) -178 

Variable Operating Cost ($MM/yr) -201 

Lower Energy Density Gasoline ($MM/yr) 1,848 

Total Cost ($MM/yr) 823 

Capital Costs (c/gal) -0.40 

Fixed Operating Cost (c/gal) -0.11 

Variable Operating Cost (c/gal) -0.12 

Lower Energy Density Gasoline (c/gal) 1.13 

Total Cost Excluding Subsidies (c/gal) 0.50 

99 EPA typically assesses social benefits and costs of a rulemaking. However, this analysis is more limited in its 

scope by examining the average cost of production of ethanol and gasoline without accounting for the effects of 

farm subsidies that tend to distort the market price of agricultural commodities. 

315 
Our analysis shows that when considering all the costs associated with these fuel changes 

resulting from the expanded use of ethanol that these various possible gasoline use scenarios will 

cost the U.S. $820 million in the year 2012. Expressed as per-gallon costs, these fuel changes 

would cost the U.S. 0.50 cent per gallon of gasoline. 

Table 7.4-15 expresses the total and per-gallon gasoline costs for the RFS case with the 

federal and state ethanol subsidies included. The federal tax subsidy is 51 cents per gallon for 

each gallon of new ethanol blended into gasoline. The state tax subsidies apply in 5 states and 

range from 1.6 to 29 cents per gallon. The cost reduction to the fuel industry and consumers are 

estimated by multiplying the subsidy times the volume of new ethanol estimated to be used in the 

state. 

Table 7.4-15. 

Estimated RFS Case Cost including Ethanol Consumption Subsidies 

(million dollars per year and cents per gallon; 2004 dollars, 7% ROI before taxes) 

RFS Case 

6.7 Billion Gals 

Incremental to Reference Case 

Total Cost ($MM/yr) 823 

Federal Subsidy ($MM/yr) -1376 

State Subsidies ($MM/yr) -5 

Revised Total Cost ($MM/yr) -558 

Per-Gallon Cost Excluding Subsidies (c/gal) 0.50 

Federal Subsidy (c/gal) -0.84 

State Subsidies (c/gal) -0.003 

Total Cost Including Subsidies (c/gal) -0.34 

The cost including subsidies better represents gasoline’s production cost as might be 

reflected to the fuel industry as a whole and to consumers “at the pump” because the federal and 

state subsidies tends to hide a portion of the actual costs. Our analysis estimates that the fuel 

industry and consumers will see a 0.34 cent per gallon decrease in the apparent cost of producing 

gasoline for the RFS case. 

7.4.3.4.2 Estimated Costs for the EIA Case 

Table 7.4-16 summarizes the costs for the EIA case. The costs in this table exclude 

federal and state ethanol consumption subsidies. The costs are reported by different cost 

components as well as the aggregated total and the per-gallon costs. This estimate of costs 

reflects the changes in gasoline that are occurring with the much expanded use of ethanol, 

including the removal of MTBE and reuse of MTBE feedstocks. The operating costs include the 

labor, utility and other operating costs and are a direct output from the refinery model, adjusted 

for ethanol’s production cost at this higher volume including ethanol distribution costs. The 

fixed costs are 3 percent of the capital costs. The costs associated with lower energy density 

gasoline, as shown in Table 7.4-9, are estimated by the fractional change in energy content times 

the wholesale price of gasoline. The increment of the EIA case to the RFS case indicates the 

economic impact of the additional volume of ethanol between the two cases. 

316
Table 7.4-16. 

Summary of EIA Case Costs without Ethanol Consumption Subsidies 

(million dollars per year and c/gal, except as noted; 2004 dollars, 7% ROI before taxes) 

 EIA Case 

9.6 Billion Gals 

Incremental to Reference Case 

EIA Case 

9.6 Billion Gals 

Incremental to RFS Case 

Capital Costs ($MM) -7,311 -1,433 

Amortized Capital Costs ($MM/yr) -804 -158 

Fixed Operating Cost ($MM/yr) -222 -43 

Variable Operating Cost ($MM/yr) -491 -290 

Lower Energy Density Gasoline 

($MM/yr) 

3,255 1407 

Total Cost ($MM/yr) 1739 915 

Capital Costs (c/gal) -0.49 -0.10 

Fixed Operating Cost (c/gal) -0.14 -0.03 

Variable Operating Cost (c/gal) -0.30 -0.18 

Lower Energy Density Gasoline (c/gal) 1.98 0.86 

Total Cost Excluding Subsidies (c/gal) 1.06 0.56 

Our analysis shows that when considering all the costs associated with these fuel changes 

resulting from the expanded use of subsidized ethanol that these various possible gasoline use 

scenarios will cost the U.S. $1,740 million in the year 2012 for the EIA case. Expressed as pergallon costs, these fuel changes would cost the U.S. about 1.1 cents per gallon of gasoline. The 

incremental volume of ethanol added between the RFS and EIA cases is expected to cost $915 

million in the year 2012, resulting in a 0.56 cent per gallon cost. 

Table 7.4-17 expresses the total and per-gallon gasoline costs for the EIA case with the 

federal and state ethanol subsidies included. The federal tax subsidy is 51 cents per gallon for 

each gallon of new ethanol blended into gasoline. The state tax subsidies apply in 5 states and 

range from 1.6 to 29 cents per gallon. The cost reduction to the fuel industry and consumers are 

estimated by multiplying the subsidy times the volume of new ethanol estimated to be used in the 

state. 

317
Table 7.4-17. 

Estimated EIA Case Cost including Ethanol Consumption Subsidies 

(million dollars per year and cents per gallon; 2004 dollars, 7% ROI before taxes) 

 EIA Case 

9.6 Billion Gals 

Incremental to Reference Case 

EIA Case 

9.6 Billion Gals 

Incremental to RFS Case 

Total Cost ($MM/yr) 1739 915 

Federal Subsidy ($MM/yr) -2865 -1489 

State Subsidies ($MM/yr) -31 -26 

Revised Total Cost ($MM/yr) -1158 -600 

Per-Gallon Cost Excluding Subsidies 

(c/gal) 

1.06 0.56 

Federal Subsidy (c/gal) -1.74 -0.90 

State Subsidies (c/gal) -0.02 -0.02 

Total Cost Including Subsidies 

(c/gal) 

-0.71 -0.37 

The cost including subsidies better represents gasoline’s production cost as might be 

reflected to the fuel industry as a whole and to consumers “at the pump” because the federal and 

state subsidies tends to hide a portion of the actual costs. Our analysis estimates that the fuel 

industry and consumers will see a 0.71 cent per gallon decrease in the apparent cost of producing 

gasoline for the EIA case. Incremental to the RFS case, the consumer would be expected to see a 

0.37 cent per gallon price decrease “at the pump.” 

7.4.3.4.3 Sensitivity Cost Analyses for the RFS and EIA Cases 

In Table 7.1-5 above, we presented various corn-ethanol production cost estimates based 

on varying corn and dried distillers grain prices. We entered a range of low and high production 

ethanol cost estimates from that table into our cost spreadsheet created from the output from the 

LP refinery cost modeling. The range of ethanol production costs that we chose represents a 

reasonable bound around the possible range of future ethanol production costs. This allowed us 

to estimate the cost of using ethanol at these other possible ethanol production costs at the 

ethanol volumes analyzed for the RFS and EIA cases. We present these costs in Table 7.4-18. 

We did not conduct sensitivity analyses around higher or lower crude oil prices.100 

 This sensitivity analysis conducted at lower and higher ethanol production costs can also be used as a surrogate 

for a sensitivity analysis of higher and lower crude oil prices. Analyzing a lower ethanol cost is similar to analyzing 

a higher crude oil price with ethanol production costs at the levels we analyzed them at which was 126 and 131 cents 

per gallon, and vice versa for our sensitivity analysis at the higher ethanol production cost. 

318

100
Table 7.4-18. 

Summary of the Sensitivity Cost Analysis at Higher and Lower Ethanol Production Costs 

(Costs in 2012, 2004 dollars, 7% ROI before taxes) 

Ethanol Production 

Cost 

Units Costs 

RFS Case 

Costs 

EIA Case 

Cost without $MM/yr -260 -846 

0.86 Subsidies c/gal -0.16 -0.52 

Cost with $MM/yr -1640 -3740 

Subsidies c/gal -1.00 -2.28 

Cost without $MM/yr 2930 5784 

2.04 Subsidies c/gal 1.79 3.53 

Cost with 

Subsidies 

$MM/yr 1546 2890 

c/gal 0.95 1.76 

7.4.4 Impact on Diesel Prices 

Biodiesel fuel is added to highway and nonroad diesel fuel, which increases the volume 

and therefore the supply of diesel fuel and thereby reduces the demand for refinery-produced 

diesel fuel. In this section, we estimate the overall cost impact, considering how much refinery 

based diesel fuel is displaced by the forecasted production volume of biodiesel fuel. The cost 

impacts are evaluated considering the production cost of biodiesel with and without the subsidy 

from the Biodiesel Blenders Tax credit program. Additionally, the diesel cost impacts are 

quantified with refinery diesel prices as forecasted by Jacobs’s which are based on EIA’s AEO 

2006. 

We estimate the net effect that biodiesel production has on overall cost for diesel fuel in 

year 2012 using total production costs for biodiesel and diesel fuel. The costs are evaluated 

based on how much refinery based diesel fuel is displaced by the biodiesel volumes as forecasted 

by EIA, accounting for energy density differences between the fuels. The cost impact is 

estimated from a 2012 year basis, by multiplying the production costs of each fuel by the 

respective changes in volumes for biodiesel and estimated displaced diesel fuel. We further 

assume that all of the forecasted bio-diesel fuel volume is used as transport fuel, neglecting 

minor uses in the heating oil market. 

For this analysis, the production costs for biodiesel fuel are based on the costs generated 

using the USDA, NREL, EIA and the design vendors estimates in the preceding sections. We 

average these results to developed costs for soy oil and yellow grease feedstocks. Additionally, 

the production costs are based on EIA’s projection in 2012 that half of the total biodiesel volume 

will be made from soy oil feedstock with the remaining volume being produced from yellow 

grease. To these estimates, we add distribution costs of 21.5 c/gal to the biodiesel production 

costs, reflecting the distribution estimates derived in section 7.3.2. For the refinery diesel 

production costs in 2012, we used the projected wholesale national average diesel price of 160 

c/gal projected by the Jacobs’ pricing forecast. Distribution costs of 4 c/gal were added to the 

Jacobs’s wholesale diesel price projection, to account for the additional costs to move diesel fuel 

from the wholesale market to end use terminals. 

319
Our estimate for the reduction in refinery produced diesel fuel is based on EIA’s forecast 

for approximately 300 MM gallons of biodiesel in 2012, along with the 2012 Reference Case 

year biodiesel production volume of 28 MM gallons. With this and accounting for differences in 

energy density between biodiesel and diesel fuel, we estimate in 2012 that the additional 

biodiesel production reduces the need for 250 MM gallons of refinery produced diesel fuel. 

Table 7.4-19 contains the energy densities used in this analysis. 

Table 7.4-19. 

Energy Content of Fuels per Gallon 

Fuel Lower Heating Value (BTU/gallon) 

Biodiesel 117,093 

Refinery Produced Diesel 128,700 

For all RFS case scenarios, the net effect of biodiesel production on diesel fuel costs, 

including the biodiesel blenders’ subsidy, is a reduction in the cost of transport diesel fuel costs 

by $114 MM per year, which equates to a fuel cost reduction of about 0.20 c/gal101. Without the 

subsidy, the transport diesel fuel costs are increased by $91 MM per year, or an increase of 0.16 

c/gal. 

7.5 Other Potential Economic Impacts 

Ideally, we would prefer to assess all economic and environmental impacts of increased 

ethanol use and decreased fossil fuel use in a holistic manner. Such an analysis is beyond the 

scope of this RIA. However, we can approximate some of the impacts of increased ethanol 

production and use, and we can discuss other impacts qualitatively. The preceding discussion 

quantifies the impact of expanded use of renewable fuels on the cost of gasoline and diesel fuel. 

It does so by quantifying the direct costs of ethanol production, as well as the direct costs of state 

and federal tax subsidies for the renewable fuels, which are financed through tax payments. 

There are many other economic impacts associated with the use of renewable fuels and the fossil 

fuels they replace which go well beyond the scope of the analysis conducted for the RIA. We 

have not attempted to quantify all of them here. For example, increased renewable fuel 

production and use may have adverse impacts on surface and ground water quality and soil 

erosion, while decreased fossil fuel, distribution and use may have positive impacts. To quantify 

the economic impact associated with this would require extensive analysis of the likely responses 

of farmers to the increased demand for renewable fuels, the cost of actions taken to remedy the 

impacts, and the cost of any resulting health and welfare impacts. 

101 Based on EIA’s AEO 2006, the total volume of highway and off-road diesel fuel consumed in 2012 was 

estimated at 58.9 billion gallons. 

320
Furthermore, the renewable fuel production costs assumed in our analysis do not reflect 

the entire cost to society associated with the production of the corn and soybean feedstocks used 

in their production due other state and federal agricultural policies. Direct payments, countercyclical payments, marketing loans, and subsidized crop insurance are all examples of policies 

outside of this rulemaking that impact the price of corn and soybeans that are not reflected in the 

production cost for ethanol and biodiesel, but do impact costs borne by consumers indirectly 

through taxes. Quantifying the incremental impacts of this rulemaking on the effects of these 

pre-existing programs would represent a significant challenge. However, the challenge is 

complicated even more by the direct and indirect economic support provided for the production, 

supply, and distribution of the fossil fuels which would be replaced by these renewable fuels. 

Again, any assessment of the overall costs to society for increase renewable use would have to 

look at the economic support provided across the entire fuel supply. Such an analysis is well 

beyond the scope of this RIA. 

Despite our inability to fully capture all the potential impacts on the cost to society of 

increased renewable fuel use, two potential impacts were touched on briefly in our analysis, and 

these are discussed in this subsection. 

Economic Impacts of Emission Changes 

As discussed in Chapters 4.1 and 5.1, we estimate that there may be an increase in 

emissions and a corresponding small increase in ozone resulting from the expanded use of 

renewable fuels. Our vehicle and equipment emission estimates are highly uncertain, however, 

given the lack of data in particular on vehicles and engines complying with the latest standards. 

However, to the extent that there are emission and ozone increases resulting from the expanded 

use of renewable fuels, there can be a cost associated with them. In some cases, areas that see an 

increase in emissions resulting from renewable fuel use may be forced to take other actions to 

offset these emission increases. In other cases, particularly in attainment areas, the impact, while 

not affecting attainment, may adversely impact air quality and human health. It is difficult to 

provide any quantitative estimate of what the mitigation costs might be to offset emission 

increases, or to quantify the health impacts resulting from the air quality impacts. Not only are 

the emission and air quality impacts highly uncertain, but they are also very location dependent. 

While we have made projections on where the ethanol use may rise or fall for the purposes of 

estimating nationwide fuel cost impacts and potential emissions impacts, these projections are 

much less reliable when trying to predict specific local air quality impacts. 

Despite all of the above caveats, we have attempted to provide a rough estimate of the 

potential national-level cost impacts; As a surrogate for additional emission control costs in 

nonattainment areas and potential health impacts in attainment areas, we looked at the potential 

health costs associated with the secondary nitrate PM resulting from the decreases in NOx 

emissions estimated in previous EPA rules. We note again that we actually expect an overall 

decrease in ambient PM2.5 formation due to the increased use of ethanol in fuel (See Chapter 

5.2).102 Thus, we expect most areas to have lower health impact costs and certainly lower 

abatement costs related to PM control. 

102 Overall, we expect that the decrease in secondary organic PM is likely to exceed the increase in secondary nitrate 

PM. In 2006, NOx emissions from gasoline-fueled vehicles and equipment comprise about 37% of national NOx 

321
In recent rulemakings we monetized PM emission impacts, including those resulting from 

changes in secondarily formed PM2.5 due to NOx emission changes. Using this information as a 

guide, we provide a screening-level estimate of the monetized PM-related health impacts 

associated with an increase in NOx emissions associated with the final rule. For this analysis, we 

derived a dollar-per-ton value based on recent benefits modeling conducted for the Clean Air 

Nonroad Diesel Rule (CAND).ZZZZZ This value ($8,000 in PM-related monetized health 

impacts per ton of NOx reduced) is based on air quality modeling conducted in 2004 for the 

CAND rule. This benefits transfer method is consistent with approaches used in other recent 

mobile and stationary source rules.103 We refer the reader to the final CAND RIA for more 

details on this benefits transfer approach. The dollar-per-ton value represents monetized health 

impacts in 2015 (in year 2000 dollars). 

We combined the dollar-per-ton estimate of monetized health effects with the projected 

2015 emission changes presented in Table 4.4-1, which includes emissions from gasoline 

vehicles and equipment and renewable fuel production and distribution. We estimate that the 

potential PM2.5-related monetized impact associated with NOx emissions from increased use of 

ethanol to be up to $290 million for the RFS control scenario, and up to $340 million for the EIA 

control scenario. Note that this impact is based on monetized changes in health effects, 

including changes in mortality risk, chronic bronchitis, nonfatal heart attacks, respiratory hospital 

admissions, asthma attacks, and other minor health endpoints. It is also important to point out 

that this value does not represent the cumulative monetized health impacts associated with the 

potential PM changes associated with the future use of ethanol described above. 

This estimate is subject to a number of additional caveats. The dollar-per-ton values 

reflect specific geographic patterns of emissions reductions and specific air quality and benefits 

modeling assumptions which are derived from previous analyses and will not match those 

associated with increased ethanol use in fuel for two reasons. One, the geographical distribution 

of the emission sources affected by increased ethanol use differs from that addressed in the 

CAND rulemaking. Two, the CAND rule was national in scope and the emission reductions 

were spread out across the entire nation. Increased ethanol use will be very geographically 

focused. Many major population centers will not experience an increase in ethanol use as their 

fuel already contains ethanol. Care should be taken when applying these estimates to emission 

reductions that occur in any specific location, since the dollars-per-ton for emission reductions in 

specific locations may be very different than the national average. Given these caveats and the 

emissions from mobile sources. In contrast, gasoline-fueled vehicles and equipment comprise almost 90% of 

national gaseous aromatic VOC mobile source emissions. The percentage increase in national NOx emissions due to 

increased ethanol use should be smaller than the percentage decrease in national emissions of gaseous aromatics. 

Finally, in most urban areas, ambient levels of secondary organic PM exceed those of secondary nitrate PM. Thus, 

directionally, we expect a net reduction in ambient PM levels due to increased ethanol use. However, we are unable 

to quantify this reduction at this time. 

103 See: Clean Air Nonroad Diesel final rule (69 FR 38958, June 29, 2004); Nonroad Large Spark-Ignition Engines 

and Recreational Engines standards (67 FR 68241, November 8, 2002); Final Industrial Boilers and Process Heaters 

NESHAP (69 FR 55217, September 13, 2004); Final Reciprocating Internal Combustion Engines NESHAP (69 FR 

33473, June 15, 2004); Final Clean Air Visibility Rule (EPA-452/R-05-004, June 15, 2005); Ozone Implementation 

Rule (70 FR 71611, November 29, 2005). 

322
potential decrease in ambient PM2.5 due to the decrease in aromatic fuel content, we can not say 

for certain in which direction the total monetized PM-related health impact will be. In reality 

there may be an overall reduction in PM-related health costs, despite the increase due to 

increased NOx emissions. 

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105

Chapter 8: Agricultural Sector Impacts 

As described in the Preamble, we used the Forest and Agricultural Sector Optimization 

Model (FASOM) to estimate the U.S. agricultural impacts of increasing renewable fuel volumes 

to 7.5 billion gallons per year (BGY) by 2012, as required by the Renewable Fuels Standard 

(RFS) and to 9.9 BGY, the volume of renewable fuel the Energy Information Administration 

(EIA) predicts for the year 2012 in the Annual Energy Outlook 2006.

104 Although these 

renewable fuel volumes are lower than current market predictions, these assumptions were 

established during the NPRM and are used throughout this FRM. 

FASOM is a long term economic model of the U.S. agriculture sector that maximizes 

total revenues for producers while meeting the demands of consumers. Using a number of 

inputs, FASOM determines which crops, livestock, and processed agricultural products will be 

produced in the U.S. In each model simulation, crops 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, 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.105 

FASOM uses supply and demand curves for the 11 major U.S. domestic regions,106 

which are calibrated to historic price and production data. FASOM also includes detailed supply 

and demand data for corn, wheat, soybeans, rice and sorghum across 37 foreign regions.107 

FASOM maintains transportation costs to all regions and then uses all of this information to 

determine U.S. exports to the point where prices are then equated in all markets.108 

104 We analyzed the U.S. agricultural impacts of producing renewable fuels domestically after adjusting for 

equivalence values of cellulosic ethanol and biodiesel and projected U.S. imports. For the RFS Case, we assumed 

440 million gallons of corn based ethanol will be imported, while we assumed 630 million gallons of corn based 

ethanol will be imported for the EIA Case. For both cases, we assume 250 million gallons of cellulosic ethanol will 

be produced (with a 2.5 equivalence value), and 300 million gallons of biodiesel will be produced (with a 1.5 

equivalence value). 

 FASOM calculates output in five year increments. For this analysis, 2010 and 2015 data were interpolated to 

estimate 2012 values. 

106 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. 

107 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. 

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8.1 Commodity Prices 

8.1.1 Corn and DDGS Prices 

FASOM predicts that as renewable fuel volumes increase, agricultural prices over a range 

of products (not just corn and soybean renewable fuel feedstocks) will increase as well. Since the 

principal feedstock for ethanol is corn, corn prices are anticipated to rise. For consistency, all of 

the dollar estimates are presented in 2004 dollars. In the RFS Case, corn prices increase to 

$2.50/bushel by 2012 (compared to a Reference Case price of $2.32/bushel in 2012). With the 

higher renewable fuels volumes in the EIA Case, corn prices rise to $2.71/bushel (2004$) by 

2012. (See Table 8.1-1) To place this difference in perspective, in 2012, corn prices are about 8 

percent higher in the RFS Case and 17 percent higher in the EIA Case relative to the Reference 

Case.109 

Table 8.1-1. Corn and DDGS Prices in 2012 

Reference Case RFS Case EIA Case 

Corn Price $2.32/bushel $2.50/bushel $2.71/bushel 

Distillers Dried Grains with 

Solubles (DDGS) Price 

$85.55/ton $83.35/ton $86.15/ton 

The cost of producing ethanol is dependent upon, among other factors, the price of corn 

and the price of related byproducts. As part of the analytical approach described in the NPRM, 

we used FASOM to estimate the future prices of the major ethanol production byproduct: 

distillers dried grains with solubles (DDGS). FASOM estimates that the price of DDGS will 

remain relatively constant with the renewable volume scenarios that we are examining in this 

rulemaking. An increase in DDGS supply is anticipated to be offset by an increase in DDGS 

demand as technology improves to pelletize and distribute DDGS to a wider market. DDGS 

prices in the U.S. in 2012 are predicted to be $83.35/ton in the RFS Case and $86.15/ton in the 

EIA Case. (See Table 8.1-1) Hence, the overall price of DDGS remains within 3 percent of the 

DDGS Reference Case price. 

Note that the DDGS price given here is the price an ethanol producer would expect to 

receive at the plant gate. FASOM predicts a higher value for the DDGS at the place of end use, 

108 For additional details on the FASOM model, see the report by Professor Bruce McCarl, Texas A&M University, 

“The Impacts of the Renewable Fuel Standard Program on the U.S. Agricultural Sector,” February 2007, included in 

the docket. 

109 The current price of corn in the U.S. is approximately $3.50 per bushel (2004$), which is considerably higher 

than the FASOM prediction and is a likely a result of the fact that recent demand for corn for ethanol is higher than 

the currently available stocks. The model results for 2012 reflects medium-term spatial equilibrium prices, where 

rising demand for corn is met by rising supply -- due to increased acres planted to corn and to increased corn yields 

per acre. Note that while the model assumes that markets for corn and related agricultural commodities will settle at 

a price of $2.50 per bushel (in the RFS case) by 2012, that this may be a conservative estimate to the extent that the 

agricultural sector is able to adjust to the increased use of corn in ethanol production by 2012. 

325
based on its nutritional ability to be substituted as half soy meal and half corn in animal feed. 

The difference between its feed value and the ethanol plant gate price is made up of the cost of 

handling and shipping, which may include pelletizing or other measures required to support a 

national commodity market for DDGS. 

8.1.2 Soybean and Soybean Byproducts 

FASOM predicts relatively modest changes in soybean prices as a result of the increases 

in the renewable fuel volumes examined in this rulemaking. In the RFS Case, soybean prices rise 

to $5.44/bushel (2004$) by 2012 (compared to a Reference Case price of $5.26/bushel in 2012). 

In the EIA Case, soybean prices rise to $5.47/bushel (2004$) by 2012. (See Table 8.1-2) 

Soybeans prices are expected to increase by about 3 percent (RFS Case) and 4 percent (EIA 

Case) relative to the Reference Case by 2012. The slightly higher prices of soybeans reflect the 

consequences of the higher demand for soybeans for renewable fuels as well as the slightly 

higher input costs (e.g., land prices). It is also expected that in medium-term the acres planted to 

soybeans will fall, due to increased corn plantings, which will also increase soybean prices. 

Table 8.1-2. Soybean and Soybean Meal Prices in 2012 

Reference Case RFS Case EIA Case 

Soybean Price $5.26/bushel $5.44/bushel $5.47/bushel 

Soybean Meal Price $176.70/ton $171.73/ton $170.05/ton 

Soybean meal is produced when crushing soybeans and extracting soybean oil, the 

primary feedstock of biodiesel in the U.S. Under the RFS scenario, FASOM estimates the price 

of soybean meal will decrease by about 3 percent in 2012, relative to the Reference Case.110 

(See Table 8.1-2) This decrease is slightly larger under the EIA scenario, with the price of 

soybean meal dropping by about 4 percent. Several factors influence the small change in 

soybean meal prices. First, more acres of soybeans are being planted to rotate with increased 

planting of corn, and this leads to increased soybean supplies. Second, increased DDGS supplies 

can substitute for soybean meal as a feed ingredient by reducing the soybean meal needed in feed 

rations using higher levels of DDGS. Third, the size of the livestock herd is smaller due to higher 

meat prices, reducing the demand for animal feeds overall. 

110 The current price of soybeans in the U.S. is considerably higher than the FASOM prediction and is a likely a 

result of the fact that the market expects acres planted to soybeans in the short term are likely to decline due to 

increased corn plantings. As with the corn results,.the model reflects medium-term spatial equilibrium prices, where 

rising demand for corn is met by rising supply -- due to increased acres planted to corn and to increased corn yields 

per acre by 2012. Similarly, over time, farmers will begin to plant more soybeans in response to relatively higher 

short-term prices. The model expects soybean prices to reach an equilibrium price of $5.44 per bushel (in the RFS 

case) by 2012. 

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8.2 Impact on U.S. Farm Income 

The increase in renewable fuel production provides a significant increase in farm income 

to the U.S. agricultural sector. FASOM predicts that in 2012, U.S. farm income from the sale of 

agricultural commodities will increase by $2.65 billion dollars in the RFS Case and $5.41 billion 

in the EIA Case. (See Figure 8.2-1) The RFS and EIA farm income changes represent roughly a 

5 and 10 percent increase, respectively, in U.S. farm income from the sale of farm commodities 

over the Reference Case of roughly $53 billion111. Most of the increase in net income is likely to 

be concentrated in rural areas, and may contribute to rural wealth creation. 

Figure 8.2-1. Change in Net Farm Income Relative to Reference Case in 2012 

$2.65 

$5.41 

0.00 

1.00 

2.00 

3.00 

4.00 

5.00 

6.00 

Billio

n 2

0

0

4 D

ollars 

2012 

FASOM RFS FASOM EIA 

8.3 Impact on Employment 

Agricultural employment was not directly modeled but is likely to be very small since 

modern farm practices are not labor intensive and increases in production as modeled here will 

have negligible impact on direct farm employment. Some additional employment will result 

111 While U.S. government farm payments are currently part of the U.S. farm income, what programs will be in 

place in 2012 and their impact on farm income is unclear. For our modeling, we assumed the support programs 

were in place in 2010 but none were in place in 2015; interpolation between 2010 and 2015 provided the assumed 

impact in 2012. 

327
from ethanol plant construction and operation. 30 to 50 people per ethanol production facility 

seems typical. 

8.4 Commodity Use Change 

8.4.1 Corn and Ethanol Byproducts 

For this analysis, U.S. corn uses are broken down into four categories: domestic (i.e., 

household) consumption, ethanol production, livestock feed, and U.S. corn exports. (See Figure 

8. 4-1) As the demand for corn increases to produce more renewable fuel, U.S. corn utilization 

patterns are expected to be altered. In 2005, approximately 13 percent of all corn produced in the 

U.S. was used for ethanol production. With the two renewable fuel volumes that we are 

examining, the percentage of corn feedstock used for renewable fuels increases significantly. By 

2012, in the RFS Case, 20 percent of all corn produced in the U.S. is used to produce ethanol. In 

2012, in the EIA Case, 26 percent of all corn is used to produce fuel ethanol. These estimates are 

similar to the percentages included in the NPRM. 

The increasing use of corn for ethanol raises the price of corn which has a direct impact 

on the other uses of corn. FASOM predicts higher U.S. corn prices leads to lower U.S. exports 

of corn. U.S. corn exports drop from about 2 billion bushels in the Reference Case to 1.6 billion 

bushels in the RFS Case and 1.3 billion bushels in the EIA Case by 2012. In value terms, U.S. 

exports of corn fall by $573 million in the RFS Case and by $1.29 billion in the EIA Case in 

2012. 

Figure 8.4-1. Corn Uses in 2012 

100% 

80% 

60% 

40% 

20% 

0%

Feed

FASOM: Reference Case FASOM: RFS FASOM: EIA 

Ethanol Exports Domestic consumption 

P

ercent Corn Use 

328
Higher U.S. prices for corn due to increased demand for ethanol production results in 

decreased use of corn for U.S. livestock feed. The amount of corn used for livestock feeding 

decreases by about 320 million bushels in the RFS Case and by about 690 million bushels in the 

EIA Case relative to the Reference Case. Substitutes are available for corn as a feedstock, and 

this market is highly price sensitive. One alternate feedstock is DDGS because feed ration using 

increased levels of DDGS would need less corn. The relatively flat prices for DDGS predicted 

across all ethanol volume scenarios results from the significant increase in the demand for DDGS 

as a feed ingredient parallels the increase in supply of DDGS. FASOM estimates that DDGS use 

for livestock feeding for the RFS Case will almost double by 2012, increasing from 8.5 million 

tons to 15.2 million tons. Under the EIA Case, FASOM predicts that DDGS will increase to 

22.2 million tons by 2012. (See Figure 8.4-2) Domestic (i.e., household) consumption of corn 

for food use declines slightly with the different renewable fuel volumes analyzed in this 

rulemaking. 

Figure 8.4-2. Livestock Feed Sources in 2012 

600 

500 

400 

300 

200 

100 

0 

Corn 

FASOM Reference Case FASOM RFS FASOM EIA 

DDGS Hay Silage Soybean Meal Wheat Sorghum Barley Oats Gluten Meal 

Milli

on Tons 

329
8.4.2 Soybean and Soybean Byproducts 

As described previously, only a modest increase in the demand for soybeans are expected 

to be used to produce biodiesel in the renewable fuel scenarios analyzed in this rulemaking. 

Although changes in soybean uses in this analysis are limited, U.S. exports are expected to drop 

by 41.8 million bushels (RFS Case) and 35.6 million bushels (EIA Case). In terms of export 

earnings, U.S. exports of soybeans fall by $220 million in the RFS Case and by $194 million in 

the EIA Case in 2012. 

8.5 U.S. Land Use Patterns and Land Prices 

8.5.1 Corn Acreage 

FASOM predicts that total production of corn in the U.S. in 2012 will be 11.9 billion 

bushels under the RFS Scenario and 12.1 billion bushels under the EIA Scenario (compared to 

11.7 billion bushels in the Reference Case)112. (See Table 8.5-1) With higher renewable fuel 

volumes, more corn will be produced in the U.S. Increased U.S. corn production can result from 

two sources: greater productivity on existing acres of land devoted to corn or from “new” acres 

that are brought into the corn production. Much of the high quality, suitable land in the U.S. is 

already being used to produce corn. Improvement in the productivity of growing corn on existing 

U.S. land is projected to grow by roughly 1 percent annually through 2012. As a result, most of 

the increased demand for corn from increased use of renewable fuels will be met from increased 

productivity on existing acres of corn relative to the 2005 baseline year. However, corn 

production from new acres plays an important role in corn supply. FASOM estimates an 

increase in land devoted to corn production of 1.6 million acres (RFS Case) and 2.6 million acres 

(EIA Case) in 2012 compared to the Reference Case. 

Table 8.5-1. 

U.S. Corn Acres Harvested, Corn Production, and Agricultural Land Prices in 2012 

Reference Case RFS Case EIA Case 

Corn Acres Harvested 

(million acres) 

78.5 80.1 81.1 

Total Corn Production 

(billion bushels) 

11.7 11.9 12.1 

Land Prices (percent 

increase relative to 

Reference Case) 

N/A 8.4% 16.8% 

112 FASOM includes corn equivalent feeding of by products.in the estimate for total corn production. 

330 
Higher renewable fuel volumes will have a direct impact on the value of U.S. agricultural 

land. As demand for corn and other farm products increases, the price of U.S. farm land will 

also increase. The FASOM analysis shows that in 2012, higher renewable fuel volumes increase 

average agricultural land prices in the U.S. by about 8 percent (RFS Case) and 17 percent (EIA 

Case). 

8.5.2 Soybean Acreage 

Increasing use of biodiesel fuel in the renewable fuel scenarios does not cause a 

significant change in U.S. soybean production. Soybean production stays relatively flat at 3.3 

billion bushels in all three scenarios analyzed. (See Table 8.5-2) Soybean acreage increases 

modestly as well in the renewable fuel scenarios examined. In the RFS Case, total soybean acres 

are 74.6 million. For the EIA Case, total soybean acres are 74.4 million acres, compared to 73.4 

million acres of soybeans in the Reference Case. 

Table 8.5-2. U.S. Soybean Acres Harvested and Soybean Production in 2012 

Reference Case RFS Case EIA Case 

Soybean Acres Harvested 

(million acres) 

73.4 74.6 74.4 

Total Soybean Production 

(billion bushels) 

3.3 3.3 3.3 

8.5.3 CRP Acreage 

Current lands in the Conservation Reserve Program (CRP) total approximately 40 million 

acres. To qualify for inclusion in the CRP, the acres must have been at one time in active 

agricultural use. Farmers are paid to take these lands out of production and place them in CRP to 

provide environmental benefits, including limiting erosion and providing wildlife habitat. 

Farmers put land into the CRP voluntarily, considering among other factors the value of the land 

if it were to remain in agricultural production versus the amount paid under the CRP contract. 

The amount of government payments can change over time. 

For this analysis, we have assumed current per-acre payment levels to landowners are 

maintained through 2012. However higher commodity prices and higher land rents associated 

with higher renewable fuel volumes would likely require higher CRP payments to maintain the 

same level of CRP enrollments. The RFS and EIA renewable fuel volumes are estimated to 

result in CRP withdrawals of 2.3 million and 2.5 million acres, respectively, relative to the 

Reference Case. Most of the CRP lands are not likely to go into corn or soybean production 

since much of the CRP lands tend to be marginal lands due to their location and productivity. 

For example, only a relatively small portion of CRP lands are in the Corn Belt. Instead, 

additional corn or soybeans acres will probably be planted on lands that were previously used for 

other crops or pasture, for example, wheat, grain, sorghum or planted forage crops. It is 

331
expected that some of the land removed from CRP will be used for these other agricultural 

purposes. Table 8.5-3 depicts the estimate of CRP impacts. 

Table 8.5-3. CRP Acreage Changes Relative to Reference Case 

RFS Case EIA Case 

Reduction in CRP Acreage (million acres) 2.3 2.5 

8.6 Fertilizer Use 

Under the RFS scenario, the total amount of nitrogen applied on all farms increases by 

1.2 percent, or 480,000 pounds, relative to the Reference Case in 2012. Under the EIA scenario, 

the total amount of nitrogen applied on all farms increases by 2 percent, or 790,000 pounds, 

relative to the Reference Case in 2012. (See Table 8.6-1) We note that this percent increase in 

fertilizer is largely accounted for by the 2 percent increase in land used for corn production and 1 

percent increase in land for soybean production. The fact that the amount of nitrogen used 

increases at a smaller percent than the amount of land increase for corn production suggest that 

much of the corn production land is already in agricultural use (with fertilizer applied) and is not 

likely to be land newly released from CRP. 

Table 8.6-1. Nitrogen and Phosphorous Use in 2012 

Reference Case RFS Case EIA Case 

Total Nitrogen Applied 

(million pounds) 

40.28 40.76 41.07 

Total Phosphorous Applied 

(million pounds) 

4.24 4.27 4.29 

Under the RFS scenario, the total amount of phosphorous applied on all farms increases 

by 0.7 percent, or 30,000 pounds, relative to the Reference Case in 2012. Under the EIA 

scenario, the total amount of phosphorous applied on all farms increases by 1.2 percent, or 

50,000 pounds, relative to the Reference Case in 2012. See Table 8.6-1. 

8.7 Environmental Analysis 

Although this analysis does not include a comprehensive and integrated environmental 

assessment of the impacts in the agricultural sector of higher renewable fuel volumes from this 

rulemaking, we looked at two factors directly impacted by the production of agricultural crops 

332
that may relate to environmental impacts. FASOM does estimate the amount of fertilizer used 

and changes in CRP land, two indicators that could be associated with water pollution.113 

Marathon commented that it believes that EPA’s assessment of environmental impacts 

does not consider all environmental impacts and is therefore incomplete, especially with respect 

to water quality impacts. As described above, our analysis predicts a modest increase in 

fertilizer use and modest withdrawals of CRP lands due to the higher renewable fuel volumes. 

While increased agricultural development would likely increase pressure on environmentally 

sensitive areas such as wetlands and prairie lands and rural ecosystems in general, FASOM does 

not represent this level of land detail in the national model and therefore cannot quantify any 

potential impacts on these subsets of land types. To the extent that CRP withdrawals are 

managed in an environmentally sustainable way, however, water pollution impacts would be 

minimized. 

Increasing worldwide demand for biofuels and decreasing U.S. exports of feedstocks 

used in producing renewable fuels will likely lead to increased prices, production, and different 

trade patterns for renewable fuel feedstocks (i.e., corn and soybeans) in parts of the world outside 

of the U.S. FASOM includes the export effect as it contains supply curves for rest of world 

production of key agricultural products, but it does not contain a mechanism for appraising world 

environmental implications since FASOM is a domestic model of the U.S. agricultural sector. 

Therefore, this analysis focuses only on impacts of the higher renewable fuels volumes in the 

U.S. 

8.8 U.S. Food Prices 

Despite the wider use of U.S. agricultural feedstocks, principally corn, for renewable 

fuels, FASOM estimates only a modest increase in U.S. household food costs. Annual wholesale 

U.S. food costs are estimated to increase by approximately $7 per person with the RFS 

renewable volumes and by about $12 per person annually with the EIA renewable volumes by 

2012. (See Figure 8.8-1) Agricultural costs are only a portion of ultimate household food costs 

so significant increases in corn prices and, to a lesser degree, soybean prices results in a much 

smaller relative increase in household food costs. 

113 The FASOM model can describe the proportion of fertilizer that potentially will affect groundwater quality and 

surface water quality. FASOM also details the extent to which shifts in agricultural production may affect soil 

erosion and carbon sequestration. In the short timeframe available, we were not able to devote significant efforts to 

this type of analysis, but this area of inquiry could be investigated more extensively in the future. We do note that 

we capture the sequestration impacts in the GREET analysis. 

333
Figure 8.8-1. Increase in Annual Food Costs Per Person 

Relative to Reference Case in 2012

2004 D

ollars 

14 

12 

10 

8 

6 

4 

2 

0 

$7.0 

$11.9 

2012 

FASOM RFS FASOM EIA 

FASOM estimates a relatively modest increase in U.S. prices for meat and agricultural 

products associated with the higher renewable fuel volumes. When evaluating changes in overall 

U.S. food prices, FASOM uses the All Farm Products Price Index, which is a weighted average 

of prices received by farmers at the "farm gate" for crop and livestock products relative to the 

Reference Case.114 FASOM estimates a 4 percent increase in the RFS Scenario and a 7 percent 

increase in the EIA Scenario in the weighted price of all farm products. (See Table 8.8-1) 

To evaluate changes in U.S. meat prices, FASOM uses the All Meat Products Price Index 

which is a weighted average of the prices that farmers receive for meat products at the farm gate. 

This index is based upon changes in the weighted average of beef, pork, chicken, and turkey 

prices. U.S. meat prices that farmers receive in 2012 are estimated to increase by 0.3% in the 

RFS Case and by 1.3% in the EIA Case compared to the Reference Case. 

114 The All Farm Products Price Index includes: cotton, corn, soybeans, wheat, sorghum, rice, oats, barley, silage, 

hay, sugarcane, sugar beet, potatoes, tomatoes, oranges, grapefruit, switch grass, hybrid poplar, willow, beef, cows, 

milk, pigs, lamb, wool, horses and mules, eggs, chicken, and turkey. 

334
Table 8.8-1. Increase in the All Farm Products Price Index and the All Meat Products 

Price Index Relative to the Reference Case in 2012 

RFS Case EIA Case 

All Farm Products Price 3.8% increase 6.9% increase 

All Meat Products Price 0.3% increase 1.3% increase 

Because corn is a major component of the All Crop Price Index, a significant change in 

corn prices will result in a pronounced change in this index. The impact of corn price changes 

on the Meat Price Index will be less pronounced for two reasons. First, as corn prices rise, meat 

producers will modify feed rations and production systems to reduce their corn usage. Second, 

there will also be substitution among meats leading to higher consumption of meat from animals 

using less of the higher priced corn (e.g., increased production of poultry products relative to 

beef products). 

335
Chapter 9: Small-Business Flexibility Analysis 

This chapter presents our Small Business Flexibility Analysis (SBFA) which evaluates 

the potential impacts of the new 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 those entities that we believe are small 

entities (see section 9.3, below). As a part of this analysis, we conducted outreach with those 

entities to gather information and recommendations from these entities on how to reduce the 

impact of the rule on small businesses. 

9.1 Requirements of the Regulatory Flexibility Act 

We are generally required under the Regulatory Flexibility Act (RFA) to conduct a 

regulatory flexibility analysis unless we certify that the requirements of a regulation will not 

cause a significant impact on a substantial number of small entities. The key elements of the 

RFA include: 

• a description of and, where feasible, an estimate of the number of small entities to which 

the proposed rule will apply; 

• the projected reporting, record keeping, and other compliance requirements of the 

proposed 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 proposed rule; and, 

• any significant alternatives to the proposed rule which accomplish the stated objectives of 

applicable statutes and which minimize any significant economic impact of the proposed 

rule on small entities. 

The RFA was amended by SBREFA to ensure that concerns regarding small entities are 

adequately considered during the development of new regulations that affect them. Although we 

are not required by the Clean Air Act to provide special treatment to small businesses, the RFA 

requires us to carefully consider the economic impacts that our proposed rules will have on small 

entities. Specifically, the RFA requires us to determine, to the extent feasible, our rule’s 

economic impact on small entities, explore regulatory options for reducing any significant 

economic impact on a substantial number of such entities, and explain our ultimate choice of 

regulatory approach. 

We have concluded that the final RFS rule will not have a significant impact on a 

substantial number of small entities. We based this conclusion on several criteria. First, the 

336
industry is expected to be overcomplying by a wide margin independent of the standard, thus 

causing compliance costs to be minimal. Second, the Energy Policy Act of 2005 (Energy Policy 

Act) already provides relief from the renewable fuels standards until 2011 for the majority of the 

small entities; and lastly, we are extending this relief to the remaining small entities. This is 

discussed further below. 

9.2 Need for the Rulemaking and Rulemaking Objectives 

A detailed discussion on the need for and objectives of this rule are in the preamble to the 

final rule. As previously stated, EPA is required to promulgate regulations implementing a 

renewable fuel program under Section 1501 of the Energy Policy Act, which amended the Clean 

Air Act by adding Section 211(o). The Energy Policy Act requires EPA to establish a program 

to ensure that U.S. gasoline contains specific volumes of renewable fuel for each calendar year 

beginning in 2006, to increase the amount of renewable fuel used in vehicles and engines in the 

U.S. 

9.3 Description of Affected Entities 

9.3.1 Definition 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 (see Table 9-1); (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. Table 9.3.1-1 

provides an overview of the primary SBA small business categories potentially affected by this 

regulation. 

Table 9.3.1-1. Small Business Definitions 

Industry Defined as small entity by SBA if: NAICS Codes a 

Gasoline refiners ≤1,500 employees115 324110 a

 North American Industrial Classification System 

9.3.2 Summary of Small Entities to Which the Rulemaking Will Apply 

The refiners that are potentially affected by this proposed rule are those that produce 

gasoline. For our recent rulemaking “Control of Hazardous Air Pollutants from Mobile Sources” 

115 In the Draft RIA, we also referred to a 125,000 barrels of crude per day (bpcd) crude capacity limit. This 

criterion was inadvertently used and is not applicable for this program (as it only applies in cases of government 

procurement). We note that the number of small entities remains the same whether this criterion is used or not. 

337
(72 FR 8428, February 26, 2007), we performed an industry characterization to determine the 

universe of potentially affected gasoline refiners. Information about the characteristics of 

refiners comes from sources including the Energy Information Administration within the U.S. 

Department of Energy, and from Hoover’s (a division of Dun and Bradstreet). The refining 

industry is located primarily in NAICS code 324110. 

The industry characterization was then used to determine which refiners met the SBA 

definition of a small refiner. From the industry characterization, and further analysis following 

the Notice of Proposed Rulemaking (71 FR 55552, September 22, 2006), we determined that 

there were 15 gasoline refiners (owning 16 refineries) that met the definition of a small refiner. 

It should be noted that because of the dynamics in the refining industry (e.g., mergers and 

acquisitions), the actual number of refiners that ultimately qualify for small refiner status could 

be different from this estimate. 

Title XV the Energy Policy Act provides, at Section 1501(a)(2) [42 U.S.C. 

7545(o)(9)(A)-(D)], special provisions for “small refineries”, which includes a temporary 

exemption from the standards until calendar year 2011. Further, the Energy Policy Act states 

that EPA must use the definition of “small refinery” and apply the special provisions provided 

for small refineries in the RFS program. The Energy Policy Act defines the term “small 

refinery” as “...a refinery for which the average aggregate daily crude oil throughput for a 

calendar year...does not exceed 75,000 barrels.” 

A small refinery (as defined by the Energy Policy Act) is very different from a small 

refiner (as defined in SBA’s regulations at 13 CFR 121.201). Per 13 CFR 121.201, and as stated 

above in Table 9-1, a small refiner is a small business that employs less than or equal to 1,500 

employees. A small refinery, per the Energy Policy Act, is a small-capacity refinery and could 

be owned by a larger refiner that exceeds the criterion specified in SBA’s small entity definition; 

whereas small refiners generally only own a few (and more often than not, only one) refineries. 

In our analysis of the potentially affected small refiners, we found that 42 refineries met 

the Energy Policy Act’s definition of a small refinery. Of these, we determined that 13 of these 

refineries were owned by small refiners. Therefore, 12 of the 15 small refiners owned refineries 

that also met the Energy Policy Act’s definition of a small refinery. As a result, we believe that 

all but three small refiners would automatically be granted relief by implementing the provisions 

specified in the Energy Policy Act. 

9.4 Issues Raised By Public Comments 

During the public comment period we received numerous comments regarding various 

aspects of the proposed rule, including our proposed small refiner provisions. The following 

section provides a summary of the comments that we received on our proposed provisions. More 

information on these comments can be found in the Final Summary and Analysis of Comments, 

which is a part of the rulemaking record. 

338
9.4.1 Extension of the Small Refinery Exemption to Small Refiners 

Commenters that supported the provision extending the small refinery exemption to small 

refiners generally stated that they believe that a small refiner exemption is necessary as those 

entities that would qualify as small refiners are generally at an economic disadvantage due to 

their company size – whereas the Energy Policy Act only recognizes facilities, based on the size 

of each location. These commenters also stated that they have concerns with the cost and the 

availability of credits under the RFS program, and believe that provisions for small refiners are 

necessary to help mitigate any significant adverse economic impact on these entities. 

Commenters that opposed the provision commented that they believe that EPA exceeded 

its discretionary authority, that there appears to be no basis on which the Agency can legitimately 

expand this statutory exemption to add small refiners, and that Congress “clearly did not intend 

that the exemption be broadened to also include small refiners.” One commenter also stated that 

it does not believe that small refiner provisions are necessary because the RFS program does not 

require costly capital investments like previous fuel regulations. 

9.4.2 Application Deadline 

We proposed that refiners would need to apply for the small refinery exemption, and that 

the exemption would be effective 60 days after receipt of the application by EPA (unless EPA 

notifies the applicant that the application was not approved or that additional documentation is 

required). We received comments on this provision in which commenters stated that requiring 

small refinery applications was inconsistent with the language set out in the Energy Policy Act. 

The commenters stated that the Energy Policy Act intended that small refineries would 

automatically receive the small refinery exemption upon the effective date of the standard, and 

that these parties should not be considered obligated parties in 2007 even if they do not submit a 

small refinery application. 

9.4.3 Provisions for Foreign Small Refineries and Refiners 

For consistency with prior gasoline-related fuel programs, we also proposed to extend the 

RFS small refinery (and small refiner) exemption to foreign refiners, and we requested comment 

on this provision. We received some comments in which commenters stated that they believe 

that there is no reason to extend the small refinery exemption to these refiners. One commenter 

even stated that it believed that such an allowance would be unlawful. 

9.4.4 Other 

We received some comments which stated that EPA needed to clarify whether or not 

exempt small refineries (and small refiners) could separate a RIN simply by owning a batch of 

fuel. We also received a comment which stated that it was not clear in the proposed rule whether 

or not small refineries (and small refiners) blending ethanol at a terminal or any location without 

formally opting into the program could separate RINs. 

339
9.5 Related Federal Rules 

Other current and proposed Federal rules that are related to this rule are: the Mobile 

Source Air Toxics (MSAT2) rule (72 FR 8428, February 26, 2007), the Tier 2 Vehicle/Gasoline 

Sulfur rulemaking (65 FR 6698, February 10, 2000), and the fuel sulfur rules for highway diesel 

(66 FR 5002, January 18, 2001) and nonroad diesel (69 FR 38958, June 29, 2004). 

9.6 Projected Reporting, Recordkeeping, and Other Compliance 

Requirements 

For any fuel control program, EPA must have the assurance that refiners meet the 

applicable standards. Thus, requirements are imposed to ensure that compliance obligations are 

met. 

The recordkeeping, reporting and compliance provisions of this program are fairly 

consistent with those currently in place for our other 40 CFR part 80 fuel programs, including the 

highway and nonroad diesel and MSAT regulations. These provisions include: 

· 

· 

· 

Registration (the registration numbers will also be used in the RINs) 

Submission of annual reports summarizing a refiner’s annual gasoline production and a 

demonstration of its compliance with the renewable fuels standard and submission of 

annual reports detailing and tracking a refiner’s RINs; EPA’s Central Data Exchange will 

be used for report submissions 

Recordkeeping will consist of the retention of all compliance documents (such as Product 

Transfer Documents and all reports submitted to EPA) for at least five years 

For a more detailed discussion of these provisions, please see section IV of the preamble 

to this final rule. 

9.7 Steps to Minimize Significant Economic Impact on Small Entities 

As stated above, we conducted outreach to a number of stakeholders that met the 

definition of a small entity to gain feedback and advice on the needs of small businesses and 

potential challenges that these entities may face. The feedback that we received from these 

entities as a result of these meetings was used during the development of the proposed rule for 

developing regulatory alternatives to mitigate the impacts of the rulemaking on small businesses. 

General concerns raised by these entities were the potential difficulty and costs of compliance 

with the upcoming standards given the other fuel compliance requirements that the fuel refining 

industry is subject to. Below we discuss the regulatory flexibility alternatives and provisions 

which are being finalized in this action. 

While we do not believe that the RFS program with just the statutorily-prescribed 

temporary relief for small refineries would have a significant economic impact on a substantial 

number of small entities, we continue to believe that some refiners, due to their size, generally 

340
face greater challenges compared to larger refiners. These refiners generally have greater 

difficulty in raising and securing capital for investing in capital improvements and in competing 

for engineering resources and projects. This rulemaking does not require that refiners make 

capital improvements, however there are still costs associated with meeting the standard. Thus, 

we find it appropriate to extend the small refinery temporary exemption, as set out in the Energy 

Policy Act, to small refiners. Under this exemption, any gasoline produced at a refinery owned 

by a small refiner will not be counted in determining the renewable fuel obligation of a refiner 

until January 1, 2011; further, the small refiner may exclude gasoline produced at its refineries 

from its compliance calculations. Beginning in 2011, refineries owned by small refiners will be 

required to meet the same renewable fuel obligation as all other refineries. 

Past fuels rulemakings have included a provision that, for the purposes of the regulatory 

flexibility provisions for small entities, a refiner must also have an average crude capacity of no 

more than 155,000 barrels of crude per day (bpcd). To be consistent with these previous rules, 

we are finalizing in this rule that refiners that meet this criterion (in addition to having no more 

than 1,500 total corporate employees) will be considered small refiners for the purposes of the 

regulatory flexibility provisions for RFS program. Further, the refiner must have produced 

gasoline at its refineries by processing crude oil through refinery processing units. We are also 

finalizing that eligibility will be based on 2004 data. 

We agree with statements from commenters that the Energy Policy Act did in fact intend 

to provide the small refinery exemption without the need for the submission of small refinery 

applications, and that these parties should receive the exemption upon the effective date of the 

rule. We also believe that this should be the case for small refiners as well. Therefore, we are 

finalizing that small refiners will also receive the exemption immediately upon the effective date 

of the rule. However, to ensure that only those refiners who meet the criteria above receive this 

exemption, we believe that it is necessary for refiners to verify that they do in fact meet the 

criteria. Therefore, these refiners will also be required to submit a verification letter showing 

that they meet the criteria for qualification as a small refiner for the regulatory flexibility 

provisions. This letter will be similar to the small refiner status applications required under other 

EPA fuel programs (and must contain all the required elements specified at §80.1142 of the 

regulations), except the letter will not be due prior to the program. Small refiner status 

verification letters for this rule that are later found to contain false or inaccurate information will 

be void as of the effective date of this rule. Small refiners who subsequently do not meet all of 

the RFS program’s regulatory flexibility qualification criteria (i.e., cease producing gasoline by 

processing crude oil, employ more than 1,500 people, or exceed the 155,000 bpcd crude oil 

capacity limit) as a result of a merger with or acquisition of or by another entity, are disqualified 

as small refiners, except in the case of a merger between two previously approved small refiners. 

As in other EPA programs, where such disqualification occurs, the refiner must notify EPA in 

writing no later than 20 days following the disqualifying event. 

We are finalizing the proposed provision allowing foreign refiners to apply for a small 

refinery or small refiner exemption under the RFS program. The Energy Policy Act definition of 

“small refinery” is not limited to domestic facilities, and we believe that we have the discretion 

to apply the definition of small refinery, and the similar relief that we are providing to small 

refiners, to foreign producers. We believe that this provision is necessary for consistency with 

341
prior fuel programs (anti-dumping, MSAT, and the fuel sulfur rules) which allowed foreign 

refiners to receive such exemptions. Under this provision, gasoline produced at approved 

foreign small refineries, and by approved foreign small refiners, will be exempt from the RFS 

standard such that obligated parties (importers or blenders) would not count these volumes 

towards their renewable volume obligations. 

We are also finalizing the proposed provision that the automatic five year exemption, and 

any small refinery extended exemptions (extensions of the small refiners exemption will only be 

available to small refineries), may be waived upon notification to EPA. Gasoline produced by a 

small refiner who waives its exemption will be included in the RFS program and will be included 

in the gasoline used to determine the refiner’s renewable fuel obligation. If a refiner waives the 

exemption, the refiner will be able to separate and transfer RINs like any other obligated party. 

However, exempt small refiners cannot separate a RIN simply by owning a batch, a RIN can 

only be separated by these parties once the volume of renewable fuel is blended with gasoline or 

diesel to produce a motor vehicle fuel (as stated in the regulations at §80.1129). If a small 

refiner does not waive its small refiner exemption, it can still separate and transfer RINs, but 

only for the renewable fuel that the refiner itself blends into gasoline (i.e., the refinery operates 

as an oxygenate blender facility). Lastly, exempt small refiners who blend ethanol can separate 

RINs from batches without formally opting in to the program. 

9.8 Conclusions 

After considering the economic impacts of today’s proposed rule on small entities, we do 

not believe that this action will have a significant economic impact on a substantial number of 

small entities. While the Energy Policy Act provided for a temporary exemption for small 

refineries from the requirements of today’s proposed rule, these parties will have to comply with 

the requirements following the exemption period. Therefore, we had to take into account the 

economic effects of the program on small entities when they would need to comply with the 

standard. As described in section VI of the preamble to this final rule, the annual projections of 

ethanol production are greater than the annual renewable fuel volumes required by the Energy 

Policy Act. For example, in 2011, when the Energy Policy Act’s small refinery exemption ends, 

over one billion gallons in excess RINs are projected to be available. Further, excess RINs are 

anticipated for each year of the program. Due to this projected excess supply in comparison to 

the standard, the cost of RINs should be very low—near the level of the transaction costs. 

Due to the low cost to affected small entities, and the projected RIN availability, as well 

as the temporary relief provided to small refineries and small refiners, we do not believe that this 

program will impose a significant economic impact on a substantial number of small entities. 

342
Endnotes 

A Renewable Fuels Association (RFA), Ethanol Biorefinery Locations (Updated October 16, 

2006). 

B

 Ethanol Producer Magazine (EPM), Plant List (Downloaded October 18, 2006) monthly 

magazine publications (June 2006 through October 2006). 

C

 Ethanol Producer Magazine (EPM), monthly magazine publications (June 2006, July 2006, 

August 2006, September 2006 and October 2006). 

D ICF International, Ethanol Industry Profile (September 30, 2006). 

E

 BioFuels Journal, News & Information for the Ethanol and BioFuels Industries (breaking news 

posted June 16, 2006 through October 18, 2006). 

F

 Renewable Fuels Association (RFA), Ethanol Biorefinery Locations (Updated June 19, 2006). 

G Ethanol Producer Magazine (EPM), U.S. & Canada Fuel Ethanol Plant Map (Spring 2006). 

H International Fuel Quality Center (IFQC), Special Biofuels Report #75 (April 11, 2006). 

I

 “Biodiesel Performance, Costs, and Use”, Anthony Radich, EIA page 6. 

J

 NBB Survey September 13, 2006 “U.S. Biodiesel Production Capacity”. 

K From Independent Biodiesel Feasibility Group Presentation. 

L

 “Pipeline Considerations for Ethanol”, Department of Agricultural Economics, Kansas State 

University, August 2002. http://www.agmrc.org/NR/rdonlyres/4EE0E81C-C607-4C3F-BBCFB75B7395C881/0/ksupipelineethl.pdf , and “Shipping Ethanol Through Pipelines”, American 

Petroleum Institute, April 6, 2006, http://apiep.api.org/printerformat.cfm?ContentID=54FD1A11-95FA-4B7C-ACE1D7C6F121FB1C. 

M Infrastructure Requirements for an Expanded Fuel Ethanol Industry, Downstream Alternatives 

Inc., January 15, 2002. 

N “Ethanol Industry’s Interest in River Sites Grows”, River Transport News, Vol. 15, No. 10, 

May 22, 2006. 

343
O Infrastructure Requirements for an Expanded Fuel Ethanol Industry, Downstream Alternatives 

Inc., January 15, 2002. 

P

 Infrastructure Requirements for an Expanded Fuel Ethanol Industry, Downstream Alternatives 

Inc., January 15, 2002. 

Q F.O. Licht, “World Ethanol Markets, The Outlook to 2015” (2006). 

R

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S

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T

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Gasoline Areas (Updated February 23, 2004) and U.S. EPA Office of Transportation & Air 

Quality, 2004 RFG Fuel Survey Results 

(http://www.epa.gov/otaq/regs/fuels/rfg/properf/rfgperf.htm). 

U Source: U.S. EPA Office of Transportation and Air Quality, State Winter Oxygenated Fuel 

Program Requirements for Attainment or Maintenance of CO NAAQS (November 2005). 

V National Ethanol Vehicle Coalition (http://www.e85fuel.com/e85101/faqs/number_ffvs.php). 

W EIA 2004 Petroleum Marketing Annually (Table 48: Prime Supplier Sales Volumes of Motor 

Gasoline by Grade, Formulation, PAD District, and State). 

X EIA Historical RFG MTBE Usage (file received from EIA representative on March 9, 2006). 

Y EIA Monthly Energy Review, June 2006 (Table 10.1: Renewable Energy Consumption by 

Source, Appendix A: Thermal Conversion Factors). 

Z

 FHWA Highway Statistics 2004: Estimated Use of Gasohol (April 2006). 

AA EPA Reformulated Gasoline (RFG) Survey 

(http://www.epa.gov/otaq/regs/fuels/rfg/properf/rfgperf.htm). 

BB Alliance of Automobile Manufacturers (AAM) North American Fuel Survey 2004. The 

report and data can be purchased through the AAM website, 

http://autoalliance.org/fuel/fuel_surveys.php, or by contacting the Alliance at 1401 Eye Street, 

N.W., Suite 900, Washington, DC 20005, Tel: (202) 326-5500. 

CC EIA Monthly Energy Review, June 2006 (Table 10.1: Renewable Energy Consumption by 

Source, Appendix A: Thermal Conversion Factors). 

DD EIA Historical RFG MTBE Usage (file received from EIA representative on March 9, 2006). 

344
EE U.S. EPA, State Actions Banning MTBE (Statewide), June 2004. 

FF EIA 2004 Petroleum Marketing Annually (Table 48: Prime Supplier Sales Volumes of Motor 

Gasoline by Grade, Formulation, PADD, and State). 

GG EIA Monthly Energy Review June 2006 (Table 10.1: Renewable Energy Consumption by 

Source, Appendix A: Thermal Conversion Factors). 

HH EPA Reformulated Gasoline (RFG) Survey 

(http://www.epa.gov/otaq/regs/fuels/rfg/properf/rfgperf.htm) 

II AAM North American Fuel Survey 2004 (http://autoalliance.org/fuel/fuel_surveys.php). 

JJ EIA Annual Energy Outlook 2006 (Table 2: Energy Consumption by Sector and Source). 

KK International Fuel Quality Center (IFQC), Special Report: United States – State Renewable 

Content Standards (June 6, 2006). 

LL Renewable Fuel News and World Refining & News Today (January 2006). 

MM Modeling Impacts of Energy Bill RFS, Jacobs Consultancy December 2006. 

NN The Energy Act Section 1504, promulgated on May 8, 2006 at 71 FR 26691. 

OOEIA 2004 Petroleum Marketing Annually (Table 32: Conventional Motor Gasoline Prices by 

Grade, Sales Type, PAD District, and State). 

PP EIA NEMS Petroleum Market Model Documentation, Appendix I, Table I4. 

QQ American Coalition for Ethanol, STATUS: State by State Ethanol Handbook 2006 

(supplemented by information obtained from state websites and conversations with state 

government officials). 

RR Final Regulatory Impact Analysis for Reformulated Gasoline, Table VI-A6, December 13, 

1993. 

SS U.S. EPA, Final Regulatory Impact Analysis for Reformulated Gasoline, Table VI-A6, 

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TT U.S. EPA, EPA’s National Mobile Inventory Model (NMIM), A Consolidated Emissions 

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345
UU U.S. EPA, Guide on Federal and State Summer RVP Standards for Conventional Gasoline 

Only, EPA420-B-05-012, November 2005. 

VV Alliance of Automobile Manufacturers North American Fuel Survey 2005. The report and 

data can be purchased through the AAM website, http://autoalliance.org/fuel/fuel_surveys.php, 

or by contacting the Alliance at 1401 Eye Street, N.W., Suite 900, Washington, DC 20005, Tel: 

(202) 326-5500. Information about the data and methods is contained in the report Survey 

Sampling and Testing Details (May 2005), which may be downloaded from the AAM website at 

no charge. 

WW U.S. EPA website www.epa.gov/otaq/rfg/whereyoulive.htm, as of April 11, 2006. 

XX U.S. EPA, A Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions, Draft 

Technical Report, EPA420-P-02-001, October 2002. 

YY Caffrey, Peter J., and Paul A. Machiele, U.S. EPA, “In-Use Volatility Impact of 

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ZZ “Analysis of California’s Request for Waiver of the Reformulated Gasoline Oxygen Content 

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CCC “ExxonMobil: LEV/ULEV Gasoline Oxygenate Study”. Available on the California Air 

Resources Board’s Website: http://www.arb.ca.gov/fuels/gasoline/carfg3/carfg3.htm. 

DDD “Toyota Study: Effects of Ethanol on Emissions of Gasoline LDVs”. Available on the 

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EEE Schifter, I., Diaz, L., and Lopez-Salinas, E. “A Predictive Model to Correlate Fuel 

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FFF “California Waiver Decision Document,” US EPA, EPA420-S-05-005, June 2005. 

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346
III

HHH Haskew, Harold M., Thomas F. Liberty and Dennis McClement, “Fuel Permeation from 

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347
SSS “Nonroad Evaporative Emission Rates,” Table 2, U.S. Environmental Protection Agency, 

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YYY Federal Highway Administration, Highway Statistics 2004, Section V, Table VM-1 “Annual 

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ZZZ U.S. EPA, User’s Guide for the Final NONROAD2005 Model, EPA420-R-05-013, December 

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AAAA U.S. EPA. 2006. Technical Support Document for the Proposed Mobile Source Air Toxics 

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in Docket EPA-HQ-OAR-2003-0053. 

348
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349
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XXXX Ibid. 

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350
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NNNNN Energy Policy Act of 2005, Section 1501(a)(2). 

OOOOO Historical data at 

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351
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QQQQQ http://www.iogen.ca/company/about/index.html. 

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TTTTT Lignocellulosic Biomass to Ethanol Process Design and Economics Utilizing Co-Current 

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Wooley, Mark Ruth, John Sheehan, and Kelly Ibsen, Biotechnology Center for Fuels and 

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ZZZZZ

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352