# Draft Regulatory Impact Analysis: Changes to Renewable Fuel Standard Program

# Draft Regulatory Impact Analysis: Changes to Renewable Fuel Standard Program

Assessment and Standards Division Office of T ransportation and Air Quality U.S. Environmental Protection Agency

NOTICE

This tec hnical report does not necessarily represent final EP A decisions or positions. It is intended to present technical analysis of issues using data that are cur rently available. The purpose in the release of such reports is to facilitate the exchange of technical information and to inform the public of technical developments.

EPA-420-D-09-001 May 2009

## Table of Contents

Statement of Need

....................................................................................................... iii

Overview

..................................................................................................................... iv

List of Acronyms a nd Abbreviations

......................... ............................................... vi

Chapter 1 : Renewable Fuel Production and Consumption

........................................ 11

Chapter 2 : Lifecycle GHG Analysis

......................................................................... 278

Chapter 3 : Impacts of the Pr ogram on Non-GHG Pollutants

................................... 436

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

.................. 516

Chapter 5 : Economic Impacts and Benefits

.............................................................. 642

Chapter 6 : Impacts on Water

..................................................................................... 705

Chapter 7 : Initial Regulat ory Flexibility Analysis

.................................................... 733

Appendix A: Biodiesel E ffects on Heavy-Duty Highway Engines and Vehicles

..... 743

Endnotes

..................................................................................................................... 771

ii

## Statement of Need

The current Renewable Fuel Standard (RFS) program was originally adopted by EPA to implement the provisions of the Energy Policy Ac t of 2005 (EPAct), which added section 211(o) to the Clean Air Act (CAA). With the passage of the Energy Independence and Security Act of 2007 (EISA), Congress recently made several impor tant revisions to these renewable fuel requirements. This proposal would revise the RFS program regulations to implement these EISA provisions.

iii

## Overview

Chapter 1: Renewable Fuel Production and Consumption This chapter describes the various feedstocks and renewable fuel types that could potentially be used to meet the EISA biofuel standards. The availability and challenges of harvesting, storing, and transporting these feedstocks are discusse d, as well as the diffe rent biofuel production technologies, industry plans, and potential growth projections for future f acilities. A discussion of biofuel distribution an d consumption is included.

Chapter 2: Lifecycle GHG Analysis This chapter describes the methodology used to determine the lifecycle greenhouse gas (GHG) emissions of the renewable fuels required by EISA, and to determine which qualify for the four GHG reduction thresholds established in EISA. Future inclusion of other feedstocks and fuel is discussed, as well as the overall GHG benefits of the RFS program.

Chapter 3: Impacts of the Program on Non-GHG Pollutants This chapter discusses the expected impacts of this proposed program on emissions of hydrocarbons (HC), oxides of nitrogen (NOx), car bon monoxide (CO), particulate matter (PM10 and PM2.5), sulfur oxides (SOx), ammonia (NH3), ethanol, and air toxic emissions of benzene, 1,3-butadiene, acetaldehyde, formaldehyde, acrolein, a nd naphthalene. Emissions from vehicles and off-road equipment, as well as emissions from the entire fuel production and distribution chain are considered.

Chapter 4: Impacts on Cost of Re newable Fuels, Gasoline, and Diesel The impact of the proposed program on the pr oduction and distribution costs of ethanol, biodiesel, gasoline and diesel ar e discussed. Per-gallon and nati onwide costs are presented with and without ethanol subsidies.

Chapter 5: Economic Impacts and Benefits of the Program This chapter summarizes the impacts of the RFS2 proposal on the U. S. and international agricultural sector, U.S. petroleu m imports, and the consequences of reduced oil imports on U.S. energy security. Also, it examin es the greenhouse gas benefits and the co-pollutant health and environmental impacts from the wider use of biofuels in the U.S. from this proposal.

Chapter 6: Impacts on Water The impacts of this proposed program on diffe rent bodies of water ar e discussed. Aspects include feedstock production, ecol ogical impacts, climate change , ethanol production, biodiesel production, and drinking water and public health issues.

Chapter 7: Initial Regul atory Flexibility Analysis The Initial Regulatory Flexibility Analysis (IRFA) evaluates the potential impacts of the proposed standards on small entities. In deve loping the IRFA, we conducted outreach and held meetings with representatives from the various small entities that could be affected by the rulemaking. Small business recommendations are discussed.

ivAppendix EPA conducted a comprehensive anal ysis of the emission impacts of biodiesel using heavy-duty, in-use diesel chassis and engine exhaust em issions data. The emission impacts on NOx, PM, HC, and CO of 20 volume percent biodiesel fu els produced from various animal- and plantbased feedstock materials tested under several cycles in this analysis.

v

## List of Acronyms and Abbreviations

ACE American Coalition for Ethanol ACS American Cancer Society ADM Archer Daniels Midland AEO Annual Energy Outlook (an EIA publication) AHC Aromatic hydrocarbons ARMS Agricultural Resource Management Survey ASTM American Society of Testing and Materials B0, B5, B20, etc Percent of biodiesel , e.g., B5= 5% biodiesel, 95% diesel bbl Barrel BEA Bureau of Economic Analysis Bgal, bgal, bilgal, bg Billions of gallons BGY Billions of gallons per year BPCD Barrels Per calendar day BPSD Barrels per stream day bpd, bbls/day Barrels Per Day Brix A measurement of the sugar conten t of a solution at a given temperature BTL Biomass-to-liquid BTU British Thermal Unit BU Bushel Bu/acre Bushels per acre BZ Benzene C Carbon C&D Construction and Demolition CA California CAA Clean Air Act CAIR Clean Air Interstate Rule CARB California Air Resources Board CaRFG3 California Phase 3 RFG CBG Cleaner Burning Gasoline CBI Caribbean Basin Initiative CB05 Carbon Bond 05 CD Census Division CFEIS EPA’s Certifica tion and Fuel Economy Information System CFR Code of Federal Regulations c/gal Cents per gallon CG Conventional Gasoline CH 3 CHO Acetaldehyde CH 3 C(O)OO· Acetyl peroxy radical CH 3 C(O)OONO 2 Peroxyacetyl nitrate CHF Congestive heart failure CHP Combined Heat an d Power Technology CIMT Carotid intima-media thickness CMAQ Community Multi-scale Air Quality model CO Carbon Monoxide CO2 Carbon Dioxide COHb Carboxyhemoglobin Co-op Cooperative CRC Coordinating Research Council CRGNSA Columbia River Gorge National Scenic Area CRP Conservation Reserve Program

viCTL Coal-to-liquid DDGS Distillers’ Dried Grains with Solubles DGS Distillers’ Grains with Solubles DHHS Department of Health and Human Services DOE Department of Energy DRIA Draft Regulatory Impact Analysis dt

Dry ton E&C Engineering and Construction E0 Gasoline Blend which Does Not Contain Ethanol E10 Gasoline Blend containing a nominal 10 percent ethanol by volume E85 Gasoline Blend containing 85 percent ethanol by volume E200 Percent of Fuel Evaporated at 200 Degrees F (ASTM D 86) E300 Percent of Fuel Evaporated at 300 Degrees F (ASTM D 86) EIA Energy Information Administration (part of the U.S. Department of Energy) EISA Energy Independence and Security Act Energy Act Energy Policy Act of 2005 (also the Act) EPA Environmental Protection Agency EPAct Energy Policy Act of 2005 (also ‘the Energy Act’ or ‘the Act’) ETBE Ethyl Tertiary Butyl Ether ETOH Ethanol EU European Union ex CA Excluding California F, °F Fahrenheit F-T Fischer-Tropsch FAME Fatty acid methyl ester FAPRI Farm and Agricultural Policy Research Institute FASOM Forestry and Agriculture Sector Optimization Model FBP Feed Boiling Point (also Final Boiling Point) FCC Fluidized Catalytic Cracker FCCU Fluidized Catalytic Cracking Unit FHWA Federal Highway Administration FOEB Fuel Oil Equivalent Barrel FR Federal Register FRM Final Rulemaking FRTP Fixed Reduction Trigger Point FFV Flexible Fuel Vehicle FTP Federal test procedure g/Btu Grams per Btu g/day Grams per day Gal, gal Gallon GDP Gross Domestic Product GEOS Goddard Earth Observing System GHG Greenhouse Gases GPA Geographic Phase-in Area GREET Greenhouse Gas, Regulated Emissions, and Energy Use in Transportation model GWP Global warming potentials ha Hectare H 2 O Water HC Hydrocarbon(s) HCO Heavy Cycle Oil (a refinery stream) HCHO Formaldehyde HDN Naphtha Hydrotreater (also Hydro-Denitrogenation Unit) HEI Health Effects Institute

viiHNO 3 Nitric acid HSR Heavy Straight Run (a refinery stream) HVGO Heavy Vacuum Gas Oil (a refinery stream) IARC International Agency fo r Research on Carcinogens IBP Initial Boiling Point IRFA Initial Regulatory Flexibility Analysis k Thousand kbbl Thousand barrels kg kilogram kwh Kilowatt Hour L, l Liter Lb, lb Pound LCC Land Capability Classification LCO Light Cycle Oil (a refinery stream) LEV Low emission vehicle LLE Liquid-Liquid Extraction LNS Light Naphtha Splitter LP Linear Programming (a type of refinery model) LSR Light Straight Run (a refinery stream)

m 2 Square meter MCIP Meteorology-Chemistry Interface Processor mg/m 3 Milligrams per cubic meter MGY, MMgy Million Gallons per Year

mm Millimeter MM Million MMBTU Million British Thermal Units MMbbls/cd Millions of barrels per calendar day MMGal/yr Millions of gallons per year MOBILE (5, 6, 6.2) EPA’s Motor Vehicle Emission Inventory Model (versions) MON Motor Octane Number MOVES Motor Vehicle Emissions Simulator MOVES2006 EPA’s Next Generation Highway Vehicle Emission Model MSAT Mobile Source Air Toxics MSAT1 2001 Mobile Source Air Toxics Rule MSAT2 2006 Proposed Mobile Source Air Toxics Rule MSW Municipal Solid Waste Mt Metric ton MTBE Methyl Tertiary-Butyl Ether N Nitrogen NAAQS National Ambient Air Quality Standards NAICS North American Indust rial Classification System NASS National Agricultural Statistics Service NATA National Air Toxic Assessment NBB National Biodiesel Board NCGA National Corn Growers Association NCI National Cancer Institute NCLAN National Crop Loss Assessment Network NCSU North Carolina State University NGL Natural gas plant liquids NH3 Ammonia NIOSH National Institute of Oc cupational Safety and Health NMHC Non-Methane Hydrocarbons NMIM National Mobile Inventory Model (EPA software tool)

viiiNMOG Non-methane organic gases NONROAD EPA’s Non-road Engine Emission Model NONROAD2005 EPA’s Non-road Engine Emission Model Released in 2005 NO Nitric oxide NO 2 Nitrogen dioxide NO x Oxides of nitrogen NPRM Notice of Proposed Rulemaking NRC National Research Council NRCS Natural Resource Conservation Service NREL National Renewable Energy Laboratory O 3 Ozone OA Organic aerosol OC Organic carbon ·OH Hydroxyl radical OM Organic mass OMB Office of Management and Budget OMHCE Organic Material Hydrocarbon Equivalent ORD Office of Resear ch and Development ORNL Oak Ridge National Laboratory OTAQ Office of Transportation and Air Quality Oxy-fuel, oxyfuel Winter oxygenated fuel program PADD Petroleum Administration for Defense District PAHs Polycyclic aromatic hydrocarbons PAN Peroxyacetyl nitrate PM Particulate Matter PM 10 Coarse Particle PM 2.5 Fine Particle PM AQCD Particulate Matter Air Quality Criteria Document PMA Petroleum Marketing Annual (an EIA publication) POM Polycyclic Organic Matter PONA Paraffin, Olefin, Naphthene, Aromatic ppb Parts per billion
ppm Parts Per million PPN Peroxypropionyl nitrate PRTP Percentage Reduction Trigger Point PSI Pounds per Square Inch QBtu Quadrillion btu Quadrillion 10 15 (R+M)/2 Octane calculation (RON+MON)/2 R&D Research and Development RBOB Reformulated Blendstock for Oxygenate Blending rd Renewable diesel RFA Regulatory Flexibility Act RFG Reformulated Gasoline RFS Renewable Fuels Standard RFS1 Renewable Fuels Standard Program promulgated in 2007. RFS2 Renewable Fuels Standard Changes RIA Regulatory Impact Analysis RIMS Regional Input-Output Modeling System RIN Renewable Identification Number RON Research octane number RPMG Renewable Products Marketing Group RSM Response Surface Model

ixRVP Reid Vapor Pressure S Sulfur SBA Small Business Administration SBAR Panel, or ‘the Panel’ Small Business Advocacy Review Panel SBREFA Small Business Regulatory Enforcement Fairness Act (of 1996) scf Standard cubic feet SER Small Entity Representative SI Spark Ignition SOA Secondary Organic Aerosol SOC Secondary organic carbon SOC Soil organic carbon SOx Oxides of Sulfur SULEV Super ultra low emission vehicle SVOC Semi-volatile organic compound T50 Temperature at which 50% (by volum e) of fuel evapor ates (ASTM D 86) T90 Temperature at which 90% (by volum e) of fuel evapor ates (ASTM D 86) TAME Tertiary Amyl Methyl Ether Ton 2000 lbs Tonne Metric tonne (equivalent to 1.1 tons); also metric ton TRQ Tariff rate quotas ULEV Ultra low emission vehicle U.S.C. United States Code USDA U.S. Department of Agriculture VGO Vacuum Gas Oil (a refinery stream) VMT Vehicle Miles Traveled VOC Volatile Organic Compound vol% Percent by volume, volume percent WDGS Wet Distillers Grain w/ Solubles wt% Percent by weight, weight percent
yr, y Year

x11 

## Chapter 1: Renewable Fuel Production and Consumption

1.1 Biofuel Feedstock Availability

Currently, the main feedstocks used for re newable fuel production are corn for ethanol and soy for biodiesel. As technologies improve , we expect more emphasis on using cellulosic feedstocks such as agricultural residues, forestry residues, etc. However, limitations may occur due to concerns over sustainable removal rates fo r initial cellulosic feedst ocks. Thus, dedicated energy crops which are touted as requiring low fertilizer and ener gy inputs as well as having the ability of being grown on margin al lands may also enter the ma rket. The following sections discuss the current and potential availability of biofuel feedstocks and the potential challenges that must be overcome in order for enough feed stock to be collected to meet the EISA requirement of 36 billion gallons of renewable fuel by 2022.

1.1.1 Starch/Sugar Feedstocks

The following sections describe starch and s ugar feedstocks that can be used to produce ethanol. Currently, the majority of ethanol is produced from corn /grain. Recently, there have also been plans to convert sugarcane grown in the U.S. into ethanol. We also describe feedstocks used in the producti on of ethanol ou tside the U.S.

1.1.1.1 Corn/Grain Ethanol

Today’s ethanol is primarily corn-based etha nol, which accounts for the majority of the estimated 9.0 billion gallons of domestic fuel ethanol produced in 2008. According to multiple sources, anywhere from 12-18 billion gallons of corn ethanol could be produced by the 2015-16 timeframe, see Table 1.1-1. 1 Studies indicate, however, that current sustainable production of corn for fuel in the U.S. may be no more than 15 billion gallons per year at most, and perhaps only 12-14 billion gallons. 2, 3 For this proposal, we modeled 15 b illion gallons of corn ethanol as required to meet the EISA standards using the agricultural models FASOM and FAPRI in order to assess the impact on crop acreage, crop alloca tion to fuel vs. other uses, and costs. See Chapter 5 of the DRIA for more details on the m odeling. In addition to acres harvested and crop allocation, factors such as crop yields and ethanol yield per bushel of feedstock impact the amount of biofuel that can be produced.Table 1.1-1. Corn Ethanol Production Foreca st Parameters and Corresponding Years
Source (cited in text above) Fuel Volumes/Year (billion gallons) Acres Planted (millions) Yield (bu/acre) Corn Allocation to Ethanol Ethanol Conversion (gal/bu) b
USDA Baseline 12/2016 90 170 31% 2.76

USDA Study 15/2016 92 170 37% 2.8 NCGA Analysis c 12.8-17.8/2016 76-78 a 178-193 33-40% 2.9-3.0

This Proposal 15/2022 92/87 a 183 35% 2.85

a Acres harvested
b Our proposed scenarios assume all figu res include denaturant, but most refere nces do not specify; Differences also occur depending on whether dry or wet mills are assumed, wet mills have slightly lower yields

c National Corn Growers Association

Corn is mainly grown in 12 states within the United States: Illinois, Indiana, Iowa, Kansas, Kentucky, Michigan, Minnesota, Miss ouri, Nebraska, Ohio, South Dakota, and Wisconsin. See Table 1.1-2.

Table 1.1-2. U.S. Corn Harvested in 2007 by State

Total Harvested State (Acres) Total 72,222,500 Illinois 13,050,000 Indiana 6,351,700 Iowa 13,850,000 Kansas 3,700,000 Kentucky 1,360,000 Michigan 2,315,900 Minnesota 7,784,500 Missouri 3,239,200 Nebraska 9,199,700 Ohio 3,604,300 South Dakota 4,488,400 Wisconsin 3,278,800

Corn yield per acre has been increasing at an average rate of about 1.7 percent per year for the past three decades. A, 4 See Figure 1.1-1

. In our economic modeling assessment, the national average corn yield is approximately 183 bu/acre in 2022, although yi elds are calculated at the regional level. Theref ore, the national average depend s on crop production in each region in a given year (see Chapter 5 of this DRIA). These yield incr eases over time are generally consistent with the United States Depart ment of Agriculture (USDA) projections. 5

A Calculated from 1977-2007.

12Figure 1.1-1. U.S. Corn Yields (1978-2008)

The percent of U.S. corn produced allocated to ethanol has increased in recent years. In 2006, the percent of U.S. corn used for ethanol was around 20 percent and in 2007 the percent had increased to 23 percent. Today, the majority of corn is still being used as animal feed (44 percent), with smaller portions going to ethano l (33 percent), exports (16 percent), human food, and seed. 6 The FASOM projects that approximately 35 percent of corn would need to be allocated to the ethanol industry by 2022. The amount of corn allocat ed to fuel vs. other uses has caused much controversy over the production and us e of corn-based ethanol in the past few years. There is concern that the use of corn for fuel could potentia lly divert corn needed to feed people. On the other hand, it is entirely possibl e that other countries (e .g. Argentina or Brazil) could increase their production of corn to match the increase in demand for food and fuel, thus meeting both needs. As many factors are in play, we can not be certain of the future. We rely on our modeling results to help inform us of the potential impacts.

Over the last 15 years, ethanol indus try optimization of cooking, mashing, and fermentation conditions has increased the amount of ethanol produced fro m a bushel of corn. According to USDA reports, by about 2010 we can e xpect all plants on-lin e to yield an average of 2.76 gallons per bushel. 7, 8 In addition, based on discussions with USDA, we believe it is reasonable to expect an increase in corn kernel starch content of 2-4 percent over the next decade. Combining these figures, we project indu stry average denatured ethanol yields to reach 2.85 gallons per bushel by 2022 for dry mills and 2.63 gallons per bushel for wet mills. See section 1.4 of this DRIA for more information on corn ethanol biofuel production technologies,
e.g. dry mill vs. wet mill.

1.1.1.2 Sugar Ethanol

Currently, there are no U.S. plants pr oducing ethanol from sugar feedstocks. 9 Brazil and several other countries are pr oducing ethanol from sugarcane, sugar beets, and molasses,

13showing that it is economically feasible to conv ert these feedstocks into ethanol (see section 1.1.1.3). However, the economics of producing ethanol from sugar feedstocks in these countries is not directly comparable to the economics of producing ethanol from sugar feedstocks in the U.S. Over the longer term, the profitability of producing ethanol from su garcane, sugar beets, and molasses depends on the prices of these cr ops, the costs of conversion, and the price of gasoline.

Recent news indicates that th ere are plans in the U.S. to produce ethanol from sugar feedstocks. For instance, sugar cane has been grown in California’ s Imperial Valley specifically for the purpose of making ethanol and using the can e’s biomass to generate electricity to power the ethanol distillery as we ll as export excess electricity to the electric grid. 10 There are at least two projects being developed at this time that could result in several hundred million gallons of ethanol produced. One company is California Ethanol and Power which is currently in the development stage and plans to build a facili ty that produces 60 milli on gallon per year of sugarcane ethanol and 50 megawatts of electricity. 11 The company plans to break ground by late 2009 and be operational by 2011. The sugarcane is being grown on marginal and existing cropland that is unsuitable for food crops and will replace forage crops like alfalfa, Bermuda grass, Klein grass, etc. Harv esting is expected to be fully mechanized. Another company is Pacific West Energy LLC which plans to produc e 12-15 million gallons per year of ethanol on the island of Kauai in Hawaii, perhaps as early as 2010. Hawaii is well suited for sugarcane ethanol production due to several factors, including lower costs fo r feedstock compared to those in the continental U.S., high prices for elec tricity and liquid fuels, and state production incentives. 12 Thus, there is potential for these project s and perhaps others to help contribute to the EISA biofuels mandate.

1.1.1.3 Imported Ethanol

In 2006, around 47% of world fuel ethanol production was produced from sugar crops, i.e. sugarcane and sugar beets, with the remainder mostly from grains. 13 Sugar beets are mainly grown in France, Germany, and in the U.S., with th e majority of the feedstock typically used to produce sugar for food and feed. Compared to sugar beets, sugarcane is produced in much higher volume and has been able to support a growing sugar and etha nol market. Due to a higher availability of sugarcane feedstock for ethanol production, we expect that imported ethanol to the U.S. will likely come from sugarcane.

World production of sugarcane is approximate ly 1.4 billion metric tons (MT) and is concentrated mainly in tropical regions, partic ularly in Latin America, Africa, and South and Southeast Asia. Roughly 100 count ries produce sugarcane today. 14 Brazil is currently the world’s largest producer of sugarcane (487 milli on MT in the 2007/8 harvest season) and offers the greatest potential for growth, due primarily to the availability of suitable lands for expanding sugarcane cultivation. 15

As far as land availability is concer ned, Brazil has large potential for expanding sugarcane production. Sugarcane area in Braz il is forecasted to grow by 50% by 2010, with construction and extension of mills especially in the Centre-South. Currently expansions have mainly been into pastureland areas and not into native scrubland. In Br azil, just 20% of the

14arable land is cultivated, totaling 156 million acres. The following Table 1.1-3., describes the land available/used in 2007. As there are 494 m illion acres of pastureland and a considerable area of unused arable land (190 million acres), it is believed that there could be a large expansion in sugarcane. 16

Table 1.1-3. Brazil Land Areas in 2007. 17
Million Acres Brazil (Total Area) 2100

Total Preserved Areas and Other Uses* 1260 Total Arable Area 840

Cultivated Land (All Crops) 156

Soybeans 51 Corn 35 Oranges 2 Sugar Cane 19

Sugar Area 11 Ethanol Area 8 Pastureland 494 Available land (ag, livestock) 190 *Areas include Amazon Rain Forest, protected areas, conservation and reforestation areas, cities and towns, roads, lakes, and rivers.

The statistics above, however, do not indi cate whether the land av ailable requires any additional usage of water or has the proper soil and clim ate conditions for sugar cane. According to one study, there is at least 148 million acres of additional land availa ble with proper soil and climate conditions for sugar cane without utiliz ing environmentally protected land (i.e. Amazon and native reserves) and wit hout the use of irrigation. 18 This translates to approximately 90 billion gallons of ethanol potential (using a yield of approximately 600 gal/acre which is a conservative estimate based on exis ting technology). Although it is not probable that all this land will be converted to sugar cane ethanol, the estimate puts into prospective the large potential for sugarcane ethanol to be produced in Brazil.

Another study commissioned by the Brazilian Government produced an analysis in which Brazil’s arable land was evaluated for its suitability for cane. The benefit of this study is that it provides more detail on the land quality and yield assumptions used in its estimates than the study and statistics shown above. 19 The study eliminated areas protected by environmental regulations and those with a slop e greater than 12% (those not suitable for mechanized farming). The following Table 1.1-4 shows the available land calculated for suga rcane expansion at various crop yields with and w ithout irrigation and the potential volumes that could be attained. As can be seen, there are potentially large areas of land available for sugarcane expansion in Brazil.

15Table 1.1-4. Potential Volumes Utilizing Available Land for Sugarcane Expansion B, 19 , 20

Potential Ethanol Yield (gal/acre) Potential Area (million acres) Potential Ethanol Volume (billion gallons) w/o irrigation w/ irrigation w/o irrigation w/ irrigation High Good Average Inadequate 659 592 524

0 20 94 281 242 369 414 224 143 13 62 166 143 193 217

0 0 Total 894 894 373 422

The actual potential for ethanol from sugar cane will, however, be further limited by the amount of sugarcane diverted towards food and other uses. Taking into account demands for food and feed, the Oak Ridge National Laborator y (ORNL) Biofuel Feedstock Assessment for Selected Countries report sugge sts that perhaps more than 20 billion gallons of gasoline equivalent fuel could be produced from availabl e sugarcane supply by 2017. Brazil is estimated to produce approximately 2/3 of the potential su pply. The majority of this supply would likely be consumed within the country, with the leftove r potentially available for export to the U.S. and other countries. Recent government and industr y estimates indicate that approximately 3.8-4.2 bgal of ethanol could be available for expor t from Brazil by 2022 (with close to 17 billion gallons being produced and 13 bi llion gallons consumed domes tically). See section 1.5.2.1.1 of this DRIA for further details on Brazilian et hanol production and consumption. Thus, there appears to be a large enough potential for Brazil to increase production of sugarcane to meet its internal demands as well as export to the United States and other countries.

Countries other than Brazil lack the land re sources, appropriate soils, and climate for large expansion of sugarcane production. 21 India and China are the second and third largest producers, however, most of the cultivatable la nd area is already in us e and government policies discourage reallocation of arable land for biofuel producti on. Although Argentina and Columbia have significant underutilized land s available, these resources gene rally do not have suitable soil and climate characteristics for sugarcane producti on. Due to these factor s, Brazil is the most likely country able to produce substantial volum es of sugarcane for biofuel production in the future.

1.1.2 Cellulosic Biofuel Feedstocks

Various cellulosic feedstocks can potentially be used to produce cellulosic biofuel. These include agricultural residues, fo rest residues, urban waste, an d dedicated energy crops. We describe each type in the following sections.

B Adapted from CGEE, ABDI, Unicamp, and NIPE, Scaling Up the Ethanol Program in Brazil. Assumed a conversion factor of 20 gallons of ethanol per tonne of sugarcane feedstock to compute gal/acre. A “high” potential refers to ethanol yields that are high er than current industry averages, while “good” refers to good quality land and productivity that is about equal to the current average. Explanations for “Average” and “Inadequate” were not provided.

161.1.2.1 Agricultural Residues

We estimated how much crop residue coul d potentially be produced, and of that, how much could be removed or harvested to determ ine the total amount that will be available to produce ethanol in 2022. The amount of residue that can be harvested is limited by how much residue must be left on the field to maintain soil health and by the mechanical efficiency (inefficiency) of the harvesting operation. We discuss harvesting limitations due to maintaining soil health below, while mechanical efficiencies, storage, and transport issues are discussed in section 1.3 of this DRIA. Feedstock costs ar e discussed in Chapter 4 of the DRIA.

Corn ( Zea mays L.) and wheat are receiving the most attention across the industry due to their concentrated production areas and because be tween them they generate about the majority of total residue that’s produced. This also m eans they will more likely be able to support commercial scale production. In aggregate, th e other residues provide fairly significant quantities of material, but because they are spread out, e.g., less densely planted both in the field and in a county or state, they are viewed as le ss likely to support commer cial operations. Later, we discuss how these other lower-quantity residue crops may supplement the larger operations.

We analyzed various reports on the availability of agricu ltural residues. These are summarized in Table 1.1-5. Th e agricultural residue estimate s in Table 1.1-5 are based on historical/recent data, and thus, could be consid ered conservative in comparison to the future (2022) which would typically have higher crops yields or increases in acres harvested.

Table 1.1-5. Estimated Agricultural Re sidue Feedstock Availability (per year) 22, 23, 24, 25, 26

Source Total Available Total Sustainably Removable Crops Analyzed

USDA > 500 million tons not specified Eight leading U.S. crops, e.g. corn, wheat, soy, oats, barley, rice (did not specify other two)

NREL 495 million tons 173 million tons Corn, wheat, soybeans, cotton, sorghum, barley, oats, rice, ry e, canola, beans, peas, peanuts, potatoes, safflower, sunflower, sugarcane, and flaxseed

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

Walsh not specified 144 million tons at $40/dry ton, ~150 million tons at >$40/dry ton for corn; 7 million tons at $40/dry ton, ~10-11 million tons at >$40/dry ton for wheat Corn and wheat

Graham 216 million tons 65 million tons at 30% removal rate and current conditions; 112 million tons at 50% removal rate using no-till conditions Corn

Based on our FASOM modeling, corn stove r was chosen as the most economical agricultural feedstock to be used to produce ethanol in order to meet the 16 Bgal EISA cellulosic biofuel requirement. We estimate that by 2022 gr eater than 400 million tons of corn stover could be produced. Approximately 82 million tons woul d be needed to produce 7.8 billion gallons of

17cellulosic biofuel that ou r modeling projects to co me from corn stover by 2022. C See Table 1.1­ 6. Smaller amounts will be required from sugarcan e (bagasse) as well as sweet sorghum pulp to produce another 1.3 billion gall ons of cellulosic biofuel. D Thus, the residue collected to meet EISA would be a small fraction of the tota l residue produced. S ee section 1.5.3.4 for more details on the use of agricu ltural residues for our cellulo sic plant siting analysis.

C Assuming conversion yield of 94 gal/dry ton
D Bagasse is technically a by-product of the sugarcane process and not an agricultural residue, we include it here for simplification. Sweet sorghum pulp is also a by-product of sweet sorghum processing.

18Table 1.1-6. FASOM Estimated Agricultural Residue Feed stock Availability in 2022 (million tons) E

State/Region Barley Corn Oats Rice Sorghum Wheat Total Alabama 0.0 1.9 0.0 0.0 0.1 0. 2 2. 2 Arizona 0.1 0.1 0.0 0.0 0.0 0.4 0.7 Arkansas 0.0 2.7 0.0 6.3 1.5 2.8 13.3 California 0.4 1.6 0.1 1.9 0.1 3.1 7.3 Colorado 0.3 5.5 0.0 0.0 0.4 7.7 13.9 Connecticut 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Delaware 0.0 0. 2 0.0 0.0 0.0 0.0 0.3 Florida 0.0 0. 2 0.0 0.0 0.0 0.0 0. 2 Georgia 0.0 5.7 0.1 0.0 0.1 0.3 6. 2 Idaho 1.3 0.4 0.0 0.0 0.0 4.5 6. 2 Illinois 0.0 70.6 0.0 0.0 0.4 4.1 75.1 Indiana 0.0 34.6 0.0 0.0 0.0 1.8 36.4 Iowa 0.0 79. 2 0.4 0.0 0. 2 0.1 79.9 Kansas 0.0 16.1 0.0 0.0 7.5 23. 2 47.0 Kentucky 0.1 7.5 0.0 0.0 0.1 1.6 9.3 Louisiana 0.0 0.5 0.0 4.0 0.1 0.1 4.8 Maine 0.0 0.1 0.0 0.0 0.0 0.0 0. 2 Maryland 0.1 2.0 0.0 0.0 0.0 0.6 2.8 Massachusetts 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Michigan 0.0 9.6 0.1 0.0 0.0 2.6 12.3 Minnesota 0. 2 40.3 0.3 0.0 0.0 3.8 44.6 Mississippi 0.0 4.6 0.0 0.7 0.5 1. 2 7.0 Missouri 0.0 12.9 0.1 0.4 3.0 2.4 18.8 Montana 1.1 0.0 0.1 0.0 0.0 6.5 7.8 Nebraska 0.0 59.6 0.1 0.0 1. 2 6.7 67.5 Nevada 0.0 0.0 0.0 0.0 0.0 0.0 0.1 NewHampshire 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NewJersey 0.0 0. 2 0.0 0.0 0.0 0.1 0.3 NewMexico 0.0 0.5 0.0 0.0 0.7 1.6 2.8 NewYork 0.0 2.1 0.3 0.0 0.0 0.5 3.0 NorthCarolina 0.1 4.6 0.1 0.0 0.1 1.5 6.4 NorthDakota 1.8 5.7 0. 2 0.0 0.0 14.0 21.7 Ohio 0.0 18. 2 0. 2 0.0 0.0 4.0 22.4 Oklahoma 0.0 0.3 0.0 0.0 0.8 9.7 10.9 Oregon 0. 2 0. 2 0.1 0.0 0.0 0.6 1.1 Pennsylvania 0.1 3. 2 0. 2 0.0 0.0 0.7 4. 2 RhodeIsland 0.0 0.0 0.0 0.0 0.0 0.0 0.0 SouthCarolina 0.0 1.1 0.0 0.0 0.0 1.0 2. 2 SouthDakota 0.1 17.1 0. 2 0.0 0.5 6.8 24.7 Tennessee 0.0 2.7 0.0 0.0 0.1 0.9 3.8 Texas 0.0 15. 2 0.1 1. 2 5.5 3.5 25.6 Utah 0.1 0.1 0.0 0.0 0.0 1.0 1. 2 Vermont 0.0 0. 2 0.0 0.0 0.0 0.0 0. 2 Virginia 0.1 1. 2 0.0 0.0 0.0 0.7 1.9 Washington 1.1 0.8 0.0 0.0 0.0 6.9 8.8 WestVirginia 0.0 0.1 0.0 0.0 0.0 0.0 0.1 Wisconsin 0.0 14. 2 0. 2 0.0 0.0 1. 2 15.7 Wyoming 0. 2 0.3 0.0 0.0 0.0 0.4 0.9 Total 7.7 444.4 3.1 14. 6 23.1 128.8 621.8

E Assumes straw to grain ratio for barley and wheat (1.5:1) and for corn, oats, rice, and sorghum (1:1); Also assumes 0.024 ton/bu for barley and oats, 0.028 ton/bu for corn, 0.05 ton/cwt for rice and sorghum, and 0.03 on/bu for wheat.

19Soil Health

In terms of soil health, residues perform many positive functions for agricultural soils. Recent studies and reviews have attempted to address these issues. Despite a few shortcomings, existing research can be used to some extent to guide practices or make estimates, especially for corn stover harvest in the Corn Belt, which has been studied mo re extensively than the other residues except, perhaps, wheat.

In a review by five USDA Agricultural Resear ch Service (ARS) scientists, Wilhelm et al. acknowledged the complexity of interactions between soil type, climate, and management when considering crop residue effects on soil. They recommended that removal rates be based on regional yield, climatic cond itions and cultural practices, with no specific rates given. 27 Using the Revised Universal Soil Loss Equation ( RUSLE) technology and the Wind Erosion Equation (WEQ), Nelson predicted safe re sidue removal rates for minimizing soil loss in the Eastern and Midwestern U.S. These predictions varied wi dely over time and location as a result of the complex interactions discussed by Wilhelm et al. 28, 29 In another recent review, sponsored by the U.S. Department of Energy (DOE), Mann et al. concluded that before specific recommendations could be made, more information was needed on the long term effects of residue harvest, including: 1) water quality; 2) soil biota; 3) transformations of different forms of soil organic carbon (SOC); and 4) subsoil SOC dynamics. 30 Current USDA Natural Resource Conservation Service (NRCS) practice standards for residue ma nagement shy away from specific residue quantities and point to the use of the RUSLE2 model for guidan ce (without specifics on how to do so). 31 Despite broad recognition of the need for specific guidelines for residue removal, none yet exist.

With the upsurge in biofuels and the obvious prospects of removing significant quantities of residue, many questions remain regarding the l ong-term effects on soils of residue removal. Residues haven’t yet been removed at the contem plated rates over a period sufficiently long for the effects to be clearly determined. Another di fficulty is that while the effects of removing a residue may appear to one observer to have aff ected the soil in a certain manner, it may not be completely clear that the observed effects were to tally related to the residue removal or, were in fact related to a change or to combinations of ch anges in other variables that were simply missed. A second observer may view the same results in an honest, but different manner. There are so many variables with so many diff erent interactions among them that assigning effects is very difficult at best. There simply are no real-world da ta available for determining long-term effects. Nevertheless, we can describe some of the in teractions that take place and how they can potentially affect soil health.

Soil Erosion

Soil erosion is an extremely important nati onal issue. Most, if not all, agricultural cropland in the United States experiences some de gree of soil erosion each year due to rainfall (water) and/or wind forces. Rainfa ll erosion (sheet and rill) occurs when rain directly strikes the soil, dislodging partic les in the top layer. F When soil becomes saturated, particles are

F rill: A small intermittent watercourse with steep sides, usually only a few inches deep; www.hancockcoingov.org/surveyor/drainage_glossary_of_terms.asp

.

20transported down the slope of the field. Soil er osion due to wind occurs in much the same manner as rainfall with wind forces dislodging so il particles and carryi ng them along and above the field surface (creep and saltation) or suspending them above the field. G While eroded soil does not disappear, the erosion process moves soil pa rticles to other locations in the field (either downslope or downwind) where they can be tran sferred into waterways or onto non-croplands.

The amount of soil erosion th at agricultural cropland experiences is a function of many factors: field operations (fi eld preparation, tillage, etc.) in prep aration for the next crop, timing of field operations, present throughout the year, soil type, field charac teristics such as field slope, and the amount of residue (cover) left on the field from harvest until the next crop planting. Crop rotation cover provided by agricu ltural crop residues, both fallen and standing, helps to minimize rainfall and wind energy as it stri kes or blows across the ground as well as helping to keep soil particles from being transported after they have been dislodged. Climatic conditions such as rainfall, wind, temperature, etc. must be accounted for. Studies predict that up to 30% of surface residue can be removed from some no-till syst ems without increased er osion or runoff.

The NRCS has established tolerable soil loss limits (T values) for all soil types in all counties throughout the United States. The tolerable soil loss values denote the maximum rate of soil erosion that can occur for a particular so il type that does not lead to prolonged soil deterioration and/or loss of productiv ity. Tolerable soil loss limits take into account the rate of topsoil formation, role of topsoil formation, loss of nutrients, erosion rate at which gully erosion would commence, and potential eros ion-control factors that farmers would be able to implement. However, T values are not a function of the type of crop grown.

Soil Tilth

Another important aspect associated with soil conservation involves soil tilth. Soil tilth is defined as the physical condition of the soil as relate d to its ease of tillage , fitness as a seed bed, impedance to seedling emergence and root pene tration, and all other physical conditions that influence crop development. Tilth depends upon soil granulation and its stability (soil workability) as well as organic matter content, mo isture content, porosity, water retention, degree of aeration, rate of water inf iltration, drainage, and capillary-w ater capacity, all of which are affected by crop residue removal. Preliminary va lues of required tilth have been estimated by the NRCS.

Tillage

Various tillage operations are associated w ith management of ag ricultural crop residues and planting preparation throughout the year. Type and number of tillage operations employed for any particular crop from the time of harvest un til the next planting have a tremendous effect on the amount of soil lost to erosion during the year, and hence, the amount of residue that can possibly be removed for energy purposes. It mu st be noted that even though crop residues may be used for energy purposes, the farmer is, firs t and foremost, in the business of producing grain. Therefore, he will be concerned with using those tillage operations that will provide him with the

G saltation: the movement of sand-sized particles by a skipping and bouncing action in the direction the wind is blowing uizlet.com/print/10948/

21highest possible yield at the next harvest, and not necessarily those that tend to maximize erosion control on his lands. 32

In summary, all agricultura l cropland upon which nearly a ny crop is grown within a particular county can exhibit a wi de variation in soil erodbility, field slope and length, climate conditions, and management practices. Within any one particular c ounty there can be many different soil types (50 or more) used to grow ag ricultural crops. In addition, and possibly more importantly, not all soil types within a county ma y be suitable for agricultural crop production. Some soils possess characteristics that make them highly susceptible to erosion that may not be able to sustain certain cropping practices. Production of conventional agricultural crops on these lands may severely and/or permanently reduce the soil’s ability to provide sustained, economical production. For this reason, the NRCS implemente d a land capability classification (LCC) that ranges from I (one) to VIII (eight) that is applied to all soils within a county.

Organic Matter, Carbon, and Nutrients

With added nitrogen fertilizers, residues can increase soil organic matter (SOM). However, roots appear to be the largest contri butor to new SOM, maki ng residues less important for carbon accrual. Residue removal leading to higher erosion and runoff rates would greatly decrease SOM and nutrients. Residue harvest woul d also require increased fertilizer inputs to make up for nutrients removed in the plant material. When re turned to the land, crop residue also replenishes soil organic carbon (SOC) that typically has alr eady been reduced 30 to 50% of precultivation levels through crop production activities. Soil organic carbon retains and recycles nutrients, improves soil structure, enhances wa ter exchange characteristics and aeration, and sustains microbial life within the soil. It’s been reported that crop yield and the value of environmental services (C and N sequestration) were greater for so ils with greater SOC. Limited research has shown that removing stover reduces grain and stover yield of subsequent crops and further lowers soil organic matter levels. 33

Beneficial and Deleterious Soil Organisms

Residue removal can result in detrimenta l changes in many biological soil quality indicators including soil carbon, mi crobial activity, fungal bioma ss and earthworm populations, indicating reduced soil functi on. Some disease-producing organi sms are enhanced by residue removal, others by residue reten tion, depending on crop and region.

Available Water and Drought Resistance

Residue cover can reduce evaporation from th e soil surface, thereby conserving moisture and increasing the number of days a crop can survive in drought conditions. Improved soil physical properties related to cr op residues, such as reduced bulk density, e.g., the soil is looser and lighter, and greater aggregate st ability, also lead to better wa ter infiltration and retention.

22Soil Temperature and Crop Yield

In colder climates, residues are linked to re duced yields due to lower soil temperatures resulting in poor germination. Stubble mulc hing, as opposed to residue chopping, can help overcome this problem. Even though residue-a ssociated yield reductions have been found on poorly drained, fine–textured soils, these soils of ten have low erosion ri sk and residues might safely be removed.

Summary

Despite the many important benefits of crop residues, research shows their effects can vary. For instance, some reports showed lower yields in systems with high crop residues due to increased disease or poor germination; others reported higher yields when soil moisture is limiting. Other studies suggest that residues do not contribute significantly to soil carbon. Many studies found that additional N fe rtilizer is needed when residues are left on soils to avoid N uptake (immobilization) from soil or allow for soil carbon accrual. Fo r appropriate residue removal recommendations, the conditions leading to these varied effect s of residues must be elucidated.

Soil health as related to residue removal is an extremely complex issue for which, as yet, there are no specific guidelines for residue remo val. Wrong decisions, ca rried out over extended periods could have far reaching deleterious effects. Sustainable residue re moval rates for biofuel production vary by system, according to such fact ors as management and cropping practice, crop yield, climate, topography, soil type and existing soil quality. Keeping in mind that gravimetric rates are not the same as percent soil cover (% mass is not the same as % coverage), appropriate conversion is necessary and varies by crop a nd region. While areas with low slopes and high yields may support residue harvest, in many area s the residue amounts required to maintain soil quality could be even higher than current practices. What is meant by ‘high’ and ‘low’ slopes has yet to be absolutely determined, which dete rmination also depends on soil type and other cropping practices. Removal rates wi ll need to be reduced as clim ates become warmer or more humid, for lower C:N residue or lo wer yielding crops, as soil distur bance (e.g. tillage) increases, or as soils become coarser textured, compared to the conditions in which most studies occurred (in the U.S. Midwest Corn Belt for no-till corn). 34 The most important aspect of this is that any or all of the interacting variables that dete rmine how much residue can be removed, can, and usually do, change from year-to-ye ar, across both wide re gions of the country as well as across single counties and farms. A change in one variab le nearly always changes how all the variables interact.

Given all the issues we’ve discussed regard ing residue removal and soil health, rather than try to predict, county-by-county how much residue will be available, we assumed in our FASOM modeling that the available amount will be somewhere between 0% and 50%, at least until the issues we’ve been discus sing are settled. We based the amount removable based on the tillage practice: 0% removed for conventional ti llage, 35% removed for conservational tillage, and 50% removed for no-till for corn stover. 35 Removal rates for wheat straw were based on the billion-ton study. 36 We believe that given the uncertaintie s in removal rates, our assumptions are reasonable.

231.1.2.2 Forest Residues

There is a substantial amount of forestland here in the U.S. It is estimated that 749 million acres, or one-third, of the U.S. land area is forested. Of this forested land, two-thirds (504 million acres) is considered timberland which contains more than 20 ft 3 of woody material per acre – the other one-third of the fo rest land contains less than 20 ft 3 of woody material per acre. Most of this forested land, 58 percent, is privat ely owned, another 29 percen t of the forest land is publicly owned, and 13 percent is owned by the forest industry. A higher percentage of the land is privately owned in the East, and a higher percentage of the land is publicly owned in the West.

Of the 749 million acres of forestland, 77 are re served as parks or wilderness and would likely be considered off limits for harvesting for biomass. Also, 168 million acres of timberland is believed to be not suitable for harvesting for biomass because of poor soil, lack of moisture, high elevation, or rockiness. 37

The U.S. forestry industry harvests a portion of this forest land to produce its products, and in the process of doing so, it generates woody residues that can be recovered for the purpose of producing cellulosic biofuels. Major sources of solid wast e wood generated in the U.S. include forestry residues, primary and secondary mill residues, and urban wood residues. In addition, forests which are not currently harveste d for wood could be thin ned. This thinning of the forests would not just be to provide biomass, but as part of a strategy which may be beneficial for the forests, or to avoid external costs such as forest fires. Each of these categories is further described below:

Forestry residues

In-forest operations generally include four major sources of materials: logging residues, other removals, fuelwood, and fuel treatment wood. 38 In the process of removing, or logging, the larger woody portion of the trees (5 inch diameter and greater), the logging industry creates logging residues. Logging residues typically include tops of harvested trees and unwanted trees cut or knocked down and left on site, including dead and cull trees. Other removals are growing stock and other sources cut and burned or otherwis e destroyed in the process of converting forest land to non-forest uses, such as for making way for new housing or industrial developments. They also include growing stock removed in fore stry cultural operations. Forest residues are also available from fuelwood, which is harveste d wood used in the residential and industrial sectors for energy. Thus, forest residues are already being created or harvested today.

Primary and secondary mill residues

Harvested wood from forests is converted into consumer products at wood processing mills. Primary mills convert roundwood products (i.e., tree trunks and logs) into other wood products, including sawmills that produce lumber, pulp mills, veneer mills, etc. Secondary mills use the products from primary mills to produce ot her products such as millwork, containers and pallets, buildings and mobile homes, furniture, flooring and paper and paper products. While primary and secondary mills are typically separa te facilities, both primary wood processing and secondary conversion to finished consumer products can occur in the same facility. 39

24Both primary and secondary mills produce residue and waste woody material. For example, the residue generated by primary mills includes bark, slabs and edgings, sawdust and peeler cares. This waste material could be used as feedstock to produce biofuels.

Urban wood residues

The two principal sources of urban wood resi dues are municipal solid waste (MSW) and construction and demolition (C&D) debris. Munici pal solid waste contains solid wood from both wastewood and yard trimmings. Yard trimmings include herbaceous material and woody trimmings. Information on the composition of MS W is limited. However, it has been assumed in one report that approximately two thirds of the total volum e consists of woody material. 40 Construction waste is made of contemporary building materials with little contamination. Sources include new residential construction, new nonresidential building construction and repair and remodeling of existing buildings. Demolition waste, on the other hand, is a heterogeneous mixture of material from demolishi ng buildings and structures and is difficult to separate as it is often contaminated. The pot ential contribution of urba n wood residues to the production of biofuels will be addresse d in the section 1.1.2.3 of this DRIA.

The Thinning of Forests

While the above categories are associated with existing forest harves ting or other removal activities, the thinning of forests would largel y be a new activity. Many U.S. forests have become overgrown and very dense with forest ma terial, and a portion of this overgrown forest will die, dry out and decay. This decaying forest material can provide a source of fuel for forest fires that are expensive to fight or contain. Over the previ ous 10 years forest fires have consumed 49 million acres and cost the U.S. taxpayer $8.2 billion. 41 This cost does not include the additional cost due to the loss of human lif e, the loss of personal property and the impact on the environment. Thinning forests involves the removal of excess forest material from the forests that could help to prevent some of these forest fires, or at le ast help to reduce their impact. Also, thinning these forests to pr event them from beco ming overly dense could potentially help them to remain healthier. There are many thinning operations today, but the material is burned or left to decompose in stead. The removed excess woody material from overgrown forests could provide a sour ce of biomass for producing biofuels.

Despite the availability of woody residues fo r producing cellulosic biofuels, there are several obstacles for woody residues that are not present when utilizing feedstocks such as agricultural residues. For instance, forestlands will likely be managed less intensively than agricultural lands because forests provide multiple -use benefits (e.g., wild life habitat, recreation, and ecological and environmental services). 42 This in effect makes it more difficult to take steps to increase the productivity of fo rest areas. Also, there are fact ors or site conditions that can affect tree growth, including poor soils, lack of moisture, high elevation, and rockiness. The limits caused by some of these factors would likely not be overcome, resulting in lower productivity than what could be theoretically possible. Also, a couple of these factors, the high elevation and rockiness, results in areas of forestland which is inaccessible by forestry equipment. Forestry residues are also demande d for other purposes other than for production of

25a transportation fuel (e.g. for process fuel). These reasons would make it more challenging to collect and use woody residues in large quanti ties compared to agricultural residues.

On the other hand, there may be some bene fits to the use of woody residues. One example is the removal of excess forestry biomass to reduce the risk of fi res and/or to improve forest health. In addition, resources such as primary and secondary mill residues and urban wood residues are alread y collected at the processing facil ity and it seems probable that some cellulosic facilities could be co-located to m ills and/or landfills to in crease the likelihood of having close and steady feedstocks readily available. Some states may also be endowed with larger wood resources than agricultural residues.

The following sections further describe some of the additional factor s to consider when determining the potential availa bility of woody feedstocks.

Harvest, Transport, and Storage

The largest portion of woody material to pr oduce biofuels is avai lable as forestry residues. However, unlike residues such as primary or secondary mill residues and urban wood residues that could be available on-site at a processi ng facility, forestry residues would need to be collected and transported similarly to conve ntional forest products. The amount of residues potentially available is a function of harvest amount, logging method, and type and location of timberlands. 43 In addition, residue availability is lim ited by economic factors. According to one study, “the actual operations of harvesting, colle cting, processing and tran sporting loose forest residues are costly and present an economic barrier to recove ry and utilization of wood for energy”. 44 Thus, there are still challenges that need to be addressed before large-scale use of forestry residues is possible.

Currently, the most cost-effective method of r ecovering forest residue for biomass is inwoods chipping. 45 This method is suitable for operations where there is w hole-tree skidding to roadside, good road access to chip vans and ch ippers, and sufficient biomass volume per acre. However, in-woods chipping systems are not as effective when ground-based skidding is restricted or when there are no merchantable prod ucts other than biomass. In addition, the chip vans designed to haul wood chips were built fo r highway use and often do not have sufficient suspension systems for remote forest roads. Th ere are also high costs for wood grinders with low production rates. 46 Fortunately, there have been deve lopments in alternative methods to reduce the costs of bioma ss collection systems.

Much of the focus has been on developing me thods of densifying residues in order to increase productivity of hand ling operations (i.e. hauling, skidding, and loading). New approaches to removing forestry residues are currently being evaluated (e.g. slash bundling machines, horizontal grinders, and roll on/off cont ainer transport). One of the advantages of using slash bundling machines, for example, is the ability to store biomass longer than in chip form. Storing biomass at roadside in the form of biomass bundles could provide a more secure and stable biomass supply than with chips which are smaller and have greater surface area for potential weathering. The use of horizontal grinders was also f ound to be the best at reducing

26bundles. Utilizing roll on/off containers allows fo r recovery of residue from difficult-to-access locations and in such situations could be co mpetitive with regular highway chip vans.

While these are just some of the ways to improve recovery operations for forestry residues, these methods still have challenges. For example, there are some difficulties with bundling of brittle residues or short, large di ameter pieces. In addition, some residues may include rocks or trash that can result in additional saw mainte nance and reduced utilization. With millions of acres of forest, there is no single residue treatment option that will meet the needs of all situations. Forest land managers w ill need to weigh the different options for dealing with forest residues to determine the most co st-effective means for residue removal in their specific locations.

In making estimates of potential forest resi due availability, certain assumptions about accessibility and recoverability are typically made. For example, some studies assume that residue collection is completed at the same time as harvesting, meaning that all residues are regarded as one hundred percent accessible. 47 This might become possi ble due to integrated harvesting systems which could harvest forest biomass in a single pass operation such that residual forest residue for producing biofuels co uld be produced along with conventional forest products. 48 Other estimates for accessibility have been lower, with about sixty percent of North American temperate forest considered accessible (not reserved or high-elevation and within 15 miles of major transportation infrastructure). 49 In terms of recoverabi lity, some studies have assumed sixty-five percent of logging residues and fifty percent of other removal residues as being recoverable while others re port an average potential recovery of sixty percent and as much as sixty-five percent when utilizing newer technology. 50

Sustainable Removal

While there has been some discussion of su stainable removal practices for crop residues, there has been less review on the topic for woody residues. As forest residues have been traditionally left in the forest to decompose, there remains much to be learned about the harvesting of forest residues in a sustainable way that still leaves sufficient nutrients to maintain the forest and to replenish the soil. This is reiterated in reports on woody residue removal which emphasize the need for more detailed studies on th e range of ecological eff ects, from wildlife to soils.

Currently, practices for how mu ch forest residue shall be maintained in the forest to maintain forest health vary substantially. For example, a district for one study on the removal of forestry residues required about 5 tons per acre be left whereas othe r districts had no such requirements. 51 In a different source, a summary of na tional forest land management plans from 1995 indicated about 60 percent of western national fore st timberland base to be suitable for timber production operations. 52 This issue is not only applicable in the United States, but also in Europe, where the use of forest biomass for ener gy is also being considered. A Swedish study showed that the main incentive for forest owners not to sell forestry resi dues was concerns for soil fertility. 53 Therefore, although there have been limitations to th e amount of residue suitable for removal there has yet to be consensus over the optimal amount.

27Yet another issue regarding sustainable remova l is the affect of forest residue extraction on biodiversity. The removal of forest residue may affect biodi versity because lower amounts of wood in the forest imply fewer habitats for sp ecies using wood for breeding. Species may also be threatened because certain insects colonize in wood that may be burned for energy purposes. Quantitative predictions about how much habitat loss various species can tolerate are almost impossible to make. Instead, one study recomm ended making qualitative predictions on which types of habitats or wood types are most threat ened. For instance, this study examined Sweden’s forest fuel extractions and conc luded that coniferous wood can be harvested to a rather large extent, whereas deciduous tree species s hould be retained to a larger degree. 54 As different regions will certainly have species specific to th eir own regions, more research is necessary to determine appropriate recommendati ons on maintaining biodiversity.

Another issue that has been considered is the occurrence of soil disturbance due to the use of forest residue collection equipment. Studies have shown that the growth of woody plants and yields of harvestable plant products are decr eased by soil compaction from residue collection equipment, because of the combined effects of high soil strength, decrease d infiltration of water and poor soil aeration. 55 In another study, the use of a residue bundling machine caused some measurable amounts of soil disturbance and an increase in “soil e xposed” area at some locations. 56 Thus, it is important to limit the severity of soil disturbances with minimal passes and relatively low ground pressure.

Energy Content of Forest Residue and Biofuel

Woody material obtained by the harvesting or thinning of forest is somewhat more energy dense compared to other forms of biomass. On its Biomass Program webpage, the Department of Energy lists the higher heating valu es (lower heating values were not available) for many different types of biomass for dry samples. 57 These values for woody biomass are summarized in Table 1.1-7.

Table 1.1-7. Energy Content of Forest Material

Tree name Higher Heating Value (BTU/lb dry wood) Hybrid Popular 8,384 - 8,491 Black Locust 8,409 - 8,582 Eucalyptus 8,384 - 8,432 American Sycamore 8,354 - 8,481 Eastern Cottonwood 8,431 Monterey Pine 8,422

Because woody material is energy dense, it can produce a large amount of renewable fuels per ton of feedstock. Based on the data in FASOM, we assumed that 89 gallons of ethanol could be produced per each ton of dry ( 15 percent moisture) w oody feedstock in 2022.

28Quantity of Forest Residue

The quantity of forest residue availabl e to produce biofuels was estimated by two different studies. We summarize those two studi es, and then summarize data which we received directly from the U.S. Forest Service

Billion Ton Study

A landmark assessment of the potential bioma ss available from existing forest land in the U.S. was recently conducted by the USDA and the Department of Energy (DOE). 58 This landmark assessment was titled “Biomass as Feedstock for a Bioenergy and Bioproducts Industry: The Technical Feasibil ity of a Billion Ton Supply,” whic h is also known as the Billion Ton Study. We reviewed this study and are summa rizing much of the information contained in that report here because it is very useful bac kground about U.S. forest land and its potential contribution to biofuels.

The total forest inventory is estimated to be about 20.2 billion dry tons. The report authors estimated that about 2.2 percent of the to tal forest inventory is harvested each year, which corresponds to 444 million dry tons. This removal rate is estimated to be less than the annual average forest growth, which suggests, at least on an aggregate basis, that this removal rate is sustainable. It is estimated that 78 percent of this removal was for roundwood products (sawlogs, pulpwood, veneer logs and fuel w ood), 16 percent was loggi ng residue and about 6 percent was classified as other removals. Thus , the Billion Ton study authors estimate that 67 million dry tons of logging residue would potenti ally be available for biofuel production, which is comprised of 49 million dry tons of primary logging residue, and 18 million dry tons of other removals. The Billion Ton study estimates that 65 percent of the total logging and other residue would be recovered for use. The two reasons cite d for not collecting the ot her 35 percent is that some of the logging residue is comprised of sm all pieces, such as small branches and leaves, which would not be economically recoverable, and that it would be n ecessary leave behind a portion of the logging residue would be to protect the sustainability of the forest as well as the wildlife which thrives in the forest. For thes e reasons, the Billion T on Study authors estimated that 41 million dry tons of forest residue could be sustainably removed from the U.S. forests as byproduct from existing logging operati ons. Virtually all this rem oval is from privately owned land where the logging operations occur today.

Additional forest residue is available downstream of the l ogging operations at mills. In the process of making their products, primary wood processing mills create some waste or residue. However, almost all of this waste w ood is recovered or burned for process heat. For example, the bark from the logged wood is burned as fuel or converted into mulch. However, the Billion Ton authors estimated that just under 2 million dry tons per year of residue would be available from the primary wood processing m ills as feedstock for producing biofuels.

The Billion Ton study estimates that additiona l wood waste could also be available from secondary wood processing mills, which refine crude wood into more refined products. The report authors could not find a ny data on how much residue is produced by these secondary wood processing mills, however, a study of these facilities did provide an estimate.

29Approximately 15.6 million dry tons per year woul d be available from the smaller of these secondary wood processing mills, however, the report estimated that only 40 percent, or 6 million dry tons per year, would be available for biofuels production.

Another industry which processes the harvested wood is the pulp and paper mills. These companies process wood into fi ber to make paper and cardboard. Most of the pulp and paper mills use the Kraft process or sulfate pulping pr ocess which converts half of the woody material into fiber, while the other half is a byproduct termed black liquor. The black liquor contains a substantial amount of biomass. The pulp and pape r industry is already us ing all of this black liquor, plus purchasing and using so me fossil fuels, to generate th e electricity and heat that it needs for its plants. Therefore, the authors of the Billion Ton Study estimated that there would not be any residue available from the pul p and paper industry to produce biofuels.

The Billion Ton study estimates that anothe r potential source of biomass from forests would be the selective thinning of forests to help reduce the risk of fire, or to facilitate the fighting of fires in the case that fires break out (as discussed a bove). Using a forest evaluation tool called the Fuel Treatment Evaluator, the Forest Service has estimated tree densities for forests all across the U.S. and has identified fo rests which contain excess woody material. The forests which contain excess woody material are candidates for pr oviding additional biomass for producing biofuels. The Forest Service has es timated that the tota l amount of excess woody material to be 8.4 billion dry tons.

The Forest Service next estimated the portion of this excess woody mate rial that could be harvested for biofuels production. Despite the fact that this inventory ex ists today, the Billion Ton Study authors assumed that this excess wo ody inventory would be used over a 30 year period to reflect a sustainable removal rate. Th is assumption reduces the total yearly available amount of excess woody biomass to 280 million dry t ons per year. Another limiting factor is that much of our nations forest is remote, thus, only 60 percen t of this excess woody material was estimated to be removable for use. The next assumption made is that the best of this woody material, which is the woody material more than 5 inches in diameter and which comprises 70 percent of this material, would be used for feedstock for the logging industry. Thus, the remaining 30 percent would be residue that would serve as feedstock for the biofuels industry. Finally, the last assumption made is that of the excess woody material harvested, 15 percent would be lost between harvesting and use, thus the total amount of w oody biomass was adjusted to be 15 percent lower. These assumptions re sult in 18 million dry t ons of additional woody biomass that could be used to supply the biofue ls industry annually, and 42 million dry tons that would supply the logging industry.

As shown below in Table 1.1-8, the Billion T on Study estimates that a total of 67 million try tonnes per year would be av ailable from non-urban forests.

30Table 1.1-8. Quantity of Forest Biomass Available for Producing Biofuels Quantity (million dry tons) Logging Residue 41 Primary Mill Residue 2 Secondary Mill Residue 6 Forest Thinnings 18 Total 67

The Billion Ton Study authors projected that forest harvesting and mill activity will increase in the future, thus increasing the amoun t of forest residues that would be available for producing biofuels. The authors estimated the fu ture forest residue supply in the year 2050 and concluded that the logging residue is expected to increase from 41 million dry tons to 64 million dry tons. Also in 2050, the primary and secondary mill residue quantity is projected to increase from a total of 8 million dry tons per year to a total of 24 million dry tons per year. No estimate was provided for any increase, or decrease, in the amount of forest woody ma terial that would be available from thinning forests. If the projec ted 39 million dry ton increases in forest residue comes to fruition, then the total amount of forest residue that would be available for producing biofuels in 2050 would be 106 million dry tons per year. We are prim arily interested in compliance with the RFS2 biofuels st andard in 2022, which is just over 1/3 rd of the way between today and 2050. Thus, by interpolating the projec ted future forest resi due in 2022 relative to current levels and those in 2050, the report suppo rts the conclusion that 79 million dry tons of forest residue would be available in 2022 .

U.S. Cellulosic Biomass Study

Another estimate for the amount of forest resi due that could be used to produce biofuels was made by Marie Walsh, formerly of th e Oak Ridge National Laboratory (ORNL). 59 The report was titled “US Cellulosic Biomass Supplies and Distribution” and it is dated January of 2008. This report also uses the Forest Service da ta base for its estimates, so its conclusions resemble those of the Billion Ton study. Howe ver, an important difference between this Cellulosic Biomass Study and the Billion Ton Study is that Marie Walsh estimated a cost curve for amount of biomass available for her Cellulo sic Biomass study for multiple future years.

In this report, Marie Walsh estimates th at 63 million dry tons of logging residue is created in the lower 48 states. Of this total amount of logging re sidue, 65 percent is estimated to be accessible by roads, and not all the accessible logging residue is considered recoverable because some of it is too small to recover. This study also estima tes the cost for recovering this available logging residue for future years for five year intervals th rough 2030. The amount of logging residue available at different price points and for different years is summarized in Table 1.1-9.

31Table 1.1-9. Quantity of Logging Residue Available at Varying Prices

$20/dt $25/dt $30/dt $35/dt $40/ dt $45/dt $50/dt $75/dt $100/dt 2007 0.06 1.84 6.22 10.89 24.02 31.29 31.29 36.19 38.50 2010 0.065 1.81 6.41 13.23 29.37 38.70 38.70 45.02 47.89 2015 0.065 1.95 6.80 13.62 29.99 39.35 39.35 45.71 48.60 2020 0.067 2.10 7.22 14.41 31.51 41.20 41.20 47.79 50.77 2025 0.067 2.17 7.46 14.81 32.32 42.19 42.19 48.90 51.95 2030 0.068 2.25 7.70 15.22 33.12 43.17 43.17 50.01 53.13

Marie Walsh also identified the quantity of woody material that would be available at specific prices from other removal supplies – trees removed to make way for the construction of buildings. Marie Walsh estimates that a total of approximately 24 million dry tons of forest residue falls within this category. She estimated that perhaps 50 percent of this material would be available for biofuel production. Marie Wa lsh added the other removal supplies to the logging residue and estimated their availability at different price points, increasing the available biomass by 25 percent. The combined total is summarized in Table 1.1-10.

Table 1.1-10. Quantity of Forest Residue and Other Removals Available at Varying Prices

$20/dt $25/dt $30/dt $35/dt $40/ dt $45/dt $50/dt $75/dt $100/dt 2007 0.09 2.63 10.49 15.16 32.16 41.62 41.62 47.71 50.49 2010 0.09 2.63 10.76 17.59 38.08 49.17 49.17 56.68 60.03 2015 0.09 2.79 11.26 18.08 38.87 50.00 50.00 57.56 60.93 2020 0.09 2.96 11.80 19.00 40.58 52.04 52.04 59.84 63.31 2025 0.10 3.07 12.15 19.50 41.56 53.21 53.21 61.15 64.68 2030 0.10 3.17 12.51 30.02 42.55 54.39 54.39 62.47 66.07

This report also estimates the amount of pr imary and secondary mill residues available for biofuels production. Like the Billion Ton stud y, Marie Walsh also concludes that only a very small amount of primary mill residue is estimat ed to be currently unused and available for producing biofuels. She concludes that out of the 88.7 million dry tons of primary mill residue which are generated, that only 1.3 million dry tons is not used for fuel, fiber or other sources as discussed above. However, she provides an addi tional assessment that, at the right price, the primary mill residue could be drawn away from these other users of the primary mill residue. The assumption is that for fiber uses, the prim ary mill residue could be drawn away from the current users at 35% of the product price. For othe r uses, including for fuel, it is assumed that at 65% of the market price of the raw wood value, the primary mill residue could be purchased away from the current users. Table 1.1-11 belo w estimates the price that specific estimated primary mill residue volumes could be available for producing biofuels.

32Table 1.1-11. Quantity of Primary Mill Residue is Available at Varying Prices

$20/dt $25/dt $30/dt $35/dt $40/ dt $45/dt $50/dt $75/dt $100/dt 2007 0.43 4.93 6.03 19.34 20.14 41.46 42.38 50.31 51.04 2010 0.55 5.70 7.29 21.91 22.80 46.03 47.37 56.29 57.33 2015 0.56 5.93 7.51 22.88 23.77 48.00 49.34 58.55 59.61 2020 0.58 6.16 7.74 23.85 24.73 49.97 51.31 60.82 61.88 2025 0.59 6.34 7.93 24.58 25.47 51.46 52.82 62.55 63.61 2030 0.60 6.52 8.12 25.31 26.20 52.96 54.31 64.28 65.35

The author also attempted to estimate the amount of secondary mill residue that could be available for producing biofuels. She observed th at data is scant on the amount of secondary mill residue. She referenced a study (Rooney, 1998) that estimated that only a very small volume of secondary mill residue would be availa ble for producing biofuels. Of 12.5 million dry tons of secondary mill residue which is generated, only 1.2 million dry tons is available for producing biofuels. Unlike the analysis conducte d for primary mill residue, the author did not attempt to estimate the extent that biofuels producers could bid the secondary mill residue away from the current users.

Marie Walsh also assumes that three very di fficult-to-quantify sources of forest material could be available as biomass for producing biofuels. One of these potential sources is the forest material that could be available through the thinning of overgrown fo rests to help reduce the fire risk within these forests. Marie Walsh refe renced one study which estimated that 100 to 200 million acres of overgrown forest could be harvested. No estimate, however, was provided for the amount of this forest material that c ould be available from forest thinning.

Another potential source of fo rest material for biofuel pr oduction that the study discussed is a portion of the estimated 35.4 million tons of fuel wood used to heat homes and to provide heat for industries. The author cited a report wh ich estimated that fuel wood use decreased from 1986 to 2000, but began to increase again and is expected to increase through 2050. This presumably means that if the demand for fuel w ood is lower than previously, that some of that fuel wood could be available for producing biofuels . However, in this report, Marie Walsh did not make any firm estimate for this.

The Marie Walsh report also discussed that forest pulpwood supply is exceeding demand in the Southeast. The demand of forest pul pwood decreased from 131 to 121 million tons per year from 1993 to 2003, and this demand is exp ected to further decrease through 2020, and some have projected that this decrease in de mand will continue beyond 2020. During the period between 1993 and 2003, pulpwood acreage and manage ment intensity have increased, which suggests that the Southeast is a nd will continue to be over supp lied. This oversupply of forest pulpwood could potentially provide additional biomass to the biofuels industry, although she did not provide any firm estimate for this.

While both of these studies pr ovide quality assessments for the total amount of forest residue available for producing biomass, they bo th have an important limitation as well. The

33limitation is that these reports did not assess whet her the forest residue in any particular area, along with other potential bioma ss, is of sufficient density to adequately supply a potential cellulosic biofuel plant. This feedstock density assessment must also consider the feedstock availability requirements made by cellulosic pl ant investors or banks, which may choose to require that a certain excess amount of feedstock be available to justify the use of that biomass in a cellulosic ethanol plant. Without considering these limitations, these studies may overestimate the quantity of biomass that would be truly usable and also the ultimate amount of biofuel that could be produced.

U.S. Forest Service Data

To assess forest residue suppl y within the feedstock density and supply constraints, we obtained county-by-county forest residue data from the U.S. Forest Service. 60 The information was provided by the subcategories of logging residue, primary mill residue, timberland thinnings, and other removals. The information also included urban forest residue, however, because that material is included with the other MSW, we did not consider it here. Like the studies discussed above, the national forest lands are omitted from consideration, and the urban forest residue is not considered here, but in the section discus sing MSW. The information was also provided at different price points. The quantities of forest residues are summarized by source type in Tables 1.1-12, 1.1-13 and 1.1-14. To avoid presenting a huge amount of data, we aggregated the county data by state, and we are presenting the data at sp ecific price points: $30/dry ton, $45/dry ton and $70/dry ton.

34Table 1.1-12. Volume of Forest Residue Available for Producing Biofuel Biomass Available at $30/ton Logging Residue Other Removals Timberland Thinnings Primary Mill Residu e Total Quantity Alabama 2,405,083 507,240 867,038 7,117 3,786,478 Arizona 17,698 44,871 66,171 1,351 130,091 Arkansas 1,703,543 770,985 738,165 12,889 3,225,582 California 669,740 0 1,742,702 65,088 2,477,530 Colorado 18,405 14 0 2,302 20,721 Connecticut 8,391 30,678 20,929 3,949 63,948 Delaware 30,101 24,218 9,835 0 64,155 Florida 1,070,430 515,407 481,893 2,202 2,069,932 Georgia 3,113,907 993,262 1,107,254 45,138 5,259,561 Idaho 253,145 0 83,095 6,006 342,247 Illinois 278,202 235,178 230,863 18,523 762,766 Indiana 562,483 104,173 396,225 10,627 1,073,508 Iowa 112,098 55,160 97,983 159 265,400 Kansas 14,658 88,405 19,353 8,720 131,136 Kentucky 1,027,977 664,358 689,896 55,196 2,437,427 Louisiana 2,634,279 880,585 601,848 30,075 4,146,788 Maine 2,412,877 940 160,628 42,483 2,616,927 Maryland 181,443 830 81,988 17,067 281,327 Massachusetts 70,921 62,087 27,602 0 160,610 Michigan 758,926 244,952 655,280 13,763 1,672,922 Minnesota 697,614 662,985 265,424 26,878 1,652,900 Mississippi 3,097,069 710,142 850,688 95,138 4,753,038 Missouri 774,868 530,292 684,154 79,787 2,069,100 Montana 262,670 0 133,185 9,136 404,990 Nebraska 21,145 18,771 23,414 4,971 68,302 Nevada 29 105 0 0 134 New Hampshire 314,642 348 95,604 7,019 417,613 New Jersey 5,918 77 4,847 1,437 12,279 New Mexico 23,858 2,557 51,796 4,902 83,113 New York 734,006 109,342 326,672 27,390 1,197,410 North Carolina 2,026,330 1,259,265 1,121,627 12,811 4,420,033 North Dakota 2,906 15,202 7,644 265 26,017 Ohio 370,795 18,106 167,351 22,600 578,853 Oklahoma 347,738 197,587 106,086 495 651,906 Oregon 1,520,552 63 1,055,405 16,316 2,592,335 Pennsylvania 1,087,327 1,372 449,956 170,972 1,709,626 Rhode Island 1,769 45,721 5,600 389 53,478 South Carolina 1,429,102 696,577 603,700 1,051 2,730,431 South Dakota 13,944 28,873 5,986 2,294 51,096 Tennessee 633,412 489,840 847,812 187,583 2,158,647 Texas 1,233,553 436,927 371,437 3,021 2,044,938 Utah 5,946 0 19,817 4,437 30,200 Vermont 209,752 37,304 96,790 0 343,845 Virginia 1,483,346 813,600 873,740 39,366 3,210,052 Washington 1,282,288 44 1,850,958 21,446 3,154,736 West Virginia 976,712 49,428 323,306 118,779 1,468,225 Wisconsin 1,137,600 982,264 520,587 60,410 2,700,862 Wyoming 22,685 0 28,100 34,014 84,799 Total 37,061,885 12,330,137 18,970,435 1,295,560 69,658,018

35Table 1.1-13. Tons of Forest Residue Available for Producing Biofuel Biomass Available at $45/ton Logging Residue Other Removals Timberland Thinnings Primary Mill Residue Total Quantity Alabama 2,405,083 507,240 1,012,090 7,117 3,931,530 Arizona 27,131 49,020 69,934 1,351 147,436 Arkansas 1,703,543 770,985 858,827 12,889 3,346,244 California 1,166,955 0 1,898,937 65,088 3,130,980 Colorado 20,112 22 61,238 2,302 83,674 Connecticut 8,601 32,190 20,929 3,949 65,670 Delaware 35,863 28,290 13,400 0 77,554 Florida 1,070,430 515,407 533,194 2,202 2,121,234 Georgia 3,113,907 993,262 1,288,591 45,138 5,440,898 Idaho 432,605 0 105,188 6,006 543,799 Illinois 278,305 235,178 230,863 18,523 762,869 Indiana 562,928 104,173 443,691 10,627 1,121,419 Iowa 112,100 55,215 99,102 159 266,576 Kansas 14,658 88,405 19,353 8,720 131,136 Kentucky 1,027,977 664,358 814,743 55,196 2,562,273 Louisiana 2,634,279 880,585 661,023 30,075 4,205,963 Maine 2,561,023 989 204,885 42,483 2,809,379 Maryland 189,159 842 81,988 17,067 289,054 Massachusetts 78,254 66,382 27,602 0 172,239 Michigan 783,465 257,201 820,603 13,763 1,875,033 Minnesota 717,037 683,787 319,980 26,878 1,747,683 Mississippi 3,097,069 710,142 935,870 95,138 4,838,219 Missouri 774,868 530,292 932,163 79,787 2,317,110 Montana 431,194 0 141,549 9,136 581,879 Nebraska 21,419 18,867 23,414 4,971 68,672 Nevada 44 142 0 0 186 New Hampshir e 331,037 395 115,132 7,019 453,583 New Jersey 6,368 79 4,847 1,437 12,731 New Mexico 34,478 2,575 53,724 4,902 95,679 New York 768,914 113,104 379,391 27,390 1,288,799 North Carolina 2,026,330 1,259,265 1,336,840 12,811 4,635,245 North Dakota 2,907 15,202 7,644 265 26,018 Ohio 372,045 18,139 177,144 22,600 589,927 Oklahoma 347,738 197,587 125,400 495 671,220 Oregon 2,502,187 68 1,095,253 16,316 3,613,824 Pennsylvania 1,092,836 1,386 604,355 170,972 1,869,549 Rhode Island 1,913 50,079 5,600 389 57,981 South Carolina 1,429,102 696,577 704,036 1,051 2,830,767 South Dakota 23,743 31,161 6,505 2,294 63,704 Tennessee 633,412 489,840 1,015,395 187,583 2,326,230 Texas 1,233,553 436,927 438,374 3,021 2,111,876 Utah 7,515 0 21,571 4,437 33,524 Vermont 217,084 38,363 107,673 0 363,120 Virginia 1,483,346 813,600 1,048,745 39,366 3,385,057 Washington 2,135,174 46 1,963,678 21,446 4,120,344 West Virginia 976,712 49,428 482,367 118,779 1,627,287 Wisconsin 1,153,876 998,604 654,054 60,410 2,866,943 Wyoming 36,327 0 36,405 34,014 106,745 Total 40,084,609 12,405,402 22,003,291 1,295,560 75,788,862

36Table 1.1-14. Tons of forest residue Availa ble for Producing Biofuels Biomass available at $70/ton Logging Residue Other Removals Timberland Thinnings Primary M ill Residu e Total Quantity Alabama 2,405,083 507,240 1,163,309 7,117 4,082,749 Arizona 27,131 49,020 77,357 1,351 154,859 Arkansas 1,703,543 770,985 984,188 12,889 3,471,605 California 1,166,955 0 2,001,231 65,088 3,233,274 Colorado 20,112 22 61,238 2,302 83,674 Connecticut 8,601 32,190 20,929 3,949 65,670 Delaware 35,863 28,290 13,400 0 77,554 Florida 1,070,430 515,407 664,706 2,202 2,252,745 Georgia 3,113,907 993,262 1,553,823 45,138 5,706,130 Idaho 432,605 0 123,852 6,006 562,463 Illinois 278,305 235,178 230,863 18,523 762,869 Indiana 562,928 104,173 443,691 10,627 1,121,419 Iowa 112,100 55,215 99,102 159 266,576 Kansas 14,658 88,405 19,353 8,720 131,136 Kentucky 1,027,977 664,358 927,808 55,196 2,675,339 Louisiana 2,634,279 880,585 750,104 30,075 4,295,044 Maine 2,561,023 989 332,233 42,483 2,936,728 Maryland 189,159 842 81,988 17,067 289,054 Massachusetts 78,254 66,382 27,602 0 172,239 Michigan 783,465 257,201 1,066,214 13,763 2,120,643 Minnesota 717,037 683,787 401,197 26,878 1,828,900 Mississippi 3,097,069 710,142 1,033,196 95,138 4,935,545 Missouri 774,868 530,292 1,287,857 79,787 2,672,803 Montana 431,194 0 166,045 9,136 606,375 Nebraska 21,419 18,867 23,414 4,971 68,672 Nevada 44 142 0 0 186 New Hampshire 331,037 395 116,195 7,019 454,646 New Jersey 6,368 79 4,847 1,437 12,731 New Mexico 34,478 2,575 64,375 4,902 106,330 New York 768,914 113,104 385,701 27,390 1,295,109 North Carolina 2,026,330 1,259,265 1,600,910 12,811 4,899,315 North Dakota 2,907 15,202 7,644 265 26,018 Ohio 372,045 18,139 177,144 22,600 589,927 Oklahoma 347,738 197,587 163,268 495 709,088 Oregon 2,502,187 68 1,133,187 16,316 3,651,758 Pennsylvania 1,092,836 1,386 680,995 170,972 1,946,189 Rhode Island 1,913 50,079 5,600 389 57,981 South Carolina 1,429,102 696,577 791,111 1,051 2,917,842 South Dakota 23,743 31,161 8,258 2,294 65,457 Tennessee 633,412 489,840 1,033,100 187,583 2,343,935 Texas 1,233,553 436,927 507,340 3,021 2,180,841 Utah 7,515 0 29,434 4,437 41,386 Vermont 217,084 38,363 142,210 0 397,658 Virginia 1,483,346 813,600 1,260,733 39,366 3,597,045 Washington 2,135,174 46 2,059,970 21,446 4,216,636 West Virginia 976,712 49,428 575,278 118,779 1,720,198 Wisconsin 1,153,876 998,604 841,550 60,410 3,054,440 Wyoming 36,327 0 43,195 34,014 113,536 Total 40,084,609 12,405,402 25,186,746 1,295,560 78,972,317

37The U.S. Forest Service data re veals that there is a lot of forest material in the Southeast, the far Northeast and the Northwest portions of the U.S. The data also shows that the price curve for this forest material is fair ly flat over the range summarized here. This suggests that the forests which are already accessible by roads provide access to low cost forest material from the thinning of timberland. However, to access more and more of the timberland, the costs ramp up quickly to gain access to more of the timberland.

Summary

We compared the quantity of potential bioma ss supplies projected to be available in 2022 by the two studies and the data that the Forest Service provided us in Table 1.1-15.

Table 1.1-15. Forest Biomass Availability in 2022 at Di fferent Prices (million dry tons/yr) Price ($/ton) 30 45 70 Billion Ton Study 79 U.S. Cellulosic Biofuels 20 103 118

Forest Service Data 70 76 79

1.1.2.3 Urban wastes

Cellulosic feedstocks available at the lowest cost to the ethanol producer will likely be chosen first. This suggests that urban waste wh ich is already being gath ered today and typically incurs a fee for its disposal may be among the fi rst to be used. Urban wood wastes are used today in a variety of ways. Most commonly, wastes are ground into mulch, dumped into landfills, or incinerated with other MSW or construction and demolition (C&D) debris. Estimating the amount of urban waste availabl e for biofuel production involves understanding the types of materials that can be found in urban waste, potential competing uses of urban waste, and the challenges with separating a mixed feedstock.

1.1.2.3.1 Municipal Solid Waste

MSW consists of paper, glass, metals, plastics, wood, yard trimmings, food scraps, rubber, leather, textiles, etc. See Figure 1.1- 2 for the percent compos ition of MSW generated (before recycling) in 2006. 61 Construction and demolition debris is not included in the estimate.

38Figure 1.1-2. Total MSW Generation (by Material), 2006 251 Million Tons (Before Recycling).

The portion of MSW containing cellulosic material and typi cally the focus for biofuel production is wood and yard trimmings. In a ddition, paper, which made up approximately 34 percent of the total MSW generate d in 2006, could potentially be converted to cellulosic biofuel. Food scraps could also be convert ed to cellulosic biofuel, howev er, it was noted by an industry group that this feedstock could be more difficult to convert to biofuel due to challenges with separation and the materials degrading.

Although recycling/recovery ra tes are increasing over time, th ere appears to still be a large fraction of biogenic material that ends up unused and in landf ills. In order to project the portion of material that can poten tially be used for biofuel purposes, we must understand how the composition of landfilled material changes over time. To do this, we first analyzed the trends from 1960-2006 for the percent composition of total MSW generated from paper/paperboard, wood, and yard trimmings over time as shown in Table 1.1-16 in order to project the percent composition of total MSW generated for the year 2022 for those categories (i.e. calculated to be 26.9% paper, 5.5% wood, and 12.7% yard trimmings). 62 In general, there appears to be a decrease in the percentage of total MSW gene rated from paper and yard trimmings while the composition of wood relatively remained stable.

Table 1.1-16. Percent Composition of Total MSW Generated (including recyclable material): Paper, Wood, and Yard Trimmings
1960 1970 1980 1990 2000 2002 2004 2005 2006

... 2022 Material Paper/paperboard 34 36.6 36.4 35.4 36.8 35.1 35.1 34.3 33.9

... 26.9 Wood 3.4 3.1 4.6 6.0 5. 5 5.6 5.5 5.6 5.5

... 5.5 Yard Trimmings 22.7 19.2 18.1 17. 1 12.8 13.0 12.7 12.9 12.9

... 12.7

We also analyzed the trends from 1960- 2006 for the percent composition of total MSW discarded (i.e. after recycling has occurred) to project the percent compositions for the year 2022

39(i.e. calculated to be 11% paper, 8% wood, and 2% yard trimmings), see Table 1.1-17 and Table 1.1-18. Comparing Table 1.1-16 and Table 1.1-17, we note that there is a lo wer percent of paper and yard trimmings that is discarded than generated for MSW. This makes sense because a large percentage of these materials are recycled. Ot her than recycling, some MSW material is also combusted for energy use. This material we as sume would be unavailable for biofuel use, and therefore report in Table 1.1-18 the percent composition of total MSW discarded after accounting for both recycling and co mbustion for energy use. Therefore, we have taken into account MSW that could potentially be diverted towards recycling instead of future biofuel production.

Table 1.1-17. Percent Composition of Total MSW Discarded (not including recycled material): Paper, Wood, and Yard Trimmings
1960 1970 1980 1990 2000 2002 2004 2005 2006 Material Paper/paperboard 30.2 33.2 31.7 30.5 29.7 27.3 27.3 25.5 24.3 Wood 3.7 3.3 5.1 7.0 7.0 7.2 7.3 7.4 7.4 Yard Trimmings 24.2 20.5 20. 1 17.9 8.7 9.0 7.0 7.2 7.3

Table 1.1-18. Percent Composition of Total MSW Discarded (not including recycled or combusted mate rial): Paper, Wood, and Yard Trimmings
1960 1970 1980 1990 2000 2002 2004 2005 2006

... 2022 Material Paper/paperboard 30.2 33.1 28.8 26. 1 25.5 23.5 23.5 22.1 21.3

... 11.2 Wood 3.7 3.3 4.6 6.0 6. 0 6.2 6.3 6.4 6.5

... 7.6 Yard Trimmings 24.2 20.4 18.3 15. 3 7.5 7.7 6.0 6.2 6.4

... 1.9

The total amount of MSW generated (prior to recycling) is assumed to increase over time due to population growth. Biocycle magazine ( 2006) reports MSW estimates for each state in the U.S. based off of 2004 population data. 63 We used U.S. Census Bureau population projections by state to scale up or down th e MSW estimates depending on whether the state populations increase or decrea se by 2022. The total amount of MSW generated (prior to recycling) was estimated to be 391 million tons. As we are interested in the volume of MSW available for biofuel use, we focused only on wast e estimated to be landfilled, which is a portion of the total MSW generated. We used estimates on the percentage of MSW landfilled by state from Biocycle in order to estimate the amount of MSW potentially availabl e to biofuels (after recycling).

Knowing the total amount of MSW landfilled is only part of the picture. We also need to understand the types of cellulosic material likel y to make up the MSW landfilled. For this, we were able to gather state com position data (i.e. percent wood vs. paper vs. other materials) of landfills for MSW generated , however, we were in fact intere sted in acquiring state composition 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74 data for the MSW landfilled

. Using the state composition data, we estimated the percent composition of MSW la ndfilled by state using a ratio of percent composition of national material generated (estim ated in Table 1.1-16) a nd landfilled (estimated in Table 1.1-18) and state pe rcent composition data for MSW generated (gathered from the

40multiple state reports). We then multiplied th e volume of MSW (in tons) generated for each state in the year 2022 by the per cent of MSW estimated to be la ndfilled (provided in Biocycle) and by the percent composition of MSW landfilled by state. Some stat es did not provide composition data, therefore, we estimated average percentages based on the states within a similar location in the U.S. where data was pr ovided (e.g. if Utah data was unavailable, we assumed compositions would be similar to other rocky mountain states).

Furthermore, the amount of MSW potentia lly available is limited by assumptions on percent moisture and percent contamination. We assumed that paper, wood, and yard trimmings have a 10%, 20%, and 40% moisture content, respectively. 75, 76 We also assumed that wood is approximately 50% contaminated, due to objects su ch as nails, paint, chemicals, etc. typically associated with such feedstocks. Paper and yard trimmings are assumed to be mostly uncontaminated, assuming 75% uncontaminated. We account for contamination because it is likely to affect the quality of the wood wast e and could potentially cause problems in the processing steps of cellulosic material to biofuel. Thus, for this analysis we assumed that the estimated contaminated portions would not be us ed for biofuel production. In addition, not all yard trimming can be assumed to be wood, only 25% is assumed to be from wood. 77 We estimate that 22 million tons could be available from paper, 0.2 million tons from yard trimmings and 4 million tons from wood.

At the time of this proposal we did not in clude food scraps in our estimates for urban wastes as this source was observe d to have difficulties in separation and more easily degraded, making an assessment of this feedstock complex. Food scraps made up 12.4% of the total MSW generated in 2006, meaning that potentially 31 m illion tons (and perhaps more by the year 2022) could be available for cel lulosic biofuel production.

1.1.2.3.2 Construction and Demolition (C&D) Debris

C&D debris mostly comes from building de molition and renovation, and the rest comes from new construction. 78 Roughly equal percentages of building-related waste are estimated to come from the residential and commercial buildi ng sectors. The composition of C&D materials varies significantly, depending on the type of project from whic h it is being generated. For example, materials from older buildings is like ly to contain plaster a nd lead piping, while new construction materials may contai n significant amounts of drywall, laminates, and plastics. For building materials, EPA estimates the overall percen tage of debris in C&D materials falls within the following ranges:

41Table 1.1-19. Percentage Composition of C&D Debris (by volume) Concrete and mixed rubble 40-50% Wood 20-30% Drywall 5-15% Asphalt roofing 1-10% Metals 1-5% Bricks 1-5% Plastics 1-5%

In 1996, total C&D debris generated was estimated to be approximately124 million metric tons. 79 As seen in Table 1.1-19 above, only a por tion of this, however, would be made of woody material. We based our estimate of C&D wood in 2022 on the equation adopted from Wiltsee’s analysis. 80 The equation estimated C&D wood based on population size. We estimated approximately 31 million tons could be available from this resource by 2022; however, we assumed that 50% of that could potentially be contaminated. Thus, we estimate that only 15.4 million tons would be available for biofuels.

1.1.2.4 Dedicated Energy Crops

Crops developed and grown specifically as a renewable source of ce llulosic material for biofuel production are not yet commercial, but have the potential for negating some of the problems surrounding other feedstocks. Currentl y, crops such as corn that are grown and harvested for energy uses in the United States ar e also used for agricultural purposes and serve many important uses other than biofuel productio n. This competition could be reduced by the use of non-agricultural feedstocks for cellulosic biofuel production. Urban wastes and forest and agriculture residues will likely be the first feedstocks used in cellulosic biof uel production; However, there are many uncertainties over land av ailability and sustainable removal rates for residues.

Most energy crops will be pere nnial species grown from roots or rhizomes that remain in the ground after harvesting the above-ground biomass. While most agricultural crops are annual species, perennials are considered beneficial in many ways. Dedicated perennial energy crops have the potential to grow on marginal lands, produce high yields, and may have low input needs. Once a perennial crop is established costs are reduced, as the need for tillage is lowered. The root system that remains in the soil can al so facilitate the acquis ition of nutrients thus decreasing the need for large fertilizer inputs. In southern climates, pere nnials have the potential for higher yield per acre of land than other annual crops. This is due to the fact that perennial plants develop more quickly in the spring and th e canopy of foliage can sustain for longer in the fall. This makes it possible for the plants to be more photosynthetically active and have a more efficient energy conversion system. Perennial energy crops also in crease soil productivity, sequester carbon, and provide refuge for wildlife.

421.1.2.4.1 Types of Energy Crops

The following sections describe several of the most commonly discussed dedicated energy crops (switchgrass, miscanthus, and hybr id poplars) as well as some less familiarly known crops.

1.1.2.4.1.1 Switchgrass

The energy crop that has received the most attention is switchgrass. Switchgrass ( Panicum virgatum ) is perennial warm season grass that is native to the United States. It typically reaches heights of 3-5 f eet, but can grow to more than 10 feet in some southern regions. It has a deep root system that extends many feet below the earth. It may be the ideal energy crop mainly because it can tolerate many soil types and climates from drought c onditions to floods. It is also resistant to many pests and diseases . The photosynthetic path way of switchgrass (and other perennials) allows it to produce high biomass yields with low amounts of chemical input. In the spring, switchgrass develops a photosynthe tic canopy of biomass more quickly, and it also persists longer in the fall than annual plants, allowing for a high net conversion of solar energy
81 per year.

Highly variable yields have been estimate d at 1-12 dry tons/acre per year (3-30 dry tons/ha) depending on soil, locati on, and variety. A yield of 4-5.5 tons/acre (10-13 tons/ha) is a reasonable commercial average today. 82 In a long term study sponsored by the DOE, average yield after 10 years of growth was 4.8-7.6 t ons/acre (12-19 tons/ha) for switchgrass when harvested annually. H, 83 Biannual harvests were also done e xperimentally to try and achieve the maximum yields possible but show little differen ce in total yield. Biannual harvests result in approximately 70% of the yield for the first cut and 30% for the second. 84

Water and nitrogen availability are the ma in resources that lim it production of warmweather grasses such as switchgr ass. Nitrogen accessibility fo r these plants depends on many factors. Harvesting frequency, soil content, and removal rates all affect the nitrogen available to the plant. In the previously mentioned study by S.B. McLaughlin, initial nitrogen fertilization rates were 40-120 kg/acre (36-107 lbs/acre); however they discove red that a reduction to only 20 kg/ha (17.8 lbs/acre) of n itrogen was sufficient to produce simila r yields in single cut systems in the mid-Atlantic region. 85 Reduced nitrogen amounts were similar in other regions of the country. In comparison, the US fertilization ra te for corn is an average of 138 lbs/acre.

With commercial growth of switchgrass, gr owers would sell the crop for conversion to cellulosic ethanol. Our economic modeling shows that in order to meet the cellulosic biofuel goal of 16 Bgal set by the EISA mandates, dedica ted energy crops will need to be utilized. I

H Switchgrass variety used in this study was Alamo. Other varieties could result in different yields.
I Assuming 16 Bgal cellulosic biofuel total, 2.2 Bgal from urban waste, and 3.8 Bgal from forestry biomass; 10 Bgal of cellulosic biofuel for ag residues and/or energy crops would be needed.

431.1.2.4.1.2 Miscanthus

Miscanthus is a tall perennial grass that has been evaluated as a potential energy crop most extensively in Europe where it is alre ady being produced for biofuel. The genus is primarily tropic or sub-tropic in origin but ther e is a wide climactic range at the species level. 86 This characteristic makes it more suitable for establishment over the ranging climates of North America. Giant miscanthus ( Miscanthus x gigantus ) is a hybrid variety that can grow 12-14 feet tall. It is a cold-tolerant warm season grass and has similar characteri stics of switchgrass with high yields and low amounts of input. 87

Research on miscanthus is curr ently being conducted at the Univ ersity of Illinois. In the Midwest, the growing season is April to Octobe r. The plant grows large green foliage that maximizes in approximately late August. As the temperature falls the foliage fades and drops off leaving the stem. The stem is the commercially important part of the plant and resembles bamboo. Stems can reach nine feet in length, 1⁄2 to 3⁄4 in diameter, and are harvested in the winter after drying occurs. 88

Establishment of a crop takes approximately 2 years, with maximum yields reached in the third year depending on soil fertility. The grass could require 4-5 years on low quality soils. In established crops 5-10 shoots per sq uare foot can be developed. Yi elds in various studies from the University of Illinois were 9-16 tons/acre in va rious regions in Illinois. The southern regions of the state with poor so il quality also saw high yields illustra ting that miscanthus is suitable for growth and high achievable yields on marginal land. 89 Yields in Europe ranged widely, with irrigated crops reaching12 tons/acre and un-irri gated yields of 4-10 tons/acre in the fall. According to trials conducted in Europe, the quality of miscanthus biomass for conversion to biofuel improves by delaying harvesting until after the winter months and the plant has time to dry sufficiently. However, this reduced yields by 30 percent. 90 In comparison to switchgrass, research out of Illinois also concluded that misc anthus can yield more biomass for conversion to biofuel because of its even higher photosynthetic efficiency and longer growing season. 91 In terms of input, miscanthus uses nitrogen extremel y efficiently and therefore does not need to be fertilized for high yields to be achieved. There is also no need for pesticides; however, herbicides have been used to control weed populations. 92

Challenges in growing and producing miscanthus crop include high establishment costs, problems in winter survival during the first year, and high water needs. European cost estimates are similar to other perennial pl ants at approximately $64 per dr y ton; however they estimate that a growing cycle of 10-12 year s is required to r ecover the star t-up costs of $267 per planted

acre. 93 The bulk of the high initial cost comes from planting and harvesting machinery. Establishment of a stronger market for grow ing these energy crops, as well as increased knowledge of propagation of the species , will inevitably lo wer overhead costs. 94

1.1.2.4.1.3 Hybrid Poplar

The poplar tree ( Populus trichocarpa ) is another option being i nvestigated for use as a dedicated energy crop. Herbaceous or woody pe rennial plants have some of the same characteristics of the perennial grasses that ma ke them suitable for possible use as an energy

44crop. They retain significant amounts of root biomass below gr ound, require little tillage thus decreasing labor and erosion, grow fast large canopies, and require less fertiliza tion than their agricultural counterparts.

Technological advances in harvesting and genetic s must be utilized in order to produce species that will be more suitable for use as an energy crop. Genetic information has helped to understand the characteristics the poplar tree. The complex genetic information obtained from the genome of this plant will make possible the e ngineering of faster growing trees with more biomass available for harvest. 95

1.1.2.4.1.4 Other Potential Feedstocks

Several other perennial plants have the possibi lity to be used as dedicated energy crops. As previously described, the char acteristics of perennial species make some optimal for use in this capacity. Because these plants have not been grown in agricultural sectors, they have not been extensively researched and developed for optimization. Corn is a crop that has been scientifically studied for decades because of its continued importance in the market. Dedicated energy crops must see this type of investment to bring about further knowledge of basic biology which will lead to advances in breeding and ev entual domestication of the species that have promise. The DOE along with university research have implicated several other plants as potential energy crops. These incl ude additional types of grasses su ch as reed canary grass and sorghum. Hybrid willow, silver maple, blac k locust, sweetgum, a nd eucalyptus are other perennial woody plants th at are possibilities. 96

Significantly accelerated testing and selec tion for populations will be necessary in establishing these plants. Breeding for desired traits and adaptability across a wide array of environments in multiple physiologic and geographic regions will be necessary. No single species of dedicated energy plant will be optim al for all areas of th e country, especially considering the amount of biofuels needed. Temperature, rainfall, and soil composition are highly variable across the contin ental United States; therefore, using a diverse group of plant species optimal for each growing region is a lik ely strategy. With current information and characteristics of each plant, the DOE has es timated where the possible growing areas could occur (see Figure 1.1-3). 97

45Figure 1.1-3. Possible Geographic Distribution of Dedi cated Biomass Crops

A U.S Department of Energy. Breaking the Biological Barriers to Cellulosic Ethanol: A Joint Research Agenda. (2006).

1.1.2.4.2 Land Assessment

There is evidence that perennial species are suitable for growth on marginal lands that are not useful for growth of food crops. A new study by Elliot Campbell out of Stanford University assessed abandoned land availability a nd the potential for this land to be used for energy crops. Because of the increased de mand for biomass energy, using abandoned crop or pasture lands to grow so me of these crops could be a better alternative than converting forested areas or using food agriculture lands. This study estimated the amount of global abandoned land available, the amount of biomass that could be grown on these lands, and the corresponding energy that could be obtained. Hi storical land use data, satellite imagery, and a global ecosystem model were used for the estimates. They c onsidered “abandoned land” as land that was previously used for pasture or crops but has sin ce been abandoned and not converted to urban or forested areas. Historical land use data was obtained from the History Database of the Global Environment 3.0 (HYDE) which consisted of gridde d maps which show the fraction of crop and pasture land within each grid cell for decades between 1700 and 2000. Also, the Center for Sustainability and the Global Environment (SAG E) land use database was used to check and supplement the HYDE database. They used a MODI S satellite map to exclude areas that have transitioned into forest or urban areas. Two different mathematical approaches were then used to estimate a conservative and a high estimate of total land available. Biomass production was estimated using the Carnegie-Ames-Stanford Approach ecosystem model which takes into account climate data, soil texture, land cover and the normalized difference vegetation index (NDVI), but does not take into account fertilizer use or irrigation, whic h could increase yields (see Figure 1.1-4). 98

46Figure 1.1-4. Global View of Crop and Pasture Abandoned Lands

Obtained from: Campbell, J.E. at al. The global potential of bioenergy on abandoned agriculture lands. Environ. Sci. Technology. (2008). (A) SAGE supplemental data showing abandoned crop lands. (B) HYDE data showing crop lands that have been converted to uses other than cropping. (C) HYDE data showing former pasture lands that have been converted to uses other than for pasture. (D) HYDE data showing an av erage of total abandoned agri culture land (excludes areas of land use transition of crop to pasture, pasture to crop, agriculture to forest, and agriculture to urban).

The low and high estimates for global ab andoned land, excluding forested and urban areas are 385 and 472 million hectares. The authors found that these lands could produce between 1.6 and 2.1 billion tons of biomass resp ectively. In the United States an average of approximately 58 million ha (146 million acres) of abandoned land was estimated. Assuming natural growth on these lands, approximately 321 million tons/year of biomass could be produced. At 80 gallons of ethanol per ton of biomass, there w ould be the potential to produce approximately 26 billion gallons from a grass crop such as switchgrass. It is pointed out that there will be significant differences between crop types and management st yles which will effect growth and yields. There is not a specified crop type; however, as the author’s point out that growing conventional crops such as corn on this land could increase erosion and polluted runoff. Growing perennial grasses such as switchgrass, fo r use as a feedstock would be the best option. 99 Although perennial grasses are able to grow on these lands, yields may be lower than they would be on more suitable agricultural lands.

On a state-by-state basis, the areas with the highest amount of available abandoned lands are in the West. Wyoming, Utah, Oregon, New Me xico, Nevada, Colorado and California all contribute over 5 million abandoned acres to th e total. Texas has the largest amount of abandoned land estimated at 10.37 million acres. Midwestern states including Iowa, Wisconsin, Illinois, and Ohio have appr oximately 3-4 million acres of abandoned land each (see Table 1.1­ 20). These lands may be more conducive to crop pr oduction than the more arid parts of the West. However, the condition and quality of these lands is unknown at this time. It would be difficult to estimate the specific types of energy crops th at could be grown on these lands. Also, in the DOE assessment previously referenced, most of the Western states are not implicated as areas of possible biomass growth (above Figure 1.1-3).

47Table 1.1-20. Abandoned Agriculture Land and Potential Production by State 100

State Area (Million ha) A rea (Million acres) Production (MM tons biomass/yr) Ethanol Production Rate (gallons/tons) Alabama 1.4 3.46 13.2 3.82 Alaska 0.3 0.74 0.4 0.54 Arizona 1.9 4.69 2.4 0.51 Arkansas 1.1 2.72 11.1 4.09 California 3.6 8.89 13.2 1.48 Colorado 2.7 6.67 8.1 1.21 Connecticut 0.1 0.25 0.6 2.43

Delaware 0.1 0.25 0.5 2.02 District of Columbia 0 0 0 0 Florida 0.5 1.24 2.7 2.19 Georgia 1.6 3.95 15.2 3.85

Idaho 1.4 3.46 4.7 1.36 Illinois 1.6 3.95 11.4 2.88 Indiana 1.2 2.96 8.5 2.87

Iowa 1.6 3.95 12.7 3.21 Kansas 0.3 0.74 1.8 2.43 Kentuck y 0.8 1.98 6.7 3.39 Louisiana 0.9 2.22 7.8 3.51

Maine 0.1 0.25 0.8 3.24 Maryland 0.4 0.99 2.7 2.73 Massachusetts 0.2 0.49 1.1 2.23 Michigan 1.5 3.71 9 2.43 Minnesota 1.6 3.95 10.7 2.71 Mississippi 1 2.47 9.1 3.68

Missouri 1.5 3.71 14.1 3.81 Montana 1.7 4.2 6.8 1.62 Nebraska 0.4 0.99 2.2 2.23 Nevada 2.1 5.19 3 0.58 New Hampshire 0 0 0.3 0

New Jerse y 0.2 0.49 1.9 3.85 New Mexico 3 7.41 5.4 0.73

New York 1.7 4.2 10.2 2.43 North Carolina 0.7 1.73 6.2 3.59 North Dakota 1 2.47 4.4 1.78 Ohio 1.4 3.46 8.9 2.57 Oklahoma 1.1 2.72 8.8 3.24

Oregon 2.2 5.43 8.2 1.51 Pennsylvania 1 2.47 8.2 3.32 Rhode Island 0 0 0.2 0 South Carolina 0.8 1.98 7.3 3.69 South Dakota 0.3 0.74 2 2.7

Tennessee 1.1 2.72 10.3 3.79

Texas 4.2 10.37 25.3 2.44 Utah 2.6 6.42 4.7 0.73 Vermont 0.1 0.25 1 4.05 Virginia 0.7 1.73 6.7 3.88 Washington 0.9 2.22 4 1.8 West Virginia 0.1 0.25 0.5 2.02

Wisconsin 1.4 3.46 9.9 2.86 Wyoming 2.8 6.92 6.1 0.88

Totals 58.9 145.5 321

Total Ethanol Volume a 25.68 Bgal Ethanol/yr

a. Assuming 80 gal/ton conversion rate

48The estimates of abandoned agricultural land do not include land enrolled in the Conservation Reserve Program (CRP), which could be an additional source of land available for energy crops. Land in this program is farmland th at is converted to tree s, grass, and areas for wildlife cover, but is considered crop land by the models in the abandoned land study. Environmental benefits of this land include th e creation of wildlife habitat, increasing soil productivity, reducing soil erosion and improving ground and surface water quality. 101 As of July 2008, there were 34.67 million acres under the CRP contract which is down 2.1 million acres from last year. 102 Approximately 28 million CRP acres are growing with native or introduced grasses, suggesting th at there is a significant amount of switchgrass already in the environment. Figure 1.1-21 shows the land allocation in the United States in 2007. 103 Recently, the 2008 Farm Bill capped the number of acres in the CRP at 32 million acres for 2010-2012. Following historical trends, it is assumed that so me of these acres will go into crop production. While some of this land may go for biofuel production, the bene fits of producing energy crops will have to be weighed against the bene fits of having the land in the CRP.

Figure 1.1-5. CRP land in 2007

USDA. Farm Services Agency. Conservation Reserve Program Summary and Enrollment Statistics. FY 2007

1.1.3 Biodiesel & Renewable Diesel Feedstocks

In general, plant and animal oils are va luable commodities with many uses other than transportation fuel. Therefore we expect the primary limiting factor in the supply of both biodiesel and renewable diesel to be feedstock availabil ity and price. Primary drivers for this are increasing worldwide demand as incomes rise in developing countries, as well as increased recognition that these material s have value based on their en ergy or hydrocarbon content as substitutes for petroleum. Expansion of bi odiesel market volumes beyond the mandates is dependent on it being able to compete on a price basis with the petroleum diesel being displaced.

The primary feedstock for domestic biodiesel production in the U.S. has historically been soybean oil, with other plant a nd animal fats and recycled greases making up a small portion of

49the biodiesel pool. We estimate that 2-4 percen t of biodiesel came from waste cooking oils and greases in calendar year 2007. 104

1.1.3.1 Virgin Plant Oils

Agricultural commodity modeling we have done for this proposal (s ee Chapter 5 of this document) suggests that soybean oil production will stay relatively flat in the future, meaning supplies will be tight and pri ces supported at a high level as biofuels demand increases. Modeling scenarios conducted for the year 2022 w ith the EISA mandates indicates that domestic soy oil production could support about 560 million gallons of biodiesel production. The model also projects that some food-gr ade corn oil will also be direct ed to biodiesel production based on its price and availability, resulti ng in an additional 109 million gallons of fuel from this source in 2022. These materials are most likely to be pr ocessed by biodiesel plan ts due to the large available capacity of these facil ities and their proximity to soybean production. Compared to other feedstocks, virgin plan t oils are most easily proce ssed into biofuel via simple transesterification due to their homogeneity of composition and lack of contaminates.

1.1.3.2 Corn Oil Extracted During Ethanol Production

A source of feedstock which could provide si gnificant volume is oil extracted from corn or fermentation co-products in the dry mill et hanol production process. Often called corn fractionation or dry separation, th ese are a collection of proce sses used get additional product streams of value from the corn. This idea is not new, as existing wet mill plants create several streams of product from their corn input, including oil. In a dry mill setting, the kernel can be separated into the bran, starch, and germ compon ents ahead of fermentation, or alternatively, oil can be extracted from the distillers’ grains af ter fermentation. Both have advantages and disadvantages related to plant ca pital cost and energy consumpti on, as well as yield of ethanol and the other coproducts.

Extraction of oil from the thin stillage or di stillers’ grains with so lubles (DGS) streams is a proven technology that can be retrofit to ex isting plants relativel y cheaply. Front-end separation requires more intensive capital invest ment than is required to extract oil from the DGS, and therefore is best designed into the plan t at the time of construction. However, it yields a larger array of co-products, a nd generally also results in etha nol process energy savings since less unfermentable material is going through the process train. We expect that this technology will be increasingly deployed in new plants in pa rallel to existing plants pursuing extraction of oil from DGS. For the sake of simplicity, for this proposal we have chosen to focus for cost and volume estimates on the DGS extraction process.

Specifically, our estimates come from a process developed and marketed by GS Cleantech, Inc., though there are others who will likely develop and market similar processes. Depending on the configuration, this system can extract 20-50 percent of the oil from the fermentation co-products, producing a distressed corn oil stream which can be used as feedstock by biodiesel facilities. Since it offers another stream of revenue from the corn flowing into ethanol plants, we assumed approximately 40 per cent of projected total ethanol production will implement this or other oil extraction process by 2022, generating approximately 150 million

50gallons per year of corn oil biofuel feedstock. 105, J We expect this material to be processed in biodiesel plants for the same reasons given above fo r soy oil. At this time it is uncertain whether there will be third party aggregat ors of this extracted oil, or whether individual ethanol plants will contract directly with nearby biodiesel faci lities, which may ultimately impact where and how this feedstock is processed.

1.1.3.3 Yellow Grease and Other Rendered Fats

Rendered animal fats and reclaimed cooking oils and greases are another potentially significant source of biodiesel feedstock. In 2007, Tyson F oods announced plans to produce renewable diesel at multiple sites in joint ve nture with ConocoPhillips and Syntroleum Corp, suggesting these operations are t echnologically feasible if mark et conditions are right.

National Renderer’s Association gives a quantity of approximately 11 billion lbs of fats and greases available annually for all uses, a nd suggests this will grow by 1% per year. 106 This figure is broken down into several categories, and includes “yellow grease” and “other grease” collected and processed by rendering companie s each year. NRA defi nes yellow grease as material primarily derived from restaurant gr ease or cooking oil (they do not define “other grease” but we can assume this is trap grease or other reclaimed material ). Adding together the NRA’s “yellow grease” and “other grease” categorie s, we arrive at 2.7 billion lbs per year (all figures there are for 2005).

Similarly, a 2004 report prepared for New York State Energy Research and Development Authority by LECG, LLC describe s yellow grease as material pr oduced by restaurants and food service. 107 (This report describes grease recovere d from sewer traps as brown grease, and suggests it is too low in quality to be used for biodiesel production. ) Based on USDA and US Census data, LECG shows production of yellow greas e by restaurants to be on the order of 9 lbs per capita per year, equivalent to about 2.7 billion lbs/yr. Unfo rtunately, it's not clear whether this quantity would include or be in addition to the NRA figures , but given the similarity of numbers, it seems reasonable to suspect that the NRA total includes the same sources of grease as assessed by LECG.

Thus, the figures we use here assume that the NRA figures already include collection of a large portion of restaurant a nd trap grease by rendering compan ies; we have not included additional waste greases that othe r studies have suggested might be available base d on per-capita use of cooking oils, wastewater treatment disposal, etc. Perhaps there is some additional waste grease out there not bein g collected or counted by NRA that is, or could be, aggregated and funneled into biofuel production, but there is unfort unately no good way for us to determine this.

In addition to the named sources above, there is also a written statement by David Meeker of the NRA asserting that not more than 30% of the 11 billion lbs could be directed to biofuel production on a long-term basis. 108 So, along with the total volume of fats and greases as estimated above, we have 0.3 x 11 billion lbs per year / 7.6 lbs per gallon = 434 million gallons. With 1% annual growth between 2005 (t he year of the figures cited) and 2022, we

J The projected fraction of plants doing corn oil extraction was based on a conversation with someone working in the ethanol industry, as well as Table 3 of the Mueller report referenced in this paragraph.

51arrive at a potential 514 million gallons of biodies el per year from all av ailable sources. Our projections in this proposal suggest that 375 milli on gallons or 73% of this will be actually be utilized for biofuel. This figure was deri ved from FASOM modeling of other sources of feedstock we expect to be availa ble (such as soy oil), as well as our assumptions about corn oil extracted at ethanol plants, in the context of the standard set forth in the EISA. This 73% seems reasonable considering market inefficiencies an d potential competition from other high-value, non-fuel uses of these feedstocks.

Much of biodiesel producti on seems to rely on niches of feedstock availability and market outlets. Thus we could assume that the 2-4% of biodiesel volume to be produced from fats and greases as reported by FO Licht for 2007 w ill continue indefinitely. We speculate that much of any new fat and grease feedstock use woul d be routed to renewa ble diesel in the long term, however, because that process appears to ha ve lower operating costs for this material, and because there have been intent or construc tion announcements for RD plants to utilize a significant portion of the 375 MMgal. Furthermore, we suspect that some of the feedstock these plants will use may be obtained through direct c ontract or joint venture with animal processing or rendering operations, such that it would not typically be available on the open market to 5 or 10 or 20 MMgal/yr biodiesel plants in a different county or state. Thus we chose to assume the vast majority of this feedstock would be rout ed to RD production by the time of the fully phasedin RFS2 program, and ignored it s use in biodiesel production.

1.1.3.4 Jatropha

Jatropha is a genus of plants, consisting of both shrubs and trees, some of which hold some promise as a feedstock for the producti on of biofuels. One species in particular, Jatropha

curcas , yields seeds that contain between 25-45 pe rcent lipids, which can be processed to produce biodiesel. Claims regard ing the production potential of J. curcas have led to the popularity of the crop as a potential biofuel feedstock. In particular , these claims state that the
crop:

-grows on marginal lands and reclaims wasteland, -is tolerant of drought, -requires low nut rient input, -requires low labor inputs, -does not compete with food production, and -has a high oil yield.

J. curcas has been traditionally cultivated for li ving fences, to conserve soil moisture, reclaim soil, control erosion, and used locally in soap production, insect icide, and medicinal application. 109, 110 J. curcas is also a toxic plant. Safety of large-scale J. curcas plantations is also a concern, especially for children. Accidental ingestion can have severe consequences. “Hundreds of cases have been reported from di fferent parts of India. In Meerut (in Uttar Pradesh), over 50 children were recently hospitalized due to jatropha poisoning.” 111 Even two J.

curcas seeds are like a strong purgative, while four to five seeds can cause death.

52J. curcas has recently been cultivated as an energy crop. J. curcas originated in Central America, mainly growing in arid and semi-ari d conditions; now it is al so found in the tropical regions of Africa, Asia, and North and South America. 112 Because of J. curcas’ deep root system, it can grow in lands that have been prev iously heavily cultivated or otherwise have low levels of essential minerals and nutrients in the t op levels of soil; this results in the recycling of nutrients from deeper soil levels. 113, 114 In addition, because the plan t is a perennial (living up to 50 years) the root system stays in place, which can significantly reduce er osion and even reverse desertification. 115, 116 However, J. curcas may not produce efficiently on marginal lands without significant extra expense.

1.1.3.4.1 Jatropha Input and Cultivation

As a wild plant which has not yet been domesticated, J. curcas may be considered drought-tolerant. However, “there is little known on wa ter use efficiency of J. curcas as a crop.” 117 Even though J. curcas can survive moderate droughts by dropping its leaves, the effect of water starvation on seed yield and oil cont ent in the seeds is mostly unknown. No studies relating water use and yield of Jatropha curcas were available. Water use efficiency of sister species Jatropha pandurifolia and Jatropha gossypifolia are reported as 3.68 and 2.52 mol CO 2 / mol H 2 O, respectively. 118 This is similar to that of other oil seed species like soybean, with a water use efficiency of 3.90 mol CO 2 / mol H 2 O. 1 19 Thus, it is conceivable that water requirements of J. curcas will be similar to that of other oil seed species; a lthough the plant may survive droughts, it may not produce efficiently or economically when it is water-deprived. 120 Obviously, further studies re lating water use to crop produc tion must be performed.

Because J. curcas has been observed growing on low quality soils with low nutrient amounts, it was assumed that the plant would be able to grow as a crop in these conditions. However, research indicates that J. curcas growth and production of s eed is severely limited by soil fertility. 121, 122 In the initial growth pha se of the plantation, when competition between plants for radiation, water, and nutrients is negligib le, “nutrient content in mature leaves is not significantly affected by crop density.” 123 However, in the competition phase, “nutrient uptake from the soil was negatively correlated with plant density;” 124, 125 fertilization with J. curcas seedcake significantly increased seed yield. 126 This indicates that J. curcas plantations should at least be fertilized with remaining seedcake (bulk of seed after oil is pressed out), which is very nutrient rich. Thus, this seedcake cannot be sold as a fertilizer, or burned for energy production to make the cultivation of the plant more cost effective, as was suggest ed in various studies, 127, 128
if J. curcas cultivation is to require low external nut rient input. Even in the case that the seedcake and other oil extr action by-products is recycled to ma intain soil fertility, “initiating a plantation on low or non fertile soils ... implies the need to use other fertilizers, at least at the start, to boost crop growth and s eed production in the initial stages.” 129 The long-term impact of monocultures of jatropha on soil heal th has not been studied. Although J. curcas can potentially grow on marginal land, whether it is safe to plant on current crop land is still unknown. There are some indications that J. curcas will not be sustainable, unless specific steps are taken to ensure the plantations’ long term health. 130, 131

Because it was observed that J. curcas thrived on marginal soils, it was assumed that the labor required to maintain the crop and harvest the seeds would be minimal. However, in order to

53prepare J. curcas for use as an energy crop, significant la bor is required, including: preparing land, setting up nurseries, planting, irrigating, fertilizing, pruning, harvesting, and processing. 132 At present, there are no data to show that any of these labo r intensive activities, which are assumed in the cultivation of ot her perennials, can be eliminate d. Labor input required for crop maintenance actually rose from 22 person days / ha / year to 70 person days / ha / year from the 1 st to 6 th year in one study, indicating that there will be no reduction in labor inputs required even as the plantations mature. 133 Especially in the United States, where labor costs are high, this is a severe hindrance for the cultivation of J. curcas on a large scale. Furthermore, the development of any sort of mechanized harvesting will be problematic, as “continuous flowering results in a sequence of reproductive development stages on the same branch, from mature fruits at the base, to green fruits in the middle, and flowers at the t op of the branch;” seeds s till need to be picked by hand. 134, 135

Because it was assumed that J. curcas could thrive on marginal land with low resource inputs, it was thought that the crop’s cultivation would not comp ete with that of food crops. 136 However, until present, most studies on the crop’s oil production have been carried out given optimal growing conditions that may be unrea listic in many parts of the world. “A major constraint for the extended use of J. curcas seems to be the lack of knowledge about its potential yield under sub-optimal and marginal conditions;” it is unknown whether J. curcas can produce economically on marginal lands. 137 Even if J. curcas could be grown successfully on these lands, it is likely that, in order to achieve ec onomic yields, significant resource inputs will be needed. In this case, food crops could likely be grown on these same lands anyways; in addition, the cultivation of J. curcas will draw scarce water and labor resources from food crops. There are examples of farmers leaving successful food crop plantations to cultivate J. curcas , leading to increased food scarcity. 138 Finally, even if su ccessful cultivation of J. curcas were entirely possible on marginal lands with low inputs, the widespread success of the crop “might lead to rapid expansion of production at the cost of food crops.” 139 Although advances are being made on several of these fronts, currently, J. curcas can likely not be cultivated on a large scale without displacing food corps because of land, water, and labor constraints.

1.1.3.4.2 Jatropha Yield

Optimal seed production of 1.5-7.5 tons/h a occurs with 1200 to 1500 mm water/year under different trial conditions. 1 40 However, a minimum of 5 00 to 600 mm water/year is required to produce approximately 1 seed ton/ha. 141 It is unknown whether this variation is a result of environmental or genetic variation, or a combination of bot h. Maximum production is attained in mature plantations, about 3-5 year s after planting, with a maximum productive life of over 30 years. 142, 143

The seed of J. curcas has a lipid content of r oughly 25-45 percent by weight. 144, 145 However, the oil content of the seed was “signifi cantly higher in soils that had not been used for arable farming before (42.3 vs 35 percent). 146 Thus, assuming a seed oil content of 35 percent and an extraction efficiency of 75 percent, this would yield 404-2040 kg oil / ha or 439-2217 liter oil/ha. 147 This is somewhat higher than other oil producing crops like soybean, sesame, sunflower, rapeseed, and castor with a range of 3 75-1200 liter oil/ha. However, at this time, it is difficult to assess production potentials on marginal land because of the lack of data. “The hype

54in J. curcas oil production is not sufficiently sup ported by hard data on crop production, well controlled or optimal management production conditions, and environmental impact.” 148, 149, 150, 151

Table 1.1-21. Jatropha Yield 152, 153, 154 Conservative projection Optimistic projection Oil 500 liter/ha (53 gal/ac re) 2000 liter/ha (213.8 gal/acre) Biodiesel 460 liter/ha (49 gal/acr e) 1840 liter/ha (196.7 gal/acre) (1 L/ha = 0.1069 US gallons per acre)

J. curcas is only currently present in 2 states (Florida and Hawaii) in the United States. 155
Because of J. curcas’ intolerance to frost, only small por tions of the United States can be considered for cultivation. Areas with the most suitable climate conditions for cultivation can extend as far as 30° N in latitude; this would include the southern parts of Texas and Louisiana, and most of Florida. 156 Assuming no irrigation (as J. curcas is assumed to be a low-input crop), an absolute minimum of 500 mm of rainfall is re quired for substantial pro duction, this occurs in the easternmost portions of Texas, and all of Louisiana and Florida. 157 The University of Florida has done some breeding and genetic manipulation of J. curcas , with the goal of increasing hardiness and tolerance to colder climat es; no results have yet been published. 158

Table 1.1-22. Timeline (for US cultivation) 159, 160 Conservative projection Optimistic projection Test plantations 5-10 years 2-5 years Commercialization 10-20 years 6 years

Cost data for US cultivation of J curcas is not available. Becaus e of high labor costs, the need to reuse seedcake as organic fertilizer (ins tead of selling it for further economic gain), and irrigation needs, J. curcas biodiesel production in the Unite d States will likely may not be economic. Other countries with si gnificantly lower labor costs ma y find it more economic to use jatropha as a biodiesel feedstock.

1.1.3.4.3 Jatropha Conclusions

It is unlikely that Jatropha curcas will be able to help the Un ited States meet its energy needs in the future without disrupting current f ood crops, water resources, and other US interests. Currently active large projects to cultivate J. curcas around the world are based on very optimistic assumptions; many of the strong selling points of J. curcas a may not be valid under more reasonable assumptions. Still very little is known about the crop’s sustainability impacts, its long-term yields, or its ab ility to be grown economically as a monoculture. Even under optimal conditions, jatropha yields are unlikely to be radically different from that of other conventional oil plants; thus, its possible impact on US energy supply is limited.

1.1.3.5 Algae

Microalgae are single-celled alg ae species that grow quickly a nd have high lipid content, and thus are a promising feedstock for biofuel production. While the majority of algae

55companies are focusing on the use of algae for bi odiesel production, it is important to note that algae can alternatively be used for producing etha nol or crude oil for gasoline or diesel which could also help contribute to the advanced biofuel mandate. K Some of the benefits of using algae as a biofuel feedst ock are that it:

-grows on marginal land, -requires low water inputs, -can recycle waste stream s from other processes, -does not compete with food production, and -has high oil yield.

1.1.3.5.1 Algae Overview

Mass cultivation of microa lgae has been ongoing since the 1950s for medical and pharmaceutical purposes. Since the 1980s, algae-to-b iofuel research has been heavily funded by governments such as Japan, France, Germany and the United States. The research program in the US was especially large. The Aquatic Sp ecies Program, backed by the National Renewable Energy Laboratory, ran from 1978-1996 to look at the use of aquatic pl ants, specifically algae, as sources of energy. From about 1982 through th e termination of the program, research concentrated on algae for biofuel pr oduction, specifically in open ponds. 161 Two branches to research large scale algaculture systems were funded: the “High Rate Pond” and the “Algae Raceway Production System” from 1980 to 1987. By 1988 several large (1,000 m 2 ) systems were designed and built at the “Outdoor Test Facility.” 1 62 However, overall productivity of the ponds was lower than expected at around 10 grams algae / m 2 / day, due to cold temperatures and native species of algae taking over the ponds.

Recently, many small companies in the United States and Europe have begun developing processes for algae-to-biodies el production, most notably, Gr eenFuel Technologies, Solazyme, LiveFuels, Solix Biofuels, and AlgaeLink. 163 Even large energy corpora tions have shown interest in algae development: Chevron, for example, has partnered with NREL to collaborate on research to produce trans portation fuel using algae. 164

Microalgae, which can have a high mass percen tage of triacylglycerols, or natural oils, can be cultivated using either of two methods. One method that is currently in use, and was studied widely by the Aquatic Species Program, i nvolves using large, open ponds to grow algae; these are often called “raceway” ponds, as their shap e is similar to an oval racetrack. They use a paddle wheel to keep the water in motion around the pond. The other method of algae cultivation utilizes closed “photobioreactors ;” these are long, clear tube s through which the algae-water mixture flows. The mixture is still exposed to sunlight and CO2 (through gas bubblers) but is shielded from contaminants that may occur in the environment.

After the algae are grown, the oil must still be extracted from the mixture. Usually this involves a dewatering process, to dry the algae, which is followed by an oil extraction process. The oil may be extracted using presses, expellers, or chemical processes. The triacylglycerols

K Algenol and Sapphire Energy, see http://www.algenol biofuels.com/ and http://www.sapphireenergy.com/

56can then be processed into biodiesel by tran sesterification, a chemical process. The transesterification process is discussed fu rther in section 1.4.4 of this document.

1.1.3.5.2 Algae Input and Cultivation

Because algae are marine species and can be grown wherever sufficient water is available, algaculture does not re quire fertile lands. While other oi l crops such as soy, sunflower, rapeseed, and jatropha need large amounts of agricultural land in order to meet a sizable portion of US liquid fuel demand, algae do not displace agricultural crops. 165, 166, 167 Algae grow most effectively in regions with high solar insolation, su ch as the US southwest, as discussed later.

When cultivated in enclosed photobioreac tors, evaporation of water in limited, and water extracted during the drying process can be mostly reclaimed. 1 68 Even in open raceway-style ponds where evaporation is not negligible, water requirements are still considerably lower than with conventional agricultural cr ops. It is estimated that, in order to produce enough algal biomass for 60 billion gallon biodiesel/year, 20-1 20 trillion gallons of wa ter/year are needed. This is several orders of magnitude lower than the 4,000 trillion gallon/year used to irrigate the entire US corn crop. 169 (This, if used exclusively for the production of conventional ethanol, would not reach the energy equivalent of 60 billion gallons of algae biodiesel).

Algae can also thrive in brackish water, w ith salt concentrations up to twice that of seawater, which is often available in salin e groundwater aquifers in the southwest. 170, 171 The salt, other minerals, and contaminants may pos e a problem to the dewatering and extraction process, depending on the method used. However, this is a technical, not fundamental, issue with the use of saltwater. Thus, water usage is not a constraint and should not displace conventional crops, as no water currently used for conventional crops will need to be diverted to algae. 172, 173

Aside from sunlight, algae require tw o main physical inputs for growth: CO 2 and nutrients. 1 74 These nutrients can be obtained from conven tional fertilizers, orfrom domestic or industrial waste sources, such as farm refuse and manure. 175 Co-locating algae farms with animal husbandry, in order to directly use the manure as a nutrient, would reduce transportation costs. 176 In addition, both of these input s can be obtained from waste streams from other energy processes. They can be coupled with coal-burning power plants or even ethanol plants, and can effectively recycle between 50% and 90% of flue gasses, depending on the size of the algae farm. 177, 178, 179 The highly controlled environment of algae photobioreactors make them especially suitable to process and recycle CO 2 in flue gasses, as the gas can be bubbled or channeled into the water. 180 One study shows approximately 1,700 power plants in the United States have enough unused surrounding land to support a commercial-scale algae biofuel system. 181 A more localized analysis of Arizona, on e of the more promising algae cultivation states, only identified 7 coal-bur ning power plants suitable for co -location with algae farms.

1.1.3.5.3 Algae Yield

Certain species of algae can produce 80 per cent of their body weight as oils, and oil levels of 20-50 percent are common. 182, 183 Thus, the projected outputs of algae farms are many

57times higher than traditional farms, on the order of 30,000 to 130,000 liter oil/ha/yr, which can be processed into 27,600 to 119,600 liter biodiesel/ha/yr (2,950 to 12,780 gallons biodiesel/acre/yr). 184, 185, 186, 187, 188 See Table 1.1-23, below.

Raceway systems are low-cost but have low productivity compared to photobioreactors. Most current research focuses on the photobioreact or system of cultivation for optimal pH, gas level, salinity, and temperatur e control, and to prevent contamination from invasive algae species. 189, 190 Photobioreactors also increase the concen tration of algae in the water, by around 30 times, somewhat simplifying the dewatering process. 1 91

Table 1.1-23. Potential Algae Yield (assuming photobioreactor system) 192, 193, 194 Conservative projection Optimistic projection
Oil 30,000 liter/ha/yr (3207 gals/acre) 130,000 liter/ha/yr (13898 gals/acre)
Biodiesel 28,000 liter/ha/yr (2993 gals/acr e) 120,000 liter/ha/yr (12829 gals/acre)

1.1.3.5.4 Algae Timeline and Cost Projections

The projected timeline for commercial pr oduction of algae oil for biofuel varies depending on expert opinion. As shown in Table 1.1-24, some estimate as early as the next few years for algae biofuel to be produc ed at commercial scale.

Table 1.1-24. Algae Timeline 195, 196, 197, 198, 199 Optimistic projection Conservative projection Commercialization 2-3 years 10 years

One company, AlgaeLink, has developed th e first made-to-orde r industrial algae production facility in the world, though it is currently selling it at high cost. 200 Also, a company called XL Renewables has developed an approach to use a potentially lower cost photobioreactor system for algae production. 201 Estimated costs are detailed in the Table 1.1-25, below.

Table 1.1-25. Algae Costs (assumi ng photobioreactor system) 202, 203 Conservative projection Optimistic projection Algae biomass $0.47/kg ($0.21/pound) $0.05/kg ($0.02/pound) Oil (recovered; estimate) $2.80/liter ($10.60/gallon) $0.20/liter ($0.75/gallon) Biodiesel $2.95/liter ($11.17/gallon) $0.35/liter ($1.32/gallon)

Concrete information about harvesting and o il extraction costs is no t available, but may be up to 50% of the cost of recovered algae oil. Refining of algae oil into biodiesel will add roughly $0.57/gallon. 2 04 Thus, a large production facility of 10,000 tons algae biomass/year using current algaculture tec hnology could produce algae oil at roughly $10.60/gallon, and algae biodiesel at $11.17/gallon under conservative projections. Assuming more optimistic projections, algae oil could be produced fo r $0.75/gallon and biodiesel for $1.32/gallon. In comparison, during 2006, the cheapes t vegetable oil available (cr ude palm oil) cost roughly $1.97/gallon, and palm oil biodiesel roughly $2.50/gallon.

581.1.3.5.5 Possible U.S. Locations

The US southwest is the most promisi ng location for economic algae-for-biofuel cultivation, due to its high solar insolation (see Figure 1.1-6), availa bility of saltwater aquifers, and relatively low current land use. 205, 206 Ideally, algae farms could be co-located with coalburning power plants in order to recycle the carbon emissions and increase algae. However, although one study states that 1,700 power plan ts throughout the United States have enough surrounding land to support a commer cial-scale algae system, only a limited number of these are in the southwest, due to lower population densities. 207

Figure 1.1-6. PV Solar Radiation in the United States

1.1.3.5.6 Algae Challenges

Unlike other feedstocks that have fundamental land use, water use, and yield/efficiency issues, the main problem facing future deve lopment of algae-to-biodiesel systems is underdeveloped technology and high cost. “Produc ing microalgal biomass is generally more expensive than growing crops.” 208 The high cost is due mainly to the relative complexity of cultivation, harvesting, and oil ex traction systems needed for algae, which translates to high capital and operating costs. 209, 210, 211, 212 “Economics of producing micr oalgal biodiesel need to improve substantially to make it competitive wi th petrodiesel, but th e level of improvement necessary appears to be attainable.” 2 13 Specific technological hurdles include temperature control, dewatering methods and lipid extraction. Harvesting the algae is particularly difficult and energy intensive, but is made somewhat eas ier with photobioreactor s, which yield higher algae concentrations in the water than open-ponds.

1.1.3.5.7 Algae Conclusions

Algae-to-biofuel production has significant potential because of high oil yields and

59ability to be cultivated on marginal land. Ind eed, algae could supply the United as much as 60 billion gallons of biodiesel/year using a land ar ea as small as 6 million acres (about 8% of Arizona’s land area). 214 However, several technical hurdles must still be overcome, and costs must come down significantly, before algae are viable as a commerc ial biofuel feedstock.

1.1.3.6 Imported Biodiesel

In 2007, the EU produced 58 percent of worl dwide biodiesel. Another 20 percent was produced in the U.S., with the remainder spl it between South America and the rest of the world. 215

In terms of historical production, EU biodies el output increased fr om 200 million gallons in 2000 to almost 1,500 million gallons in 2007. 216 According to some analysts, however, the EU does not have the capacity to expand oilseed ar eas and in comparison to other climates, the climate in the EU is not part icularly good for oilseed production. 217 Estimates for future EU biodiesel production appear to be roughly 2.5 billion gallons by 2017. 218

In addition to leading production, the EU is cu rrently the largest market for biodiesel in the world, consuming around 700 million gallons in 2005. Biodiesel consumption in 2007 increased to approximately 1,800 million gallons. 219 Even with the increase in domestic production, however, it seems likely that the EU’s fu ture biofuels consumption goals will not be
met.

In 2007, the European Commission (EC) pr oposed a binding minimum target requiring 10 percent biofuel use for transport by 2020. 220, 221 The International Energy Outlook forecasts OECD European countries will consume 147 billion gasoline ga llons equivalent (gge) of transportation fuel in 2010, growing to 151 gge by 2020. Currently, approximately 60 percent of the EU’s transportation fuel demand is met by diesel . Assuming this split continues in the future, a 10 percent biofuel requirement would translate to roughly 8 bill ion gallons of biodiesel demand by 2017. Thus, it appears likely that the EU will not be able to produce enough biodiesel to meet its goals by 2020 and will need to depend on other countries to make up the remaining 5.8 billion gallon demand (after accounting for domestic produc tion of 2.2 billion gallons as estimated from above). So not only will the EU, the largest curr ent producer of biodiesel, not have any biodiesel available for export in the future; they will likely be a net importer competing with the U.S. for surplus biodiesel supplies.

As imports of finished biodiesel (or biodies el feedstocks) are relatively new in the U.S. domestic market, volumes are currently small and not well-tracked by industry groups and EIA. Therefore, there is limited histor ical data from which to better understand the U.S.’s position to import biodiesel. Alternatively, in order to an swer these questions, we analyzed the following countries that have been noted as having a la rge potential for producing biodiesel: Brazil, Malaysia, Indonesia, and India.

Currently, there is a large interest in Braz il to develop a strong bi odiesel program. As Brazil has mandates of B2 by 2008 and B5 by 2013 much of its production of biodiesel will be consumed domestically before any biodi esel will be available for exports. 222 If the mandates as

60well as Brazil’s biodiesel production goals are me t, this would mean that approximately 500 million gallons could be available for exports by 2010 and around 600 million gallons by 2020.
223 Although this is a relatively large amount comp ared to what is produced today, this volume is still not enough to meet all of the EU’s demand for biodies el. Therefore, the amount of biodiesel exported from Brazil will likely be split among thos e countries demanding biodiesel, with the amount allocated to the U.S. uncertain at this point in time.

We also analyzed the biodiesel situation in the top two world producers of palm oil, Malaysia and Indonesia. Although Malaysia and Indonesia have a large potential for biodiesel production from palm oil, these countries appear to be marketing their exports to European and Asian nations as well as implementing their own biof uels goals. In fact, Ma laysia is anticipating biodiesel export opportuniti es in the European market and is expected to export biodiesel specifically to Europe in the range of 100-120 million gallons by 2010. 224 This volume is still relatively small in comparison to the production of biodiesel in the U.S. from domestic sources. The prospect for Indonesian palm oil biodiesel is similar as most Indonesian exports of biodiesel are to China and other Asian markets.

As far as mandates are concerned, Malaysia is planning on introducing a B5 mandate which could demand up to 166 million gallons of biodiesel. 225 Indonesia has a smaller mandate of B2.5, as a decline in fossil fuel prices and the increase in crude palm oil prices resulted in the decrease from its original B5 mandate. 226 In fact, historically, palm oil prices have been lower than soybean and rapeseed oil, suggesting thatthe use of palm oil could be the most costeffective. 227 However, in 2006, palm oil prices be gan to track soybean and rapeseed oil prices. 228 Since feedstock costs make up the larges t portion of production costs for biodiesel, this suggests that the production of biodiesel may not be profitab le if palm oil prices remain high. With Malaysia and Indonesia’s eyes set on importing biodiesel to the EU and Asian nations, possible biodiesel mandates in the future, and increases in palm oil feedstock costs, it seems unlikely that the U.S. will receive large volumes of biodiesel from this part of the world.

As described above, another potential source of biofuel feedstock is jatropha. Although the potential for India to produce biodiesel from jatropha is large, there are still great uncertainties as to whether India can meet even its own biodiesel goals (i.e., B20 by 2020) 229 , let alone the goals of other nations. Historically, India has neither imported nor exported bio-fuels for fuel purposes, nor does the government provide any financial assistance for these products. Thus, India’s lack of experience in transporting large volumes of biofuels to other nations may limit how much is availabl e internationally. Given that large- scale cultivation of jatropha has yet to be seen, and that jatropha has unpredictable yields, it seems un likely that India could provide large volumes of biodiesel to the U.S. 230

Our analysis of the countries with the most potential to produce a nd consume biodiesel in the future suggests that there will be very limited supplies of biodi esel available to the US from other nations. Supplies to the U.S. will be limited by factors such as mandates and goals of other countries, preferential shipment of biodiesel to European and Asian nations, and how quickly non-traditional crops such as jatropha can be developed. Th us, we are estimating that there will be negligible amounts of biodiesel ava ilable to the U.S. in the future.

611.1.3.7 Biodiesel Feedstock Summary

Table 1.1-26. Estimated 2022 Potential Biodiesel & Renewable Diesel Volumes Based on Feedstock Availabilit y (million gallons of fuel)

Biomass-based diesel Other advanced biofuel Totals From FASOM Outside FASOM From FASOM Outside FASOM From FASOM Outside FASOM Soy oil 551 551 Corn oil (food grade) 109 109 Corn oil (fractionation) 150 150 Etallow 14 14 28 NonETallow 29 29 57 Lard 26 26 52
YGrease 94 94 188 P o

u l t r

y f

a t 2 5 2 5 5 0 Subtotals 823 175 163 25 985 200 Totals 998 188 1,185



### 1.2 RFS2 Biofuel Volumes

Our assessment of the renewable fuel volumes required to meet the Energy Independence and Security Act (EISA) necessi tates establishing a primary set of fuel types and volumes on which to base our assessment of the impacts of the new standards. EISA contains four broad categories: cellulosic biofuel, bi omass-based diesel, total advanced biofuel, and total renewable fuel. As these categories could be met with a wide variety of fuel choices , in order to assess the impacts of the rule, we projected a set of r easonable renewable fuel volumes based on our interpretation at the time we be gan our analysis of likely fuels that could come to market.

The following subsections detail our rationale for projecting the amount and type of fuels needed to meet EISA. For cellulosic biofuel we have assumed that the entire volume will be domestically produced cellulosic ethanol. Biomass- based diesel is assumed for our analyses to be comprised of a majority of fatty-acid methyl ester (FAME) biodiesel and a smaller portion of non-co-processed renewable diesel. Subsequent to the analyses being conducted, our lifecycle analysis was completed showing that biomass-base d diesel from soy oil failed to meet the 50% GHG threshold. The analyses contai ned in this DRIA are still ba sed on the presumption that soy and other virgin plant oils used for biodies el would qualify as bi omass-based diesel. Adjustments in the analysis will be conducted fo r the FRM. The portion of the advanced biofuel category not met from cellulosic biofuel and biom ass-based diesel is assumed to come mainly from imported (sugarcane) ethanol with a smalle r amount from co-processed renewable diesel. The total renewable fuel volume not required to be comprised of a dvanced biofuels is assumed to be met with corn ethanol.

In addition, the following subsections also desc ribe other fuels that have the potential to contribute to meeting EISA, but because of their uncertainty of use, or because their use likely might be negligible we have chosen to not assu me any use for our analysis. Examples of these types of renewable fuels or ble ndstocks include bio-butanol, bioga s, cellulosic diesel, cellulosic gasoline, biofuel from algae, ja tropha, or palm, imported cellulo sic ethanol, other biomass-toliquids (BTL), and other alcohols or ethers.

621.2.1 Cellulosic Biofuel

As defined in EISA, cellulosic biofue l means renewable fuel produced from any cellulose, hemicellulose, or lignin that is derive d from renewable biomass and that has lifecycle greenhouse gas emissions, as determined by the Admini strator, that are at least 60% less than the baseline lifecycle greenhouse gas emissions.

When many people think of ce llulosic biofuel, they immediately think of cellulosic ethanol. However, cellulosic biofuel could be comprised of other alcohols synthetic gasoline, synthetic diesel fuel, and synthe tic jet fuel, propane, and biogas. Whether cellulosic biofuel is ethanol will depend on a number of factors, including production costs, the form of tax subsidies, credit programs, and issues associated with blen ding the biofuel into the fuel pool. It will also depend on the relative demand for gasoline and diesel fuel. For instance, European refineries have been undersupplying the European market with diesel fuel supply a nd oversupplying it with gasoline, and based on the recent diesel fuel price margins over gasoline, it seems that the U.S. is falling in line with Europe. Theref ore, if the U.S. trend is towa rd being relatively oversupplied with gasoline, there could be a price advantag e towards producing renewable fuels that displace diesel fuel rather than a gasoline fuel replacement like ethanol.

Current efforts in converti ng cellulosic feedstocks into fuels focus on biochemical and thermochemical conversion processes. In term s of production costs, at least for the main technologies of focus, neither biochemical nor thermochemical platforms (comparing enzymatic biochemical processing to ethanol and thermochemi cal processing to cellulosic diesel) appear to have clear advantages in cap ital costs or operating costs. 231 We further discuss these technologies in Section 1.4 of the DRIA, while feedstock and production costs are discussed in Chapter 4 of the DRIA. The economic competitive ness of cellulosic biofuels will also depend on the extent of financial support from the government. Under the Farm Bill of 2008, both cellulosic ethanol and cellulosic di esel receive the same tax subsidies ($1.01 per gallon each). The tax subsidy, however, gives ethanol produc ers a considerable advantage over those producing cellulosic diesel due to the feedst ock quantity needed per gallon produced (i.e. typically the higher the energy conten t of the product, the more feed stock that is required). On an energy basis, cellulosic et hanol would receive approximate ly $13/mmBtu while cellulosic diesel would receive approximately $8/mmBtu. In a similar manner, if we were to finalize our co-proposal for Equivalence Values such that th ey are generated based on volume rather than energy content, this would likew ise provide an advantage for th e production of cellulosic ethanol over cellulosic diesel.

One large advantage that cellulosic diesel has ov er ethanol is the ability for the fuel to be blended easily into the current distribution in frastructure at sizeable volumes. There are currently factors tending to limit th e amount of ethanol that can be blended into the fuel pool (see Section 1.7. of the DRIA for more discussion). Thus, the production of cellulosic diesel instead of cellulosic ethanol could help increase consumption of renewable fuels.

Thus, there is uncertainty as to which mix of cellulosic biofuels will be produced to fulfill the 16 Bgal mandate by 2022. The latest release of AEO 2009, for example, estimates a mixture of cellulosic diesel and ethanol produced for cellulo sic biofuel. For assessing the impacts of the

63RFS2 standards, we made the simplifying assump tion that cellulosic bi ofuel would only consist of ethanol, though market realities may also re sult in cellulosic diesel and other products.

Cellulosic biofuel could also be produced in ternationally. One example of internationally produced cellulosic biofuel is ethanol produced fr om bagasse or straw fr om sugarcane processing in Brazil. Currently, Brazil burns bagasse to produce steam and genera te bioelectricity. However, improving efficiencies over the comi ng decade may allow an increasing portion of bagasse to be allocated to othe r uses, including cellulosic biofue l, as the demand for bagasse for steam and bioelectricity coul d remain relatively constant.

One recent study assessed the biomass feedstoc k potential for select ed countries outside the United States and projected supply avai lable for export or for biofuel production. L, 232 For the study’s baseline projection in 2017, it was estima ted that approximately 21 billion ethanolequivalent gallons could be produced from cellulosic feedstocks at $36/dry tonne or less. The majority (~80%) projected is from bagasse, with the rest from forest products. Brazil was projected to have the most pot ential for cellulosic feedstock production from both bagasse and forest products. Other countries including India, China, and t hose belonging to the Caribbean Basin Initiative (CBI) also have some potenti al although much smaller feedstock supplies are projected as compared to Brazil. Although inte rnational production of cellulosic biofuel is possible, it is uncertain whether this supply would be available pr imarily to the U.S. or whether other nations would consume the fuel domestically. Therefore, for our analyses we have chosen to assume that all the cellulosic biofuel us ed to comply with RFS2 would be produced domestically.

1.2.2 Biomass-Based Diesel

Biomass-based diesel as defined in EISA means renewable fuel that is biodiesel as defined in section 312(f) of the Energy Policy Act of 1992 with lifecycle greenhouse gas emissions, as determined by the Administrator, that are at least 50% less than the baseline lifecycle greenhouse gas emissions. M Biomass-based diesel can include fatty acid methyl ester (FAME) biodiesel, renewable diesel (RD) that has not been co-processed with a petroleum feedstock, as well as cellulosic diesel. Alt hough cellulosic diesel produ ced through the FischerTropsch (F-T) process or other processes could pote ntially contribute to th e biomass-based diesel category, we have assumed for our analyses th at the fuel and its co rresponding feedstocks (cellulosic biomass) are already accounted for in the cellulosic biofuel category as discussed in the previous Section 1.2.1.

FAME and RD processes can make acceptable quality fuel from vegetable oils, fats, and greases, and thus will generally compete for the sa me feedstock pool. For our analyses, we have assumed that the volume contribution from FAME biodiesel and RD will be a function of the available feedstock types. For our analysis we assumed that virgin plant oils would be

L Countries evaluated include Argentina, Brazil, Canada, China, Colombia, India, Mexico, and CBI
M Subsequent to the analyses being conducted, our lifecycle analysis was completed showing that biomass-based diesel from soy oil failed to meet the 50% GHG threshold. The analyses contained in this DRIA are still based on the presumption that soy and other virgin plant oils used for biodiesel would qualify as biomass-based diesel. Adjustments in the analysis will be conducted for the FRM.

64preferentially processed by biodies el plants, while the majority of fats and greases would be routed to RD production. N, O This is because the RD proce ss involves hydrotreating (or thermal depolymerization), which is more severe and uses multiple chemical mechanisms to reform the fat molecules into diesel range material. The FA ME process, by contrast, relies on more specific chemical mechanisms and requires pre-treatment if the feedstocks contain more than trace amounts of free fatty acids or othe r contaminates which are typical of recycled fats and greases. In terms of volume availability of feedstocks, supplies of fats and greases are more limited than virgin vegetable oils. As a resu lt, our control case assumes the ma jority of biomass-based diesel volume is met using biodiesel facilities processing vegetable oils, with RD making up a smaller portion and using solely fats and greases.

The RD production volume must be furthe r classified as co-processed or non-coprocessed, depending on whether the renewable material was mi xed with petroleum during the hydrotreating operations (more details on this defini tion are in Section III.B.1 of the preamble). EISA specifically forbids co-processed RD from being counted as biomass-based diesel, but it can still count toward the total advanced biof uel requirement. What fraction of RD will ultimately be co-processed is uncertain at this time, since little or no commercial production of RD is currently underway, and little public information is av ailable about the comparative economics and feasibility of the two methods. We assumed in our control case that half the material will be non-co-processed and t hus qualify as biomass-based diesel.

We assumed that all biomass-based diesel would be produced from soy oil, corn oil, tallow, lard, yellow grease, and poul try fats. In the future, however, other feedstocks such as algae and jatropha may develop as discusse d previously in Section 1.1 of the DRIA.

1.2.3 Other Advanced Biofuel

As defined in EISA, advanced biofuel mean s renewable fuel, other than ethanol derived from corn starch, that has lifecycle gree nhouse gas emissions, as determined by the Administrator, that are at le ast 50% less than baseline lifecy cle greenhouse gas emissions. As defined in EISA, advanced biofuel includes the ce llulosic biofuel, biomass-based diesel, and coprocessed renewable diesel ca tegories that were mentioned in Section 1.2.1 and Section 1.2.2 above. However, EISA requires greater volumes of advanced biofuel than just the volumes required of these fuels. It is entirely possible that greater volumes of cellulosic biofuel, biomassbased diesel, and co-processed renewable diesel than required by the Act could be produced in the future. Our control case, however, does not a ssume that cellulosic biofuel and biomass-based diesel volumes will exceed those required under EISA. P As a result, to meet the total advanced biofuel volume required under EISA, advanced biof uel types are needed other than cellulosic biofuel, biomass-based diesel, and co -processed renewable diesel through 2022.

N Recent changes to federal tax subsidies and market shif ts may warrant changes to this assumption. We will reevaluate the relative production volumes of biodiesel and renewable diesel for the FRM.
O This analysis was conducted prior to the completion of our lifecycle analysis and assu mes the fuels will meet the required GHG threshold
P While cellulosic biofuel will not be limited by feedstock availability, it likely will be limited by the very aggressive ramp up in production volume for an industry which is still being demonstrated on the pilot scale and therefore is not yet commercially viable. On the other hand, biomass-based diesel derived from agricultural oils and animal fats are faced with relatively high feedstock costs which limit feedstock supply.

65We have assumed for the analyses conducte d that for our contro l case the most likely source of advanced fuel other than cellulosic biofuel, biomass-based diesel, and co-processed renewable diesel would be from imported sugarc ane ethanol. Our assess ment of international fuel ethanol production and dema nd indicate that anywhere from 3.8-4.2 Bgal of sugarcane ethanol from Brazil could be available for export by 2020/2022. If this volume were to be made available to the U.S., then there would be su fficient volume to meet the advanced biofuel standard. To calculate the amount of imported et hanol needed to meet the EISA standards, we took the difference between the total advanced bi ofuel category and cellulosic biofuel, biomassbased diesel, and co-processed renewable diesel categories. The amount of imported ethanol required by 2022 is approximately 3.2 Bgal. Re fer to Section 1.5.2 for a more detailed discussion on imported ethanol.

Recent news indicates that ther e are also plans for sugarcan e ethanol to be produced in the U.S in places where the sugar subsidy does not apply. For instance, sugarcane has been grown in California’s Imperial Valley specifica lly for the purpose of ma king ethanol and using the cane’s biomass to generate electricity to power the ethanol distillery as well as export excess electricity to the electric grid. Q There are at least two projects being developed at this time that could result in several hundred million gallons of ethanol produced. The sugarcane is being grown on marginal and existing cropland that is unsuitable for food crops and will replace forage crops like alfalfa, Bermuda grass, Klein grass, etc. Harvesting is expected to be fully mechanized. Thus, there is potential for these pr ojects and perhaps others to help contribute to the EISA biofuels mandate. This could lower the volume needed to be imported from Brazil.

Butanol is another potentia l motor vehicle fuel which could be produced from biomass and used in lieu of ethanol to comply with th e RFS2 standard. Production of butanol is being pursued by a number of companies including a partnership between BP and Dupont. Other companies which have expressed the intent to produce biobutanol are Baer Biofuels and Gevo. The near term technology being pursued for pr oducing butanol involves fe rmentation of starch compounds, although it can also be produced from cellulose. Butanol ha s several inherent advantages compared to ethanol. First, it has higher energy density th an ethanol which would improve fuel economy (mpg). Second, butanol is much less water soluble which may allow the butanol to be blended in at th e refinery and the resu lting butanol-gasoline blend then more easily shipped through pipelines. This would reduce dist ribution costs associated with ethanol’s need to be shipped separately from its gasoline bl endstock and also save on the blending costs incurred at the terminal. Third, butanol can be blended in higher concentr ations than 10% which would likely allow butanol to be blended with gasoline at high enough concentrations to avoid the need for most or all of high concentration et hanol-gasoline blends, such as E85, that require the use of fuel flexible vehicles. For example, because of butanol’s lower oxygen content, it can be blended at 16% (by volume) to match the oxyge n concentration of ethanol blended at 10% (by volume). Because of butanol’s higher energy de nsity, when blending butanol at 16% by volume, it is the renewable fuels equiva lent to blending ethanol at about 20%. Thus, butanol would enable achieving most of the RFS2 standard by blending a lower concentration of renewable fuel than having to resort to a sizab le volume of E85 as in the case of ethanol. The need to blend

Q Personal communication with Nathalie Hoffman, Managing Member of California Renewable Energies, LLC, August 27, 2008

66ethanol as E85 provides some difficult challeng es. The use of butanol may be one means of avoiding these blending difficulties.

At the same time, butanol has a couple of le ss desirable aspects relativ e to ethanol. First, butanol is lower in octane compared to ethano l – ethanol has a very high blending octane of around 115, while butanol’s octane ranges from 87 octane numbers for normal butanol and 94 octane numbers for isobutanol. Potential but anol producers are likely to pursue producing isobutanol over normal butanol because of isobutanol ’s higher octane conten t. Higher octane is a valuable attribute of any gaso line blendstock because it help s to reduce refining costs. A second negative property of butanol is that it has a much higher viscosity compared to either gasoline or ethanol. High viscos ity makes a fuel harder to pum p, and more difficult to atomize in the combustion chamber in an internal combus tion engine. The third downside to butanol is that it is more expensive to produce than ethano l, although the higher produc tion cost is partially offset by its higher energy density.

Another potential source of renewable transportation fuel is biomethane refined from biogas. Biogas is a term meaning a combustib le mixture of methane and other light gases derived from biogenic sources. It can be combuste d directly in some applications, but for use in highway vehicles it is typically purified to closely resemble fossil natural gas for which the vehicles are typically designed. The definition of biogas as given in EISA is sufficiently broad to cover combustible gases produced by biologi cal decomposition of organic matter, as in a landfill or wastewater treatment facility, as well as those produced via thermochemical decomposition of biomass.

Currently, the largest source of biogas is landfill gas coll ection, where the majority of fuel is combusted to generate electricity, with a small portion being upgraded to methane suitable for use in heavy duty vehicle fleets. Current literature suggests a pproximately 16 billion gasoline gallons equivalent of bi ogas (referring to energy content) could potentially be produced in the long term, with about two thirds coming from biomass gasification and about one third coming from waste streams such as landfills and human and animal sewage digestion. 233, 234

Because the majority of the biogas volume estimates assume biomass as a feedstock, we have chosen not to include this fuel in our analyses since we are projecting most available biomass will be used for cellulosic liquid biof uel production in the long term. The remaining biogas potentially available from waste-related sources would come from a la rge number of small streams requiring purification and connecti on to storage and/or di stribution facilities, which would involve significant economic hurdles. An additional and important source of uncertainty is whether there would be a suffici ent number of vehicles configured to consume these volumes of biogas. Thus, we expect futu re biogas fuel streams to continue to find nontransportation uses such as electrical power generation or facility heating.

1.2.4 Other Renewable Fuel

The remaining portion of total renewable fuel not met with advanced biofuel is assumed to come from corn-based ethanol. The Act essen tially sets a limit for participation in the RFS program of 15 Bgal of corn ethanol by 2022. It should be noted, however, that there is no

67specific “corn-ethanol” mandated volume, and th at any advanced biofuel produced above and beyond what is required for the advanced biofue l requirements could reduce the amount of corn ethanol needed to meet the total renewable fuel standard. This occurs in our projections during the earlier years (2009-2014) in which we project that some fuel s could compete favorably with corn ethanol (e.g. biodiesel and imported etha nol). Beginning around 2015, fuels qualifying as advanced biofuels likely will be devoted to meeting the increasingly stringent volume mandates for advanced biofuel. It is also worth noting th at more than 15 Bgal of corn ethanol could be produced and RINs generated for that volume unde r our proposed RFS2 regulations. However, obligated parties would not be re quired to purchase more than 15 Bgal worth of corn ethanol RINs.

We are assuming for our analysis that sufficient corn ethanol will be produced to meet the 15 Bgal limit. However, this assumes that in the future corn ethanol production is not limited due to environmental constraints, such as water quantity issues (see Section 6.10 of this DRIA). This also assumes that in the future either corn ethanol plants are constructed or modified to meet the 20% GHG threshold, or that sufficient corn et hanol production exists that is grandfathered and not required to meet the 20% threshold. Our current projection is that up to 15 Bgal could be grandfathered, but actual volumes will be determined at the time of facility registration. Refer to Section 1.5.1.4 for more information. Since our current lifecycle analysis estimates that much of the current corn ethanol would not meet the 20% GHG reduction threshold required of nongrandfathered facilities without f acility upgrades, then if actual grandfathered corn volumes are less than 15 Bgal it may be necessary to meet the volume mandate with other renewable fuels or through the use of advanced t echnologies that could improve the corn ethanol lifecycle GHG estimates.

1.2.5 Control Case for Analyses

Table 1.2-1 summarizes the fuel types used for the control case a nd their corresponding volumes for the years 2009-2022.

6869Table 1.2-1.Control Case Projected Renewable Fuel Volumes (billion gallons)YearAdvanced BiofuelNon-AdvancedBiofuelTotal RenewableFuelCellulosicBiofuelBiomass-Based DieselaOther Advanced BiofuelCellulosicEthanolFAMEbBiodieselNon-Co-processedRenewable DieselCo-processedRenewable DieselImportedEthanolCorn Ethanol20090.000.500.000.000.509.8510.8520100.100.640.010.010.2911.5512.6020110.250.770.030.030.1612.2913.5320120.500.960.040.040.1812.9414.6620131.000.940.060.060.1913.7516.0020141.750.930.070.070.3614.4017.5820153.000.910.090.090.8315.0019.9220164.250.900.100.101.3115.0021.6620175.500.880.120.121.7815.0023.4020187.000.870.130.132.2515.0025.3820198.500.850.150.152.7215.0027.37202010.500.840.160.162.7015.0029.36202113.500.830.170.172.6715.0032.34202216.000.810.190.193.1415.0035.33aBiomass-Based Diesel includes FAME biodiesel, cellulosic diesel, and non-co-processed renewable diesel.bFatty acid methyl ester (FAME) biodiesel70 We needed to make this projection soon afte r EISA was signed to allow sufficient time to conduct our long lead-time analyses. As a result, we used the same ethanol-equivalence basis for these projections as was used in the RFS1 rulema king. However, as described in Section III.D.1 of the preamble, we are also co-proposing that volumes of re newable fuel be counted on a straight gallon-for-gallon basis under RFS2, such that all Equi valence Values would be 1.0. While the control case volumes used as the basis fo r our analyses do not reflect this approach to Equivalence Values, the net effect on projected volumes is very small; instead of 36 billion gallons of renewable fuel in 2022, our control ca se includes 35.3 billion gallons. We do not believe that this difference will substantively aff ect the analyses that are based on our projected control case volumes.

1.2.6 Construction Feasibility for Cellulosic Ethanol Industry

Start-up of cellulosic ethanol plants is expected to begin in earnest with a few small plants in 2010-11, followed by addition of industry capacity continuing at an increasing pace due to more plant starts per year as well as increasi ng plant size. This is typical as an industry progresses up the learning curve, and investors become more confident and are willing to fund larger, more efficient plants. During the period from 2010-12, we also expect a slowing of starch ethanol plant construction, such that engineering and constr uction personnel and equipment fabricators would potentially be able to transition to work on cellulosic ethanol facilities.

Here we examine the build rate required to construct cellulosic plants in time to meet the standards in Table 1.2-1, and we comp are this to the historic build ra te of capacity in the starch ethanol industry. Figu re 1.2-1 depicts these construction trends.Figure 1.2-1. Historic and projected U.S. et hanol production trends 2001-2022.

0 5 10 15 20 25 30 35

200 2 2004 2 006 20 08 201 0 2012 20 14 201 6 2018 2 020 202 2Plants/yr

0 5 10 15 20 25 30 35Bgal/yr Starch Plants Cellulosic Plants Total Ethanol Capacity

2008 and later data are projected

Historical plant build rates fo r starch ethanol were derived from figures in Chapter 1.5 of this DRIA (see Figure 1.5-1). Average plant capacity figures were estimated from existing capacity and plant counts, and we project that the recent trend toward larger plant sizes continues going forward. Approximately 200 starch ethanol plants are expected to be operating by 2022.

For cellulosic ethanol plant construction, we assumed new plant size would begin relatively small at 40 million gal/yr for 2010- 13, increasing to 80 million gal/yr for 2014-17, and 100 million gal/yr afterwards. Given the volume sta ndards laid out in the EISA, as well as the number of cellulosic ethanol plants projected in Chapter 1.5 to be roughly 180 by 2022 (see Table 1.5-36), we arrive at a ma ximum required build rate of approximately 2 billion gal/yr from 2018-2022. This is similar to the rate of starch et hanol construction in recent years. Table 1.2-2 shows a summary of the figures used in the analysis.

71Table 1.2-2. Summary of figures used in the ethanol plant construction rate analysis 2001-2022.
Starch Ethanol Cellulosic Ethanol Build Avg Plant Capacity Industry Build Avg Plant Capacity Industry Rate Capacity Change Capacity Rate Capacity Change Capacity Year Plants/yr Mgal/yr Bgal/yr Bgal/yr Plants/yr Mgal/yr Bgal/yr Bgal/yr 2001 1.8 2002 6 50 0.3 2.1 2003 14 50 0.7 2.8 2004 12 50 0.6 3.4 2005 10 50 0.5 3.9 2006 20 50 1.0 4.9 2007 16 100 1.6 6.5 2008 27 100 2.7 9.2 2009 20 100 2.0 11.2 2010 15 100 1.5 12.7 2 40 0.1 0.1 2011 10 100 1.0 13.7 5 40 0.2 0.3 2012 8 100 0.8 14.5 7 40 0.3 0.6 2013 5 100 0.5 15.0 10 40 0.4 1.0 2014 15.0 12 80 1.0 1.9 2015 15.0 15 80 1.2 3.1 2016 15.0 17 80 1.4 4.5 2017 15.0 18 80 1.4 5.9 2018 15.0 20 100 2.0 7.9 2019 15.0 20 100 2.0 9.9 2020 15.0 20 100 2.0 11.9 2021 15.0 20 100 2.0 13.9 2022 15.0 20 100 2.0 15.9

This work suggests that it may be feasible to construct plants quickly enough to meet the standard if plant starts can reach a rate as high as for starch et hanol in recent years. While cellulosic plant technology is stil l developing, it is expected that the plants will be considerably more complex and expensive to construct than the starch ethanol plants being built today. Therefore, we believe the market will need to react even more quickly with capital funding, as well as design and construction resources.



### 1.3 Agricultural Residue Harv esting, Storage & Transport

The amount of agricultural residue that can be removed is limited by how much residue must be left on the field to maintain soil health and by the mechanical efficiency (inefficiency) of the harvesting operation. We have already disc ussed sustainable removal rates in Section 1.1 and will discuss the technical ch allenges related to the harvesting, storage, and transport of agricultural residues in th e following sections.

1.3.1 Harvesting Technologies

The amount of residue that can be harves ted from any one field is dependent on the combined mechanical inefficienci es of the harvesting operation and on the health of the soil.

72Even if 100% of the residue could be removed, fr om a soil health perspective, the mechanical inefficiencies of the harvest operation make it impossible to remove that much residue. A controlling principle is that incr easing amounts of dry matter are lo st as the stover is handled by increasing numbers of different machines.

Corn stover harvest, at present, requires mu ltiple machines: combines, shredders, rakes, balers, bale wagons, and stackers just to get the stove r bales to the side of the field; dry matter is lost during each operation. Current ly, there are no harvesting machines designed specifically for residue harvest, other than perhaps, for small gr ain straws that use common hay equipment. One proposal for corn stover harvest is to shut the sp reader off on the grain combine in order to form a windrow, of sorts, following which the windrow is baled. 235 However, modern combines leave most of the stalk standing. In or der to harvest as much of the stove r as possible, it is necessary to shred the standing stalks and then rake all of it together prior to baling. 236 The baler pickup must be set high enough to avoid picking up dirt and dirt clods , the dirt-particles fr om which are very hard on harvesting equipment and that would demand a cleanup stage in downstream processing, which of itself would translate into overall dry ma tter losses. As such, it is likely that the baler will leave some amount of stover.

Small grain straws, such as those from wheat , oats, barley, and rice have been harvested for many years. A significant difference between the harvesting equipment used for corn stover and these grains, is that the small grain plan t is cut off near the ground and passes through the combine at the time of harvest. It falls to th e ground from the harveste r into somewhat of a windrow; in some cases, the windrow many need to be raked together before baling to gain maximum removal efficiency. Since the whole grai n plant had dried prior to harvest, it’s not necessary to wait for the straw to dry before it’s baled. Small grain straws can be baled, hauled, and stacked in standard small bales or in larger 3’ x 4’ x 8’ square bales with current hay equipment.

Sugarcane bagasse is not harvested, in the se nse we’ve discussed ‘a harvest.’ It is a byproduct of sugar production from sugarcane, de livered by truck and trailer from the sugar processing facility to the ethanol plant.

We anticipate that by 2022, the corn stover ha rvest will actually be reduced to a singlepass operation during which the amount of residue left on the field will be less a function of harvest efficiency and more a function of the farmer/grower and the harvesting company being able to determine how much residue must be le ft to maintain soil health. A combine designed specifically for the job must still be constructed, but we expect that it will cut the whole stalk a few inches above the soil, leav ing some stalk anchored to th e ground. A single-pass harvester could cut the entire pl ant a few inches above the ground and pull all of it, e.g., stalks, leaves, cobs, and grain into the combine, where they become a single, mixed grain and stover stream. The harvester blows the entire stream into tr actor-pulled grain-carts that run along-side the harvester. When a cart is filled, it is replaced by an em pty cart, and the full cart is hauled to the field side, where it’s unloaded into bulk ‘walking -floor’ semi trailers, a nd hauled to a co-op or depot type elevator/facility for fu rther processing and storage. At the elevator, the stover/grain mix is unloaded into equipment for further proc essing before it’s sent to storage. Although a facility (equipment, buildings, et c.) at an elevator for separating the corn grain from the stover

73has not been constructed, we anticipate that it could operate very much like a modern grain harvester/combine, except it will obviously be st ationary. The entire stream could be fed, by chain or belt, where it drops between a cylinder covered with rough steel bars and a piece of equipment called a concave. As the cobs are ru bbed between the steel bars and concave, the corn grain rubs off and drops ont o a perforated belt; most of th e stover remains are larger than corn grain pieces, and is moved rearward toward the spreader. The corn grain and small stover particle fall through and are carried to a chaffer.

One of the issues yet to be solved is how the proper quantity of residue required for soil maintenance is either left on or returned to the field and how it will be evenly spread. This of course could add another operati on or two and would likely require heavy equipment to travel on fields, which would undoubtedly exacerbate comp action problems. We don’t anticipate any major future changes in the way the small grain straws will be harvested.

1.3.2 Feedstock Storage

We expect corn stover bales and small grain st raw bales will be stor ed in similar ways. There has been some discussion about storing bale s (of any of the feedstocks) out in the open, on gravel; few suggest storing them on dirt. Large, round stover bales store better in the open than large square bales, in that rain and particularly snow collect on flat surfaces more readily than on round. Another important issue that impacts whether round or square bales are used is that round bales can usually be stacked only three-bales high, in a kind of pyramid-shaped stack. One result of stacking round bales higher is that because of the low bale density, they tend to deform and the stack becomes unstable and there is an increase d chance of dry matter loss. Square bales can be stacked as many as five-high and remain stable.

As discussed in Section 1.3.1, we anticipate that the stover harvest could become a single-pass operation. Equipment at the elevator/depot separate s the stover from the grain, following which the stover is chopped and dried. This distributed preprocessing facility can provide significant cost benef its by producing a higher value fe edstock with improved handling, transporting, and merchandising potential. In addition, data suppor ting the pr eferential deconstruction of feedstock materials due to their bio-composite st ructure identifies the potential for significant improvements in equipment effi ciencies and compositional quality upgrades. 237 The stover, now with flowability characteristics similar to small cereal grains, is moved by standard grain loading and unloading systems into large corrugated steel bins for intermediate storage. In this harvest format, the stover is handled by only two machines before it reaches the roadside and never hits the ground. Dry matte r losses should be significantly reduced.

Harvesting wet stover as chopped material, simila r to animal forage, and ensiling it in a silo bag can be done successfully, with dry-ma tter (DM) matter loss at about 11% after seven months storage. Harvesting wet stover by baling and tube wrapping was also successful with DM losses of 3.6%. Dry stover ba les stored indoors or outdoors had average DM losses of 5% and 15%, respectively. Wrapping dry bales in ne t wrap and storing on a well drained surface significantly reduced DM loss compared to storing twine wrapped dry bales on the ground. 238

74Indoor storage is, in most cases, a concrete slab with a roof, supported by poles, with open sides (pole-barn). Depending on the number of ba les to be stored, the slab must be sized to include aprons around all four edges with aisl es between stacks to accommodate stacking and hauling equipment and for fire safety. Corn stove r is usually harvested only once per year during an approximately 50- to 70-day period; this peri od is bounded by the time the corn grain harvest begins and the onset of winter we ather, which is usually wet, and could include snow cover. As a result, the quantity of stover an ethanol plants expects to use until the next harvest (~one-year) must be harvested and stored during the same peri od. We don’t expect that a full-year’s worth of feedstock for most ethanol plants wi ll be stored at the plant site; for larger plants the storage site would cover several hundred acres and the logist ics of hauling and stack ing the required number of bales would be impossible. If the bales are to be stored as large, sq uare baled, in satellite storage, we anticipate that pole-barn type stor age will be used. A similar slab and pole-barn would be constructed at the ethanol plant to store three- or four-days of feedstock. R

Sugarcane bagasse is high in moisture, typi cally 60 to 75 percent, that is packed in storage and sealed as much as possible to reduce the amount of oxygen that would otherwise promote rotting. On the other ha nd, the anaerobic fermentation in th e ensilage creates lactic acid that acts as a preservative.

In the future, as we described in Secti on 1.3.1, we expect stover will be ground into particles that are small and stab le enough that they can be handled and stored using methods and equipment currently used for grains of all kinds, e.g., silos or tanks.

1.3.3. Feedstock Transport

Baled Format: Following the baling operation, we expect stover bale s will be picked up from the field in 10-bale loads, by vehicles desi gned for that purpose (such vehicles are currently used to gather hay bales); the bales are subsequently unloaded or dropped at the field-edge. Later, the bales are loaded onto wagons pulled by high-speed tractors th at haul as many as 20­ bales per load to satellite stor age (the pole-barns described in Section 1.3.2). The bales are unloaded and stacked for storage until they are need ed at the ethanol plant. Transport to the plant is by over-the-road trucks a nd trailers that can haul net-load s of up to about 45- to 50-tons. However, because the bale density is low (on aver age, about half the weight of a similarly sized hay bale), the maximum number of bales a truc k can haul usually weighs much less than the maximum allowable weight. We expect small grai n straws will be handled in much the same way. Sugarcane bagasse can contain as much as 60% to 75% moisture and is usually hauled in open-topped metal trailers from which the baga sse can be dumped onto belts or into auger hoppers that convey it to ensilage type storage.

As we pointed out in the prev ious paragraph, low bale density usually means that trucks and trailers can’t haul the maximum allowabl e weight, which translates into increased transportation costs. We anticipate that dryi ng and grinding the stover (and possibly the other residues) will increase its bulk de nsity and allow the transports to haul maximum weight loads to the ethanol plant.

R We discuss this information in greater detail in subsection 4.1.1.2 of this chapter.

75Grain Elevator Format: According to proce ss requirement, an ethanol plant could pick up its feedstock from the elevator/depot in trucks and trailers for transport to the facility. We believe stover feedstock in the ground format will have a significantly higher bulk-density than baled stover, which should translate into lower transportation costs.



### 1.4 Biofuel Production Technologies

Biofuel production technologi es continue to evolve with research and development efforts focused on reducing costs and increasing efficiencies. Improvements include increasing conversion yields for various feedstocks, reduci ng energy and materials usage, eliminating or reducing wastes, finding alternat ive uses for by-products, etc. For those technologies not yet commercial, researchers are combining their inno vative ideas to develop cost-effective processes to produce biofuel at low enough costs to compet e with their petroleum counterparts. The following sections describe both proven and new technologies which may be used to produce renewable fuels to meet the EISA 36 billion gallon standard by 2022.

1.4.1 Corn Ethanol

There are two primary processes for conve rting corn (and other similarly processed grains) into ethanol: wet milling and dry milling. The main difference between the two is in the treatment of the grain. Dry mill plants grind th e entire kernel (shown below in Figure 1.4-1) and generally produce only one primary co-product: distillers grains w ith solubles (DGS). The coproduct is sold wet (WDGS) or dried (DDGS) to the agricultural market as animal feed. Wet mill ethanol plants separate the grain kernel pr ior to processing into its component parts and produce other co-products (usually gluten feed, gl uten meal, and food-grade corn oil) in addition to DGS. Each process is described in great er detail in the subsections that follow.

Figure 1.4-1. Components of the Corn Kernel

ENDOSPERM Contains nearly all the starch used to make ethanol and also contains protein used in animal feed.

TIP CAP Attachment point of the kernel to the cob. PERICARP/BRAN The protective covering of the kernel. Contains fiber used in human and animal food products.

GERM
Contains corn oil used in food applications as well as biodiesel production.

761.4.1.1 Dry Milling Technology 239

In traditional dry mill plants, first the corn is screened to remove any unwanted debris. Then, it goes through a hammer mill where it is gr ound into course flour also know as “meal.” Next the meal is cooked to physically and ch emically prepare the starch for fermentation.

The first step of the cooking process is to form a hot slurry. The meal is mixed with water, the pH is adjusted, and an alpha-amylase enzyme is added. The slurry is heated to 180– 190°F for about 30–45 minutes to reduce viscosity.

The second step in the cooking process is lique faction, which occurs in two steps. First the hot slurry is pumped through a pressurized jet cooker at approximately 220°F and held for about 5 minutes. The mixture is then cooled by an atmospheric or vacuum flash condenser. After cooling, the mixture is held for 1–2 hours at 180–190°F to give the alpha-amylase enzyme time to break down the starch in to short-chain carbohydrates al so know as “dextrins.” Once cooking is complete, a pH and temperature adjust ment is made, a second enzyme (glucoamylase) is added, and the resulting mixture (also know as “mash”) is pumped into the fermentation tanks.

During the fermentation process, the glucoamy lase enzyme breaks down the dextrins to form simple sugars. Yeast is added to convert the sugar into ethanol and carbon dioxide. The mash is then allowed to ferment for 50–60 hours. The result is a mixture that contains 10-15% ethanol by volume (20 to 30-proof) as well as solids from the grain and added yeast.

From here, the fermented mash is pumped into a multi-column distillation system where additional heat is added. The co lumns utilize the differences in the boiling points of ethanol and water to boil off and separate the ethanol. By th e time the product stream leaves the distillation columns, it contains about 95% ethanol by volume (190-proof). The residue from this process, called stillage, contains non-fermentable solids and water and is pumped out from the bottom of the columns into the centrifuges.

The final step in the ethanol production pro cess is dehydration to remove the remaining 5% water. The ethanol is passed through a molecu lar sieve to physically se parate the water from the ethanol based on the different sizes of the molecules. This result is 200-proof anhydrous (waterless) ethanol. At this poi nt, denaturant is added (maki ng it unfit for human consumption) and the ethanol is placed into storage.

During the ethanol production process, tw o primary co-product s are created: carbon dioxide and distillers gr ains. As yeast ferment the sugar, they release large amounts of carbon dioxide gas. In some plants it’s released into the atmosphere, but where local markets exist, it’s captured and purified with a scrubber and sold to the food processing industry for use in carbonated beverages and flas h-freezing applications.

The stillage from the bottom of the distilla tion columns contains so lids from the grain and added yeast as well as liquid from the water ad ded during the process. It is separated via centrifuge into thin stillage (a liquid with 5–10% solids) and wet distillers grain.

77Some of the thin stillage is routed back to the cooking tanks as makeup or “backset” water, reducing the amount of fresh water requi red by the cooking process. The rest is sent through a multiple-effect evaporation system where it is concentrated into a condensed distillers solubles or “syrup” containing 25–50% solids. This syrup, which is high in protein and fat content, is then mixed back in with the distillers grain to make wet distillers grains with solubles.

Wet distillers grains with solubles (WDGS ) contain most of the nutritive value of the original feedstock (plus a dded yeast) and can be easily conveyed as a wet cake for transport. As such, WDGS makes an excellent cattle ration for local feedlots and dair ies. However, WDGS must be used soon after it’s produced because th e wet grains spoil easi ly. Since many ethanol plants are located in areas wh ere there are not enough nearby cattle to utilize all the feed, a portion or all of the WDGS is sent through a dr ying system to remove moisture and extend the shelf life. The resulting dried distillers grains with solubles (DDGS) are commonly used as a high-protein ingredient in cattle, swine, poultry, and fish diets. Distille rs grains are also being researched for human consumption. A schematic of a typical dry-mill ethanol plant is shown below in Figure 1.4-2.

78Figure 1.4-2. Dry Milling Process

1.4.1.2 Wet Milling Technology 240

In wet mill plants, first the corn is soaked or "steeped" in a dilute sulfurous acid solution for 24-48 hours. The steeping process facilitates th e separation of the corn kernel into germ, fiber, gluten, and starch.

After steeping, the corn slurry is processed through a series of grinders to separate out the germ. The germ is either extracted on-site or sold to crushers who extract the corn oil. The corn oil in its crude state can be sold to the biodiesel or renewable diesel industry. However, most wet mill plants refine the product into food-grade corn oil for use in cooking applications. The remaining fiber, gluten and star ch components are further segreg ated using centrifugal, screen, and hydroclonic separators.

The steeping liquor is concentr ated in an evaporator. This concentrated product, heavy steep water, is co-dried with th e fiber component and is then sold as corn gluten feed to the livestock industry. Heavy steep water is also sold by itself as a feed ingred ient and is used as a component in Ice Ban, an environmentally-friend ly alternative to salt for removing ice from

79roads.

The gluten component (protein) is filtered and dried to produce the corn gluten meal coproduct. This product is highly s ought after as a feed ingredie nt in poultry broiler operations.

The starch and any remaining water from the mash is generally processed in one of three ways: fermented into ethanol, dried and sold as dried or modified corn starch, or processed into corn syrup. If made into ethanol, the fermentatio n process is very similar to the dry mill ethanol production process described above. A schematic of the wet milling process is shown below in Figure 1.4-3.
Figure 1.4-3. Wet Milling Process

1.4.1.3 Advanced Technologies

A number of corn ethanol plants are pursu ing technological advancements such as cold starch fermentation, dry fractionation, and corn oi l extraction to improve plant yields and reduce energy requirements. Research is also being done that looks at adding membranes to reduce ethanol distillation requirements. Finally a growing number of companies are utilizing alternative boiler fuels and/or incorporating combined heat and power (CHP) technology into their facilities to reduce to pl ant energy requirements, and in some cases, produce excess power for the grid. The advanced t echnologies currently being pursed by the corn ethanol industry are described in more detail below.

80Cold Starch Fermentation 241 242

POET Biorefining, the United States ’ largest corn ethanol producer S , has developed a cold starch fermentation process that uses raw-star ch hydrolysis to convert starch to sugar, which then ferments to ethanol without heat. The patent-pending POET technology eliminates the cooking process that has been part of ethanol production for years. According to POET, the BPXTM process not only reduces energy costs, but also releases additiona l starch for conversion to ethanol, increases protein content and quality of co-products, increa ses co-product flowability, potentially increases plant throughp ut, and significantly decreases plant emissions. The benefits of the process include reduced energy costs, in creased ethanol yields, increased nutrient quality in the distillers grains and decr eased plant emissions. At the time of our industry assessment, BPXTM was reportedly used in 20 of POET's 22 U.S. ethanol plants. According to POET, the BPXTM process, which yields 20% ethanol in fe rmentation, increases theo retical ethanol yields from the industry standard of 2.7 gallons of ethanol per bushel of corn up to 3 gallons per bushel. Earlier this year, POET also announced that it wa s funding a research collaboration with Iowa State University to help improve the efficiency of the BPXTM process.

Dry Fractionation

Dry fractionation is a mechanical separation of the corn kernel into its three component pieces, the germ, bran, and endosperm before fermentation. This separation decreases the amount of non-fermentable material sent through the process and allows each of the components to be processed separately to produce new, hi gher-value co-products. As shown in Figure 1.4-1, the germ is a small, non-fermentable part of th e kernel consisting predominantly of protein and oil. Food grade corn oil can be extracted from the germ. After the oil has been extracted, the remainder of the germ can then be blended into the DGS to increase its protein content. The bran, or pericarp, is the protectiv e outer covering of the kernel. The bran can be sold as cattle feed, human fiber additive, or corn fiber for oil extraction. It can also be burned to reduce the amount of coal or natural gas required for ethanol production. The endosperm, which contains approximately 98% of the starch a nd is the only fermentable portion of the kernel, is sent to the fermentation vessels. Decreasing the amount of non-fermentable materials (germ and bran) in the process has many beneficial e ffects, including increasing the pr oduction capacity of the plant, decreasing the energy required to dry the DGS , and potentially decreasing the enzyme requirement of the plant but up to 30%. Dry frac tionation is currently only used by a few ethanol plants; however several compan ies, including ICM, Delta-T, and POET currently offer dry fractionation options for new or existing plants.

Corn Oil Extraction 243

Several dry mill corn ethanol plants have implemented corn oil extraction to produce fuel-grade corn oil for the biodies el industry. The crude corn oil can either be extracted from the thin stillage (the non-ethanol li quid left after fermentation) befo re it enters the evaporator, or from the DGS after it has been dried. While the corn oil is of a lower qual ity and value than that produced from corn fractionation, the equipmen t can be easily added to existing ethanol production facilities and is relatively inexpensive. In additi on to generating an additional

S At the time of our May 2008 plant assessment. For more information, refer to Figure 1.5-4 in Section 1.5.1.1.

81revenue stream from the fuel-grade corn oil, reducing the oil content of the DGS improves its flowability and concentrates its protein content. The de-fatted DGS is more marketable than DGS containing corn oil as higher quantities can be included in dairy and beef cattle feed.

Membrane Replacement

Several companies are currently working to produce commercially viable polymeric membranes that could potentially reduce the ener gy used in distillation and eliminate the need for molecular sieve units currently used in most ethanol plants. One such company, Vaperma, has partnered with GreenField Ethanol to prove the viability of its Siftek TM technology. Siftek TM membranes have been successfully installed in GreenField’s Tiverton, Ontario demonstration plant and are scheduled to be installed in their Chatham, On tario plant, which produces 187 million liters of ethanol per year, by the end of 2008. Vaperma claims its Siftek TM membranes are capable of producing a fuel grade ethanol product from an ethanol/water mixture that contains as much as 60% water. These membra nes would replace the rectif ier unit as well as the molecular sieves used in a conventional ethanol plant, po tentially reducing the energy consumption of the ethanol dehydration pro cess by up to 50%. Another way for these membranes to be used is to tr eat the ethanol/water vapor colle cted when the molecular sieve units are regenerated. This stream is usua lly recycled to the rectifier and makes up approximately one third of the feed to the rectifying column. Using Siftek TM technology to treat this stream reduces the feed to the rectif ier, reducing energy cons umption and increasing production rate by 20% or more. While membrane replacement technology has the potential to significantly reduce the energy demands of an ethanol plant, they are likel y at least a couple of years from being commercially available. It is not expected that membrane replacement units would be retrofitted into existing plants due to th e significant capital costs. These two factors will effectively limit the use of membrane separa tion units to new ethanol plants built in 2010 or later.

An alternative method of membrane repl acement is to use ethanol-permeating membranes to eliminate the need for the beer column, followed by a water-selective membrane for final dehydration. Eliminating the need for the beer column as we ll as the rectifier and molecular sieve units would significantly reduce the capital costs of an ethanol plant, as well as lowering the energy requirements of ethanol separation. While th is technology has the potential to significantly lower the cost and energy demands of an ethanol plant, it is highly unlikely that it will be available for near term commercialization. It has therefore not been considered section 1.5.1.3 on the forecasted growth of advanced ethanol technologies. If more information becomes available we will consider including this method in the energy usage forecast for the final rule.

Combined Heat and Power 244

Ethanol production is a rela tively resource-intensive proce ss that requires the use of water, electricity, and steam. In most cases , water and electricity are purchased from the municipality and steam is produced on-site using boilers fired by natural gas, coal, or in some cases, alternative fuels (describ ed in more detail below). T However a growing number of ethanol

T There are a few ethanol producers that we have been ma de aware of from EPA Region 7 that pull their steam from a nearby utility.

82producers are pursuing combined heat and pow er (CHP) technology. CHP, also known as cogeneration, is a mechanism for improving overall plant efficiency by using a single fuel to generate both power and thermal energy. The most common configuration in ethanol plants involves using the boiler to power a turbine gene rator unit that produces electricity, and using waste heat to make process steam. In some cas es, the generator produces excess electricity that can be sold to the grid. While the thermal energy demand for an et hanol plant using CHP technology is slightly higher than that of a conventional plant, th e additional energy used is far less than what would be required to produce the same amount of electricity in a central power plant. The increased efficiency is due to the abi lity of the ethanol plant to effectively utilize the waste heat from the electric ity generation process.

The CHP system can be owned and operated solely by the ethanol plant, or jointly operated with the local utility company. In thes e cases it is common for the utility company to purchase the generator and to spli t the cost of the generator fuel with the ethanol plant. The utility company receives the elect ricity produced, while the ethanol plant uses the waste heat. These arrangements reduce the energy costs for bot h parties, as well as reducing the green house gas emissions that would be produced by operati ng the generator and boiler separately. An illustration of the more common CHP configuration typically seen in ethanol plants is shown below in Figure 1.4-4.

Figure 1.4-4. Steam Boiler with Steam Turbine

Alternative Boiler Fuels

In addition to CHP (or sometimes in comb ination), a growing number of ethanol producers are turning to alternative fuel sources to replace traditional boiler fuels (i.e., natural gas and coal), improve their carbon footprint, and/ or become more self-sustainable. Alternative boiler fuels currently used or being pursued by the ethanol i ndustry include biomass (wood and other organic feedstocks), co-products from the et hanol production process (b ran, thin stillage or

83syrup), manure biogas (methane from nearby animal feedlots), and landfill gas (generated from the digestion of munici pal solid waste).

For a breakdown of current and near-term U utilization of CHP t echnology and alternative boiler fuels, refer to Sections 1.5.1.1 and 1.5.1.2. For our 2022 projections of the potential utilization of these and other advanced technologies, refer to Section 1.5.1.3.

1.4.2 Ethanol from Sugarcane Juice and Molasses

The production of ethanol from sugarcan e juice or molasses is the least complicated method to use biomass to produce ethanol since suga rcane contains six-carbon sugars that can be directly fermented. This is currently the method used to produce ethanol in Brazil. In contrast, starch or cellulosic feedstocks require additional steps before suga rs are released for use in the fermentation step. In addition to ethanol, sugarc ane also yields trash, bagasse, filter cake mud, and vinasse. These by-produc ts are described below:

Trash (Leaves and Tops)

The tops, brown and green leaves of suga rcane are commonly referred to as trash. Sugarcane trash is not currently harvested for production of ethanol . Much of the U.S. sugarcane trash is currently mechanically harvested and de livered to the factory with stalks, where it detrimentally affects industrial processing. Howe ver, there has been growing interest to use hydrolysis (cellulosic) technologies to convert tr ash to ethanol because it is easier to hydrolyze than bagasse, as the trash contains less ligni n. Sugarcane trash can also be converted into bioelectricity.

Bagasse

Bagasse is the fibrous material left over after juice is extrac ted from the crushed stalk of the sugarcane plant. It mainly consists of hemicellulose, cellulo se and lignin valued mainly for being a fossil fuel and wood substitute in steam energy generation. U.S. factories and other industrial units have used bagasse mainly for steam production, but a few are producing electricity (co-generation) as we ll. In Brazil, some facilities are able to produce more energy than needed and have exported excess electricity to the grid.

Filter Cake Mud

Filter cake is the dried, leftover solid material from precipitated mud after the sugarcane juice clarification (via lime addition) at the facili ty. It is sometimes reap plied to sugarcane fields as a fertilizer.

Vinasse

Vinasse is the liquid waste product from the etha nol distillation process. It is rich in minerals, organic material, and water. It is prod uced and used throughout the harvest in Brazil.

U Based on current company plants.

84Some countries are allowed to spray vinasse on s ugarcane crop as fertilizer (not allowed in the U.S.). There is environmental legislation which pr ohibits inappropriate dis posal of vinasse into rivers, lakes, the ocean, and soils.

In the production of sugarcane ethanol and sugar from sugarcane juice or molasses, the cane stalks are shredded and the juice is extrac ted across tandem mills or a diffuser. The juice contains most of the soluble sugars and the left over sugarcane fiber is bagasse. Next, the cane juice is filtered then heated and limed to precip itate impurities during the clarification process. The resultant clarified juice is then concentrated across an evaporation st ation (14-16°Brix up to 65°Brix). The syrup produced is then further ev aporated in vacuum pans and seed crystallized, leading to a mixture of sucrose crystals su rrounded by molasses with a concentration of 91­ 93°Brix. The sugar crystals and molasses are s ubsequently separated by centrifugation. In ethanol production in Brazil, the sugars in juice or diluted are fermented into ethanol by the addition of yeast. Fermentation varies from 4-12 hours, with ethanol yields ranging from 80­ 90%. The fermented mixture is then distille d to produce hydrous (96 % ethanol) or anhydrous ethanol (99.7 % ethanol). The production of anhydrous ethanol is normally done by addition of cyclohexane. See Figure 1.4.5 for a diagram of the sugarcane ethanol and sugar production
process. 245 The production of sugar (for food and e xport) or ethanol depends on the supply and demand changes for both products.

Figure 1.4-5. Simplified Overview of Sugarca ne Ethanol and Sugar Production Process

1.4.3 Cellulosic Biofuel

The following sections contain descriptions of cellulosic et hanol and cellulosic diesel production technologies. Section 1.4.3.1 introduces the two primary pathways for the production of cellulosic ethanol, through biochemical and thermochemical processes while Section 1.4.3.2 discusses cellulosic diesel which is produced th rough thermochemical processes. We end the section with specific company descriptions of ce llulosic biofuel technologies and briefly describe how they differ from gene ric process discussions.

851.4.3.1 Cellulosic Ethanol

Cellulosic biomass has long been recognized as a potential source of mixed sugars for fermentation to fuel ethanol. The Germans may have been one of the earliest to try commercializing a process to produce ethanol fr om a cellulosic feedst ock, probably from wood in the late 1890s. They used dilute acid to hydrolyze the cellulose to glucose and xylose, but were able to only produce a little less than 20 gallons per ton of feedstock; they soon improved the process enough to generate yields of around 50 gallons per ton. Eventually, two commercialsized plants that used dilute su lfuric acid hydrolysis were construc ted in the U.S. Yields from the U.S. facilities were roughly half that of the German plants, however, the production rate from U.S. facilities was significantly higher. Lumb er production decreased following World War I, which resulted in the close down of cellulosic plants. 2 46, 247 Although corn-grain ethanol was used in the early 20 th Century, especially by high-performance race cars and as an additive to raise gasoline octane, petroleum-derive d gasoline eventually replaced it as the primary fuel for automobiles and light-duty trucks. From the early 1970’s and up through th e present, ethanol from corn, has been increasingly used as a fue l; however, recently, ethanol from cellulose is being viewed with increasing interest.

Several processing options are currently available to conve rt cellulosic biomass into ethanol. These conversion technologies generally fall into two main categories: biochemical and thermochemical. Biochemical conversion refers to the fermentation of sugars liberated from the breakdown of biomass feedstock. Thermochemi cal conversion includes the gasification and pyrolysis of biomass material into a synthesis gas for subsequent fermentation or catalysis. The main benefit of gasification/pyr olysis over the biochemical route is that thermochemical processes can more easily conve rt low-carbohydrate or “non-ferm entable” biomass materials such as forest and wood residues to alcohol fuels and can more readily accept a wider variety of feedstocks. 248 However, the thermochemical process does have some drawbacks, such as tar production and clean-up gas procedures that require additional capit al investment.

Since commercial production of cellulosic et hanol has not yet begun, it is unclear which process options will prove most viable or whether additional variations will emerge. At least in the near future, there have been plans to build both stand-alone biochemical and thermochemical ethanol processing plants. In addition, some i nvestors are currently s upporting research and development in both cellulosic processing proced ures, neither choosing one conversion over the other. 249 The following subsections describe the pro cess steps, current challenges, and targeted areas for improvement for each conversion method.

1.4.3.1.1 Biochemical Conversion

Unlike grain feedstocks where the major car bohydrate is starch, li gnocellulosic biomass is composed mainly of cellulose (40-60 %) and hemicellulose (20-40 %). The remainder consists of lignin, a complex polymer which serves as a stiffening and hydrophobic (waterhating) agent in cell walls. 250 Cellulose and hemicellulose are made up of sugars residues linked together in long chains called polysaccharides. Once hydrolyzed, they can be fermented into ethanol. Currently, lignin cannot be fermented into ethanol, bu t could be burned as a by-product to generate electricity.

86Both starch (corn grain) and cellulosic feedstocks must be hydrolyzed prior to fermentation. Structural differences at the molecular level make it far more difficult, and therefore more costly, to hydroly ze cellulosic biomass than it is to hydrolyze starch. Glucose, C 6 H 12 O 6 , the repeating monomer in bot h starch and cellulose, is a six-sided ring, similar in conformation to the classic ‘cha ir’ conformation of cyclohexane or benzene, except one carbon atom in the ring is replaced by an oxygen atom. For uniformity (and ease) of discussion, it is generally assumed that the first carbon atom ne xt to the oxygen, is ca rbon #1; the numbering, 2­ 5, continues around the ring with oxygen in the 6 th position; one of the four bonds of the fifth carbon atom is attached to the oxygen atom to co mplete the ring, one is attached to hydrogen atom and the fourth to a -CH 2 OH group. Thus, a glucose molecule/monomer is a six-sided molecule, but not a six-carbon ring (although there are six-carbon molecules present, one of which is in the –methylhydroxy group).

The main difference between starch and cellulosic plant matter is that starch polysaccharides are made up of α -glucose monomers, uniformly strung together by α -linked 1,4­ glucosidic bonds whereas cellulosic polysaccharides are made up of β -glucose monomers, strung together through β -linked 1,4-glucosidic bonds. In starch with the α -conformation, the hydroxyl group on carbon #1 is in the axial or α -position, which causes the -OH’s on each successive glucose monomer to end up on the same side of the polymer. There are also 1,6-linked glucose branches that occur irregularly on approxi mately one in twenty-five glucose units. 251 The -OH groups on the same side of the polymer, along w ith the randomly attached 1,6-glucose branches, leaves starch polymers relatively weak, flexible, and able to easily wrap and twist together to form tiny granules, e.g., common, everyday corn starch.

Cellulosic polysaccharides are in the β -conformation with th e hydroxyl group on carbon #1 is positioned away from the ring, in the equatorial or β -position, which causes the -OH’s on each successive glucose monomer, added to the chain, to end up on opposite sides of the polymer. The hydroxyl groups lined up evenly and uniformly along opposite sides of each polymer strand allow intra-molecu lar hydrogen bonds to develop w ithin each monomer. They also allow inter-molecular hydrogen bonds to deve lop between adjacent polymers to form tight, rigid, strong, mostly straight pol ymer bundles called microfibrils that act as the core constituent in the formation of plant cell walls that are also insoluble in water and resistant to chemical attack. The β -conformation and the resulting hydroge n bonds stabilize the glucose chair structure to help minimize the polymer’s flexibility (which hinders hydrolysis) and to add to its strength.

The second cellulosic co mponent is called hemicellulose. It consists mainly of a random mixture of highly branched and heavily substitu ted five- and six-carbon rings. The five-carbon residues are usually D-xylose and L-arabinose; the six-carbon re sidues are usually D-galactose, D-glucose, and D-mannose, and uronic and acetic aci d. Hemicellulose is not as rigid or strong as cellulose, but does contribute addi tional strength and helps protect the plant cell wall against attack by microbes or water. Hemicellulose is relatively easy to hydrol yze, due to its highly branched, somewhat random or non-uniform structure.

Lignin, the third principle component, is a complex, cross-linked polymeric, high molecular weight substance derive d principally from coniferyl al cohol by extens ive condensation

87polymerization. Covalently bonded to the hemicellulo se, it is essentially a glue-like polymer that covers the cellulose and hemicellulose polymer cell walls and helps hold them together, provides additional strength, helps resist microbial de cay, and perhaps most importantly, for this discussion, inhibits hydrolysis. Its molecular weight is around 10,000. 252 While both cellulose and hemicellulose contribute to the amount of fermentable sugars for ethanol production, lignin does not, but can be combusted to provide proces s energy in a biochemical plant or used as feedstock to a thermochemical process. 253

To review, a significant part of the reason it is more diffi cult and more costly to produce ethanol from cellulosic feedstocks, such as corn stover, has to do with the differences in the molecular structures of simple st arch and those of cellulosic plan t matter. That is, as a plant grows, glucose monomers are a dded to the polysaccharide chains of the plant cell walls through condensation reactions. In general, condensation is a chemical process by which two molecules are joined together to make a larger, more co mplex molecule, and a molecule of water is a byproduct of the reaction. In the formation of polysaccharides, and enzyme catalyzes the reaction wherein the -OH group on carbon #1 of one monomer, or glucose residue, reacts with the -OH on carbon #4 or #6 of another residue. An H-OH (H 2 O or water) molecule is removed leaving an -O- that links the monomers toge ther to form the polysaccharide chain. Again, depending on the direction of the –OH group at car bon 1, it may be called an alpha (as in starch) or a beta (as in cellulose) linkage. 254

Hydrolysis is the reverse reaction. The -H from an H-OH (water) molecule is added to one monomer and the remaining -OH is added to its pair, e.g., to the next monomer on the chain, to regenerate separate glucose monomers. Du ring starch hydrolysis, water and water borne hydrolyzing enzymes can easily penetrate the ra ndomly formed polymers (the tiny granular particles or bundles) in order to break the bonds to release gluc ose monomers. However, the cellulosic or glucan polymers formed in tightly packed, dense, rigid microf ibrils are especially resistant to water and hydrolyzing enzymes. Xy lan, the main constituent of hemicellulose, is more easily hydrolyzed than cellulose, but not easily fermented. Cellulose is not easily hydrolyzed, but readily ferments. These are two of the major problems that must be satisfactorily resolved before cellulosic ethanol can become a competitive fuel.

Biochemical conversion processes typically us e dilute acid with enzymes or concentrated acid to convert cellulosic biomass to sugar for fermentation to ethanol. Concentrated acid hydrolysis is fairly well devel oped and is being pursued to comm ercialization in certain niche situations. For example, concentrated acid hydr olysis is suitable for feedstocks such as municipal solid wastes which have largely heterogeneous mixtures. 255 Concentrated acid hydrolysis is typically much faster than enzymatic approaches, albeit at the cost of reduced sugar yields due to undesira ble side reactions. 256 Enzymatic hydrolysis is mostly suitable for homogeneous mixtures because specific enzyme s are needed to convert a given type of feedstock. During the period covered by this pro posed rule, the cost to enzymatically hydrolyze cellulose is expected to dec line significantly as these tec hnologies continue to improve. 257

In general, steps of the biochemical proce ss include: feedstock pr etreatment, hydrolysis, saccharification and fermentation, ethanol dehydra tion, and lignin recovery. Refer to Figure 1.4­

886 for an illustration of the enzymatic biochemical production process. We used NREL’s study as a guide to describe, somewhat generi cally, how such a process might work. 258

Figure 1.4-6. Cellulosic Ethanol Biochemical Production Process (Enzymatic )

Feedstock (e.g. corn stover)

Feedstock Pretreatment

Hydrolysis

Saccharification Cellulase & enzymes Fermentation

Ethanol Dehydration Lignin Recovery

Fuel Ethanol Lignin

Stage 1 – Feedstock Pretreatment

Lignocellulosic biomass, such as the corn stover we’re currently discussing, must undergo at least some pretreatment prior to hydrolysis. During the early years of cellulosic ethanol production (e.g., 2010 to 2015), we anticipate that this stage will likel y occur within the facility. In the out years covered by this rule (2022) we believe that this stage will likely be moved outside the plant gate (e.g., upstream of th e ethanol plant) to reduce transportation costs that are typically high due to the low density of th is type of biomass. The biomass is pretreated with either a physical or chemi cal pretreatment method to help the polysacharides become more accessible to hydrolysis. Studies have shown a di rect correlation between the removal of lignin and hemicellulose and the digestibility of cellulose. 259

Physical pretreatment nearly always incl udes size reduction by some type of grinding, shredding, or chopping. For example, in order to biochemically process wood chips, e.g., poplar trees or willows, the chips must be reduced in size to 1-mm or less in order to increase the surface area for contact with acid, en zymes, etc. Breaking up a 5-in tree stem into 1-mm pieces

89would consume a large amount of energy. On the other hand, corn stover chips for a biochemical process can range up to a maximum size of 1.5 inches. 260

Chemicals are also used for pretreatment . The most common chemical pretreatment methods for cellulosic feedstocks are dilute acid, hot water, alkaline, orga nic solvent, ammonia, sulfur dioxide, carbon dioxide, or other chemicals to make the biomass more digestible by the 261, 262 enzymes. These chemicals cause the biomass to react quite differently. 263 For example, instead of hydrolyzing the hemicellulose (as in aci dic pretreatments), an alkaline approach tends to leave the hemicellulose and cellulose intact. Enzymes are therefore required to digest both hemicellulose and cellulose at the same ti me when a basic pretreatment is used.

Different pretreatment appro aches also affect the amounts of degradation products (e.g. furfurals, acetates) that occur from the decom position of hemicellulose and lignin. This is important since these degradation products can inhibit microorganisms in the fermentation step. A well known pretreatment meth od that does not degrade biomass sugars or produce fermentation inhibitors is ammonia fiber expa nsion (AFEX). During AFEX, liquid ammonia is added to the cellulosic material follow ed by a rapid pressure release.

Each type of feedstock, whether softwoods, co rn stover or bagasse, requires a particular combination of pretreatment methods to optimi ze the yields of that feedstock, minimize the degradation of the substrate, and maximize the su gar yield. Pretreatment of cellulosic biomass in a cost-effective manner is a major challenge of cellulose-ethanol t echnology research and development. 264

Stage 2 – Pretreatment a nd Hydrolyzate Conditioning

NREL refers to this stage as a combinati on of pretreatment and hydrolysis. In their process flow diagram, the washed and sized-reduced feed is directly heated with steam and mixed with dilute sulfuric acid. The pr ocess converts, primarily, the hemicellulose polysaccharides xylan, mannam, arabinan and galact an, to produce the mixed sugars and futher helps prepare the ce llulose for hydrolysis. A small amount of glucan in the hemicellulose and in the cellulose is converted into glocose. The r undown from the acid hydrolysis reactor is fed to a blowdown tank that subsequently feeds a filter press. The filter press produces two main streams, a filter cake and a liquid filtrate, also called hydrolyzate. The filter cake carries the unhydrolyzed portions of the feed (e.g., glucan s) among other insollubles, while the liquid carries that part of the feed that was hydrolyzed, mainly the xyloses.

The liquid portion is neutrali zed to remove gypsum and other contaminants that would be toxic to downstream enzymes. The cake is wa shed, mixed back with the detoxified liquid hydrolyzate, and fed to the saccharification reac tors to hydrolyze the glucan polysaccharides.

Stage 3 – Saccharification and Co-Fermentation

We should point out that this is not ‘Sim ultaneous Saccharification and Fermentation’ (SSF). Saccharification, in the process we’re di scussing, takes place primar ily in several reactors along with other intermediate treatments such as filtering and detoxifying. Using a cellulase

90enzyme cocktail, saccharification of the cellulo se to glucose occurs first at an elevated temperature to take advantage of increased enzyme activity, which reduces the quantity of required enzyme as well as the reaction time.

The cellulase enzymes used to convert cellulose to sugars can be obtained in two ways. The first option is for a plant to produce it on-site. The sec ond option requires the plant to purchase the enzymes from off-site enzyme manuf acturers. Due to a jo int research effort by DOE, Genencor International, and Novozymes Bi otech, the cost for pr oduction of cellulase enzymes has been drastically reduced. Such re search and development in areas of enzyme production have reduced the cost of cellulolytic enzymes by a factor of 10 to 30, down to 20 to 30 cents per gallon of ethanol produced. 265, 266, 267 It is estimated, however, that enzyme costs will have to be further reduced to a level comparab le to those used to produce ethanol from corn grain at a cost of 3 to 4 cents per gallon of et hanol. The current challenge is to develop the correct enzyme “cocktails” to re flect differences in the physical and chemical characteristics of all the various types of cellulosic materials. It may be easier, theref ore, to process single feedstocks (more homogeneous) rather than multi ple feedstocks, in whic h variations are more likely.

Following cellulose saccha rification, both the glucose and xylose sugars are cofermented. Although xylan, the hemicellulose polysaccharide, is more easily hydrolyzed than glucan (cellulose polysaccharides), the xylose su gar is more difficult to ferment than is the glucose sugar. Different microbes as well as different residence times and process conditions may be required for each.

Because xylan can make up as much as 25% of plant matter it is imperative that as much of it as possible be fermented; the economic viab ility of biochemically produced ethanol depends heavily it. This continues to be high on the li st of problems researchers are working on, but good progress has been made toward fermenting a high er percentage of xylose during the past few
268 years.

Stage 3A – Consolidated Bioprocessing e.g., Simultaneous Saccharification and Fermentation (SSF)

During the past few years, researchers have been looking for ways to combine saccharification and fermentation into a single step through the use of enzyme/microbe cocktails. If successful, we expect there could be significant capital cost savings in that fewer reactors and other support equipment and piping would be n ecessary. Also, it may be possible to reduce processing times if hydrolysis reactions can take place simultaneously, rather than sequentially. Such strategies are known as consolidated biop rocessing (CBP). CBP, ho wever, is currently hampered by the relative inability of yeast to process recombinant cellulases (enzymes that help convert cellulose to sugars), and the relative lag in the development of molecular biological methods to manipulate organisms th at secrete cellulases naturally. 269

91Stage 4 – Ethanol Dehydration

NREL’s process model indicates that th e fermentation reactor rundown stream, now called ‘beer,’ runs down the beer column feed surge tank. The b eer column feed consists of about 83% water and only 5.5% etha nol; the balance of the mixture is very complex, but consists mostly of lignin. The beer co lumn removes the dissolved CO 2 overhead and produces a water/ethanol bottom stream that is fed to a r ectification column. According to NREL’s model, the rectification column bottoms would be mostly water with about 0.05% ethanol that’s recycled back to the process. The rectifica tion column overhead that consists of about 92.5% ethanol and 7.5% water, is fed to a molecular sieve that pr oduces a 99.5 wt.% ethanol product stream with about 0.5 wt.% water. Gasoline, a denaturant, is added to produce a 95 % to 98 % ethanol fuel.

Stage 5 – Lignin Recovery

Following the saccharification and fermentati on of the xylan and glucan to ethanol, the lignin is gradually concentrated with other solids into a moist cak e-like product that is about 48% insoluble solids. About 80% of the 48% insoluble solids is essentially lignin microbial cells, and other unconverted biomass remnants, (e.g., cellulose, xylose, glucan, xylan, other oligomers, etc.,) from the process. This material can be either combusted to provi de process heat for the biochemical operation, for a co-located starch ethanol plant, or as we discuss in the following section, could be used as feedst ock for a thermochemical unit.

1.4.3.1.2 Thermochemical Conversion

Thermochemical conversion involves biom ass being broken down into intermediates using heat and upgraded to fuels using a combina tion of heat and pressure in the presence of catalysts. 270 Thermochemical processes include py rolysis (absence of oxygen), gasification (partial oxidation in the presence of a gasifying agent, usually air, oxygen, and/or steam), and combustion (complete oxidation). The former two conversion processes, pyrolysis and gasification, can be used to convert biomass into energy carriers for tran sportation use. It is important to note that these proce ssing steps are also applicable to other feedstocks (e.g., coal or natural gas); the only difference is that a renewable feedstock is used (i.e. biomass) to produce cellulosic biofuel. A thermoch emical unit can also complement a biochemical processing plant to enhance the economics of an integrated bior efinery by converting lignin-rich, non-fermentable material left over from high-starch or cellulosic feed stocks conversion. 271 We discuss the gasification and pyrolysis processes below.

Gasification

Compared to corn ethanol or biochemical cellulosic ethanol plants, the use of biomass gasification may allow for greater flexibility to u tilize different biomass feedstocks at a specific plant. Mixed biomass feedstocks may be used, based on availability of long-term suppliers, seasonal availability, harvest cycle, and costs. Agricultural residue, energy crops, forest residue, and municipal solid waste have been discussed as potential feedstocks. Geographic location, availability of biomass, the ex istence of biomass suppliers, and costs would all likely influence

92the mix of biomass feedstocks utilized. The ge neral steps of the gasification thermochemical process include: feedstock handling, gasification, gas cleanup and conditi oning, fuel synthesis, and separation. Refer to Figure 1.4-7 for a schema tic of the thermochemi cal cellulosic ethanol production process through gasification.

Figure 1.4-7. Cellulosic Ethanol Therm ochemical Gasification Process

Feedstock (e.g. forest residue)

Feedstock Handling

Gasification

Gas Cleanup &

Conditioning

Fuel Synthesis

Separation

Fuel Ethanol (Other mixed alcohols if using catalyst)

Stage 1 – Feedstock Handling

The particle size requirement for a ther mochemical process is around 10-mm to 100-mm in diameter. 272 Once the feed is ground to the proper size, flue gases from the char combustor and tar reformer catalyst regenerator dry the feed from around 30% to 40% moisture to the level required by the gasifier.

Stage 2 – Gasification

There are two general classes of gasifiers. First, partial oxidation (POx) gasifiers (directly-heated gasifiers) us e the exothermic reaction betwee n oxygen and organics to provide the heat necessary to devolatilize biomass and to convert residual carbon -rich chars. In POx gasifiers, the heat to drive the process is genera ted internally within the gasifier. A disadvantage

93of POx gasifiers is that oxygen production is expe nsive and typically requir es large plant sizes to improve economics.

The second general class, called indirect ga sification, uses steam gasifiers to accomplish gasification through heat transfer fr om a hot solid or through a heat transfer surface. Either the byproduct char and/or a portion of the product gas can be combusted with air (external to the gasifier itself) to provide the energy required for gasification. Although steam gasifiers have the advantage of not requiring oxygen, most operate at low pressure and th erefore require product gas compression for downstream purifi cation and synthesi s unit operations. 273, 274

There are different subcategor ies of gasifiers which are eith er directly or indirectly heated. One subcategory is termed a bubbling fl uidized bed gasifier a nd it employes a bubbling fluidized bed of inert material and the reactant (biomass) is also bubbled through the fluidized bed. A second variant is the circulating fluidized bed gasifier which is similar to the bubbling fluidized bed reactor except that a high feedstock and air flow rate circul ates the fluidized bed out of and back into the reactor. For the flui dized bed, the bed material may either be inert alumina or sand which helps the heat transfer. There are also fixed bed reactors which either feed the reacting gas (oxygen or air) upward or downward thr ough a fixed bed of the reactant (biomass). Because of the tar formed when usi ng biomass as a feedstock, a second reactor is sometimes added which solely targets converting the tar to syn-gas. If the biomas feedstock is ground to a sufficiently small particle size, or liquefied, the biomass is considered to be “entrained” in the reactor, a nd the reactor is defined as an entrained flow reactor.

Indirect gasification using an entrained flow gasifier is desc ribed for this example. The gasification process begins as the biomass is fed to the reactor containing a heat transfer media, such as sand, and is partially reacted with air (or oxygen) which is introduced to the bottom of the reactor. The air serves as the carrier-gas and as the ox idant for partially oxidizing the biomass to syn-gas, carbon monoxide and hydroge n. In addition to the syngas produced, char and coke are also formed. The heat for the endothermic gasification re actions is supplied by circulating heat transfer media (e .g. sythetic sand) between the gasifier and the char combustor. The heat generated by the combustion of the char a nd coke heats the heat tr ansfer media to over 1800°F. The syngas is separated from th e sand and ash and sent to gas cleanup.

Stage 3 – Gas Cleanup & Conditioning

Once the biomass is gasified and converted to syngas, the syngas must be cleaned and conditioned. This raw syngas has a low to me dium energy content depending on the gasifying agent and consists mainly of CO, H 2 , CO 2 , H 2 O, N 2 , and hydrocarbons. The minor components, tars, sulfur, nitrogen oxide s, alkali metals, and particulates have the potential to negatively affect the syngas conversion steps. Therefore, unwanted impurities are removed in a gas cleanup step and the gas composition is further modified during gas conditioning. Gas conditioning steps include sulfur polishing to remove trace levels of H 2 S and water-gas shift to adjust the final H 2 /CO ratio for optimized fuel synthesis.

94Stage 4 – Fuel Synthesis

After cleanup and conditioning, the “clean” s yngas is comprised of essentially CO and H 2 . The syngas is then converted into a liquid fu el by either a catalytic process or through the use of a microorganism. The fuel producer has the choice of producing di esel fuel or alcohols from syngas by optimizing the type of catalyst used and the H 2 /CO ratio. Diesel fuel has historically been the primary focus of such pr ocesses, as it produces a high quality distillate product, however, with the 45 cent tax subsidy currently available for ethanol production, it may be economically advantageous for fuel producers to convert syngas to ethanol instead of to diesel fuel. Production of cellulosic diesel is discu ssed in further detail in the following Section 1.4.3.2.

Conceptual designs and techno-economic m odels have been developed for ethanol production via mixed alcohol synthesis using catalytic processes. The proposed mixed alcohol process produces a mixture of ethanol along with higher normal alcohols (e.g., n-propanol, nbutanol, and n-pentanol). The by-product hi gher normal alcohols have value as commodity chemicals and fuel additives. Typically the mi xed alcohol products descri bed in literature are often high in methanol, but contain a wide distri bution of several different alcohols. A concept proposed in literature is to completely recycle this methanol in order to increase the production of ethanol and higher alcohols wh ich are generally more valuable. This concept was modeled by NREL for the thermochemical production of etha nol for the year 2012. Total mixed alcohol yield was 94.1 gallons per dry ton, in which 85% of the total alcohol product was ethanol. This was made possible through the addition of an almo st complete recycle of methanol within the
275 process.

In contrast to the catalytic processing of syngas to produce fuels, the fermentation process requires a special microorganism (Clostridium ljungdahlii) that will convert the syngas to ethanol. 276 This combined syngas and fermentation process has the benefit of having a significantly faster processing time, on the or der of minutes, as compared to the typical biochemical process on the order of days. 277

Stage 5 – Alcohol Separation

The liquid rundown from the low-pressure separator is dehydrat ed in vapor-phase molecular sieves, producing the dehydrated mixed alcohol feed into a methanol/ethanol overhead stream and a mixed, higher molecular weight al cohol bottom stream. The overhead stream is further separated into a methanol stream and an ethanol stream.

Heat & Power

A carefully integrated conventional steam cycle produces process heat and electricity (excess electricity is exported). Pre-heaters, steam generators, and super-heaters generate steam that drives turbines on compressors and electr ical generators. The heat balance around a thermochemical unit or thermochemical/biochemi cal combined unit must be carefully designed and tuned in order to avoid unnecessary heat losses. 278

95Pyrolysis

Pyrolysis oils, or bio-oils, are produced by condensing the decomposed biomass instead of introducing a gasifying agent. 279 The decomposition occurs at lower temperatures than gasification processes, and produces liquid oil instead of a synthesi s gas. The reaction can occur both with or without the use of catalysts. The oil produced va ries in oxygen content or viscosity according to the feedstock used. The oil must have particulates and ash removed in filtration to create a homogenous product and is further upg raded to hydrocarbon fuels via hydrotreating and hydrocracking processing, which reduces its total oxygen content. Some believe that pyrolysis could have a significant economic advantage over ot her cellulosic ethanol approaches since very little has been done in terms of optimizing the process, and as such, there are still many possibilities yet to be explored. 280

1.4.3.2 Cellulosic Diesel

Technology

Another example of a thermochemical proc ess is a cellulosic diesel fuel technology which is also termed biomass-to-liquids (B TL). The BTL process produces a syngas from biomass and then the syngas is fed to a Fischer- Tropsch (FT) reactor to primarily produce diesel fuel.

The BTL method removes contaminants from the gasification stream prior to the reactions that form the liquid compounds. The re sulting liquid fuel is essentially contaminantfree and is very similar to petroleum-based diesel fuel – in fact, its cetane number is higher than petroleum-based diesel fuel making it somewhat be tter in quality. Thus it can be easily blended with or used interchangeably with petroleum-based diesel fuel.

BTL plants, like thermochemical ethanol plan ts, are capital intensive plants with many subunits associated with them. The first coup le of steps of BTL pl ants, including biomass processing and gasification, are similar to the ther mochemical cellulosic et hanol plants described above. However, once the syngas is produced, it is then cooled producing high pressure steam, and is scrubbed to remove particulate matter. Impurities such as me rcury, arsenic and trace metals are removed by a sulfur impregnated carb on reactor. The syngas is further treated in either a Selexol or Rectisol unit to remove hydrogen sulfide a nd concentrated carbon dioxide (CO2). The CO2 can be captured and sold to a bottling company, used for enhanced the oil recovery from oil wells, or even sequestered in the ground for additional life cycle benefits. The syngas is sent to a water shift reactor to whic h causes a shift to more hydrogen and less carbon monoxide, which is necessary to establish an optimal mix of hydrogen and carbon for the downstream Fischer-Tropsch (FT) reactor.

The cleaned and water-shifted syngas is sent to the FT reactor where the carbon monoxide and hydrogen are reacted over a FT catalyst . The FT catalyst is either iron-based or cobalt-based. The cobalt catalyst is more expens ive, although it does not require a recycle, while the less expensive iron catalyst doe s require a recycle. The FT r eactor creates a syncrude, which is variety of hydrocarbons that boil over a wide distillation range (a mix of heavy and light

96hydrocarbons). The syncrude from the FT reactor is sent to a distillation column where it is separated into various components based on thei r vapor pressure, mainly liquid petroleum gas (LPG), naphtha, distillate and wax fractions . The heavier compounds are hydrocracked to maximize the production of diesel fuel. The distillate boiling compounds have high cetane and thus are of high quality for blending into diesel fuel. Conversely, the naphtha material is very low in octane thus, it would either have to be upgraded, or blended down with high octane blendstocks (i.e., ethanol), or be upgraded to a hi gher octane blendstock to have much value for use in gasoline. The naphtha could also be sold as feedstock for the petrochemical market for manufacturing chemical products su ch as ethylene and benzene.

The unreacted carbon monoxide and hydrogen a nd any gaseous hydrocarbon material are burned to produce electricity in a turbine. The waste heat from the gas turbine along with the steam created to cool the syn-gas, may be sent to steam turbines to produce additional electricity. Most of the electricity would be used within the BTL pl ant, however, some could be sold to raise additional revenues, particularly in the summer when electric ity demand and prices are high.

Industry Characterization

No commercial BTL plants currently exist in the U.S., however, there are several BTL pilot plants. Choren is currently building a 1 m illion ton per year commercial Plant in Freiberg/ Saxony Germany that is expected to start up in 2011 or 2012. Initia lly, the plant will use biomass from nearby forests, the wood-pro cessing industry and straw from farmland.

Although coal-to-liquids (CTL) plants use coal as a feedstock, the pr ocess is essentially the same, therefore, CTL pilot and full scale plants help to demonstrate the BTL plant technology. Examples of CTL pilot projects in clude a 10-15 barrel per day (BPD) operation in Colorado (Rentech) and a 30 BPD plant in New Jersey (Headwaters Inc.). Internationally, commercial sized CTL plants are currently in opera tion in South Africa (Sasol) and have been in operation for a number of decades. These plants produce approximately 2.4 billion gallons per year (or 160,000 barrels per day) of fuel products. These liqui d fuel products can include finished Fischer-Tropsch diesel, Fischer-Tropsch naphtha (a gasoline blendstock), methanol, and dimethyl ether, among others. Worldwide, addi tional CTL plants are being considered or constructed in China, Indonesia, In dia, Australia, and Malaysia.

An inventory of current domestic CTL pl ants under considera tion (including those without CCS, with CCS, and/or biomass co-feed) i nvolves fifteen projects ranging in size from smaller projects (1,800 BPD) to large commercial plants up to 80,000 BPD. Most plants being considered are in the conceptual stage or are in the project feasibility stage. V Two plants are currently in the design phase (a 13,000 BPD and a 5,000 BPD plant) while one plant is in the construction phase (a 1,800 BPD plant). W Based on discussions with plant developers, three of the plant sites under cons ideration are currently assessing the use of both biomass co-firing and

V DOE (2007), “Clean Coal Today: A Newsletter about In novative Technologies for Coal Utilization,” Office of Fossil Energy, U.S. Department of Energy, DOE/FE-0509, Issue No. 72, Summer 2007, Table 1, p. 6.
W The plants currently listed under design status are located near Medicine Bow, WY (DKRW Advanced Fuels, 13,000 BPD) and near Gilberton, PA (WMPI, 5,000 BPD). The current plant under construction is in East Dubuque, IL (Rentech, 1,800 BPD).

97carbon capture to reduce GHG emissions. The plant fu rthest in developing coal and biomass to liquids (CBTL) with carbon capture (with CO 2 intended for reuse in enhanced oil recovery applications) would be located in Wellsvill e, OH (a 50,000 BPD plant, Baard Energy).

1.4.3.3 Developing Technologies

The following sections describe specific companies and thei r cellulosic biofuel technologies which the companies have develope d or are developing. This summary is not meant to be an unabridged list of cellulosic biofue l technologies, but rather a description of some of the more prominent or interesting cellulosic biofuel technologies. These technologies are variants of the biochemical or thermochemi cal platforms described above in Section 1.4.3.1.1 and 1.4.3.1.2. The process technology summaries provided below are those stated by the respective companies. EPA has not confir med the statements made concerning these technologies, nor have we confirmed the process conditions and the process flow steps necessary for any of these companies. As some of thes e technologies have slightly different process designs from the general descriptions f ound in Section 1.4.3.1 and Section 1.4.3.2 and may warrant additional considerati on in the final rule, we found it important to discuss their differences below.

Amyris Biotechnologies

A new second generation process from Am yris Biotechnologies has a fermentation process that uses custom designed yeast cells to make renewable fuels that are substitutes for petroleum gasoline and petroleum diesel fuel. The technology is modular in design and can be collocated with existing ethanol plants.

Amyris’s yeast cells are the ke y drivers of their conversion pr ocess. The process uses the same feedstocks that are currently used to make corn ethanol, wh ich could be sugar cane or corn. At this time, no public informa tion is available to derive pr oduction cost estimates for the Amyris process. Amyris has a 100 gallon per we ek pilot plant operating in Emeryville, CA, and is constructing a commercial demonstration plan t in Brazil in 2010 to showcase their technology.

The produced diesel fuel and is compatib le with the existing petroleum distribution system and provides better storage and cold flow properties than biodiesel.

Bell Bio-Energy

A biochemical biofuel technology has been de veloped which uses genetically engineered bacteria to convert cellulosic feedstocks to s ynthetic hydrocarbon fuels. Depending on the types of bacteria used, this process can produce specific hydrocarbon types which can either be methane or other light hydrocar bons, gasoline, diesel or jet fuel type hydrocarbon compounds. For example, if a bacterium is chosen to produc e gasoline, the bacteria may only produce octane, the eight carbon hydrocarbon molecule that boils within the distillation temperature range of gasoline.

98After the inventors of this process completed their development work, they discussed their technology with the Department of Defense which became interested in this technology for providing fuels to their land and air based vehicl es. The military agreed to partially fund the establishment of pilot plants at different military bases. The bases are: Fort Benning and Fort Stewart in Georgia, For Bragg in North Carolina, Fort AP Hill in Virginia, Fort Drum in New York and Fort Lewis in Washington, and at a faci lity in San Pedro, California. The first of these pilot plants began operating at Fort Stewart, GA in late 2008 and the total expected production capacity for these pilot plants is 0.04 million gallons per year. The in tent is to next build a large commercial demonstration plant of at least 150 m illion gallons per year with a startup date in 2011.

The technology works by first grinding the cellulosic feedstock into a smaller size and then immersing the ground cellulose with bacteria into water. The bacteria begin to digest the cellulose after only several hours, but require 30 days to fully digest the cellulose. The produced fuel is constantly removed from the reaction vess el, and some organic mate rial is also produced which can be marketed as potting soil. The process produces over 84 gallons of renewable product per ton of feedstock and the simplicity of the process re sults in low capital costs per volume produced. The process is believed to be cost competitive with petroleum fuels when crude oil is priced at or above $30 per barrel assuming that the cellulosic feedstocks cost $40 per dry ton.

BlueFire Ethanol

BlueFire Ethanol has a commercial acid hydr olysis technology process that converts cellulosic materials into ethanol. The technolo gy can make ethanol from urban trash, rice and wheat straws, wood waste and othe r agricultural residues. Acid hydrolysis is the main reaction mechanism to convert cellulosic and hemicellulosic material into simple sugars such as hexose and pentose or "C6 and C5" sugars. Fermentation of these sugars with microbes converts these sugars into ethanol. This process for convert ing cellulosic and hemicellulosic material into ethanol via acid hydrolysis and fermentation has been around for many decades; though it has not been economically competitive as the cost was not competitive with transport fuel made from petroleum. BlueFire’s process is claimed to offer several improvements to existing acid hydrolysis technology, giving hi gher ethanol yields and lower production costs.

Bluefire uses a proprietary concentrated ac id hydrolysis system a nd several other process improvements to make ethanol production more economically attractive than older acid hydrolysis methods. Some of Bl uefire’s stated improvements include a more efficient acid recovery system; higher sugar purities and concen trations; use of more efficient microbes to ferment C6 and C5 sugars into ethanol; the processes ability to use biomass feedstock’s containing silica. The Bluefire process consis ts of the following main components; feedstock preparation; decrystallization/hydrolysis reaction; filtration of solids and liquids; separation of the acid and sugars; fermentation of the sugars and product separation. For product separation, ethanol effluent is separated using distillation and then de hydrated with molecular sieve technology.

99Bluefire has successfully operated a pilot plan t for five years near their headquarters in Southern California. BlueFire is in the process of building its first commercial facility which will be located in Lancaster California. Th e plant could start up as early as 2010 and will produce up to 3.2 million gallons of cellulosic etha nol per year, using feedstock derived from cellulosic feedstocks from municipal solid wast e (MSW) Blue Fire is planning to start up another cellulosic ethanol plant which they call their Mecca plant also using MSW as feedstock, and which will produce about 17 million gallons per year. No start up date has been announced for their Mecca plant.

Cello-Energy

The Cello-Energy process is a catalytic depolymerization technology. At moderate pressure and temperature, the Cello-Energy process catalytically removes the oxygen and minerals from the hydrocarbons that comprise cellulose. This resu lts in a mixture of short chain (3, 6 and 9 carbon) hydrocarbon compounds. These short chain hydrocarbon compounds are polymerized to form compounds that boil in the diesel boiling range, though the process can also be adjusted to produce gasoline or jet fuel. The resulting diesel fuel meets the ASTM standards, is in the range of 50 to 55 cetane and typically contains 3 ppm of su lfur. The resulting diesel fuel has been tested in Caterpiller engines to demonstrate the viability of the fuel.

The Cello-Energy process is reported to c onvert 94% of the hydrocarbon material to diesel fuel, although a very small amount of h eavier hydrocarbons is also produced. The Cello Energy Process could be totally sel f-sufficient by routing 12% of th e product to run generators to produce the electricity that it needs. The only energy input is electric ity - no natural gas nor water is used in the process.. The Cello proce ss is on the order of 82 % efficient at converting the feedstock energy conten t into the energy content of the produc t, which is very high compared to most of today's biochemical and thermochem ical processes which are on the order of 50 % efficient, or less.

Because of the simplicity of the process, the capital costs are very low. A 50 million gallon per year plant only incurs a total cost of $45 million. This is typical of the capital costs incurred when refiners expand their refineries, a ve ry low cost for a grassroots plant. Because of its high efficiency in converti ng feedstocks into liquid fuel, the production and operating costs are estimated to be very low. By using so me waste feedstocks today, production costs are reported to be less than $0.50 per gallon. However, even with feedstock costs in the $70 to $80 per ton range, which is the cost we used in our cost analysis, total costs would remain less than $1.00 per gallon of diesel fuel.

Cello-Energy was founded 16 years ago and after the chemistry was worked out, they built their first pilot plant in 1998. They next converted their pilot pl ant in 2004 to a larger continuously-operating pilot plant that produced 4 million gallons per year of diesel fuel. In December 2008, Cello started up a 20 million gallon per year commercial demonstration plant. Cello energy already has chosen locations to construct and start up three 50 million gallons per year plants by the end of 2010. This includes a fa cility in conjunction with the State of Georgia Energy Innovation Center, and two additional plants in Alabama. Cello is already making plans to construct and start-up ten more plants the y ear after. Cello explai ned that they will use

100prefabrication techniques so that these plants can readily be constructe d, shipped and installed anywhere in the U.S.

Choren

Choren has a technology called Carbo-V, whic h is a Fischer-Tropsch process that can be used to make diesel fuel. The process can pro cess a wide variety biomass and recycled material materials as feedstocks. The process converts ag riculture biomass, fore stry biomass, biogenic waste and recycling substances in to a synthesis gas which can be further converted to a diesel fuel using a Fischer-Tropsch reac tor. The Carbo-V process can al so be configured without the Fischer-Tropsch hydrocracking technology, so as to produce el ectricity, heat and power, methanol, and other chemical feedstocks.

The principal aspect of the Carbo-V Pro cess is a three-stage gasification process consisting of low temperature gasification, hi gh temperature gasification and endothermic entrained bed gasification. In the first stage, biomass is partially oxidized with air or oxygen at temperatures between 400 and 500 °C. This brea ks down the feedstock into a gas containing tar and solid carbon. In the second stag e, the tar is oxidized at te mperature higher than the ash’s melting point, converting the tar into a synthesi s gas. In the third stage, solid carbon is mechanically pulverized and blown into the ho t gasification stream. The fluidized carbon endothermicly reacts with the gasification stream and is converted into a synthesis gas. In the next Fischer-Tropsch stage of the process, the sy nthesis gas (CO and H2) reacts with the aid of a catalyst to form hydrocarbons. The resulting hyd rocarbons produced from the three stages can then be sent to a hydrocracking proce ss to primarily produce diesel fuel.

Choren will be building building a commercial Plant in Freiberg/ Saxony Germany that is expected to be operational in 2011 or 2012. Init ially, the plant will use biomass from nearby forests, the wood-processing i ndustry and straw from farmland.

Coskata

The Coskata process is a gasification-ba sed technology which produces ethanol from biomass and other forms of carbon through a biof ermentation route. A wide variety of feedstocks can be used, municipal waste, agri culture waste and other carbonaceous containing material. Since this process us es combustion and biofermentation, it is not easily classifiable as either a biochemical or thermochemical produ ction method. This process requires that the biomass or carboneous material be pr ocessed to a small particle size an d then it is injected into a gasifier.

The gasifier combusts any dry carboneous f eed stocks into syngas, comprised primarily of carbon monoxide and hydrogen. The syngas produ ced is fermented in a reactor by microorganisms, which convert the carbon monoxide a nd hydrogen directly into ethanol. The microorganisms are low cost and can process a wi de range of carbon m onoxide and hydrogen molar ratios in the syngas, providing feedstock processing flexibility. No other enzymes are required by this process for producing et hanol, providing significant cost savings over current cellulosic and corn based fermentation production methods. The Coskata process is conducted at low

101pressures, which offers savings on capital and energy costs. Additional energy savings can be realized by employing membrane technology to sepa rate ethanol from the reactor decant liquid. This technology uses gravity and filtration to r ecover ethanol, resulting in significant savings on distillation capital and energy costs used in cellulosic and corn based production methods. Initial ethanol production cost es timates are lower than the biochemical and thermochemical cellulosic technologies descri bed in Section 1.4.3.1.1 and 1.4.3.1.2.

For woody biomass, Coskata estimates that each ton of this feedstock would generate about 100 gallons of ethanol and small amounts of ash which would be burned to supply energy needs for the process. Corn stover is expected to yield similar ethanol yields as woody biomass feed stocks, though details about yields from th e various feed supply stocks are not yet public.

Coskata has a bench scale pilo t plant in Warrenville, IL, a nd projects that its larger 40,000 gallon per year pilot plant will be operational later on in 2009. Coskata is targeting to design and build a 50 million gallons per year commercial demonstration plant will be operational in 2011

Dynamotive Energy Systems

Dynamotive Energy Systems Corporation has announced a pyrolysis technology that uses medium temperatures and oxygen free reactions to convert dry waste biomass and energy crops into fuels that can be used in power/heat gene ration and transportation vehicles. Additionally, the process can make feedstock’s th at can be used to produce chemi cals. The process is flexible on the types of biomass feedstock’s that can be processed. The fuel produced from the Dynamotive process is called “BioOil” and cont ains up to 25% water, though the water is intimately mixed and does not easily separate into anot her phase with time. Since the bio oil contains significant amounts of water, it is not di rectly useable as fuel in conventional vehicles and would have to be converted via another catalytic conversion processing step. The additional catalytic step envisioned for this would combus t the material into a synthesis gas which would then be converted into diesel fu el or bio-methanol vi a a catalytic reaction (the BTL process). The diesel fuel produced is expected to be compa tible with existing petroleum diesel fuels.

Three products are produced by the Dynamotive process, bio oil (60-75% by weight), char (15-20% wt.) and non-condensable gases (10-20% wt.). The char produced is similar to coke and can be used as fuel by other industries while the gases yielded from the process can be used to supply about 75% of th e energy requirements of the pyrolysis process. The pyrolysis process operates at a reactor temperat ures of about 400-500 degrees Celsius.

The process has two small demonstration plan ts, one operating and a nother at the end of its construction phase. These plan ts are being run to produce bio o il for use in the power sector, though Dynamotive has not announced plans for building a facility to make transportation fuels. The operating demonstration plant is located in West Lorne Ontario, Canada. This plant started operation in early 2005 and operates with a capacity of 130 tons per day of waste sawdust. The other demonstration plant is being built with a st ated capacity of 200 tons per day. This plant will be located in Guelph, Ontario, Canada and will process 66,000 dry tons of biomass a year.

102This process uses waste construction and demolition wood from the private sector and will use about 3,000 tons of recycled wood.

POET

POET has over twenty years of producing conventional ethanol in 23 plants in seven states with production capability of one billion gallons of ethanol annua lly. POET has expanded their production capability to include cellulosic et hanol technology. POET’s cellulosic technology will make ethanol from plant materials li ke corn stalks, switch grass, wood chips and refuse. In February 2007, POET was selected by DOE for an award totaling $80 million for federal funding for a commercial cellulosic ethanol plant, which will be located in Emmetsburg, Iowa. As such, POET will be one of the first to build a cellulosic plant on a commercial scale. POET’s commercial demonstration plant is project ed to produce 25 million gallons per year and start up in 2011. It will make cellulosic ethanol from plant materials such as corn cobs and perhaps other cellulosic feedstocks.

Range Fuels

Range Fuels produces cellulosic ethanol via a two step thermochemical process, similar to that discussed above for the cellulosic ethanol process via the thermochemical pathway. Their technology converts biomass to syngas followed by catalytic conversion of the syngas to alcohols. Range claims that their technology produces more ethanol than other cellulosic technologies based on yields per energy input. Th is is a two step process which can use many forms of non food biomass, such as agriculture waste, wood, and corn stocks. Additionally, the technology can process feed stocks with variable water content.

In the Range process, biomass feedstock ar e converted by heat, pressure and steam into syngas, which is then scrubbed and cleaned befo re entering into the second stage. The second stage uses catalyst to convert the syngas into mixed alcohols, which are then separated and purified into alcohol fuels using di stillation equipment. Overall, the Range process is simple as no enzymes or living organisms are used for the main conversion reactions.

Range has operated a pilot plant for over 7 years using ove r 20 different nonfood feedstocks. Range broke ground building its firs t commercial plant late in late 2008 and is expected to be operational in 2010. This plant will be located in Soperton, Georgia and is partially funded from proceeds of a DOE grant. The plant will use wood, grasses, and corn stover as feedstocks. Although the initial plan t is designed to produce 10 million gallons of ethanol per year, the plant is expected to be expanded to produce up to 60 million gallons per year of ethanol.

Virent Bioreforming

Another new process unveiled by Virent called “Bioforming Process” functions similarly as the gasoline reforming process used in the refining industry. While re finery-based reforming raises natural gasoline’s octane value and pr oduces organic chemicals, benzene, xylene and toluene as a byproduct, Bioforming reforms biom ass-derived sugars into hydrocarbons for

103blending into gasoline and diesel fuel. Th e process however, operates at much lower temperatures and pressures than reforming used by the refining industry. The Bioforming process is being developed through a partnership with Shell, Carg ill, Honda and the University of Wisconsin. Virent currently has 16 pilot plants in operation. At this stage, though, the data is limited. It appears that Bioforming is a promis ing technology, as production costs estimates are low in comparison to most other renewable a nd biomass production processes while the products are compatible with traditional petroleum stocks.

Biomass feedstocks for the Bioforming process are sugar feeds, such a corn syrup, sucrose, glycerol, sorbitol, xylose, glucose, ce llulose and hemi cellulose. These are primarily converted into gasoline and diesel fuel, though other hydrocarbons such as jet fuel, LPG, benzene, toluene, xylene, hydrogen, natural gas ca n also be produced. Water is also produced, as the reforming process removes oxygen from the suga r feeds. The resultin g properties and energy content of gasoline and diesel produced though are physically comparable to those yielded from refining industry, probably allowing movement in existing petroleum distribution systems, saving on shipping costs. Additi onally, variable operating costs are low because no distillation equipment is needed to separate the produced gasoline, diesel and other hydrocarbons, as these separate naturally from the aqueous solutions generated in the reforming process. The net energy costs are also low due to low operating pressures and temperatures.

1.4.4 Biodiesel & Renewable Diesel Production

Biodiesel

Plant oils and animal fats consist primarily of triglycerides, a type of a molecule consisting of a group of three hydro carbon chains (saturated or ol efinic) linked to a three-carbon backbone via carboxylic acid esters (see Figure 1 .4-8). Biodiesel is made by removing the chains from the triglyceride molecules and adding me thanol to their ends to form methyl esters. Glycerin is formed as a by-product from the th ree-carbon backbones that remain. For relatively pure triglycerides, such as virgin plant oils, th e primary reaction is catalyzed by an alkaline pH and takes place in a stirred vessel at mild temperature and pressure conditions.

104F igure 1.4-8. Overview of biodiesel conversion process



# +
transesterification

methyl esters (biodiesel) glycerin

triglyceride 

# +
transesterification

methyl esters (biodiesel) glycerin

triglyceride

In the case of feedstocks containing more th an a few percent free fatty acids (FFAs), such as rendered fats and waste greases, addition of base will result in the formation of soap, an undesirable process contaminate when present above trace levels. To avoid this, these feedstocks first undergo an acid pre-treatment step to esterify the FFAs before proceeding to the base-catalyzed triglyceride tran sesterification reaction. Feedst ocks with small amounts of FFAs may be converted in a basic environment if the soaps can be removed from the fuel product.

Once the chemical conversions are complete, the mixture is neutralized, washed, and coproducts and unreacted alcohol are r ecovered. At that point the biodi esel is subjected to quality control testing and then releas ed for sale. Figure 1.4-9 show s a process flow diagram for a typical biodiesel producti on process that uses virgin plant oil as feedstock; processes using waste fats or greases would include a an acid esterification step upstream of the transesterification reactor shown here. Plants that also produ ce other oleochemicals of ten have distillation equipment at the end of the process capable of pu rifying the methyl esters to a high degree or separating them by molecular weig ht. These plants may use this equipment to produce a very high purity biodiesel product. We estimate that less than 10% of current biodiesel production is distilled. 281

105Trans esterification Reactor

Acid Acidulation

Separator

Acidulation Biodiesel Wash Tank

Separator Figure 1.4-9. Schematic of typical biodie sel production from virgin plant oil

- Vegetable Oils

Methanol Catalyst (Base)

Glycerin Stripper Column Methanol Rectification Column Glycerin Product Methanol / Water Crude Biodiesel

To Methanol Storage Glycerin Methanol

Water Water

Gray Water

Biodiesel / Methanol Stripper Biodiesel

Methanol / Water Filter Biodiesel Product Filter

Transesterification Reactor

Acid Vegetable Oils

Methanol Catalyst (Base)

Acidulation

Separator

Acidulation

Glycerin Stripper Column Methanol Rectification Column Glycerin Product Methanol / Water Biodiesel Wash Tank Crude Biodiesel

To Methanol Storage Glycerin Methanol

Water Water

Separator Gray Water

Biodiesel / Methanol Stripper Biodiesel

Methanol / Water Filter Biodiesel Product Filter

Some differences exist between large and sm all plants that are worth mentioning given the very wide range of plant capacities existing in this industry. Larger plants (greater than 10 million gallons per year) are more likely to employ continuous flow processes, which afford certain efficiencies of scale and steady-state operation. On the othe r hand, small plants (less than one million gallons per year) are most likely to produce fuel batch-by-batch, which may give them more flexibility to change feedstock types or slow output on short notice. Smaller plants are less likely to be able to afford an on-site laboratory or quality control specialist, which may cause them hardship as fuel quality standards tighten and/or are more stringently enforced. Third-party labs exist for this pu rpose, but they pose challenges such as significant per-test costs and multi-day turnaround times that require holdi ng of product batches until results are received.

The biodiesel production pro cess is relatively simple a nd economical, and there is sufficient existing U.S. capacity to produce al l the biodiesel required to comply with the biomass-based diesel standard in this ru lemaking. With U.S. 2008 biodiesel production estimated to be 650 million gallons, most made as described above, the process is viewed as demonstrated and reliable. Thus, we do not expe ct large changes in the process technology used to make biodiesel going into the future. That sa id, it is worth noting some potential changes as existing plants strive to comply with changing fuel quality standards, or as new plants are occasionally built to take advantage of specific market niches.

106One such change may be an increase in distil lation of biodiesel. EPA requires biodiesel to meet the ASTM D-6751 specification for B100 in order to be legally blended into diesel fuel for use in vehicles. Earlier this year, ASTM am ended this specification to require a cold filter plugging test, which effectively mandates very low levels of FFAs, st erol glucosides, and partially-converted triglycerides in the finish ed biodiesel. There are a variety of process parameters a biodiesel producer can adjust to reduce the levels of these compounds in the finished fuel, but one very effective way to en sure a high purity product is through distillation. At this time it is unclear to what extent dist illation will be relied upon for compliance with the amended biodiesel specification. An increase in distillation would in crease per-gallon energy inputs to the process significantl y, which will make the biodiesel more costly and result in higher GHG emissions.

Another potential change is a shift to use of heterogeneous (i .e., solid phase) catalysts for transesterification rather than the sodium or potassium methylat e liquids used in most plants now. This is expected to reduce the neutraliz ing/washing/separating st eps required downstream of the reactor, potentially resulti ng in a higher purity product and decreasing process energy use
to some extent.

Some industry forecasts suggest animal fats and waste greases will make up an increasing share of biodiesel feedstocks due to their lower costs and lower upstream GHG impacts. Because most fats and greases contain significan t levels of FFAs, this shift will cause more plants to use acid pre-treatment, increasing pr ocess complexity and per-gallon energy use.

Renewable Diesel

The renewable diesel production process conve rts vegetable oils and animal fats into diesel fuel using thermal depolymerization, which is similar to hydrotreating used in petroleum refining to remove sulfur. The process uses hydrogen and catalyst to remove oxygen from the triglyceride molecules in th e feedstocks oils via a d ecarboxylation and hydro-oxygenation reaction, yielding some light petroleum products and water as byproducts. The reactions also saturate the olefin bonds in th e feedstock oils, converting them to paraffins. All of these reactions consume significant amounts of hydrogen. The extent of these reactions depends on the process conditions, as some of the carbon backbone of the oils can be cracked to naphtha and lighter products with higher seve rity. For our analysis though, we assume no such cracking and predict high selectivity to diesel -range material with a small am ount of propane and water as byproducts. Figure 1.4-10 shows a flow diagram of the primary steps of renewable diesel production.

107S, N, metal removal Flash separator Hydrotreating reactor

S, N, metal removal Figure 1.4-10. Process flow diagram of primar y steps in renewable diesel production

Wa t e r , l i g h t hy droc arb o ns

Mixed grease

feedstock Hydrogen

Heat in Heat out

Liquid fuel for blending or further separation S, N, metal removal

Mixed grease

feedstock Hydrogen

Heat in Flash separator Heat out

Liquid fuel for blending or further separation Hydrotreating reactor

Renewable diesel will be produced either at a stand-alone facility or within the boundaries of an existing petroleum refinery. For the stand-alone facility, feedstock is brought in and finished fuel is transported out to market. This type of facility may be co-located with a rendering facility to minimize feedstock transpor tation and storage costs. For production within the boundaries of a refinery, the feed material may either be processed in a segregated unit (new or revamped), or co-processed with petroleum in an existing unit. In any case, the feedstock will require pre-treatment in a unit th at removes contaminates such as sulfur, nitrogen, and other compounds that may poison hydrotreating catalysts.

Since December, 2007, ConocoPhilips has been producing a small amount (300-500 bbl/day) of renewable diesel at their Borger, Texas, refinery from beef tallow generated by Tyson Foods, Inc. in Amarillo, Texas. 282 In 2007, Dynamic Fuels, LLC., was formed by Syntroleum Corp. and Tyson Foods, Inc., to produ ce liquid renewable fuels. This fall Dynamic Fuels has announced ground-breakin g for a 75 million gallon per year plant (5,000 bbl/day) in Geismar, Louisiana, that will use Tyson meat pr ocessing fats as feedstock to Syntroleum’s BioSynfining process. Start-up is scheduled fo r 2010, with the primary product being high-quality diesel fuel that will be fungible with in the existing petroleum supply system. 283 This facility plans to utilize supplies of hydroge n available in the industrial park where it will be located, as well as rail and shipping infrastr ucture already in place nearby. 284

Syntroleum Corp was founded in 1984 and hol ds a number of patent s in gas-to-liquids and biomass-to-liquids conversion processes. One of these technologies they call Synfining, a process for upgrading Fischer-Trops ch paraffins to compounds with properties more favorable

108for diesel fuel. They have furt her adapted this process to use a variety of fats and oils as feedstocks, calling it Bio-Synfining. It is this process that will be used in the Geismar facility.

Looking internationally, the Finnish comp any Neste Oil began operating a 3,200 bbl/day process in Finland in 2007 to c onvert vegetable oils into renewa ble diesel. This company has plans to construct similar faci lities in Singapore and the Neth erlands by 2010, and eventually plans to bring on-line plants th at will convert bi omass to liquid fuels using gasification. 285

Since thermochemical production of hydrocar bon fuels from fats and biomass is a relatively new endeavor to conduct on a commerci al scale, we expect continued innovation and fine-tuning of the technology as th ese processes evolve from thei r roots in Fischer-Tropsch and petroleum hydrotreating processes. (This discussion ties in with cellulosic diesel in section 1.4.3.2.)



### 1.5 Biofuel Industry Characterization & Projected Growth

In this section we will discuss the curr ent state of the biof uel industry and how production might grow in the future under th e proposed RFS2 program. The bulk of the discussion will focus on ethanol production, but we will also provide insight on biodiesel, renewable diesel, and the evolving cellulosic diesel industry. We will start by discussing current corn ethanol production and how the industry might continue to expand under the RFS2 program as well as employ more advanced processing te chnologies. From there we will discuss the availability of imported ethanol from Brazil and Caribbean Basin Initiative (CBI) countries to help meet the Advanced Biofuel Standard. Following this discussion, we will characterize the present state of the cellulosic biofuel industry and talk abou t the likelihood of near-term commercialization based on industr y plans and technological break throughs aided by state and federal grants, tax incentives, and loan guarantee programs. And furthermore, why we believe the industry is on track for meeting the 2010 Cellul osic Biofuel Standard set by EISA. We will end the discussion by forecasting where potential cellu losic biofuel plants could be located in the U.S. based on feedstock availability. This info rmation, along with project ed corn ethanol plant locations and imported ethanol locations, will be us ed as an input into the distribution analysis (discussed later in Section 1.6) and the emissions and air quality modeling (discussed in Chapter 3). Finally, we will conclude our industry charac terization by discussing the present state of the biomass-based diesel industry and how we expect biodiesel production to gr ow in the future and renewable diesel production to emerge. For a more in-depth discussion of corn ethanol, imported ethanol, cellulosic et hanol/diesel, biodiesel, and renewable diesel processing technologies, refer to Section 1.4.

1.5.1 Corn Ethanol

The majority of domestic biofuel production currently comes from plants processing corn and other similarly-processed grai ns in the Midwest. However, there are a handful of plants located outside the Corn Belt and a few plants pr ocessing simple sugars from food or beverage waste. In this subsection, we will talk about th e present state of the co rn ethanol industry and how we expect things might change in the futu re under the proposed RFS2 program. At the end

109of this section (Section 1.5.1.5) we will summ arize an earlier May 2008 corn ethanol industry assessment that was used to project future corn/s tarch ethanol plant locations for various impact analyses associated with this proposed rule.

1.5.1.1 Historic/Current Production

The United States is currently the largest et hanol producer in the world. In 2008, the U.S. produced almost 9 billion gallons of fuel et hanol for domestic consumption, the majority of which came from domestically-grown corn. 286 Although the U.S. ethanol industry has been in existence since the 1970s, it has ra pidly expanded over the past few years due to the phase-out of methyl tertiary butyl ether (MTBE) X , elevated crude oil prices, state mandates and tax incentives, the introduction of the Federal Volume Ethanol Excise Tax Credit (VEETC) Y , and the implementation of the existing RFS1 program Z . As shown in Figure 1.5-1, U.S. ethanol production has grown exponentially over the past decade.

X For more information on how the phase-out of MTBE helped spur ethanol production/consumption, refer to Section 1.7.1.1.
Y On October 22, 2004, President Bush signed into law H.R. 4520, the American Jobs Creation Act of 2004 (JOBS Bill), which created the Volumetric Et hanol Excise Tax Credit (VEETC). The $0.51/gal VEETC for ethanol blender replaced the former fuel excise tax exem ption, blender’s credit, and pure ethano l fuel credit. However, the recentlyenacted Farm Bill has modified the alcohol credit so that corn ethanol gets a reduced credit of $0.45/gal. The 2008 Farm Bill also gives cellulosic biofuel a credit of $1.01/gal. Both credits appear to be valid through the end of 2012.

Z On May 1, 2007, EPA published a final rule (72 FR 23900) implementing the Renewable Fuel Standard (RFS) required by the Energy Policy Act of 2005. The RFS requires that 4.0 billion gallons of renewable fuel be blended into gasoline/diesel by 2006, growing to 7.5 billion gallons by 2012.

1101999 2000 2001 2002 20 0 3 2004 2005 20 0 6 20 07 20 0 8 Figure 1.5-1. Historical Growth in U.S. Corn/Starch Ethanol Production 287

1.4 1.6 1.7 2.0 2.7 3.3 3.8 4.6 6.3 8.9

0 1 2 3 4 5 6 7 8 9 10Corn/Starch Ethanol Production (Bgal)

Year

As of April 2009, there were 169 fuel etha nol plants operating in the U.S. with a combined estimated production capac ity of 10.5 billion gallons per year. AA 288 289 This does not include a number of ethanol pl ants that are currently idled. BB The majority of today’s ethanol (over 91% by volume) is produced exclusively fro m corn. Another 11% comes from a blend of corn and/or similarly processed grains (milo, wheat , or barley) and less than half a percent is produced from cheese whey, waste beverages, a nd sugars/starches combined. A summary of U.S. ethanol production by feedstock is presented in Table 1.5-1.

AA Our April 2009 corn/starch ethanol industry characterization was based on a variety of data sources including: Renewable Fuels Association (RFA) Ethanol Biorefinery Locations (updated March 31, 2009); Ethanol Producer Magazine (EPM) Current plant list (last modified on April 7, 2009), and ethanol producer websites. The baseline does not include ethanol plants whose primary business is industrial or food-grade ethanol production nor does it include plants that might be located in the Virgin Islands or U.S. territories. Where ap plicable, current ethanol plant production levels have been used in lieu of nameplate capacities to estimate plant production. The April 2009 information presented in this section reflects our most recent knowledge of the corn /starch ethanol industry. However, for various NPRM impact analyses, an earlier May 2008 industry assessment was used. For more on this assessment, refer to Section 1.5.1.5.
BB In addition to idled plants, the assessment does not incl ude idled production capacity at facilities that are currently operating at 50% or less than their nameplate capacity.

111Table 1.5-1. Current Corn/Starch Ethanol Production Capacity by Feedstock

Plant Feedstock (Primary Listed First) Capacity MGY % o f Capacity No. o f Plants % of Plants Corn a 9,605 91.2% 144 85.2% Corn, Milo b 717 6.8% 14 8.3% Corn, Wheat 130 1.2% 1 0.6% Milo 3 0.0% 1 0.6% Wheat, Milo 50 0.5% 1 0.6% Cheese Whey 5 0.0% 1 0.6% Waste Beverages c 19 0.2% 5 3.0% Waste Sugars & Starches d 7 0.1% 2 1.2% Total 10,535 100% 169 100%

a Includes one facility processing seed corn, two facilities also operating pilot-level cellulosic ethanol plants, and four facilities with plans to build pilot-level cellulosic ethanol plants or incorporate biomass feedstocks in the future.

b Includes one facility processing a small amount of molasses in addition to corn and milo.

c Includes two facilities processing brewery waste.

d Includes one facility processing potato waste that intends to add corn in the future.

As shown in Table 1.5-1, of the 169 operati ng plants, 161 plants processing corn and/or other similarly processed grains. Of these facilities, 150 utilize dry-milling technologies and the remaining 11 plants rely on wet-milling processes. Dry mill ethanol plants grind the entire kernel and generally produce only one primary co-pr oduct: distillers grains with solubles (DGS). The co-product is sold wet (WDGS) or dried (DDGS ) to the agricultural market as animal feed. However, there are a growing number of dr y mill ethanol plants pursuing front-end corn fractionation or back-end corn oil extraction to produce fuel-grade corn oil for the biodiesel industry. There are also additiona l plants pursuing cold starch fermentation and other energysaving processing technologies. For more on th e dry-milling and wet-milling processes as well as emerging advanced technologi es, refer to Section 1.4.1.

In contrast to traditional dry mill plants, wet mill facilities separate the kernel prior to processing into its component parts (germ, fiber, protein, and starch) and produce other coproducts (usually gluten feed, gluten meal, and food-grade corn oil) in addition to DGS. Wet mill plants are generally more costly to build bu t are larger in size on average. As such, 11.5% of the current grain ethanol production comes from the 11 previously-mentioned wet mill facilities listed in Table 1.5-2.

112Table 1.5-2. Existing Wet Mill Corn Ethanol Plants

Ethanol Plant/Company Location Capacity MGY % of Tot Capacity Archer Daniels Midland a
Archer Daniels Midland a Archer Daniels Midland a Archer Daniels Midland a
Archer Daniels Midland a Aventine Renewable Energy Cargill, Inc. Cargill, Inc. Grain Processing Corp Penford Products Tate & Lyle Cedar Rapids, IA Clinton, IA Columbus, NE Decatur, IL Marshall, MN Pekin, IL Eddyville, IA Blair, NE Muscatine, IA Cedar Rapids, IA Loudon, TN 250 190

95 290

40 100

35 85 20 45 66 2.4% 1.8% 0.9% 2.8% 0.4% 0.9% 0.3% 0.8% 0.2% 0.4% 0.6% Total 1,216 11.5%

a Estimated plant capacities.

The remaining eight ethanol plants pr ocess cheese whey, waste beverages or sugars/starches and operate differently than their grain-based counterparts. These small production facilities do not require milling a nd operate a simpler enzymatic fermentation process. A summary of today’s average etha nol plant sizes is found in Table 1.5-3 below.

Table 1.5-3. Average Corn/Starch Plant Sizes Processing Technology Capacity MGY % o f Capacity No. o f Plants % of Plants Avg. Size MGY Dry Milling a Wet Milling Other a 9,289 1,216 30 88.2% 11.5%

0.3% 150 11

8 88.8% 6.5% 4.7% 61.9 110.5 3.8 Total 10,535 100.0% 169 100.0% 62.3

a Includes four facilities that plan on incorporating cellulosic feedstocks at their existing corn ethanol plants in the future. If plans come to fruition, these plants will need additional front end technology to supplement their existing dry mill processes.

b Facilities that do not process traditional grain-based crops and thus do not require milling. This category includes plants processing cheese whey, waste beverages or sugars & starches.

Ethanol production is a rela tively resource-intensive proce ss that requires the use of water, electricity, and steam. CC Steam needed to heat the pro cess is generally produced on-site or by other dedicated boilers. Th e ethanol industry relies primarily on natural gas. Of today’s

CC For more on plant energy requirements, refer to Section 1.5.1.3.

113169 ethanol production faciliti es, 142 burn natural gas DD (exclusively), three burn a combination of natural gas and biomass, one recently started burning a combination of natural gas, landfill syngas and wood, and two burn a combination of na tural gas and syrup from the process. In addition, 20 plants burn coal as their primary fuel and one bur ns a combination of coal and biomass. Our research suggests that 25 plants currently utilize cogenera tion or combined heat and power (CHP) technology, although others may exist. CHP is a mechanism for improving plant energy efficiency by using a single fuel to generate both power and thermal energy. For more on this technology, refer to Section 1.4.1.3. A summary of the energy sources and CHP technology utilized by today’s ethano l plants is found in Table 1.5-4.

Table 1.5-4. Current Corn/Starch Ethanol Production Capacity by Energy Source
Plant Energy Source (Primary Listed First) Capacity MGY % of Capacity No. o f Plants % o f Plants CHP Tech. Coal a Coal, Biomass Natural Gas b Natural Gas, Biomass c Natural Gas, Landfill Biogas, Wood Natural Gas, Syrup 1,868 50 8,294

113 110 101 17.7% 0.5% 78.7%

1.1% 1.0% 1.0% 20 1 142 3 1 2 11.8 % 0.6% 84.0%

1.8% 0.6% 1.2 % 9 0 15

1 0 0 Total 10,535 100.0% 169 100.0% 25

a Includes four plants that are permitted to burn biomass, tires, petroleum coke, and wood waste in addition to coal and one facility that intends to transition to biomass in the future.

b Includes one facility that intends to switch to biomass, one facility that intends to burn thin stillage biogas, and two facilities that might switch to coal in the future.

c Includes one facility processing bran in addition to natural gas.

Since the majority of ethanol is made from co rn, it is no surprise that most of the plants are located in the Midwest near the Corn Belt. Of today’s 169 ethanol production facilities, 151 are located in the 15 states comprising PADD 2. For a map of the Petroleum Administration for Defense Districts or PADDs, refer to Figure 1.5-2.

DD Facilities were assumed to burn natural gas if the plant bo iler fuel was unspecified or unavailable on the public domain.

114Figure 1.5-2. Petroleum Administration for Defense Districts

As a region, PADD 2 accounts for 94% (or almost 10 billion gallons) of today’s estimated ethanol production capac ity, as shown in Table 1.5-5.

Table 1.5-5. Current Corn/Starch Ethanol Production Capacity by PADD

PADD Capacity MGY % of Capacity No. o f Plants % o f Plants PADD 1 PADD 2 PADD 3 PADD 4 PADD 5 150 9,900

194 160 131 1.4% 94.0%

1.8% 1.5% 1.2% 3 151 3 7 5 1.8% 89.3%

1.8% 4.1% 3.0% Total 10,535 100.0% 169 100.0%

Leading the Midwest in etha nol production are Iowa, Nebr aska, Illinois, Nebraska, Minnesota, and South Dakota. T ogether, these five states’ 98 ethanol plants account for twothirds (or just over 7 billion gallons) of the nation’s ethano l production capacity. However, although the majority of ethanol production comes from PADD 2, there are a growing number of plants situated outside the trad itional Corn Belt. Our April 2 009 industry assessment indicates that Arizona, California, Colorado, Georgia, Idaho, Kentucky, Mi ssissippi, New York, Oklahoma, Oregon, Texas and Wyoming all have one or more operational ethanol plants. Some of these facilities ship in feedstocks (namely co rn) from the Midwest, others rely on locally grown/produced feedstocks, while others rely on a combination of the two. A summary of ethanol production capacity by stat e is presented in Table 1.5-6.

115Table 1.5-6. Current Corn/Starch Ethanol Production Capacity by State

State Capacity MGY % of Capacity No. o f Plants % o f Plants Iowa 3,011 28.6% 37 21.9% Illinois 1,142 10.8% 11 6.5% Nebraska 1,091 10.4% 19 11.2% Minnesota 951 9.0% 18 10.7% South Dakota 836 7.9% 13 7.7% Indiana 714 6.8% 9 5.3% Wisconsin 529 5.0% 9 5.3% Kansas 439 4.2% 11 6.5% Ohio 320 3.0% 5 3.0% Missouri 261 2.5% 6 3.6% Michigan 215 2.0% 4 2.4%
Tennessee 176 1.7% 2 1.2% North Dakota 175 1.7% 4 2.4% Colorado 146 1.4% 5 3.0%
Texas 140 1.3% 2 1.2% Georgia 100 1.0% 2 1.2% Mississippi 54 0.5% 1 0.6% New York 50 0.5% 1 0.6% Arizona 50 0.5% 1 0.6% California 41 0.4% 3 1.8% Kentucky 40 0.4% 2 1.2% Oregon 40 0.4% 1 0.6% Wyoming 9 0.1% 1 0.6% Idaho 5 0.0% 1 0.6% Oklahoma 2 0.0% 1 0.6% Total 10,535 100.0% 169 100.0%

The U.S. ethanol industry is currently comp rised of a mixture of company-owned plants and locally-owned farmer cooperatives (co-ops). The majority of today’s ethanol production facilities are company-owned and, on average, thes e plants are larger in size than farmer-owned co-ops. Accordingly, compan y-owned plants account for almost 80% of today’s ethanol production capacity. 290 Furthermore, 30% of the total domestic product comes from 38 plants owned by just three different companies – POET Biorefining, Archer Daniels Midland (ADM), and Valero Renewables (refer to Figure 1.5-3 be low). Valero recently entered into the renewable fuels business by acquiring five co rn ethanol plants and one cons truction site from bankrupted VeraSun Energy Corporation. Valero has since purchased two more VeraSun plants, but at the time of our April 2009 assessment these plants had not been brought back online yet.

116Figure 1.5-3. Current Corn/Starch Ethanol Production Capacity by Company

Other, 5,549 POET Biorefining, 1,535

,

Aventine Renewable Energy, 207 Archer Daniels Midland, 1,100
BioFuel Energy Corp., 230 Valero Renewables, 560

Glacial Lakes Energy, LLC, 250 Hawkeye Renewables, LLC, 460 Green Plains Renewable The Andersons, 315 Energy, Inc., 330

1.5.1.2 Forecasted Production Under RFS2

As highlighted above, 10.5 billion gallons of corn/starch ethanol plant capacity was online as of April 2009. So even if no additi onal capacity was added, U.S. ethanol production would grow from 2008 to 2009, pr ovided facilities continue to operate at or above today’s production levels. And despite today’s temporar y unfavorable market conditions (i.e., low ethanol market values), we expect the ethanol in dustry will continue to expand in the future under RFS2. Although there is not a set corn ethanol standard, EISA allows for 15 billion gallons of the 36-billion gallon rene wable fuel standard to be met by conventional biofuels. And we expect that corn and other sugar or starch -based ethanol will fulfill this requirement. Furthermore, we project that all new corn/sta rch ethanol plant capacity brought online under RFS2 would either meet the conventi onal biofuel GHG threshold requirement EE or meet the grandfathering requirement (for more information, refer to Section 1.5.1.4).

In addition to the 169 corn/starc h ethanol plants that are curren tly online today, 36 plants are presently idled. Some of these constructe d facilities (namely smaller ethanol plants) have been idled for quite some time, whereas other plan ts have just recently be en put into “hot idle” mode. A number of ethanol producers (e.g., Vera Sun) are idling operatio ns, putting projects on hold, selling off plants, and even filing for Chapter 11 bankruptcy. In addition, we are aware of

EE The lifecycle assessment values which assume a 2% discount rate over a 100-year timeframe.

117two facilities that are currently operating at 50% or less than their nameplate capacity. As crude oil and gasoline prices rise agai n in the future, corn ethanol produ ction will become more viable again and we expect that these plants will resu me operations. At the time of our April 2009 ethanol industry assessment, ther e were also 19 new ethanol plants under construction in the U.S, and two plant expansion projects underway. FF While many of these projects are also on hold due to current economic conditions, we expect these facilities will ev entually come online under the RFS2 program. A summary of the projected industry growth is found in Table 1.5-7.

Table 1.5-7. Potential Corn/Starch Ethanol Industry Expansion Under RFS2
Growth in Ethanol Production

Plants Currently Online Idled Plants / Capacity a New Construction Projects Expansion Projects Total

Plant Capacity (MGY) 10,535 2,471 1,955 80 15,042
Total No. of Plants 169 36 19 2 226

a Includes the idled plant capacity of the two facilities that are currently operating at 50% or less than nameplate capacity.

While theoretically it only takes 12-18 months to build an ethanol plant GG , the rate at which new plant capacity comes online will be dictated by market conditions, which will in part be influenced by the RFS2 requirements. As mentioned above, today’s proposed program will create a growing demand for corn ethanol reachi ng 15 billion gallons by 2015. However, it is possible that market conditions could drive demand even higher. Whether the nation will overcomply with the corn ethanol standard is uncertain and will be determined by feedstock availability/pricing, crude oil pr icing, and the relative ethanol/gasoline price relationship. To measure the impacts of the proposed RFS2 progr am, we assumed that corn ethanol production would not exceed 15 billion gallons. We also as sumed that all growth would come from new plants or plant expansion proj ects (in addition to idled plan ts being brought back online). HH However, it is possible that some of the requ ired growth could come from minor process improvements (e.g., debottlenecking) at existing facilities.

Once all the aforementioned projects are co mplete, we project that there will be 226 corn/starch ethanol plants operati ng in the U.S. with a combined production capacity of around 15 billion gallons per year. Much like today’s ethanol industry, the overwhelming majority of new plant capacity (93% by volume) is expected to come from corn-fed pl ants. Another 7% is

FF Based on Renewable Fuels Association (RFA), Ethanol Biorefinery Locations – Under Construction/Expansions (updated April 4, 2008); Ethanol Producer Magazine (EPM), Under Construction plant list (last modified on April 14, 2008), ethanol producer websites, and follow-up correspondence with ethanol producers. It is worth noting that for our industry assessment, “under construction” implies that more than just a ground breaking ceremony has taken place. To determine whether a fac ility “commenced construction” by Dece mber 19, 2007 and thus will be grandfathered under the proposed RFS2, we will rely on the Expanded registration Process (r efer to Section III.C of the Preamble).
GG For more information on our estimated plant build rates, refer to Section 1.2.5.
HH For our NPRM impact analysis, we re lied on an earlier May 2008 industry assessment. For more information, refer to Section 1.5.1.5.

118forecasted to come from plants processing a blend of corn and/or other grains, and a very small increase is projected to come from idled cheese whey and waste beverage plants coming back online. A summary of the forecasted ethanol production by feedstock under the RFS2 program is found in Table 1.5-8.

Table 1.5-8. Projected RFS2 Ethanol Production Capacity by Feedstock

Plant Feedstock (Primary Listed First) Additional Production Total RFS2 Estimate Capacity MGY No. of Plants Capacity MGY No. o f Plants Corn a 4,197 49 13,802 193 Corn, Milo b 185 3 902 17 Corn, Wheat 8 1 138 2 Corn, Wheat, Milo 110 2 110 2 Milo 0 0 3 1 Wheat, Milo 0 0 50 1 Cheese Whey 3 1 8 2 Waste Beverages c 4 1 23 6 Waste Sugars & Starches d 0 0 7 2 Total 4,507 57 15,042 226

a Includes one facility processing seed corn, another facility processing small amounts of whey, two facilities also operating pilot-level cellulosic ethanol plants, and four facilities with plans to build pilot-level cellulosic ethanol plants or incorporate biomass feedstocks in the future. bIncludes one facility processing a small amount of molasses in addition to corn and milo.

c Includes two facilities processing brewery waste.

d Includes one facility processing potato waste that intends to add corn in the future.

Based on current industry plans, the majority of additional corn/grain ethanol production capacity (almost 84% by volume) is predicted to come from new or expanded plants burning natural gas. II Additionally, we are forecasting one ne w plant and a reopening of another plant relying on manure biogas. We ar e also predicting expansions at three coal-fired plants. JJ Of the 55 new ethanol plants, our research indicates that five would util ize cogeneration, bringing the total number of CHP facilities to 30. A summar y of the forecasted near-term ethanol plant energy sources is found in Table 1.5-9.

II Facilities were assumed to burn natural gas if the plant bo iler fuel was unspecified or unavailable on the public domain.
JJ Two of the three coal-fired plant expa nsions appear as new plants in Table 1.5-8. This is because two of the expansion projects consist of adding dry milling plant ca pacity to an existing wet mill plant. However, our interpretation is that these facilities will rely on the same (potentially expanded) coal-fired boilers for process steam. Since all the aforementioned coal-fired ethanol production f acilities appear to have commenced construction prior to December 19, 2007, we project that the ethanol produced at these facilities will be grandfathered under the proposed RFS2 rule. For more on our grandfathered volume estimate, refer to Section 1.5.1.4.

119Table 1.5-9. Projected Near-Term Corn/Starch Ethanol Production Capacity by Energy Source

Plant Energy Source (Primary Listed First) Additional Production Total RFS2 Estimate
Capacity MGY No. of Plants Capacity MGY No. of Plants CHP Tech. Coal a 610 2 2,478 22 11 Coal, Biomass 0 0 50 1 0 Manure Biogas 134 2 134 2 0 Natural Gas b 3,763 53 12,056 195 18 Natural Gas, Biomass c 0 0 113 3 1 Natural Gas, Landfill Biogas, Wood 0 0 110 1 0 Natural Gas, Syrup 0 0 101 2 0 Total 4,507 57 15,042 226 30

a Includes six plants that are permitted to burn biomass, tires, petroleum coke, and wood waste in addition to coal and one facility that intends to transition to biomass in the future.

b Includes one facility that intends to switch to biomass , one facility that intends to burn thin stillage biogas, and six facilities that might switch to coal in the future.

c Includes one facility processing bran in addition to natural gas.

The information in Table 1.5-9 is based on s hort-term industry pro duction plans at the time of our April 2009 plant assessment. However, we anticipate even more growth in advanced ethanol production technol ogies in the future under the proposed RFS2 progra m. We project that fuel prices will drive a large number of corn ethanol plants to transition from conventional boiler fuels to advanced biomass-based feedstocks. We also believe that fossil fuel/electricity prices will drive a number of ethanol producers to pursue CHP technology. For more on our projected 2022 utilization of these technologies under the RFS2 program, refer to Section 1.5.1.3.

Under the proposed RFS2 program, the majority of new ethanol production is expected to originate from PADD 2, close to where the corn is grown. However, there are a growing number of “destination” ethanol plants being built outsid e the Midwest in response to state production subsidies, E10/E85 retail pump incentives, and state mandates. A summary of the forecasted ethanol production by PADD under th e RFS2 program can be found in Table 1.5-10.

120Table 1.5-10. Projected RFS2 Corn/Starch Ethanol Production Capacity by PADD

PADD New Plants/Exp. Total 2022 Est. Capacity MGY No. o f Plants Capacity MGY No. o f Plants
PADD 1 PADD 2 PADD 3 PADD 4 PADD 5 214 5,002

215

70 328 2 47

2 2 5 264 13,620 385 230 499 4 187 5 9 11 Total 5,829 58 14,998 216

Based on current production plans, we projec t that Iowa, Nebraska, and Illinois will continue to dominate ethanol production with a collective production cap acity of about 7.4 billion gallons. Indiana and Mi nnesota are projected to be the fourth and fifth largest producers displacing South Dakota (today’s fifth largest pr oducer according to Table 1.5-6). Ethanol production is expected to grow in other Midwest states and there are also a growing number of plants that are being built outsi de the Corn Belt. After the proposed RFS2 program is fully implemented, we estimate that more than half of the United States will have corn/starch ethanol production as shown below in Table 1.5-11.

121Table 1.5-11. Projected RFS2 Corn/Starch Ethanol Production Capacity by State

State Additional Production Total RFS2 Estimate
Capacity MGY No. o f Plants Capacity MGY No. of Plants Iowa 833 8 3,844 45 Nebraska 827 9 1,918 28 Illinois 538 7 1,680 18 Indiana 506 5 1,220 14 Minnesota 188 4 1,139 22 South Dakota 172 2 1,008 15 Wisconsin 27 1 556 10 Ohio 224 3 544 8
Kansas 80 2 519 13 Texas 210 2 350 4 North Dakota 171 2 346 6 Michigan 50 1 265 5 Missouri 0 0 261 6 Tennessee 60 0 236 2 California 195 4 236 7 New York 114 1 164 2 Oregon 113 1 153 2 Colorado 0 0 146 5 Georgia 0 0 100 2 North Carolina 60 1 60 1 Idaho 50 1 55 2 Washington 55 1 55 1 Mississippi 0 0 54 1 Arizona 0 0 50 1 Kentucky 0 0 40 2 New Mexico 30 1 30 1 Wyoming 0 0 9 1 Florida 4 1 4 1 Oklahoma 0 0 2 1 Total 4,507 57 15,042 226

The majority of future ethanol plants are expected to be company-owned. Of the 57 plants we are expecting to be built or brought back online und er the proposed RFS2 program, 51 are expected to be owned by corporations. The leading ethanol producers will likely continue to be Archer Daniels Midland and POET Biorefinin g, each with over 1.5 bi llion gallons of annual corn ethanol production capacity. Valero Rene wables, who recently purchased eight ethanol plants (including a development s ite) from VeraSun Energy is expected to be the third largest

122Other, 7,816

White Energy, 250

Glacial Lakes Energy, LLC, 250

Green Plains Renewable Energy, Inc., 330 The Andersons, 315

Abengoa Bioenergy Corp., 369 Aventine Renewable Energy, 427 Hawkeye Renewables, LLC, 460 Archer Daniels Midland, 1650

POET Biorefining, 1535

Valero Renewables, 890

TBD Ownership (former

VeraSun plants), 750 ethanol producer with almost 900 million gallons of production capacity. How the rest of the company rankings fall out will be largely dependent on who purchases the remaining eight VeraSun ethanol plants (currently owned by ba nks and holding companies) totaling 750 million gallons of annual capacity. A summary of the pr ojected ethanol plant ow nership under the RFS2 program is found in Figure1.5-4.

Figure 1.5-4. Forecasted Corn/Starch Ethanol Production Capacity by Company

1.5.1.3 Forecasted Growth in Ad vanced Processing Technologies

While we can get a good idea of what the etha nol industry will look like in the near term by looking at currently existing et hanol plants and those under cons truction, further analysis is needed to forecast what the ethanol industry wi ll look like in 2022. Significant changes in the primary fuel source and overall energy efficiency of ethanol production plants are likely to occur. The high price of natu ral gas has many ethanol plants considering alternative fuel sources. Greater biofuel availa bility and potential low life cy cle green house gas emissions incentives may further encourage ethanol producer s to transition from fossil fuels to biomass based fuels. As ethanol plants become more efficient and require less energy, their ability to use biofuels increases. Two of the biggest drawback s to using biofuels currently are handling and storage costs. Due to the lower de nsity of biofuels, as compared to coal, a larger area is required to store biomass with an equivalent heating value of coal. Handling costs are also increased as a larger volume of fuel must be moved. These negative impacts would be less significant in an ethanol plant using less energy. Lower overall energy use would also allow waste products and

123locally produced biofuels to make up a larger portion of the total fuel supply, reducing the purchase and transportation costs of the biofuels. It is likely that plants currently using natural gas would transition to using bioga s and those using coal would tr ansition to using solid biomass due to their ability to make these transitions without investing in new boiler equipment. The same factors, cost and lower life cycle green house gas emissions , are expected to increase the number of ethanol producers using combined heat and power (CHP) technology. Projections for the primary feedstock and use of CHP technol ogy from 2020 to 2030 are summarized in Table 1.5-12 below.

Table 1.5-12. KK Projected Primary Fuel Sources and CHP Usage
2020 2022 2025 2030 Natural Gas Boiler 54% 49% 42% 31% Natural Gas CHP 11% 12% 13% 15% Coal Boiler 0% 0% 0% 0% Coal CHP 4% 4% 4% 4% Biomass Boiler 10% 11% 12% 15% Biomass CHP 9% 10% 12% 15% Biogas Boiler 12% 14% 16% 20%

The energy efficiency of ethanol plants are also expected to change significantly. New technologies are expected to both increase the e fficiency of units currently used in ethanol production, as well as provide en ergy saving alternatives to c onventional production practices. Increasing energy efficiency is a pr iority in many ethanol plants as is can dramatically increase profitability by reducing energy costs, the sec ond highest cost of etha nol production behind raw materials. Several groups are currently working on technologies that could impact the ethanol industry. The Department of Energy’s (DOE ) Super Boiler program is expected to produce boilers with an efficiency of 94% by 2020. The National Electrical Manu facturers Association’s (NEMA) premium efficiency motors are expected to be adopted more widely in the coming years. Electricity generation efficiency is also ex pected to increase, both on site at plants with CHP technology, and at central pow er plants supplying electricity to ethanol plants through the national grid. The projected energy savings from the energy efficiency improvements to units used in conventional ethanol plants in 2022 re lative to 2007 is 32.1%. The projected energy savings from 2015 to 2030 are summar ized in Table 1.5-13 below.

KK Data based on “An Analysis of the Projected Energy Us e of Future Dry Mill Corn Ethanol Plants (2010-2030)”; Steffen Mueller, University of Illinoi s at Chicago Energy Resource Center, Oc tober 10, 2007, Available online at: http://www.chpcentermw.org/pdfs/2 007CornEethanolEnergySys.pdf

124Table 1.5-13. LL Projected Energy Savings from Conv entional Production Equipment
2007 2015 2020 2022 2025 2030 Boiler, Efficiency 82.0% 86.0% 90.0% 91.6% 94.0% 94.0% Energy Savings Relative to 2007

- 1.2% 8.9% 10.5% 12.8% 12.8% Motor, Efficiency 90.0% 92.0% 93.0% 93.8% 95.0% 95.0% Energy Savings Relative to 2007

- 2.2% 3.2% 4.0% 5.3% 5.3% 10 MW Industrial Turbine, Efficiency 31.0% 33.0% 34.0% 34.0% 34.0% 34.0% Energy Savings Relative to 2007

- 6.1% 8.8% 8.8% 8.8% 8.8% Central Power Plant, Efficiency 30.5% 32.5% 33.5% 33.5% 33.5% 33.5% Energy Savings Relative to 2007

- 6.1% 8.8% 8.8% 8.8% 8.8% Total Energy Savings Relative to 2007

- 15.6% 29.7% 32.1% 35.7% 35.7%

The same factors that drive ethanol producer s to increase the energy efficiency of their equipment may also move them to consider energy saving changes to the ethanol production process. Several process changes, including raw starch hydrolysis, corn fractionation, corn oil extraction, and membrane separati on, are likely to be adopted to varying degrees. The degree to which they are adopted will depend on many factor s, including technology availability, capital cost of implementation, energy cost savings, an d co-product revenue gene ration. A description of each of these technologies, in cluding the challenges and benefits of their implementation, can be found in Section 1.4.1.3. The projected imp act of the implementation of these technologies on ethanol production energy usage is to decr ease the thermal energy usage by 12% and to increase the electrical energy usage by 3.8%. Th ese numbers are based on a plant that is drying 100% of its distillers grains and solubles (DGS). Plants that dry less than 100% of their DGS would be likely to realize smalle r benefits from these technologies . The projected penetration of these technologies, and the associated energy use impact, is summarized in Table 1.5-14 below.

LL Data based on “An Analysis of the Projected Energy Us e of Future Dry Mill Corn Ethanol Plants (2010-2030)”; Steffen Mueller, University of Illinois at Chi cago Energy Resource Center, October 10, 2007,

125Table 1.5-14. MM Projected Energy Savings from Process Changes Percent of all Plants Adopting Process Process Improvement 2020 2022 2025 2030 Raw Starch Hydrolysis 20% 22% 25% 30% Corn Fractionation 18% 20% 24% 30% Corn Oil Extraction 20% 22% 25% 30% Membrane Separation 3% 5% 5% 5% Energy Reduction from Base Process (Thermal) Raw Starch Hydrolysis 16% 16% 17% 17% Corn Fractionation 31% 31% 31% 32% Corn Oil Extraction 4% 4% 5% 5% Membrane Separation 29% 29% 29% 29% Weighted Average Savings (Thermal) 10.45% 12.05% 14.39% 17.65% Energy Reduction from Base Process (Electrical) Raw Starch Hydrolysis 0% 0% 0% 0% Corn Fractionation -9% -9% -8% -8% Corn Oil Extraction -9% -9% -8% -8% Membrane Separation 0% 0% 0% 0% Weighted Average Savings (Electrical) -3.42% -3.78% -3.92% -4.80%

The combined effect of the energy efficiency improvements and the production process changes are expected to reduce the energy required to produce a gallon of et hanol significantly. We project a thermal energy use reduction of 42.1% and an electrical energy use reduction of 25.8% by 2022. Based on the USDA’s 2007 estimated energy requirements of 38,717 BTU thermal energy/gallon ethanol and 3,242 BTU el ectrical energy/gallon ethanol for a dry mill ethanol plant fueled by natural gas drying 100% of its DDGS, we project that a similar plant in 2022 would have energy requirements of 22,435 BT U thermal energy/gallon ethanol and 2,406 BTU electrical energy/gallon ethanol.

As these technologies are incorporated into more ethanol plants, or make progress towards commercial viability we are able to make better estimates as to their impacts on energy usage and expected penetration in the ethanol industry. Additionally, recent energy price fluctuations and decreasing ethanol profit marg ins have likely impacted ethanol producers’ ability and willingness to invest in new technolo gy. For the final rule our projections of the technology penetration and its impact on energy use will be reevaluated and revised as necessary. The cost impacts of these technologies will also be analyzed.

1.5.1.4 Projected Grandfathered Corn Ethanol Volume

As explained in the Section III.B.3 of th e Preamble, renewable fuel produced from new facilities which commenced construction after December 19, 2007 must achieve at least a 20% reduction in lifecycle greenhouse ga s emissions compared to base line lifecycle greenhouse gas

MM Data based on “An Analysis of the Projected Energy Us e of Future Dry Mill Corn Ethanol Plants (2010-2030)”; Steffen Mueller, University of Illinoi s at Chicago Energy Resource Center, Oc tober 10, 2007, with the exception of Membrane Separation data based on conversation with Leland Vane, EPA

126emissions in order to generate RINs under the proposed RFS2 program. NN However, facilities that commenced construction before December 19 , 2007 are exempt or “grandfathered” from the 20% GHG reduction requirement. In addition, faci lities that commenced construction in 2008 or 2009 are grandfathered if they burn natural ga s, biomass, or any combination thereof.

The ethanol plants (and corre sponding production volumes) th at are grandfathered under the RFS2 rule will be determined based on information received during the expanded registration process (refer to Section III.C of the Preamble). However, for the NPRM we thought it would be a useful exercise to estimate the volume of corn ethanol that might be grandfathered under this rule.

To do so, we stated with our assessment of ethanol plants that were operational, idled, under construction, or expanding at the time of ou r April 2009 assessment (refer to Table 1.5-7). This amounts to around 15 billion gallons or the en tire conventional biofuel standard. As such, for this exercise we assumed that these corn et hanol plants would be re presentative of those in production in the future under the RFS2 program. As shown in Table 1.5-9, 12.5 billion gallons are expected to come from 203 plants burning na tural gas, biomass, or a combination thereof and the remaining 2.5 billion gallons are expected to come from 23 plants burning coal as their primary fuel.

As illustrated in Figure 1.5-5 below, the 203 natural gas and/or biomass-fired plants would be grandfathered or “deemed complain t” under the proposed grandfathering provisions due to the fact that they commenced cons truction by 2008/2009 (refer to proposed regulation §80.1403). In addition, the 23 coal-fired plants would be grandfathered because it appears they were all either online or commen ced construction prior to Decemb er 19, 2007 as summarized in Table 1.5-15.

NN In accordance with Section 211(o)(2)(A)(i) of the Clean Air Act as amended by EISA.

127Figure 1.5-5. Estimate of Grandfathered Volume of Corn Ethanol Under RFS2

128Table 1.5-15. Projected Coal-Fired Ethanol Plants

Plant/Company Name City Operational Under Construction Capacity

MGY Est. Prod Capacity MGY Est. Prod Ag Processing Inc. (AGP) Hastings, NE 52 1992 Archer Daniels Midland (ADM) Cedar Rapids, IA 250 N/A a Archer Daniels Midland (ADM) Cedar Rapids, IA 275 TBD b Archer Daniels Midland (ADM) Clinton, IA 190 N/A a Archer Daniels Midland (ADM) Columbus, NE 95 1994 Archer Daniels Midland (ADM) Columbus, NE 275 TBD b Archer Daniels Midland (ADM) Decatur, IL 290 N/A a Archer Daniels Midland (ADM) Marshall, MN 40 1988 Archer Daniels Midland (ADM) Peoria, IL 210 N/A a Archer Daniels Midland (ADM) Walhalla, ND 25 N/A a Aventine Renewable Energy, Inc. Pekin, IL 100 1981 Blue Flint Ethanol LLC Underwood, ND 50 Feb-07 Cargill Inc. Eddyville, IA 35 Sep-92 Cargill, Inc. Blair, NE 85 Apr-94 Chief Ethanol Fuels Inc. Hastings, NE 62 1985 Corn LP c Goldfield, IA 50 Dec-05 Grain Processing Corp Muscatine, IA 20 May-00 Heron Lake BioEnergy, LLC Heron Lake, MN 50 Oct-07 Lincolnway Energy LLC Nevada, IA 50 May-06 Red Trail Energy, LLC Richardton, ND 50 Jan-07 Riverland Biofuels Canton, IL 38 Oct-08 d Southwest Iowa Renewable Energy (SIRE) Council Bluffs, IA 110 Feb-08 e Tate & Lyle f Loudon, TN 66 N/A 60 TBD

a It is unclear exactly when these five ADM ethanol plants came online. However, based on permitting information, it appears the y commenced construction prior to December 17, 2007.

b It appears these dry-mill ADM expansion projects are currently on hold at the moment. However, based on permitting information and an August 28, 2007 e-mail construction update, it appears these expansion projects began prior to December 17, 2007.

c This Corn LP plant burns biomass in addition to coal.

d This plant was was originally proposed in 2001 as Central Illinois Energy. Local farmers provided start-up capital but due to l ack of additional financing, ground wasn’t broken on the plant until October 2006. The project went over budget and Central Illinois Energy was forced to file for bankruptcy in December 2007. Riverland Biofuels took over the project, completed construction of the plant, and started producing ethanol in October 2008. Based on the information provided, it appears this plant commenced construction prior to December 17, 2 007.

e A "groundbreaking" ceremony was held on November 11, 2006, a final permit was issued on April 19, 2007, construction was well u
f It is unclear exactly when this Tate & Lyle ethanol plant came online and when the expansion project began. However, since bot h p r

1.5.1.5 May 2008 Assessment of the Corn Ethanol Industry

The information presented above in Sections 1.5.1.1 and 1.5.1.2 (as well as 1.5.1.4) represents our most current asse ssment of the corn/starch ethanol industry. However, we relied on an earlier May 2008 industry assessment to re flect existing corn ethanol production and project future growth under the proposed R FS2 program. This was the best information available at the time our distribution, cost, emi ssion and air quality analyses were conducted. This was before the economic recession forced ma ny companies to idle plants, file for Chapter 11 bankruptcy and/or to sell off ethanol plants/projects. As a result, the starting ethanol

129production is slightly different from the informa tion presented in Section 1.5.1.1. However, since we assume that many of the idled plants/pro jects would come back on line in the future; the final RFS2 projections are not that different from the information presented in Section 1.5.1.2. A summary of the relevant May 2008 industry inform ation used to support various NPRM analyses is presented below.

Previously Existing Corn/Starch Ethanol Production

At the time of our May 2008 corn ethanol pl ant assessment, there we re 158 fuel ethanol plants operating in the U.S. with a combined production capacity of 9.2 billion gallons per
OO 291 292 year. The majority of ethanol (nearly 89% by volume) was produced exclusively from corn. Another 11% came from a blend of corn and/or similarly processed grains (milo, wheat, or barley) and less than half a percent was produ ced from cheese whey, waste beverages, and sugars/starches combined. A summary of the feed stocks utilized by the U.S. ethanol industry as of May 2008 is found in Table 1.5-16.

OO Our May 2008 corn/starch ethanol industry characterization was based on a variety of data sources including: Renewable Fuels Association (RFA) Ethanol Biorefinery Locations (updated April 2, 2008); Ethanol Producer Magazine (EPM) Current plant list (last modified on Apr il 14, 2008), and ethanol producer websites. The baseline does not include ethanol plants whose primary business is industrial or food-grade ethanol production. Where applicable, ethanol plant production levels were used in lieu of nameplate capacities to estimate plant production. The baseline does not include U.S. plants that were idled as of May 2008 or plants that might be located in the Virgin Islands or U.S. territories.

130Table 1.5-16. May 2008 Corn/Starch Ethanol Production Capacity by Feedstock

Plant Feedstock (Primary Listed First) Capacity MGY % of Capacity No. o f Plants % o f Plants Corn a 8,141 88.8% 131 82.9% Corn, Milo b 704 7.7% 14 8.9% Corn, Wheat 130 1.4% 1 0.6% Corn, Wheat, Milo 115 1.3% 2 1.3% Milo 3 0.0% 1 0.6% Wheat, Milo 50 0.5% 1 0.6% Cheese Whey 8 0.1% 2 1.3% Waste Beverages c 13 0.1% 4 2.5% Waste Sugars & Starches d 7 0.1% 2 1.3% Total 9,169 100% 158 100%

a Includes one facility processing seed corn, one facility also operating a pilot-level cellulosic ethanol plant, and six facilities with plans to build pilot-level cellulosic ethanol plants or incorporate biomass feedstocks in the future.

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

c Includes two facilities processing brewery waste.

d Includes one facility processing potato waste that intends to add corn in the future.

The corn ethanol industry relies primarily on natural gas. At the time of our May 2008 plant assessment, 134 of the 158 corn/starch etha nol plants burned natural gas (exclusively). PP In addition, three burned a combination of natural gas and biomass, one burned a combination of natural gas, landfill syngas and wood, while one burned a combination of natural gas and syrup from the process. In addition, 18 plants burne d coal as their primary fuel and one burned a combination of coal and biomass. Our research suggested that 24 plants utilized cogeneration or combined heat and power (CHP) technology at the time of our assessment. A summary of the energy sources and CHP technology utilized by the U.S. ethanol industry as of May 2008 is found in Table 1.5-17.

PP Facilities were assumed to burn natural gas if the plant bo iler fuel was unspecified or unavailable on the public domain.

131Table 1.5-17. May 2008 Corn/Starch Ethanol Production Capacity by Energy Source
Plant Energy Source (Primary Listed First) Capacity MGY % o f Capacity No. o f Plants % o f Plants CHP Tech. Coal a Coal, Biomass Natural Gas b Natural Gas, Biomass c Natural Gas, Landfill Syngas, Wood Natural Gas, Syrup 1,720 50 7,141

113 100

46 18.8% 0.5% 77.9%

1.2% 1.1% 0.5% 18 1 134 3 1 1 11.4% 0.6% 84.8%

1.9% 0.6% 0.6% 8 0 15

1 0 0 Total 9,169 100.0% 158 100.0% 24

a Includes four plants that are permitted to burn biomass, tires, petroleum coke, and wood waste in addition to coal and one facility that intends to transition to biomass in the future.

b Includes one facility that intends to bu rn thin stillage biogas, five facilities that intend to transition to coal, and one facility that intends to switch to biomass in the future.

c Includes one facility pr ocessing bran in addition to natural gas.

Besides a few plants located outside of the Co rn Belt, the majority of ethanol is produced in PADD close to where the corn is grown. At the time of our May 2008 ethanol industry characterization, PADD 2 accounted for 94% (or 8.6 billion gall ons) of the estimated ethanol production capacity as shown in Table 1.5-18 below.

Table 1.5-18. May 2008 Corn/Starch Ethanol Production Capacity by PADD

PADD Capacity MGY % o f Capacity No. o f Plants % o f Plants PADD 1 PADD 2 PADD 3 PADD 4 PADD 5 50 8,619

170 160 171 0.5% 94.0%

1.9% 1.7% 1.9% 2 140 3 7 6 1.3% 88.6%

1.9% 4.4% 3.8% Total 9,169 100.0% 158 100.0%

Leading the Midwest in ethanol production were Iowa, Nebr aska, Illinois, South Dakota and Minnesota. Together, these five states’ 93 ethanol plants accounted for 67 percent of the nation’s ethanol production capacity in May 2008. Fo r a map of the ethanol plant locations and a summary of ethanol production capa city by state, refer to Figure 1.5-6 and Table 1.5-19 below.

132Figure 1.5-6. May 2008 Corn/Starch Ethanol Plant Locations

133Table 1.5-19. May 2008 Corn/Starch Ethanol Production Capacity by State

State Capacity MGY % o f Capacity No. o f Plants % o f Plants
Iowa 2,282 24.9% 30 19.0% Nebraska 1,278 13.9% 22 13.9% Illinois 941 10.3% 9 5.7% South Dakota 892 9.7% 14 8.9% Minnesota 749 8.2% 18 11.4% Indiana 540 5.9% 7 4.4% Wisconsin 479 5.2% 8 5.1%
Kansas 464 5.1% 12 7.6% Ohio 345 3.8% 4 2.5% Michigan 214 2.3% 4 2.5% Missouri 202 2.2% 5 3.2% Colorado 146 1.6% 5 3.2%
Texas 140 1.5% 2 1.3% North Dakota 125 1.4% 3 1.9% California 81 0.9% 4 2.5%
Tennessee 66 0.7% 1 0.6% New York 50 0.5% 1 0.6% Arizona 50 0.5% 1 0.6% Kentucky 40 0.4% 2 1.3% Oregon 40 0.4% 1 0.6% New Mexico 30 0.3% 1 0.6% Wy oming 9 0.1% 1 0.6% Idaho 5 0.1% 1 0.6% Oklahoma 2 0.0% 1 0.6% Georgia 0 0.0% 1 0.6% Total 9,169 100.0% 158 100.0%

134Preliminary Forecasted Growth in Co rn/Starch Ethanol Production Under RFS2

According to our industry assessment, there were 59 ethanol plants under construction or expanding as of May 2008 with a combined prod uction capacity of 5.2 billion gallons per
year. QQ These projects were at various phases of construction from conducting land stabilization work, to constructing tanks and installing ancillar y equipment, to completing start-up activities. Unfavorable market conditions have recently driv en a number of producers to delay construction or scrap plans altogether. However, we believe that implementation of the RFS2 program will help revitalize ethanol production. As such, we assumed that all this capacity would eventually come online as well as a number of other projects that were at advanced stages of planning at the time of our May 2008 industry assessment.

Once all the aforementioned projects are comp lete, we projected that there would be 216 corn/starch ethanol plants opera ting in the U.S. with a combin ed production capacity of about 15 billion gallons per year. Much like today’s ethanol production facili ties, the overwhelming majority of new plant capacity (95% by volume) was expected to come from corn-fed plants. The remainder was forecasted to come from plan ts processing a blend of corn and milo. A summary of the forecasted ethanol production by feedstock under the RFS2 program based on our May 2008 plant assessment is found in Table 1.5-20.

QQ Based on Renewable Fuels Association (RFA), Ethanol Biorefinery Locations – Under Construction/Expansions (updated April 4, 2008); Ethanol Producer Magazine (EPM), Under Construction plant list (last modified on April 14, 2008), ethanol producer websites, and follow-up correspondence with ethanol producers.

135Table 1.5-20. May 2008 Projected RFS2 Ethanol Production Capacity by Feedstock

Plant Feedstock (Primary Listed First) New Plants/Exp. Total RFS2 Est.
Capacity MGY No. of Plants Capacity MGY No. of Plants Corn a 5,526 54 13,666 185 Corn, Milo b 303 4 1,007 18 Corn, Wheat 0 0 130 1 Corn, Wheat, Milo 0 0 115 2 Milo 0 0 3 1 Wheat, Milo 0 0 50 1 Cheese Whey 0 0 8 2 Waste Beverages c 0 0 13 4 Waste Sugars & Starches d 0 0 7 2 Total 5,829 58 14,998 216

a Includes one facility processing seed corn, one facility also operating a pilot-level cellulosic ethanol plant, and six facilities with plans to build pilot-level cellulosic ethanol plants or incorporate biomass feedstocks in the future.

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

c Includes two facilities processing brewery waste.

d Includes one facility processing potato waste that intends to add corn in the future.

Based on May 2008 industry plans, the majo rity of new corn/grain ethanol production capacity (82% by volume) was predicted to come from new or expanded plants burning natural gas. Additionally, we forecasted one new plant burning a combination of natural gas and syrup (from the process) and an expansion at an existing facility burni ng natural gas and biomass. Our predictions also suggest two new coal-fired ethanol plants and th ree expansions at existing coalfired plants. RR Finally, we projected three new plants burning alternative fuels – one relying on manure biogas, one burning biomass, and one burning a combination of biomass and thin stillage from the process. SS Our research indicated that nine of the 58 new plants would utilize cogeneration, bringing the total number of CHP facilitie s to 33. A summary of the forecasted ethanol plant energy sources in 2022 under the RFS2 program is found in Table 1.5-21.

RR Two of the three coal-fired plant expa nsions appear as new plants in Table 1.5-21. This is because two of the expansion projects consist of adding dry milling plant ca pacity to an existing wet mill plant. However, our interpretation is that these facilities will rely on the same (potentially expanded) coal-fired boilers for process steam. Since all the aforementioned coal-fired ethanol production f acilities appear to have commenced construction prior to December 19, 2007, we project that the ethanol produced at these facilities will be grandfathered under the proposed RFS2 rule. For more on our grandfathered volume estimate, refer to Section 1.5.1.4.
SS Thin stillage is a process liquid with 5–10 percent solids taken ou t of the distillers grains via centrifuge. However, construction on this altern atively fuel ethanol plant near Heybu rn, ID was recen tly terminated. Accordingly, this plant was not included in our April 2009 RFS2 projections.

136Table 1.5-21. Projected Near-Term Corn/Starch Ethanol Production Capacity by Energy Source (Based on May 2008 Ethanol Industry Characterization)

Plant Energy Source (Primary Listed First) New Plants/Exp. Total RFS2 Est.
Capacity MGY No. of Plants Capacity MGY No. of Plants CHP Tech.
Biomass 88 1 88 1 0
Coal a 740 4 2,460 22 12 Coal, Biomass 0 0 50 1 0 Manure Biogas 115 1 115 1 0
Natural Gas b 4,776 50 11,917 184 19 Natural Gas, Biomass c 40 0 153 3 1 Natural Gas, Landfill Biogas, Wood 0 0 100 1 0 Natural Gas, Syrup 50 1 96 2 0 Thin Stillage Biogas, Biomass 20 1 20 1 1
Total 5,829 58 14,998 216 33

a Includes four existing plants and two under construction facilities that are permitted to burn biomass, tires, petroleum coke, and wood waste in addition to coal. Also includes one facility that intends to transition to biomass
in the future.

b Includes one facility that intends to burn thin stillage biogas, six facilities that intend to transition to coal, and one facility that intends to switch to biomass in the future.

c Includes one facility processing bran in addition to natural gas.

The information presented in Table 1.5-21 is based on near-term production plans at the time of our May 2008 industry asse ssment. As mentioned in Section 1.5.1.2, we anticipate additional growth in advanced ethanol productio n technologies in the fu ture under the proposed RFS2 program. For more on our projected 2022 utiliza tion of these technologi es under the RFS2 program, refer to Section 1.5.1.3.

Based on our May 2008 assessment, 85% of new ethanol production capacity under RFS2 is expected to originate from PADD 2. For a summary of this and other forecasted PADDlevel production projections, refe r to Table 1.5-22.

137Table 1.5-22. May 2008 Projected RFS2 Corn/Starch Etha nol Production Capacity by PADD

PADD New Plants/Exp. Total RFS2 Est.
Capacity MGY No. of Plants Capacity MGY No. of Plants PADD 1 PADD 2 PADD 3 PADD 4 PADD 5 214 5,002

215

70 328 2
47

2

2
5 264 13,620 385 230 499 4 187 5 9 11 Total 5,829 58 14,998 216

Our May 2008 assessment suggested that Iowa, Ne braska, and Illinois would continue to dominate ethanol production under RFS2 with a collec tive annual production capacity of about 7.5 billion gallons. Minnesota and Indiana were projected to be the f ourth and fifth largest ethanol producers. A map of the forecasted co rn ethanol plant locations based on our May 2008 assessment is provided in Figure 1.5-7 and a summa ry of the ethanol production capacity by state is presented in Table 1.5-23.

Figure 1.5-7. May 2008 Projected RFS2 Corn/Sta rch Ethanol Plant Locations

138Table 1.5-23. May 2008 Projected RFS2 Corn/Starch Ethanol Production Capacity by State

State New Plants/Exp. Total RFS2 Est.
Capacity MGY No. of Plants Capacity MGY No. of Plants Iowa 1,573 13 3,854 43 Nebraska 959 7 2,237 29 Illinois 465 4 1,406 13 Minnesota 440 4 1,189 22 Indiana 470 5 1,010 12 South Dakota 100 1 992 15 Kansas 203 4 667 16 Wisconsin 70 1 549 9 Ohio 185 3 530 7 Texas 215 2 355 4 North Dakota 210 2 335 5 Michigan 107 1 321 5 Missouri 60 1 262 6 California 160 3 241 7 Tennessee 160 1 226 2 New York 114 1 164 2 Oregon 113 1 153 2 Colorado 0 0 146 5 Georgia 100 1 100 2 Idaho 70 2 75 3 Washington 55 1 55 1 Arizona 0 0 50 1 Kentucky 0 0 40 2 New Mexico 0 0 30 1 Wyoming 0 0 9 1 Oklahoma 0 0 2 1 Total 5,829 58 14,998 216

1.5.2 Imported Ethanol

1.5.2.1 Historic/Current Imports and Exports

In order to assess the potential for U.S. imported ethanol, we examined the chief countries that are currently produ cing or consuming relatively large volumes of ethanol. In particular, we chose to focus on Brazil, the Eu ropean Union (EU), Japan, India, and China to determine whether each country will likely be an im porter or exporter of etha nol in the future. The following sections first describe the ethanol demands of each of these countries due to enacted or proposed mandates and goals as well as their ability to supply those demands with

139domestically produced ethanol. We conclude the analysis by examining the most likely pathways for imported ethanol, namely through the Caribbean Basin Initiativ e (CBI) and directly from Brazil.

1.5.2.1.1 Brazil

Much of the potential of impor ted ethanol will depend on the ability for Brazil to supply ethanol to the United States and ot her countries. This is becau se Brazil has been a top producer and is the top exporter of ethanol in the world. In fact, many count ries are interested in Brazilian produced sugarcane ethanol because it is currently the least costly method for producing ethanol. No other nation is seen as being able to suppl y any reasonably large amount of ethanol, and as such, it is important to devote much of the import potential discussion to Brazil.

Brazil has been steadily increasing its expor ts of ethanol, with total exports escalating from under 700 million gallons in 2005 to over 1200 million gallons in 2008. As seen in Figure 1.5-8, Brazil exports ethanol to many different countries around th e globe. Prior to 2006, the majority of Brazilian ethanol exports flowed to the EU and Caribbean due to favorable economics. In 2006, the majority of Brazilian et hanol exports (52%) went to the U.S as a result of the withdrawal of MTBE from the U.S. fuel pool and an increased price of ethanol. As countries create their own biofuels mandates a nd goals, they will also be looking to other countries such as Brazil to supply large amounts of biofuels. Countries may essentially be “competing” against one another fo r Brazilian ethanol in the futu re to meet their mandates and goals.

Figure 1.5-8. Brazil Ethanol Export s (Includes all types of ethanol). 293, 294, 295Million Gallons 1400

1200

1000

800

600

400

200

0
2005 2006 2007 2008 Others

Venezuela
Japan

India

Mexico
Nigeria

South Korea

Caribbean

EU

U.S.

140Brazil currently produces both hydrous and a nhydrous ethanol. Hydrous ethanol has 96% ethanol and 4% water in its composition, whereas anhydrous ethanol is made up of 99.5% ethanol and 0.5% water. 296 In 2006, total ethanol consumption made up 17.2% (9.9% hydrous and 7.3% anhydrous) of the vehicle fuel pool w ith the rest being: 27.2% Gasoline A (pure gasoline before blending with ethanol), 3.2% vehicular natural gas (VNG), 3.3% B2 (diesel blended with 2% biodiesel), and 48.9% Diesel. 297 Of light duty vehicles only, ethanol accounts for 36.1% of the total fuel pool in Brazil.

While hydrous ethanol is used di rectly in Otto-cycle motors (100%), anhydrous ethanol is mixed with pure gasoline at 20-25% by volume. Production of anhydrous ethanol to be mixed with gasoline has fallen sin ce the 2005/2006 harvest, on account of the smaller share of cars running exclusively on gasoline. This was especi ally due to the success of flex vehicles with Brazilian customers. 298 In fact, sales of flex-fuel vehicles (FFVs) in Brazil, those that can use any mixture of gasoline and ethanol from 0 to 100%, have grown dramatically, with domestic FFV sales representing 91% of vehicles sold in 2008. 299 Hydrous ethanol production, on the other hand, has grown almost c onstantly in recent years. However, in the 2008/2009 crop year, growth in hydrous ethanol was much larger, w ith hydrous ethanol accounti ng for 65% of ethanol production in Brazil. Figure 1.5-9 shows the hi storical production of hydrous and anhydrous ethanol in Brazil.

Figure 1.5-9. Historical Ethanol Produc tion of Hydrous and Anhydrous Ethanol in Brazil. 300



### 5,000 Hydrous Ethanol 4,500 Anhydrous Ethanol 4,000Million Gallons 

### 3,500
3,000
2,500
2,000
1,500
1,000

500

0199719981999200020012002200320042005200620072008

141In contrast to Brazil, ethanol consumed in the U.S. must first be converted to anhydrous ethanol before its use in conventional or FFV vehicles. This differs from Brazil because Brazilian FFVs can use hydrous ethanol, or E 100 (100% ethanol), wher eas U.S. FFVs can only use up to E85 (85% ethanol and 15% gasoline by volume). As a result, if hydrous ethanol is exported from Brazil, it mu st be dehydrated somewhere else before it is used in the U.S. This is the case for the majority of ethanol exported from Brazil to the Caribbean, where it is dehydrated and often re-exported to the U.S. for consumption.

In terms of future ethanol production, however, there has been much speculation about Brazil’s ability to increase production. Sugarcan e analyst Datagro recentl y stated that Brazil’s ethanol fuel production w ould have to grow by approximately 800 million gallons a year through 2025 to keep up with demand at home and abroad. 301 Estimates of future ethanol production in Brazil range greatly. See Figure 1.5-10. Brazil’ s government has adopted plans to meet global demand by tripling production by 2020. 302 This would mean a total capacity of approximately 12.7 billion gallons, to be achieved through a co mbination of efficiency gains, greenfield projects, and infrastructure expansions. Estimates for the investment required tend to range from $2 billion to $4 billion a year . Other estimates indicate that based on current projects the required investment in capacity e xpansion is $3-4 billion annually. 303 If global demand were to increase much more than Brazil is planning, th en capacity would need to expand even further and thus greater investment dol lars would be required.

Figure 1.5-10. Estimated Brazilian Ethanol Production Volumes 304



### 25,000

2000 2010 2020 2030 2040 FAPRI 2007 Agricultural Outlook OECD-FAO Agricultural Outlook Bioenergia (2007)

Rothkopf (2006)

EPE (2007)

Unica 20,000

15,000

10,000

5,000

0Million Gallons

142To meet the growing demand, the Brazilian sugar and ethanol industry is already rapidly expanding and numerous mills have been planne d. This includes over 90 new plants (354 existing plants in 2007/2008) that will be set up in the next five years, allowing the expansion of processing from 383 to 560 million tonnes of sugarcane by 2010/2011. 305, 306 Brazil’s stateowned development bank BNDES said the country is set to invest $13. 1 billion between 2007 and 2011 in 89 of these new sugar and ethanol mills. 307 Some estimate even more, where investments in sugarcane processi ng factories are expected to t op $23 billion over the next four
years. 308 Recent investments also include a projec t by Odebrecht, a Brazilian engineering company that will invest $2.6 billion dollars ov er the next decade to build 12-15 plants with a combined capacity to crush at least 30-40 M t/year of sugarcane and produce ~ 400 million gallons per year of ethanol. 309 Even U.S. ethanol producer ADM is preparing to enter the sugarcane business in Brazil. A recent quote by ADM’s sen ior vice president of strategy, Steve Mills, said that sugar cane ethanol is now “a key co mponent” of ADM’s short-term strategy and, “We’re devoting a lot of time and energy to this area. We’re not talking about something 10 years down the road. It’s on the front burner.” 310 To put these estimates of production capacity expansion into perspective, 383 million tonnes of sugar cane yielded 26.2 million tonnes of raw sugar and approximately 4.2 billion gallons of ethanol in 2005. If we assume the same split between sugar and ethanol then by 2010 we could expect an additional ~2 billion gallons of ethanol produced from Brazil each year from now until 2010/2011.

In addition to expanding sugarcane production and ethanol production plant capacity, Brazil will need to improve its current ethanol distribution infrastructur e. Brazil’s transport system is predominantly road-based. 311 Railroad infrastructure and u se of a waterway system is lacking, as well as very low availa bility of multi-mode terminals. Logistics currently represent 22% of the export expenses and is one of the area s where costs need to be reduced in order for Brazilian ethanol to become more competitive abroad. 312

One way of dealing with this l ack of infrastructure is to invest in enlarging the pipeline network. Petrobras, Brazil’s largest petroleum refi ner is planning to build a pipeline to transport ethanol destined for export from the states of Sao Paulo, Minas Gerais, Mato Grosso, Mato Grosso do Sul, Goias, and Parana. The pipeline is anticipated to go onlin e in October 2010, with $232 million invested in the project. By 2012 Petr obras will spend more than $1.6 billion to improve logistics infrastructure to transport Brazilian production. By 2011, Petrobras has the goal of exporting 920 million gallons per year. 313 Recent updates indicate a perhaps more optimistic timeframe for completion, stating that Petrobras will complete the first of two ethanol pipelines in 2009. 314 One of the pipelines will run from Goias state in Brazil’s center-west to Petrobras’s Paulinia refinery in Sao Paulo State. The proj ect is called PMCC Projetos de Transporte de Alcool. The line is expected to have the capacity to ship 3.2 million gallons of ethanol annually. 315

Other competitors include the joint ventur e from Cosan, Copersucar, and Crystalsev which will make initial investment s of $11.5 million apiece to inst all an ethanol-only pipeline between the oil refinery in Paulinia, to an etha nol offloading terminal on the state’s coast. In addition, at least three major pr ivate equity groups (Infinity, Cl ean Energy Brazil, and Brenco) plan to invest $1 billion in a 683 mile long ethanol pipeline ex pected to be completed by 2011 with a capacity to deliver 1.1 million gallons of ethanol a year. In total, it is estimated that Brazil

143will need to invest $1 billion each year for the next 15 years in infrastructure to keep pace with capacity expansion and export demand. 316

Another area that requires investment is in R&D and education. Currently, Brazil produces only 0.08 engineers for every 1000 people, compared to 0.2 in the US, 0.33 in the EU, and 0.8 in Korea. 317 In addition, there are deficiencies in basic education in Brazil’s north and northeast regions. Since certain types of education require a l ong lead time (e.g. scientific training) Brazil will need to continue to invest in training and professional development for the sector’s labor pool to meet the growing demand in the biofuels industry.

Before ethanol can be exported to othe r countries, Brazil’s own domestic fuel consumption should be met. Brazil currently ha s an ethanol mandate of 25% (as of July 1, 2007). 318 The Brazilian ethanol to gasoline mix is set by the Brazilian government, which has the flexibility to adjust the ethanol mandate of 20-25% by volume ethanol to gas ratio. Ethanol currently represents nearly 30% of the light vehicle fuel market in Brazil. 319 Approximately 80% of new vehicles sold in Brazil in 2006 were fl ex fuel (1.6 million vehicles). The current increase in domestic consumption of ethanol in Brazil is partially due to the success of the flexfueled vehicle.

At some point in the future, Brazil’s light ve hicle fleet will likely become saturated with FFVs in preference to gasoline. As such, the rate in domestic de mand for ethanol is expected to begin to slow. 320 Thus, as domestic demand begins to level off, some experts believe that there is a significant possibility that e xports will become more relevant in market share terms. Figure 1.5-11 shows various estimates for future Br azilian ethanol domestic consumption.

144Figure 1.5-11. Estimated Brazilian Ethanol Consumption Volumes 321



### 0 5,000 10,000 15,000 20,000 25,000Million Gallons 

### Canaplan, Director Carvalho FAPRI 2007 Agricultural Outlook EPE (2007)

Unica

2000 2010 2020 2030 2040

After domestic consumption is met, the rest of the ethanol can be av ailable for exports to other countries. Potential worldwide exports ba sically equal the total pr oduction minus the total consumption. Given the available data, onl y three sources estimated both production and consumption for some of the years during 2010-2022. As such, these values were used to compute reasonable export volumes for Brazil as seen in Figure 1.5-12. Estimates from EPE and Unica indicate that as much as 3.8-4.2 billi on gallons could be e xported by Brazil in the 2020/2022 timeframe. Longer timeframe estimates from sugarcane analyst Datagro project international ethanol sales to gr ow to 6.6 billion gallons by 2025. 322

145Figure 1.5-12. Estimated Brazilian Export Volumes



### 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000Million Gallons 

### EPE (2007)

Unica

FAPRI 2007 Agricultural Outlook

2010 2012 2014 2016 2018 2020 2022

1.5.2.1.2 The European Union (EU)

Although the EU market has largely focused on biodiesel, ethanol has increasingly become important. Fuel ethanol demands are increasing due to the introduction of mandatory blending targets. In 2001 an EU Directive established that by 2005 biofuels should cover 2% of the total fuel consumption (energy basis), while the target for 2010 was set at 5.75%. However, in recent years the average biofuel contribution has been much less (i.e. 0.5%, 0.6% and 1% in 2003, 2004, and 2005, respectively). 323

The EU is currently considering a binding ta rget requiring 10% biofuel use for transport by 2020, although the target has recently come under criticism due to concerns over deforestation and food security problems. 324 The International Energy Outlook (IEO) 2007 forecasts OECD European countries will consum e 159 billion gasoline gallons equivalent (gge) of transport fuel in 2010, growing to 167 gge by 2030. 325 Assuming a split of nearly 70% fuel volume consumed as diesel and 30% consumed as gasoline (current use), a 10% by energy requirement would require roughly 7.3 billion gallons of ethanol in 2022. TT, 326 However, this may be a slight overestimate of gasoline use sinc e the overall gasoline consumption in the EU is declining as a result of the in creasing popularity of more economi c diesel-powered cars. Other sources indicate smaller gasoline consumpti on volumes are possible by the 2020/2022 timeframe which when translated equals 5.2-5.4 bill ion gallons of ethanol assuming a 10% energy requirement. 327, 328

TT Assuming energy contents 115,000 Btu/gal for gasoline and 77,930 Btu/gal for denatured ethanol and 164 gge in

146 2022However, many believe that even 10% is unrealistic and are calling for a lower percentage for biofuels. Most recently, the EU Energy Committee has passed draft legislation that if approved is expected to forbid the 27 EU countries from importing ethanol produced with excessive use of natural res ources such as water or stim ulates forest devastation. 329 As a result, Brazilian stakeholders are mon itoring such potential modifications in EU biofuels policy as it will affect the potential demand for ethanol from the EU.

Fuel ethanol production in the EU has been much lower than this, with less than 1 billion gallons produced historically (457 million gallons in 2007). 330 Germany, Spain, France, Poland, and Sweden represent almost 90% of the producti on in 2007. According to the “World Biofuels: FAPRI 2007 Agricultural Outlook”, ethanol production in the EU is expected to grow to 1.5 billion gallons by 2016. Taking this into account , the EU would need to import approximately 4­ 6 billion gallons of ethanol in order to meet th e 10% volume requirement if only traditional crops are used. It should be noted, however, th at the EU production estimates in the FAPRI Agricultural Outlook does not reflect the EC direc tive volume requirement since the targets were announced after the report was is sued. This could spur greater production in the EU and reduce the amount needed from ethanol exporters such as Brazil.

As of March 1, 2009, the installed capacity of the EU ethanol i ndustry is 1.4 billion gallons, while a further 1 billion gallons are under construction and anothe r 3 billion gallons has been announced. 331, 332 Totaling these capacity estimates, th e EU would have 5 billion gallons ethanol capacity. While not all the announced projects in the EU will be comp leted, this gives an estimate of how fast and large et hanol production in the EU could grow. If we assumed that EU could produce this volume by 2022, as much as 2 bill ion gallons would need to be imported from other countries assuming a 7.3 billion gallon demand due to the 10% mandate. It appears likely from the above analysis that th e EU will continue to be a net importer of biofuels under most future scenarios.

Due to constraints in etha nol production from current available technologies and feedstocks, the EU is continuing its devel opment of second generation biofuels based on cellulosic materials. The majority of attention has been in Sweden, and to a lesser extent, the UK, Spain, and Netherlands. If ethanol from cellulosics proves feasible and commercial-scale production is realized in the future, additional et hanol supplies from such feedstocks may help lessen the amount needed from imports.

1.5.2.1.3 Japan

Until recently, Japan did not produce much etha nol and imported the majority of their consumption. 333 Now the government is showing signs of encouraging biofuels production by promoting (not mandating) a 3% blend of ethanol in gasoline. At the very least, a non-mandatory 3% blend will create a demand of 106-132 million gallons of ethanol. 334 This is similar to Japan’s Agency for Natural Resources and Ener gy target to replace 132.1 million gallons of transportation fuel by 2010, using ethanol and biodiesel. 335

With the passage of legislation calling for a 3% ethanol mandate in gasoline blend, Brazilian ethanol companies are hopeful that tr ade will increase substantially with Japan (this

147may even reach over 1.5 billion gallons annually if a 10% ethanol blend was implemented nationwide in Japan, approximately 500 million gallons with E3 blends). 336 While in the future use of greater than E3 blends in Japan may be unlikely, the Japanese government has mandated that all gasoline powered vehicles run on E-10 bl ends by 2030 and will also enact legislation to require all new vehicles to be E-10 compatible by 2012. 337, 338

The prospect for large domestic production of ethanol in Japan appears to be quite small due to limitations on feedstock. The Agricultur e Ministry states that Japan has enough feedstock to produce 26.4 million gallons per year, however , the Ministry of Environment (MOE) expects Japan to meet only 10% of the 132.1 million gall on target (or 13.2 million gallons) with domestic ethanol production. 339 The Ministry of Agriculture and Fisheries (MAFF), on the other hand, predicts that Japan could reasonably expect to supply approximately 95 million gallons. Even with these higher domestic produc tion estimates, Japan would stil l be a net importer of fuel ethanol if the biofuels target is met. The potential estimated demand for imported ethanol ranges from 37 million gallons to 1572 million gallons de pending on the type of mandate assumed and the differences in the estimates of domestic ethanol production.

Table 1.5-24 details new plan ts that are under construction. The total capacity is much lower than the 132.1 million gallons of ethanol go al and further supports the argument that Japan will likely be heavily dependent on imports.

Table 1.5-24. Upcoming/Recent Plants in Japan 340
Plant Capacity Feedstock Year 3 from Agriculture ~4 million Local crops: rice, low-quality Ministry gallons/year wheat, sugarbeet, sugarcane Hokkaido 3.96 million Sugar beets that aren’t 2007­ Prefectural Union of gallons/year; targeted for domestic sugar construction Agricultural start producing and substandard wheat 2009- Cooperatives 3961 gal/year unusable for food production Nippon Steel Plant 104.89 gallons Food waste from: April 2007­ daily (38,285 supermarkets, restaurants, operational gallons/year) schools, hospitals. (10 tons annually) Mitsui Engineering Agricultural wastes: felled 2010: trial & Shipbuilding Co. oil palm trunks, empty fruit bunches, fibrous fruit wastes, kernel shells operations if ag. waste is successful

Up until now, Japan has seen Brazil as the only possible reliable supplier of ethanol. In early 2005, Japan and Brazil signed an agreemen t for a bilateral biofuels program to export Brazilian ethanol and biodiesel to Japan. Japan’s investment will be used to install new ethanol facilities, increase acrea ge of sugarcane production, and mo dernize the infrastructure necessary for the transportation of ethanol.

One such partnership is between Brazilia n oil company (Petrobr as) and trading house Mitsui & Co., with financial s upport from Japan Bank for International Cooperation are in the

148process of analyzing 40 projects evaluated at $8 billion for building 40 new processing plants which produce alcohol and sugar fr om sugar cane. According to Paulo Roberto Costa, head of Petrobras’ supply division, “Our target is to produce ethanol to be exported only to Japan.” Within two to three months (perhaps at the end of 2007) Petrobras will sign a pioneer contract to produce a total of 1 billion liter s (264 million gallons) of alco hol annually at five processing plants in the states of Mato Grosso, Goias, and Minas Gerais. Each of the five processing plants will produce approximately 50 million gallons per year within the next 2 1⁄2 years, and the whole production will be exported to Japan. In orde r to help convince Japan that Petrobras has adequate ethanol supplies it has no ted that their process ing facilities will not be able to produce sugar, only alcohol. 341 With this amount (264 million gallons) slated for Japan only, other countries will have to either deve lop their own contracts with Braz il to ensure a stable supply or risk receiving the leftover supply.

Petrobras also recently bought a 90 percent st ake in Exxon Mobil’s Ok inawa oil refinery that may serve as a staging point for Brazilian ethanol exports to Japan and the rest of Asia. This may help mitigate one of the main problems for Petrobras and other major exporters, a lack of offloading infrastructure. 342

Another challenge is the distribut ion of ethanol in Japan. As ethanol in blends of E5 or higher have shown to be corrosive to aluminum and rubber car parts, Japan is looking into using ETBE blends of 7% and even 20-25% instead of ethanol. 343 The Petroleum Association of Japan has announced that gasoline containing ETBE blends of 7% will be available for general public consumption by 2010. As ETBE is produced using ethanol as a feedstock, this could create a domestic ethanol demand of 90-100 million gallons. 344

1.5.2.1.4 India

India has the potential to in crease its ethanol production due to its long agricultural tradition. Unfortunately it is st ill in the very young st ages of raising its bi ofuel industry, which leaves some analysts doubting this program’s stability. 345

Currently, India imports more than 70% of its energy needs. In addition to not being able to sustain its own current energy demands, the UN is predicting that India will become the most populated country by 2030 (surpass ing China), and it will be c onsuming about a third of the world’s energy by 2050.

The amount of ethanol blended in to gasoline in India has fluctuated in recent years. In October 2004, ethanol blending in gasoline for ex ample, had to be halted because of a lower sugar output due to a drought, which increased prices. However, production started back up in late 2005 when a fuller sugarcane molasses crop b ecame available. Then in September 2006 the government announced the second phase of the EBP program that mandates 5% blending ethanol with gasoline in 20 states and eight union terri tories. The mandate was effective starting in November 2006 and would have re quired about 145 million gallons to be used. However, the program only started with 10 states and was not im plemented in other states due to high state taxes, excise duties and levies.

149A recent USDA Attaché report estimated that only 66 million gallons of ethanol will be blended with gasoline in 2006/07, compared with the original target of 145 million gallons. The government plans to extend the et hanol blend ratio to 10% in a third stage once the program is extended to all target states. Some local s ources report plans to introduce E-10 mandates in several cane-producing states such as Tamil Nadu, Andhra Pradesh, and Maharashtra. 346 The original plan on a minimum of 10% blend of ethanol by Octobe r 2008, however, was put on hold because of the sharp fall in crude oil prices and because of technical conc erns raised by the Society of Indian Automobile Manufacturers. The main concern is from vehicles with older engines that may not be able to use a 10% blend without engine m odifications (e.g. twowheelers). Regardless, the govern ment announced as part of its new biofuels policy to raise the blending level to 20% of total fu el usage by 2017 (includes biodiesel). 347

India has about 300 distillers with a produc tion capacity of about 845 million gallons. Due to the government’s ethanol policy, over 110 distilleries have modified their plants to include an ethanol production line, with a to tal production capacity of 343 million gallons per year, enough to meet the estimated demand for E- 5. For an E-10 mandate, however, the current ethanol production capacity would need to be enhanced.

As of May 2006, India’s largest sugar a nd ethanol manufacturing company (Bajaj Hindusthan) is trying to acquire et hanol production plants in other countries with large sugarcane outputs (i.e. Brazil—but no concrete plans have been made). This way, the company can increase its production from 30.86 million gallons to 77.15 million gallons. Yet after several months of courting mill owners in Brazil, the company has not been able to come to any concrete agreements. 348

Some oil companies are instead pushing for impo rts of ethanol. However, as of today, there is still an import dut y of 198.96% on the cif value for denatured and 59.08% for undenatured ethanol. 349 The c.i.f. (cost, insurance, and fr eight) value represents the landed value of the merchandise at the first port of arrival in a given country. In comparison to the U.S. which has a tariff of 54 cents per gallon (with 45 cents per gallon offset by the ethanol subsidy) and a smaller ad valorem tax of 2.5% for denatured ethanol, import duties in India are much higher.

The analysis of India’s biofuels developmen ts appears to indicate that it will be selfsustaining if E5 is mandated (as noted by the suga r industry). However, as India strives to meet its E10 goal, it may need to rely on imports from other countries. As noted above, India’s own domestic production may grow from its current production of 66 million gallons of ethanol, with production capacity expanding to 343 million ga llons per year. At E5 and E10 mandates, approximately 145 million gallons and 290 million gallons per year of ethanol, respectively, is required. Therefore, depending on the amount of ethanol that India chooses to mandate, India could either be an importer of 230 million gallons of ethanol or be able to meet its goals with domestically produced ethanol.

1501.5.2.1.5 China

In 2007, China was the world’s fourth la rgest fuel ethanol producer, producing 486 million gallons. 350 Approximately 80% of fuel ethanol currently made in China is from corn. 351 Less than 20 percent of China’s corn crop is used for the industrial sector, and only 44.5 percent of that is put towards fuel ethanol. 352

China began mandating fuel ethanol blended into gasoline since June 2002. 353 In 2004, the Chinese government introduced an ethanol mandate of 10% ethanol blended in gasoline (E10) in several provinces (Helong jiang, Jilin, Liaoning, Henan, and Anhui). This mandate was further expanded to 27 cities in the provinces of Shandong, Jiangsu, Hebei, and Hubei in 2006. To keep up with fuel demand, a National Plan ca lls for fuel ethanol to rise from approximately 330 million gallons of ethanol per year to 660 million gallons by 2010 and 3.3 billion gallons by 2020. UU, 354

However, there have been recent concerns in China about the security of their food supply and the inflationary impact of biofuels which use grains as feedstock. With a population of 1.3 billion people, corn growers have to meet the demand for food (which might increase about 0.22 million tons) while al so providing feedstock for fuel . In addition, they supply livestock feed for which demand is estimated to rise to roughly 100 million metric tons for 2007. 355

In response to these food and feed dema nds for corn according to the National Development and Reform Commissi on (NDRC), China has stopped approvals for industrial corn processing for three years (until 2010) and has suspended approved projects which have not yet started construction. 356 Over the next three years, corn consumption by the deep-processing sector (i.e. transformation of corn into industrial products like etha nol) will be restricted to 26 percent of China's total corn consumption.

The National Development and Reform Co mmission (NDRC) stated in their 11 th Five Year Plan (2006-2010) that the production of ap proximately 2 billion gallons of grain-based ethanol will not threaten the country ’s grain security. Currently, ther e are four fuel ethanol plants operating in the country which have a production capacity of approximately 300 million gallons (since 2005): Jilin Fuel Alc ohol Company Ltd, Anhui Fengyua n Petrochemical Ltd, Henan Tianguan Group and the Heilongjiang Hu arun Jinyu Ltd (China Resources). 357, 358 These plants were established after 2000 to addr ess a surplus of grains in China at the time. Plans for ethanol plant expansions could reach up to approximate ly 550 million gallons of ethanol by 2007. These companies, as well as announced expansions ar e listed in the following Table 1.5-25.

UU Assuming a conversion of 1 million tonnes of ethanol equals 330 million gallons.

151Table 1.5-25. Current and Future Fuel Ethanol Production in China 359

Province Company Name Feedstock 2005 Production (Gal) 2007 Production (Gal) Heilongjiang China Resources Alc ohol Co. Corn 33,423,025 33,423,025 Jilin Jilin Fuel Ethanol Co. Corn 100,269,074 200,538,149 Henan Henan Tian Guan Fuel-Ethanol Co. Wheat 66,846,050 66,846,050 Anhui Anhui BBCA Biochemical Co. Corn 106,953,679 106,953,679 Guangxi China Resources Alcohol Co. Cassava 0 36,765,327

Hebei China Resources Alcohol Co. Sweet potato, corn, etc. 0 76,872,957 Hubei Tian Guan Fuel-Ethanol Co. Grains 0 33,423,025 Total 307,491,828 554,822,211

As seen in the above table, several distilleries have been look ing into alternative feedstocks. 360 Examples of alternative feedstocks in clude sorghum, wheat, cassava, and sweet potato. These crops, however, are grown in much smaller quantities as compared to corn, and if China ethanol production expands from cassava, for example, China will have to rely on imported cassava. 361 China may soon become a major importe r of ethanol, especi ally if the E10 blend is extended across the count ry. With a nationwide E10 bl end in 2020, biofuels demand would be approximately 7.6 billion gallons of ethanol. 362 Even if the National Plan which calls for China’s domestic fuel et hanol production to reach 3.3 bi llion gallons by 2020 is met, a nationwide E10 blend would result in a supply shortfall of about 4.3 billion gallons of ethanol. 363 Another study, the “World Biof uels: FAPRI 2007 Agricultural Outlook” also indicates that China would be a net importer of ethanol in the future (out to 2016), where domestic production only reaches approximately 1 billi on gallons. In addition, if onl y food crops are assumed to be used to produce ethanol, China is esti mated to only produce 0.6 billion gallons. 364 Assuming a possible E10 mandate nationwide and the projections for domes tically produced ethanol, China would need to import approximately 4.3-7. 0 billion gallons of ethanol per year.

1.5.2.1.6 Other Countries

Although Brazil is the largest e xporter of ethanol, there may still be other countries that could provide additional ethanol to the U.S. In fact, trace amounts of ethanol entered the U.S. market from Argentina, Canada, Netherlands, and Pakistan in 2006. 365 The North American Free Trade Agreement (NAFTA) is similar to the Caribbean Basin Initiative (CBI) in that it welcomes tariff-free ethanol imports from Canada and Mexico.

In addition, there may also be other count ries that are beginning biofuels programs and could demand smaller volumes of ethanol in the future. We provide a list of the potential mandates and goals for other countries below in Ta ble 1.5-26. This list is not meant to be allinclusive, but rather to give idea of the biofuel outlook in other countries.

152Table 1.5-26 Potential Mandates and Goals for Various Countries 366, 367, 368, 369, 370, 371
Argentina Argentine President Nestor Kirchner signed a law in February 2007 implementing tax breaks and fuel-content mandates for biofuels. The Biofuels Act includes tax breaks for companies investing in the biofuels sector and mandates 5% ethanol in gasoline by 2010. The Secretary for Agriculture estimates that the country would need 200 million liters per year of ethanol (528,000 gallons) to satisfy the E5 requirements. Australia The Australian government has set a biofuels target of 93 Mgal by 2010 according to the 'Biofuels for Cleaner Transport' 2001 election policy. This target was never mandated in legislative form. Queensland - In early August 2006 a mandate for a minimum of 5% ethanol from December 21, 2010. New South Wales (NSW )- Beginning in September 2007, fuel supplied to wholesalers in New South Wales will be required to contain 2% ethanol. Proponents of ethanol in the region want to increase the mandate to 4% in 2009 and 10% in 2010. Australian Capital Territory (ACT) - The ACT does not plan to mandate ethanol. Generally this territory follows the policies of NSW because most of their fuel supplies are sourced from NSW. Victoria - Biofuels target of 5% of fuel market by 2010 (106 Mgal), this includes biodiesel. South Australia - No plans to mandate of set a target for biofuels use. Northern Territory - No plans to mandate or set a target for biofuels known. Western Australia - No plans to mandate or set a target for biofuels known. Tasmania - The alternative fuels policy is currently based on CNG use. No plans to mandate or set a target for biofuels known. Canada On June 26, 2008, the Canadian Senate passed Bill C-33, which will require the use of 5% renewable content in gasoline by 2010. Canada's Government General Michaelle Jean signed the bill after it was passed in the senate, making it official. Saskatchewan - Enacted in October 2006 a 7.5% ethanol mandate in gasoline (approximately 131 Mgal) Ontario - Enacted in January 2007 a 5% ethanol mandate in gasoline British Columbia - Bill C-16 to pass soon, 5% ethanol by 2010 to support federal plan Alberta - Has not set its own standard as it prefers a national approach Manitoba - As of Jan. 1, 2008, 8.5% in gasoline (approximately 130 Mgal) Quebec - 5% ethanol in gasoline by 2012, expects source to be met with cellulosic ethanol production Nova Scotia - No goals for biofuels New Brunswick - No goals for biofuels Newfoundland Labrador,PIE - Interest on the East Coast, but nothing as of May 2008 North West Territories, Yukon, Nunavut - No goals for biofuels Columbia In September 2001, the Colombian Government issued Law 693, which made it mandatory to use 10% ethanol blends in gasoline in cities with populations larger than 500,000 inhabitants by the year 2008. The law went into effect in September 2005. Ethanol production, however, could not cover the entire country's demand, and thus the government established a phase-in period throughout the country for mandatory ethanol use. Mexico There is currently no specific bio-fuels promotion program operation in Mexico. On April 26, 2007, the Mexican congress approved the Law for Promotion and Development of Biofuels (LPBD). The law, though lacking in any actual mandates, sets the state for further legislative actions on biofuels development and use.

1.5.2.1.7 Summary of Pote ntial Import/Export Demands

For the main countries we have analyzed from above, there appears to be a large potential demand from the EU, Japan, India, and China for imported ethanol. See Table 1.5-27 for a summary of potential import demand by 2020/2022. Total impor t potential demand from all these countries would range from approximately 5-15 billion gallons. If these countries decide to meet their mandated ethanol blends or enact new mandates, this could greatly increase the amount that each country would demand from other countries, primarily from Brazil. As shown in Section 1.5.2.1.1, Brazil is only expected to export 3.8-4.2 b illion gallons by 2022. This is significantly below the volume we estimated that could be potentially demanded by other

153countries in the future. Therefore, it is likel y that unless Brazil incre ases production much more than its government projects, the EU, Japan, India, and China will not be able to meet their stated goals. This also indicates that the U.S. will likely compete with other foreign countries for exports from Brazil. This analysis, however, onl y considers non-cellulosic biofuel potential. If cellulosic biofuel production develops in these countries, it is enti rely possible that the biofuel demands could be lower due to greater supplies.

Table 1.5-27. Potential Import Demand: EU, Japan, India, and China by 2020/2022 (billion gallons). VV Country EU Japan India China Total Potential Domestic Production (non-cellulosic) 1.5-5.0 0-0.1 0.1-0.3 0.6-3.3 Potential Domestic Consumption

Petrobras Contract n/a 0.3 n/a n/a E3 n/a 0.5 n/a n/a E5 n/a n/a 0.1 n/a E10 (or 10% by energy for EU) 5.2-7.3 1.6 0.3 7.6 7% ETBE n/a 0.1 n/a n/a Potential Import Demand 0.2-5.8 0.1-1.6 0-0.2 4.3-7.0 4.6-14.6

1.5.2.1.8 Potential for U.S. Ethanol Exports

Ethanol exports in the U.S. have averag ed only approximately 100 million gallons per year since 2000, mostly to Cana da, Mexico, and the E.U. See Fi gure 1.5-13. There is a trend over the past five years of exporting larger quan tities to fewer countries, with declining volumes to Asia and increasing volumes to the E.U. and I ndia. Based on this hist orical data, it does not seem likely that volume of ethanol exports wi ll change drastically in the future.

VV Ranges are calculated assuming the potential values for production and consumption

154

### 0 20 40 60 80 100 120 140Million Gallons 

### Others Mexico India Canada Japan EU Figure 1.5-13. U.S. Ethanol Exports (includes hydrou s, dehydrated, and denatured volumes). 372



### 2001 2002 2003 2004 2005

1.5.2.2 Projected Growth Under RFS2

As long as imported ethanol is cost-competitive with gasoline, there will continue be a demand for it. As our analysis from above shows, Brazil is the only countr y that will likely be able to provide a significant volume of ethanol to the U.S. Accordingly, Brazil will ship ethanol to countries via the most cost-effective way.

The pathway Brazil chooses to ship ethanol wi ll likely depend on the tariffs and taxes put in place by other nations. Speci fically, the U.S. places a 54 cent ta riff on all imported ethanol (as well as a 2.5 percent ad valorem tax for un-denatu red ethanol and a 1.9 percent tax for denatured ethanol). A key reason for establishing a tariff w as to offset a tax incentive for ethanol-blended gasoline, which is set at 45 cen ts per gallon of pure ethanol. WW This analysis assumes that both the tax subsidy and the tariff will continue in the future.

The tariff can be avoided by first shipping et hanol to countries unde r the Caribbean Basin Initiative (CBI) and then to the U.S. Historical ly, the majority of CBI ethanol to the U.S. comes from dehydrating ethanol from Br azil. Legislation and agreements since the 1980s have waived or significantly reduced the tariff on imports fr om Canada, Mexico, and those nations covered under the CBI. There are currently nineteen coun tries that can benefit from the CBI program. These countries are: Antigua and Barbuda, Aruba, Bahamas, Barbados, Belize, British Virgin

WW Prior to the 2008 Farm Bill, the tax incentive was set at 54 cents per gallon

155Islands, Costa Rica, Dominica, Grenada, Guyana, Haiti, Jamaica, Montserrat, Netherlands Antilles, Panama, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, and Trinidad and Tobago. 373

Under the Caribbean Basin Economic Recove ry Act (CBERA), which created the CBI, countries in Central America a nd the Caribbean have had duty- free access to the United States since 1989 for ethanol produced from regional feed stocks. Although most an alysts believe there is sufficient land available for sugar cane pr oduction in some CBI na tions, there has been insufficient economic potential to spur sugar cane planting for ethanol production. 374 Ethanol derived from non-regional feedstocks has been limited to 7 percent of total U.S. ethanol consumption (based on figures from the previous year). There are also country-specific allocations for El Salvador (5.2 million gallons in first year (2006) and an annual increase of 1.3 million gallons per year, not to exceed 10% of CBI quota) and Costa Ri ca (31 million gallons annually) established by the U.S. Free Trade Agreement with Central America and the Dominican Republic (CAFTA-DR). 375 Thus far, Costa Rica, El Salvador, Jamaica, Trinidad and Tobago, and the U.S. Virgin Islands (since 2007) are the only countries that have ever exported ethanol to the U.S. under the CBI quota.

Historically, the CBI nations have had little ethanol production capaci ty of their own but have supplemented it by importing hydrous Brazili an ethanol where it is further dehydrated before being re-exported to the U.S. duty-free. CBI countries have also relied on surplus wine alcohol from France, Italy, Spain and ot her Mediterranean c ountries in the past. 376 According to the United States International Trade Commission, the majority of fuel ethanol imports to the United States came through CBI countries be tween 1996 and 2003. Although CBI ethanol imports to the U.S. in 2006 totaled over 170 million gallons, imports to the U.S. from Brazil totaled 3 times that amount, or approximately 430 million gallons. This data indicates that in 2006 it was economical to import sign ificant quantities of ethanol di rectly from other nations due to the withdrawal of MTBE and high oil prices. However, it is not clear on how much of this volume the tariff was paid, as there have been ot her means for importers to avoid the tariff.

In the past, companies have also impo rted ethanol from Brazil through a duty drawback. 377 The drawback is a looph ole in the tax rules which allowed companies to import ethanol and then receive a rebate on taxes paid on the ethanol wh en jet fuel is sold for export within three years. The draw back considered ethanol and jet fuel as similar commodities (finished petroleum derivatives). 378, 379 Most recently, however, Sena te Representative Charles Grassley from Iowa included a provision into th e 2008 Farm bill that ended such refunds. The provision states that “any dut y paid under subheading 9901.00.50 of the Harmonized Tariff Schedule of the United States on imports of ethyl alcohol or a mixture of ethyl alcohol may not be refunded if the exported article upon which a drawback claim is based does not contain ethyl alcohol or a mixtur e of ethyl alcohol.” 380 The provision became effective on or after October 1, 2008 and companies have until October 1, 2010 to apply for a duty drawback on prior transactions. With the loophole closed, it is antici pated that there may be less ethanol directly exported from Brazil in the future. 381

CBI countries have not yet exceeded the ta riff rate quotas (TRQs) for ethanol imports. The TRQ has been limited to 7 percent of tota l U.S. ethanol consumption (based on figures from

156the previous year). The fill rate, or percent of the TRQ used, has ranged from 22-77% between 1990 and 2008. See Figure 1.5-14. Thus, there is still considerab le room for growth in CBI imported ethanol.

Figure 1.5-14. U.S. Fuel Ethanol CBERA TRQ, 1990-2009



### 0 100 200 300 400 500 600 700Million Gallons 

### TRQ Used TRQ Available1990199219941996199820002002200420062008

*TRQ Used data for 2009 is not yet available.

In October 2003, the California Energy Commission (CEC) reported four active CBI ethanol dehydration plants, two in Jamaica, one in Costa Rica, and one in El Salvador. At the time, CEC concluded that reprocessing capacity w as the limiting factor on CBI imports, with a total of 90 million gallons per year. 382 Since then, several companies have expanded plants or announced new plants as described below:

• Jamaica - In 2005, Petrojam Ethanol Limited (PEL), upgr aded and expanded their ethanol dehydration plant in Jamaica to a capacity of 40 million US gallons. Currently, the production of anhydrous (fuel gr ade) ethanol at the plan t is based on a marketing agreement with the Brazilian company, Coim ex Trading, where the feedstock - hydrous ethanol is supplied from Brazil

. 383

, 384 Jamaica Broilers Group (JBG) launched fuel ethanol production at its 60 million gallon dehydra tion plant this year. The first shipment of 5.5 million gallons of ethanol , which ar rived in June 2007, will be converted to anhydrous ethanol for export to the U.S. JB G has a deal with Bauche Energy for the supply of 50 million gallons of hydrous ethano l out of Brazil for the first year of operation. 385 Jamaica Ethanol Processing Ltd, whic h is ED & F Man’s subsidiary on the island, has a small plant that dehydrates ethanol from Brazil at a capacity of 55 million gallons.

157• Costa Rica - LAICA (cane co-op) has a plant currently dehydrating ethanol at 38 million gallons. 386

• El Salvador and Panama - In 2004, it was reported that Cargill and Chevron Texaco had announced plans to construct ne w dehydration plants in El Salvador and Panama. These plants could produce 60 million gallons per year and between 50 and 100 million gallons per year, respectively. 387 Plants currently in operation in clude Gasohol de El Salvador (Liza/Vitol) at 100 million gallons per year and ARFS (CASA/Cargill/Crystalsev) at 60 million gallons per year. 388

• Trinidad - EthylChem Inc. has reported plans to build an ethanol dehydration operation at the Petrotrin Refinery in Point-a-Pierre, a sout hern port city in Trinidad. The plant will dehydrate ethanol at a capacity of 100 million gallons per year and is expected to be complete between March and April of 2007. 389 The cost to build the plant is estimated at $20 million. 390 It is probable, however, that not al l the ethanol would be exclusively for U.S. consumption. According to Ron White, the executive director of Ethylchem, "While EthylChem intends to export the fuel to the United States the company is examining the possibility of shipping the product to other markets in the world". 391 Another company, Angostura Ltd., started processing ethanol in 2005. 392 The plant has an overall capacity of 100 million gallons per year, with 50 milli on gallons per year in the first phase. 393

• Others - An idled ethanol plant in Haiti has attr acted some investors and there are also projects in the works in G uyana, the Dominican Republic and Aruba. The U.S. Virgin Islands has one plant dehydrating ethanol at 100 million gallons per year capacity (Geonet). 394 A new ethanol dehydrat ion plant is proposed to be built at the Bulk Terminal Facility near Spring Garden Highway in Barbados, with construction expected to commence by the end of 2008. 395 There is a proposal to build a US$36 million ethanol plant near Bridgetown, Barbados. The pl ant is expected to produce about 132 million gallons by refining ethanol imported from Brazil. 396

In total, current fuel etha nol plant capacity for dehydrat ion in the Caribbean is 500 million gallons per year. Plans to expand total approximately 200 million gallons. 397 This means that there could be 700 million gallons per y ear of fuel ethanol capacity in the next few
years.

Some stakeholders, however, have expressed concern that the CBI countries are not as stable for investment. Both Brazilian ethanol and European wine alcohol are susceptible to factors including availability, price fluctuat ions, trade regulations, currency movements and freight rates. Availability of European surp lus wine alcohol has diminished since the World Trade Organization (WTO) placed limitations on export subsidies and has found new markets in Spain and Sweden. 398 CBI countries also need to compete fo r Brazilian ethanol. For example, Angostura’s ethanol subsidiary, Tr inidad Bulk Traders Ltd., wasn ’t profitable in 2006 because it could not get enough fuel from Brazil. 399

There are other prohibitive fact ors to CBI ethanol production that exist. For instance, many of the CBI countries have no oil, natural gas or coal. Permitting is often a huge challenge and fresh water is typically scarce. 400

158In addition, increasing si gnificantly beyond the 7% limit may be challenging. Few Caribbean countries are in any position to produc e ethanol from domestic feedstocks such as sugar cane. Currently, all three plants exist in Central America (CATSA in Costa Rica, Pantleon Group in Guatemala, and Pellas Group in Nicaragua ). Capacity for each plant is approximately 10 million gallons per year. The majority of this domestic fuel ethanol is shipped to the EU for fuel use rather than US due to higher opport unity prices and simila r tariff free treatment. 401 In addition, the governments of Trinidad, St. Kitts and Barbados have already decided the sugar sectors of their islands are not worth further investment. Rum distillers such as Trinidad’s Angostura and Jamaica’s Appleton Ltd. have also had to import molasses from Fiji for their spirits. 402 Thus, it may take years before Caribbean countries are able to domestically produce large volumes of ethanol. As noted above, however, as dehydration capacity gets close to the US CBI quota, processors will need to c onsider blending indigenous ethanol.

As a result of the economic benefit of shipping ethanol th rough CBI nations, we anticipate that the majority of the TRQ will be met in the future. If we assume that 90 percent of the TRQ is met and that total domestic ethanol (c orn and cellulosic ethanol) consumed in the prior year was 28.5 Bgal, then approximately 1. 8 Bgal of ethanol coul d enter the U.S. through CBI countries. XX Brazilian ethanol exports not ente ring the CBI will compete on the open market with the rest of the world demanding so me portion of direct Br azilian ethanol. We calculated the amount of direct Brazilian ethanol exported in 2022 to the U.S. as the total imported ethanol required to meet the Act s ubtracted by imported etha nol from CBI countries (e.g. 3.14 Bgal of imported ethanol is required to meet EISA in 2022, while 1.8 Bgal can enter CBI due to the TRQ limit, thus leaving 1.34 Bgal needed directly from Brazil). The total imported ethanol required by the Act was projecte d for each year based on the required volumes needed to meet the advanced biofuel standard after accounting for the volumes from cellulosic biofuel, biodiesel, and renewabl e diesel. See Table 1.5-28.

Table 1.5-28. Projected Contribution of Ethanol from CBI Countries and Direct Brazilian exports in 2022 (billion gallons)
Ethanol From CBI Countries Ethanol Directly From Brazil Total Imported Ethanol 1.80 1.34 3.14

The amount of Brazilian ethanol available for direct ship ment to the U.S. will be dependent on the biofuels mandates and goals se t by other foreign countri es (i.e., the EU, Japan, India, and China). Our estimates show that there could be a potential demand for imported ethanol of 4.6-14.6 billion gallons by 2020/ 2022 from these countries as noted in Section 1.5.2.1.7. This is due to th e fact that some countries are una ble to produce large volumes of ethanol because of e.g. land constraints or lo w production capacity. Therefore, unless Brazil increases production much more than its governme nt projects there may be a limited supply for imported ethanol to satisfy all foreign country mandates and goals.

XX Total Domestic Ethanol is based on the amount needed to meet EISA (i.e. for 2021: 15 Bgal Corn Ethanol, 13.5 Bgal Cellulosic Ethanol; for 2022: 15 Bgal Corn Ethanol, 16 Bgal Cellulosic Ethanol).

1591.5.2.2.1 Origin of Projected Imports

A summary of our pr ojections of the breakdown of et hanol imports that would come directly from the Brazil versus that which would come through th e CBI countries is contained in Table 1.5-19. To estimate the future brea kdown of ethanol imports from CBI countries by country of origin, we evaluate d historical ethanol import data from the International Trade Commission (ITC) and trends regard ing potential growth in such im ports. Table 1.5-29 contains 2005-2007 data from the ITC on ethanol imports from CBI countries. 403 Table 1.5-30 contains January – March 2008 data from the ITC on ethanol imports from CBI countries. 404

Table 1.5-29. Ethanol Import s from CBI Countries 2005-2007 2005 2006 2007 % of CBI

imports Volume (Million Gallons) % of CBI imports Volume (Million Gallons) % of CBI imports Volume (Million Gallons) Costa Rica 32% 33.4 22% 35.9 17% 39.3 El Salvador 23% 23.7 23% 38.5 32% 73.3 Jamaica 35% 36.3 40% 66.8 33% 75.2 Trinidad and Tobago 10% 10 15% 24.8 19% 42.7

Source: International Trade Commission

Table 1.5-30. Ethanol Imports from CB I Countries, January through March 2008 January February March % of CBI

imports Volume (Million Gallons) % of CBI imports Volume (Million Gallons) % of CBI imports Volume (Million Gallons) Costa Rica 26% 5.4 27% 5.4 0 0 El Salvador 13% 2.6 0 0 23% 4.6 Jamaica 19% 4.0 32% 6.4 39% 7.9 Trinidad and Tobago 20% 4.1 21% 4.2 29% 6

Virgin Islands 22% 4.6 21% 4.2 9% 1.9

Source: International Trade Commission

Based on our review of the January thr ough March 2008 data, we assumed that ethanol exports from the Virgin Islands wo uld continue to grow to equal those of Trinidad and Tobago in 2022. By accommodating this assumption into our review of 2005 though 2007 historical ethanol import data, we arrive d at our projections regarding the future breakdown of ethanol imports from CBI countries which is contained in Table 1.5-31.

160Table 1.5-31. Projected Future Breakdown of Ethanol Imports from CBI Countries % of Total Ethanol Imports from CBI Countries Costa Rica El Salvador Jamaica Trinidad and Tobago Virgin Islands 20% 20% 30% 15% 15%

1.5.2.2.2 Destination of Projected Imports

For our distribution and air qua lity analyses, we had to make a determination as to where the projected imported ethanol would likely enter the United States. As mentioned above, we started by looking at historical ethanol import data and made assumptions as to which countries would likely contribute to the CBI ethanol volumes in Table 1.5-19, and to what extent (see Table 1.5-22).

From there, we looked at 2006-2007 import data and estimated the gene ral destination of Brazilian ethanol and the five contri buting CBI countries’ domestic imports. 405 Based on these countries’ geographic locations and import histories, we estimated that in 2022 82% of the ethanol would be imported to the East and Gulf Coasts and the remaining 18% would go to the West Coast and Hawaii. The des tination of imports from Brazil and the CBI countries in 2022 is detailed in Table 1.5-32.

Table 1.5-32. 2022 Projected Destination of Ethanol Imports from Brazil and CBI Countries Based on 2006-2007 Import Data Origin Destination of Ethanol Impor ts (% of imported volume) West Coast Hawaii East & Gulf Coasts Costa Rica 83% 35% 47% El Salvador 18% 9% 88% Jamaica 3% 0% 17% Trinidad & Tobago 0% 32% 68% Virgin Islands 3% 9% 88% Brazil (direct) 7% 0% 93% Total 11% 7% 82%
Source: Energy Information Administration historical gasoline and ethanol import data: http://www.eia.doe.gov/oil_gas/petroleum/data_publi cations/company_level_imports/cli_historical.html

To estimate the 2022 ethanol import locations on a finer level, we looked at coastal ports that had received ethanol or fini shed gasoline imports in 2006. We chose to include ports which imported finished gasoline (in addition to ethanol) because we believe finished gasoline will be one of the first petroleum products to be replaced under the proposed RFS2 rule. And

161presumably, these ports cities already have ex isting gasoline storage tanks that could be retrofitted to accommodate fuel ethanol. All together, we arrived at 28 potential ports in 16 coastal states that could r eceive ethanol imports in 2022 (refer to Figure 1.5-14 below). YY

To determine how much ethanol would arri ve at each port location, we started by examining each receiving state’s imported etha nol consumption potential. To do this, we considered each state’s maximum ethanol cons umption potential (based on projected gasoline energy demand) and deducted the projected 2022 corn and cellulo sic ethanol production (detailed in Sections 1.5.1.2 and 1.5.3.4, respec tively). While we are not concluding that each state would necessarily consume all the ethanol it produces, we believe this serves as an appropriate assumption in determining each port state’s relative imported ethanol consumption potential. Once we determined the amount of im ported ethanol that each state would receive in 2022 under RFS2, for states with multiple ethanol ports, we allocated the ethanol among port locations based on each port county’s relative energy demand - using projected 2022 vehicle miles traveled (VMT) from EPA’s MOVES mo del 2022 VMT. A summary of the projected ethanol imports volumes by port locati on is found in Figure 1.5-15.

YY We are considering adding Hampton Roads, VA and Baltim ore, MD to the list of future ethanol import locations and may adjust our an alysis for the final rule accordingly.

162Figure 1.5-15. Projected RFS2 Ethanol Import Loca tions and Volumes (Million Gallons) ZZ

1.5.3 Cellulosic Biofuel

In this subsection, we will discuss the state of the evolvi ng cellulosic biofuel industry. The bulk of this discussion focuses on the cellulo sic ethanol industry sinc e it is the cellulosic biofuel we assumed for our RFS2 volume and im pact analyses. However, as discussed in Section 1.2.1 (and throughout this package) cellulosic diesel coul d also count towards meeting the 16 billion gallon cellulosic biofuel standard. We are becoming aware of more and more companies pursuing plans to produce diesel thro ugh Fischer-Tropsch gasification of biomass as well as thermal, thermochemical, and biologi cal depolymerization of biomass to produce synthetic diesel fuel. We plan on doing a mo re robust assessment of these companies and technologies for the final rulemaking. In the meantime, a discussion of the cellulosic diesel production plans we are aware of is provided in Section 1.5.3.3. For more on the cellulosic biofuel conversion technologies men tioned in this section, refer to Section 1.4.3. Finally, we end this section with a discussi on of how the nation is on trac k for meeting the 2010 cellulsic biofuel standard and where more commercial-le vel plants might be located in 2022 based on feedstock availability.

ZZ We are considering adding Hampton Roads, VA and Baltim ore, MD to the list of future ethanol import locations and may adjust our an alysis for the final rule accordingly.

1631.5.3.1 Cellulosic Ethanol Production/Plans

Presently, all commercial-level ethanol pr oduced in the U.S. comes from plants processing corn and other grains or starches. Ho wever, cellulosic feedstocks have the potential to greatly expand domestic ethanol production, bo th volumetrically and geographically. Before the fuel can be economically produced at commerci al levels in today’s marketplace, technical and logistical barriers must be overcome. In addition to today’s RFS2 program which sets aggressive goals for all ethanol production, the Department of Energy (DOE) and other federal and state agencies are helpi ng to spur industry growth.

The cellulosic ethanol industry is essentially in its infancy. No commercial-scale plants are currently operating in the U.S. However, numerous state and federal agencies, national laboratories, universities, and private companies are working to wards making cellulosic ethanol commercially viable. These organizations are pursuing second-generation conversion technologies and experimenting with a variety of feedstocks. Some r esearchers are focusing on processing corn residues, e.g., corn stover, c obs, and/or fiber. So me are focusing on other agricultural residues such as sugarcane bagasse, ri ce, and wheat straw. Others are looking at waste products such as forestry residues, citrus residues, pulp or paper mill waste, municipal solid waste (MSW), and construction and demolition (C&D) debris. Dedicated energy crops including switchgrass and poplar trees are also be ing investigated.

The majority of the groups pursuing ce llulosic ethanol prod uction are currently conducting laboratory research or experimenting with pilot- or demonstration-level production. Based on an April 2009 assessment of information available on the public domain, there are presently 25 small cellulosic ethanol plants operating in the United States. AAA Most of these facilities operate intermittently and produce insi gnificant volumes of ethanol. A list of these facilities and their locations, feedstocks, ethanol production capacities (if applicable/available), estimated operation dates, and t echnologies is provided in Table 1.5-33. The date listed in the table indicates when the facili ty first began operations.

AAA Our April 2009 cellulosic ethanol industry assessment was based on researching DOE- and USDA-supported projects, plants referenced in HART’s Ethanol & Biodiesel News (through the April 14, 2009 issue), plants included on the Cellulosic Ethanol Site ( http://www.thecesite.com/ ), and plants referenced on other biofuel industry websites.

164165Table 1.5-33.Operational Pilot- and Demonstration-Scale Cellulosic Ethanol PlantsCompany or Organization NameLocationFeedstocksProd Cap(MGY)Est. Op.DateConv.Tech.aAbengoa Bioenergy CorporationbYork, NEWheat straw, corn stover, energy crops0.02Sep-07BioAE BiofuelsbButte, MTSwitchgrass, small-grain straw, corn stoverN/AAug-08BioArkenol Technology CenterOrange, CABiomassN/A1994BioAuburn University / Masada Resources GroupcAuburn, ALWoodN/A1995BioBioengineering Resources, Inc. (BRI)Fayetteville, AKMSW, wood waste, coal0.041998ThermBPI & Universal EntechPhoenix, AZPaper waste (sorted MSW)0.012004BioChemrec & WeyerhaeuserNew Bern, NCPaper mill wasteN/A1996ThermClearFuels Technology / Hawaii Natural Energy InstituteKauai, HISugarcane bagasseN/A2004ThermCornell University’s Biofuels Research Laboratory (BRL)Ithaca, NYPerennial grasses, woody biomassN/AJan-09BioCoskataWarrenville, ILReformed natural gas, biomassN/AMar-08ThermDOE National Renewable Energy Laboratory (NREL)Golden, COCorn stover, other biomassN/A2001BothGulf Coast EnergyLivingston, ALWood waste (sorted MSW)0.20Dec-08ThermMascoma CorporationRome, NYWood chips0.20Feb-09BioNovozymesFranklinton, NCCorn stoverN/AN/ABioPan Gen Global (formerly Colusa Biomass)Colusa County, CARice straw & hullsN/A1995BioPearson Technologies Inc. (PTI)Aberdeen, MSWood residues, rice strawN/A2001ThermPOET Research CenterbScotland, SDCorn cobs & fiberN/AN/ABioPOET Project BellbScotland, SDCorn cobs & fiber0.02Jan-09BioPureVision Technology, Inc.Fort Lupton, COCorn stover, wood, sugarcane bagasseN/A2003BioPureVision Technology, Inc.Fort Lupton, COCorn stover, wood, sugarcane bagasseN/A2009BioRange FuelsBroomfield, COWood wasteN/AMar-09ThermUSDA Citrus & Subtropical Products LaboratoryWinter Haven, FLCitrus residuesN/A1990BioVereniumJennings, LASugarcane bagasse0.052006BioVereniumJennings, LASugarcane bagasse, wood, energycane1.50Feb-09BioWestern Biomass Energy, LLC. (WBE)Upton, WYWood waste (softwood)1.502007Bio>3.5 MGYTotal Existing Production CapacityaBio = biochemical cellulosic ethanol technology, Therm = thermochemical cellulosic ethanol technology, Both = investigating both technologies.bCellulosic pilot plant is collocated with a corn ethanol plant.cIn December of 2007, Masada donated the Tennessee Valley Authority's biomass pilot plant facilities and equipment in Muscle Shoals, AL to Auburn University in Auburn, AL.166 As shown in Table 1.5-33, today’s cellulosi c ethanol plants are r un by a combination of academic, government, and private organizations. Some of the privatel y-owned companies are existing corn ethanol producers, but the major ity are start-up companies entering the ethanol industry for the first time. Verenium Corp. r eceived a $10 million grant from DOE to help build their demonstration-level plant in Jennings, LA that just recently came online.

To date, the majority of cellulosic etha nol research has focused on biochemical pretreatment technologies, i.e., the use of acids a nd/or enzymes to break down cellulosic materials into fermentable sugars. However, there are a growing number of co mpanies investigating thermochemical conversion which involves gasi fication of biomass into a synthesis gas or pyrolysis of biomass into a bio-crude oil for pr ocessing. Cellulosic diesel, a diesel fuel replacement discussed more in Section 1.5.3.3, can also be made from thermochemical conversion processes. Many compan ies are also researching the potential of co-firing biomass to produce plant energy in addition to biofuels. For more on the biofuel processing technologies, refer to Section 1.4.3

In addition to the 25 existing facilities, th ree additional cellulosi c ethanol plants are currently under construction in the United States. Like the existing plants, two are pilot-level facilities that are still working towards proving their conversion technolo gies. However, Range Fuels, a company that received $76 million from DOE and an $80 loan guarantee from USDA funding to build one of the first commercial-scale cellulosic ethanol plan ts in the U.S., is currently building a 40 million gallon per year plant in Soperton, GA. BBB At this time, the company is just working on the initial 10 milli on gallon per year phase). A summary of the cellulosic ethanol plants currently under construc tion in the U.S., is presented in Table 1.5-34.

As shown in Tables 1.5-33 and 1.5-34, unlike corn ethanol production, which is primarily located in the Midwest near the Corn Belt, ce llulosic ethanol production is spread throughout the country. The geographic distributi on of plants is due to the wi de variety and availability of cellulosic feedstocks. Corn stover is found primarily in th e Midwest, while the Pacific Northwest, the Northeast, and the Southeast all have forestry residues. Some southern states have access to sugarcane bagasse and citrus waste while MSW and C&D debris are available in highly populated areas throughout the country. For more information on cellulosic feedstock availability, refer to Section 1.1.2.

BBB Range Fuels’ ultimate goal is to expand the Soperton, GA facility to produce 100 million gallons of cellulosic ethanol per year.167Table 1.5-34.Small-Scale Cellulosic Ethanol Plants Currently Under ConstructionCompany/Plant NameLocationFeedstocksProd Cap(MGY)Est. Op.DateConv.Tech.aCoskataDuPont Dansico Cellulosic Ethanol (DDCE)Range FuelsMadison, PAVonore, TNSoperton, GAMSW, natural gas, woodchips, bagasse, switchgrassCorn cobs then switchgrassWood waste, switchgrass0.040.2510.00Jul-09Dec-09Dec-09ThermBioThermTotal Under Construction Production Capacity>10 MGYaBio = biochemical cellulosic ethanol technology, Therm = thermochemical cellulosic ethanol technology.This page intentionally left blank.168169 Increased public interest, government s upport, technological advancement, and the recently-enacted EISA have helped spur many pl ans for new cellulosic biofuel plants. Although more and more plants are being announced, mo st are limited in si ze and contingent upon technology breakthroughs and effici ency improvements at the pilo t and demonstration level. Additionally, because cellulosic biofuel productio n has not yet been proven on a commercial level, financing of these projects has primarily been through venture capital and similar funding mechanisms, as opposed to conventional bank loans.

Consequently, recently-announced Federal gran t and loan guarantee programs may serve as a significant asset to the cellulosic biofuel industry in this area. In February 2007, DOE announced that it would invest up to $385 million in six commercial-scale ethanol projects over the next four years. 406 Since the announcement, two of th e companies have forfeited their funding. Iogen has decided to locate its first commercial-scale plant in Canada and Alico has discontinued plans to build an et hanol plant all together. The f our remaining “pioneer” plants (including Range Fuels) hold promise and could very well be some of the first plants to demonstrate the commercial-scale viability of cel lulosic biofuel production. However, there is still more to be learned at the pilot level. Although technologies needed to convert cellulosic feedstocks into ethanol (and diesel) are becoming more and more understood, there are still a number of efficiency improvements that need to occur before cellulo sic biofuel production can compete in today’s marketplace.

In May 2007, DOE announced that it woul d provide up to $200 million to help fund small-scale cellulosic biorefiner ies experimenting with novel pr ocessing technologies that could later be expanded to commer cial production facilities. 407 Four recipients were announced in January 2008 and three more were announced in April 2008. 408, 409 Three months later, DOE announced that it would provide $40 million more to help fund two additional small-scale plants. 410 Of the nine total small-scale plants, se ven were pursuing cellulosic ethanol production (including Verenium Corp.) a nd two were planning on producing cellulosic diesel. However, Lignol Innovations, recently suspended plans to build a 2.5 million gallon per year ethanol plant in Grand Junction, CO due to market uncertainty.

The Department of Energy has also introduced a loan guarantee program to help reduce risk and spur investment in projects that empl oy new, clean energy tech nologies. In October 2007, DOE issued final regulations and invi ted 16 project sponsors who submitted preapplications to submit full appl ications for loan guarantees. 411 Of those who were invited to participate, five were pursuing cellulosic bi ofuel production. However, only three of the companies appear to still be eligible. CCC Of the three remaining companies, two are pursuing cellulosic ethanol produc tion (and are also DOE grant recipien ts) and one is pur suing cellulosic diesel production. The U.S. Department of Ag riculture is also providing an $80 million loan guarantee to Range Fuels to help support commerci alization of cellulosic et hanol. For a list of the cellulosic biofuel projects rece iving DOE grants and/or loan guarantees that are still on the table, refer to Table 1.5-35. For more informati on on the cellulosic diesel companies listed in this table, and other privately f unded plants and proposed projec ts, refer to Section 1.5.3.2.

CCC Iogen and Alico have also forfeited po tential loan guarantees from DOE.170Table 1.5-35.Cellulosic Biofuel Plants Receiving DOE or USDA SupportDDDCompany/Plant NameLocationFeedstocksProd Cap(MGY)Est. Op.DateaConv.Tech.bDOEFundingFed. LoanGuaranteeCellulosic EthanolAbengoa Bioenergy Corp.cHugoton, KSCorn stover, wheat straw, milo stubble, switchgrass11.42012Bio$76MMBlueFire Mecca, LLC.El Sobrante, CAWoodchips, grass cuttings, and other yard waste17.0TBDBio$40MMDOEEcofin / AlltechSpringfield, KYCorn cobs1.32010Bio$30MMICM Inc.cSt. Joseph, MOCorn fiber/stover, sorghum, switchgrass1.52010Bio$30MMMascoma CorporationKinross, MIdWood fiber40.02012Bio$26MMPacific EthanolcBoardman , ORWheat straw, wood chips, corn stover2.7TBDBio$24MMPOET Project LibertycEmmetsburg, IACorn cobs & fiber25.02011Bio$80MMDOERange FuelseSoperton, GAWood waste, switchgrass40.02011Therm$76MMUSDARSE Pulp & ChemicalOld Town, MEWoody biomass2.22010Bio$30MMVerenium Corp.Jennings, LASugarcane bagasse, wood, energycane1.5OnlineBio$10MMfCellulosic DieselChoren U.S.A.Flambeau River BiofuelsNew Page CorporationgTBD (Southeast U.S.)Park Falls, WIWisconsin Rapids, WIAg residues, forestry biomass, biogenic wasteForestry residuesWoody biomass, mill residuesTBD6.05.5TBD20102012FTFTFT$30MM$30MMDOEaBased on current production plans. In many cases, these estimated production dates are further out than the originally-announced construction timeline.bBio = biochemical cellulosic ethanol technology, Therm = thermochemical cellulosic ethanol technology, FT = Fischer-Tropsch biomass-to-liquids diesel technology.cCellulosic ethanol plant will be collocated with a corn ethanol plant.dMascoma was originally issued a DOE grant to help build a demonstration-level plant in Vonore, TN. However, that location hassince been rescinded and Mascoma plans to use theDOE funding towards their planned commercial-level plant in Kinross, MI. This project, a joint venture between Mascoma and JMLongyear, is also known as Frontier RenewableResources.eThe first 10 MGY phase is currently under construction.fEstimated DOE funding.gProject formerly owned by Stora Enso.DDDBased on information provided on DOE plant fact sheetsand 7/14/08 DOE Small-Scale Biorefinery Project Overview(http://www.doe.gov/media/Small_Scale_Biorefineries_Matrix.pdf.) supplemented with information provided on company websites.171 In addition to the aforementioned co mpanies receiving DOE funding, numerous privately-funded companies have an nounced plans to build cellulosic ethanol plants in the U.S. These projects vary in scope and status. Th e facilities range in size from pilot and demonstration-level plants (similar to those curre ntly operational or under construction), to small commercial plants (similar to the four commerci al-scale plants receivi ng DOE funding), to large commercial plants (similar in si ze to an average corn ethanol pl ant). These facilities are at various states of planning from conducting feasibility studies, to securing funding/financing, to acquiring construction permits, to securing E&C contractors. Some of the projects (“planned” plants) are further along in the planning stage while others are in the very early stages of planning (“proposed” plants). There are also several companies with even less definitive cellulosic biofuel production plan s. We will continue to tr ack these companies and plan on providing more information on these projects in the final rulemaking. All in all, while it is uncertain which (if any) of these production plan s will come to fruition, we believe the success of such projects will be greatly improved by the impending success of the DOE-supported projects. A summary of the “planned” and “pro posed” cellulosic ethanol plants that we are aware of based on our April 2009 industry assessment is provided in Tables 1.5-36 and 1.5-37 below.172Table 1.5-36.Planned Cellulosic Ethanol Plants/CapacityCompany/Plant NameLocationFeedstocksProd Cap(MGY)Est. Op.DateConv.Tech.aAbengoa Bioenergy CorporationHugoton, KSbCorn stover, wheat straw, milo stubble, switchg11.4Early 2012BioBlueFire Ethanol, Inc.Lancaster, CAWoodchips, grass cuttings, and other yard wast3.9TBDBioBlueFire Mecca, LLC.El Sobrante, CAWoodchips, grass cuttings, and other yard wast17.0TBDBioClearFuels TechnologycKauai, HISugarcane bagasse, cane trash, wood waste1.5End of 2010ThermCoskataTBD (Southeast U.S.)Woody biomass50.02012ThermEcofin / AlltechSpringfield, KYCorn cobs1.32010BioFulcrum BioenergydStorey County, NVPost-recycled organic waste from MSW10.52010ThermICM Inc.St. Joseph, MObCorn fiber/stover, sorghum, switchgrass1.52010BioMascoma CorporationeKinross, MIWood fiber40.02012BioPacific EthanolBoardman , ORbWheat straw, wood chips, corn stover2.7Early 2011BioPOET Project LibertyEmmetsburg, IAbCorn cobs & fiber25.0End of 2011BioRange FuelsSoperton, GAWood waste, switchgrass30.02011ThermRSE Pulp & ChemicalOld Town, MEWoody biomass2.22010BioSoutheast Renewable Fuels LLCClewiston, FLSweet sorghum, bagasse20.0End of 2010UnknVerenium / BPLake Okeechobee, FLEnergycane, high-biomass sorghum36.02011BioZeaChemBoardman, ORWood chips, saw dust, logging debris1.52010Both255 MGYTotal Planned Plant CapacityaBio = biochemical cellulosic ethanol technology, Therm = thermochemical cellulosic ethanol technology, Both = investigating both technologies, Unkn =unknown pretreatment technology.bCellulosic ethanol plant will be collocated with a corn ethanol plant.cLocated at the Gay & Robinson sugar mill.dProject also known as known as Sierra Biofuels.eThis project, a joint venture between Mascoma and JM Longyear, is also known as Frontier Renewable Resources.fThe first 10 MGY phase is currently under construction in Soperton, GA. Once this second 30 MGY phase is added, the plant willbe capable of producing 40MGY of cellulosic ethanol.173Table 1.5-37. Proposed Cellulosic Ethanol Plants/CapacityCompany/Plant NameLocationFeedstocksProd Cap(MGY)Conv.Tech.aAEE DistilleriesNew Milford, CTForest residues, sawdust and yard wastes24.0UnknAgresti BiofuelsPike County, KYMSW20.0BioCentral Minnesota Ethanol Co-op (CMEC)Little Falls, MNbWoodchips10.0BioChemrec & NewPage CorpEscanaba, MIPaper mill waste13.0ThermCitrus EnergyClewiston, FLCitrus residues4.0BioCoskataTBD (Southeast U.S.)Woody biomass50.0cThermEnerkemPontotoc, MSMSW20.0UnknFlorida Crystals Corp.Okeelanta, FLSugarcane bagasse, yard waste1.5BioGenaholLake County, INMSW35.0ThermGulf Coast EnergyLivingston, ALWood waste35.0ThermGulf Coast EnergyMossy Head, FLWood waste35.0ThermGulf Coast EnergyCleveland, TNWood waste35.0ThermNew Age Energy / LiquafactionMoses Lake, WAbWheat straw, corn stover, sorghum40.0BioOrion Ethanol / Dimmitt EthanolDimmitt, TXbCorn Stover10.0UnknPan Gen Global (formerly Colusa Biomass)Colusa County, CARice straw & hulls12.5BioPencor Masada OxyNolMiddletown, NYMSW9.0BioRange FuelsSoperton, GAWood waste, switchgrass60.0cThermRaven BiofuelsTBD (MS)Wood waste20.0BioRaven BiofuelsTBD (WA)Wood waste20.0BioRaven BiofuelsTBD (ID)Corn cobs20.0BioRaven BiofuelsTBD (PA)Wood waste20.0BioSoutheast Renewable Fuels LLCClewiston, FLSweet sorghum, bagasse80.0cUnkn384 MGYTotal Proposed Plant CapacityaBio = biochemical cellulosic ethanol technology, Therm = thermochemical cellulosic ethanol technology, Unkn = cellulosic conversion technology is unknown.bCellulosic ethanol plant will be collocated with a corn ethanol plant.cPlanned expansion project. Production capacity is in addition to the volumes listed in Tables 1.5-34 and 1.5-36.174 1.5.3.2 Federal/State Production Incentives

In addition to helping fund a series of sm all-scale cellulosic biofuel plants, the Department of Energy, along with the U.S. Depa rtment of Agriculture (USDA), is helping to fund critical research to help ma ke cellulosic ethanol production mo re commercially viable. In March 2007, DOE awarded $23 million in grants to four companies and one university to develop more efficient mi crobes for ethanol refining. 412 In June 2007, DOE and USDA awarded $8.3 million to 10 universities, laboratories, and research centers to conduct genomics research on woody plant tissue for bioenergy. 413 Later that same month, DOE announced its plan to spend $375 million to build three bioenergy resear ch centers dedicated to accelerating research and development of cellulosic ethanol and other biofuels. The centers, which will each focus on different feedstocks and biologi cal research challenges, will be located in Oak Ridge, TN, Madison, WI, and Berkeley, CA. 414 In December 2007, DOE awarded $7.7 million to one company, one university, and two research cen ters to demonstrate the thermochemical conversion process of turning grasses, stover, and other cellulosic materials into biofuel. 415 In February 2008, DOE awarded another $33.8 million to three companies and one research center to support the development of commercially-viable enzymes to support cellulose hydrolysis, a critical step in the biochemical breakdown of cellulosic feedstocks. 416 Finally, in March 2008, DOE and USDA awarded $18 million to 18 univers ities and research institutes to conduct research and development of biomass-based pr oducts, biofuels, bioenergy, and related
processes. 417 Since 2007, DOE has announced more than $1 billion and since 2006, USDA has invested almost $600 million for the research, deve lopment, and demonstration of new biofuel technology.

Numerous states are offeri ng grants and tax incentives to help encourage biofuel production. The majority of efforts are cente red on expanding ethanol production, and more recently, cellulosic ethanol production. Acco rding to a July 2008 assessment of DOE’s Energy Efficiency and Renewable Ener gy (EERE) website, 33 states currently offer some form of ethanol production incentive. 418 The incentives range from support for ethanol producers to support for research and development companies to support for feedstock suppliers. As shown in Table 1.5-38, 30 states offer ethanol pr oducers support, 19 states offer R&D companies support, and four states offer f eedstock suppliers support.Table 1.5-38. State Ethanol Production Incentives

State Ethanol Production R&D Feedstocks Grant Tax Credit Grant Tax Credit Grant Tax Credit Arkansas California Colorado Connecticut
F l o r i d a Georgia Hawaii Illinois Indiana
Iowa
Kansas Kentucky Maine Maryland Michigan Minnesota Mississippi Missouri
Montana New Mexico New York North Carolina North Dakota Oklahoma Oregon Pennsylvania South Carolina South Dakota
Texas Vermont Virginia Washington Wyoming X
X

X

X

X
X

X
X

X
X

X

X
X X
X
X

X
X
X
X
X
X

X
X
X
X
X
X
X
X
X
X

X
X

X

X
X X
X
X
X

X
X

X

X

X

X X

X
X

X
X

X

X

X

X

X X

X

X X

The grant and tax credit programs offered to et hanol producers are sim ilar for most of the states that have them. The grants provide funds to help companies cover expenses associated with opening a production facility. These expen ses can include land acquisition, site permitting, and plant construction. They t ypically require the gr ant amount to be matched by the producers’ own funding. The tax credits are given to faci lities after they start production and are based on the number of gallons of ethanol produced at each facility. The incentives offered to R&D companies have similar stipulations as the produc tion incentives. However, the grants are less likely to require the organization doing the research to provide matching funds. States that offer tax breaks to biofuel R&D companies tend to ei ther give tax credits based on the company’s

175research expenses, or give property tax breaks to the lab facilities. Feedstock support is generally in the form of grants given to feedstock processi ng and/or grain hand ling facilities or tax credits issued to feedstock producers and distributors.

Out of the 33 states offering ethanol producti on incentives (shown in Table 1.5-29), only Kansas, Maryland, and South Carolina offer specific incentives towards cellulosic ethanol production or research. Kansas offers revenue bonds through the Kansas Development Finance Authority to help fund constructi on or expansion of a cellulosic ethanol plant. Additionally, these newly-built or expanded facilities are exempt from state property tax for 10 years. Maryland offers a credit towards state income ta x for 10 percent of cellulosic ethanol research and development expenses. They also have a $0.20 per gallon production credit for cellulosic ethanol. South Carolina gives a $0.30 per gallon production credit to cellulosic ethanol producers that meet certain requirements.

In addition to the production incentives described above, a group of states in the Midwest have joined together to pursue ethanol and other biofuel producti on and usage goals as part of the Midwest Energy Security and C limate Stewardship Platform. 419 As of June 2008, Indiana, Iowa, Kansas, Michigan, Minnesota, North Dakota, Ohio, South Dakota, and Wisconsin had all committed to these goals which emphasize energy independence through the growth of cellulosic ethanol production and availability of E85. The Platform goals are to produce cellulosic ethanol on a commercial level by 2012 and to have E85 o ffered at one-third of refueling stations by 2025. They also want to reduce the energy in tensity of ethanol produc tion and supply 50% of their transportation fuel needs by re gionally produced biofuels by 2025.

Finally, the passage of the Food, Conser vation, and Energy Act of 2008 (also known as the “2008 Farm Bill”) is also projected to spur cellulosic ethanol production and use. 420 The 2008 Farm Bill modifies the existing $0.51 per gallon alcohol blender credit to give preference to ethanol and other biofue ls produced from cellulosic feedstoc ks. Starting in 2009, corn ethanol will receive a reduced tax credit of $0.45/gal while cellulosic biofuel will earn a credit of $1.01/gal. EEE The 2008 Farm Bill also has provisions that enable USDA to assist with the commercialization of second-generation biofuels. Section 9003 authorizes loan guarantees for the development, construction and retrofitting of commercial scale biorefineries. Section 9004 provides payments to biorefiner ies to replace fossil fuels with renewable biomass. Section 9005 provides payments to producers to support and en sure production of advanced biofuels. And finally, Section 9008 provides competitive grants, contracts and financial assistance to enable eligible entities to carry out research, developm ent, and demonstration of biofuels and biomassbased based products.

1.5.3.3 Cellulosic Diesel Production/Plans

There has been an emphasis towards ethanol as a renewable fuel which seems to have focused the research and development priorities, as well as the government incentives, towards ethanol as a cellulosic biofuel. This focus is obvious by th e number of U.S. companies developing cellulosic ethanol technologies compared to those developing cellulosic diesel fuel

EEE Refer to Part II, Subparts A and B (Sections 15321 and 15331 ).

176technologies. Ethanol does have a regulatory advantage in that it is already registered to be used in gasoline. Cellulosic diesel fuel, however, must be registered by EPA to be used as a blendstock in highway diesel fuel, or used as its own fuel.

Several issues associated with the future use of ethanol may cause entrepreneurial companies to focus more of th eir research and development efforts towards other cellulosic biofuels such as cellulosic diesel fuel. Recen tly, the greater worldwid e demand for diesel fuel has increased diesel fuel pr ices relative to those of gasoline. This price difference is likely to continue. Worldwide diesel fuel demand is expected to continue to increase faster than gasoline demand. This is true for the U.S. as well because the expected increase in vehicle efficiency will cause gasoline demand by the light-d uty fleet of vehicles here in th e U.S. to diminish relative to that of heavy-duty diesel vehicles. Furtherm ore, the more aggressive pursuit of corn and cellulosic ethanol technologies will tend to furt her expand the gasoline pool even as gasoline demand falls. Looking towards the future, distri buting and blending in mo re ethanol as E85 is challenging in that it would likel y require that ethanol be pri ced substantially lower than gasoline. The potential for a mid-ethanol ble nd waiver, allowing the bl ending and use of higher ethanol blends like E15, could alleviate some of the challenges for using substantial volumes of cellulosic ethanol. However, reaching RFS2 vol umes will ultimately require another solution.

Despite the past emphasis towards ethanol, Congress has recognized that cellulosic diesel fuel be provided the same opportunity as cellulosic ethanol. The very significant $1.01 federal subsidy for developing technologies fo r converting cellulosic feedstocks into fuels also applies to cellulosic diesel fuel. Additionally, DOE and US DA have provided some grant money or loan guarantees to cellulosic diesel fuel technologies as summari zed above in Table 1.5-35.

In Table 1.5-39 we provide a list of the two companies which are commercially producing cellulosic diesel fuel , or conducting development wo rk on cellulosic diesel fuel technologies through pilot plant operations. Desp ite the focus of most government research and support for ethanol, the company which appears to be the furthest along among all the cellulosic biofuel companies is Cello Energy which has started up a 20 million gallon per year demonstration diesel fuel production plant, the only commercial scale cellulosic biofuel plant in the U.S. In addition, Bell Bio-Energy is aggr essively developing its diesel fuel technology through the eventual establishment of 7 pilot plants in different Army bases across the country.

177Table 1.5-39. Current Cellulosic Diesel Plants

Company/Plant Name Location Feedstocks Prod Cap (MGY) Est Op Date Conv Tech Cello Energy Bell BioEnergy Bay Minette, AL Fort Stewart, GA Wood Chips, Hay Wood Chips 20 0.01 2008 2008 Cat Depoly Biochm

As mentioned above, Bell Bio-Energy is committed to constructing 7 pilot plants in 7 different Army bases across the country. Also, Bell Bio-Energy is projecting that they will be able to construct and start up a 150 million gall on per year cellulosic diesel plant sometime in 2011. There are several other comp anies planning on building cellulo sic diesel fuel plants in the U.S. Flambeau River Biofuels, Baard Energy and New Page/Chemrec are all biomass to liquids technologies which primarily produce diesel fuel through the thermochemical/Fischer Tropsch route. The earliest of these is Flambeau Rive r Biofuels which is plan ning on starting up their plant in 2010. The Baard Energy plant is primar ily a clean coal plant which will cofire biomass with the coal.

Table 1.5-40. Under Construction & Planned Cellulosic Diesel Plants

Company/Plant Name Location Feedstocks Prod Cap (MGY) Est Op Date Conv Tech Cello Energy TBD (AL) Forest Residue Catalytic

Cello Energy TBD (AL) and Hay
Forest Residue 50 2010 Depoly Catalytic

Cello Energy TBD (GA) and Hay Forest Residue 50 2010 Depoly Catalytic and Hay 50 2010 Depoly Bell Bio-Energy Fort Lewis, WA Cellulose 0.01 2009 Bio Bell Bio-Energy Fort Drum, NY Cellulose 0.01 2009 Bio Bell Bio-Energy Fort AP Hill, VA Cellulose 0.01 2009 Bio Bell Bio-Energy Fort Bragg, NC Cellulose 0.01 2009 Bio Bell Bio-Energy Fort Benning, GA Cellulose 0.01 2009 Bio Bell Bio-Energy San Pedro, CA Cellulose 0.01 2009 Bio Bell Bio-Energy ? Cellulose 150 2011 Bio Flambeau River Biofuels Park Falls, WI Forest Residue 6.00 2010 Therm-FT Baard Energy Wellsville, OH Forest and Ag Residue; cofired with coal 17 2013 Therm-FT

New Page/Chemrec Rapids, WI Forest Residue 5.50 2012 Therm-FT

For a number of technologies summarized in Table 1.5-41, there are no immediate plans for producing cellulosic hydrocarbon biofuels in the U.S., howev er, they are strong candidates for eventual production in the U.S. Choren h as a pilot plant in Germany and is planning to

178construct a demonstration plant in Germany in 2011 or 2012. They did receive a commitment for a loan from DOE for building a plant in the U.S., so they ha ve already have a commitment for at least some funding for a plant in the U.S. Also, Choren has develo ped a presence here in the U.S. UOP/ENI is building a pyrolysis plant in Italy in 2010. Since announcing that plant, UOP has formed a partnership with Ensyn which h as been using pyrolysis to make a variety of food additives and other chemicals in Canada and he re in the U.S. for many years. The pyrolysis technology being developed by UOP tends to produce more naphtha than diesel fuel. Logos Technologies received almost $20 million as a grant from the Department of Defense to develop aviation fuels from cellulose. Logos is pooling the expertise and experi ence of 12 organizations, half of them are universities a nd the other half are private comp anies, to derive low cost and highly productive technologies which could include the thermochemical and biochemical pathways. Swift Fuels has developed a techno logy for producing a high oc tane aviation gasoline from cellulose which would avoid the use of lead as an additive. The next several companies have developed technologies for converting sugar/starches to various hydrocarbon fuels. Virent is a U.S. based company which is reforming sugars to produce hydrocarbon based fuels. Amyris, Gevo and LS9 are all companies which are developing biochemical technologies for converting sugars/starch to hydroc arbons. For these companies to make the step to using cellulosic feedstocks, th ey would either have to devel op the technology of converting the cellulose to sugars, or partner wi th other companies which have al ready mastered that step.

Table 1.5-41. Propective Cellulosic Hydrocarbo n Technologies in the U.S. Company/Plant Name Feedstocks Conv Tech Choren UOP/ENI and Ensyn Logos Technologies Swift Fuels Virent Amyris Gevo/Cargil and Bye Energy LS9 Wood Chips and Ag residue Celllulose Cellulose Cellulose Sugars/Cellulose Sugars Sugars Sugars Therm-FT Pyrolysis Various Biochem Reforming Biochem Biochem Biochem

1.5.3.4 Meeting the Proposed 2010 Cellulosic Biofuel Standard

As described in Section III.E.b of the Pr eamble, not only does this notice of proposed rulemaking (NPRM) lay out the proposed biof uel standards through 2022, it sets the biofuel standards applicable in 2010. In accordance with EISA and the regulations set forth in this NPRM, the nation is required to blend 100 millio n gallons of cellulosic biofuel into U.S. transportation fuels by 2010. We believe our nation’ s vehicles and distribu tion infrastructure are more than capable of accommodating this volume of cellulosic biofuel. Accordingly, we have focused our feasibility assessmen t on production feasibility.

Building off our April 2009 cellulosic biofue l industry characterizations presented above in Sections 1.5.3.1 and 1.5.3.3, we considered existing, under construc tion, and planned nearterm plants with the po tential to produce cellulosic ethanol or cellulosic diesel fuel in 2010. All

179together, we came up with 24 pilot- or demonstra tion-level plants and 7 commercial-level plants that we believe would allow us to meet the 100 million gallon cellulosi c biofuel standard in 2010. For plants scheduled to come on part wa y through the year, we ass umed only a percentage of the total production capacity would count to wards meeting the RFS2 standard. Still, we estimated that just over 100 million gallons of ce llulosic biofuel could be produced and applied towards meeting the RFS2 standard in 2010. A nd since the majority (a bout 72%) of the volume is projected to come from ce llulosic diesel fuel, the ethanol-equivalent compliance volume would be closed to 145 million gallons. For a su mmary of the plants that we believe could enable our nation to meet the 100 million gallon cellu lsic biofuel standard in 2010, refer to Table 1.5-42.

180Table 1.5-42. Projected Cellulosic Biofuel Production Capacity in 2010

Company or Organization Name Location Prod Cap (MGY) Est. Op. Date Est. 2010 Million Gallons Est 2010ETOHEquiv. Million Gallons
Cellulosic Ethanol
BPI & Universal Entech Phoenix, AZ 0.01 Online 0.01 0.01 POET Project Bell Scotland, SD 0.02 Online 0.02 0.02 Abengoa Bioenergy Corporation York, NE 0.02 Online 0.02 0.02 Bioengineering Resources, Inc. (BRI) Fayetteville, AK 0.04 Online 0.04 0.04
Verenium Jennings, LA 0.05 Online 0.05 0.05 Gulf Coast Energy Livingston, AL 0.20 Online 0.20 0.20 Mascoma Corporation Rome, NY 0.20 Online 0.20 0.20
Verenium Jennings, LA 1.50 Online 1.50 1.50 Western Biomass Energy, LLC. (WBE) Upton, WY 1.50 Online 1.50 1.50
Coskata Madison, PA 0.04 Jul-09 0.04 0.04 DuPont Dansico Cellulosic Ethanol (DDCE) Vonore, TN 0.25 Dec-09 0.25 0.25 Range Fuels Soperton, GA 10.00 Dec-09 10.00 10.00
Ecofin / Alltech Springfield, KY 1.30 2010 0.65 0.65 Fulcrum Bioenergy Storey County, NV 10.50 2010 5.25 5.25
ICM Inc. St. Joseph, MO 1.50 2010 0.75 0.75 RSE Pulp & Chemical Old Town, ME 2.20 2010 1.10 1.10
ZeaChem Boardman, OR 1.50 2010 0.75 0.75 ClearFuels Technology Kauai, HI 1.50 End of 2010 0.38 0.38
Southeast Renewable Fuels LLC Clewiston, FL 20.00 End of 2010 5.00 5.00
Cellulosic Diesel Cello Energy Bay Minette, AL 20.00 Online 20.00 32.00 Bell Bio-Energy Fort Stewart, GA 0.01 2008 0.01 0.01 Bell Bio-Energy Fort Lewis, WA 0.01 2009 0.01 0.01 Bell Bio-Energy Fort Drum, NY 0.01 2009 0.01 0.01 Bell Bio-Energy Fort AP Hill, VA 0.01 2009 0.01 0.01 Bell Bio-Energy Fort Bragg, NC 0.01 2009 0.01 0.01 Bell Bio-Energy Fort Benning, GA 0.01 2009 0.01 0.01 Bell Bio-Energy San Pedro, CA 0.01 2009 0.01 0.01 Cello Energy TBD (AL) 50.00 2010 16.67 26.67 Cello Energy TBD (AL) 50.00 2010 16.67 26.67 Cello Energy TBD (GA) 50.00 2010 16.67 26.67
Flambeau River Biofuels Park Falls, WI 6.00 2010 3.00 4.80
100.74 144.57 Total 2010 Production Forecast

It is possible that some of the plants highlighted below may experience construction delays and not come online as soon as currently projec ted. It is also possible that a portion of the biofuel produced at th ese facilities may not me et the cellulosic biofuel definition based on the feedstocks and/or the fuel’s GHG reduction po tential (refer to §80. 1401). However, on the contrary, it is possible that othe r cellulosic biofuel plants could come online in this timeframe - especially if construction were to begin soon. Leading corn ethanol producers Archer Daniels

181Midland and POET Biorefining ar e both pursuing cellulosic ethanol production. ADM is currently investigati ng cellulosic ethanol pr oduction through a cooperativ e research agreement with Purdue University. The ADM-Purdue proj ect is focused on comme rcializing the use of highly-efficient yeast to conve rts cellulosic materials into ethanol through fermentation. 421 In addition, ADM is also collaborating with Deere & Company and Monsanto Company on research that explores economically-sustainable me thods for the harvest, storage and transport of corn stover for the production of animal feed, process energy, and/or cellulosic biofuel. 422 Depending on discoveries made during the resear ch, it is possible th at ADM could begin processing small amounts of corn stover and pr oducing an appreciable volume of cellulosic biofuel at some of its existing U.S. ethanol plants in this timeframe. In addition, POET recently opened a 20,000 gallon-per-year demonstration-level cellulosic ethanol plant at its Scotland, SD research center (shown above in Table 1.5-39). It has plans to bring a 25 million-gallon-per-year plant online in Emmetsburg, IA in 2011. Howe ver, POET recently announced that it plans to produce cellulosic ethanol at six more of its exis ting corn ethanol plants in Iowa in the future. 423 Depending on how aggressive they are with pursu ing these cellulosic ethanol production plans, POET could further contribute to meeting the 2010 cellulosic biofuel standard. In addition, a number of companies are currently building or operating demonstration- level cellulosic biofuel plants overseas. It is possible that these compan ies could export their production or decide to build a commercial plant in the United States in response to the proposed RFS2 standards. Finally, there could also be a num ber of start-up companies that we are unaware of that could contribute to meeting the 2010 cellulosi c biofuel standard.

1.5.3.5 Projected Cellulosic Feed stocks and Facilities Under RFS2

As noted in Section 1.5.3.1, cellulosic ethanol production capacity needs to expand greatly in order to meet the cellulosic biof uel mandate of 16 billion gallons by 2022. While current production plans provide an initial idea of the types of feedstocks and potential plant locations that are being considered by biof uel producers, future production will be highly dependent on acquiring relative ly cost-effective feedstocks in sufficient quantities.

A wide variety of feedstocks can be u sed for cellulosic ethanol production, including: agricultural residues, forestry biomass, municipal solid waste, construction and demolition waste, and energy crops. These feedstocks are much mo re difficult to convert into ethanol than traditional starch/corn crops or at least require new and different processes because of the more complex structure of cellulosic material.

At least initially, the focus will be on feedstocks that are readily available, already produced or collected for other reasons, and even waste biomass which currently incurs a disposal fee. Consequently, initial volumes of cellulosic biofuels may benefit from low-cost feedstocks. However, to reach 16 Bgal will lik ely require reliance on more expensive feedstock sources purposely grown and or harvested fo r conversion into cellulosic biofuel.

To determine the likely cellulosic feed stocks for production of 16 billion gallons cellulosic biofuel by 2022, we analyzed the data and results from various sources. Sources include agricultural modeling from the Forest ry Agriculture Sector Optimization Model (FASOM) to establish the most economical agricu lture residues and energy crops (see Section

1825.1 for more details on the FASOM), consulta tion with USDA-Forestry Sector experts for forestry biomass supply curves, and feed stock assessment estimates for urban waste. FFF

An important assumption in our analysis proj ecting which feedstocks will be used for producing cellulosic ethanol is th at an excess of feedstock woul d have to be available for producing the biofuel. Banks are anticipated to require excess feedstock supply as a safety factor to ensure that the plant will have adequate feedstock available for the plant, despite any feedstock emergency, such as a fire, drought, inf estation of pests etc. For our analysis we assumed that twice the feedstock of MSW, C&D waste, and forest residue would have to be available to justify the building of a cellulosic ethanol plant. For corn stover, we assumed 50 percent more feedstock than necessary. We used a lower safety factor for corn stover because it could be possible to remove a la rger percentage of the corn st over in any year (usually only 50 percent or less of corn stover is assumed to be sustainably removed in any one year). GGG

Another assumption that we made is that if multiple feedstoc ks are available in an area, each would be used as feedstocks for a prospec tive cellulosic ethanol plant. For example, a particular area might comprise a small or medium sized city, some forest and some agricultural land. We would include the MSW and C&D wastes available from the city along with the corn stover and forest residue for proj ecting the feedstock that would be processed by the particular cellulosic ethanol plant.

Previously, Section 1.1 descr ibed the total potential availa bility of various cellulosic feedstocks. Due to EISA, a substantial amount of the available feedstocks will need to be used for biofuels production. This section discusses the estimated am ounts from each feedstock type needed to meet the EISA requirement of 16 Bgal of cellulosic biofuel by 2022 and also explains our projections for the locations of potential cellulosic biofuel faci lities based on feedstock availability.

Urban Waste

Cellulosic feedstocks available at the lowest cost to the ethanol producer will likely be chosen first. This suggests that urban waste which is already being ga thered today and which incurs a fee for its disposal ma y be among the first to be used. Urban wood wastes are used in a variety of ways. Most commonly, wastes are ground into mulch, dumped into land-fills, or incinerated with other municipal solid wast e (MSW) or construction and demolition (C&D) debris. Urban wood wastes include a variety of wood resources such as wood-based municipal solid waste (MSW) and wood debris from construction and demolition.

Urban waste (MSW wood, paper, and C&D de bris) was estimated to be located near large population centers. We estimated a total of 42 million dry tons of MSW (paper, wood, and

FFF It is important to note that our plant siting analysis for cellulosic ethanol facilities used the most current version of outputs from FASOM at the time, which was from April 2008. Since then, FASOM has been updated to reflect better assumptions. Therefore, the version used for the NPRM in Section 5.1 is slightly different than the one we used here. We do not believe that the differences between the two versions are enough to have a major impact on the plant siting analysis.
GGG The FASOM results do not take into consideration these feedstock safety margins. Safety margins were used, however, for the plant siting analysis described in this section.

183yard trimmings) and C&D wood waste could be available nationwide for producing biofuels after factoring in the various assumptions on percent landfilled, per cent moisture, percent contaminated, etc. as described in Section 1.1.2. 3 on feedstock availability. We further assumed that approximately 25 million dr y tons (of the total 42 billion dry tons) woul d be used, however, since many areas of the U.S. (e.g. much of the Rocky Mountain states) would not have a cellulosic facility in close enough proximity.

We arrived at the 25 million dry tons with the following an alyses. First, using the MSW and C&D wood waste estimates by state we calcul ated the tons of MSW and C&D wood waste material generated per person per state. We used the estimate of MSW and C&D wood waste material generated per person per state (i.e. t ons/person) along with data on the population sizes of the largest cities within the state to allocate the total waste material in a state to specific cities.

Much of the materials are already being co llected and may be avai lable to a potential ethanol producer at negligible costs. For instance, some additional sorting or size reduction may increase costs, however, tipping f ees (fees for materials that are di scarded at landf ills) can also be avoided which would decrease the potential cost the feedstoc k. Chapter 4 of this DRIA further discusses feedstock costs. Assuming that the majority of this waste is of negligible cost to a potential ethanol producer, we calculated a minimum size for a cellulosic plant dedicated to MSW and C&D wood waste for various locations in the U.S. Sizes ranged from 9-60 million gallons per year.

First we did not consider tho se cities, while small, may still be able to justify a cellulosic ethanol plant because some other source of biom ass is also available th at combined with the MSW and C&D wood waste can supply the cellulosi c ethanol plant with sufficient feedstock. However, where non-MSW and C&D wood waste feed stocks are not available, we needed to estimate what the minimum plant size would be that would be competitive with other cellulosic ethanol plants.

We conducted this analysis early on before NREL provided us with the cost information for a biochemical cellulosic ethanol plant. In stead we used a representation made by NREL in 2007 for of a thermochemical ethanol plant. Usi ng that cellulosic plant model we estimated the production cost for a 100 million gallon per year thermochemical plant which processed a cellulosic feedstock. We conducted this analysis in different part s of the country using different capital cost factors that account for how capital costs vary in differe nt parts of the country. The different regions were Petroleum Administrati on for Defense Districts (PADDs) for which we have plant installation costs. In each part of the country, we estimated the cost of the ethanol produced processing the cellulosic f eedstock assuming that the feedstock cost about $70 per dry ton. Next, we set the feedstock costs to zero cost in our cost sp readsheet and determined at what plant size, when scaling the capital costs as the plan t size became smaller, the resulting cellulosic production costs matched those of the non-MSW a nd C&D wood waste plants. See Table 1.5-43.

184Table 1.5-43 Breakeven Plant Size for MSW and C&D Wood Waste Cellulosic Ethanol Plants

PADD 1 PADD 2 PADD 3 PADD 4 PADD 5 CA

Ethanol Production Cost (c/gal) 1.33 1.24 1.10 1.29 1.19 1.57

Breakeven Plant Size (million gals/yr) 28 19 9 23 15 60

We then identified the citi es that had large enough MSW a nd C&D wood waste to justify a dedicated cellulosic faci lity. By dedicated cellulosic fa cility, we mean that only MSW and C&D wood waste is used as a feedstock, as oppo sed to a facility that has multiple mixed feedstocks. Nineteen facilities were identified to meet such criteria, as shown in Table 1.5-44. The total contribution from dedi cated cellulosic MSW and C&D wood waste is approximately 640 million gallons. Table 1.5-44. Projected Dedicated Cellulosic MSW and C&D Wood Waste Facilities by Location and Size for 2022 State County PADD City Size of Facility (Mgal)

1 Alabama Jefferson Birmington 3 11 2 Arizona Maricopa Phoenix 5 20 3 California Los Angeles Los Angeles 5 56 4 California Riverside Riverside 5 24 5 California San Franci sco San Francisco 5 17 6 Colorado Adams Denver 4 28 7 Florida Miami Fort Lauderdale 1 31 8 Georgia Cobb Atlanta 1 43 9 Illinois Cook Chicago 2 79 10 Michigan Oakland Detroit 2 33 11 Nevada Clark Las Vegas 5 17 12 New York New York City New York 1 72 13 Oregon Clackamas Portland 4 15 14 Pennsylvania Philadelphia Philadelphia 1 42 15 Texas Dallas Dallas 3 52 16 Texas Fort Bend Houston 3 49 17 Texas Bexar San Antonio 3 16 18 Texas Travis Austin 3 14 19 Washington King Seattle 5 17

We did assume that in areas with other ce llulosic feedstocks (f orest and agricultural residue), that the MSW would be used even if the MSW could not justify the installation of a plant on its own. MSW used in mi xed feedstock plants is shown in Table 1.5-36. Therefore, we have estimated that urban waste could help c ontribute to the production of approximately 2.2

185billion gallons of ethanol. HHH Subsequent to initiating our an alysis, however, we realized that the the renewable biomass definition in the stat ute may preclude the use of most MSW. See Section III.B.1 of the Preamble for a discussion of renewable biomass. When the definition of renewable biomass is finalized, it could preclude the use of waste paper and C&D waste for use in producing cellulosic biofuel for use toward the RFS2 standard. If this is the case, our FRM analysis will be adjusted to reflect this.

Agricultural and Forestry Residues

The next category of feedstocks chosen will likely be those that are readily produced but have not yet been commercially collected. This in cludes both agricultu ral and forestry residues.

Agricultural residues are exp ected to play an important ro le early on in the development of the cellulosic ethanol industry du e to the fact that they are al ready being grown. Agricultural crop residues are biomass that remains in the field after the harvest of agricultural crops. The most common residue types include corn stover (the stalks, leaves, and/or cobs), straw from wheat, rice, barley, or oats, and bagasse from su garcane. The eight leading U.S. crops produce more than 500 million tons of residues each year , although only a fraction can be used for fuel and or energy production due to sustainability and conservation constraints. 424 Crop residues can be found all over the United States, but are prim arily concentrated in the Midwest since corn stover accounts for half of all available agricultural residues.

Agricultural residues play an important role in maintaining and improving soil tilth, protecting the soil surface from wa ter and wind erosion, and helping to maintain nutrient levels. Thus, collection and removal of ag ricultural residues must take into account concerns about the potential for increased erosion, reduced crop pr oductivity, depletion of soil carbon and nutrients, and water pollution. Sustainable removal rates for agricultural r esidues have been estimated in various studies, many showing tremendous variabili ty due to local differences in soil and erosion conditions, soil type, landscape (slope), tillage pr actices, crop rotation managements, and the use of cover crops. These are described in more detail in Section 1.1. One of the most recent studies by top experts in the field showed that under cu rrent rotation and tillage practices, ~30% of stover (about 59 million metric tonnes) produced in the US could be collected, taking into consideration erosion, soil moisture concerns, and nutrient replacement costs. The same study showed that if farmers chose to convert to no -till corn management and total stover production did not change, then ~50% of stover (100 millio n metric tonnes) could be collected without causing erosion to exceed the tolerable soil loss. 425 This study, however, did not consider possible soil carbon loss which other studies indicate may be a greater constraint to environmentally sustainable feedstock harvest than that needed to control water and wind erosion. 426 Experts agree that additio nal studies are needed to fu rther evaluate how soil carbon and other factors affect sustainable removal rate s. Despite unclear guidelines for sustainable removal rates due to the uncer tainties explained above, our agricultural mode ling analysis assumes that 0% of stover is removable for conven tional tilled lands, 35% of stover is removable for conservation tilled lands, and 50% is removabl e for no-till lands. In general, these removal guidelines are appropriate only for the Midwest, where the majority of corn is currently grown.

HHH Assuming approximately 90 gal/dry ton ethanol conversion yield

186As already noted, removal rates will vary w ithin regions due to local differences. Given the current understanding of sustainable removal rates, we believe that such assumptions are reasonably justified. We invite comment on th ese assumptions. Based on our research we also note that residue maintenance requirements for the amount of biomass that must remain on the land to ensure soil quality is another appro ach for modeling sustaina ble residue collection quantities, therefore we also in vite comment on this approach. This approach would likely be more accurate for all landscapes as site specific conditions such as soil type, topography, etc. could be taken into account. This would prevent site specific soil erosion and soil quality concerns that would inevitably exist when using average values for residue removal rates across all soils and landscapes. At the time of our an alyses we had limited data on which to accurately apply this approach and therefore assumed th e removal guidelines based on tillage practices. Refer to the Section 1.1 of this DRIA for mo re discussion on sustainable removal rates.

Our agricultural modeling (FASOM) suggests that corn stover will make up the majority of agricultural residues used by 2022 to m eet the EISA cellulosic biofuel standard (approximately 83 million dry tons used to produce 7.8 billion gallons of cellulosic ethanol). III Smaller contributions are expected to come from bagasse, which is a by-product from the production of sugarcane, (1.2 bga l ethanol) and sweet sorghum pulp (0.1 bgal ethanol). At the time of this proposal, FASOM was able to model agricultural residues but not forestry biomass as potential feedstocks. As a resu lt, we relied on USDA-Forest Service (FS) for information on the forestry sector.

Using the assumptions from FASOM on residue and ethanol yields, we determined if it is possible to site potential cellulosi c plants based on the acres curre ntly harvested. We identified that there are enough harvested acr es to produce 7.8 Bgal of et hanol from corn stover by 2022 without having to rely on new lands . Therefore, the siting of many of the cellulosic facilities will likely be located where corn is typically grown today. See Table 1.5-45 for a summary of the states producing corn stover, and their projected volume contribution to meeting the EISA cellulosic requirement by 2022.

III Assuming 94 gal/dry ton ethanol conversion yield for corn stover in 2022

187188 Table 1.5-45. Projected Ethanol Produced to Me et EISA in 2022 from Corn Stover JJJ

Total Sustainably Sustainably Harvested Total Residue Total Residue Removable Residue Removable

Acres Yield Available Yield Residue Available Residue Used State (in 2022) (tons/acre) (Million tons) (tons/acre) (Million tons) (Million tons) Total 71,784,020 n/a 380 n/a 122 82 Illinois 12,994,100 5.43 71 1.62 21 15 Indiana 6,209,463 5.58 35 2.09 13 10 Iowa 14,482,313 5.47 79 1.59 23 17 Kansas 3,026,615 5.33 16 1.65 5 3 Kentucky 1,473,023 5.08 7 2.04 3 1 Michigan 2,238,321 4.30 10 1.79 4 3 Minnesota 7,509,658 5.37 40 1.60 12 8 Missouri 2,732,875 4.73 13 2.93 8 5 Nebraska 10,135,162 5.88 60 1.28 13 9 Ohio 3,712,612 4.91 18 1.89 7 5 South Dakota 4,268,425 4.01 17 1.41 6 4 Wisconsin 3,001,454 4.74 14 2.33 7 5

JJJ Corn stover is given in dry tons/acre and assumes an ethanol yield of 94 gal/dry ton; Sustainably removable residue yields assumes only the fraction that can be removed after accounting for sustainable removal rates based on tillage practices. This table gives approximate averages by state based on our agricultural modeling, actual yields will vary greatly depending on specific soil type, slope, etc. Also, the values above are calculated using the FASOM data outputs from April 2008 and thus are slightly different from those found in Section 5.1 of this DRIA which use more updated runs.189 Sugarcane, on the other hand, is grown mainly in Florida, Louisiana, and Texas, although plans are underway to also grow sugarcane in California as well. See Section 1.1.1.2 of this DRIA for more discussion on sugarc ane ethanol produced in the U.S. If all the sugarcane acres harvested today in the U.S. were used to produce ethanol from th e bagasse, using the assumptions from FASOM on residue and etha nol yields, only approximately 700 million gallons could be produced, see Table 1.5-46. FA SOM, however, predicts that the production of 1.2 billion gallons of ethanol could be economi cally feasible from sugarcane bagasse. This means that between now and 2022, more suga rcane may be grown, allowing for more availability of bagasse in the future.

Table 1.5-46. Projected Ethanol Produced to Meet EISA in 2022 from Sugarcane Bagasse

Total Ba g asse Ethanol Total Harvested Yield Residue Used Produced State Acres (in 2007) (tons/acre) (Million tons) (Million gallons)
Total 810,800 n/a 10 707 Florida 382,000 14.71 6 389 Louisiana 389,600 10.25 4 277 Texas 39,200 15.23 1 41

Using FASOM, we analyzed the types of land likely to be supplanted by additional sugarcane acres in 2022 in the stat es of Florida, Louisiana, and Texas. In Florida, sugarcane crops appear to replace mainly corn, soy, and hay acres. In Louisiana, s ugarcane crops appear to have replaced mainly corn, soy, wheat, sorghum , and hay acres. In Texas, sugarcane crops appear to have replaced mainly soy and sor ghum crops. For these three states we gathered available data on corn, soy, wheat, and sorghum acres currently harvested by county (data on hay acres were unavailable and appeared to show small changes compared to corn and soy). 427 We then identified the top counties (i n terms of acres available) in cl ose proximity to each other that could potentially be converted from corn to sugarcane crops, soy to sugarcane, wheat to sugarcane, etc in order to produce enough ethanol for half a billion gallons.

Sweet sorghum pulp is predicted to be used to produce approximately 0.1 billion gallons of ethanol. According to the National Agricu lture and Statistics Service (NASS) of the Department of Agriculture, there is not curren t available data on sweet sorghum acres grown in the United States. Therefore, we used FASOM to predict the types of crops that sweet sorghum is mainly replacing, which is corn and soybeans. Similar to the analysis done for sugarcane, we identified the top counties (in terms of acres availa ble) in close proximity to each other that could potentially be converted from corn to sweet so rghum crops and soy to sweet sorghum crops in order to produce enough ethanol for 0.1 billion gallons.

The U.S. has vast amounts of forest resources that could po tentially provide feedstock for the production of cellulosic biof uel. One of the major sources of woody biomass could come from logging residues. The U.S. timber industr y harvests over 235 million dry tons annually and produces large volumes of non-merchantable wood and residues during the process. 428 Loggingresidues are produced in conven tional harvest operations, forest management activities, and clearing operations. In 2004, these operations generated approximately 67 million dry tons/year of forest residues that were le ft uncollected at harvest sites. 429 Other feedstocks include those from other removal residues, thinnings from timberland, and primary mill residues.

Harvesting of forestry residue and other woody material can be conducted throughout the year. Thus, unlike agricultural residue which must be moved to secondary storage, forest material could be “stored on the stump.” A voiding the need for secondary storage and the transportation costs for moving the feedstock there potentially provid es a significant cost advantage for forest residue over agricultural re sidue. This could allow forest residue to be transported from further distances away from the cellulosic plant compared to agricultural residue at the same feedstock price. Section 1.1.2.2 further detail s some of challenges with using forestry biomass as a feedstock.

EISA does not allow forestry material from national forests and virgin forests that could be used to produce biofuels to count toward s the renewable fuels requirement under EISA. Therefore, we required forestry residue estimates that excluded su ch material. Most recently, the USDA-FS provided forestry biomass supply curv es for various sources (i .e., logging residues, other removal residues, thinnings from timberland, etc.). This information suggested that a total of 76 million dry tons of forest material coul d be available for produc ing biofuels (excluding forest biomass material containe d in national forests as require d under the Act). See Section 1.1.2.2 for more information on forest residue feedstock availability. However, much of the forest material is in small pockets of forest which because of its regiona l low density, could not help to justify the establishmen t of a cellulosic ethanol plant. After conduc ting our availability analysis, we estimated that approximately 44 milli on dry tons of forest ma terial could be used, which would make up approximately one fourth, or 3.8 billion gallons, of the 16 billion gallons of cellulosic biofuel required to meet EISA.

Dedicated Energy Crops

While urban waste, agricultural residues, and forest residues will likely be the first feedstocks used in the production of cellulosic biofuel, there may be limitations to their use due to land availability, sustainabl e removal rates, etc. Energy crops which are not yet grown commercially but have the potential for high yields and a series of environmental benefits could help provide additional feedstocks in the future . Dedicated energy crops are plant species grown specifically as renewable fuel feedstocks. Vari ous perennial plants have been researched as potential dedicated feedstocks. These include switchgrass, mixed prairi e grasses, hybrid poplar, miscanthus, and willow trees.

In addition to estimating the extent that agricultural residues might contribute to cellulosic ethanol production, FASOM also esti mated the contribution th at energy crops might provide. KKK FASOM covers all cropland and pasturel and in production in the 48 conterminous United States, however it does not contain all categories of gra ssland and rangeland captured in USDA’s Major Land Use data sets. Therefore, it is possible there is land appropriate for

KKK Assuming 16 Bgal cellulosic biofuel total, 2.2 Bgal from Urban Waste, and 3.8 Bgal from Forestry Biomass; 10 Bgal of cellulosic biofuel fo r ag residues and/or ener gy crops would be needed.

190growing dedicated energy crops that is not currently modeled in FASOM. Furthermore, we constrained FASOM to be consistent with the 2008 Farm Bill and assumed 32 million acres would stay in CRP. LLL These constraints on land availability may have contributed to the model choosing a substantial amount of agricultural residues mostly as corn stover and a relatively small portion of energy crops as being economically viable feedstocks. The use of other models, such as USDA’s Regional Environment and Ag riculture Programming (REAP) model and University of Tennessee’s POLYSYS model, have shown that the u se of energy crops in order to meet EISA may be more significant than our curr ent FASOM modeling results. MMM As such, we plan to revisit these land availability assumptions in order to arrive at a more consistent basis for the FRM.

Given the constraints outlin ed above, FASOM projects th at 0.9 billion gallons of cellulosic ethanol from switchgrass is econom ically feasible by 2022. The majority of switchgrass is projected to likely be grown in Oklahoma, where the majority of acres are replacing wheat and hay. A sma ller portion is expected to come from West Virginia and New Hampshire where hay is mainly replaced. Simila r to the analysis done for sugarcane and sweet sorghum, we identified the top counties (in terms of acres available) in close proximity to each other that could potentially be converted from wheat to switchg rass or hay to switchgrass in order to produce enough etha nol for 0.9 billion gallons.

Summary of Cellulosic Feedstocks for 2022

Table 1.5-47 summarizes our internal estimate of cellulosic feedstocks and their corresponding volume contributi on to 16 billion gallons cellu losic biofuel by 2022 for the purposes of our impacts assessment.

LLL Beside the economic incentive of a farmer payment to ke ep land in CRP, local environmental interests may also fight to maintain CRP land for wildlife preservation. Also, we did not know what portion of the CRP is wetlands which likely could not support harvesting equipment.
MMM Biomass Research and Development Initiative (BR&DI ), “Increasing Feedstock Production for Biofuels: Economic Drivers, Environmental Implications, and th e Role of Research,” http://www.brdisolutions.com, December 2008.

191Table 1.5-47. Cellulosic Feedstocks Assume d to Meet EISA in 2022 Feedstock Volume (Bgal) Agricultural Residues 9.1 Corn Stover 7.8 Bagasse NNN 1.2 Sweet Sorghum Pulp 0.1 Forestry Biomass 3.8 Urban Waste 2.2 Dedicated Energy Crops (Switchgrass) 0.9

Total 16.0

Cellulosic Plant Siting

Future cellulosic biofuel plan t siting was based on the types of feedstocks that would be most economical as shown in Table 1.5-35, above. As cellulosic bi ofuel refineries will likely be located close to biomass resources in order to take advantage of lower transportation costs, we’ve assessed the potential areas in the U.S. that grow the various feedstocks chosen. To do this, we used data on harvested acres by county for crops that are currently grown today, such as corn stover and sugarcane (for bagasse). 430 In some cases, crops are not currently grown, but have the potential to replace othe r crops or pastureland (e.g. dedi cated energy crops). We used the output from our economic modeling (FASOM) to help us determine which types of land are likely to be replaced by newly grown crops. For forestry biomass, USDA-Forestry service provided supply curve data by county showing th e available tons produced. Urban waste (MSW wood, paper, and C&D debris) was estimated to be located near la rge population centers.

Our analyses also take into account the loca tions of planned cellulosic facilities as well as any corn facilities or pulp and paper mills when we project where cellulosic plants are located into the future. While not all planned cellulosi c facilities will likely co me to fruition, it was important to look at the locations of these facilities as their locations are li kely to be chosen for good reasons (i.e. close to resourc es, infrastructure in place, etc. ). We analyzed current corn facilities and pulp and paper mill sites as well since th ey are likely to be close to their respective feedstocks (i.e. corn stover and wood residues) and could have many synergies with cellulosic biofuel production, such as shar ed steam and electricity produc tion. However, this does not mean that we have chosen to place cellulosic fac ilities at all the locations where there are current corn facilities and pulp and paper mills. The locations are only used to help select areas that could be preferential towards building a cellulosic facility

Using feedstock availability data by county/ city, we located potential cellulosic sites across the U.S. that could justify the construc tion of a cellulosic plant facility. Table 1.5-48 shows the volume of cellulosic facilities by feedstock by state projected for 2022. The total

NNN Bagasse is a byproduct of sugarcane crushing and not technically an agricultural residue. Sweet sorghum pulp is also a byproduct of sweet sorghum processing. We have included it under this heading for simplification due to sugarcane being an agricultural feedstock.

192volumes given in Table 1.5-35 match the total vo lumes given in Table 1.5-36 within a couple hundred million gallons. As these differences are relatively small, we believe the cellulosic facilities sited are a good estimate of potential locations.

Table 1.5-48. Projected Cellulosic Ethanol Volumes by State (million gallons in 2022)

State Total Volume Ag Volume Energy Crop Volume Urban Waste Volume Forestry Volume Alabama 532 0 0 140 392 Arkansas 298 0 0 0 298 California 450 0 0 221 229 Colorado 28 0 0 28 0 Florida 421 390 0 31 0 Georgia 437 0 0 67 370 Illinois 1,525 1,270 0 198 58 Indiana 1,109 948 0 101 60 Iowa 1,697 1,635 0 32 30 Kansas 310 250 0 29 32 Kentucky 70 70 0 0 0 Louisiana 1,001 590 0 103 308 Maine 191 0 0 2 189 Michigan 505 283 0 171 51 Minnesota 876 750 0 50 76 Mississippi 214 0 0 22 192 Missouri 654 504 0 78 72 Montana 92 0 0 9 83 Nebraska 956 851 0 31 75 Nevada 17 0 0 17 0 New Hampshire 171 0 35 29 107 New York 72 0 0 72 0 North Carolina 315 0 0 98 217 Ohio 598 410 0 156 32 Oklahoma 793 0 777 0 16 Oregon 244 0 0 44 200 Pennsylvania 42 0 0 42 0 South Carolina 213 0 0 57 156 South Dakota 434 350 0 6 78 Tennessee 97 0 0 19 78 Texas 576 300 0 131 145 Virginia 197 0 0 95 102 Washington 175 0 0 17 158 West Virginia 149 0 101 0 48 Wisconsin 581 432 0 43 106
Total Volume 16,039 9,034 913 2,139 3,955

Each of the cellulosic plan ts was chosen to produce approximately 100 million gallons per year of ethanol. In some cases we had to r esort to lower volumes due to limited resources in

193a given area. In other cases, we used greater than 100 million gallons per year because relatively close materials were available that would othe rwise go unused. In addition, we limited biomass transport distances to be approximately 100 m iles each way or less (radius from proposed facility), as large transport dist ances are economically prohibitive. We found that the majority of corn stover cellulosic f acilities required smaller transpor t distances than the assumed 100 mile limit due to relatively close proximity to available feedstocks. Forest resi dues, on the other hand, typically required greater distan ces as collectable material appear ed to be sparser. The following Table 1.5-49 lists the 180 cellulo sic ethanol facilities that we pr oject could be used to produce 16 Bgal of cellulosic biofuel by 2022. See Figure 1.5-16 for a visual representation of the locations of these facilities.

Table 1.5-49. Projected Cellulosic Facilities (million gallons in 2022)

County State Total Volume (million gallons/yr) Escambia Alabama 112 Greene Alabama 108 Morgan Alabama 96 Russell Alabama 101 Talledega Alabama 115 Cleveland Arkansas 99 Howard Arkansas 97 Woodruff Arkansas 102 Butte California 94 Orange California 133 San Joaquin California 120 Siskiyou California 102 Adams Colorado 28 Broward Florida 31 Hendry Florida 90 Palm Beach Florida 100 Palm Beach Florida 100 Palm Beach Florida 100 Glynn Georgia 108 Grady Georgia 130 Richmond Georgia 101 Treutlen Georgia 98 Bureau Illinois 130 Carroll Illinois 77 Champaign Illinois 89 Coles Illinois 77 De Witt Illinois 100 Du Page Illinois 128 Grundy Illinois 77 Iroquois Illinois 80 Knox Illinois 89 Menard Illinois 99 Montgomery Illinois 78

194Morgan Ogle Richland Shelby Tazewell Washington Illinois Illinois Illinois Illinois Illinois Illinois 67 95 81 68 107 85 Benton Clinton Daviess De Kalb Fulton Jasper Jennings Madison Morgan Parke Union Vanderburgh Wells Indiana Indiana Indiana Indiana Indiana Indiana Indiana Indiana Indiana Indiana Indiana Indiana Indiana 92 80 93 91 74 82 94 78 100 92 82 74 77 Benton Buchanan Buena Vista Cerro Gordo Chickasaw Des Moines Dubuque Franklin Grundy Guthrie Ida Mahaska Muscatine O Brien Page Palo Alto Pottawattamie Sioux Story Union Webster Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa Iowa 69 83 84 79 82 87 70 80 83 85 88 80 83 80 81 75 84 72 89 76 86 Logan Nemaha Sedgwick Stevens Kansas Kansas Kansas Kansas 75 78 71 87 Webster Kentucky 70 Bienville E. Baton Rouge E. Carroll Jeff Davis Allen Louisiana Louisiana Louisiana Louisiana Louisiana 115 106 103 87 50

195Avoyelles Iberville La Fourche Lafayette Pt. Coupe St Landry Louisiana Louisiana Louisiana Louisiana Louisiana Louisiana 100 90 50 100 100 100 Penobscot Piscataquis Maine Maine 100 91 Calhoun Ionia Tuscola Van Buren Wayne Michigan Michigan Michigan Michigan Michigan 109 117 105 89 85 Chippewa Dakota Dodge Faribault Lyon Martin Rock Sibley Stearns Stevens Minnesota Minnesota Minnesota Minnesota Minnesota Minnesota Minnesota Minnesota Minnesota Minnesota 92 114 86 88 84 95 73 102 68 76 Forrest Grenada Mississippi Mississippi 107 107 Audrain Chariton Clark Gentry New Madrid Ray St. Louis Missouri Missouri Missouri Missouri Missouri Missouri Missouri 86 74 89 95 84 100 125 Sanders Montana 92 Boone Custer Harlan Hitchcock Holt Lancaster Lincoln Nuckolls Saunders Wayne York Nebraska Nebraska Nebraska Nebraska Nebraska Nebraska Nebraska Nebraska Nebraska Nebraska Nebraska 98 84 78 83 91 74 81 76 100 96 94 Clark Nevada 17 Carroll Carroll New Hampshire New Hampshire 136 35 West Chester New York 72 Cumberland Forsyth North Carolina North Carolina 110 104

196Martin North Carolina 102 Auglaize Clinton Franklin Logan Portage Richland Wood Ohio Ohio Ohio Ohio Ohio Ohio Ohio 80 100 77 75 98 83 85 Craig Grady Hughes Kingfisher Lincoln Muskogee Osage Oklahoma Oklahoma Oklahoma Oklahoma Oklahoma Oklahoma Oklahoma 130 108 91 110 120 118 116 Lane Yamhill Oregon Oregon 126 118 Montgomery Pennsylvania 42 Berkeley Spartanburg South Carolina South Carolina 105 108 Day Edmunds Kingsbury Lake Turner South Dakota South Dakota South Dakota South Dakota South Dakota 85 80 98 83 89 Monroe Tennessee 97 Angelina Bexar Cameron Dallas Harris Hidalgo Travis Willacy Texas Texas Texas Texas Texas Texas Texas Texas 114 16 100 52 80 100 14 100 Halifax Prince George Virginia Virginia 98 99 Chelan Thurston Washington Washington 78 97 Harrison West Virginia 149 Calumet Dane Dunn Eau Claire Grant Jefferson Marquette Wood Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin 91 76 63 65 68 94 65 59 Total 16039

197Figure 1.5-16. Projected Cellulosic Facilities

Bagasse Plan t

MSW Plant

Switchgrass Plant

A g. Residue Plant

Forest Residue Plant

It is important to note, however, that th ere are many more factor s other than feedstock availability to consider when eventually siting a plant. We have not taken into account, for example, water constraints, availability of permits, and sufficient personnel for specific locations. As many of the corn stover facilities ar e projected to be locate d close to corn starch facilities, there is the potential for competition fo r clean water supplies. Therefore, as more and more facilities draw on limited resources, it ma y become apparent that various locations are infeasible. Nevertheless, our plant siting an alysis provides a reas onable approximation for analysis purposes since it is not in tended to predict precisely where actual plants will be located. Other work is currently being done that will help address some of these issues, but at the time of this proposal, was not yet available. 431

As we are projecting the locat ion of cellulosic plants in 2022, it is important to keep in mind the various uncertainties in the analysis. For example, futu re analyses could determine better recommendations for sustainable removal ra tes. In the case where lower removal rates are recommended, agricultural residues may be more limited and could promote more growth in dedicated energy crops. Given th e information we have to date, we believe our projected locations for cellulosic facilities represent a reasonable forecast fo r estimating the impacts of this rule.

1.5.4 Biodiesel & Renewable Diesel

Biodiesel

The biodiesel industry differs significantly in profile from th e ethanol industry, in that it is comprised of plants with a wide variety of sizes, ranging from less than one million gallons to more than 50 million gallons per year producti on capacity, using feedstock ranging from virgin soy oil to recycled cooking grease and rendered fats. The industry capacity has expanded

198rapidly, going from a sparse network of sm all businesses selling locally to one with large companies selling internationally in less than a decade. Approximately 176 plants reported being on-line for production in 2008, with a mean size of 13 million gallons per year and a median size of just 5 million gallons per year. Figure 1.5-17 below shows historical aggregate capacity, sales volumes, and apparent cap acity utilization rates.

Figure 1.5-17. Recent biodiesel industr y capacity and production trends.

0 500 1,000 1,500 2,000 2,500

2003 2004 2005 2006 2007 2008Million Gallons

0% 5% 10% 15% 20% 25% 30% 35%Utilization Capacity

Production

Utilization

a

a 2008 capacity figure is as of January; annual production figures were not available for 2008 at the time of this writing.

The average utilization capacity has stayed around 30% for the past few years due to continued expansion of on-line cap acity despite apparently adequa te existing capacity. We can speculate that this is primarily because of the relatively low capital cost (typically 5-10% of operating cost) of these plants, whic h enables them to operate only part of the year or at reduced capacity, depending on feedstock prices or other ma rket conditions. Besides fuel, some of these plants also produce non-fuel oleo chemicals for use in detergents, lubricants or other products, providing additional sources of revenue for part of the industry.

In order to conduct our emissions and dist ribution analyses, we needed to have an industry characterization at the time of the fully phased-in program, the year 2022. This was not a simple task because of the unusual nature of the biodiesel industry. We generally assumed that the demand for biodiesel would stabilize at appr oximately the level of the standard, which seemed reasonable given the relatively high cost for biodiesel feedstocks as well as the uncertain status of the current tax incentives. We estimat ed how many plants would continue to produce biodiesel and where they might be located based on three factor s: state incentives for production and sales, BQ-9000 certification, and capabilities fo r handling multiple feedstock types. Plants

19913/15

105/123 0.8/1 0.4/0.5 35/85 41/60

113/158

56/60 9.2/22 75/80 13/30 17/20

38/45

NJ: 38/40 CT: 3.3/4 MA: 0.4/0.5

RI: 0.2/0.3

42/50

67/80 25/30

FL: 17/18 8.3/10 88/105

0.4/0.5

7.5/8

Biodiesel Production/Capacity (million gal/yr) in 2022 13/15

105/123 0.8/1 0.4/0.5 35/85 41/60

113/158

56/60 9.2/22 75/80 13/30 17/20

38/45

NJ: 38/40 CT: 3.3/4 MA: 0.4/0.5

RI: 0.2/0.3

42/50

67/80 25/30

FL: 17/18 8.3/10 88/105

0.4/0.5

7.5/8 with more of these advantages were expected to be more likely to survive over those that had fewer. We projected that a number of very small plants processing waste greases/fats would continue to operate based on local market niches regardless of these cr iteria. In an effort to be realistic in this forecast, other practical considerations were ma de, such as avoiding siting several plants in the same state (e xcept in the Midwest).

We project that between now and 2022 plants will continue to compete and consolidate tomake fewer plants of larger size. During this period most plants will have added the pretreatment and feedstock segregati on capacity to process any mix of feedstock types available in their area. By the out-years of this analysis, we expect production capacity use factors to reach 80-90% in nearly all fuel plan ts. Multi-product plants will retain the capacity to produce biodiesel, but it is not expected to be their primary produc t due to higher margins for more specialized products like surfactants, lubricants, or renewabl e oleochemical feedstocks for resale. The map in Figure 1.5-18 represents what the industry might look like by 2022. Table 1.5­ 50 summarizes key parameters of the industry as it is currently and in the 2022 forecast.

Figure 1.5-18. Biodiese l industry forecast for 2022

88/105

35/85 41/60

8.3/10 MA: 0.4/0.5
17/20 RI: 0.2/0.3

0.4/0.5 0.4/0.5 113/158

9.2/22 75/80 13/30 38/45

NJ: 38/40 CT: 3.3/4

7.5/8 0.8/1 56/60

42/50 13/15 25/30

67/80

105/123

Biodiesel Production/Capacity (million gal/yr) in 2022 FL: 17/18

200Table 1.5-50. Summary of current biodiesel industry and forecast. 432

2008 2022 Total production capacity on-line (million gal/yr) 2,610 1,050 Number of operating plants 176 35 Median plant size (million gal/yr) 5 30 Total biodiesel production (million gal) 700 810 Average capacity factor 0.27 0.77

Renewable Diesel

Since December, 2007, ConocoPhillips h as been producing a small amount (300-500 bbl/day) of renewable diesel at their Borger, Te xas, refinery from beef tallow generated by Tyson Foods, Inc. in Amarillo, Texas. 433 In 2007, Dynamic Fuels, LLC., was formed by Syntroleum Corp. and Tyson Foods, Inc., to produ ce liquid renewable fuels. This fall Dynamic Fuels has announced ground-breakin g for a 75 million gallon per year plant (5,000 bbl/day) in Geismar, Louisiana, that will use Tyson meat processing fats as feedstock to Syntroleum’s BioSynfining process. Start-up is scheduled for 2010, with the primary product being high-quality diesel fuel that will be fungible with in the existing petroleum supply system. 434

The earlier years of our industry projec tion are based primarily on feasibility and construction announcements by Dynamic Fuels, LLC, while the later years are based on the available supply of rendered fats and greases as f eedstocks, assuming the continued expansion of fuel conversion facilities to ut ilize them. We project a mix ofstand-alone and refinery colocated facilities. Stand-alone facilities receive feedstocks, pro cess them, and ship out finished products, and are generally self-sufficient in terms of material and ener gy. Conversion facilities that are located within the boundaries of existing petroleum refineries can take advantage of utilities such as steam and hydrogen already available on -site, as well as hydrocarbon blendstocks, and tankage and pipeli ne, rail, and port terminals. Material that is processed at existing refineries can either be co-processed in the same equipment trains with petroleum, or processed separately and blended just before leaving the facility.

The Geismar, Lousiana, facility plans to utilize supplies of hydr ogen available in the industrial park where it will be located, as well as rail and shipping infrastructure already in place nearby. 435 However, it is not co-l ocated with existing petrol eum production, and therefore would be considered a stand-alon e facility in our analyses.

Based on public material released by Syntroleum earlier this y ear, it appears that a second plant of 150 million gallons per year capacity is also under consideration for construction, but location, timeline, and feedstock so urce are not known at this time. 436 Table 1.5-51 shows the industry forecast used in our analyses of renewa ble diesel production in 2022. The Geismar, Lousiana, facility comprises part of the 125 million gallons per year in 2022 of new facility fuel shown in the table below, while the facility ex pansion values represent production we expect to come on-line at existing refineries in the future.

201Table 1.5-51. Projected renewable di esel volumes (million gallons in 2022) Expansion at existing refinery New stand-alone facility Co-processed with petroleum 188

- Not co-processed with petroleum 63 125



### 1.6 Biofuel Distribution

1.6.1 Biofuel Distribution Overview

The discussion in this section pertains to th e distribution of biofuels within the U.S. Significant volumes of etha nol would be imported into the U.S. to meet the volume of ethanol that we project would be used to Satisfy the RFS2 standards. The importa tion of ethanol into the U.S. under the RFS2 standards is discussed in Section 1.5.2 of this DRIA.

The modes of distributing rene wable fuels to the end user vary depending on constraints arising from their physical/chemi cal nature and their point of origination. Some fuels are compatible with the existing fuel distribution sy stem, while others curren tly require segregation from other fuels up to the point where they are bl ended with petroleum based fuels. The location of renewable fuel production plants is also often dictated by the n eed to be close to the source of the feedstocks used rather than to fuel demand centers or to take advantage of the existing pipeline distribution syst em for petroleum products. OOO Once blended with petroleum-based fuels, some biofuels (such as E10, B2, and B5) can be handled in the conventional fuel distribution downstream of the te rminal (including retail) and used in conventional vehicles. PPP However, E85 must be used in flex-fuel vehicl es and dispensed from retail equipment that has been manufactured to tolerate the high ethanol concentration. Hence, the distribution of renewable fuels raises unique concerns and in many instances requires the addition of new transportation, storage, blending, and retail equipment.

Significant challenges must be faced in reconfiguring the distribution system to accommodate the large volumes of ethanol and to a lesser extent biodiesel that we project would be used to meet the proposed standards. While some uncertainties remain, th ere is no barrier that appears insurmountable. The res ponse of the transportation syst em to date to the recent unprecedented increase in ethanol use is encouraging. A recent U.S. Depa rtment of Agriculture (USDA) report concluded that l ogistical concerns have not ha mpered ethanol production growth to date, but that concerns may arise about the adequacy of tr ansportation infrastructure as the growth in ethanol production continues. 437

Considerations related to th e distribution of ethanol, biodi esel, and renewable diesel are discussed in the following sections as well as the chang es to each segment in the distribution system that would be needed to support the vol umes of these biofuels that we project would be

OOO A discussion of the projected locations of ethanol produc tion facilities can be found in chapter 1.5 of this DRIA.
PPP E10 is a mixture of 10% ethanol and 90% gasoline. B2 is a mixture of 2% biodiesel and 98% petroleum-based diesel. B5 is a mixture of 5% biodiesel and 95% petroleum-based diesel.

202used to satisfy the RFS2 standards. The costs associated with making the necessary changes to the fuel distribution infras tructure are discussed in S ection 4.2 of this DRIA.

Considerable efforts are underway by indivi dual companies in th e fuel distribution system, consortiums of such companies, indus try associations, independent study groups, and inter-agency governmental organizations to evaluate what steps might be necessary to facilitate the necessary upgrades to the distribution syst em to support compliance with the RFS2 standards. QQQ EPA will continue to participate in or monitor these efforts as appropriate to keep abreast of potential problems in the biofuel distribution system which might interfere with the use of the volumes of biofuels that we project will be needed to comply with the RFS2 standards.

To the extent that biofuels other than et hanol and biodies el are produced in response to the RFS2 standards, the need for added segrega tion during distribution mi ght be lessened. For example, we expect renewable di esel fuel could be handled like a petroleum-based diesel blend stock in the distribution system. Distillate fuel produced from cellulosic feedstocks could also likely be treated as a petroleum-based diesel fuel blendstock or finished diesel fuel in the distribution system. Likewise, bio-gasoline or bio-butanol coul d likely be treated as petroleumbased gasoline blendstocks. RRR The ability to treat such bio-based blendstocks in that same fashion as petroleum-based fuel blendstocks mi ght enhance the possibility for transport by pipeline. However, the location of the production plants for such biofuels relative to petroleum pipeline origination points would continue to be an issue limiting the usefulness of existing pipelines. SSS

1.6.1.1 Overview of Ethanol Distribution

Denatured ethanol is shipped from pr oduction and import fac ilities to petroleum terminals where it is blended w ith gasoline. Pipelines are the preferred me thod of shipping large volumes of petroleum products over long distan ces because of the relative low cost and reliability. However, ethanol is not commonly shipped by pipeline currently because it can cause stress corrosion cracking in pi peline walls and its affinity for water and solvency can result in product contamination concerns. The location of ethanol production and demand centers also would tend to limit the extent to which existing petroleum pipelines could be used to ship ethanol. Shipping ethanol in pipelines that carry di stillate fuels as well as gasoline also presents unique difficulties in coping w ith the volumes of a distillate -ethanol mixture which would typically result. TTT It is not possible to re-process this mixture in the way that diesel-gasoline

QQQ For example: 1) The “Biomass Research and Development Board”, an inter-governmental group, has formed a group that is focused on evaluating biofuels distribution infrastructure issues. 2) The National Commission on Energy Policy, an independent organization, has formed a Biofuels Infrastructure Task Force composed of industry experts to evaluate what steps need to be taken to support the distribution of the volumes of biofuels mandated under the Energy Information and security Act (EISA) on which th e RFS2 standards are based. 3) The Association of Oil Pipelines is conducting research to evaluate what steps a necessary to allow the distribution of ethanol blends by pipeline.
RRR Biogasoline might also potentially be treated as finished fuel.
SSS The projected location of biofuel plants would not be affected by the choice of wh ether they are designed to produce ethanol, distillate fuel, bio-gasoline, or butanol. Proximity to the feedstock would continue to be the predominate consideration.
TTT Different grades of gasoline and diesel fuel are typically shipped in multi-product pipelines in batches that abut each other. To the extent possible, products are sequenced in a way to allow the interface mixture between batches

203mixtures resulting from pipeline shipment are currently handled. UUU Substantial testing and analysis is currently underway to resolve the se concerns so that et hanol may be shipped by pipeline either in a batch mode or blended with petroleum-based fuel. 438 By the time of the publication of this proposal, result s of these evaluations may be available regarding what actions are necessary by multi-product pipelines to overcome safety and product contamination concerns associated with shipping 10% ethanol blends. A s hort gasoline pipeline in Florida also started shipping commercial batches of dena tured ethanol in December of 2008. 439 Thus, there is the potential that existing petroleum pipelines in some areas of the county may play a role in the shipment of ethanol from th e points of production/importation to petroleum terminals.

However, the location of ethanol plants in relation to existing pipe line origination points will continue to limit the role that can be pl ayed by pipelines in the shipment of ethanol. VVV Current corn/starch ethanol produc tion facilities are primarily locat ed in the Midwest far from the origination points of most existing pr oduct pipelines and the primary gasoline demand
centers. WWW This is also projected to primarily be the case for future corn/starch ethanol plants. XXX While the projected future cellulosic etha nol plants are expected to be somewhat more dispersed throughout the country, we proj ect that most will be located in the Midwest. YYY Some imported ethanol could be brought into ports near th e origination points of product pipelines in the gulf coast and the Northeast. Neve rtheless, the majority of ethanol will continue to be produced at locations distant from the origination points of product pipelines and gasoline demand centers. The gathering of ethanol from production facilities locate d in the Midwest and shipment by barge down the Mississippi for intro duction to pipelines in the gulf coast is under consideration. However, the a dditional handling steps to bring th e ethanol to the pipeline origin points in this manner would dimi nish the potential benefit of shipment by existing petroleum pipelines compared to direct shipment by rail.

Evaluations are also currently underway rega rding the feasibility of constructing a new dedicated ethanol pipeline from the Midwest to the East coast. 440 Under such an approach, ethanol would be gathered from a number of Mi dwest production facilities to provide sufficient volume to justify pipeline operati on. To the extent that etha nol production would be further concentrated in the Midwest due to the citing cell ulosic ethanol plants there, this would tend to help justify the cost of installi ng a dedicated ethanol pipeline. There are substant ial issues that would need to be addressed before construc tion on such a pipeline could proceed, including those associated with securing new right of ways and establishing sufficient surety regarding the return on the several billion dollar investment.

to be cut into one of the adjoining products. In cases where diesel fuel abuts gasoline in the pipeline, the resulting mixture must typically reprocessed into its component parts by distillation for resale as gasoline and diesel fuel.
UUU We believe that it may not currently possible to separate ethanol from a gasoline/diesel mixture sufficiently by distillation. Hence, a significant amount of ethanol may remain in the gasoline and diesel fractions separated by distillation. Gasoline-ethanol mixtures can be blended into finished gasoline provided the applicable maximum allowed ethanol concentration is not exceeded. However, dies el-ethanol mixtures can not be used as motor fuel.
VVV Some small petroleum product refineries are currently limited in their ability to ship products by pipeline because their relatively low vo lumes are not sufficient to justify connection to the pipeline distribution system.
WWW The location of current corn/starch ethanol production f acilities is discussed in Section 1.5.1.1 of this DRIA.
XXX The projected growth in corn/starch ethanol plants under the proposed RFS2 standards is discussed in Section 1.5.1.2 of this DRIA.
YYY The projected location of cellulosic ethanol plants is discussed in Section 1.5.3 of this DRIA.

204Due to the uncertainties regarding the degree to which pipelines will be able to participate in the transportation of ethanol, we assumed that ethanol will con tinue to be transported by rail, barge, and truck to the terminal where it would be blended into gasoline. There is substantial room for the distribution by these modes to be fu rther optimized primarily through the increased shipment by unit train and installation of additio nal hub delivery terminals that can accept large volumes of ethanol for further distri bution to satellite terminals. To the extent that pipelines do eventually play a role in the distribution of ethanol, this could tend to the reduce distribution costs and improve reliability in supply.

The United States Department of Agriculture estimated that in 2005, approximately 60% of ethanol was transported by ra il, 30% was transporte d by tank truck, and 10% was transported by barge. 441 When practicable, shipment by unit train is the preferred method of rail shipment rather than shipping on a manifest rail car basis. ZZZ Manifest rail car shipment refers to the shipment of ethanol in rail tanks cars that are incorporated into trains which are composed of a variety of other commodities. The use of unit trai ns, sometimes referred to as a virtual pipeline, substantially reduces shipping costs and improves reliability compared to manifest rail car shipment. Unit trains are composed entirely of 70-100 ethanol tank cars , and are dedicated to shuttle back and forth to large hub terminals. AAAA Unit train receipt facilities can either be located at petroleum terminals (whe n there is sufficient room availa ble) or at rail terminals. When unit train facilities are lo cated at rail facilities, ethanol is further shipped to petroleum terminals by tank truck or barge.

Unit trains can be composed at a single etha nol production plant or if a group of plants are not large enough to support such service individually, can be formed at a central facility which gathers ethanol from a number of producers. The Manly Terminal in Iowa, the first such ethanol gathering facility, accepts ethanol from a number of nearby etha nol production facilities for shipment by unit train. Regional (Class 2) ra ilroad companies are an important link bringing ethanol to gathering facilities for assembly into unit trains for long-distance shipment by larger (Class 1) railroads.

Ethanol is sometimes carried by multiple modes before finally arriving at the terminal where it is blended into gasoline. The practice of pumping ethanol directly from rail cars to tanks trucks for further delivery to petroleum termin als at rail terminals is an important means of bringing ethanol into developing markets. BBBB This practice delays the need to install substantial ethanol handling and storage facilities at rail receip t facilities. However, we believe that it may not be a viable long term soluti on due to the logistical demands associated with ensuring the safe transfer of ethanol directly from rail cars to tank trucks. We anticipat e that once the ethanol distribution system is fully developed all ethanol shipped to rail terminals will be delivered into storage tanks prior to further di stribution to petroleum terminals. Similarly, the practice of sequential delivery of gasoline and ethanol into a tank truck at the terminal so that mixing to the finished fuel specification tak es place within the tank truck during delivery is an important

AAAA Hub ethanol receipt terminals can be located at large petroleum te rminals or at rail terminals.
BBBB This practice is sometimes referred to as transloading.

205means of bringing ethanol to developing markets. CCCC This practice allo ws the blending of ethanol in markets prior to the installation of in-line ethanol blending equipment at petroleum terminals. In-line blending refers to the prac tice of delivering a pre- blended ethanol/gasoline mixture into the tank truck. Due to the improved efficiency of delivery and greater assurance of achieving the target blend ratio and a properly mixed final blend that accompanies in-line blending, we anticipate that all terminals will eventually install in-line blending equipment. DDDD

As is the case for conventional gasoline, deliv ery of ethanol blends to retailers and fleets operators is accomplished by tank truck. Typically no changes are needed to dispense E10 at retail and fleet facilities. Howeve r, some retail facilities in part s of the country that have not used E10 in the past are projec ted to need to prepare their sy stems for E10 service primarily by removing water from their storage tanks. E10 can be used in conventional engines. E85 requires specially certified retail dispensing hardware an d can only be used in flexible fuel vehicles. EEEE

1.6.1.2 Overview of Biodiesel Distribution

Biodiesel is currently trans ported from production plants to petroleum terminals by tank truck, manifest rail car, and by barge where it is blended with petroleum-based diesel fuel. Unblended biodiesel must be tran sported and stored in insulated/ heated containers in colder climes to prevent gelling. Insulated/heated contai ners are not needed for biodiesel that has been blended with petroleum-based diesel fuel in low concentrations (i.e. B2, B5, B20). Biodiesel plants are not as dependent on bei ng located close to feedstock s ources as are corn and cellulosic ethanol plants. FFFF This has allowed some biodiesel plants to be located adjacent to petroleum terminals. Biodiesel production facilities are more geographical ly dispersed than ethanol facilities and the production vol umes also tend to be smaller than ethanol facilities. GGGG These characteristics in combination with the smaller volu mes of biodiesel that we project will be used under the RFS2 standards compared to ethanol allo w relatively more biodiese l to be used within trucking distance of the production faci lity. However, we project that there will continue to be a strong and growing demand for bi odiesel as a blending component in heating oil which could not be satisfied alone by local sourc es of production. It is likely th at State biodiesel mandates will also need to be satisfied in part by out-of-State production. Fleets are also likely to continue to be a substantial biodiesel user, a nd these will not always be located close to biodiesel producers. Thus, we are assuming that a substantial fraction of biodiesel will continue to be shipped long distances to market. Downstream of the petroleum terminal, B2 and B5 can be distributed in the same manner as petroleum diesel and used in conventional diesel engines.

CCCC This practice is sometimes referred to as splash blending . The term splash blending can also refer to blending ethanol with a gasoline blendstock that is not formulated in anticipation of the addition of ethanol. Matched blending refers to the practice of blending ethanol with a specially formulated gasoline blendstock that in itself can not typically be sold as finished gasoline.
DDDD Most terminals are likely to install in-line ethanol blending equipment by 2013 as the use of E10 becomes virtually nationwide. The growth in ethanol consumption under the proposed RFS2 standards is discussed in Section 1.7.1.2 of this DRIA.
EEEE A discussion of Underwriters Laboratories certification requirements for E85 dispensing equipment can be found at http://ulstandardsinfonet.ul.com/outscope/outscope.asp?fn=0087a.html
FFFF Biodiesel feedstocks are typically preprocessed to oil pr ior to shipment to biodiesel production facilities. This can substantially reduce the volume of feedstocks shipped to biodiesel plants relative to ethanol plants.
GGGG Section 1.5.4 of this DRIA contains a discussion of our projections regarding the location of biodiesel production facilities.

206Concerns remain regarding the shipment of biodiesel by pipeline (either by batch mode or in blends with diesel fuel) related to the potential contamina tion of other products (particularly jet fuel), the solvency of bi odiesel, and compatibility with pipeline gaskets and seals. The smaller anticipated volumes of biodiesel and the more dispersed and sma ller production facilities relative to ethanol also make bi odiesel a less attractive candidate for shipment by pipeline. Due to the uncertainties regarding the suitability of transporting biodiesel by pipeline, we assumed that biodiesel which needs to be transported ove r long distance will continue be carried by rail car and to a lesser extent by barg e. Due to the relatively sma ll plant size and dispersion of biodiesel plants, we anticipate the volumes of bi odiesel that can be gathered at a single location will continue to be insufficient to justify shipment by unit train. To the extent that pipelines do eventually play a role in the distribution of biodiesel, this co uld tend to the reduce distribution costs and improve reliability in supply.

1.6.1.3 Overview of Renewable Diesel Distribution

We believe that renewable diesel fuel will be confirmed to be sufficiently similar to petroleum-based diesel fuel blendstocks with respect to distribution system compatibility. Hence, renewable diesel fuel could be treated in the same manner as any petroleum-based diesel fuel blendstock with respect to transport in the existing petroleum distribution system. Approximately two-thirds of rene wable diesel fuel is projected to be produced at petroleum refineries. HHHH The transport of such renewable dies el fuel would not di ffer from petroleumbased diesel fuel since it would be blended to produce a finished diesel fuel before leaving the refinery. The other one-third of renewable diesel fuel is projecte d to be produced at stand-alone facilities located more closely to sources of feedstocks. We antic ipate that such renewable diesel fuel would be shipped by tank trucks to near by petroleum terminals where it would be blended directly into diesel fu el storage tanks. Because of its hi gh cetane, we anticipate that all renewable diesel fuel would likely be blended with petroleum based diesel fuel prior to use. Downstream of the terminal, renewable/petroleu m diesel fuel mixtures w ould be distributed the same as petroleum diesel.

1.6.2 Shipment of Ethanol to Petroleum Terminals

There is considerable uncertainty regardi ng how ethanol would be shipped to petroleum terminals once the market matures under the RFS2 standards. Our evalua tion for this NPRM is based on an assessment of current trends in et hanol distribution and engineering judgment regarding how these trends are likely to develop over time. As discussed below, there is a lack of data on existing biofuels distribution f acilities and capabiliti es as well as uncertainties regarding how the distribution system would resp ond to increased biofuels volumes. Hence, we were compelled to make certain assumptions ba sed on a review of ava ilable literature and discussions with industry. A study was recently completed for EPA by Oakridge National Laboratories (ORNL) which m odeled the transportation of ethanol from production/import facilities to petroleum terminals. 442 The ORNL model optimizes freight flows over the rail, marine, and road distribution net while addressing the use of multiple shipping modes. We plan

HHHH A discussion of the projected location of renewable di esel fuel production facilities is contained in Section 1.5.4 of this DRIA. Renewable diesel fuel produced at petroleum refineries would either be co-processed with crude oil or processed in separate units located at the refinery for blending with other diesel blendstocks at the refinery.

207to use the results of the ORNL study to adjust our projections regarding how ethanol would be shipped to petroleum terminals for the final rule, particularly with respec t to how much ethanol can be shipped directly by tank truck from pr oduction facilities and the relative roles of unit train, manifest rail, and barg e in the distribution chain.

USDA estimated that approximately 30% of direct ethanol deliv eries from production facilities to petroleum terminals in 2005 were accomplished by tank truck. 443 As the total volume of ethanol used increases an d ethanol is shipped to more markets that are distant from production centers (e.g. in the S outheast), we expect that shipments by rail would increase and direct tank truck shipments to petroleum termin als to decrease to 20%. We believe that for transportation distances of less th an 200 miles all shipments of ethanol to petroleum terminals would be by tank truck. Tran sportation by tank truck for a di stance over 200 miles typically requires an overnight layover which significantly incr eases costs. Thus, we anticipate that only a limited volume of ethanol would be transported by tank truck for a distance of between 200 and 300 miles and essentially none over 300 miles once the market matures.

The amount of ethanol that can be sh ipped directly by truck from production plants/import centers to petroleum terminals is a function of the relativ e location of production plants/import centers to petroleum terminals, the ethanol productio n/import capacity of production/import facilities, and th e potential ethanol demand at each terminal. Ethanol demand at a given terminal is governed by ethanol cost considerations relative to gasoline, State ethanol use incentives, the penetration of E85 into the market, and the total gasoline motor vehicle demand served the terminal. As an input to th e ORNL study, we provi ded our estimates of the location of ethanol production pl ants and imports centers as well as our estimate of how much ethanol would be used on a State-by-State basis. IIII ORNL allocated ethanol usage to terminals located within a given State on a population weig hted basis given the terminals service areas. Thus, we anticipate that the OR NL study will provide an enhan ced estimate of the volume of ethanol that could be shipped to petroleum terminals by tank truck.

Where shipping distances are greater than 300 miles, we assumed that unit train would be the preferred means of tr ansport where ethanol volumes are suffici ently large. We estimated that shipment by manifest rail car w ould continue to be an important means of bringing ethanol to a subset of petroleum terminals th at could not support the receip t of unit trains or otherwise receive ethanol from hub terminals th at are equipped for unit train receipt. JJJJ We assumed that 10% of ethanol production/imports w ould be shipped to petroleum term inals by manifest rail car. We also assumed that shipment by barge would continue to be a sign ificant means of moving ethanol from certain hub terminals in the Northe ast that receive ethanol via unit train to other Northeast terminals. We anticipa te that the analysis of the rela tive merits of the various modes for long distance transportation of ethanol inhe rent in the ORNL modeling work will provide additional basis for us to adjust our estimates of the relative extent to which the different modes would be utilized.

IIII A discussion of our estimates of where ethanol would be used under the RFS2 standards is contained is contained in Section 1.7 of this DRIA. A discussion of our estimates of where ethanol would be produced / imported in response to the RFS2 standards is contained in Section 1.5 of this DRIA.
JJJJ Manifest rail car shipment refers to the practice of shipping several ethanol rail cars in a train that is composed of rail cars carrying various products.

208The extent to which multiple shipping links are needed to bring ethanol to petroleum terminal depends on two principal factors. The fi rst is whether the ethanol production facility is configured to support shipment by unit train. The Manly unit train ga thering facility was constructed to serve a number of ethanol production plants that we re either too small to support unit train service themselves or othe rwise chose not to install a unit train shipping facility. Our analysis indicates that the vast majority of new ethanol produc tion plants will have a production capacity of at least 70 million gallons per year (MGY). KKKK The current industry rule of thumb is that ethanol production plants need to have a production capacity of approximately 80 million MGY to support the installation of a unit train loading facility. Th is appears to be based on the assumption of an ~2 week cycle time for unit trai n rail cars which we be lieve is widely accepted for ethanol shipments by unit train, and the use of a 100 car unit train. LLLL A 70 MGY ethanol plant could only support the shipment of a 100 ca r unit train every 17 days , resulting in a 20% less efficient use of rail cars compared to a 2 week cycle. Rail car leas e costs are estimated at $750 per month per car and thus can c onstitute a significant fraction to total ethanol freight costs.

Ethanol unit trains range betw een a minimum of 70 to a maxi mum of 100 rail cars. By reducing the number of cars in a unit train to 90, a 70 MGY et hanol could support a two week rail car cycle time. Thus, we believe that a 70 MGY ethanol plant should be able to support the installation of a unit train shipping facility. It is also likely that the le vel of ethanol demand at some downstream ethanol unit trai n receipt facilities w ould be better suited to the receipt of smaller sized unit train than 100 rail cars. We anticipate that all ethanol production facilities of sufficient scale to support unit train service other than those currently served by the Manly facility will choose to install such service due to the asso ciated market advantages. We assume that the new production plants th at are too small to support in stallation of unit train service would primarily serve local mark ets by tank truck and ship the re mainder of their production to terminal by manifest rail car. Based on the above discussion, we are assuming that no additional unit train rail gathering facilities would be need ed. Hence, we are projecting that no additional manifest rail car shipments to form unit trains w ould be needed as a result of the RFS2 standards.

The second factor influencing the need for second ary shipments of ethanol is the extent to which ethanol will be shipped to hub terminals vi a unit train for further distribution to satellite terminals and the degree to wh ich such hub terminals are located at petroleum terminals as opposed to being located at rail terminals. A si gnificant challenge facing terminals and one that is currently limiting the volume of ethanol that can be used is the abil ity to receive ethanol by rail. Only a small fraction of petroleum termin als currently have rail receipt capability and a number likely have space constrai nts (particularly those in the No rtheast) or are located too far from the rail net to allow in stallation of such capability.

There is significant uncertainty regarding the extent to which additional rail receipt facilities can be accommodated at petroleum terminals. This is true regard ing the installation of manifest rail receipt capability as well as unit train receipt capability. Comprehensive data on the current rail receipt capability at petroleum terminals or on logistical considerations regarding the addition of such capability do es not exist. For the purpose of our analysis, we are assuming

KKKK A discussion of the projected locations of ethanol pr oduction facilities can be found in chapter 1.5 of this DRIA.
LLLL Rail car cycle time for shipment of ethanol by manifest rail car is typically one month.

209that 50% of new ethanol unit tr ain facilities would be located at petroleum terminals and 50% would be located at rail terminal s. The ORNL study estimated th at all new ethanol rail receipt capability would be installed at existing rail te rminals given the limited ability to install such capability at petroleum terminals. We intend to review our estimates regarding the location of the additional ethanol rail receipt facilities for the final rule in light of the ORNL study.

Ethanol not blended at hub petroleum terminals or shipped to rail terminals would need to be further shipped to smaller “satellite” petroleum terminals. We assume that 20% of the ethanol received by unit train at a petroleu m terminal would be blended into gasoline at that terminal. The remaining 80% would be further shipped to satellite petroleum terminals. We are assuming that 90% of such secondary shipments would be done by tank truck and 10% by barge. We also assumed the ethanol shipped by unit train to ra il terminals would be shipped to petroleum terminals by the same means. At one ethanol unit train hub facility at a rail terminal, there are plans to ship ethanol by a short pi peline to a nearby petroleum terminal. We anticipate that this practice will expand as the industry matures. Ho wever, due to a lack of information on the extent to which short pipelines could feasibly play such a role, and to provide a conservatively high estimate of ethanol distribution costs, we assumed that all secondary shipments from rail terminals would be by tank truck and barge. We anticipate that the results from the ORNL modeling work will provide the means for us to modify our estimates of the extent to which secondary shipments of ethanol will need to be made from unit train receipt facilities at petroleum terminals and the breakdown of the transportation modes that would be used for secondary shipments from all ethanol unit train receipt facilities (including those at rail terminals).

Our estimate of how ethanol would be sh ipped to petroleum terminals in 2022 is summarized in Table 1.6-1. A discussion of ethanol fr eight costs can be found in Section 4.2 of this DRIA.

210Table 1.6-1. Estimated 2022 Ethanol Shipping Volumes by Link/Mode of Shipment

Shipping Link / Shipment Mode Million Gallons of Ethanol Shipped Annually in 2022

Total Volume Change from Reference Case b From production facilities by manifest rail car and tank tr uck to unit train gathering facility 1,000 0

From production/import facility to petroleum terminal by tank truck 6,830 (20% production/import vol.) a 4,190 From production or unit train gathering facility to petroleum terminal by unit train 11,950 (35% production/import vol.) a 7,340

From production/import facility to petroleum terminal by manifest rail

car 3,410 (10% production/import vol.) a 2,100

From production or unit train gathering facility to rail terminal by unit train 11,950 (35% production/import vol.) a 7,340

From petroleum terminals that receive ethanol by unit train by barge to satellite petroleum terminals 960 590

From petroleum terminals that receive ethanol by unit train by tank truck to satellite petroleum terminals 8,600 5,280

From rail terminals that receive ethanol by unit train or port facilities that receive imported ethanol by barge to satellite petroleum terminals 1,190 730

From rail terminals that receive ethanol by unit train by tank truck to satellite petroleum terminals 10,750 6,600

a 34.14 BGY of ethanol would be produced/imported by 2022 to satisfy the RFS2 standards. b Total – the AEO 2007 based 2022 reference case absent the RFS2 standards. Our ethanol consumption projections under RFS2 standards are discussed in Section 1.7.1 of this DRIA.

1.6.3 Shipment of Biodiesel to Petroleum Terminals

We projected the volumes of biodiesel that would be used on a St ate-by-State basis to meet anticipated State biodeisel mandates/incen tives and the estimated demand for biodiesel as a blending component in heating oil. MMMM Using the estimated loca tions of biodiesel production

MMMM A discussion of our estimates regarding the location of biodiesel production facilities and centers of biodiesel demand is contained in Section 1.5.4 of this DRIA.

211facilities and their volumes, we evaluated the most efficient m eans of meeting this projected demand while minimizing shipping distances (and cost). The remaining biodiesel production volume that was not taken up meeting this demand was assumed to be used in the same State it was produced to the extent that th e State’s diesel fuel did not al ready contain 5% biodiesel. We believe that this should provide a somewh at conservatively hi gh estimate of biodiesel distribution costs since bi odiesel might be used in excess of 5% even absent a State mandate. If the State was already saturated with 5% biodi esel, the remaining volume was assumed to be shipped out of State within a 1,000 mile shipping distance. A 1,000 mile shipping distance was selected to ensure that all biodiesel not used to satisfy a State ma ndate or for bio-heat could find a market. It is likely that some fraction would not need to travel quite as far. Therefore, this assumption is also likely to result in a conservatively high estimate of biodiesel freight costs. It was assumed that biodiesel production volumes will con tinue to be insufficiently concentrated to justify shipment by unit train. Where distances are beyond 300 miles, ship ment by manifest rail was assumed to be the preferred option other than in cases on the East coast where there were apparent barge routes from production to de mand centers. In no case was it assumed that biodiesel would need to be shipped by multiple m odes prior to delivery at the petroleum terminal where it is blended with petrol eum-based distillate fuel.

Table 1.6-2 contains our estimate of how biodiesel would be sh ipped to petroleum terminals in 2022. Additional discussion on our es timate of how biodiesel used to comply with the RFS2 standards would be tran sported to petroleum terminals can be found in Section 4.2 of this DRIA on biodiesel freight costs.

Table 1.6-2. Estimated 2022 Biodiesel Shipping Vo lumes by Link/Mode of Shipment

Shipping Link / Shipment Mode Million Gallons of Biodiesel Shipped Annually in 2022

Total Volume Change from Reference Case b From production facility to petroleum terminal by tank truck 696 (86% of total production volume) a 369

From production facility to petroleum terminal by manifest rail car 109 (13% of total production volume) a 5

From production facility to petroleum terminal by barge 5 (1% of total production volume) a 3

a 810 MGY of biodiesel would be produced in 2022 to satisfy the RFS2 b Total – the AEO 2007 based 2022 reference case absent the RFS2 standards. Our ethanol consumption projections under RFS2 standards are discussed in Section 1.7.2 of this DRIA.

1.6.4 Shipment of Renewable Di esel to Petroleum Terminals

We project that the distribu tion of the 2/3 of renewable di esel fuel (251 MGY in 2022, or potentially earlier) produced to petroleum terminals would be tran sparent with respect the impact

212on the distribution system. NNNN We assume that the remain ing 125 MGY would be shipped to nearby terminals or petroleum refineries (within a 200 mile radius) by tank truck to be blended with petroleum-based diesel fuel.

1.6.5 Changes in Freight Tonnage Movements Due to RFS2

In order to evaluate the magnitude of the ch allenge to the distribution system up to the point of receipt at the terminal, we compared the growth in freight tonnage for all commodities from the AEO 2007 reference case to the growth in freight tonnage under the RFS2 standards in which ethanol increases, as does the feedstock (corn) and co-products (distillers grains). We did not include a consideration of th e transportation of cellulosic feedstocks in this analysis. The distribution of cellulosic feedstoc ks is discussed in Section 1.3. 3 of this DRIA. For purposes of this analysis, we focused on onl y the ethanol portion of the renewable fuel goals for ease of calculation and because ethanol represents the vast majority of the total fuel. The resulting calculations serve as an indicator of changes in freight tonnages associated with increases in renewable fuels. We calculated the freight tonnag e for the total of all modes of transport as well as the individual cases of rail, truck, and barge.

In calculating the AEO 2007 reference case percen t growth rate in to tal freight tonnage, we used data compiled by the Federal Highway Administration to cal culate the tonnages associated with these commodities. We then cal culated the growth in freight tonnage for 2022 under the RFS2 standards and compared the di fference with the AEO 2007 reference case. The comparisons indicate that across all transport mo des, the incremental increase in freight tonnage of ethanol and accompanying feedstocks and co-pr oducts associated with the increased ethanol volume under the RFS2 standards are small. The percent increase for total freight across all modes (rail, barge, truck, and pipeline) by 2022 is 0.9 percent. Because pipelines currently do not carry ethanol, and the increase in volume of ethanol displaces a corresponding volume of gasoline, pipelines showed a decrease in tonnage car ried. The displaced gasoline also resulted in some decrease in tonnage in other modes that s lightly reduced the overall increases in tonnage reflected in the totals.

To further evaluate the magnitude of the increase in freight tonnage under the RFS2 standards, we calculated the porti on of the total freight tonnage fr om the rail, barge, and truck modes made up of ethanol-related freight fo r both the AEO 2007 based 2022 reference case and control cases. The freight associated with ethanol constitutes only a very small portion of the total freight tonnage for all commodities. Specif ically, ethanol freight represents approximately 0.5 and 2.5% of total freight for the AEO 2007 based 2022 reference case and RFS2 standards case, respectively. For the rail mode only, the fright associated with ethanol represents approximately 3.2% of the total for the RFS2 case. The results of this analysis, suggest that it should be feasible for the distribution infrastructur e upstream of the terminal to accommodate the additional freight associated with this RFS2 standards especially given the lead time available. Specific issues related to transportation by rail , barge, and tank truck are discussed in the

NNNN A discussion of the projected location of renewable di esel fuel production facilities is contained in Section 1.5.4 of this DRIA. Renewable diesel fuel produced at petroleum refineries would either be co-processed with crude oil or processed in separate units located at the refinery for blending with other diesel blendstocks at the refinery.

213following sections. Our estimate of the percent changes in total freigh t tonnage by mode for 2022 required to satisfy the RFS2 standa rds is contained in Table 1.6-3.

Table 1.6-3. Estimate of Percent Changes in Tota l Freight Tonnage by Mode for 2022 Reference Case RFS2 case % change a

Total frei g ht tonna g e for all commodities (thousands of tons) 25,161,284 25,673,207 1.99% Portion of total freight to nnage made up of ethanolrelated freight (thousands of tons) 135,018 643,418

Percent ethanol related freight to total freight 0.54% 2.51%

Total freight tonnages for specific transport modes (thousands of tons) Reference Case RFS2 case % change a

Truck 16,521,816 16,884,804 2.15%

Rail 2,552,227 2,636,173 3.18%

Water 714,483 734,292 2.70%
Pipeline 5,372,758 5,417,937 0.83%

a Total – the AEO 2007 based 2022 reference case absent the RFS2 standards. Our ethanol consumption projections under RFS2 standards are discussed in Section 1.7.1 of this DRIA.

We intend to incorporate the results of the recently completed study by Oak Ridge National Laboratories (ORNL) on the potential constraints in ethanol distribution into the analysis for the final rule. The ORNL study concluded that th e increase in ethanol transport would have minimal impacts on the overall tran sportation system. However, the ORNL study did identify localized areas where significant upg rades to the rail dist ribution system would likely be needed.

1.6.6 Rail Transportation System Accommodations

Many improvements to the freight rail system will be required in the next 15 years to keep pace with the large increase in the overall freight demand. Much of the projected increase in rail freight demand is associated with the expected rapid growth of inter-modal rail transport. Most of the needed upgrades to the freight rail system are not specific to the transport of renewable fuels and would be needed irrespectiv e of today’s proposed rule. The modifications required to satisfy the increase in demand include upgrading tracks to allow the use of heavier trains at faster speeds, the modernization of tr ain braking systems to a llow for increased traffic on rail lines, the installation of rail sidings to facilitate train stag ing and passage through bottlenecks.

214Some industry groups OOOO and governmental agencies in discussions with EPA, and in testimony provided for the Surface Transportation Board (STB) expressed concerns about the ability of the rail system to keep pace with large increase in demand even under the AEO 2007 reference case (27% by 2022). For example, the electric power industry has had difficulty keeping sufficient stores of coal in inventory at power plants due to rail transport difficulties and has expressed concerns that this situation will be exacerbated if rail congestion worsens. One of the more sensitive bottleneck areas with respect to the movement of ethanol from the Midwest to the East coast is Chicago. Th e City of Chicago commissioned its own analysis of rail capacity and congestion, which found that the lack of rail capacity is “no longe r limited to a few choke points, hubs, and heavily utilized co rridors.” Instead, the report finds , the lack of rail capacity is “nationwide, affecting almost a ll the nation’s critically important trade gateways, rail hubs, and intercity freight corridors.” This is due, in pa rt, to the lack of critic al linkages between the 27 major rail yards located in the Chicago-land area.

To help improve east-west rail connections through the city, federal, state, and local officials announced an agreement in 2006 to invest $330 million over three-years in city-wide rail infrastructure designed to improve the flow of rail traffi c through the area. The State of Illinois, the City of Chicago, and seven Class I rail carriers, as well as Amtrak and Metra, the area's transit system, also committed $1.5 billion in improvements. Chicago is the largest rail hub in the country with more than 1,200 trains passing through it daily carrying 75% of the nation's freight valued at $350 bi llion; 37,500 rail freight cars p ass through the city every day projected to increase to 67,000 by 2020. Chicago is the only city where all six Class I railroads converge and exchange freight. Th e plan calls for the cr eation of five rail corridors to aid in alleviating the bottleneck.

Significant private and public resources are focused on maki ng the modifications to the rail system to cope with the in crease in demand. Rail carriers repor t that they typically invest 16 to 18 billion dollars a year in infrastructure improvements. 444 Substantial government loans are also available to small rail companies to help make needed improvements by way of the Railroad Rehabilitation and Improvement Finance (RRIF) Program PPPP , administered by Federal Railroad Administration (FRA), as well as Section 45G Railroad Track Ma intenance Credits, offered by the Internal Revenue Service (IRS).

The RRIF program offers loans to railroad s for a variety of ca pital purposes including track and equipment rehabilitation at “cost of money” for 25 year terms. Typically, short line railroads cannot secure this kind of funding in the private markets. Under this program, FRA is

OOOO Industry groups include the Alliance of Automobile Manufacturers, American Chemistry Council, and the National Industrial Transportation League; governmental agencies include the Federal Railroad Administration (FRA), the General Accountability Offi ce (GAO), and the American Associa tion of State Highway Transportation and Officials (AASHTO). Testimony for the STB public hearings includes Ex Parte No. 671, Rail Capacity and Infrastructure Requirements and Ex Parte No. 672, Rail Transportation and Resources Critical to the Nation’s Energy Supply.
PPPP The RRIF program was established by the Transporta tion Equity Act for the 21st Century (TEA-21) and amended by the Safe Accountable, Flexible and Effi cient Transportation Equity Act: a Legacy for Users (SAFETEA-LU). RRIF funding may be used to: acquire, improve, or rehabilitate intermodal or rail equipment or facilities, including track, components of track, bridges, yards, buildings and shops; refinance outstanding debt incurred for the purposes listed above; and develop or establish new intermodal or railroad facilities.

215authorized to provide direct loans and loan guara ntees up to $35.0 billion. Up to $7.0 billion is reserved for projects benefiting freight railroads other than Class I carriers. However, the program has lent less than $650 million to non-pa ssenger rail carriers since 2002, according to the FRA/RRIF website.

The American Association of State Hi ghway Transportation Officials (AASHTO) estimates that between $ 175 billion and $195 billion must be invested over a 20-year period to upgrade the rail system to handle the anticipated growth in freight demand, according to the report’s base-case scenario. 445 The report suggests that railroad s should be able to provide up to $142 billion from revenue and borrowing, but that the remainder would have to come from other sources including, but not limited, to loans, tax cr edits, sale of assets, and other forms or publicsector participation. Given the reported historic al investment in rail in frastructure, it may be reasonable to assume that rail carriers would be able to manage the $7.1 billion in annual investment from rail carriers th at AASHTO projects would be needed to keep pace with the projected increase in freight demand.

The Association of Ameri can Railroads (AAR) estimates 446 that meeting the increase in demand for rail freight transportation will require an investment in infrastructure of $148 billion (in 2007 dollars) over the next 28 years and that Cl ass I railroads' share is projected to be $135 billion, with $13 billion pr ojected for short line and re gional freight railroads.

In testimony before the STB, Class I railroad s committed to working with all parties in the ethanol logistical chains to provide safe, co st-effective, and reliable ethanol transportation services as well as to resolve past freight rail capacity difficulties. Presumably, this commitment extends to the projected three-percent increase in overall freight tonna ge envisioned herein.

However, the Government Accounting Offi ce (GAO) found that it is not possible to independently confirm statements made by Class I rail carriers regarding future investment plans. QQQQ In addition, questions persist regardi ng allocation of these investments, with the Alliance of Automobile Manufacturers, Amer ican Chemistry Council, National Industrial Transportation League, and others expressing concern that their infrastructural needs may be neglected by the Class I railroads in favor of mo re lucrative intermodal traffic. Moreover, the GAO has raised questions regarding the competiti ve nature and extent of Class I freight rail transport. This raises some concern that providing suffici ent resources to facilitate the transport of increasing volumes of et hanol and biodiesel might not be a first priority for rail carriers. In response to GAO concerns, the Surface Transp ortation Board (STB) agreed to undertake a rigorous analysis of competition in the freight railroad industry. RRRR

QQQQ The railroads interviewed by GAO were generally unwilli ng to discuss their future investment plans with the GAO. Therefore, GAO was unable to comment on how Class I freight rail companies are likely to choose among their competing investment priorities for the future, includ ing those of the rail infrastructure, GAO testimony Before the Subcommittee on Surface Transporta tion and Merchant Marine, Senate Committee on Commerce, Science, and Transportation, U.S. Senate, Freight Railroads Preliminary Observations on Rates, Competition, and Capacity Issues, Statement of JayEtta Z. Hecker, Director, Physical Infrastructure Issues, GAO, GAO-06-898T Washington, D.C.: June, 21, 2006).
RRRR GAO, Freight Railroads: Industry Health Has Improv ed, but Concerns about Competition and Capacity Should Be Addressed, GAO-07-94 (Washington, D.C.: Oct. 6, 2006); GAO, Freight Railroads: Updated Information on Rates and Other Industry Trends , GAO-07-291R Freight Railroads (Washington, D.C.: Aug. 15,

216Given the broad importance to the U.S. ec onomy of meeting the an ticipated increase in freight rail demand, and the substa ntial resources that seem likely to be focused on this cause, we believe that overall freight rail capacity woul d not be a limiting factor to the successful implementation of the biofuel requirements under th e RFS2 standards. Evidence from the recent ramp up of ethanol use has also shown that ra il carriers are enthusiastically pursuing the shipment of ethanol, although there is some indication that the Cl ass I freight rail industry will expect ethanol to primarily be shipped by unit train from facilities that assemble unit trains which are developed and paid for by the ethanol industry.

Class 2 railroads have been particularly activ e in gathering sufficient numbers of ethanol cars to allow Class 1 railroads to ship ethanol by unit train. Based on this recent experience, we believe that ethanol will be able to compete su ccessfully with other co mmodities in securing its share of freight rail service.

While many changes to the overall freight rail system are expected to occur irrespective of today’s proposed rule, a number of ethanol-s pecific modifications will be needed. For instance, a number of additional ra il terminals are likely to be confi gured for receipt of unit trains of ethanol for further distributi on by tank truck or other means to petroleum terminals. Each ethanol rail car holds ap proximately 29,500 gallons. SSSS Thus, each 100 car unit train would deliver 2,950,000 gallons of ethanol. We believe that it would be reasonable for an ethanol unit train facility to accept a unit train approximately every 3 days. Thus, we assumed that each unit train facility could handle 120 unit trains per y ear which translates to 354 million gallons per year for each unit train facility. Based on the volumes we projected to be transported by unit train in Table 1.6-1 of this DRIA, this translates to a total of 68 unit train receipt facilities for all ethanol used by 2022 and 42 such facilities to handle the volume attributed to the RFS2 standards alone (relative to the AEO 2007 based 2022 reference case). As discussed in Section 1.6.2 of this DRIA, we are assumi ng that half of the un it train receipt facili ties needed to cope with the additional volumes of ethanol projected to be used to comply with the RFS2 standards would be constructed at petroleum terminals (i.e. 21) and ha lf would be cons tructed at rail terminals.

As discussed in Section 1.6.2 of this DRIA, the recently completed ORNL study estimated that all new ethanol ra il receipt capability w ould be installed at existing rail terminals given the limited ability to instal l such capability at petroleum terminals. We intend to review our estimates regarding the location of the additional ethanol rail receipt facilities for the final rule in light of the ORNL study.

The placement of ethanol receipt fa cilities at rail term inals would be particularly useful in situations where petroleum terminals would find it difficult or impossible to install their own ethanol rail receipt capability. If the projected number of ethanol rail receipt facil ities can not be accommodated at petroleum terminals then additi onal ethanol unit train re ceipt facilities would

2007). STB's final report, entitled Report to the U.S. STB on Competition and Related Issues in the U.S. Freight Railroad Industry , is expected to be completed November, 1, 2008.
SSSS Ethanol cars have a nominal capacity of 30,000 gallons.

217be needed at rail terminals. TTTT We do not have information on the extent to which there is available space at rail terminals to accommodate ethanol receipt facilit ies. However, the majority of the required space would be needed to locate the train itself, the facilities for which would already be in place at ra il terminals. We expect the other space requirements for truck loading and storage tanks would be relatively modest and could be accommodated at a sufficient number of rail terminals to meet the projecte d need. The need for long-term demand to be established prior the construction of such facilities would likely m ean that the needed facilities would at best come on line in a just-in-time basis. We anticipate that ethanol storage will typically be installed at rail term inal ethanol receipt hubs over the long run. However, the ability to rely on transloading while ethanol storage fa cilities at rail termin al ethanol receipt hub facilities are constructed w ould help to ease the introduction of downstream rail receipt capability.

We anticipate that rail terminals would only install the minimum amount of ethanol storage capacity in order to maintain steady de liveries by unit train. This is based on the assumption that petroleum terminals would be the on es to maintain an inventory buffer to ensure a steady supply of ethanol to blend with gasoline. UUUU Under this model, rail terminals would empty their ethanol storage by sh ipment to petroleum terminals as soon as possible. Therefore, we believe that it not appropria te to apply the formula we used to estimate the ethanol storage tank requirements at petroleum terminals which is based on the premise of always having ethanol on hand even the face of temporary upsets in deliv ery. Each delivery of an ethanol unit train contains 2.95 million gallons. To ensure that a ra il terminal is always ready to unload an ethanol unit train, we believe that it would be sufficien t to size the ethanol storage capacity at 6 times unit train capacity (17.7 million gallons). This should allow for a reasonable amount of irregularity in both the receipt of ethanol by unit train into rail terminals and the delivery of ethanol from rail terminal storage to petroleum terminals. For all of the projected 21 rail terminal ethanol unit train receipt facilities, this totals 371.7 million gallons or 8.85 million barrels of ethanol storage. All of the ethanol storage at rail terminals would need to be new construction, since we do not believe there would be existing tankage at rail terminals that could be converted to ethanol service.

A substantial number of additi onal rail cars would be needed to transport the volumes of ethanol and to a lesser extent bi odiesel that are projected to be used in response to the RFS2 standards. Our estimate of the number of rail cars needed is based on the an assumed cycle time of 2 weeks for shipment by unit train and one mo nth for shipment by manifest rail car which we believe is conservatively high gi ven current industry experience. VVVV Biodiesel rail cars are typically somewhat smaller than those used for ethanol. We assumed a deliverable volume of 25,600 gallons for biodiesel rail cars and 29,000 for ethanol rail ca rs. We estimated the number of rail cars that would be need ed to transport ethanol and biod iesel using the projected volumes of ethanol and biodiesel that we expect would be shipped by unit train and manifest rail (see

TTTT As discussed in Section 1.6.6 of this DRIA, we estimate that 148 additional manifest rail receipt facilities would be constructed at petroleum terminals to handle the volumes of ethanol that we project would be shipped by manifest rail.
UUUU A discussion of the projected demand for ethanol storage is contained in Section 1.6.6 of this DRIA.
VVVV Rail car cycle time refers to the time needed to complete one delivery and return to the origin including the time to prepare for the next shipment.

218Tables 1.6-1 and 1.6-2) and the assumed rail car volumes and cycle times. As discussed in Section 1.6.1.3, we project that renewable diesel fuel would not n eed to be transported by rail. Our estimate of the number of rail cars to transport the volume of renewable fuels projected to be used by 2022 to satisfy the RFS2 standa rds is contained in Table 1.6-4.

Table 1.6-4. Estimated Number of Rail Cars Needed by 2022 for Shipment of Ethanol and Biodiesel

Number of Rail Cars Needed to Transport Ethanol

Total Volume Volume Attributed to RFS2 Standards a Ethanol Rail Cars 43,398 26,644 Biodiesel Rail Cars 665 353 Ethanol & Biodiesel Rail Cars 44,063 26,997

a Total – the AEO 2007 based 2022 reference case absent the RFS2 standards

Our analysis of ethanol and bi odiesel rail car production capa city indicates that access to these cars should not represent a serious impediment to meeting the requirements under the RFS2 standards. Ethanol tank car production ha s increased approximately 30% per year since 2003, with over 21,000 tank cars expected to be produced in 2007. To accommodate the increased demand for ethanol ta nk cars, rail car producers conv erted existing boxcar production facilities to tank production facili ties and brought on additional work shifts to adjust to rapidly changing to market conditions.

1.6.7 Marine Transportation System Accommodations

The American Waterway’s Association has expressed concerns about the need to upgrade the inland waterway system in order to k eep pace with the anticipated increase in overall freight demand. The majority of these concerns have been focused on the need to upgrade the river lock system on the Missi ssippi river to acco mmodate longer barge tows and on dredging inland waterways to allow for movement of fully lo aded vessels. We do not anticipate that a substantial fraction of renewable/alternative fuels will be transported via these arteries. Thus, we do not believe that the ability to ship ethanol/biodiesel by inland marine will represent a serious barrier to the implementation of implementation of the requirements under RFS2 standards. Substantial quantities of the corn ethanol co-produ ct dried distiller grains (DDG) is expected to be exported from the Midwest via the Mississippi river as the US demand for DDG becomes saturated. We anticipate that the volume of exported DDG would take the place of corn that would be shifted from export to domestic use in the production of etha nol. Thus, we do not expect the increase in DDG exports to result in a substantial incr ease in river freight traffic.

A number of new barges would be needed to transport the volumes of ethanol and to a lesser extent biodiesel that are projected to be used in respons e to the RFS2 standards. We assumed the use of tank barges with a carrying ca pacity of 10,000 barrels (42,000 gallons). We understand that the tank barge industr y is trending towards the use of tank barges with a carrying capacity of 30,000 barrels. Thus, our assumed u se of 10,000 barrel barges may overstate the number of barges that would be needed. We assumed a 2 week barge cycle time, which we

219understand to be typical given the markets wh ere we expect most barge shipments would
WWWW occur.

We estimated the number of barges that w ould be needed to transport ethanol and biodiesel using the projected volum es of ethanol and biodi esel that we expect would be shipped by barge (see Tables 1.6-1 and 1.6-2), and th e assumed barge volume and cycle time. As discussed in Section 1.6.1.3, we project that renewable diesel fuel would not need to be transported by barge. Our estimate of the number of barges to transport the volume of renewable fuels projected to be used by 2022 to satisfy the RFS2 standards is contained in Table 1.6-5.

Table 1.6-5. Estimated Number of Barges Needed by 2022 for Shipment of Ethanol and Biodiesel

Number of Barges Needed to Transport Ethanol and Biodiesel

Total Volume Volume Attributed to RFS2 Standards a
Ethanol Barges 213 131 Biodiesel Barges 20 11 Ethanol & Biodiesel Barges 233 142

a Total – the AEO 2007 based 2022 refe rence case absent the RFS2 standards

The U.S. tank barge fleet currently numbers 3,600. 447 In 2004, over 500 barges of all types were added to the U.S. ba rge fleet. Given the gradual ramp up in demand for shipment of biofuels by barge over time, we be lieve that the additi on to the fleet of the 142 barges estimated to be needed to transport biofuels can be accommodated by the industry.

As discussed in Section 1.5.2.2 of this DRIA , we project that impor ts of ethanol will ramp up significantly to 3.14 BGY by 2022. XXXX To estimate which ports would receive ethanol imports we gave priority to ports that have a history of receivi ng ethanol imports from Brazil and Caribbean Basin Initiative Counties YYYY according to company-level hi storical fuel import data from the Energy Information Administration (EIA). 448 Additional ports were selected from those that have a history of r eceiving finished gasoline imports. Ports were selected in States that could not satisfy their internal ethanol de mand from in-State production and from those ports that were closest to large demand centers. We estimate that a total of 28 ports would receive imported ethanol by 2022 (see Figure 1.6-1). ZZZZ We estimate that the 12 ports which did not

WWWW We believe most barge shipments of biofuels would originate and terminate in the Northeast. Cycle time refers to the time needed to complete one delivery and return to the origin including the time to prepare for the next shipment.
XXXX See chapter 1.5.2 of this DRIA for a discussion of our projection of ethanol imports.
YYYY Caribbean Basin Initiative countries receive special exem ptions from U.S. ethanol import tariffs (See Section 1.5 of this DRIA regarding the source of ethanol imports and for additional discussion regarding how we estimated where ethanol imports would enter the U.S..
ZZZZ Paulsboro/Gloucester NJ were treated as separate ports fo r purposes of our capital cost analysis but were treated as a single port for the purposes of ou r ethanol freight costs analysis due to th eir close proximity. The same is true for Belfast and Searsport ME.

220receive ethanol in the past w ould need to install/modify et hanol receipt facilities including piping, pumps, vapor handling syst ems, and ethanol storage tanks while ports that had received ethanol in the past would primarily need to inst all additional ethanol stor age tanks. We believe that all of these ports also serve as petroleum term inals. Hence, the need for additional ethanol storage as well as outgoing ethanol shipping facilities would be cove red within the context of our estimation of the upgrades needed to petroleum te rminal facilities.

Figure 1.6-1. Projected Ethanol Import Locations and Volumes (MGY) in 2022

Seattle, WA (73)

Vancouver, WA (16)

Portland, OR (13)

San Francisco (25) & Selby, CA (81)

Los Angeles, CA (539)

Honolulu, HI (19)

Port Arthur (24), Houston (424), & Brownsville, TX (34) Tampa, FL (121)
Port Everglades, FL (179 ) Port Canaveral, FL (64) Jacksonville, FL (106) Savannah, GA (198) Charleston, SC (79)

Wilmington, NC (153) Philadelphia, PA (246) Newark (56), Perth Amboy (102), & Paulsboro, NJ (39) New York, NY (299) New Haven (36) & Bridgeport, CT (42) Providence, RI (24)

Boston, MA (143) Belfast/Searsport (<1) & Portland, ME (1)

Total U.S. Ethanol Imports in 2022 = 3,140 Million Gallons

Note: We are considering adding Ha mpton Roads VA, and Baltimore MD to the list of future ethanol import locations and may adjust our analys is for the final rule accordingly. This figure is the same as figure 1.5-13 and is repeated here for reference purposes.

1.6.8 Accommodations to the to the Road Transportation System

We estimated the number of tank trucks that would be needed to transport ethanol, biodiesel, and renewable diesel fuel using the projected volumes of ethanol and biodiesel that we expect would be shipped by tank truck (see Table 1. 6-1, Table 1.6-2, and Section 1.6.4 of this DRIA ), and the assumed tank truck volume and cycle times. In all cases, we assumed a tank truck capacity of 8,000 gallons. Larger tank trucks are permitted in some areas, so this assumption will tend to overestimate of the num ber of tank trucks needed. We assumed 312 truck shipping days per year and two 8 hour shifts. For shipments of ethanol from production/import facilities to petroleum termin als we assumed 3.75 shipments per day (4 hours for round trip). For secondary ethanol shipments from hub to satellite terminals, we assumed 6

221shipments per day (2.5 hours per round trip). For shipments of biodiesel and renewable diesel fuel from production facilities to terminals, we assumed 6 shipments per day. We believe that the shorter shipping distance for tank truck tr ansport from biodiesel pr oduction facilities is justified based on the greater dispersion of such f acilities relative to ethano l facilities and the fact that some would be located at petroleum terminals.

Tables 1.6-6 and 1.6-7 contain our estimates of the number of new tractor trailer tank trucks and truck drivers that would be need ed by 2022 to transport the additional volumes of ethanol, biodiesel, and renewable diesel fuel that are projected to be used in response to the proposed RFS2 standards. Our est imate of the number of truck dr ivers that would be needed is based on the assumption that each tank truck would be operated for two shifts with one driver for each shift.

Table 1.6-6. Estimated Number of Tank Trucks Needed by 2022 to Transport Ethanol, Biodi esel, and Renewable Diesel Fuel

Number of Tank Trucks N eeded for the Transport of Ethanol, Biodiesel, and Renewable Diesel Fuel Total Volume Volume Attributed to RFS2 Standards a Ethanol 2,022 1,241 Biodiesel 74 39 Renewable Diesel 8 8 All Biofuels 2,106 1,288

a Total – the AEO 2007 based 2022 refe rence case absent the RFS2 standards

Table 1.6-7. Estimated Number of Tank Truck Drivers needed by 2022 to Transport Ethanol, Biodi esel, and Renewable Diesel Fuel

Number of Tank Truck Driv ers Needed to Transport Ethanol, Biodiesel, and Renewable Diesel Fuel

Total Volume Volume Attributed to RFS2 Standards a Ethanol 4,044 2,483 Biodiesel 148 78 Renewable Diesel 16 16 All Biofuels 4,208 2,577

a Total – the AEO 2007 based 2022 refe rence case absent the RFS2 standards

According to a 2005 study commissioned by th e American Trucking Association (ATA), the motor carrier industry will face a shortage of qualified professional long-haul truck drivers by 2014. 449 In the study, ATA found that the long-haul, heavy-duty truck transp ortation industry in the United States is currently e xperiencing a national shortage of 20,000 truck drivers and, if the

222current trend continues, that shortage of long-haul truck drivers could increase to 111,000 by 2014. ATA projected the need for additional 54,000 drivers each year. The trucking industry is active in a number of efforts to attract and retrain a sufficien t number of new truck drivers including ATA’s National Truck Driver Recr uiting Campaign and Driver Tuition Finance Program.

As discussed above, we estimate that the growth in the transportation of biofuels by truck through 2022 due to the RFS2 sta ndards would result in the need for a total of approximately 2,600 additional trucks drivers. Given the relati vely small number of new truck drivers needed to transport the volumes of biofuels projected to be used to comply with the RFS2 standards through 2022 compared to the to tal expected increase in demand for drivers over the same time period (>750,000), we do not expe ct that the implementation of the RFS2 standards would substantially exacerbate the potential for an ove rall shortage of truck drivers. However, specially-certified drivers are re quired to transport biofuels be cause these fuels are classified as hazardous liquids. Thus, there may be a heightened level of concern about the ability to secure a sufficient number of such specially-certified drivers to transport biofuels. The trucking industry is involved in efforts to streamline the certifica tion of drivers for hazardous liquids transport. We do not anticipate that the need for special hazardous liquids certification for biofuels truck drivers of would substantially interfere with th e ability to transport the projected volumes of biofuels by tank truck. We proj ect that tank truck deliveries of biofuels would typically be accomplished within an 8 hour shift allowing the driver to return home each evening. A AAAA The ATA sponsored study indicated that there was particular difficulty in attracting and retaining drivers for long haul routes the k eep the driver away from home overnight. Thus, driving a tank truck (with typical 8 hour shift) may be relatively more attractive compared to a long haul truck driving position.

Truck transport of biofuel f eedstocks to production plants a nd finished biofuels and coproducts from these plants naturally is concen trated on routes to and from these production plants. This may raise concerns about the potential impact on road congestion and road maintenance in areas in the proxi mity of these facilities. We do not expect that such potential concerns would represent a barrier to the implemen tation of the RFS2 standards. The potential impact on local road infrastructure and the ability of the road net to be upgraded to handle the increased traffic load is an inherent part in the placement of new biofuel production facilities. Consequently, we expect that any issues or concerns would be dealt with at the local level. The transport of biofuel feedstocks is disc ussed in Section 1.3.3 of this DRIA.

1.6.9 Petroleum Terminal Accommodations

Terminals will need to install additional st orage capacity to accommodate the volume of ethanol/biodiesel that we anticipate will be used in response to the RFS2 standards. By 2022, an additional 20.96 BGY of ethanol would be used as a result of the RFS2 standards. B BBBB We

AAAAA A small fraction of biofuels deliverie s may require a sleep-over on the road of the driver due to limitations on the amount of time a driver can spend behind the wheel in a day.
BBBBB A total of 34.14 BGY of ethanol would be used by 2022 under the RFS2 standards. 13.18 BGY is projected to have been used in the absence of the RFS2 standards under the AEO 2007 based 2022 reference case. See

223estimate that it would be necessary to maintain an inventory le vel of 15% of annual ethanol consumption at the terminal level in order to provide a sufficient downstream buffer to ensure consistent supply. We chose a working inventor y level of 15% rather than the 10% that is typical for petroleum-based fuels to comp ensate for the potential increase in temporary disruptions in ethanol delivery compared to petr oleum-based fuels. We believe that this is appropriate due to the reliance on rail, barge, and truck for the tr ansport of biofuels as opposed to use of pipelines for the shipment of petrol eum-based fuels. Accommodating a 15% working inventory equates to 3.14 BG or 74.9 million barr els of additional ethanol storage at terminals due to the RFS2 standard. We further estimate that an additional 30% of ethanol storage (22.4 million barrels) would be needed as working space to accommodate ethanol deliveries. C CCCC Thus we project that a total of 97.3 million barrels of additional ethanol storage would be needed at petroleum terminals due to the RFS2 standards. D DDDD

Overall demand for the gasoline motor vehicl e fuel is expected to remain relatively constant through 2022. E EEEE Thus, much of the demand fo r new ethanol storage could be accommodated by modifying storage tanks that had pr eviously been used for the gasoline that would be displaced by ethanol. Due to the lowe r energy density of etha nol relative to gasoline (67%), we project that only 67% of the demand for new ethanol storage (65.2 million barrels) could be accommodated by modifyi ng existing gasoline tanks for etha nol service. Therefore, we project that 32.1 million barrels of new ethanol stor age tanks would be need to be constructed at petroleum terminals.

By 2022, an additional 430 MGY of biodiesel would be used as a result of the RFS2 standards. F FFFF We used the same terminal tankage si zing factors used above for ethanol to estimate the volume of new biodiesel storage vo lume that would be needed at petroleum terminals. This results in an estimated need for an additional 77.4 million gallons or 1.84 million barrels of new biodiesel st orage space at petroleum terminals as a result of the RFS2 standards. Our projections of the growth in diesel fuel demand indicate that there would not be the opportunity to switch tanks currently in petroleum-based diesel fuel service to biodiesel service. Thus, all of the demand for new biodiesel stor age would need to be satisfied through the construction of new tanks. Biodiese l storage tanks would need to be insulated/heated in colder climes.

Concerns have been raised by terminal operators in the East ern U.S. about the ability of some terminals to install the n eeded storage capacity due to space constraints and difficulties in securing permits. 450 We acknowledge that it may not be possible for some terminals that have

Section 1.5 of this DRIA for a discussion of projected ethanol volumes that would be used under the RFS2 standards and under the reference cases.
CCCCC Petroleum terminals typically allow an additional 30 pe rcent of storage capacity (in relation to the amount provided for working inventor y) to accommodate the receipt of petroleum products.
DDDDD We also project that 8.8 million barrels of new ethano l storage would be needed at rail terminals that act as ethanol unit train receipt facilities as disc ussed in Section 1. 6.4 of this DRIA.
EEEEE Our projections of the motor fuel demand can be found in chapter 1.2 of this DRIA.
FFFFF A total of 810MGY of biodiesel would be used by 2022 under the RFS2 standards. 380 MGY is projected to have been used in the absence of the RFS2 standards under the AEO 2007 based 2022 reference case. See Section 1.5 of this DRIA for a discussion of projected biodiesel volumes that would be used under the RFS2 standards and under the reference cases.

224become surrounded by urban growth over time to install additional stor age tanks within the boundaries of their existing f acilities. However, we believe that there are ways to manage this situation. The areas served by existing terminals often overlap. In such cases, one terminal might be space constrained while another servi ng the same area may be able to install the additional capacity to meet the increase in dema nd. Terminals with limited ethanol storage (or no access to rail/barge ethanol shipments) could receive truck shipments of ethanol from terminals with more substantial ethanol storag e (and rail/barge receip t) capacity. The trend towards locating ethanol receipt a nd storage capability at rail te rminals located near petroleum terminals is likely to be an important factor in reducing the need for la rge volume ethanol receipt and storage facilities at some pe troleum terminals. In cases where it is impossible for existing terminals to sufficiently expand th eir storage capacity due to a lack of adjacent available land or difficulties in securing the necessary permits or to make arrangements to sufficiently reduce the need for such additional storage, new satellite st orage or new separate te rminal facilities may be need for additional ethanol storage. However, we believe that there will be few (if any) such situations.

As discussed below, we project that all term inals that distribute gasoline would install ethanol blending capability in response to the RFS2 standards. However, we estimate that only 44% of terminals that distribute diesel would install biodiesel blending/storage capability under the RFS2 standards. Therefore, in the case of biodiesel, those terminals that would experience that most difficulty in installi ng new storage capacity could for go bringing biodiesel into their terminal

Another question is whether the storage tank c onstruction industry woul d be able to keep pace with the increased demand for new tanks th at would result from today’s proposal. The storage tank construction industry recently experi enced a sharp increase in demand after years of relatively slack demand for new tankage. Much of this increase in demand was due to the unprecedented increase in the use of ethanol. St orage tank construction companies had been increasing their capabilities which had been pared back during lean times. Given the projected gradual increase in the need for biofuel storage tanks, it seems reasonable to conclude that the storage tank construction industr y would be able to keep pace with the projected demand.

Petroleum terminals would n eed to install additional equi pment to blend ethanol with gasoline as well as making other miscellaneous u pgrades to piping, pumps, seals, and vapor recovery systems to ensure ethanol compatibility . There are currently 1,063 petroleum terminals that carry gasoline. 451 We project that 899 of these terminal s (85% of the total) would install E10 blending equipment absent the implementation of today’s proposed requirements in order to support the consumption of 13.18 BGY of ethano l by 2022 under the reference case. This is based on 85% of the gasoline needing to be blended with ethanol in order to consume 13.18 BGY of ethanol considering the pr ojected use levels of E10 vers us E85 and total motor vehicle fuel consumption in 2022. G GGGG We project that essentially a ll gasoline would be either E10 or E85 by 2022 under the RFS2 standards. Thus, we est imate that all terminal s would need to have ethanol blending capability to sup port the use of the volume of etha nol we project would be used under the RFS2 standards. Based on our projec tion that 899 terminals would install ethanol

GGGGG A discussion of the volumes of biofuels we estimate would be used absent the RFS2 standards is located in chapter 1.2 of this DRIA.

225blending capability absent the RFS2 standards, we estimate that 164 term inals would need to install ethanol blending equipment as a r esult of the proposed requirements.

We estimate that E85 would need to be reasona bly available in 70% of the nation in order to support the use of the proj ected volume of E85 needed to comply with the proposed standards. H HHHH To provide a conservatively high estima te, we are projecting that 90% of all gasoline terminals (931) would n eed to install E85 blending capability by 2022. The remaining terminals (132 out of a total of 1,063) would only have E10 ble nding capability in 2022. We estimate that the terminals which would have installed E10 blending capability absent the proposed standards would upgrade their E10 blending facilities to accommodate E85 as well as E10. This is based on the assump tion that those terminals that we re the first to blend E10 would also be the first to begin bl ending E85. Input from termin al operators in dicates that the modification of E10 blending equipment to hand le E85 primarily involves an upgrade to the blending equipment software. I IIII We estimate that the vapor re covery systems at all terminals that had not received ethanol before would need to be upgr aded to handle ethanol-blended gasoline.

We considered the following in estimati ng the number of terminals that would store/blend biodiesel in response to the RFS2 standards. We estimate that 853 terminals handle diesel fuel. 452 We estimate that approximately 62.5 bill ion gallons of diesel fuel would be used in 2022. J JJJJ Thus, the average diesel throughput per terminal would be approximately 73.2 MGY. We estimate that on a national average basis biodiesel would represent approximately 2.9% of the diesel fuel pool. For the purposes of our calculation of the number of terminals that would carry biodiesel, we assumed th at 2.9% of the diesel fuel th ey dispense would be biodiesel. This is likely to result in a conservatively hi gh number of terminals that would need to carry biodiesel, since those terminals that do carry biodiesel would be expected too blend at higher than the national average concentration. Assu ming that 2.9% of a terminal’s diesel fuel throughput would be biodiesel, we arrive at an estimate that 377 terminals would need to blend biodiesel to support the proj ected use of 810 MGY of biodiesel by 2022 under the RFS2 standards. We estimate that 200 terminals woul d need to store/blend biodiesel in order to support the use of volume of biodiesel that we es timate would be used as a result of the RFS2 standards relative to the AE O 2007 380 MGY 2022 baseline.

New equipment will be needed at terminals to facilitate the receipt of ethanol by truck, manifest rail car, and by unit train. As discussed in Section 1.6.3 of this DRIA, we estimated that half of the ethanol unit train receipt facilit ies (21) needed as a result of the RFS2 standards would be placed at petroleum terminals and half would be placed at rail terminals for the purpose of this analysis. K KKKK In estimating the number of additi onal manifest rail car receipt facilities

HHHHH A discussion of our E85 use projections is contained in chapter 1.7 of this DRIA.
IIIII Additional ethanol storage and modifications to terminal piping would also be needed to supply additional quantity of ethanol needed to blend E85.
JJJJJ A discussion of our estimate of biodiesel use in relation to the use of petroleum-based diesel is contained in Section 1.5.4 of this DRIA.
KKKKK We intend to review our estimate of the number of et hanol rail receipt facilities in light of the recent ORNL report which assumed that all such new rail receipt capab ility would be placed at rail te rminals due to the difficulties in accommodating such facilities at petroleum terminals. Th is applies to manifest rail as well as unit train receipt facilities.

226that would be needed at petroleum terminals, we assumed 5 deliveries per month of 8 cars at each facility. This would result in the deliver y of 1,180,000 gallons of ethanol per month which we believe to be reasonable, giving the an ticipated motor vehicl e gasoline throughput at terminals we expect would receive ethanol ship ments by manifest rail car. Based on the ethanol volumes we projected to be transp orted by manifest rail car in Ta ble 1.6-1 of this DRIA, this translates to a total of 241 manif est rail car receipt facilities fo r all ethanol used by 2022 and 148 such facilities to handle the vol ume attributed to the RFS2 sta ndards alone (relative to the AEO 2007 based 2022 reference case). Lacking any speci fic data on the number of terminals that currently have rail receipt capability, we estimate that 50% of the terminals that install ethanol rail receipt capability would ha ve already had an ac tive rail connecti on and that 50% would need to install a short spur to c onnect to a nearby rail line.

As discussed in Section 1.6.3 of this DRIA, we expect that to the extent that the projected number of ethanol rail re ceipt facilities could not be cited at petroleum terminals that they would be cited at rail terminals. Petroleum terminals within trucking distance of each other are also likely to cooperate so that only one would need to install rail receipt capability. Given the timeframe during which the projected volumes of ethanol ramp up, we believe that these means can be utilized to ensure that a sufficient numbe r of terminals have access to ethanol shipped by rail although some will need to rely on sec ondary shipment by truck from large ethanol hub receipt facilities.

We project that those termin als that would receive domes tic shipments of ethanol by barge would have already insta lled the necessary barge offloading equipment. Terminals that also serve as points of entry for imported ethanol which did not previously receive shipments of ethanol would need to install facilities to accep t marine shipments of ethanol. We estimate that there would be a total of 29 terminals that r eceive imported ethanol by 2022 and that 12 of these terminals would not have received ethanol shipments previously. L LLLL We do not project the need for additional facilities at terminals to accept receipts of biodiesel and renewable diesel fuel. We expect that the facilities projected to be installed for ethanol could be used for biodiesel and renewable diesel fuel. In the case of bi odiesel, new heated/insulated transfer piping would be needed from the receipt facil ity to the storage tank.

The Independent Fuel Terminals Operators Association (IFTOA) stated that terminals are concerned that the market would not be able to adapt in time to en sure that the necessary distribution infrastructure accommod ations are in place to support compliance with the timetable for the implementation of the RFS2 standards. 453 Based on this concern, in a presentation at the 2008 SAE government-industry conference IFTOA s uggested that EPA should consider reducing and or slowing the pace of the implementation of the RFS2 standards in order to allow the market sufficient time to adjust. 454 We do not believe that there is sufficient reason at this time to conclude that the distribu tion system could not adapt suffi ciently within the time frame established by statute for the implementation of the RFS2 standards. We will continue to monitor the progress made to by th e distribution system to make the necessary changes. To the extent that the fuel distributi on industry experiences difficulty in installing the needed storage tanks and blending equipment, the continuation of the interim practic es of transloading and splash blending would help to bridge the gap.

LLLLL See Section 6.1.5 of this DRIA for a discussion of ethanol import locations.

227The RFG and anti-dumping regulations currently require certified g asoline to be blended with denatured ethanol to produ ce E 85. The gasoline must meet all applicable RFG and antidumping standards for the time and place where it is sold. We understand that some parties may be blending butanes and or pentanes into gasoline before it is ble nded with denatured ethanol in order to meet ASTM minimum volatility specifica tions for E85 that were set to ensure proper driveability, particularly in the winter. M MMMM If terminal operators a dd blendstocks to finished gasoline for use in manufacturing E85, the terminal operator would need to register as a refiner with EPA and meet all applicable standards for refiners.

Testing has shown that much of in-use E85 does not meet minimum ASTM volatility specifications. N NNNN However, it is unclear if noncomp liance with these specifications has resulted in a commensurate adver se impact on driveability. This has prompted a re-evaluation of the fuel volatility requirements for in-use E85 vehicles and wh ether the ASTM E85 volatility specifications might be relaxed or the minimum amount of ethanol in E85 adjusted dowanwards to facilitate compliance with the current volatility requirements. O OOOO For the purpose of our analysis, we are assuming that certified gasoline currently on hand at terminals can be used to make up the non-ethanol portion of E85. P PPPP

We will continue to evaluate the extent th at special blendstocks would be used to manufacture E85 at terminals particularly in light of the outcome of the ASTM process and adjust our estimates for the final rule as appropriate. Input from industry i ndicates that it is not common for gasoline terminals to have butane/pent ane storage capability. This input further indicates that the space requirements for c iting a pressurized butane storage vessel make installation of butane stor age at some terminals impractical. If special gasoline blendstocks not typically found at terminals are needed for the manufacture of E85, there could be substantial additional costs and logistical challenges associated with shipping these ble ndstocks to terminals.

1.6.10 Additional E85 Retail Facilities

As discussed in Section 1.7.1.2 of this DRIA , we estimate that end-users would need to have reasonable access to E85 in 70% of the nation by 2022 given our proj ections regarding the population of flexible fuel vehicles ( FFVs) and E85 refueling frequencies. Q QQQQ We define reasonable access as one in four g asoline retail facilities offering E85 in fashion consistent with the way they currently offer gasoline. We select ed one in four based on a review of the number of facilities that have been postulated to be need ed to support the introduction of alternative fuels vehicles such as hydrogen and natural gas vehicles, the number of facilities that currently offer

MMMMM “Specification for Fuel Ethanol (Ed75-Ed85) for Spar k-Ignition Engines”, American Society for Testing and Materials standard ASTM D5798.
NNNNN Coordinating Research Council (CRC) report No. E-79 -2, Summary of the Study of E85 Fuel in the USA Winter 2006-2007, May 2007. http://www.crcao.o rg/reports/recentstudi es2007/E-79-2/E-79­ 2%20E85%20Summary%20Report%202007.pdf
OOOOO CRC Cold Start and Warm-up E85 Driveability Program, http://www.crcao.com/about/Annual %20Report/2007%20Annual%20Rep ort/Perform/CM-133.htm
PPPPP This is different from the approach taken in the refinery modeling which assumed that special blendstocks would be used to blend E85. A discussion of the refinery modeling can be found in Section 4 of this DRIA. QQQQQ Chapter 1.7 of this DRIA contains a discussion of our projections regarding the prevalence of flexible fuel vehicles in the vehicle fleet and the relative volumes of E85 versus E10 that would be used in these vehicles.

228diesel fuel, and industry estimates re garding the number of E85 fac ilities that would be needed. One in five to one in three retail facilities h as been discussed as a reasonable rule of thumb regarding the number of retail facilities needed to support the wi despread introduction of alternative fuel vehicles.

We estimate that approximately one in three fu el retail facilities (32% ) offered diesel fuel in 1999 based on our review of fuel retailer survey data. 455 The National Association of Convenience Stores (NACS) reported that in 2006, 36.6% of the respondents to their survey offered diesel fuel. 456 We believe that given that NACS members typically do not include truck stop operators (who all offer diesel fu el) that that it is most likely that the number of diesel fuel retailers has increased si nce 1999. Since fuel retailers make most of their money from in-store sales as opposed to fuel sales, it seems likely that more retailer s recognized an opportunity to attract additional customers by o ffering diesel fuel since 1999. In any event, the number of diesel fuel refueling facilities available in 1999 or 2006 has not hindered the use of diesel fuel vehicles. Unlike diesel fuel vehi cles that can refuel only on diese l fuel, flex fuel vehicles can refuel on gasoline as well as E85. We project that fl ex-fuel vehicles would only refuel on E85 70% of the time in order to meet the ethanol consumption goals neces sary to support the RFS2 standards. R RRRR Thus, we believe that the percentage of total fuel retail facilities that sell diesel fuel (32% based on our 1999 estimate) serves as an upper bound regarding the number of E85 retail facilities that would be needed.

At the same many time fleet operators were di vesting of their in- house fueling facilities because of new environmental regulations, most retailers were installing equipment to blend mid-grade gasoline at the pump rather than store a separate mid-grade gasoline. This allowed for a significant number of retailer s to begin offering diesel fuel at relatively low capital cost by converting storage tanks that ha d been dedicated to mid-grade gasoline storage to diesel fuel service. A number of retail facili ties (40% of the total that inst alled diesel fuel tanks had low annual diesel throughput volumes of less th an 60,000 gallons per year in 2000. 457 Only 5% of total diesel retail sales are estim ated to be sold at these low-volume retailers. Given that the installation of some diesel retail facilities was not strictly driven on the ex pectation or realization of substantial throughput, it seems reasonable to assume that some fraction of low-volume retailers may not be absolutely necessary to ensure adequate di esel availability. Therefore, somewhat less than 32% of retail facilities might actually be need ed to ensure adequate diesel fuel availability. We believe that this comparison to the number of diesel fuel retail facilities available supports our estimate thatone in four retail facilities would be sufficient to provide reasonable access to E85.

By multiplying the one-in-four reasonable access assumption by the assumed 70% of the retail market that would need to have reasonable access to E85, and the total number of retail facilities, we arrived at our estimate that ther e would need to be a total of 28,750 E85 retail locations to facilitate the consumption of the amo unt of ethanol that we project would be used by 2022 in response to the requirements under the RFS2 standards. The National Ethanol Vehicle Coalition (NEVC) estimates there are currently 1,767 E85 refueling facilities. 458 However, the NEVC estimate includes E85 refu eling facilities that are not open to the general public. 459
“NEAR85” estimates that there ar e currently 1,247 E85 retail facilities. The Near85 estimate

RRRRR A discussion of E85 refueling rates is contained in Section 1.7.1.2.4 of this DRIA.

229includes only retail facili ties. Based on these data, we are ass uming that there are approximately 1,300 E85 retail facilities currently in service for the purposes of our E85 retail facility cost analysis. By growing the num ber of E85 retail faci lities by the same proporti on as the growth in our projections for the growth in ethanol u se under the AEO 2007 based 2022 reference case, we estimate that 4,500 E85 refueling f acilities would be in place by 2022 absent the RFS2 standards. Thus, we estimate that today’s ru le would result in the need fo r an additional 24,250 retail E85 facilities by 2022.

We project that a number of E85 facilities would install multiple E85 pump stands. We based our estimate on the number of facilities that would install multiple E85 pump stands on an evaluation of how the anticipated E85 use levels translate to daily throughput per delivery nozzle. We estimate that by 2022 approximately 27.1 BGY of E85 woul d be used under the RFS2 standards. S SSSS We are assuming that all existing E85 refueling facilities and those that would be constructed absent the RFS2 standards have one pump stand each. There are two delivery nozzles (i.e. refueling pos itions) per each pump stand. Assuming that 40% of new E85 retail facilities constructed due to the RFS2 sta ndards install 2 pump stands (4 nozzles) and the remainder install a single pump stand results in a total of 76,900 E85 fueling positions (nozzles) by 2022. We assumed an average E85 refueling volum e of 15 gallons. This translates to an annual E85 throughput per refueling position of 3 53,000 gallons or one refueling event every 22 minutes. We believe this may be a reasonable, albeit optimistic, assumption regarding the future use rates of E85 refueling equipment. To evalua te the validity of this assessment, we reviewed historical data regarding th e use rates for refueling equipment for other fuels.

The National Association of Convenience Stores (NACS) reports throughput per refueling position. 460 For all types of fuel dispensed , NACS reports that from 2001 through 2006, the annual throughput varied from approxi mately 142,000 to 164,000 gallons per refueling position. These data include reports on the sales of all fuels including premium, mid-grade, and regular gasoline, diesel fuel and other fuels. Th e most appropriate compar ison would be made to throughput from refueling positions that dispense only regular gasoline since the use of E85 would primarily displace regular gasoline sales. However, this is not possible given that most gasoline is dispensed from blender pumps that can dispense any gasoline grade. Hence, we chose to make the comparison to throughput over dispensers that offer all gasoline grades, which may tend to underestimate the potential utilization rate of dispenser that dispenses only regular grade gasoline.

NACS reports that there is an average of 8. 6 refueling positions at the retail facilities that responded to their survey. NACS reports th at 36.6% of stores sold diesel fuel and 15.1% sold “other” fuels (i.e. not diesel, regu lar, mid-grade, or premium gasoline). T TTTT To estimate how many refueling positions are dedicated to diesel fuel and “other” fuels, we assumed that retailers offer diesel fuel from one pump with two nozzles, and other fu els from one pump with one nozzle. By multiplying the percentage of reta ilers that offer diesel fuel/other fuel by the assumed refueling positions for these fuels where they are present, we arrived at an estimate of 0.9 refueling positions per facility on average dedi cated to diesel fuel and other fuel. This translates to an average of 7.7 refueling positions per facility that dispenses gasoline. NACS

SSSSS A discussion of our E85 use projections is contained in Section 1.7.1.2 of this DRIA.
TTTTT In many cases, we expect that the “other” fuel is kerosene.

230reports that 92.7% of fuel volumes sold by respondents to their survey is gasoline (of all grades). By dividing 92.7% of the total average throughput for all fuels per facility reported by NACS by 7.7 refueling positions, we arrived at an estim ate of annual gasoline throughput per nozzle of 177,000 gallons for 2003. U UUUU The estimated E85 throughput per nozzle under the assumption that 40% of new facilities would install four E85 re fueling positions is nearly twice this figure.

We anticipate that most new E85 refueling f acilities would be insta lled at larger fuel retailers because these would be most forward looking with respect to attracting new customers, would be most likely to have the needed space, and be would have the be st access to capital. A higher throughput could be expected at larger fuel re tailers compared to that across all retail facilities reflected in the NACS survey. Thus, we believe that the projected level of E85 refueling position throughput of 353,000 gallons per year may be attainable, albeit ambitious. We performed a sensitivity analysis to evaluate the effect on E85 refu eling position throughput if additional E85 pumps were installed.

If each of the 28,750 E85 refueling faciliti es that we estimate would be needed by 2022 installed 2 E85 pumps/4 nozzles, the annua l E85 throughput per nozzle would be 236,000 gallons. If each installed 3 E 85 pumps/6 nozzles the annual E85 throughput per nozzle would be 157,000 which is consistent with current gasoline pu mp utilization rates. We anticipate that some of the additional E85 pumps would take the pl ace of existing gasoline pumps as E10 use is displaced by E85 use. It is also possible that pumps could be configured to dispense E10 as well as E85.

We assumed that one new 8,000 gallon E85 unde rground storage tank would be installed at each E85 refueling facility. Based on our estimate of 28,750 E 85 refueling facilities and the use of 27.125 BGY of E85 by 2022, annual E85 thr oughput per facility would be approximately 943,000 gallons. This translates to one 7,000 ga llon E85 delivery by tank truck every 2.7 days. V VVVV We believe that this is a reasonable fuel delivery rate, particularly in light of our projection that new E85 faci lities would likely be installed at the larger fuel retail facilities. We believe that a number of larger retailers have multiple gasoline storage tanks. Although not necessary to support E85 retail facility thr oughput, as E85 throughput ramps up and gasoline throughput decreases, some facilities may choose to retrofit one of their g asoline storage tanks to E85. However, we understand that it may be difficult or impossible to confirm th e compatibility of some existing underground storag e tanks and other tank-related hardware. In fact, several states (e.g. California) will not allow existing underground storage tanks to be upgraded to store ethanol blends greater than 10% due to compatibility concerns.

Approximately 1,960 new E85 facilities would need to be added each year from 2009 through 2022 in order to satisfy this goal of 28,750 facilities by 2022. The addition of 1,960 new E85 retail facilities a year is ve ry ambitious given that there are currently onl y 1,300 E85 retail facilities in service. Neverthe less, we believe the addition of these numbers of new E85 facilities may be possible for the industries that manuf acture and install E85 retail equipment. Underwriters Laboratories recently finalized its certification requirements for E85 retail

UUUUU The year 2003 had the highest average throughput per refueling position over the years 2001- 2006.
VVVVV A 7,000 delivery volume was assumed to provide adequate reserve at the retail facility to ensure consistent availability.

231equipment. 461 Equipment manufactures are currently ev aluating the changes that will be needed to meet these requirements. However, we anticipate the needed changes will not substantially increase the difficulty in the ma nufacture of such equipment compared to equipment which is specifically manufactured for dispensing E85 today.

Petroleum retailers expressed concerns about th eir ability to bear th e cost of installing new E85 refueling equipment because of the diffi culty of obtaining the necessary capital and to achieve adequate capital recovery . We estimated the costs under both our primary scenario where only 40% of E85 retailers install 2 pumps and under th e sensitivity case where all E85 retail facilities are assumed to have 3 E85 pumps. W WWWW A full discussion of our estimated of E85 facility costs is contained in Section 4.2 of this DRIA. We assumed all new pump stands under both scenarios. This will tend to provide a conservatively high estimate of costs since we expect that some E10 dispensers would be replaced by E85 dispensers under the 3 pump per facility sensitivity case. Under the 3-pump sens itivity case, we also included the cost of retrofitting an existing gasoline storage tank to pr ovide additional E85 storage capacity at 50% of new E85 facilities. Under our primary case where we assume that 40% of new facilities install 2 E85 pumps and the remainder woul d have one pump, we estimate that the cost of installing E85 refueling equipment will average $122,000 per faci lity which equates to $3 billion by 2022. These costs include the installation of an underg round storage tank, piping, dispensers, leak detection, and other ancillary equipm ent that is compatible with E85. X XXXX Under the sensitivity case, where all E85 facilities are assumed to ha ve 3 E85 pumps, we estimate the cost for facilities that did not offer E 85 before would be approximately $166,000 per facility. For those facilities that already offered E85, the cost of installing 2 additi onal pump stands is estimated at $46,000 per facility. The total cost of E85 refu eling facilities under the 3-pump sensitivity case is estimated at 4.2 billion by 2022.

Today’s proposal does not c ontain a requirement for reta ilers to carry E85. We understand that retailers will only install E85 facilities if it is economically advantageous for them to do so and that they will price their E85 and E10 in a manner to recover these costs. As discussed earlier we expect that larger fuel re tailers would be most lik ely to install new E85 refueling facilities. While the $3 billion total cost for E85 refueling facilities is a substantial sum under our primary E85 facility scenario, it equate s to just 1.2 cents per gallon of E85 throughput. Under the 3-pump sensitivity case, the total E85 f acility cost of $4.2 billion equates to 1.7 cents per gallons of E85 throughput. Y YYYY Therefore, we do not believe th at the cost of installing E85 refueling equipment will be unmanageable to reta ilers given the very large projected consumer demand for E85. Government incentives are also av ailable to help defer the cost of installing E85 retail equipment and expansions of these incentives are under consideration. Z ZZZZ

WWWWW Section 4.2 of this DRIA contains a discussion of estimated E85 retail facility costs.
XXXXX 40 CFR 280.32 states that underground storage tank syst ems must be made of or lined with materials that are compatible with the substance stored in the system.
YYYYY Our estimates of the cost of the E85 retail facilities that would be needed to support the use of the volume of ethanol that we project would be used under the RFS2 standards is contained in Section 4.2 of this DRIA.
ZZZZZ See Section 1.7.1.2.3 of this DRIA for a discussion of government incentives to install E85 retail refueling equipment.

232Petroleum retailers also expressed concern rega rding their ability to discount the price of E85 sufficiently to persuade flexible fuel vehi cle owners to choose E 85 given the lower energy density of ethanol. This issue is discu ssed in Section 1.7.1.2.5 of this DRIA.

1.6.11 Potential Impact on the Fuel Distribut ion System if a Waiver is Granted for MidLevel Ethanol Blends and Ancillery Issues that would need to be Resolved to Facilitate the Introduction of Mid-Level Ethanol Blends

As discussed in Section 1.7.1.3.1 of this DRIA, there is inter est in evaluating the suitability of midlevel ethanol blen ds (e.g. E15) for use in non-flex fuel vehicles to facilitate the consumption of the large volumes of ethanol that w ould be needed to satisfy the RFS2 standards. While a waiver may legalize the use of mid-le vel ethanol blends under the CAA, there are a number of other actions that would have to occu r to bring mid-level blends to retail. The time needed to take these actions coul d delay the penetration of mid- level ethanol blends into the market. The CAA only provides a 1 pound RVP waiv er for ethanol blends of 10 volume percent or less. Lacking such an RVP waiver, a spec ial low-RVP gasoline blendstock would be needed at terminals to all the formulation of mid-level ethanol blends that are complaint with EPA RVP requirements. Providing such a separate gasoline blendstock could presen t significant logistical challenges and costs to the fuel distribution system. A AAAAA In addition, a number of changes would be needed to EPA regulati ons including those pertaining to reformulated and conventional gasoline, and deposit control additives to acco mmodate and mid-level ethanol blends. Such changes would need to be made through the notice and comment process similar to today’s action. Independent of EPA’s regulations, most states require that fuel comply with the applicable ASTM International (formally known as the American Standards for Testing and Materials) specification. The development of an ASTM International specification for mid-level ethanol blends through an industry consensu s process is currently being initiated.

There are a number of requireme nts regarding the fire and leak protection safety of retail fuel dispensing and storage equipment. EPA’ s Office of Underground Storage Tanks (OUST) requires that UST systems must be compatible with the fuel stored in the system. These requirements pertain to all components of the system including the storage tank, connecting piping, pumps, seals and leak de tection equipment. The Occupational Safety and Health Administration (OSHA) requires that retail fu el handling equipment be listed with an independent standards body such as Underwriters Laborat ories (UL). No independent standards body has listed fuel handling equipment for mid-le vel ethanol blends. Furthermore, UL has stated that it would not expand lis tings for in-use fuel retail equipment originally listed for E10 blends to cover grea ter than E10 blends. B BBBBB

AAAAAA It may be possible for refiners to formulate a gasoline blendstock that would be suitable for manufacturing mid-level ethanol blends and E10 at the terminal. While this would avoid the logistical problems associated with maintaining separate blendstocks, there could be significant additional refining costs.
BBBBBB UL stated that they have data which indicates that the use of fuel dispensers certified for up to E10 blends to dispense blends up to a maximum ethanol content of 15 volume percent would not result in critical safety concerns ( http://www.ul.com/newsro om/newsrel/nr021909.html ). Based on this, UL stated that it would support authorities having jurisdiction who decide to permit legacy equipment originally certified for up to E10 blends to be used to dispense up to 15 volume percent ethanol. The UL announcement did address the compatibility of underground storage tank systems with greater than E10 blends.

233States typically adopt fire safety codes fr om either the National Fire Protection Association (NFPA) or th e International Code Council (ICC). These orga nizations currently do not have provisions that would allow the mid-level ethanol bl ends to be stored/dispensed from existing equipment at retail. Local safety offici als (e.g. fire marshals) referred to as “Authorities Having Jurisdiction” (AHJ’s) often require a UL certification for fuel re tail storage/dispensing equipment although some will accept other subs tantiation of equipment safety such as a manufacture certification. Fuel retailers must also satisfy the requirements of the insurance company that they are insured through which may be more stringent than the legal requirements.

The Department of Energy and EPA are currently working with industr y to evaluate what changes may be necessary to underground storage ta nk systems, fuel dispensers, and refueling vapor recovery equipment at fuel retail facilities to handle a mid-le vel ethanol blend. If existing equipment proves tolerant to a mid-level ethanol blend, this could substantially facilitate its introduction at retail. If the data supports the suitability of legacy retail equipment to store/dispense a mid-level blend, then the pr ocess of seeking acceptan ce by the standard bodies discussed above could commence. The normal processes used by these standards bodies can be lengthy. For example, the NFPA has a 3 year cycle for evaluating changes to its codes with proposals for the current cycle due this June. C CCCCC Thus, apart from the need to technically evaluate the suitability of legacy retail equipm ent to handle a mid-level ethanol blend, the need to secure recognition from standards bodies could delay the introduction of a mid-level ethanol blend at retail should a wa iver be granted by EPA.

If some components of the above ground existing retail hardware are found to be incompatible with a mid-level et hanol blend, it may be possible for them to be replaced through normal attrition. For example the “hanging hard ware” which includes the nozzle and hose from the dispenser is typically replaced every 3 to 5 years. It is al so possible that only minor changes might be needed to equipment that has a longer service life which might be accomplished without too much difficulty/cost. However, if extensive new equipment is needed and particularly if this involves th e breaking of concrete, we believ e that it is unlikely that fuel retailer would opt to install equipment specifi cally for a mid-level ethanol blend given the projected future need for retail equipment capable of handling E85. D DDDDD The deployment of mid-level ethanol blends would still not get us all the way to meeting the RFS2 volumes requirements. Once the nation hit the mid-level blend wall, refiners a nd marketers would still need to invest another $2-3 billion in E85 in frastructure (depending on th e scenario) or find other non-ethanol alternativ es to meet the RFS2 volume requirements. E EEEEE

Fuel retail operators are concerned about the potential liab ility from the misfueling of vehicles/equipment that might not be covered if a partial waiver is granted for mid-level ethanol blends. We anticipated that a dditional fuel pump labeling requirem ents would be needed. It is

CCCCCC It is unclear whether sufficient material could be assembled by industry in time for the consideration of dispensing E15 from legacy retail equipment be entered into the currently NFPA 3 year cycle.
DDDDDD A discussed previously, significant penetration of E85 is projected to be needed to facilitate the use of the volumes of ethanol we project would be needed to satisfy the requirements of the EISA.
EEEEEE For more information on retail and ethanol consumption impacts of mid-level ethanol blend assessment, refer to Sections 1.6.11 and 1.7.1. 3 of the DRIA, respectively.

234unclear what other measures might be needed to ad equately prevent misfueli ng if a partial waiver is granted.

Finally, auto manufactures woul d need to extend the warranty coverage for vehicles covered under a waiver in order to give consum ers adequate confidence to use mid-level ethanol blends. Owner manuals for non-FFV vehicles in the existing fleet are wr itten to void vehicle warranties if the vehicle is operated on ethanol levels greater than 10%.

Given our assumption that no ethanol would be transported by pipeli ne, we expect that there would be no impact on the ethanol distribution system thr ough the terminal level. If shipment of ethanol by pipeline does become pr acticable, there would obvi ously be implications regarding which ethanol blend ratio would be most desirable to ship . At the terminal level, the amount of ethanol blended into gasoline as it is delivered into tanks woul d be adjusted to the desired mid-level blend ratio. We expect that only minimal changes to the software for the blending equipment would be needed to accommodate the delivery of mid-level blends up to the point of delivery to retail facilities.



### 1.7 Biofuel Consumption Feasibility

1.7.1 Ethanol Consumption

1.7.1.1 Historical/Current Ethanol Consumption

Ethanol and ethanol-gasoline blends have a long history as automotiv e fuels. However, cheap gasoline/blendstocks kept ethanol from making a significant presence in the transportation sector until the end of the 20 th century when environmental regulations and tax incentives helped to stimulate growth.

In 1978, the U.S. passed the Energy Tax Act which provided an excise tax exemption for ethanol blended into gasoline th at would later be modified through subsequent regulations. F FFFFF In the 1980s, EPA initiated a phase-out of leaded g asoline which created some interest in ethanol as a gasoline oxygenate. Upon passage of th e 1990 CAA amendments, states implemented winter oxygenated fuel (“oxyfue l”) programs to monitor carbon monoxide emissions. EPA also established the reformulated gasoline (RFG) progr am to help reduce emissions of smog-forming and toxic pollutants. Both the oxyfuel and RFG programs called for oxygenated gasoline. However, petroleum-derived ethers, namely me thyl tertiary butyl ether (MTBE), dominated oxygenate use until drinking wate r contamination concerns prom pted a switch to ethanol. Additional support came in 2004 with the passage of the Volumetric Ethanol Excise Tax Credit (VEETC). The VEETC provided do mestic ethanol blenders w ith a $0.51/gal tax credit, replacing the patchwork of existing subsidies. G GGGGG The phase-out of MTBE and the

FFFFFF Gasohol, a fuel containing at least 10 percent biomass-derived ethanol, received a partial exemption from the federal gasoline excise tax. This exemption was implemented in 1979 and a blender’s tax credit and a pure alcohol fuel credit were added to the mix in 1980.
GGGGGG The recently enacted Food, Conserva tion, and Energy Act of 2008 (“2008 Farm Bill”) replaces the $0.51/gal ethanol blender credit with a $0.45/gal corn ethanol blender credit and also introduces a $1.01/gal cellulosic biofuel

235introduction of the VEETC along with state ma ndates and tax incentiv es created a growing demand for ethanol that surpassed the traditional oxyfuel and RFG markets. By the end of 2004, not only was ethanol the lead oxygenate, it was found to be blended into a growing number of states’ conventional gasoline. According to our RFS1 baseline oxygenate assessment, in 2004, ethanol was found to be blended into 15 states’ conven tional gasoline, 10 stat es’ winter oxyfuel, and 15 states’ RFG as shown in Table 1.7-1. 462

producer credit. Both credits are eff ective January 1, 2009. The 2008 Farm Bill is discussed in more detail in later parts of this subsection.

236Table 1.7-1. 2004 U.S. Ethanol Usage by State (Million Gallons)

State CG Oxy RFG Total
California 0.0 0.0 853.0 853.0
Illinois 110.8 0.0 310.7 421.5
New York 0.0 0.0 301.2 301.2
Minnesota 268.4 0.0 0.0 268.4
Ohio 191.6 0.0 0.0 191.6 New Jersey 0.0 0.0 187.6 187.6
Connecticut 0.0 0.0 152.2 152.2
Indiana 105.3 0.0 42.7 148.0
Missouri 42.9 0.0 79.1 122.0
Iowa 116.7 0.0 0.0 116.7
Wisconsin 47.3 0.0 61.3 108.6
Arizona 0.0 13.5 74.8 88.3
Colorado 0.0 80.2 0.0 80.2 Michigan 76.9 0.0 0.0 76.9 Kentucky 0.0 0.0 49.9 49.9
Hawaii 45.2 0.0 0.0 45.2

Kansas 40.8 0.0 0.0 40.8

Texas 0.0 13.1 26.3 39.5
Nebraska 37.2 0.0 0.0 37.2
Alabama 31.4 0.0 0.0 31.4 Oregon 0.0 30.8 0.0 30.8
South Dakota 23.9 0.0 0.0 23.9
Nevada 0.0 23.1 0.0 23.1
Massachusetts 0.0 0.0 18.4 18.4 Washington 0.0 17.9 0.0 17.9
North Dakota 10.5 0.0 0.0 10.5
New Mexico 0.0 8.0 0.0 8.0
Alaska 0.0 3.4 0.0 3.4
Utah 0.0 1.9 0.0 1.9

Montana 0.0 1.1 0.0 1.1
Rhode Island 0.0 0.0 0.3 0.3 Maryland 0.0 0.0 0.3 0.3
Florida 0.1 0.0 0.0 0.1 Virginia a 0.0 0.0 0.0 0.0

Total 1,149.0 193.0 2,158.0 3,500.0

a Approximately 3,200 gallons of ethanol use according to FHWA.

237In the years that followed, rising crude oil prices and othe r favorable market conditions continued to drive ethanol usage. In May 2007, EPA promulgated a Rene wable Fuel Standard (“RFS1”) in response to the Energy Policy Act of 2005. 463 The RFS1 program set a floor for renewable fuel use reaching 7.5 billion gallons by 2012, the majority of which was ethanol. The country is currently on track fo r exceeding the RFS1 requirement s and meeting th e introductory years of today’s proposed RFS2 program. For a summary of the growth in U.S. ethanol usage over the past decade, refer to Table 1.7-2.

Table 1.7-2. U.S. Ethanol Consumption (Including Imports)

Year Total Ethanol Use a
Trillion BTU Bgal
1999 2000
2001 2002 2003
2004 2005 2006 2007
2008 120 138
144 171 233
292 334 451 566
792 1.4 1.6
1.7 2.0 2.8
3.5 4.0 5.3 6.7
9.4

a EIA Monthly Energy Review March 2009 (Table 10.2)

According to EIA’s March 2009 Monthly Energy Review, the U.S. consumed approximately 9.4 billion gallons of et hanol in the transp ortation sector in 2008. 464 If ethanol consumption continues to grow (as it did from 2005-2008), the nation sh ould be on track for meeting the 2009 volume requirements set forth by EI SA (10.3 billion gallons of ethanol, based on our volume interpretations explained in Sec tion 1.2). For a graphi cal representation of monthly ethanol usage over the past fe w years, refer to Figure 1.7-1.

238Monthly Ethanol Usage (Bgal) Figure 1.7-1. 2005-2008 Monthly Ethanol Consum ption (Including Imports) 465

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2 2008

2007

0.1 2006

2005

0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month

Not only is ethanol usage projected to grow in future ye ars under the proposed RFS2 program, it is expected to come from more dive rsified sources. The Food, Conservation, and Energy Act of 2008 (also known as the “2008 Farm Bill” ) is expected to spur cellulosic ethanol production and use. 466 The recently-enacted Farm Bill modifies the existing $0.51 per gallon alcohol blender credit to give pr eference to ethanol and other biof uels produced from cellulosic feedstocks. As of January 1, 2009, corn etha nol receives a reduced credit of $0.45/gal and cellulosic biofuel earns a credit of $1.01/gal. H HHHHH

Through the years, there have also been sev eral policy initiatives to increase the number of flexible fuel vehicles (FFVs) capable of c onsuming up to 85 volume percent ethanol blends (E85). The Alternative Motor Vehicle Fuels Act of 1988 provi ded automakers with Corporate Average Fuel Economy (CAFE) credits for produc ing alternative-fuel ve hicles, including FFVs as well as CNG and propane vehicles. Furthe rmore, the Energy Policy Act of 1992 required government fleets to begin purchasing alternative-fu el vehicles, and the majority of fleets chose FFVs. 467 As a result of these two policy measur es, there are over 7 million FFVs on the road today. 468 These vehicles increase our nation’s etha nol consumption potential beyond what is capable with conventional vehicles. Howe ver, most FFVs are currently refueling on conventional gasoline (E0 or E10) due to lim ited E85 availability and the fact that E85 is

HHHHHH Refer to Part II, Subparts A and B, Sections 15321 and 15331.

239typically priced 20-30 cents per ga llon higher than gasoline on an energy equivalent basis. As such, we are not currently tapping into the full et hanol consumption potential of our FFV fleet. However, we expect refueling patterns to ch ange in the future under the proposed RFS2
program.

1.7.1.2 Increased Ethanol Use under RFS2

To meet the RFS2 standards, ethanol consum ption will need to be much higher than both today’s levels and those pr ojected to occur absent RFS2. The Energy Information Administration (EIA) projected that under business-as-usual c onditions, ethanol usage would grow to just over 13 billion gallons by 2022. 469 This represents signifi cant growth from today’s usage; however, this volume of ethanol is capa ble of being consumed by today’s vehicle fleet albeit with some fuel infrastructure improvements. I IIIII Although EIA projected a small percentage of ethanol to be bl ended as E85 in 2022, 13 billion ga llons of ethanol could also be consumed by displacing about 90% of our c ountry’s forecasted gasoline energy demand with E10. The maximum amount of ethanol our countr y is capable of consum ing as E10 compared to the projected RFS2 ethanol volumes is shown below in Figure 1.7-2.

IIIIII For more on distribution accommodations, refer to Section 1.6.

2402010 2011 201 2 2013 2014 201 5 2016 2017 2018 2019 2020 2021 20 2 2 Figure 1.7-2. J JJJJJ Max E10 Ethanol Consumption Comp ared to RFS2 Requirements

0 5 10 15 20 25 30 35 40Billion Gallons of Ethanol Ce l l u l o s i c E t h a n o l
I m p or t ed E t ha no l

Co r n E t h a n o l

E1 0 Bl e n d W a l l

Year

As shown in Figure 1.7-2, under the proposed RFS2 program, we are projected to hit the E10 “blend wall” of about 14-15 billion gallons by 2013. This volume corresponds to 100% E10 nationwide. However, if gasolin e demand falls, or if E10 cannot get distributed nationwide, the nation could hit the blend wall sooner. Regard less, to get beyond the blend wall and consume more than 14-15 billion gallons of ethanol, we are going to need to see significant increases in the number FFVs on the road, the number of E85 re tailers, and the FFV E85 refueling frequency. In the subsections that follow, we will highlight the variables that imp act our nation’s ethanol consumption potential and, more specifically, wh at measures the market may need to take in order to consume 34 billion gallons of ethanol by 2022 (assuming the cellulosic biofuel standard and the majority of the advanced biofuel standard are met with ethanol).

As explained in Section 1. 2, our primary RFS2 analysis focuses on ethanol as the main biofuel in the future. In addi tion, from an ethanol consumpti on standpoint, we have focused on an E10/E85 world. While E0 is capable of co-existing with E10 a nd E85 for a while, we assumed that E10 would replace E0 as expeditiously as possible and that all subsequent ethanol growth would come from E85. Furthermore, for our primary analysis, we assumed that no ethanol consumption would come from the mid-level ethanol ble nds (i.e., E15 or E20) as they are not currently approved for u se in non-FFVs. However, based on public interest and a recent

JJJJJJ Based on AEO 2009 gasoline energy projections and the assumption that the cellulosic biofuel standard and the majority of the advanced biofuel standard would be met with ethanol.

241waiver application submitted by Growth En ergy, we will discuss the potential volume implications of mid-level ethanol ble nds in the subsections that follow.

We acknowledge that, if approved, mid-level ethanol blends could help the nation meet the proposed RFS2 volume requirements. Firs t, non-FFVs could consum e more ethanol per gallon of “gasoline”. This could result in greate r ethanol consumption nati onwide. In addition, mid-level blends could allow refiners to conti nue to price ethanol rela tive to gasoline (as it currently is for E10). For these reasons, it is pos sible that mid-level ethanol blends could help the nation get beyond the E10 blend wall. Howeve r, as explained in Section 1.6,11 there are numerous actions that would need to be taken to bring mid-level ethanol blends to market. In addition, mid-level ethanol blends alone (even if made available nationwide) are not capable of fulfilling the RFS2 requirements in later years. As explained in Section 1.7.1.3, we would essentially hit another blend wall 1-6 years later depending on the intermediate blend (i.e., E15 vs. E20), how quickly it could be brought to market, and how wi dely mid-level ethanol blends were distributed. Nevertheless, this time coul d be very valuable when it comes to expanding E85/FFV infrastructure and/or commercializing other non-ethanol cellulosic biofuels.

In summary, our primary analysis fo cuses on an E10/E85 world because mid-level ethanol blends are not currently approved for use in conventi onal gasoline vehicles and nonroad equipment. K KKKKK As a sensitivity analysis, we have an alyzed what might need to be done to bring mid-level ethanol blends to market (should a sub-sim waiver be granted) and the extent to which such blends could help our nation get beyond the E10 ble nd wall meet the RFS2 ethanol standards.

1.7.1.2.1 Projected Gasoline Energy Demand

The maximum amount of ethanol our country is capable of consuming in any given year is a function of the total gaso line energy demanded by the tran sportation sector. Our nation’s gasoline energy demand is dependent on the numbe r of gasoline-powered vehicles on the road, their average fuel economy, vehicle miles traveled (VMT), and driving pa tterns. For analysis purposes, we relied on the gasoline ener gy projections reporte d by EIA in AEO 2008. L LLLLL Unlike AEO 2007, AEO 2008 takes the fuel eco nomy improvements set by EISA into consideration and also assumes a slight dieseliza tion of the vehicle fleet. The result is a 15% reduction in the projected 2022 gasoline energy demand from AEO 2007 to AEO 2008. 470, 471 As shown in Figure 1.7-3, EIA basically has ga soline energy demand (petroleum-based gasoline plus ethanol) flattening out, and even slightly decreasing, as we move into the future and implement the EISA vehicle standards. The r ecently-published AEO 2009 shows a similar trend, but with a slightly lower ove rall energy demand.

KKKKKK For more on the potential approval pathways for mid- level ethanol blends, refer to Section V.D.3.b of the Preamble.
LLLLLL For subsequent blend wall discussions/graphs, we relied on the most r ecent AEO 2009 projections. However for our detailed ethanol consumption analysis presented in this section, we relied on AEO 2008. We adjusted the gasoline energy demand reported in AEO Table 2 to reflect lower heating values for ethanol. In AEO 2007, EIA used a higher heating value of 83,333 BTU/gal for ethanol and in AEO 2008, EIA used a higher heating value of 84,262 for ethanol. Our analyses were based on applying a lower heating value of 77,930 BTU/gal to represent denatured fuel-grade ethanol.

24220 0 4 2 00

5 2 00

6 200 7 2008 20 0 9 2 01

0 2 01

1 201 2 2013 20 1 4 2 01

5 2 01

6 Figure 1.7-3. Forecasted Gasoline Energy Demand

0 5 10 15 20 25Quadrillion BTUs

AEO 2007

AEO 2008

AEO 2009

Year

1.7.1.2.2 Projected Growth in Flexible Fuel Vehicles

According to DOE’s Department of Energy Efficiency and Renewa ble Energy, there are currently over 7 million FFVs on the ro ad today capable of consuming E85. M MMMMM And that number is growing steadily. Automakers are in corporating more and more FFVs into their lightduty production plans. While the FFV system (i.e., fuel tank, sensor, delivery system, etc.) used to be an option on some vehicles, most FFV pr oducers are moving in the direction of converting entire product lines over to E85-capable systems. Still, the number of FFVs that will be manufactured and purchased in future years is un certain. To reflect this uncertainty, we have analyzed multiple FFV production scenarios. N NNNNN However, for our primary ethanol usage and cost analyses we have focused on the “Op timistic FFV Production Scenario” (described in more detail below). The other FFV production scenarios we consid ered are presented at the end of this subsection.

In response to the President Bush’s “20-in-10” plan of re ducing American gasoline usage by 20% in 10 years, domestic automakers responded with aggressive FFV production goals.

MMMMMM DOE Energy Efficiency and Renewable Energy August 2008 estimate (worksheet available at www.eere.energy.gov/afdc/data/index.html)
NNNNNN We also analyzed multiple E85 acce ss scenarios. For more informati on, refer to Sec tion 1.7.1.2.3.

243General Motors, Ford and Chrysler (referred to hereafter as “The Detroit 3”) announced plans to produce 50% FFVs by 2012. 472 Assuming that The Detroit 3 continue to maintain 50% marketshare and that total vehicle sales rema in around 16 million per year, at least 4 million FFVs will be produced by the 2012 model year. Based on 2008 offerings, we assumed that approximately 80% of The Detroit 3’s FFV comm itment would be met by light-duty trucks and the remaining 20% would be cars. 473, O OOOOO For analysis purposes, we also assumed that all the FFVs in existence today were produced by The Detroit 3 (a nd therefore share the same aforementioned FFV car/truck ratio) and that pr oduction would ramp up linearly beginning in 2008 to reach the 2012 commitment. P PPPPP

Although non-domestic automakers have not made any official FFV production commitments, Nissan, Mercedes, Izuzu, and Mazda a ll included at least one flexible fuel vehicle in their 2008 model year offerings. 474 And we anticipate that additional FFVs (or FFV options) will be added in the future. Ultimately, we predict that non-domestic FFV production could be as high as 25%, or about 2 million FFVs per year. While we are not forecasting an official FFV production commitment from the non-domestic automa kers, we believe that this represents an aggressive, yet reasonable FFV production estimate for analysis purposes. Furthermore, based on current offerings, we assumed that the majority of non-domestic FFVs would be trucks, i.e., 100%. With respect to timing, we expect that the non-domestic automakers would ramp up FFV production later than The Detroit 3. For an alysis purposes, we assumed that non-domestic automakers would ramp up FFV production beginni ng in 2013, and like The Detroit 3, it would take about five years for them to reach their FFV production goals (or in this case, the assumed 25% production level). For a summary of th e year-by-year FFV production assumptions we made under the Optimistic FFV Production Scenario, refer to Table 1.7-3.

OOOOOO Several of the FFV production assumptions may need to be revised for the FRM in light of recent events.
PPPPPP As a simplifying assumption, we assumed that all FFVs in today’s fleet were produced by The Detroit 3 beginning in 2002.

244Table 1.7-3. Optimistic FFV Production Scenar io – FFV Production Assumptions

Year GM, Chrysler & Ford Non- Domestic Automakers
Tot FFVs FFV-Cars FFV-Trucks Tot FFVs FFV-Cars FFV-Trucks 2002 1,000,000 200,000 800,000 0 0 0 2003 1,000,000 200,000 800,000 0 0 0 2004 1,000,000 200,000 800,000 0 0 0 2005 1,000,000 200,000 800,000 0 0 0 2006 1,000,000 200,000 800,000 0 0 0 2007 1,000,000 200,000 800,000 0 0 0 2008 1,600,000 320,000 1,280,000 80,000 0 80,000 2009 2,200,000 440,000 1,760,000 160,000 0 160,000 2010 2,800,000 560,000 2,240,000 240,000 0 240,000 2011 3,400,000 680,000 2,720,000 320,000 0 320,000 2012 4,000,000 800,000 3,200,000 400,000 0 400,000 2013 4,000,000 800,000 3,200,000 720,000 0 720,000 2014 4,000,000 800,000 3,200, 000 1,040,000 0 1,040,000 2015 4,000,000 800,000 3,200, 000 1,360,000 0 1,360,000 2016 4,000,000 800,000 3,200, 000 1,680,000 0 1,680,000 2017 4,000,000 800,000 3,200, 000 2,000,000 0 2,000,000 2018 4,000,000 800,000 3,200, 000 2,000,000 0 2,000,000 2019 4,000,000 800,000 3,200, 000 2,000,000 0 2,000,000 2020 4,000,000 800,000 3,200, 000 2,000,000 0 2,000,000 2021 4,000,000 800,000 3,200, 000 2,000,000 0 2,000,000 2022 4,000,000 800,000 3,200, 000 2,000,000 0 2,000,000

Based on these FFV production assumptions an d forecasted vehicle phase-out, VMT, and fuel economy estimates provided by EPA’s MOVES Model, we cal culate that the maximum percentage of fuel (gasoline/ethanol mix) that could feasibly be consumed by FFVs in 2022 would be about 30%. Q QQQQQ The resulting gasoline energy c onsumption by vehicle type under the Optimistic FFV Production Scenario is shown below in Figure 1.7-4. For all analyses, we assumed that the percentage of gasoline en ergy consumed by nonroad, heavy-duty gasoline vehicles (HDGVs), and motorcycles would be about 8% based on historical information provided by DOE. 475

QQQQQQ For more on the EPA MOVES Model, refer to Chapter 3 of this DRIA.

24520 1 0 20 11 20 1 2 20 13 2014 2 0 15 20 1 6 20 17 20 1 8 20 19 2020 20 21 2022 Figure 1.7-4. Optimistic FFV Production Scenario - Gasoline Consumption by Vehicle Type

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%% of Gasoline Consumption FFVs Tier 2 Vehicles (Non-FFVs)
Pre-Tier 2 Vehicles Nonroad, HDGVs & Motorcycles

As mentioned above, the number of FFVs th at will be produced in the future under the RFS2 rule is relatively unknown. However, to bracket the uncertainly, we have analyzed a less optimistic FFV production scenario as well as wh at FFV production might look like under a congressional mandate. Although EPA does not have the authority to requ ire automakers to produce FFVs, there are a number of bills in Congress that are set out to do just that, e.g., The Open Fuel Standard Act of 2008. R RRRRR

For the less optimistic production scenario (r eferred to hereafter as the “Committed FFV Production Scenario”), we consid ered what production might l ook like if only The Detroit 3 followed through with their 50% by 2012 FFV comm itment, Under this scenario, we assumed the same Detroit 3 FFV production assumptions as in the Optimistic FFV Production scenario. However, we assumed that the non-domestic automakers would only produce the FFVs they are currently forecasting in their existing produc tion plans. Based on 2008 MY offerings, we assumed that the non-domestic automakers would produce approximately 5% FFVs by 2012. 476 For analysis purposes, we assumed that producti on would creep up beginning in 2008, and like the Optimistic FFV Production Scenario, and all non-domestic FFVs would be light-duty trucks.

RRRRRR Refer to Senate Bill 3303 and House Rule 6559 discussed in more detail below.

246For a summary of the year-by-year vehicl e production assumptions we made under the Committed FFV Production Scenario, refer to Table 1.7-4.

Table 1.7-4. Committed FFV Production Scenario – FFV Production Assumptions

Year GM, Chrysler & Ford Non- Domestic Automakers Tot FFVs FFV-Cars FFV-Trucks Tot FFVs FFV-Cars FFV-Trucks 2002 1,000,000 200,000 800,000 0 0 0 2003 1,000,000 200,000 800,000 0 0 0 2004 1,000,000 200,000 800,000 0 0 0 2005 1,000,000 200,000 800,000 0 0 0 2006 1,000,000 200,000 800,000 0 0 0 2007 1,000,000 200,000 800,000 0 0 0 2008 1,600,000 320,000 1,280,000 80,000 0 80,000 2009 2,200,000 440,000 1,760,000 160,000 0 160,000 2010 2,800,000 560,000 2,240,000 240,000 0 240,000 2011 3,400,000 680,000 2,720,000 320,000 0 320,000 2012 4,000,000 800,000 3,200,000 400,000 0 400,000 2013 4,000,000 800,000 3,200,000 400,000 0 400,000 2014 4,000,000 800,000 3,200,000 400,000 0 400,000 2015 4,000,000 800,000 3,200,000 400,000 0 400,000 2016 4,000,000 800,000 3,200,000 400,000 0 400,000 2017 4,000,000 800,000 3,200,000 400,000 0 400,000 2018 4,000,000 800,000 3,200,000 400,000 0 400,000 2019 4,000,000 800,000 3,200,000 400,000 0 400,000 2020 4,000,000 800,000 3,200,000 400,000 0 400,000 2021 4,000,000 800,000 3,200,000 400,000 0 400,000 2022 4,000,000 800,000 3,200,000 400,000 0 400,000

As expected, having fewer FFVs in the fleet tr anslates to this group of vehicles having a lower gasoline/ethanol consumption potential . Based on the less optimistic FFV production assumptions (summarized above in Table 1.7-4) and forecasted vehicle phase-out, VMT, and fuel economy estimates provided by EPA’s MOVES Model, we cal culate that the maximum percentage of fuel (gasoline/ethanol mix) that could feasibly be consumed by FFVs in 2022 would be about 24%. The resulting gasoline energy consumption by ve hicle type under the Committed FFV Production Scenario is shown below in Figure 1.7-5.

247Figure 1.7-5. Committed FFV Production Scenario - Gasoline Consumption by Vehicle Type

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%% of Gasoline Consumption FFVs Tier 2 Vehicles (Non-FFVs)
Pre-Tier 2 Vehicles Nonroad, HDGVs & Motorcycles

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

For our third and final FFV production scen ario, we considered what FFV production might look like under some sort of Congressional ma ndate. At the time of our analysis, we were unaware of any formal legislation being considered by Congress. However, since that time there have been multiple bills introduced by Congress. On July 22, 2008 Senator Sam Brownback (RKS) on behalf of himself and Senators Susan Collins (R-ME), Joseph Lieberman (I-CT), Ken Salazar (D-CO), and John Thune (R -SD) introduced the Open Fuel Standard Act of 2008, a bill that calls for 50% of the U.S. vehicle fleet to be FFVs capable of using high blends of ethanol or methanol (in addition to gasoline) by 2012. This number would grow to 80% by 2015. 477 A similar FFV bill was introduced by Eliot Engel (D-NY) in the House on July 22, 2008. 478

However, because our analysis was conducted before these bills were introduced, we had to come up with our own set of assumptions with respect to the number of FFVs and the implementation timeline under a theoretical ma ndate. Under our “Mandated FFV Production Scenario,” we considered the possibility that Congress could require all li ght-duty vehicles to be equipped with E85-capable fuel systems in the futu re. While we realize this is more aggressive than the mandates currently being considered by Congress, we believe this is a good upper bound for our ethanol usage analysis. Furthermore, we believe that such a mandate is feasible based on the lead-time required to engineer and implement the necessary vehicle fuel system changes.

248Under our Mandated FFV Production Scenario , we assumed that the first phase of the FFV mandate could require all au tomakers to meet the production goals set by The Detroit 3 – 50% FFVs by 2012. From there, we assumed th e mandated percentage could grow to 100% FFVs by 2015. This implementation timeline is consistent with Se nate Bill 3303 and House Rule 6559 – the mandates currently being consider ed by Congress. For a summary of the yearby-year vehicle production assumptions we ma de under the Mandated FFV Production Scenario, refer to Table 1.7-5.

Table 1.7-5. Mandated FFV Production Scenario – FFV Production Assumptions

Year GM, Chrysler & Ford Non- Domestic Automakers Tot FFVs FFV-Cars FFV-Trucks Tot FFVs FFV-Cars FFV-Trucks 2002 1,000,000 200,000 800,000 0 0 0 2003 1,000,000 200,000 800,000 0 0 0 2004 1,000,000 200,000 800,000 0 0 0 2005 1,000,000 200,000 800,000 0 0 0 2006 1,000,000 200,000 800,000 0 0 0 2007 1,000,000 200,000 800,000 0 0 0 2008 1,600,000 560,000 1,040,000 800,000 520,000 280,000 2009 2,200,000 770,000 1,430,000 1,600,000 1,040,000 560,000 2010 2,800,000 980,000 1,820,000 2,400,000 1,560,000 840,000 2011 3,400,000 1,190,000 2,210, 000 3,200,000 2,080,000 1,120,000 2012 4,000,000 1,400,000 2,600, 000 4,000,000 2,600,000 1,400,000 2013 5,333,333 1,866,667 3,466, 667 5,333,333 3,466,667 1,866,667 2014 6,666,667 2,333,333 4,333, 333 6,666,667 4,333,333 2,333,333 2015 8,000,000 2,800,000 5,200, 000 8,000,000 5,200,000 2,800,000 2016 8,000,000 2,800,000 5,200, 000 8,000,000 5,200,000 2,800,000 2017 8,000,000 2,800,000 5,200, 000 8,000,000 5,200,000 2,800,000 2018 8,000,000 2,800,000 5,200, 000 8,000,000 5,200,000 2,800,000 2019 8,000,000 2,800,000 5,200, 000 8,000,000 5,200,000 2,800,000 2020 8,000,000 2,800,000 5,200, 000 8,000,000 5,200,000 2,800,000 2021 8,000,000 2,800,000 5,200, 000 8,000,000 5,200,000 2,800,000 2022 8,000,000 2,800,000 5,200, 000 8,000,000 5,200,000 2,800,000

As expected, having more FFVs in the fleet results in these vehicles having a higher gasoline/ethanol consumption potential. Based on the mandated FFV production assumptions (summarized above in Table 1.7-5) and for ecasted vehicle phase-out, VMT, and fuel economy estimates provided by EPA’s MOVES Model, we calcu late that the maximum percentage of fuel (gasoline/ethanol mix) that could feasi bly be consumed by FFVs in 2022 would be approximately 68%. The resulting gasoline en ergy consumption by vehicle type under the Mandated FFV Production Scenario is shown below in Figure 1.7-6.

249Figure 1.7-6. Mandated FFV Production Scenario - Ga soline Consumption by Vehicle Type

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%% of Gasoline Consumption FFVs Tier 2 Vehicles (Non-FFVs)
Pre-Tier 2 Vehicles Nonroad, HDGVs & Motorcycles

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

For analysis purposes, we assumed that al l FFVs would be distributed homogeneously throughout the nation in future years. However, the reality is, FFVs may be more concentrated in certain areas of the country based on variation in regiona l product demand (and corresponding FFV offerings) and state-level FFV incentives. We plan to revisit this for the final rule and also consider the theoretical cost and refueling fr equency impacts of co-l ocating FFVs with E85 stations. In the meantime, we acknowledge that there are a number of states offering incentives to get consumers to purchase alternative fuel vehicles (AFVs). The AFV incentives range from purchasing rebates to discounted re gistration fees and even free pa rking in certain metropolitan areas. According to a July 2008 assessment of DOE’s Energy Efficiency and Renewable Energy (EERE) website, 13 states currently offer some form of AFV incentive. 479 A summary of these states is provided in Table 1.7-6 below. There are also a number of federal vehicle fleets (e.g., the U.S. Postal Service) and st ate government fleets (e.g., police departments) that require the purchase AFVs.

250Table 1.7-6. Alternative Fuel Vehicle Incentives Region States
PADD 1 GA, NC, SC, VT
PADD 2 IL, IN, KS, NE
PADD 3 LA, NM
PADD 4 MT
PADD 5 CA, OR

1.7.1.2.3 Projected Growth in E85 Access

According to the National Ethanol Vehicle Coalition (NEVC), ther e are currently over 1,900 retailers offering E85 in 45 stat es plus the District of Columbia. 480 While this represents significant industry growth, it still on ly translates to about 1% of U.S. retail stations nationwide carrying the fuel. S SSSSS, 481 As a result, most FFV owners clearly do not have reasonable access to E85. For our FFV/E85 analysis, we have defined “reasonable access” as one-in-four pumps offering E85 in a given area. T TTTTT Accordingly, just over 4% of the nation currently has reasonable access to E85, up from 3% in 2007 (based on a mid-year NEVC E85 pump estimate). U UUUUU, 482

There are a number of states promoti ng E85 usage by offering FFV/E85 awareness programs and/or retail pump incentives. There are also a growing numb er of states offering infrastructure grants to help expand E85 ava ilability. According to a July 2008 assessment of DOE’s Energy Efficiency and Renewable Energy ( EERE) website, 26 states currently offer some form infrastructure grant for facilities that bring biofuels, including E85 to market. 483 A summary of these state programs is provided in Table 1.7-7 below.

SSSSSS Based on the assumption that there were 1,733 E85 stations (number reported by NEVC on August 25, 2008) and 164,292 total gas stations (number reported by National Petroleum News in October 2007).
TTTTTT For a more detailed discussion on how we derived our one-in-four reasonable access assumption, refer to Section 1.6. For the distribution cost implications as well as the cost impacts of assuming reasonable access is greater than one-in-four pumps, refer to Section 4.2.
UUUUUU Computed as percent of stations with E85 (1,733/164,292 as of Aug 25, 2008 or 1251/164292 as of July 2007) divided by 25 % (one-in-four stations).

251Table 1.7-7. States Offering E85 Infrastructure Support Region States PADD 1 FL, ME, NY, NC, PA, SC
PADD 2 IL, IN, IA, KS, KY, MI, MN, NE, ND, OH, OK, SD, TN PADD 3 AR, NM
PADD 4 CO, ID
PADD 5 CA, OR, WA

In addition, nine Midwest states have adopted a progressiv e Energy Security and Climate Stewardship Platform. 484 As of June 2008, Indiana, Iowa, Kansas, Michigan, Minnesota, North Dakota, Ohio, South Dakota, and Wisconsin had all committed to program which emphasizes energy independence through the gr owth of E85 availability (as well as cellulosic ethanol production). The platform includ es a Regional Biofuels Promotion Plan with a goal of making E85 available at one third of all stations by 2025. In addition, on July 31, 2008, Congresswoman Stephanie Herseth Sandlin (D-S D) and John Shimkus (R-IL) intr oduced a legislative proposal that would increase the existing federal income tax credit from $30, 000 or 30% of the total cost of improvements to $100,000 or 50% of the total co st of needed alternative fuel equipment and dispensing improvements. 485 This legislation was recently reintroduced on March 26, 2009. While not signed into law, such a tax credit coul d provide a significant reta il incentive to expand E85 infrastructure.

Given the growing number of state infrast ructure incentives and the proposed Federal alternative fuel infrastructure subsidy, it is cl ear that E85 infrastructure will continue to expand in the future under the RFS2 program Howeve r, the extent to which nationwide E85 access will grow is difficult to predict, let alone quantify. To represent practical yet aggressive station growth, we considered the possibility that 70% of the nation could have reasonable access to E85 by 2022. This is roughly equivalent to all ur ban areas in the United St ates offering reasonable (one-in-four-station) access to E 85. For this analysis, we have defined “urban” areas as:

• The top 150 metropolitan statistical areas according to the U.S. Census Bureau and/or counties with the highest 150 VMT projecti ons according the EPA MOVES model.

• Federal RFG areas

• Winter oxy-fuel areas

• Summertime low-RVP areas

• Other relatively populated cities in the Midw est. Cities with populations greater than 100,000 people in states with a pr ojected ethanol surplus in 2 022. [For more information on ethanol production, refer to Sections 1.5.1 through 1.5.3 of the DRIA.]

For an illustration of the urba n areas representing about 70% of the nation’s VMT, refer to Figure 1.7-7.

252Figure 1.7-7. A Look at 70% E85 Ac cess - Concentrating Pumps in Urban Areas

We are not concluding that E 85 would only be offered in urban areas in the future. In fact, most E85 stations are curr ently located in the Midwest. Ho wever, we believe that this would be one possible way to provide 70% of the population with reasonable access to E85. From a fuel price standp oint, it makes sense that E85 might be offered in areas of the country with relatively high gasoline prices (e.g., RFG and low-RVP areas). Additionally, from an infrastructure cost standpoint, it makes sense that E85 might be offered in more populated metropolitan areas with high gasoline throughput. V VVVVV For more on retail distribution costs, refer to Section 4.2 of this DRIA.

VVVVVV We acknowledge that land costs, spatial limitations, an d air quality/regulations could potentially restrict E85 growth in densely populated metropolitan areas. However, for analysis purposes, we assumed that it would be possible for one-in-four stations to offer E85 to provid e FFV owners with reasonable access to the fuel.

2532 010 2 011 2012 2 013 2 014 2 01

5 2 01

6 2 01

7 2 0 18 2 0 19 2 020 2021 2022 Accordingly, for our primary analysis, we assumed that the percentage of the country with reasonable access to E85 could grow to 70% by 2022. This translates to about 29,000 retailers offering E85, or a bout 1:6 stations nationwide. W WWWWW According to our distribution assessment, we believe it’s possible to bring this many stations to market by 2022. And furthermore, our analysis indi cates that 29,000 stations are cap able of dispensing 29 billion gallons of E85 – the volume required to meet the RFS2 standard in 2022 in an E10/E85 world. For more information on E85 retail accommoda tions and fuel volumes required under RFS2, refer to Sections 1.6.10 and 1.7.1.4, respectively.

If we assume that reasonable E85 access grows to 70% by 2022, we can iteratively compute the corresponding nationwide E0 and E10 access assuming that a. ) each fuel retailer only carries one type of conventional gasoline (E 0 or E10) and b.) the nation does not exceed the RFS2 ethanol volume requirements presented in S ection 1.2. Under the most aggressive FFV production scenario, we estimate that E0 could theoretically remain in existence until 2016 as shown below in Figure 1.7-8. However, we an ticipate that E10 will likely replace E0 sooner based on current market trends.

Figure 1.7-8. Assumed Phase-Out of E0 and Phase-In of E10 & E85

0% 20 % 40 % 60 % 80 % 10 0%Nationwide Access to Fuel

E0 E10 E85

Year

WWWWWW Computed as the number of U.S. gas stations (164,292) times the percent of stations with reasonable access to E85 by 2022 (70%) times the definition of reasonable access (1-in-4 stations or 25%)

2542 007 200 8 2009 2 010 201 1 2012 2 013 20 14 2015 2016 20 17 20 18 2019 20 20 20 21 2022 As an upper bound, we considered the possibi lity that reasonable one-in-four E85 access could theoretically grow to 100% by 2022. While we do not believe this is very likely (after all, diesel fuel - a required fuel for compression engines is only offered at about one-third of stations nationwide), this is a meaningful sensitivity anal ysis because it corresponds to the maximum E85 retail costs under the RFS2 program (for more on E85 infrastructure costs, refer to Section 4.2 of the DRIA). For both E85 access scenarios, we a ssumed a linear phase-in from today’s estimated 4% access level as shown in Figure 1.7-9.

Figure 1.7-9. Estimated Growth in E85 Access under RFS2

100%

80%

60%

40%

20%

0% Maximum Growth in E85 Access

Practical Yet Aggressive Growth in E85 Access% of country with reasonable 1-in-4 access to E85

Year

1.7.1.2.4 Required Increase in E85 Refueling Rates

As mentioned above, there were approximately 7 million FFVs on the road in 2008. If all FFVs refueled on E85 100% of the time, this would translate to about 6.5 billion gallons of E85 use. This estimate is based on the assumpti on that FFV owners trav el approximately 12,000 miles per year and get about 18 miles per ga llon during actual in-u se driving conditions. X XXXXX
This estimate also assumes that it takes about 1.3 gallons of E85 for an FFV to travel the same distance as a gallon of gasoline due to the difference in energy density of the fuels. Y YYYYY

XXXXXX Based on the assumption that 20% of FFVs are cars (27 MPG) and 80% are trucks (20.7 MPG) and the assumption that all vehicles/FFVs achieve about 80% of the rated fuel economy under real driving conditions.
YYYYYY Based on our assumption that denatured ethanol has an average lower heating value of 77,930 BTU/gal and conventional gasoline (E0) has average lower heating value of 115,000 BTU/gal. And based on EIA’s AEO 2008

255Although we computed the theoretical E85 usage potential to be around 6.5 billion gallons in 2007, actual E85 u sage was only reported to be around 12 million gallons in 2008. Z ZZZZZ This means that, on average, FFV owne rs were only tapping in to about 0.2% of their vehicles’ E85/ethanol usage potential last year. Assuming that only 4% of the nation had reasonable one-in-four access to E85 in 2008 (as discussed above in Section 1.7.1.2.3), this equates to an estimated 5% E85 refueling fre quency for those FFVs that had reasonable access.

There are several reasons for today’s low E85 refueling frequency in areas of the country with reasonable access to the fuel. For star ters, many FFV owners may not know they are driving a vehicle that is capab le of handling E85. As mentioned above, more and more automakers are starting to produce FFVs by engi ne/product line, e.g., all 2008 Chevy Impalas are FFVs. A AAAAAA 486 Consequently, consumers (especi ally brand loyal consumers) may inadvertently buy a flexible fuel vehicle wit hout making a conscious de cision to do so. And without effective consumer awar eness programs in place, these FFV owners may never think to refuel on E85. In addition, FFV owners with reas onable access to E85 and knowledge of their vehicle’s E85 capabilities may still not choose to refuel on E85. They may feel inconvenienced by the increased E85 refueling requirements. Based on its lower energy density, FFV owners will need to stop to refuel 21% more often wh en filling up on E85 over E10 (and likewise, 24% more often when refueling on E85 over conventional gasoline). B BBBBBB In addition, some FFV owners may be deterred from refueling on E85 out of fear of reduced vehicle performance or just plain unfamiliarity with the new motor vehicle fuel. However, as we move into the future, we believe the biggest determinant will be price – whet her E85 is priced competitively with gasoline based on its reduced energy density and the fact that you need to st op more often, drive a little further to find an E85 station, and depending on th e retail configuration, wait in longer lines to fill up on E85.

To comply with the proposed RFS2 program and consume 34 billion gallons of ethanol by 2022, not only would we need more FFVs and mo re E85 retailers, we would need to see a significant increase in the current FFV E85 refu eling frequency. Under the Optimistic FFV Production Scenario (assuming prac tical growth in E85 access), our analysis suggests that FFV owners with reasonable one-in-four access to E85 w ould need to fill up on it 74% of the time in 2022 - a significant increase from today’s estimated 11% refueling frequency. As shown below in Figure 1.7-10, with fewer FFVs in the fleet, the E85 refueling frequency would need to be even higher. Similarly, with more FFVs in th e fleet, the E85 refueling frequency could be lower and still meet the proposed RFS2 requirements. However, even with an FFV mandate, our analysis suggests that we would need to see an increase from toda y’s FFV E85 refueling frequency. As expected, if the E85 access were to grow beyond 70% to 100% by 2022 (refer to dashed lines in Figure 1.7-10) th e required refueling rates would be lower. However, based on

report, E85 was assumed to contain 74 vol% ethanol and 26 vol% gasoline on average. Our analysis ignores the potential that there could be a thermal efficiency improvement when vehicles burn ethanol.
ZZZZZZ EIA Annual Energy Outlook 2008, Table 17.
AAAAAAA Refers to all mass produced 3.5 and 3.9L Chevy Impalas. However, it is our understanding that consumers may still place special or ders for non-FFVs.
BBBBBBB Based on our assumption that denatured ethanol has an average lower heating value of 77,930 BTU/gal and conventional gasoline (E0) has average lower heating value of 115,000 BTU/gal. For analysis purposes, E10 was assumed to contain 10 vol% ethanol and 90 vol% gasoline. Based on EIA’s AEO 2008 report, E85 was assumed to contain 74 vol% ethanol and 26 vol% gasoline on average.

2562 01

0 2 011 2 0 12 2013 2 01

4 2 01

5 2 016 2017 2 018 2 0 19 2 0 20 2 02

1 2 022 the cost to bring E85 to market, it is unlikely that retailers would i nvest in more E85 stations than what is necessary to meet the RFS2 program. Fo r more information on E85 infrastructure costs, refer to Section 4.2 of this DRIA.

Figure 1.7-10. C CCCCCC FFV E85 Refueling Rat es Needed to Meet th e RFS2 Requirements

0% 20% 40% 60% 80% 100%FFV E85 Refueling Frequency (Given Access) Committed FFV Production (Up to 70% E85 Access) Optimistic FFV Production (Up to 70% E85 Access) Mandated FFV Production (Up to 70% E85 Access) Committed FFV Production (Up to 100% E85 Access Optimistic FFV Production (Up to 100% E85 Access) Mandated FFV Production (Up to 100% E85 Access)

Year

As shown in Figure 1.7-10, all analyzed FFV production and E 85 access scenarios would require an increase from today’s 5% E85 refueling frequency. In or der for this to be possible, we believe there will need to be an improvement in the current E85/gasoline price relationship.

1.7.1.2.5 Market Pricing of E85 Versus Gasoline

According to an online fuel price survey, E85 is currently priced almost 30 cents per gallon higher than conventional gaso line on an energy-equivalent basis. 487 To increase our nation’s E85 refueling frequency to the levels described above, E85 needs to be priced competitively with (if not lower than) conventiona l gasoline based on its reduced energy content, increased time spent at the pump, and its limited availability. To quantify the required price differential, we started by examining 2006 gaso line price data. According to EIA’s 2006 Petroleum Marketing Annual, gasolin e was priced at $2.12 per gall on (average of all grades) at retail before taxes. 488 Applying the American Petroleum Institute’s estimated gasoline tax of

CCCCCCC Based on AEO 2008 projected gasoline energy demand.

25745.5 cents per gallon (average state plus federal taxes), this put retail gasoline around $2.58 per gallon in 2006. 489 This is slightly higher than today’s gasoline prices, but a reasonable indicator of what prices could look like in the future based on AEO 2008 fo recasted crude oil prices (refer to Table 1.7-8).

Table 1.7-8. EIA Forecasted Imported Crude Oil Prices

Year Projected Crude Oil Price ($/bbl) Change Relative to 2006 Price (%)
2006 66.02
2007 67.05 1.6%
2008 83.59 26.6%
2009 76.96 16.6%
2010 74.03 12.1%
2011 71.20 7.9%
2012 68.38 3.6%
2013 65.61 -0.6%
2014 62.72 -5.0%
2015 59.85 -9.3%
2016 56.96 -13.7%
2017 57.11 -13.5%
2018 57.99 -12.2%
2019 58.91 -10.8%
2020 59.70 -9.6%
2021 60.56 -8.3%
2022 61.47 -6.9% Source: AEO 2008, Table 12. Imported Low-Sulfur Light Crude Oil Price.

In order for E85 to be priced competitivel y with conventional gasoline (assumed to be E10 by 2022), first it would need to be priced lower based on its reduced energy density. D DDDDDD As shown below, E85 is 79 per cent as energy dense as E10.

E 85 EnergyDensity 0.74 ∗ 77,930 BTU / gal + 0.26 ∗ 115,000 BTU / gal

= = 0.79 E 10 EnergyDensity 0.10 ∗ 77,930 BTU / gal + 0.90 ∗ 115,000 BTU / gal

Accordingly, E85 should be priced at least 21% lo wer at retail to compensate. This means that E85 would need to be priced at least 54 cents less than $2.58 pe r gallon gasoline. However, we also need to take the value of FFV owners’ time into consid eration because they could be spending 21% more time at the pump if they are refueling exclusively on E85. In the U.S., a person’s time is currently valued at around $30 per hour. This value of time (VOT) estimate was

DDDDDDD Denatured ethanol was assumed to have a lower heating value of 77,930 BTU/gal and conventional gasoline (E0) was assumed to have an average lower heating value of 115,000 BTU/gal. E10 was assumed to contain 10 vol% denatured ethanol and 90 vol% gasoline and E85 was assumed to contain 74 vol% denatured ethanol and 26 vol% gasoline on average (based on EIA’s AEO 2008 report).

258Refueling frequency given“reasonable access”

Percent of stations offering E85 in areas of the country with “reasonable access”, i.e., 25% $0.21/gal Price Advantage (1997$) X based on an average of values iden tified in a review of economi cs literature and is consistent with 2005 Brownstone and Small VOT estimates. 490 Assuming it takes about six minutes for a 15-gallon refill, E85 needs to be priced an additional $0.04 per gallon less than E10.

Finally, we accounted for the fact that, as an alternative fuel, it is unlikely that E85 will ever be available nationwide. As mentioned above in Section 1.7.1. 2.3, we project that E85 will only be available at one-in-four stations in 70% of the c ountry by 2022 (under our primary, practical maximum scenario). And unlike diesel fu el, FFV owners are not required to fill up on it. So in order to get consumers to want to refuel on E85 over gasoline, there needs to be an additional price incentive at the pump acco rding to a 1997 Oakridge National Lab report. E EEEEEE, 491 As shown below in Figure 1.7-11, if an alternative fuel is only available at 25% of stations and you want people to refuel on it about 74% of the time (refer to Section 1.7.1.2.4), it needs to be given a 21 cent per gallon price advantage ( 1997$). Inflating the 1997 David Greene estimate to 2006 dollars, E85 would n eed to be priced an additional 26 cents per gallon lower than E10. For a breakdown of th e required E85 price adva ntage by program year (after the E10 blend wall), refer to Table 1.7-9.

Figure 1.7-11. Required Price Incentive for Alternativ e Fuels with Limited AvailabilityRefueling frequency given“reasonable access”

Percent of stations offering E85 in areas of the country with “reasonable access”, i.e., 25% $0.21/gal Price Advantage (1997$) X

EEEEEEE Although the 1997 David Greene study was based on as king consumers about a h ypothetical fuel that “works just as well as gasoline”, we assumed that Figure 6 from the report (pictured above) could also be used to determine the retail price incentive given to E85 to account for its limited availability . As explained in the preceding text, this was in addition to the incentives assi gned to E85 to account for its reduced energy density and additional time spent at the pump.

259Table 1.7-9. E85 Limited Availability Price Advantage by Program Year

Year Required E85 Refueling Freq Required Price Advantage 1997$ 2006$*

2013

2014
2015

2016

2017

2018
2019

2020

2021

2022 41%
45%
51%
55%
58%
61%
63%
65%
70%
74% $0.05 $0.06 $0.09 $0.11 $0.13 $0.14 $0.15 $0.17 $0.19 $0.21 $0.06 $0.08 $0.11 $0.14 $0.16 $0.18 $0.19 $0.21 $0.24 $0.26

*Computed using U.S. Department of Labor Inflation Calculator http://www.bls.gov/data/inflati on_calculator.htm

To conclude, our price analysis suggests th at E85 would need to be priced around $1.74 per gallon at retail, or about a third lower than $2 .58/gal gasoline in order for consumers to refuel on it 74% of the time in 2022. As expected, high er crude oil prices could make E85 look slightly more attractive. However, there would still need to be a strong E85 pr ice incentive at the pump in order for consumers to want to choose it. Si milarly, there w ould need to be a marked price advantage at the terminal level in order for refi ners to want to blend E85 (given transportation costs, taxes, and marketing/other costs at retail). The ethanol and respective corn break-even prices for varying crude oil pri ces are shown below. Figure 1. 7-12 shows the projected breakeven prices in 2013 (at the E10 blend wall) whil e Figure 1.7-13 shows the projected break-even prices in 2022. As shown below, the more etha nol that needs to be blended as E85 under RFS2, the less valuable it is to refiners.

2609Ethanol Breakeven Price (c/gal)Corn Breakeven Price ($/bshl) Figure 1.7-12. 2013 Ethanol & Corn Break-Even Pr ices for Selling Ethanol as E85

250 10

200

50 85 130 Crude Oil Price ($/bbl) Ethanol Breakeven Price

Corn Breakeven Price

This is what ethanol would need to be priced at in order for refiners to want to buy it and blend it as E85

This is what corn would need to be priced at in order for ethanol to be marketed at the breakeven price after the $0.45/gal subsidy. 8

150 6

5

100 4

3

50 2

1

0 0

261 7Figure 1.7-13. 2022 Ethanol & Corn Break-Even Pr ices for Selling Ethanol as E85

250 10

200

50 85 130 Crude Oil Price ($/bbl) Ethanol Breakeven Price

Corn Breakeven Price

This is what ethanol would need to be priced at in order for refiners to want to buy it and blend it as E85

This is what corn would need to be priced at in order for ethanol to be marketed at the breakeven price after the $0.45/gal subsidy. The breakeven feedstock pricing for cellulosic ethanol is TBD; however, a more generous $1.01/gal subsidy would apply 8 9Ethanol Breakeven Price (c/gal)Corn Breakeven Price ($/bshl) 7

6

5

4 150

100

3

2

1 50

0 0

It is worth noting that these are the theore tical ethanol and corn breakeven prices based on what consumers should theoretically be willing to pay for E85 at retail. However, it is possible that the average consumer might be willi ng to pay more for E85. According to earlier discussions with petroleu m retailers, E85 has to be priced at least 20% lower than gasoline today in order for consumers to refuel on it. 492 This is smaller than the 33% price differential we computed above. However, as mentioned earlier in Section 1.7.1.2.4, we expect also expect consumers motivations for filling up on E85 to shif t more towards price in the future when large volumes of ethanol need to be consumed under RFS2. For more information on gasoline/ethanol costs, refer to Section 4.4.

We are aware of the challeng es and coordinated efforts that would be required on the part of automakers, gasoline retailers , and FFV owners to get beyond the E10 blend wall. We also acknowledge that achievement of the proposed RFS2 standards is not necessarily limited to actions taken by regulated parties. However, automakers have already begun investing in FFVs and there are more and more E85-capable vehi cles being announced each year. Furthermore, more than half of U.S. gasoline retail outlets ar e “branded” stations that are either owned by or affiliated with refining companies. 493 As such, many retailers will have a vested interest in bringing E85 to market and pricing the fuel so th at it is attractive to consumers. Although the potential compliance strategies outlined in this DR IA are aggressive, we believe they are feasible

262if the right market ince ntives are put in place. In addition, we have also considered how midlevel ethanol blends might help the nation breach the blend wall.

1.7.1.3 The Impact of Mid-Le vel Ethanol Blends (E15/E20)

For our primary ethanol usage analysis, we considered that there would only be two fuels in the future, E10 and E85. And as demonstrated above, we show that it is feasible to consume 34 billion gallons of ethanol by 2022 given growth in FFV production and E85 availability and projected improvements in the current E10/E85 price relationship.

However, as explained in Section V.D.3.b of the Preamble, several organizations and government entities are interested in pursuing mid-level ethanol bl ends for use in the increasing the concentration of ethanol be yond the current 10% limit in the commercial gasoline pool. On March 6, 2009, Growth Energy and 54 ethanol manufacturers submitted an application for a waiver of the prohibition of the introduction into commerce of certain fuels and fuel additives set forth in section 211(f) of the Act. This applicat ion seeks a waiver for ethanol-gasoline blends of up to 15% by volume ethanol. The statute directs the Administrator of EPA to grant or deny this application within 270 days of r eceipt by EPA, in this instance December 1, 2009. EPA recently issued a federal register notice announcing receipt of the Growth Energy waiver application and soliciting comment on all aspects of it. Refer to 74 FR 18228 (April 21, 2009).

Although EPA has not yet made a waiver de termination, as a sensi tivity analysis, we considered the implications that adding E15 or E20 to the marketplace could have on ethanol usage and supporting fuel infrastructure should such blends be permitted. For each case, we assumed that E10 would need to continue to rema in in existence to meet the demand of legacy vehicle and non-road engine owners. We assume d that Tier 2 vehicles could handle up to 20 percent ethanol blends (i.e., E 15 or E20) and flex fuel vehi cles could handle up to E85 (if available) as shown in Table 1.7-10.

Table 1.7-10. Mid-Level Ethanol Blends Ve hicle/Engine Assumptions

Gasoline Vehicle/Engine Classification Maximum ETOH Threshold Nonroad, HDGVs & Motorcycles Up to 10% Pre-Tier 2 "Legacy" Vehicles Up to 10%
Tier 2 Vehicles Up to 20% *
Flex Fuel Vehicles (FFVs) Up to 85%

a Contingent upon vehicle test program and waiver approval

Although Tier 2 and flex-fuel ve hicles would not be reliant on E10 in a mid-level ethanol blends world, we believe that maintaining E10 (fo r all vehicles/engines) would be beneficial in providing consumer choice. E xperience in past fuel programs suggests that many consumers will not be comfortable refueling on higher ethano l blends and will blame any problems that may occur on the new fuel (regardless of the actual ca use of the vehicle problems) causing a backlash

263against the new fuel requirements. Therefore, we believe that the marketplace will continue to allow consumers the choice between mid-level et hanol blends and conventional gasoline (E10) even if a waiver were to be granted for all vehicles and engines.

For our optimistic mid-level ethanol blends scenario, we assumed that mid-level ethanol blends (E15 or E20) could be made available nationwide by 2013 (or the ti me the nation hits the E10 blend wall) by utilizing exis ting retail storage tanks. We r ealize that this is a fairly aggressive phase-in plan given all the actions that would need to be taken to bring mid-level ethanol blends to market. As such, we have also considered a more gradual 10-year phase in (discussed in more detail below). For each s cenario, we assumed that E10 would be marketed as premium-grade gasoline, the mid-level ethanol bl end (E15 or E20) would serve as regular and, like today, midgrade would be blended from th e two fuels as shown in Figure 1.7-14.

Figure 1.7-14. Potential Mid-Level Ethanol Blends Retail Configuration

We considered multiple retail tank configura tions, but decided the fuel configuration in Figure 1.7-14 would be the most likely for several reasons. For starters, marketing E15 or E20 as regular grade gasoline would allow retailers to continue to maintain the existing gasoline price relationship, i.e., regular is the cheapest fo llowed by midgrade then premium. While we acknowledge that this is not the gasoline price relationship in a ll areas of the country (e.g., in certain Midwest cities, midgrade is priced lowe r than regular), we believe it represents the predominant retail trend. Marketing the mid-leve l ethanol blend as regular would also result in the highest possible ethanol through put since, as shown below in Table 1.7-11, regular gasoline accounts for 85% of gasoline sales. Finally, we believe this configurat ion is advantageous because it allows premium gasoline to continue to be the universal fuel for all gasoline vehicles

264and engines. The downside to th is approach is that if mid- level ethanol blends were only approved for flex-fuel, Tier 2 or newer vehicles then legacy ve hicles and small engines would be forced to refuel on premium gasoline. Nonethel ess, the aforementioned retail configuration is consistent with how SIGMA/NAC S believed mid-level ethanol bl ends would be brought to market (pending approval) duri ng our discussions with them. 494 We seek comment on this approach and alternate ways that mid-level ethanol blends could be brought to market without investing in new tanks or displacing diesel fuel.

For each mid-level ethanol blend scenario , we assumed that consumers filling up on conventional gasoline would choose fuel grade based on 2006 purchasing trends. A summary of historical gasoline purchasing data as well as our ethanol content assumptions for each fuel grade is found below in Table 1.7-11. 495

Table 1.7-11. Mid-level Ethanol Blends Vehicle/Engine Assumptions

Grade of Gasoline % of CG Sales* Ethanol Content (vol%)
In E15 Case In E20 Case Regular 85% 15% 20% Midgrade 6% 13% 15%
Premium 9% 10% 10%

*Source: EIA Petroleum Marketing Annual 2006

Based on the vehicle capability assumptions outlined in Table 1.7-10, AEO 2009 gasoline energy projections, and the above refueling assumptions, we concl ude that absent E85, mid-level ethanol blends could help increase ethanol u sage to over 19 billion gallons (with E15) and 25 billion gallons (with E20).

As shown below in Figures 1.7-15 and 1.7-16, under an optimistic phase-in scenario where mid-level ethanol blends were made available nationw ide by 2013, E15 could postpone the blend wall by up to three years to 2016 and adding E20 could postpone it another three years to 2019. However, if mid-level ethanol blends were phased in more gr adually over ten years (refer to Table 1.7-12) as might occur if retailers must invest to replace existing equipment to distribute mid-level blends, E15 might only postpone the blend wa ll by one year to 2014. It appears E20 would continue to extend the ble nd wall to 2019. In both cases, we assumed that mid-level ethanol blends would eventually replac e regular gasoline and be available nationwide. However if E15 and E20 only became niche fuels (like E85 is today), th eir ability to postpone the blend wall would be si gnificantly reduced.

26520 10 2011 2012 201 3 20 14 2 015 201 6 20 17 2018 2019 202 0 20 21 2 022 Figure 1.7-15. Max ETOH Consumption under 2013 Mid-Level Et hanol Blends Phase-In Scenario

0 5 10 15 20 25 30 35 40Billion Gallons of Ethanol Cellulosic Ethanol
Imported Ethanol

Corn Ethanol

E20 Blend Wall

E15 Blend Wall

E10 Blend Wall

Year

26620 10 2011 2012 201 3 20 14 2 015 201 6 20 17 2018 2019 202 0 20 21 2 022 Figure 1.7-16 Max ETOH Consumption Under Gradual Mid-Leve l Ethanol Blends Phase-In Scenario

0 5 10 15 20 25 30 35 40

YearBillion Gallons of Ethanol Cellulosic Ethanol
Imported Ethanol

Corn Ethanol

E20 Blend Wall

E15 Blend Wall

E10 Blend Wall

Table 1.7-12 Access to Mid-Level Ethanol Bl ends Under Gradual Phase-In % of Retailers Carr y in g E15 Year or E20 2009 0% 2010 0% 2011 0% 2012 14% 2013 29% 2014 43% 2015 57% 2016 72% 2017 86% 2018 100%

2672 007 200 8 2009 2 010 201 1 2012 2 013 20 14 2015 2016 20 17 201 8 2019 20 20 202 1 2022 As demonstrated above, without E85, mid-level ethanol blends will fall short of meeting the 34 billion gallon ethanol standard. It is possi ble that other non-ethanol biofuels could come online and help the nation meet the RFS2 standard without more ethanol. However, for analysis purposes, we considered how the nation might m eet RFS2 with a combination of mid-level ethanol blends and E85. For this part of the sensitivity analysis, we maintained our assumption that mid-level ethanol blends could be availabl e nationwide by 2013. However, we also assumed that retailers would invest in E 85 infrastructure in parallel. But we do not expect that retailers would invest as much in E85 infrastructure as in the primary E10/E85 sce nario (refer to Figure 1.7-9). For analysis purposes, we assumed th at reasonable 1-in-4 access to E85 might only growth to 50% by 2022. Our logi c behind this assumption is thatgasoline retaile rs who have already made a capital investment to bring mid- level ethanol blends to market would not turn around and invest more than what was necessary in E85 infrastructure. The more conservative 50% E85 access growth curve we assumed compared to our business as usual growth estimate (discussed more in Section 1.6) is shown below in Figure 1.7-17. For more on the retail impacts of bringing mid-level ethanol blends to market, refer to Section 1.6.11.

Figure 1.7-17. Projected Growth in E85 Access in a Mid-Level Ethano l Blends World

100%

80%

60%

40%

20%

0% Conservative Growth in E85 Access

Business as Usual E85 Access% of country with reasonable 1-in-4 access to E85

Year

It is worth noting, that this graph assumes a gradual, linear phase in of E85 infrastructure reaching 50% by 2022. In reality, retailers might wait until after the E15 or E20 blend wall to really invest in E85 infrastructure. The result would be negligible E85 station growth until the 2014-2019 timeframe (depending on the mid-level et hanol blend and phase-in scenario) and then

26820 10 2011 2012 201 3 2014 20 15 201 6 2017 20 18 201 9 2 020 20 21 2022 steep growth in E85 infrastructure. While this would arguably provide re tailers with the most lead-time to postpone their E85 investments, it could r esult in unrealistic E85 station growth. As a result, for analysis purposes, we analyzed the gradual phase-in on E85 infrastructure shown above in Figure 1.7-17

In the hypothetical mid-level ethanol blends and E85 world, we assumed that automakers would only produce the “committed” number of FFVs de tailed in Table 1.7-4. To re-summarize, this involves The Detroit 3 following thr ough with their 50% FFV commitment and no significant growth in non-domestic FFV produc tion (beyond what was forecasted in 2009 MY offerings). To meet the RFS2 requirement s with mid-level ethanol blends and this lessaggressive E85/FFV scenario, would require the following FFV E85 refueling rates.

Figure 1.7-18. FFV E85 Refueling Rat es Needed to Meet th e RFS2 Requirements

0% 20% 40% 60% 80% 100%FFV E85 Refueling Frequency (Given Access) E10/E85, 70% E85 Access

E10/E15/E85, 50% E85 Access

E10/E20/E85, 50% E85 Access

Year

As shown above in Figure 1.7-18, in an E10/E15/E85 world (with committed FFV production and E85 access reaching 50% by 2022), th e required FFV E85 refueling rate would be just as high in 2022 as that in the E10/E 85 world (with optimistic FFV production and E85 access reaching 70% by 2022). This scenario woul d essentially demand the same changes to the E10/E85 price relationship as described in Sectio n 1.7.1.2.5 but could result in higher capital costs (depending o the retail cost assumptions). For this reason, E20 might make more sense than E15 if they were to both require the same retail upgrades. For more on retail

269accommodations and costs of mid-level ethanol ble nds, refer to Sections 1.6.11 and 4.2 of this DRIA.

1.7.1.4 Impacts on 2022 Fuel Volumes

As explained in Section 1. 7.1.2.1, fixing gasoline ener gy demand according to AEO 2008 essentially dictates how much ethanol can be cons umed as conventional gasoline. As a result, making different assumptions regard ing the ethanol content of conventional gasoline (i.e., E10, E15, or E20), impacts the nation’s ethanol usage potential and the timing of the respective blend walls under the proposed RFS2 program (refer to Figures 1.7-15 and 1.7-16). Adding in E85/FFVs to meet the RFS2 volume requirement s shifts ethanol usage out of conventional gasoline and into E85. As shown below in Tables 1.7-13 and 1.7-14, although holding total gasoline energy demand and the ethanol volume c onstant under RFS2 fixes the overall fuel volumes in 2022, it changes the distribution of ethanol.

270This page intentionally left blank.

271272Table 1.7-13.Comparison of Total Fuel Volumes Under Various ScenariosFuel ScenarioProjected 2022 Fuel VolumesE10E12.5E15E20E85TotalBlend WallBlend WallBlend WallE10 Nationwide (No E15, E20 or E85)E10 & E15 Nationwide (No E85)aE10 & E20 Nationwide (No E85)b147.6131.2731.270.007.390.000.00110.857.450.000.00112.760.000.000.00147.61149.51151.49RFS2 Control CaseRFS2 Control CaseRFS2 Control CaseE10/E85 Scenario (No E15 or E20)Combination E10/E15/E85 ScenarioaCombination E10/E20/E85 Scenariob124.5529.6730.340.006.410.000.0096.246.880.000.00104.1429.3021.5412.50153.85153.85153.85aAssumes E12.5 (midgrade) is blended from E10 (premium) and E15 (regular).bAssumes E15 (midgrade) is blended from E10 (premium) and E20 (regular).Table 1.7-14.Comparison of Ethanol Volumes Under Various ScenariosFuel ScenarioProjected 2022 Ethanol UsageE10E12.5E15E20E85TotalBlend WallBlend WallBlend WallE10 Nationwide (No E15, E20 or E85)E10 & E15 Nationwide (No E85)E10 & E20 Nationwide (No E85)14.763.133.130.000.920.000.0016.631.120.000.0022.550.000.000.0014.7620.6826.80RFS2 Control CaseRFS2 Control CaseRFS2 Control CaseE10/E85 Scenario (No E15 or E20)Combination E10/E15/E85 ScenarioCombination E10/E20/E85 Scenario12.452.973.030.000.800.000.0014.441.030.000.0020.8321.6915.949.2534.1434.1434.14aAssumes E12.5 (midgrade) is blended from E10 (premium) and E15 (regular).bAssumes E15 (midgrade) is blended from E10 (premium) and E20 (regular).This page intentionally left blank.274 1.7.1.5 Projected Ethanol Consumption by County in 2022

To understand the impacts of increased etha nol use on emissions and air quality (to be modeled under the final rule), we estimated where the ethanol might be used under the RFS2 program. We started by assuming that E10 would be available nationwide in 2022 (no E0), and for this county-level analysis, we focused on our primary E85/FFV scenario. We assumed practical, yet aggressive gr owth in E85 access (one-in-four station access reaching 70% by 2022). With respect to FFVs, we assumed optimis tic production (outlined in Table 1.7-4) and made the simplifying assumption that all FFVs would be distri buted homogeneously. F FFFFFF As explained in Section 1.7.1.2, th is determined the manner in which E85 would be consumed (i.e., the number of FFVs required, the number of E85 stations, and the average FFV E85 refueling frequency). However, it does not tell us where E85 would be consumed.

To estimate where E85 might be consumed under the proposed RFS2 program in 2022, we conducted a cost effectiveness study. For each area of the county, we began by looking at gasoline delivered prices. We started with stat e-level gasoline prices (e xcluding taxes) provided by EIA’s Petroleum Marketing Annual 2006. 496 We relied on Table 31 for average gasoline prices, looked to Table 34 fo r RFG prices and back-calcula ted CG prices by applying the respective gasoline fuel volumes provided in Table 48. For states requiring 7 or 7.8-lb gasoline in the summertime, we applied PADD-average low-RVP gasoline production costs derived from the Mobile Source Air Toxics (MSAT) rule 497 to come up with the r espective low-RVP and 9-lb conventional gasoline prices in these states. From there, we added in the corresponding gasoline taxes (state plus federal) according to the American Petroleum Institute (API). 498 This gave us the average retail cost of gaso line by state and fuel type.

Next we converted the gasoline prices into competitive retail E85 pr ices by adjusting for the reduced energy density of E85, the incr eased refueling time, and E85’s presumed limited availability in 2022. For a more detailed assessment, refer to Section 1.7.1. 2.5. From there, we deducted fuel taxes (assumed to be the same as gasoline), backed out marketing costs and retail profits (assumed to be $0.10 per gallon) and subt racted the terminal-to-retail transportation costs (assumed to be $0.03) to arrive at the estimated retail value of E85, a nd ultimately, the retail value of ethanol. G GGGGGG Once we computed the retail value of ethanol, we compared it to the estimated ethanol delivered price (based on transportation costs presented in Section 4.2) to come up with the respect ive E85 profit margin.

To conclude, we assigned E85 to the areas of the county with th e highest E85 profit margins, or in some cases, the least nega tive E85 profit margins until we arrived at approximately 34 billion gallons of ethanol in 2022. For a graphical representation of the areas of the country we assumed would receive/ consume E85, refer to Figure 1.7-19.

FFFFFFF We plan on looking into purchasing FFV registration data or relying on the DOE FFV study required under EISA for the final rule.
GGGGGGG For analysis purposes we assumed that E85 was taxed at the same rate as gasoline. We acknowledge that a number of states currently have reduced excise taxes or excise tax exemptions for E85. However, the extent of the tax breaks is somewhat unknown and the pot ential that these tax breaks will exist in the future is uncertain. We plan on revisiting state-level E85 excise tax exemptions for the Final Rule. For more on marketing and transportation costs, refer to Section 4.4 of the DRIA.Figure 1.7-19. Projected E85 Usage in 2022 Under RFS2

1.7.2 Biodiesel & Renewa ble Diesel Consumption

Biodiesel, like ethanol, is generally blende d at the end of the di stribution chain, just before delivery to retail outlets. Because of its ch emical properties, it is not currently considered fungible with diesel fuel, and thus its blend level in fuels offere d for sale is typically deliberate and explicit. Renewable diesel, on the other hand , is a fuel or hydrocarbon blendstock which can be blended into fungible fuel at any point in the distri bution system, such that the blend level at the final point of use is not t ypically of concern and, in fact , would probably be difficult to determine. Because of its nature, and the rela tively small volumes we are projecting (less than 0.5 billion gallons per year), we have not analyzed distribution or use impacts for renewable diesel. The remainder of this section addresses biodiesel use.

Vehicle and engine manufact urers recognize biodiesel as a lubricity improver at low levels, something that is usef ul with ultra-low sulfur diesel fuel now phasing in across the country. Therefore, most state that their products are compatible with blends up to 5%, and a few suggest blends up to 20% can be used withou t problems. Therefore, our analysis assumes blends up to 5% can find widespread use.In order to conduct our distribution and em issions analyses, we needed to forecast approximate volumes of biodiesel to be used in each state. We considered transportation diesel fuel and home heating oil as the primary uses for biodiesel. For transportation fuel estimates, we assumed that biodiesel would be preferentially used in states that have blend mandates or significant per-gallon incentives . Table 1.7-14 shows the st ates with such mandates and incentives on record as of summ er 2008, as well as the associat ed potential bi odiesel volumes based on 2005 diesel fuel use. 499 State-level forecasts were not available for transportation fuel use, thus the reliance on historic al data for this estimate.

Table 1.7-14. State biodiesel incentives as of summer 2008 and potential volumes based on 2005 data. 500

Diesel fuel use 2% biodiesel 5% biodiesel ( million g al/ y r ) ( million g al/ y r ) ( million g al/ y r ) IL per-gallon tax incentive(s) for B11+, state fleet re q uirement 1,660 33.2 State Incentive or mandate

KS p er- g allon tax incentive ( s ) for B2+ 816 16.3 LA B2 mandate with some conditions 1,734 34.7 MA B2 mandate, increasing to B15 with 491 24.5 some conditions MI p er- g allon tax incentive ( s ) for B5+ 1,071 53.5 MN B2 mandate; state fleet re q uirement 999 20.0 NC per-gallon tax incentive(s), B2 school 1,234 24.7 b us re q uiremen t ND p er- g allon tax incentive ( s ) 358 7.2 NE p er- g allon tax incentive ( s ) 547 10.9 NM B5 mandate with some conditions 475 23.7 OH p er- g allon tax incentive ( s ) 1,556 31.1 OR B2 mandate, increasing to B5 with 738 36.9 some conditions SC p er- g allon tax incentive ( s ) 764 15.3 SD p er- g allon tax incentive ( s ) 263 5.3 TX p er- g allon tax incentive ( s ) 5,339 106.8 WA B2 mandate, increasing to B5 with 1,230 24.6 some conditions Total biodiesel 468.7

Table 1.7-15 shows home heating oil use in 2005. We estimate potenti al biodiesel use in heating oil at 89 million gallons per year based on a 2% blend in all heating oil north of the Washington, DC, area (i.e., PADD 1A and 1B). Th is area was chosen because it is where the majority of heating oil is used, and should ha ve adequate biodiesel access from New Jersey, Pennsylvania, and Connecticut in our forecasted pr oduction scenarios (see Section 1.5.4). To the extent that heating oil use declines over time, th e blend levels may increase in some areas or in the shoulder seasons, such that the total biodiesel volume used in this market would not decline drastically.

276Table 1.7-15. Potential biodiesel use in he ating oil based on 2005 data. 501 Heating oil 2% biodiesel Volume Used (million gal/yr) (million gal/yr) (million gal/yr) U.S. 5,565,489 111.3 Area

PADD 1 4,759,198 95.2

PADD 1A 1,923,405 38.5 38.5 CT 545,910 10.9 ME 308,464 6.2 MA 674,324 13.5 NH 175,484 3.5 RI 136,618 2.7 VT 82,604 1.7

PADD 1B 2,529,106 50.6 50.6 DE 33,221 0.7 DC 12,832 0.3 MD 149,919 3.0 NJ 322,088 6.4 NY 1,282,899 25.7 PA 728,147 14.6

PADD 1C 306,687 6.1 FL 3,608 0.1 GA 1,520 0.0 NC 81,528 1.6 SC 8,810 0.2 VA 197,255 3.9 WV 13,966 0.3 Total used for biodiesel in heating oil 89.1

Combining these volumes gives 558 million gallons per year potential biodiesel consumption, leaving approximately 250 million gallons to be sold in blends above the projected levels shown here, or in states not included here. Further use and distribution of biodiesel is discussed in more detail in Section 4.2.2.2.

## Chapter 2: Lifecycle GHG Analysis

2.1 Lifecycle Introduction and Summary

As part of proposed revisions to the National Renewable Fuel Standard program (commonly known as the RFS program), EPA is estimating lifecycle greenhouse gas (GHG) emissions from increased renewable fuels use. The Energy Independence and Security Act of 2007 (EISA) establishes new renewable fuel cate gories and eligibility requirements, including setting the first-ever mandatory lifecycle GHG reduction thresholds for renewable fuel categories, as compared to those of average petroleum fuels used in 2005 (base year established by EISA). This chapter describes the methodology developed by EPA to determine the lifecycle GHG emissions of renewable fuels as required by EISA. This methodology was utilized to determine which renewable fuels qualify for the four different GHG reduction thresholds specified in EISA. The overall GHG benefits of the RFS program, which are based on the same methodology presented here, ar e also provided.

The GHG provisions in EISA are notable for the GHG thresholds mandated for each category of renewable fuel and also the manda ted lifecycle approach to those thresholds. Renewable fuel must achieve a 20% reduction in lifecycle greenhouse g as emissions compared to the average for gasoline or di esel sold or distribut ed as transportation fuel in 2005. Similarly, biomass-based diesel and advanced biofuels mu st achieve a 50% reduction, and cellulosic biofuels a 60% reduction. To EPA’s knowledge, the GHG reduction thresholds in EISA are the first lifecycle GHG performance requirements include d in federal law. While these thresholds do not directly constitute a greenhouse gas control program for re newable fuels, in combination with the volume mandates, they are designed to ensure significant GHG emission reductions from the use of renewable fuels and to encour age the use of GHG-reducing renewable fuels.

The definition of lifecycle greenhouse gas em issions established by Congress states that:

The term ‘lifecycle greenhouse gas emissions’ means the aggregate quantity of greenhouse gas emissions (including dire ct emissions and significant indirect emissions such as significant emissions fr om land use changes), as determined by the Administrator, related to the full fuel life cycle, including al l stages of fuel and feedstock production and distribution, from feedstock generation or extraction through the distribution and delivery and use of the finished fuel to the ultimate consumer, where the mass values for all greenhouse gases are adjusted to account for their relative global warming potential. 502

This definition requires the Agency to look br oadly at lifecycle anal yses and to develop a methodology that accounts for all of the important factors that may signi ficantly influence this assessment, including the secondary or indirect impacts of expanded biofuels use. EPA’s analysis suggests that the assessment of lifecycle GHG emissions for biofuels is significantly affected by the secondary agri cultural sector GHG impacts from increased biofuel feedstock production (e.g., changes in livestock emissions due to changes in agricultural commodity prices) and the international impact of land use change from increased biofuel feedstock

278production. Thus, these factors must be appropriately incorp orated into EPA’s lifecycle methodology to properly assess the full lifecycle GHG performance of biofuels in accordance with the EISA definition of lif ecycle GHG emissions. As discussed below, there are many other potential impacts (e.g., hypoxia in the Gulf of Mexico) that do not have a meaningful impact on the lifecycle GHG analysis and therefore fall outside the scope of this analysis.

Currently, no single model captures all of th e interactions associated with estimating lifecycle GHG emissions for renewable fuels, ta king into account the "significant indirect emissions” required by EISA. EPA’s approach has been to use the best tools available for each specific component of the lifecycle to create a comprehensive estimate of GHG emissions (See Figure 2.1-1). As required by EISA, the broad system boundaries of our analysis encompass all significant secondary agri cultural sector GHG impacts, not just impacts from land use change.

Figure 2.1-1. System Boundaries and Models and Data Sources Used

To quantify the lifecycle GHG emissions associat ed with the increase in renewable fuels mandated by EISA, we compared the differences in total GHG emissions between two future scenarios. The first assumed a “business as usual” volume, or reference case, of a particular renewable fuel based on what would likely be in the fuel pool fr om now until 2022 without EISA. The reference case includes technologica l advances, such as crop yield improvements, that are likely to occur irr espective of EISA. The second scenario assumed higher volumes of renewable fuels as mandated by EISA. Rather than focus on the imp acts associated with a specific gallon of fuel and tr acking inputs and output s across different lifecycle stages, we

279determined the overall aggregate impacts across sections of the economy in response to a given volume change in the am ount of biofuel produced. H HHHHHH Table 2.1-1 provides an overview of the volumes of renewable fuels modeled for this analysis. More details about the fuel volumes modeled, including sensitivity analysis volume runs, are provided in Section 2.6 of this Chapter.

Table 2.1-1. Renewable Fuel Volumes Modeled

Fuel Volume (Billions of Gallons)

Reference Case Control Case Net Change Corn Ethanol 12.4 15 2.6 Corn Stover Ethanol 0 7.6 7.6 Biodiesel 0.4 0.7 0.3 Sugarcane Ethanol 0.6 3.1 2.5 Switchgrass Ethanol 0 3.2 3.2

2.1.1 Results

EPA’s results suggest that biofuel induced land use change can produce significant nearterm emissions, but that biofuels have GHG emissions benefits by displacing the use of petroleum-based fuels in subsequent years that can “payback” ear lier land conversion GHG releases. Therefore, choosing an appropriate time horizon for analysis is essential when comparing biofuels with petroleum-based fuels. Whether or not to discount future emissions is also critical because it determ ines the relative importance of near-term versus longer-term emissions. The type of land (e.g., forest or grassl and) that is converted to cropland as a result of biofuel production is another factor that strongly influen ces overall lifecycle GHG reduction estimates.

Figures 2.1-2 and 2.1-3 show how EPA’s lifecycle GHG emission estimates vary by lifecycle stage for each biofuel anal yzed. We calculated a net pr esent value of emissions because it provides a common metric for direct comparis on of lifecycle emissions from biofuels and petroleum fuels. Figure 2.1-2 includes results using a 100-year time horizon and a 2% discount rate. The results in Figure 2. 1-2 use a 30-year time horizon and a 0% discount rate. Our primary estimates include GHG emissions from forest, shrub, savanna and grassland clearing (proportionally estimated based on MODIS satelli te data) resulting from biofuel production. I IIIIII The tables also show how total net lifecycle GHG emissions estim ates would vary if we assumed that all new cropland would come from forest, or all from grasslands. The choice of time horizon, discount rate, and land u se change estimates are discussed in detail in the preamble to the proposed rule.

HHHHHHH We then normalize those impacts for each gallon of fuel (or Btu) by dividing total impacts over the given volume change. Net lifecycle GHG emissions are reported as grams of carbon dioxide equivalent per million Btu (gCO2eq/mmBtu).
IIIIIII Per acre land use change emissions factors were estimated by Winrock International Inc., following 2006 Intergovernmental Panel on Climate Change guidelines

280Our results indicate that emissions produced during the fuel production stage can vary significantly for corn ethanol depending on the type of facility used to convert corn into ethanol. A typical dry mill corn ethanol production facili ty fired with natural gas produces 32,579 grams of carbon-dioxide equivalent emissions per m illion British Thermal Units (gCO2-eq./mmBtu) of ethanol produced per year. Fo r corn ethanol, emissions at th e fuel production stage can range from 60,209 gCO2-eq./mmBtu for a coal-fired dry mill in the higher emitting case; to 9,688 gCO2eq/mmBtu for a biomass-fired dry mill in th e lower emitting case. Clearly, the choice of fuel production technology can be used as a measure to reduce the climate impact of corn ethanol production.

Conversion of cellulosic feedstocks (e.g., corn stover or switchgrass) to ethanol creates a net sequestration of carbon during the fuel production stage. Et hanol is fermented with the cellulosic portion of the biomass, while process energy is generated through the unfermentable portion (mainly lignin) of incoming biomass. Based on NREL estimates, the process is assumed to generate more than 8,000 Btu of excess electricity per gallon of ethanol produced. Biomassfired electricity generation reduces GHG emissi ons by offsetting other forms of electricity production.

Figure 2.1-2. Lifecycle GHG Results Using 100-Year Net Present Value with 2% Discount Rate

160% Fuel Production

140%ions) Tailpipemiss International Land Use Change
120%EHG International Farm Inputs andG Fert N2O 100%ycle Other (fuel and feedstockifec transport)

80%ine L Domestic Soil Carbon

60%Basel International Livestockleum International Rice Methane
40%trof Pe Domestic Rice Methane

20%(% o Domestic Farm Inputs andons Fert N2O

0% Domestic Livestock

-20%GHG Emissi

Cellulosic Threshold

Advanced and Biodiesel
-40% Threshold
Conventional Threshold

Net Emissions *

*Range shows net emissions if we assume all land conversion from forest (upper bound) or all from grassland (lower bound). -128% -115% -16% 13%

-39% -44% -22%

-80%

Dry Mill Natural Gas Dry Mill Coal Dry Mill Biomass Switchgrass Corn Stover Sugarcane Soy-Based Waste Grease

GASOLINE BASELINE CORN ETHANOL CELLULOSIC ETHANOL ADVANCED ETHANOL DIESEL BASELINE BIODIESEL

281Figure 2.1-3. Lifecycle GHG Results Using 30-Year Net Present Value with 0% Discount Rate

180%

-124% -26% -26% -18% +34%

-18% +5%

-116% -26% +4%

-80%Dry Mill Natural Gas2022 Best Case DryMill NGDry Mill CoalDry Mill Biomass2022 Dry MillBiomass w/ CHPWet Mill BiomassSwitchgrassCorn StoverSugarcaneSoy-BasedWaste Grease

GASOLINE BASELINE CORN ETHANOL CELLULOSIC ETHANOL ADVANCED ETHANOL DIESEL BASELINE BIODIESEL Fuel Production

160% Tailpipe

140%GHG Emissions (% of Petroleum Baseline Lifecycle GHG Emissions)

International Land Use Change

120% International Farm Inputs and Fert N2O 100% Other (fuel and feedstock
80% transport)

Domestic Soil Carbon 60%

International Livestock 40%

International Rice Methane 20%

Domestic Rice Methane 0%

-20% Domestic Farm Inputs and Fert N2O
-40% Domestic Livestock

-60% Cellulosic Threshold

Advanced and Biodiesel Threshold

Conventional Threshold

Net Emissions *

* Range shows net emissions if we assume all land conversion from forest (upper bound) or all from grassland (lower bound).

Figures 2.1-2 and 2.1-3 also in clude indirect emissions imp acts that result from fuel production or use, such as changes in livestock inve ntories, or shifts in acreage between different types of crops. For example, the FASOM mode l projects that increasin g the production of soybased biodiesel will reduce domestic livestock and rice production, which reduces methane emissions from those sectors. To compensate for this decrease in domestic rice and livestock production, the FAPRI models project that fore ign countries will expand th eir rice and livestock production. There are similar indirect effect s on farming practices, which impact soil carbon emissions and emissions from agricultural energy use.

The benefit of using renewable fuels that di splace petroleum fuel consumption is seen primarily at the end use, or tailpipe, st age of the fuel lifecycle. Combustion CO 2 emissions for ethanol, biomass-based diesel, petroleum diesel a nd gasoline were based on the carbon content of the fuel. However, over the full lifecycle of the fuel, the CO 2 emitted from biomass-based fuels combustion does not increase atmospheric CO 2 concentrations, assuming the biogenic carbon emitted is offset by the uptake of CO 2 resulting from the growth of new biomass. As a result, CO 2 emissions from biomass-based fuels combus tion are not included in their lifecycle emissions results. Unlike CO 2 emissions, the combustion of biomass-based fuels does result in net additions of methane and N 2 O to the atmosphere. Therefore, combustion methane and N 2 O emissions are included in the lifecycle GHG emi ssions results for biomass-based fuels. Net carbon fluxes from changes in biogenic carbon r eservoirs in wooded or crop lands are accounted for separately in the land use change analysis as outlined in the agricultural sector modeling above.

2822.1.2 Greenhouse Gas Emissions from Land Use Changes

We estimated GHG emissions associated with land use changes that occur domestically and internationally as a result of increasing rene wable fuels demand in th e U.S. Key questions we addressed included the area of land convert ed to crop production, where those acreage changes would occur, the typ es of land converted to crop production, and the GHG emissions associated with different types of land conversio n. In general, we found that land use change impacts are larger when food crops (such as corn or soybeans) are used as biofuel feedstocks than when non-food crops (such as corn stover or switchgrass) are converted to biofuel. EPA recognizes that estimating the international imp acts of land use change can introduce additional uncertainty to the lifecycle GHG emissions es timates, and we present a range of results.

We used the FAPRI model to estimate international crop expansion, and the FASOM model to estimate domestic crop expansion. Th e results are summarized in Figure 2.1-4. Overall, soy-based biodiesel induces the most crop expansion per gall on of production. One reason for this is that soybeans are a relatively lo w-yield crop compared to corn and sugarcane in terms of how many gallons of biofuel can be produced per acre of crop production. Crop expansion caused by corn ethanol production is also offset to some degree by the production of distillers grains (DGs), a major co-product of dry mill ethanol production that is also used as animal feed.

Figure 2.1-4. Regional Crop Ex pansion by Renewable Fuel, 2022 (acres per thousand ethanol equivalent gallons of biofuel production)

Soy-Based Biodiesel Corn Ethanol Sugarcane Ethanol Switchgrass Ethanol

0.0 0.5 1.0 1.5 2.0 2.5

Argentina Brazil India China EU & Nigeria Other Paraguay Other Rest of S.E. Asia U.S. World Russia Africa Latin World Total America

283The percentage of each land type converte d to cropland was estimated according to MODIS satellite-based data (See Table 2.1-2). J JJJJJJ We extrapolated the recent land use trends, as determined with the satellite data, into the future to es timate the most likely places for crop expansion on a sub-regional basis, and the types of land that would most likely be converted to crop production. For the proposed rule, land use trends and emissions factors were estimated for ten countries including Argentina, Brazil, China, India, Indonesia, Malaysia, Mexico, Philippines, EU and South Africa. K KKKKKK

Table 2.1-2. Types of Land Conv erted to Cropland by Country, 2001-2004 Country Forest Grassland Savanna Shrub Argentina 8% 40% 45% 8%

Brazil 4% 18% 74% 4% China 17% 38% 23% 21% E.U. 27% 16% 36% 21% India 7% 7% 33% 53% Indonesia 34% 5% 58% 4% Malaysia 74% 3% 19% 3% Nigeria 4% 56% 36% 4% Philippines 49% 5% 44% 3% South Africa 10% 22% 53% 15%

To estimate the GHG impacts of projected la nd use changes, emissions factors were calculated by Winrock following 2006 Intergov ernmental Panel on Climate Change (IPCC) Agriculture, Forest and Othe r Land Use (AFO LU) guidelines. 503 Emissions factors were based on the initial changes in above and below ground biomass carbon stocks as well as changes in soil carbon stocks, non-CO 2 emissions from clearing with fire, and forgone forest sequestration. In general, for the countries analyzed, emission f actors for all land use typ es were highest in the South East Asian countries of Ma laysia, Indonesia and Philippines and lowest in Argentina and Nigeria.

The GHG emissions associated with converting land into crop production accumulate over time, with the largest releases occurring in th e first few years due to clearing with fire and biomass decay. After land is converted to cr op production, moderate amounts of soil carbon would continue to be released for approximately 20 years. Furthermore, when forest is cleared there would be foregone sequestrati on for approximately 80 years associated with the fact that the forest would have continued to grow had it not been cleared. Section 2.6.5 provides details about our estimates of GHG emissions from land use changes.

2.1.3 Treatment of Future Greenhouse Gas Emissions

When comparing the lifecycle GHG emissions associated with biofuels to those associated with gasoline or diesel emissions, it is critical to cons ider the timing of GHG releases from the lifecycle of each fuel t ype. With gasoline, a majority of the GHG emissions associated with extraction, conversion, and combustion are li kely to be released ove r a short period of time

JJJJJJJ The 17 MODIS imagery land cover classes were recl assified using the International Geosphere Biosphere Programme land cover dataset into five general classes: cropland, forest, grassland, savanna and shrubland.
KKKKKKK We are in the process of acquiring data for more countries to be used for the final rule.

284(i.e., annually) as crude oil is converted into gasoline or diesel fuel which qui ckly pass to market. In contrast, the land use change GHG emissions associated with the us e of a typical biofuel (e.g., corn-based ethanol) may occur ove r a longer period of time. While biomass feedstocks grown each year on new cropland can be converted to biofuels that offe r an annual GHG benefit relative to the petroleum product they replace, these benef its may be small compared to the upfront GHG emissions from land clearing. Depending on the specific biofuel in quest ion, it can take many years for the benefits of the bi ofuel to “payback” th e initial GHG releases resulting from land use change.

As shown in Figure 2.1-5, the payback period for corn ethanol produ ced in a natural gasfired dry mill is approximately 33 years; soy- based biodiesel has a payback period of about 32 years; sugarcane ethanol’s payback period is a pproximately 18 years; a nd it takes about 3 years for switchgrass ethanol use to o ffset initial land clearing emi ssions. Corn stover and waste grease biodiesel are not shown in Figure 2.1-5, because both biof uels produce GHG reductions in the first year. In our analysis , the annual gasoline and diesel b aseline emissions do not vary over time; instead, their lifecycle impacts are repli cated every year to match the lifecycle emissions from average 2005 petroleum fuel. The cumulative net emissions for corn ethanol and soy-based biodiesel cross after approximately 27 years, b ecause soy-based biodiesel has larger initial land use change emissions but lower subsequent a nnual emissions from the non-land use impacts of the lifecycle.

EISA requires EPA to determine whic h biofuel production pathways reduce GHG emissions by the required threshold amounts rela tive to the 2005 petroleum baseline. A payback period alone cannot answer that question. Since the payback pe riod is not sufficient for our analysis, we must account for th e variable time profiles of lif ecycle GHG emissions and provide a common metric that allows for a direct comparison of lifecycle emissions from biofuels and petroleum fuels. When considering the time profile of GHG emissions, two assumptions that have a significant impact on the determinati on of whether a biofuel meets the emissions reduction threshold ar e: 1) the time horizon of emissions considered in the analysis and 2) the discount rate applied to future emissions.

The importance of the time horizon for anal ysis is clearly illustrated by the payback periods of various biofuels. If we select a time horizon that is shorter than the payback period for a given biofuel, then we will determine th at the biofuel in ques tion increases GHG emissions. On the other hand, if we choose a very long time horizon, all of the biofuels we analyzed will eventually reach the GHG reducti on thresholds specified by EISA.

285Gasoline

Diesel

Soy Based Biodiesel

Corn Ethanol

Sugarcane Ethanol

Switchgrass Ethanol

Corn Ethanol Cumulative Net Emissions

Soy Based Diesel Cumulative Net Emissions

Sugarcane Ethanol Cumulative Net Emissions

Switchgrass Ethanol Cumulative Net Emissions Year 0 Land Use Emissions: -Clearing with Fire -Biomass Decay

-Soil Carbon -Foregone Sequestration

Years 1-19 Land Use Emissions:

Soil Carbon Foregone Sequestration Years 20 79 Land Use Emissions: -Foregone Sequestration Figure 2.1-5. Annual Lifecy cle GHG Emissions Over Ti me and Payback Periods

-100,000 100,000 300,000 500,000 700,000 900,000 1,100,000 1,300,000GHG Emissions (gCO2eq / mmBtu) -

-

-

- -

-100,000 100,000 300,000 500,000 700,000 900,000 1,100,000 1,300,000GHG Emissions (gCO2eq / mmBtu) Gasoline

Diesel

Soy-Based Biodiesel

Corn Ethanol

Sugarcane Ethanol

Switchgrass Ethanol

Corn Ethanol Cumulative Net Emissions

Soy-Based Diesel Cumulative Net Emissions

Sugarcane Ethanol Cumulative Net Emissions

Switchgrass Ethanol Cumulative Net Emissions Year 0 Land Use Emissions: -Clearing with Fire -Biomass Decay

-Soil Carbon -Foregone Sequestration

Years 1-19 Land Use Emissions:

-Soil Carbon -Foregone Sequestration Years 20-79 Land Use Emissions: -Foregone Sequestration

2022 2030 204 0 2050 206 0

In the proposed rule we highlight results fo r time horizons of 100 years and 30 years, and discuss the merits of both approaches. With lifecycle GHG emissions projected to occur many years into the future, the choice of whether and by how much to discount future emissions is also important. In our highlighted results, we used a 2% discount rate when analyzing GHG emissions over a 100-year time horizon (See Fi gure 2.1-2), and we apply a 0% discount rate to estimate the GHG emissions reductions associated with biofue l production over a 30-year time horizon (See Figure 2.1-3). L LLLLLL The choice of discount rate is disc ussed in detail in the preamble to the proposed rule.

While EPA believes the methodology presen ted here represents a robust and scientifically credible appro ach, we recognize that some cal culations of GHG emissions are relatively straight-forward, while others are not. The direct, domestic emissions from biofuel production are relatively well known. The indirect, international emissions are the component of our analysis with the highest level of uncerta inty. Despite uncertainties associated with international land use change, we would expect at least some interna tional land use response as demand for crop land increases as a result of this rule. Al though there ar e uncertainties associated with these estimates, it would be far less scientifically cr edible to ignore the effects of land use changes altogether than it is to use the best appro ach available to assess these known emissions sources.

Since lifecycle analysis is a new part of the RFS program, in addition to the formal comment period on the proposed rule, EPA is making multiple efforts to solicit public and expert feedback on our proposed approach. We are or ganizing a public worksh op focused specifically on lifecycle analysis, and we plan to conduct formal external peer-reviews of key components of

LLLLLLL Note that with a 0% discount rate over 30 years, emissions after year 30 are effectively discounted 100%.

286our analysis, such as the internat ional land use change assessment. Even after the final rule is issued, we recognize that the status of the science a nd availability of data w ill continue to evolve. Therefore, we plan to establish formal update procedures that incl ude further peer review of the life cycle analysis framework and methodology.

2.1.4 Overall Climate Benefits, Temperature and Sea-Level Impacts

Our analysis of the overall GHG emission impacts of this proposed rulemaking was performed in parallel with the lifecycle analys is performed to develop the individual fuel thresholds described in previous sections. Consistent with the fuel volume feasibility analysis and criteria pollutant emissions evaluation, our analysis of the GHG impacts of this proposed rulemaking was conducted by comparing the difference between a 2022 reference case and a 2022 control case with volumes of renewable fuel s meeting the RFS2 mandate. Based on the volume scenarios considered, with 36 billion ga llons of biofuel in 2022, the one-time land use change impact results in 448 million metric tons of CO2-eq. emissions. For this analysis we convert the NPV results for the 100 year 2% discount rate into an annualized stream such that the NPV of the annualized emissions will equal the NPV of the emission stream over 100 years with a 2% discount rate. This results in an annuali zed emission reduction of 160 million metric tons of CO2-eq. emissions. Usi ng a 30 year time horizon and 0% discount rate the annualized emission reduction is 150 million metric tons CO2-eq emissions. EPA also estimated the temperature and sea-level impacts of RFS2, which are explained below in Section 2.10. We also calculated the monetized GHG benefits of the RFS2 program, which are explained in DRIA Chapter 5.

2.1.5 Chapter Overview

What follows in this Chapter is a deta iled discussion of each component of EPA’s lifecycle analysis. Section 2.2 provides background about lifecycl e analysis in general, and EPA’s current approach. Section 2.3 lays out the goals and scope of our analysis. Section 2.4 explains EPA’s estimate of petroleum baseline lifecycle GHG emissions which renewable fuels are compared with. Section 2.5 discusses potential indirect impacts in the energy sector that may be caused by biofuel production. Section 2.6 pr ovides a detailed explana tion of EPA’s estimates of direct and indirect agricultura l sector impacts. Starting at S ection 2.6.5, details are provided about EPA’s estimate of emissions from land use change. Emissions produced during the conversion of renewable biomass to transportation fuel are discussed in Section 2.6.7. Section 2.7 explains our estimates of tailpipe emissions. Fuel-specific lifecycle GHG results are presented in Section 2.8, including a range of sel ective sensitivity analyses . Section 2.9 includes estimates of the overall climate benefits from EI SA’s revisions of the RFS program. Finally, Section 2.10 translates GHG emissions mitigatio n from the revised RFS program into global temperature and sea-level change projections.



### 2.2 Background for Estimating Fuel Lifecycle Greenhous e Gas Emissions

2.2.1 Lifecycle Analysis fo r the Original RFS Program

287Lifecycle modeling, often referred to as fuel cycle or well-to-wheel analysis, assesses the net impacts of a fuel throughout each stage of its production and use including production / extraction of the feedstock, feedstock transporta tion, fuel production, fu el transportation and distribution, and tailpi pe emissions. Use of a lifecycle approach to analyze different transportation fuels requires modeling and evaluation of many different input factors. The starting point for EPA’s lifecycle wo rk was the analysis conducted as part of the Renewable Fuel Standard rulemaking in response to the Energy Policy Act of 2005 (RFS1). For the RFS1 rule, we estimated GHG benefits based on lifecycle GHG modeling of the diff erent renewable fuels mandated as part of the rule. EPA’s approach for the RFS1 was to rely heavily on the “Greenhouse Gases, Regulated Emi ssions, and Energy Use in Tran sportation” (GREET) model, developed by the Department of Ener gy’s Argonne National Laboratory (ANL).

However, it has become increasingly apparent that this type of first order lifecycle modeling has notable shortcomings, especially wh en evaluating the implications of biofuel policies. M MMMMMM Recently, lifecycle ass essments have been divided into two major methodological categories: attr ibutional and consequential. 504

Attributional lifecycle assessment focuses on describing the environmentally relevant physical flows to and from a lifecycle and its subsystems. 505 Attributional analyses are based on the direct suppliers of the necessary inputs to production, and co-products are handled by allocation. This means that the environmental impacts from biofuels production are simply split between the fuel and its co-products, using av erage data to determin e the allocation shares. 506 Therefore, an attributional lifecy cle analysis of corn ethanol w ould account for the direct impacts of increased corn production, such as fertilizer and pesticide use, but it would ignore the indirect/marginal impacts of shifting crop patter ns, such as decreased so ybean production, that would also result. Attributiona l analysis also requires a choice about the basis for allocation among co-products (e.g., mass, energy content, ma rket value, displacement), which can lead to significantly different results.

Consequential lifecycle assessment captures indirect/marginal impacts that are not explicitly included in attributi onal analyses. Consequential anal yses account for activities within and outside the lifecycle that are affected by an incremental change with in the lifecycle of the product under investigation. In other word s, consequential lif ecycle analyses study the consequences of changes in production or cons umption from a market-based perspective, utilizing economic modeling to identify the ultimat e impacts of a decision, such as a policy or a single project. 507 This often implies the use of margin al data, and co-product allocation is typically avoided through system expansion. In other words, consequential analyses include alternative uses of constrained production factor s, and marginal supply and demand on affected markets. 508 Some researchers have concluded that the consequential appr oach provides more complete and accurate, but less precise results, wh ile the attributional ap proach provides more precise results but with inherent blind spots that lead to l ess accurate results. 509

MMMMMMM See also, Conceptual and Methodological Issues in Lifecycle Analysis of Transp ortation Fuels, Mark A. Delucchi, Institute of Transportation Studies, University of California, Davis, 2004, UCD-ITS-RR-04-45 for a description of issues with traditional lifecycle analysis used to model GHG impacts of biofuels and biofuel policies.

288In fact, the main criticism EPA received in reaction to our previous RFS1 lifecycle analysis, which resembled a traditional attributional lifecycle analysis, was that we did not include important secondary, indirect, or conseq uential impacts of biof uel use, specifically:

− Biomass production focused on the feedstock used (e.g., corn) and did not capture other secondary market driven agricultur al sector impacts, such as changes in other crop patterns and livestock producti on as a response to changing prices in biofuel feedstocks.

− Production of co-products from ethanol production required some type of allocation, either splittin g emissions of fuel production between fuel and coproducts or examining the use of co-produc ts in other markets. For example in the case of corn ethanol, the co-product of ethanol production is a feed product that is assumed to replace the use of co rn and soybean meal. Therefore, the emissions of producing an equivalent amount of these products are subtracted from the lifecycle assessment. This required modeling of the co-product economic market that is typically done outside of the lifecycle modeling.

− To the extent that they are included, la nd use impacts are typically confined to direct impacts, e.g., land converted to produce corn directly used for ethanol production. This does not capture effect s of land use converted to produce crops that are indirectly impacted by incr eased biofuel producti on. One specific example of this is increased corn etha nol production in the U.S. could lead to decreased crop exports resulting in in creased crop production and land use impacts internationally. Another example is corn acre increase resulting in less rice production acres and lower CH 4 emissions.

− The attributional modeling approach is not necessarily linked to a specific policy case, but is rather a general representation of biofuel production. Consideration of specific policies and interaction between di fferent fuel volumes could have very distinct impacts especially in the agricultural sector.

− The replacement of petroleum fuel was re presented by an average gallon of fuel. However, we recognize that an additiona l gallon of renewable fuel replaces the marginal gallon of petroleum fuel. To the extent that the marginal gallon of renewable fuel is from feedstocks grown on highly productive land and displaces petroleum fuel derived from oil sands or other types of crude oil that are associated with higher th an average GHG emissions, re placing these fuels could have a larger GHG benefit. Conversely to the extent the marginal gallon of renewable fuel is from feedstocks grow n on marginal land and displaces imported gasoline produced from light crude, repl acing these fuels would have a smaller GHG benefit.

− The attributional modeling approach assu mes that one Btu of renewable energy produced displaces one Btu of petroleum en ergy. In reality, the production of renewable fuels is expected to lower wo rld energy prices (as discussed in Chapter

2895.2), which in turn could lead to a marg inal increase in world energy consumption and less than a one-for-one Btu replacement.

A key aspect of the issues described above is that the attributiona l lifecycle approach typically does not includ e economic modeling and the signifi cant indirect impacts from price changes in the analysis. Therefore, to a ddress some of these issues, we have updated our lifecycle methodology to include the use of economic models to perf orm a consequential type of lifecycle analysis.

The consequential approach of incorpor ating economic models into a lifecycle assessment is not a new concept. Most not ably the Economic Input-Output Lifecycle Assessment (EIO-LCA) method has b een employed in the past. The EIO-LCA method estimates the materials and energy resources required for, and the environmental emissions resulting from, activities in the overall economy. The EIO- LCA method was theori zed and developed by economist Wassily Leontief in the 1970s based on his earlier input-output work from the 1930s for which he received the Nobel Prize in Econom ics. Researchers at th e Green Design Institute of Carnegie Mellon University operationalized this method in the mid-1990s, once sufficient computing power was widely ava ilable to perform the large-scale matrix manipulations required in real-time. This work relies on static input-output tables of the U.S. economy to determine the full economy wide impacts of produ cing a product or service.

Mark Delucchi at the Institute of Transportation Studies of the University of California Davis has developed the Lifecycl e Emissions Model (LEM) that l ooks at transportation fuels. He has also highlighted the need to look at ma rket impacts when considering biofuel production and specifically to consider land use changes. 510 There have also been several studies examining the consequential or economic based life cy cle assessment including several focusing on the agricultural sector.

Several studies and analyses conducted sin ce the completion of the RFS1 rulemaking have also contributed to our understanding of the lif ecycle environmental impacts of biofuel production. These studies, and others, have highlighted the potential impacts of biofuel production on the agricultural sec tor and specifically identified land use change impacts as an important consideration when dete rmining GHG impacts of biofuels. 511, 512 In the meantime, the dramatic increase in U.S. production of biofuels has heightened the concern about the impacts biofuels might have on land use and has increased the importance of consid ering these and other indirect impacts in lifecycle analysis.

In order to improve our lifecycle modeling for this proposed rulemaking, EPA held a series of discussions with inter ested stakeholders during July and early August 2007 and again in early 2008. Stakeholder outreach included discussions with academ ics and other researchers, the California Air Resources Board (CARB), the U.S. Departments of Agriculture and Energy, and the following stakeholders who provide d comments on RFS lifecycle analysis:

− American Petroleum Institute

− National Petroleum Refiners Association

− Renewable Fuels Association

290− National Biodiesel Board

− DuPont

− Union of Concerned Scientists

− Natural Resources Defense Council

− Environmental Defense

Through dozens of meetings with this wide range of experts and stakeholders, EPA has shared and sought input on this methodology. We also have relie d on the expertise of the U.S. Department of Agriculture (USDA) and the Depa rtment of Energy (DOE) to help inform many of the key assumptions and modeling inputs for th is analysis and have gained their support for their use. Dialogue with the State of Califor nia and the European Un ion on their parallel, ongoing efforts in GHG lifecycle analys is has also helped inform EPA’s methodology. While there is no consensus, the input we received through th ese interactions is reflected in the approach described here.

2.2.2 Updates for this Proposed Rulemaking

Based on discussions with these numerous stakeholders and review of literature, we have developed an approach for this proposed rulemaking that enhances the RFS1 modeling by including additional information and models to address concerns with our existing work by significantly strengthening and expanding our coverage of agri cultural sector impacts.

Currently, no single model can capture all of the complex interactions associated with estimating lifecycle GHG emissions for biofuels, ta king into account the "significant indirect emissions such as significant emissions from la nd use change" required by EISA. For example, some lifecycle analysis tools typically used in the past focus on process modeling—the energy and resultant emissions associated with the direct production of a fuel at a petroleum refinery or biofuel production facility. But this is only one component in th e production of the fuel. Clearly in the case of biofuels, impacts from and on the agricultural sector are important, because this sector produces feedstock for biofuel production. Commercial agricult ural operations make many of their decisions based on an economic assessment of profit maximization. Assessment of the interactions th roughout the agricultural sector requires an analysis of the commodity markets using economic models. However, existing economy wide general equilibrium economic models are not detailed enough, on their own, to capture the specific agricultural sector interactions critical to our analysis (e.g., ch anges in acres by crop type ) and would not provide the types of outputs needed for a thorough GHG anal ysis. As a result, EPA has used a set of tools that are best suited for each specific component of the analysis to create a more comprehensive estimate of GHG emissions. Where no direct links between the different models exist, specific components and outputs of each are used and combined to provide an analytical framework and the composite lifecycle assessment results.

To quantify the emissions factors associated with different steps of the production and use of various fuels (e.g., extrac tion of petroleum products , transport of feedstocks), we used the spreadsheet analysis tool developed by Argonne National Laboratories, the Greenhouse gases, Regulated Emissions, and Energy us e in Transportation (GREET) m odel. This analysis tool includes the GHG emissions associ ated with the production and combustion of fossil fuels

291(diesel fuel, gasoline, na tural gas, coal, etc.). These fossil fuels are used both in the production of biofuels, (e.g., diesel fuel u sed in farm tractors and natural gas used at ethanol plants) and could also be displaced by renewable fuel u se in the transportation sector. GREET also estimates the GHG emissions associat ed with electricity produc tion required for biofuel and petroleum fuel production. For the agricultura l sector, we also relied upon GREET to provide GHG emissions associated with the production and transport of agricult ural inputs such as fertilizer, herbicides, pesticides, etc. While GREET provides direct GHG emissions estimates associated with the extraction-through-combustion phases of fuel use, it does not capture some of the secondary impacts associated with the fuel, su ch as changes in the composition of feed used for animal production which would be expected due to changes in cost. EPA addresses these secondary impacts through other m odels described later in this section. GREET has been under development for several years and has under gone extensive peer review through multiple updates. Of the available sources of inform ation on lifecycle GHG emissions of fossil energy consumed, we believe that GREET offers the most comprehensive treatment of emissions from the covered sources. For some steps in the production of biofuels , we use more detailed models to capture some of the dynamic market inter actions that result from various policies. Here, we briefly describe the different models incorporated into ou r analysis to provide sp ecific details for various lifecycle components.

To estimate the changes in the domestic agri cultural sector (e.g., changes in crop acres resulting from increased demand for biofuel feedst ock or changes in the number of livestock due to higher corn prices) and thei r associated emissions, we used the Forestry and Agricultural Sector Optimization Model (FASOM), develo ped by Texas A&M University and others. FASOM is a partial equilibrium economic model of the U.S. forest and agricultural sectors. EPA selected the FASOM model for this analysis for several reasons. FASOM is a comprehensive forestry and agricultural sector model that tracks over 2,000 production possibilities for field crops, livestock, and biofuels for private lands in the contiguous United States. It accounts for changes in CO 2 , methane, and N 2 O from most agricultural activities and tracks carbon sequestration and carbon losses over time. Another advantage of FASOM is that it captures the impacts of all crop production, no t just biofuel feedstock. Thus, as compared to some earlier assessments of lifecycle emission, using FASOM allows us to determine secondary agricultural sector impacts, such as crop shif ting and reduced demand due to hi gher prices. It also captures changes in the livestock market (e.g., smaller he rd sizes that result from higher feed costs) and U.S. export changes. FASOM also has been used by EPA to consider U.S. forest and agricultural sector GHG mitigation options. 513

To estimate the impacts of biofuels feedstoc k production on interna tional agricultural and livestock production, we used the integrated Food and Agricultural Policy and Research Institute (FAPRI) international models, developed by Iowa State University and the University of Missouri. These models capture the biological, technical, and economic relationships among key variables within a particular commodity and across commodities. FAPRI is a worldwide agricultural sector economic model that was r un by the Center for Ag ricultural and Rural Development (CARD) at Iowa St ate University on behalf of EPA. The FAPRI models have been previously employed to examine the imp acts of World Trade Organization proposals, changes in the European Union’s Common Agricultu ral Policy, analyze farm bill proposals since

2921984, and evaluate the impact of biofuel developm ent in the United States. In addition, the FAPRI models have been used by the USDA Office of Chief Economist, Congress, and the World Bank to examine agricu ltural impacts from government policy changes, market developments, and land use shifts.

Although FASOM predicts land use and export changes in the U.S. due to greater demand for domestic biofuel feedstock, it does not assess how international agricultural production might respond to these changes in co mmodity prices and U.S. exports. The FAPRI model does predict how much crop land will change in other countries but does not predict what type of land such as forest or pasture will be affected. We used data analyses provided by Winrock International to estimate what land t ypes will be converted into crop land in each country and the GHG emissions asso ciated with the land conversions. Winrock has used 2001­ 2004 satellite data to analyze recent land use cha nges around the world that have resulted from the social, economic, and political forces that dr ive land use. Winrock has then combined the recent land use change patterns with various esti mates of carbon stocks asso ciated with different types of land at the state level. This in ternational land use assessment is an important consideration in our lifecycle GHG assessment and is explained in more detail later in this chapter.

To test the robustness of the FASOM, FAPRI a nd Winrock results, we are also evaluating the Global Trade Analysis Project (GTAP) model, a multi-region, multi-sector, computable general equilibrium model that estimates changes in wo rld agricultural produc tion. Maintained through Purdue University, GTAP projects intern ational land use change based on the economics of land conversion, rather than using the historical data approach applie d by FAPRI/Winrock. GTAP is designed to project changes in interna tional land use as a result of the change in U.S. biofuel policies, based on the relative land use va lues of cropland, forest, and pastureland. The GTAP design has the advantage of explicitly modeling the competition between different land types due to a change in policy. As further discussed in Section 2.6.5.3.3, GTAP has several disadvantages, some of which prevented its use fo r the proposal. We hope to correct several of these shortcomings between the proposed and final rule; therefore we continue to evaluate the use of the GTAP model as part of the final rule.

The assessments provided in this proposal use the values provided by the Intergovernmental Panel on Climate Change (IPCC) to estimate the impacts of N 2 O emissions from fertilizer application. However, due to concern that this may underestimate N 2 O emissions from fertilizer application 514 , we are working with the CENTURY and DAYCENT models, developed by Colorado State University to update our assessments. The DAYCENT model simulates plant-soil systems and is capable of simulating detailed daily soil water and temperature dynamics and trace gas fluxes (CH 4 , N 2 O, NOx and N 2 ). The CENTURY model is a generalized plant-soil ecosystem model that simulates plant producti on, soil carbon dynamics, soil nutrient dynamics, and soil water and temperatur e. We anticipate the results of this new modeling work will be reflected in our assessments for the final rule. More description of this ongoing work is included in the Section 2.6.2.2.

To estimate the GHG emissions associated w ith renewable fuel production, we used detailed ASPEN-based process models deve loped by USDA and DOE’s National Renewable

293Energy Laboratory (NREL). While GREET contai ns estimates for renewable fuel production, these estimates are based on existing technology. We expect biofuel production technology to improve over time, and we projected improvement s in process technology over time based on available information. These projections are discu ssed in DRIA Chapter 4. We then utilized the ASPEN-based process models to assess the impacts of these impr ovements. We also crosschecked the ASPEN-based process model predic tions by comparing them to a number of industry sources and other modeling efforts that estimate potential improvements in ethanol production over time, including the Biofuel Energy Systems Simulator (BESS) model. BESS is a software tool developed by the University of Nebraska that calculates the energy efficiency, greenhouse gas (GHG) emissions, and natural res ource requirements of co rn–to-ethanol biofuel production systems. We used the GREET model to estimate the GHG emissions associated with current technology as used by petroleum refineries, because we do not expect significant changes in petroleum refinery technology.

We used the EPA-developed Motor Vehicl e Emission Simulator (MOVES) to estimate vehicle tailpipe GHG emissions. The MOVES modeling system estimates emissions for on-road and nonroad sources, covers a broad range of polluta nts, and allows multiple scale analysis, from fine-scale analysis to na tional inventory estimation.

Finally, for the FRM we intend to use the Energy Information Administration's National Energy Modeling System (NEMS) to estimate the secondary impacts on the energy market associated with increased renewable fuel production. NEMS is a modeling system that simulates the behavior of energy markets and their interactio ns with the U.S. economy by explicitly representing the economic decision-making involved in the producti on, conversion, and consumption of energy products. NEMS should model the secondary im pacts that greater renewable fuel use may have on the prices and qu antities of other source s of energy, and the greenhouse gas emissions associated with these changes in the ener gy sector. It was not possible to complete this analysis in time for the NPRM.

While EPA is using the most state-of-the -art tools available today for each of the lifecycle components considered, we understand the concern that using multiple models necessitates integrating these models and, where possible, appl ying a common set of assumptions. As discussed later in this chapter, this is particularly important for the two agricultural sector models, FASOM and FAPRI, which are being used in combination to describe the agricultural sector impacts dom estically and internati onally. As described in more detail in the DRIA Chapter 5, we have worked with th e FAPRI and FASOM models to align several key assumptions. As a result, the projected agricu ltural impacts described in Section 2.6 are relatively consistent across both models. One outstanding issue is the differences between the modeling results associat ed with increased soybean based biod iesel production. We intend to further refine the soybean biodiesel scenarios for th e final rule.

Figure 2.2-1 graphically shows the different models used and what parts of the lifecycle they are used to represent.

294Figure 2.2-1. System Boundaries and Models Used



### 2.3 Goal and Scope of This Analysis

Lifecycle analysis is used in several ways for this proposed rulemaking. Fuel-specific GHG reductions are used to develop threshold determinations for specific fuels. Lifecycle analysis is also used to determine the overall impact of the rulemaking on GHG emissions worldwide.

The analysis conducted to assess the fuel-specific GHG performance relative to the thresholds in the standards is described in Sectio ns 2.3 through 2.8. The analysis conducted to determine the overall GHG emission impact of the rulemaking is provided in Section 2.9.

2.3.1 Goal

The proposed rulemaking involves determini ng lifecycle GHG impacts of specific fuels and fuel pathways for comparison with thresholds as defined in the legislat ion. Obligated parties will be required to use mandated quantities of renewable fuels, but only fuels that meet the GHG thresholds can qualify under the program. The lifecycle GHG reductions represent the GHG differences between renewable fuels relative to th e petroleum-based gasoline and diesel that they displace. The lifecycle methodol ogy described here is used to determine the GHG displacement

295values for different renewable fuels to be compared to the thresholds. Th erefore this analysis will provide:

− Amount of GHG emissions (in gram s, lb., tons, etc.) per amount of fuel produced (per btu) for both conventional petroleum based fuels and renewable fuels.

− Results are combined to quantify the emi ssion change per btu of renewable fuel compared to that for the conventional fuel replaced.

2.3.2 Scope

2.3.2.1 Scenario Analysis

To quantify the lifecycle GHG emissions associat ed with the increase in renewable fuel mandated by EISA, we needed to compare the im pacts of renewable fuels with EISA to a reference case without EISA. Since it is not practical or workable to conduct such an analysis and come up with factors for every year, to carry out this analysis we chose to look at the final year of the RFS2 standards when they are fully phased in. For our reference case we assumed a “business as usual” volume of a particular rene wable fuel based on what would likely be in the fuel pool in 2022 without EISA as predicted by th e Energy Information Agency’s Annual Energy Outlook (AEO) for 2007 (which took into account the economic and policy factors in existence in 2007 before EISA). For our control case we assumed the higher volumes of renewable fuels as mandated by EISA for 2022. For each individual biofuel, we analyzed the incremental impact of increasing the volume of that fuel to the total mix of biofue ls needed to meet the EISA requirements while holding volumes of other fuel s constant. Any changes between now and 2022 in factors such as crop yields, energy cost s, or production plan t efficiencies, both domestically and internationally, are reflected in both scenarios. Rather than focus on the impacts associated with a specific gallon of fuel and tracking inputs and outputs across different lifecycle stages, we determined the overall aggreg ate impacts across sections of the economy in response to a given volume change in the amount of biofuel produced. We then normalize those impacts to a gallon of fuel by dividing total impacts over the given volume change. In the case of overall rule impacts, we analyze the change in reference vs. control case volumes for all fuels together and take the absolute GHG results (e.g., do not normalize the overall rule impacts).

We did not calculate the emission impacts fo r each gallon of fuel based upon its unique production characteristics which could vary wide ly across the nation (e.g ., a gallon of ethanol produced using corn grown in Iowa may have diffe rent direct lifecycle emissions impacts than a gallon of ethanol produced at an identical facility in Nebraska using corn grown in Nebraska due to regional differences in agricultural practices. However, on a lifecycle basis, considering the indirect impacts in the context of the entire corn market they are not different). Rather, we determined the overall aggregate impacts across sections of the economy in response to a given volume change in the amount of biofuel produced. In the case of agricultural impacts, we assessed the impact on the entire U.S. agricultu ral system that would result from expanded demand for biofuel feedstock. We then normalized those impacts to a ga llon of fuel by dividing total impacts over the renewable fuel volume ch ange between our busine ss as usual case and the EISA volumes. Similarly, we estimated the t ypical emissions impact of a type of biofuel production facility (e.g., a plant th at uses the dry mill process to turn corn starch into ethanol).

296The emissions assessment from a typical facility was then ascribed to all biofuel produced across facilities using that same basic technology.

There are several important implications associ ated with this met hodology. First, this analysis focuses on the average impact of an increase in fuel produced using a technology pathway and does not distinguish the emission performance betw een biofuel production plants using the same basic production t echnology and type of feedstock. Thus it does not account for relatively small differences in facility design or operation which may a ffect the lifecycle GHG performance at that facility.

Second, the results presented here ar e based on the GHG impacts of the volumes analyzed. For corn ethanol the impact represen ts the change in GHG emissions when national corn ethanol production increases from 12.4 to 15 Bgal which are the estim ated volumes in corn ethanol production that would o ccur in 2022 without and then with EISA mandates in place. The GHG impact is then normalized to a per gallon or Btu basis in relation to gasoline. These values are used to represent every gallon of corn et hanol produced throughout the program. The per gallon impact based on different volume changes could be different than the incremental impact studied. For example, modeling a change from 0 to 15 Bgal would allow an assessment of the average GHG impact for all 15 Bgal of corn ethanol . As discussed in Section 2.8, we examine this issue through a sensitivity an alysis where we consider a ch ange in corn ethanol volumes from 8.7 to 12.4 Bgal and plan to provide a br oader set of assessments for the final rule. We described corn ethanol here, but the other rene wable fuels were assessed in the same manner.

Third, by focusing on 2022, this analysis does not track how biofuel GHG emission performance may change over time between now and 2022. While our estimates are based on the GHG emissions for each biofuel for a specific volume change in 2022, we recognize that an alternative approach would be to create incremental GHG emission curves for different volumes and different years. Section 2. 8 provides a sensitivity analysis of the year of analysis, which includes corn ethanol lifecycle GHG results for 2012 and 2017. As discussed above, we believe modeling the results in 2022 provides a represen tative assessment of the GHG impact of the additional biofuel mandated by EISA.

Finally, several of the lifecycle emission impacts for one fuel are interrelated with those of another fuel, in particular the land-use changes. For our analysis of the overall GHG impacts of the program (discussed in section 2.9), we mode led all of the fuel changes simultaneously to determine the land-use impact. Howe ver, from that analysis it is not possible to differentiate the contribution of the land-use change to one fuel vs. another. As a result, for this analysis we had to model the impacts of just one fuel change at a time. In doing this we have held the other fuel volumes constant at their mandated levels in order to best approxi mate the impacts a single fuel change would have in the context of the full RFS2 standard volumes.

We used the same approach to determine the lifecycle GHG emissions for corn ethanol, cellulosic ethanol, biodiesel produced from soyb ean (and other vegetable) oils, and biodiesel produced using waste oils as feedstock. For w aste oils, we note that no land use changes are included in the FASOM assessment, because any land use impacts are attributed to the original

297purpose of the feedstock (e.g.., the use of the vegetable oil for cooking or the production of animals for their meat), rather than the biofue l produced from the recovered waste material.

FASOM does not model feedstocks for fuels pr oduced outside the U.S. We addressed imported ethanol by analyzing the differen ce in total GHG emissi ons based on two 2022 scenarios using only the results from FAPRI modeling runs: (1 ) the business as usual reference case volume of 0.6 Bgal and (2) an RFS2 project ed volume of 3.1 Bgal of imported sugarcane ethanol.

Current models present some challenges in estimating GHG lifecycle emissions for cellulosic biofuels. The version of FASOM we used for this propo sal did not include the forestry module, and therefore, we did not model forest products or forest w aste as a renewable fuel feedstock. FASOM does have a module for the for est sector that EPA used in the past to assess forest impacts of changes in U.S. policies. Th e forestry module works much like the agricultural sector module in assessing likely outcomes; however , the data populating the forestry module is currently being updated to reflect the most recent information, and was not available in time to use in analyses for this proposal. EPA anticipates that the FASOM forestry module will be available for the final rule and intends to use it in the final rule assessmen ts. We also note that inclusion of the forestry modul e will potentially allow the FASO M model to use some currently forested lands for crop production or pasture. To the extent this occurs, our assessment of the lifecycle impacts of farm crop changes may also be impacted. In addition, FAPRI does not include switchgrass or similar energy crops, a nd can only use corn stove r or other food crop residues as feedstock in predicting cellulosic biofuel impacts. To overcome this limitation, we assumed that increased domestic switchgrass producti on would result in a decrease in U.S. crop exports, because switchgrass competes with other crops for acreage. We allowed the FAPRI model to draw on additional CRP acres to represent increased demand for acreage for energy
crops.

For biofuels made from wast es and byproducts (e.g., MSW, re ndered fats and waste oils and corn stover feedstock), we assumed no land use changes, because these biofuel feedstocks do not compete for domestic crop acreage. For co rn stover, we analyzed only the change in domestic GHG emission resulting from an increase in fertilizer replacemen t application rates to compensate for the removal of stover from the land.

Table 2.3-1 shows the different fuel scenario s considered under the different models used.

298Table 2.3-1. Fuel Volume Scenarios Considered in This Analysis

Fuel M odel Vo lu me ( Bill io n s o f G a llo n s )

Low V o l u me H i gh V o lume Net Ch an g e

C o r n Et han ol F ASO M & FAP R I 12 .4 15 2.6

8.7 12 .4 3.7

S w itc h g r as s E t h a nol F ASO M 1.1 5.7 4.6

F APRI 0 3.2 3.2

Biodi e s e l F ASO M & FAP R I 0.4 0.7 0.3

C or n St over Eth an ol F ASO M 0 7.6 7.6
Sugar c a ne Ethanne Ethan o l F APRI 0.6 3.1 2.5

2.3.2.2 System Boundaries

It is important to establish clear system boundaries in this analysis. By determining a common set of system boundaries, different fuel types can then be va lidly compared. As described in the previous section, we have assessed the direct and indirect GHG impacts in each stage of the full fuel lifecycle for biofuels and petroleum fuels. Figure 2.3-1 provides a simplified diagram describing the system studied.

Figure 2.3-1. Simplified Lifecycle System Diagram

International Agricultural

Sector Domestic Agricultural

Sector Conventional Petroleum Fuel

Production

Food / Feed Uses Transportation Sector Fuel Demand Biofuel Production

The different fuel use volume scenarios will be compared on delivering the same functions, in this case providing for both the agri cultural sector market and transportation fuels market. Within the overall system shown in Figur e 2.3-1, the unit process listed in Table 2.3-2 will be considered.

299Table 2.3-2. Unit Processes Considered Biofuel Petroleum-Based Fuel Feedstock Agriculture Crude Oil Extraction

Feedstock Transport Crude Oil Transport Feedstock Processing & Biofuel Production Refining

Biofuel Transport and Distribution Fuel Transport and Distribution Biofuel Tailpipe Emissions Fuel Tailpipe Emissions

In setting system boundaries for th is analysis, we use a cons istent approach across all fuels to determine what is incl uded in the system bounda ries considered. In this way renewable, alternative and petroleum based fuels are all co mpared based on the same system boundaries. In general this type of analysis could be expande d to cover many different sectors of the economy, with diminishing impact on the overall results. Therefore, we rely on internationally accepted lifecycle assessment standards, de veloped by the International Or ganization for Standardization (ISO), when setting the system boundaries of this analysis. Based on the ISO standards we consider environmental significance as a cut off criteria for what to include in our system boundaries.

Included in each unit process shown in Tabl e 2.3-2 are the emissions and energy use associated with that operation as well as upstream co mponents that feed into it. For example, the feedstock agriculture stage includes emissions from fuel used in tractors as well as from producing and transporting the fertilizer used in th e field. Electricity production emissions are included in almost all of the st ages shown. For direct impacts, this results in system boundaries that include operation-related activ ities, but not infrast ructure-related activities. As such, while we do include the emissions associated with the ope ration of farm equipment and trucks used for feedstock / fuel transportation we do not include the emissions asso ciated with the production of the equipment or vehicles. Furthermore, we include the emissions from the operations of biofuel production plants and petroleum refineries but we do not includ e emissions from producing the material used to construct the facilities.

This decision was based on analysis done for the RFS1 rulemaking which found that including farm equipment production energy us e and emissions incre ases ethanol lifecycle energy use and GHG emissions and decreases the percent reduction in GHG emissions from corn ethanol in comparison to gasoline by approximately 1 percent. Furt hermore, 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. For example, this e xpansion of system bounda ries would include the energy use and emissions associated with producing concrete and steel used in the petroleum refinery. The net effect of this would be a sli ght increase in both the et hanol and petroleum fuel lifecycle results and a smaller or negligib le effect on the comparison of the two.

In determining what indirect impacts to incl ude in the system boundari es of this analysis we again focus on those sources that will have the biggest effect on overall results. Therefore, for the analysis used in this proposed rulemaking we have included significant direct and indirect

300domestic and international agricult ural sector impacts. We also pl an to consider indirect effects in energy markets due to increased use of biofue ls in the transportation sector, and changes in fossil fuel markets due to increased fuel (primar ily natural gas) used in the ethanol production sector. EPA is still developing the modeling appr oach to capture these energy sector indirect effects and some preliminary analysis is presented in Section 2.5.

In comparing renewable and alternative fuels to the petroleum fuel that they replace we have made the assumption that the amount of c onventional fuel no longer consumed – that is, displaced – as a result of the use of the replacemen t renewable or alternativ e fuel is equal on an energy basis. This assumes 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). Therefore, fo r every Btu of ethanol produced there is a corresponding decrease of a Btu of gasoline use, and associated quantity of crude oil is not extracted or processed to make this gasoline. Some studies have shown that because of the increased octane content of et hanol (and specifically E85) vehi cle efficiency may actually be slightly improved with use of E 85 vs. gasoline. This would impl y that a Btu of ethanol would actually displace slightly more that a Btu of gasoline because of improved engine efficiency. However, these studies are not conclusive enough for us to include in our an alysis at this point. This is something we will be considering for the final rule and may add as a sensitivity case.

This type of modeling also does not allow for behavioral changes that could occur, what we call the “secondary oil market” impacts. Fo r example, a worldwide reduction in oil prices could result from the increased use of renewable fuels (as discussed in Chapter 5.2). To the extent world fuel prices are decreased, demand and consumption would tend to increase; this impact is labeled the “international oil takeback effect.” The result coul d be less than a Btu of gasoline displaced for each gallon of ethanol pr oduced. We have not yet conducted the modeling to quantify the magnitude of this effect or fact ored it into our lifecycle analysis. However, we are looking at options to consider this impact in the final rule analys is, as discussed in Section 2.5.

2.3.2.3 Environmental Flows Considered

The lifecycle analysis discussed here evaluates the impacts of increased renewable fuel use on greenhouse gas emissions. EISA specifies a definition of greenhou se gases to include in the analysis:

The term ‘greenhouse gas’ means carbon dioxide, hydrofluorocarbons, methane, nitrous oxide, perfluorocarbons, sulfur hexafluoride. The Administrator may include any other anthropogenically emitte d gas that is determined by the Administrator, after notice and comment, to contribute to global warming.

EISA also specifies that the mass values for all greenhouse gases are adjusted to account for their relative global warming potential.

The relative global warming contribution of emissions of various greenhouse gases is dependant on their radiative forcing, atmosphe ric lifetime, and other considerations. For

301example, on a mass basis, the radiative forcing of CH 4 is much higher than that of CO 2 , but its effective atmospheric residence time is much lo wer. The relative warming impacts of various greenhouse gases, taking into account factors su ch as atmospheric lifetime and direct warming effects, are reported on a ‘CO 2 -equivalent’ basis as global warm ing potentials (GWPs). The GWPs used in this analysis were develope d by the UN Intergovernmental Panel on Climate Change (IPCC) as listed in their Second Assessment Report, and are shown in Table 2.3-3. Second assessment report values are used to be consistent with current sta ndards for international reporting of GHG emissions.

Table 2.3-3. 100 Year Global Warming Potentials for Greenhouse Gases Greenhouse Gas GWP CO 2 1 CH 4 21 N 2 O 310

Greenhouse gases are measured in terms of CO 2 -equivalent emissions, which result from multiplying the GWP for each of the three polluta nts shown in the above table by the mass of emissions for each pollutant. The sum of impacts for CH 4 , N 2 O, and CO 2 , yields the total effective GHG impact. Other GHGs like HFCs, PFCs and SF6 are not re leased in significant amounts over the lifecycle of renewabl e or petroleum fuels, and are therefore not tr acked in this analysis. Other non-GHG climate impacts lik e albedo (light refl ectance), land surface roughness, hydrologic and energy flux, and loss of fo rest aerosols, while potentially an important aspect of climate impacts associated with land use change, are curren tly outside the scope of this analysis.

Other environmental flows besides GHG emissions are also considered in our analysis for this rulemaking. Criteria a nd toxic air pollutants are modele d and results are described in Chapter 3 of the DRIA. Water use and impacts ar e also considered and are described in Chapter 6 of the DRIA.

2.3.2.4 Data Quality

Lifecycle analysis is a data intensive process and the results are aff ected by data quality. Data quality may be defined by specific charac teristics that describe both quantitative and qualitative aspects of data, as well as the methods u sed to collect and integr ate those data into the analysis. The quality of data used can be characterized by ho w well the geographic, technical and temporal aspects of the data match the goals and scope of the analysis in question.

The quality of the data used in this analysis was classified based on its geographic, technical and temporal relevance to the goals of the study as follows:

Geographic coverage – this analysis was conducted without any regard to the geographic attributes of where emissions or energy use occurs. The benefits of this proposed rule represent global reductions in GHG emissions and en ergy use, not just those occurring in the U.S. For example, the savings associated with reducing overseas

302crude oil extraction and refi ning are included here, as are the international emissions associated with producing imported ethanol. Da ta for agricultural sector impacts include both U.S. and international defaults. Ag ricultural commodity production in other countries was based on data specific to tho se areas (e.g., fertilizer production in other countries). Land use change w as specifically modeled in diffe rent countries, impacts of land use change were based on factors representi ng sub-country level land characteristics, and for areas where data was not available averages were used.

Technology coverage – this analysis models industries that do not exist yet – cellulosic ethanol and renewable diesel for example. Therefore assumptions based on existing information and modeling were made to repr esent these industries rather than relying on existing facility data. Even for industries that currently exist there is expected to be a range of technology development over time. Fo r this analysis we have made our best projections for what the industry may look like by 2022. There is expected to be considerable variation in th e technologies used, for example combined heat and power and corn oil fractionation in a dry mill ethanol plant. To account for this we have looked at different fuel technology pathwa ys as shown in Section 2.6.7.

Temporal coverage – this analysis considered impact s in 2022. Therefore we modeled future data; we projected ethanol pr oduction in 2022 based on process models – consistent with cost analysis used in th is proposed rulemaking. For example, this assumed that future plants will be more energy efficient then current plants. Agricultural sector models also represented 2022 values including improvements in yields and cropping patterns.

2.3.2.5 Uncertainty Analysis

While EPA believes the methodology presen ted here represents a robust and scientifically credible appro ach, we recognize that some cal culations of GHG emissions are relatively straight-forward, while others are not. The direct, domestic emissions are relatively well known. These estimates are based on well-estab lished process models that can relatively accurately capture emissions impacts. For ex ample, the energy and GHG emissions used by a natural gas-fired ethanol plant to produce one gallon of ethanol can be calculated through direct observations, though this will vary somewhat be tween individual facilities. The indirect domestic emissions are also fairly well unders tood, however these results are sensitive to a number of key assumptions (e.g., current and future corn yields, energy prices, etc.). We address uncertainty in this area by testing the impact of changing these assumptions on our results. Finally, the indirect, internationa l emissions are the component of our analysis with the highest level of uncertainty. For example, identifying what type of land is converted internationally and the emissions associated with this land conversion are critical issues that have a large impact on the GHG emissions estimates. We address this uncertainty by usi ng sensitivity anal yses to test the robustness of the results based on different a ssumptions. We also identify areas of additional work that will be completed prior to the final ru lemaking. For example, while we utilized an approach using comprehensive agricultural sector models and recent satelli te data to determine the GHG emissions resulting from international la nd use impacts, we are also considering an alternative methodology (the anal yses using GTAP) that estimat es changes in land use based on

303the relative land use values of cropland, forest, and pastureland. Additionally, we are considering country-specific inform ation which may allow us to bett er predict specific trends in land use such as the degree to which marginal or abandoned pasture land w ill need to be replaced if used instead for crop production.

Despite the uncertainty associated with international land use change, we would expect at least some international land use change to occur as demand fo r crop land increases as a result of this rule. Furthermore, the conversion of crop land will lead to GHG emission from land conversion that must be accounted for in th e calculation of lifecycle GHG emissions. We believe that uncertainty in the effects and exte nt of land use changes is not a reason for not accounting for land use change emissions. Although there are uncertainties associated with these estimates, it would be far less sc ientifically credible to ignore the effects if land use change altogether than it is to use the best approach availa ble to assess these known emissions.

There are several ways of specifically deali ng with uncertainty in this analysis. One approach would be to perform statistical analys is, applying distribution cu rves to each input and running Monte Carlo analysis to determine a range of results. While this may be the most intellectually pleasing approach in theory, there ar e several significant barrier s to this approach. Most significantly, it is difficu lt to determine scientifically-defensible probability distribution functions for all (or even the most significant) in put variables. Applying functions that are not well understood may serve to misstat e uncertainty. Furthermore, an analysis that treats all variables independently would not capture the fact that th ere are links between inputs with potentially conflicting imp acts on results (e.g., higher fertilizer use linked to higher crop yields). To do this correctly, we need to determine the cova riance matrix of all vari ables, or risk further obscuring the degree of true unc ertainty in the analysis.

Another approach to examining these uncertain ties is to identify variables with the greatest impact on the overall resu lts and apply sensitivity analysis . This is the approach we have taken, for example, looking at a range of ethanol plant types, as well as ranges of land use change factors. In this way we bound the resu lts with high and low cases. The challenge in a complicated analysis with so many variables, of c ourse, is to ensure that we have identified the most significant factors influe ncing GHG emissions, and that our choices of low and high cases accurately encompass the range of possible outcomes. We note that further research on key variables will allow for a more robust assessm ent of these impacts in the future.



### 2.4 Baseline Gasoline and Diesel Fuel

2.4.1 Modeling Approach

EISA specified the baseline petroleum gaso line and diesel fuel that renewable fuels would be compared to for assessing their perfor mance in comparison to the thresholds.

The term ‘baseline lifecycle greenhouse gas emissions’ means the average lifecycle greenhouse gas emissions, as determined by the Administrator, after notice and

304opportunity for comment, for gasoline or diesel (whichever is being replaced by the renewable fuel) sold or distribut ed as transportation fuel in 2005.

To estimate the lifecycle GHG emissions asso ciated with baselin e petroleum-based gasoline and diesel fuel, we based our analysis on the GREET mode l. Lifecycle energy use and associated emissions for petroleum-based fu els in GREET are calculated based on energy efficiency metrics for the diffe rent processes involved in petrol eum-based fuels production. The energy efficiency metric is a m easure of how many Btus of input energy are needed to make a Btu of output product. GREET has assumptions on energy efficiency for different finished petroleum products as well as for different types of crude oil.

GREET also has assumptions on the mix of en ergy sources used to provide the energy input which determine GHG emissions. The GHG em issions associated with petroleum fuels arebased on the emissions from producing and combusting the input energy sources (with noncombustion sources included where applicable).

The following sections describe the GREET assum ptions for different petroleum-based fuels production processes and final products.

2.4.2 Crude Oil Extraction

GREET has assumptions for different types of crude oil extraction, conventional crude and oil sands crude. N NNNNNN The values for conventional cr ude oil are assumed to represent average crude oil extraction, including domestic and foreign production, and onshore, offshore and enhanced oil recovery processes. Oil sands re covery was used in this analysis to represent Canadian oil sands recovery. The assumptions for each type of crude oil extraction are shown here.

Conventional crude oil extraction is assumed to have an energy efficiency metric of 98%. This means that every one mmBtu of crude oil produ ced requires 1,020,408 Btu of energy input. One mmBtu of that input is crude oil feedstoc k (from the ground) that ends up in the final product and therefore only 20,408 Btu of additio nal process energy is required. Table 2.4-1 shows the breakout of the energy sources used to provide the additi onal process energy.

NNNNNNN Oil sands crude is also commonly referred to as tar sands.

305Table 2.4-1. Conventional Crude Oil Production Process Fuel Share of Input Energy (%) Energy use (Btu/mmBtu of fuel throughput) Crude oil 1.0% 204 Residual oil 1.0% 204 Diesel fuel 15.0% 3,057 Gasoline 2.0% 408 Natural gas 61.9% 12,635 Electricity 19.0% 3,872 Feed loss 0.1% 28 Total: 100% 20,408

The GHG emissions from conventional crude oil extraction are calc ulated by multiplying the Btus of the different types of energy inputs by emissions fact ors for combustion of those fuel sources. The emission factors for the different fuel types are from GREET based primarily on assumed carbon contents of the different process fuels. The emissions from producing electricity are also taken from GREET and represent average U.S. grid electricity production emissions. In addition to process energy GHG emissions, GREET also assumes there are also GHG emissions from associated flaring of natural gas at the oil well, assuming 16,800 Btu of natural gas is flared per mmBtu of crude oil produced. The GHG emissi ons from flared natura l gas are again based on emissions factors for natural gas combusti on. GREET also accounts for fugitive methane emissions from crude oil extraction and processin g, assuming that 13.15 g of fugitive methane is released per mmBtu of cr ude oil produced.

Similar to the assumptions for conventional crude oil extraction, GREET has assumptions about oil sands crude oil extraction. Oil sands can be e ither mined or recovered in situ - meaning in place, GREET has assumptions for both processes. The oil sands recovery processes include both extraction (surface or in situ) and separa tion systems to remove bitumen from sand and
water. Surface mining of oil sands involves open-pit mi ning techniques. About two metric tons of oil sands must be dug up, moved and processed to produce one barrel of oil. In situ recovery is used for bitumen deposits bu ried too deeply for mining to be practical. Cyclic steam stimulation (CSS) and steam-assisted gravity dr ainage (SAGD) are in si tu recovery methods, which include thermal injection through vertical or horizontal wells. The following Tables show the energy efficiency metric and assumed ener gy sources used in oil sands extraction. GHG emissions from the oil sands extraction process are based on applying emission factors to the different energy inputs.

306Table 2.4-2. Oil Sands Recovery: Bitumen Extraction – Surface Mining Energy Efficiency 94.8% Process Fuel Share of Input Energy (%) Energy use (Btu/mmBtu of fuel throughput) Diesel fuel 0.6% 329 Natural gas 82.3% 45,132 Electricity 17.1% 9,377 Feed loss 0.03% 14 Total: 100% 54,852

Table 2.4-3. Oil Sands Recovery: Bitu men Extraction – In-Situ Production

Energy Efficiency 84.3% Process Fuel Share of Input Energy (%) Energy use (Btu/mmBtu of fuel throughput) Natural gas 97.2% 181,011 Electricity 2.8% 5,214 Feed loss 0.01% 14 Total: 100% 186,239

Once oil sands are extracted through either th e surface mining or in situ process they are further processed for transportation. The energy efficiency metric and assumed energy input sources for the bitumen upgrading are shown in Table 2.4-4.

Table 2.4-4. Oil Sands R ecovery: Bitumen Upgrading Energy Efficiency 98.6% Process Fuel Share of Input Energy (%) Energy use (Btu/mmBtu of fuel throughput) Natural gas 97.1% 13,787 Electricity 2.8% 397 Feed loss 0.1% 14 Total: 100% 14,198

In addition to the GHG emissions from the process energy used in the upgrading process, GREET also assumes hydrogen is needed as an i nput to the upgrading process. GHG emissions from hydrogen production, based on GREET defaults, are included in the emissions of bitumen upgrading. GREET assumes 84,187 Btu of hydrogen is used per mmBtu of oil sand crude produced through surface mining, and 32,364 Btu of hydrogen is used per mmBtu of oil sand crude produced through the in situ process.

Emission factors from GREET were used for b itumen extracted in-situ , bitumen extracted by surface mining, and light crude oil extraction. Emission factors from McCann (1999) were used to calculate GHG emissions from heavy oil and Venezuela extra heavy oil by considering the incremental extraction and processing emissions relative to the average of the light crudes analyzed by McCann. 515 Instead of using McCann GHG values directly we used these results to

307determine a scaling factor for the heavy crudes co mpared to the conventional crude types. We then applied this scaling f actor to the GREET conventional crude emissions to represent emissions from heavy crude production. The resu lts of the crude oil production differentiation are shown below in Table 2.4-5.

Table 2.4-5. Scaling factors Used to Calculate Heavy Crude Extraction GHG Emissions Crude Production Emissions (gCO2-eq. / mmBtu LHV of fuel produced) Canadian

Light Brent North Sea Saudi Light Average "conventional" Heavy Oil Venezuela Extra Heavy Oil Production Emissions 6,617 5,081 7,746 6,482 6,962 15,524 Scaling Factor 1.07 2.40

The importation of heavy crude oil and extr a heavy crude oil in the U.S. comprises a relatively small but growing fraction of current total crude oil imports. Heavy crude oils are generally of higher density and hi gher viscosities compared to light er crude oils and similarly, are not as readily extracted from reserves. The American Petroleum Instit ute’s “API Gravity” is commonly used to delineate light, medium, h eavy and extra heavy crude oils. Although definitions can vary slightly, the U.S. Geological Survey (USGS) categorizes light crude oil as having an API gravity of at least 22° a nd viscosity less than 100 centipoise (cP). 516 Heavy oil, by contrast, is defined as an “asphaltic, dense, and viscous oil” with an upper API limit of 22° and viscosity between 100 – 10,000 cP. Heavy oil with an API less than 10° is defined as extraheavy oil. Natural bitumen –also referred to in the literature as oil sand, tar sand, oilimpregnated sand, asphaltic sand, rock asphalt, bituminous rock, and bitumen-bearing rock– have a similar API range as extra-heavy oils but have viscosities gr eater than 10,000 cP.

To determine the mix of crudes used in 2005 to produce the gasoline and diesel baseline required by EISA, EPA analy zed the types of crude oils used in the U.S. by examining crude oil import data from the Energy Information Admini stration for 2005, which is based on reporting by refineries. 517 The amount of crude imports reported by refineries with API less than or equal to 22° – heavy oil – was approximately 2.9 million barrels per day (mmbd) in 2005 out of the 15.2 mmbd refinery net input in the U.S. estimat ed by EIA. Figure 2.4-1 below displays the break-down of U.S. heavy crude imports by country.

308Mexico

V enezuela
C anad

a Ecuador Brazil Ch ad
N o rway

R ussia
Unit

ed Kingdom K uwait

A ngola Saudi Ara b ia

I nd

o n esia Guatam ala

Ch

i na Peru Co

l umbia N ig e ria
M alaysia O m a n

A rg ent

ina

Boliv

ia Figure 2.4-1. U.S. Imports of Heavy Cr ude Oil in 2005 (API of 22° or less)

2005 Heavy Oil Imports (<=22.0)

Source: EIA Form 814 Data

- 200,000 400,000 600,000 800,000 1,000,000 1,200,000

bb l/ dayBarrels per Day

Note that this is an underestimate of the actua l heavy crude oil, extra heavy crude oil, or bitumen used by U.S. refineries since a fraction of these crudes are upgraded at the country of origin and/or diluted with d iluents (gas condensate, natural gas liquids or light crude) and transported to the U.S. These upgraded crude oils would then appear as imports with an API greater than 22° (i.e. light or medium crude oil). EPA has identified that crude oil from Venezuela and Canada would likely have the most upgrading of extra hea vy crude oil or bitumen to synthetic crude, or syncrude , due to the nature of the r eserves and current information regarding the crude oil types extracted. The International Energy Agency estimates that in 2005, Venezuela produced approximately 565,000 bpd of s yncrude from its extra heavy oil reserves in the Orinoco oil belt. 518 An analysis of the production out put from the four members of the Orinoco Belt Strategic Associa tion shows that roughly three-four ths of the upgraded syncrude has an API greater than 22°. 519 Thus, out of the 3 mmbd of cr ude oil produced in Venezuela, approximately 19% was syncrude (i.e. 14% with AP I greater than 22° and 5% with API less than 22%). Applying these splits to the U.S. im port data would result in roughly 145,000 bpd of syncrude from extra heavy oil, 715,000 heavy oi l, and 684,000 light (or conventional) crude oils. This split would still likely unde restimate the extra heavy oil/s yncrude used by the U.S. since Venezuela tends to ship a greate r fraction of its heavier crude o ils to U.S. refineries that are specifically configured to handle heavier crude oils.

Estimates on the types of crude oil imported from Canada were also adjusted based on available information. Canadian production information for 2005 was obtained from statistical information from the Canadian A ssociation of Petroleum Producers. 520 Production of crude oil in Canada totaled 2.35 mmbd in 2005, with 35% of the volume comprised of light crude oil, 22% heavy crude oil, 23% from bitumen or synthe tic crude obtained via su rface mining, and 19% from bitumen produced from in-situ methods. Surface mining comprised over half of the Canadian bitumen extraction, but us e of in-situ techniques is now gr owing at a faster rate due to the lower availability of su rface bitumen and improvements in in-situ techniques.

309Domestic production of heavy oil was not included in the EIA import data analyzed by the EPA. 521 To account for this, various estimates were used from different sources. In 2005, we estimate that roughly 475,000 bpd of heavy oil was produced in the U. S. with the vast majority produced in California.

A fraction weighted estimate of the crude oil types used in the U.S., calculated based on the data sources and calculations described a bove, was used in our estimation of the 2005 gasoline and diesel baseline. Note that this does not include cr ude-type breakdowns for finished products imported into the U.S. from overseas (e.g . Europe) or exported a nd consumed overseas, which may change the fractions slightly. Out of the 15.3 mmbd of crude oil reported to be used in the U.S. in 2005 522 , approximately 5% was composed of bitumen-based syncrude (mainly from Canada), 1% was extra heavy crude oil b ased syncrude (mainly fr om Venezuela), and 23% was heavy crude oil, as shown in Figure 2.4-2.

Figure 2.4-2. Percent of U.S. Crude Oil by Source

U.S. Crude Oil Consumption

5% 1% 23% 72%

- 2.00 4.00 6.00 8.00 10.00 12.00million barrels per day . .

Bitumen-based Extra heavy oil-based Heavy crude oil Light or medium crude syncrude syncrude oil

The emission factors for the 2005 baseline were adjusted based on the above weighting for the different types of crude oil.

2.4.3 Crude Transport

Transport of crude oil to the U.S. was b ased on GREET defaults, which are shown in the Figure 2.4-3. The figure indicat es the percent of the different t ypes of crude oil used (Canada and Mexico are assumed to include the Canadian oil sands imports) and associated modes of transportation and miles traveled for each m ode. The GHG emissions are based on emission factors for each type of vehicl e including capacity, fuel economy, and type of fuel used.

310Figure 2.4-3. Crude Oil Transportation Assumptions

7.0% 7.0%

2,100

35.0%

1.0%

500

50.0% 50.0%

5,500 92.0%

750

8.0%

750 0.0%

8.0% 800

0.0%

800 US Terminal Petroleum Refinery Domestic:

Alaska

Domestic: US 48 States

Imported: Canada and

Mexico Imported: Offshore Countries Ocean Tanker

Pipeline

Rail Ocean Tanker
Pipeline Barge

Rail

2.4.4 Refining

The GHG emissions associated with petroleum refining can also be calculated based on the energy efficiency of the refi nery and the type of process ener gy fuel sources used. However, the refinery is producing a number of different petroleum-based products including different transportation fuels. Therefore, the total energy use and emissions from the refinery needs to be allocated to the different products.

The basis for the overall refinery energy efficiency in GREET are numerous linear programming model runs representing energy use in a typical refinery. Different refinery configurations with different slates of products produced will have different energy efficiencies. For calculating baseline GHG emi ssions associated with gasolin e and diesel fuel we are assuming an average U.S. refinery, so defaults in GREET are reasonable for our analysis.

Allocation of the total refinery energy use and emissions to the different products can be done in a number of ways. Total energy use and emissions can be allocated based on the energy, mass, or economic value of the different output products. Alternately, energy use and emissions can be based on first breaking out total refinery energy use to the different refinery sub-processes (e.g., distillation, cracking, alkylation, isomera tion, desulfurization), and then allocating energy from each sub process to the different products , for example 100% of alkylation used for gasoline. This type of sub process allocation wo uld require a level of detail that was beyond the scope of this analysis. Therefore, we used the GREET default alloca tion which in general allocated 60–65% of total refining process fuel use to gasoline production, 18–22% to diesel production, and the remaining 13–22% to other petroleum products.

The refinery modeling runs and the allocation to different products results in the energy efficiency values for different types of gaso line and diesel fuel shown in Table 2.4-6.

311Table 2.4-6. Refinery Energy Efficiency Transportation Fuel Type Energy Efficiency Conventional Gasoline 87.7% Reformulated Gasoline Feedstock 87.2% Low Sulfur Diesel Fuel 89.3%

The following table shows the process fuel breakout and energy use for each transportation fuel.

Table 2.4-7. Refinery Energy Use
Process Fuel Share of Input Energy use (Btu/mmBtu of fuel throughput) Energy

(%) Conventional Gasoline RFG Feedstock Low Sulfur Diesel Fuel Residual oil 3.0% 4,208 4,404 3,595 Natural gas 30.0% 42,075 44,037 35,946 Coal 13.0% 18,233 19,083 15,577 Electricity 4.0% 5,610 5,872 4,793 Refinery still
gas 50.0% 70,125 73,395 59,910

Total: 100% 140,251 146,789 119,821

The GHG emissions from refining are calculated by multiplying the Btus of the different types of energy inputs by emissions factors for combustion of those fuel sources. The emission factors for the different fuel types are from GREET based primarily on assumed carbon contents of the different process fuels. The emissions from producing electricit y are also taken from GREET and represent average U.S. grid electricity production emissions. In addition to process energy GHG emissions, GREET also assumes ther e are also non-combustion GHG emissions associated with petroleum refining. These emissi ons are also allocated to the different fuels based on the same energy efficiency ratios used to allocate energy use. The non-combustion CO2 emissions amount to 1,172 g CO 2 per mmBtu of conventional gasoline, 1,214 g CO 2 per mmBtu of RFG blendstock, and 1,088 g CO 2 per mmBtu of low sulfur diesel fuel.

To represent baseline gasoline we considered the types of gasoline used in 2005. Based on EIA data on sales of gasoline to end users in 2005, 57.4% w as conventional gasoline, 28.4% was reformulated gasoline and 14.2% was Californian reformulated gasoline. The California reformulated gasoline is repr esented in GREET as reformulat ed with slightly different assumptions about crude transport distances. We also considered imported finished product vs. domestically produced g asoline. According to EIA data, in 2005 6.0% of conventional gasoline used was imported and 7.7% of reformulated gasoline was imported. Imported gasoline was modeled the same as domestic for refinery emi ssions, except conventiona l crude was used to represent crude oil production impa cts, instead of a domestic mix. To represent baseline diesel fuel use we used a similar analysis and assume d 100% low-sulfur diesel fuel and 8.0% imported finished product.

2.4.5 Fuel Transport

Transport of the finished petroleum product was also included in the baseline values for gasoline and diesel fuel. Transpor t included carriage from refine ry to bulk terminal and from

312bulk terminal to retail location. Transport of imported finish ed product was also included and was assumed to be transported by ocean tanker 2, 600 miles to the U.S. Once in the U.S. both domestic and imports were assumed to be tr ansported by the modes and distances shown in Tables 2.4-8 and 2.4-9.

Table 2.4-8. Gasoline Transport Assumptions Refinery to Bulk Terminal Bulk Terminal to Refueling Station Mode Percent Distance Percent Distance Ocean Tanker 17 1,500 0 N/A Barge 4 520 0 N/A Pipeline 72 400 0 N/A Rail 7 800 0 N/A Truck 0 N/A 100 30

Table 2.4-9. Diesel Fuel Transport Assumptions Refinery to Bulk Terminal Bulk Terminal to Refueling Station Mode Percent Distance Percent Distance Ocean Tanker 12 1,500 0 N/A Barge 6 520 0 N/A Pipeline 72 400 0 N/A Rail 7 800 0 N/A Truck 0 N/A 100 30

The GHG emissions from transport of gasoline and diesel fuel were based on GREET default emission factors for each type of vehicle including capacity, fuel economy, and type of fuel used.

2.4.5 Petroleum Fuel 2005 Baselin e Well-To-Tank GHG Emissions

The results of each of the lifecycle stages fo r the petroleum fuels were combined to generate a well-to-tank lifecycle GHG value for 2005 gasoline and diesel fuel as shown in Table 2.4-10. Tailpipe combustion emissions for the two fuels are described in Section 2.7. When the tailpipe emissions are included, the well-to-wheel results for baseline gasoline and diesel fuel are 98,395 and 96,843 gCO2eq/mmBtu respectively.

Table 2.4-10. Petroleum Fuel Baseline We ll-To-Tank GHG Emissions (g/mmBtu of fuel)

CH 4 N 2 O CO 2 CO 2 -eq. Diesel Fuel 106 0.25 15,074 17,381

Gasoline 107 0.27 16,762 19,102

In December 2008, the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) released a report that estimates the average lifecycle GHG emissions from

313petroleum-based fuels sold or distributed in 2005. O OOOOOO The estimates in th e report are based on a slightly different methodol ogy than EPA’s analysis of li fecycle GHG emissions for the petroleum baseline. The NETL report is av ailable on the docket for this rulemaking.



### 2.5 Secondary Energy Sector Impacts Modeling

2.5.1 Indirect Energy Sector Impacts from Renewable Fuel Consumption

We have started an analysis using an EPA version of th e Energy Information Agency’s National Energy Modeling System (NEMS-EPA) P PPPPPP to estimate indirect impacts on energy use associated with increased renewable fuel cons umption. NEMS is a modeling system that simulates the behavior of energy markets and their interactions w ith the U.S. economy by explicitly representing the economic decision-maki ng involved in the production, conversion, and consumption of energy products. NEMS can represent the secondary impacts that greater renewable fuel use may have on the prices and qu antities of other source s of energy, and the GHG emissions associated with thes e changes in the energy sector . For example, the use of natural gas at corn ethanol production plants coul d increase the price of natural gas, and thus encourage the use of other energy sources in other sectors of the economy. Due to time constraints, we did not include indirect energy s ector impacts in the lifecycle GHG results for the proposed rule. Below we explain the preliminary results, and outline our plans to refine this analysis for the final rule.

We successfully used NEMS-EPA to simu late the RFS2 fuel volumes for this rulemaking, and the projections from that effort were used in our energy security results, and other analyses for the proposed rule. However, we had difficulties isolating volume changes for specific renewable fuel types. When we atte mpted to model the fuel-specific volume scenarios that were used for FASOM and FAPRI analysis of the agricultural sector (See Section 2.3.2 for a description of the FASOM and FAPRI fuel-speci fic scenarios), we had trouble designing model runs that held the other fuel volumes constant. For example, when we tried to use NEMS-EPA to model an increase in corn ethanol consumpti on from 12.4 to 15 billion gallons, the volumes of cellulosic ethanol consumption al so changed. We could not u se model results with variable cellulosic ethanol production to estimate the sec ondary energy sector impacts of corn ethanol, because the modeled cellulosic ethano l plants export electricity to the grid, which has significant energy sector impacts that could not be assigned to corn ethanol.

To effectively model a scenario where onl y corn ethanol consumption increased, we departed from the volume scenarios that were used for the other analyses in this rule. Table 2.5­ 1 shows renewable fuel volumes for two NEMS -EPA scenarios (Lower Corn Ethanol and Control Case) that successfully varied corn ethanol consumption and held cellulosic ethanol consumption constant.

OOOOOOO DOE/NETL. 2008. Development of Baseline Data and Analysis of Life Cycle Greenhouse Gas Emissions of Petroleum-Based Fuels. DOE/NETL-2009/1346
PPPPPPP This version is called NEMS-EPA to make it clear th at EPA, rather than EIA, conducted this analysis.

314Several assumptions were modified in order to create the differences in corn ethanol while keeping the other biofue ls (such as cellulosic ethanol and biodiesel) unchanged between the cases. In addition, a larger difference in co rn ethanol than that of the Reference to EISA cases was desired so that the cha nge was more significant relativ e to the convergence tolerance of the model.

In the Lower Corn Ethanol Case, the RFS cr edit was reduced for non-advanced biofuels (i.e. corn ethanol) and the cost of new corn ethanol plants was incr eased in order to reduce projected corn ethanol production. The Control Case includes a higher RFS requirement for total biofuels to give credit to corn ethanol above 15 billion gallons as well as a reduction in the cost of new corn ethanol plants. Both cases also in clude modifications to the cost of soy-based biodiesel as a device that keep s the biodiesel production only at the minimum EISA-mandated level in both cases. We modified the RFS requi rements such that the cellulosic requirement would be met and be binding in both cases, and re duced the advanced biofuels requirements to equal that of cellulosic. Both changes were made to minimize the change in other biofuels between the two cases. The cases al so eliminate biomass-to-liquids (which was included in EIA’s Annual Energy Outlook but not included in any of the EPA cases).

Table 2.5-1. Fuel Volumes for NEMS Corn Ethanol Scenario (Billions of Gallons Consumed Per Year)

2017 2022
Lower Corn Ethanol Case Control Case Difference Lower Corn Ethanol Case Control Case Difference Corn Ethanol 10.5 15.2 4.8 4.1 15.6 11.5 Cellulosic Ethanol 3.0 3.0 0.0 12.5 12.5 0.0 Imported Ethanol 1.5 2.2 0.7 1.2 3.0 1.8 Exported Ethanol 1.0 0.7 -0.3 1.2 0.5 -0.7 Biodiesel 1.0 1.0 0.0 1.0 1.0 0.0 Total Biofuels 16.9 22.0 5.1 20.0 32.5 12.6

We computed the difference in energy c onsumption between the Control Case and the Low Corn Ethanol Case shown in Table 2.5-1. New ethanol plants built after 2015 were modeled to use natural gas and electricity but not coal as energy sources. To isolate the indirect energy sector impacts not included in the rest of our lifecycle analysis, we excluded changes in energy use for biofuel refining, agriculture, petr oleum refining, and gasoline and diesel use for transportation. In other words, we used the NE MS-EPA analysis to proj ect how an increase in corn ethanol production would aff ect energy consumption in the residential, commercial, and non-refining industrial sectors. Table 2.5-2 shows how energy consumption in these sectors changed between the Control Case and the Lower Corn Ethanol case.

315Table 2.5-2. Projected Indirect Impacts from Corn Ethanol Production on Energy Use by Energy Source in the Residential, Commercia l, and Non-Refining Industrial Sectors (Quadrillion Btu)

2017 2022
Lower Corn Ethanol Case Control Case Difference Lower Corn Ethanol Case Control Case Difference Liquid Fuels 14.585 14.587 0.002 14.653 14.683 0.030 Liquified Petroleum Gases 2.489 2.489 0.000 2.397 2.396 -0.001 E85 0.000 0.000 0.000 0.000 0.000 0.000 Motor Gasoline 0.423 0.423 0.000 0.422 0.421 0.000 Jet Fuel 3.955 3.955 0.000 4.283 4.283 0.000 Kerosene 0.124 0.124 0.000 0.125 0.125 0.000 Distillate Fuel Oil 2.582 2.582 -0.001 2.541 2.540 -0.001 Residual Fuel Oil 1.567 1.568 0.001 1.569 1.574 0.006 Petrochemical Feedstocks 1.433 1.433 0.000 1.357 1.357 0.000 Liquid Hydrogen 0.000 0.000 0.000 0.002 0.002 0.000 Other Petroleum 2.010 2.012 0.002 1.958 1.984 0.026 Natural Gas 23.270 23.274 0.003 22.867 22.813 -0.054 Coal 24.641 24.642 0.001 26.410 26.501 0.092 Nuclear Power 8.487 8.484 -0.003 9.194 9.158 -0.036 Hydropower 2.985 2.983 -0.001 3.009 3.007 -0.003 Traditional Biomass 2.569 2.569 0.000 2.786 2.786 -0.001 Other Renewable Energy 1.786 1.785 -0.001 2.023 2.023 0.000 Other 0.180 0.180 0.000 0.175 0.175 0.000 Total 78.503 78.504 0.001 81.119 81.146 0.027

We applied emissions factors to estimate the GHG impacts of the energy consumption changes shown in Table 2.5-2. We estimated the GHG impacts in terms of GHG emissions impacts per Btu of additional corn ethanol consum ption. The results in Table 2.5-2 imply an increase in GHG emissions of 1,356 g/mmBtu in 2017, and an increase in GHG emissions of 9,290 g/mmBtu in 2022. A large part of these GHG r esults can be attributed to the increase in coal consumption and the decrease in nuclear energy consumption projected by NEMS-EPA in the Control Case relative to th e Lower Corn Ethanol Case. If we hold nuclear energy consumption constant (i.e. assume that Control Case nuclear power consumption equals nuclear power consumption in the Lower Corn Ethanol Case), the GHG impacts become an increase of 437 g/mmBtu in 2017 and an increase of 5,117 g/mmBtu in 2022.

We did not include the results of this NEMS-EPA analysis in our lifecycle GHG estimates for the proposed rule. For the final rule we intend to continue exploring the use of NEMS-EPA to estimate the indirect energy sector impacts of rene wable fuel consumption. To use the NEMS-EPA model for this purpose, we would like to gain a better understanding of why the energy sector impacts vary between years. We may also explore other models and data sources that could be utilize for th is part of the lifecycle analysis.

2.5.2 Indirect Impacts on Petroleu m Consumption for Transportation

EPA recognizes that with the increase in de mand for biofuels from the RFS2 program there could be secondary impacts on oil markets. For example, an in crease in the u se of biofuels could result in lower U.S. demand for imported o il. Lower U.S. imported oil demand could cause the world oil price to modestly decline, and resu lt in an increase in oil consumption outside of the U.S. (referred to here as the “i nternational oil takeback effect”). In addition, with the greater use of biofuels in the U.S., we estimate that th e cost of transportation fuels in the U.S. would

316increase. This increase in the costs of U.S. transportation fuels would lik ely lower the domestic demand for oil beyond the direct substitution of biof uels for gasoline and diesel. The response of U.S. oil demand to price is referred to here as the “rebound effect.” Fr om an overall standpoint, total global oil demand is the result of the two combined effects, decreases in U.S. oil demand (from the “rebound effect”) and increases in o il demand outside of the U.S. (from the “international oil takeback eff ect”). These effects work in oppos ite directions to influence the overall change in global oil dema nd. Besides providing a complete picture of oil market impacts of the RFS2 program, this issue could also be important because secondary impacts on oil demand from the RFS2 program, assuming that they can be reliably determined, could be factored into the lifecycle GHG emissions estimates.

Estimates of the “international oil takeback effect” and, to a lesser extent, the “rebound effect” are difficult to quantify. Ta ke the case of the “int ernational oil takeback effect.” While a decline in the world oil price is likely to increase world oil consumption, oil consumption patterns vary significantly across different regions of the worl d (i.e., Southeast Asia, South America). As a result, each re gion may have a different demand response to a change in the international price of crude oil. For example, in Europe, the price of crude oil comprises a much smaller portion of the overall fuel prices seen by consumers than in the U.S., since Europeans pay significantly more fuel tax than their U.S. counterparts. A dec line in the price of crude oil is likely to have a smaller impact on prices at the pump and therefore a lesser impact on demand. In many other countries, particul arly developing countries such as China and India, oil is used more widely in industrial and el ectricity generation applications, although the energy picture in China and India is evolving ra pidly. In addition, many c ountries around the world subsidize their oil consumption, and oil markets are infl uenced by monopolistic control of prices, both on the supply and demand sides. It is, therefore, not clear how oil consumption would change due to changes in the market price of oil with the current pattern of subsidies. The factors listed above illustrate the difficulty in trying to estimate the overall im pact of a reduction in world oil price on oil consumption outside of the U.S.

Even for the U.S., there is some controversy about the impact of a ch ange in the price of oil on domestic U.S. oil consumption. Since appr oximately 70 percent of the U.S. oil use is concentrated in the U.S. transportation sector, most discussion of the so called “rebound effect” revolves around the impact of a change in driving in response to a change in the price of transportation fuels. Many fact ors influence the magnitude of the U.S. rebound effect, including how fuel cost increases are passed through to pric es at the pump for consumers, the composition of adult drivers, the level of urbanization, income levels, and the amount of congestion.

In any case, the Agency recognizes that both the “international oil takeback effect” and the “rebound effect” are important to estim ate and we are examining methodologies for quantifying them for the final rule. For example, the most straightforward approach would be to utilize an elasticity estimate of global oil demand with respect to the international oil price and use this estimate to calculate the “international oil takeb ack effect.” However, a global estimate would not capture regional or country specific demand respon ses, which as indicated above could be very different. Alternatively, as anot her approach for estimating the “international oil takeback effect,” EPA is asking ORNL to expl ore an approach based upon a review of the international energy economics literature to se e if a set of bottom -up estimates of oil demand

317elasticities by world regions (i.e., Southeast Asia and South Ameri ca) and oil-using sectors (i.e., transportation and non-transportation) can be devel oped and used to addre ss this issue. If the bottom-up estimates can be developed or a global demand elasticity can be agreed upon, they will help inform the development by ORNL of an integrated petroleum/biofuels market tool to examine fuel market issues associated with the RFS2 program.



### 2.6 Biofuels Analysis

2.6.1 Modeling Approach

As mentioned in Section 2.2, to address some of the issues with our RFS1 lifecycle modeling for biofuels we have updated our methodol ogy to include the use of agricultural sector economic models. Our methodol ogy involves the use of the FASOM model to determine domestic agriculture sector-wid e impacts of increased biofuel production, and the FAPRI model to determine internati onal changes in crop production and to tal crop. Agricultural sector GHG emissions are estimated by FASO M, and FAPRI results were converted to GHG emissions based on GREET defaults and IPCC emi ssion factors. Biofuel proc ess energy use and associated GHG emissions were based on process models for the different pathways considered. Feedstock and co-product transportation GHG emissions were based on GREET defaults.

The agricultural sector models were used to determine the imp acts associated with biofuels production by comparing two similar scenari os in both models. Both agricultural sector models were run with two sim ilar volumes of the specific fuel in question, while other fuel volumes were held constant to is olate the fuel-specific impacts. Table 2.6-1 shows the 2022 fuel volumes modeled in FASOM in order to isolat e the incremental imp acts of each type of renewable fuel. Section 2.3 includes more discussion of the fuel volume scenarios.

Table 2.6-1. 2022 Fuel Volumes Modele d with FASOM (Billions of Gallons)

Control Case Biodiesel Only Case Corn Ethanol Only Case Corn Stover Ethanol Only Case Switchgrass Ethanol Only Case Biodiesel 0.6 0.2 0.6 0.6 0.6 Corn Ethanol 15.0 15.0 12.4 15.0 15.0 Corn Stover Ethanol 7.6 7.6 7.6 0.0 7.6 Switchgrass 1.1 1.1 1.1 1.1 5.7 Bagasse Ethanol 1.4 1.4 1.4 1.4 1.4

The total impacts from changes in biofue l production were calculated by taking the difference in total GHG emissions from the tw o scenarios considered. Per gallon (or mmBtu) impacts were calculated by dividing the tota l GHG emission changes by the increase in volume of fuel represented in the scenar ios. Therefore, the results pr esented in this proposed rulemaking represent the per mmBtu “average marginal” impact of the change in fuel volumes considered. In other words, the GHG impacts were estimated for a marginal increase in fuel production, and the average impact of a marginal gallon was calculated.

3182.6.2 Domestic Agriculture

GHG emissions from the domestic agricultura l sector were estimated with the FASOM model, a partial equilibrium economic model of the U.S. forest and agricultural sectors. As discussed in Section 2.3, FASOM accounts for changes in GHG emissions from most agricultural activities, including the total amount of fertilizer, chemicals, gasoline, diesel and electricity used on farms for the entire domestic agricultural sector. It al so captures changes in the soil management, livestock production and U.S. agricultural exports. More detail on the FASOM model can be found in Chapter 5 of the DRIA.

Figure 2.6-1 shows the total harvested crop ac res in the different fuel volume cases. The projected changes in total harv ested acres are modest, partly because we only modeled the incremental difference in renewable fuel volumes between the cases.

Figure 2.6-1. FASOM Projected Domestic Harvested Acres, 2022Millions of Harvested Acres 300

250

200

150

100

50

0 Corn Soybeans
SwitchGrass
Wheat
Rice Hay Sorghum
Cotton Sugarcane Sugarbeet Silage
Oats Barley
Other

Control Case Biodiesel Only Case Corn Ethanol Only Corn Stover Ethanol Switchgrass Ethanol Case Only Case Only Case
Figure 2.6-2 includes the projected changes in harvested crop acres by field crop for the fuel volume cases considered (acreage chang es are normalized per thousand gallons of renewable fuel production). In the corn ethano l scenario, corn acreage increased; area planted with soybeans, hay, sorghum and rice decre ased; and harvested acres of other crops were practically unchanged. It is not surprising that soybean acreage decreased the most when corn ethanol production increased, because corn and soybeans are often in direct competition for fertile land. As discussed more below, the sm all decrease in rice acr es had a relatively large impact on GHG emissions estimates, because do mestic rice production requires more energy than other crops and is a source of methane emissions.

Soy-based biodiesel production induced a la rge increase in harvested soybean acres, largely due to the low yield of soy-based biodiese l in terms of gallons produced per acre. Cotton

319was the only other crop that increased along with biodiesel production. The competition between corn and soybeans was evid ent again, as corn acreage saw the steep est decline. Wheat, hay, sorghum, oats and rice also dec lined when biodiesel volumes increased.

Production of ethanol from corn residue had a very small e ffect on the acreage of other crops. This was expected because corn st over production does not displace other crop production, as corn stover is a re sidual product of corn cultivation. FASOM did project minor amounts of crop shifting in the corn stover scenario, because us ing corn stover for ethanol can increase the profitability of corn production in certain regions , with subsequent impacts. The effects of corn stover harvesting on agricultural inputs, such as th e need to use more fertilizer after stover removal, are discussed below.

In the scenario where switchgrass ethanol production increased, switc hgrass was the only field crop to gain acreage. As Figure 2.6-2 shows, new switchgr ass plantings displaced a wide variety of other crops. As discussed more in DRIA Chapter 5, the FASOM runs for the proposed rule project that switchgrass will primarily be grown in Oklahoma, Texas, Virginia and West Virginia.

Figure 2.6-2. Normalized Changes in Domestic Cropland by Crop, 2022 (acres per thousand gallons of renewable fuel)

4.00

3.00

2.00

1.00

0.00

-1.00

-2.00

-3.00

-4.00 0.13 0.34

0.00 0.28

Corn Ethanol Soybean Biodiesel Corn Stover Ethanol Switchgrass Ethanol Corn and Silage

Soybeans

Switchgrass

Wheat

Rice

Sorghum

Oats

Hay

Cotton

Other Crops

Net Total CroplandHarvested Acres per Thousand Ethanol Equivalent Gallons

3202.6.2.1 Domestic Crop Inputs

FASOM utilizes data about crop inputs to build crop budgets for field crops across 11 market regions and 63 sub-regions. FASOM cr op budgets include data on yields, fertilizer, chemicals, and energy use needed to grow crops in each of th e different regions. The crop budgets are based on USDA historic data and are also projecte d into the future. The crop budgets represent an average for each region, and do not specifically calc ulate input or yield changes that could result from the use of marginal croplands or altered crop rotation patterns (e.g., continuous corn production). Q QQQQQQ Table 2.6-2 defines the 11 market regions in FASOM. DRIA Chapter 5 incl udes a detailed discussion of th e FASOM crop budgets, including assumptions about crop yields and yi eld growth rates. Below we provide a summary of some of the key FASOM assumptions that were used to estimate domestic agricultural GHG emissions.

Table 2.6-2. Definitions of 11 Market Regions in FASOM
Key Market Region Production Region (States/Subregions)

NE Northeast Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, West Virginia

LS Lake States Michigan, Minnesota, Wisconsin

CB Corn Belt All regions in Illinois, Indiana, Iowa, Missouri, Ohio (IllinoisN, IllinoisS, IndianaN, IndianaS, IowaW, IowaCent, IowaNE, IowaS, OhioNW, OhioS, OhioNE)

GP Great Plains Kansas, Nebraska, North Dakota, South Dakota

SE Southeast Virginia, North Carolina, South Carolina, Georgia, Florida

SC South Central Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Tennessee, Eastern Texas

SW Southwest (agriculture only) Oklahoma, All of Texas but the Easter n Part (Texas High Plains, Texas Rolling Plains, Texas Central Blacklands, Texas Edwards Plateau, Texas Coastal Bend, Texas South, Texas Trans Pecos)

RM Rocky Mountains Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming

PSW Pacific Southwest All regions in California (CaliforniaN, CaliforniaS)

PNWE Pacific Northwest— East side (agriculture only) Oregon and Washington, east of the Cascade mountain range

PNWW Pacific Northwest— West side (forestry only) Oregon and Washington, west of the Cascade mountain range

QQQQQQQ FASOM does not explicitly model the selection of alte rnative crop rotations. Because the model operates in 5-year time steps, it has not generally been applied to sh orter-term decisions such as changes in rotation patterns. Rather, the model data implicitly reflect average conditions for crop production (e.g., yields, input use, etc.) associated with historical rotation patterns on a regional level.

321The crop budgets included in the FASOM model include data on input use that varies by crop, management practices, and region (See DRIA Chapter 5 for a full listing). There is often considerable regional variation in the inputs used per acre, whic h suggests that total input use (and the associated GHG emissions a nd other environmental impacts) will be affected as biofuel production causes crop shifting and alters crop mana gement practices. For example, nitrogen fertilizer use is an important factor for lif ecycle GHG analysis becau se of GHG emissions from fertilizer production and use. Figure 2.6-3 includes FASOM assu mptions about average nitrogen fertilizer use by crop in 2022 for non-irrigated pr oduction without residue harvesting. Regions that have a zero nitrogen fertil izer use rate are not included in the averages. Figure 2.6-3 illustrates the relative fertilizer intensity of majo r crops. The figure indicates that corn, hay and silage are relatively fertilizer-intense crops; whereas soybeans require less than 10 pounds of nitrogen per acre (soybeans naturally fix nitrogen in the soil as they grow).

Figure 2.6-3. FASOM Average Nitrogen Fertilizer Use by Crop, 2022 Non-Irrigated, No Residue Harvesting (lbs per acre)

0 20 40 60 80 100 120 140 160 180Nitrogen Fertzilier Use (lbs per acre)

Corn Soybeans Switchgrass Wheat (hard Rice Sorghum Oats Hay Cotton red winter) (irrigated) Note: The range indicates the regions with the highes t and lowest average nitrog en fertilizer use rates.

Figure 2.6-4 shows the change in nitrogen fe rtilizer use by crop pe r thousand gallons of biofuel production. In the corn ethanol scenario, the net increase in ferti lizer use is moderated by the decrease in hay and rice acres, which use more fertilizer per acre th an corn production in most regions. Fertilizer use drops in the soy- based biodiesel scenario because soybeans, which require scarce fertilizer use, replace crops that require many tim es more fertilizer per acre. Fertilizer use increases in the corn stover ethanol scenario , because additional fertilizer is required to compensate for nitrogen lost when stover is removed for ethanol production (discussed in more deta il in DRIA Chapter 5).

322Figure 2.6-4. Normalized Changes in Domest ic Nitrogen Fertilizer Use, 2022 (lbs per thousand ethanol equi valent gallons of biofuel)

300 Corn and Silage

200 Soybeans

100 60 68 57 Switchgrass

Wheat 0

Rice -100

Sorghum -200

Oats

-334 -300 Hay

-400 Cotton

-500 Other Crops

-600 Net Total

Mechanized agriculture requires many forms of energy including diesel, gasoline, natural gas and electricity. The FASO M crop budgets include detailed energy use information by crop and region (See DRIA Chapter 5 for a full listing). Figure 2. 6-5 includes FASOM assumptions for average diesel use by crop in 2022, for non-i rrigated production without residue harvesting.Nitrogen Fertilizer Use (lbs per thousand etoh equiv. gallons)

Corn Ethanol Soybean Biodiesel Corn Stover Ethanol Switchgrass Ethanol

323Figure 2.6-5. FASOM Average Diesel Use by Crop, 2022 Non-Irrigated, No Residue Harvesting (gallons per acre)

0 5 10 15 20 25Diesel Use (gal per acre)

Corn Soybeans Switchgrass Wheat (hard Rice (irrigated) Sorghum Oats Hay Cotton red winter) Note: The range indicates the regions with the highest and lowest average diesel use rates.

Figure 2.6-6 shows the change in domestic diesel use per thousand gallons of biofuel production. Comparing the relativ e diesel intensity of the crops shown in Figure 2.6-5 and the crop shifting patterns illustrated in 2.6-2, helps to explain the overall changes in diesel use in Figure 2.6-5. Diesel use increases in the corn ethanol and soy-based biodiesel scenarios as domestic crop acreage increases. Diesel use actually declines in the corn stover scenario, in part because corn farmers shift towards conservation til lage practices. Overall diesel use declines in switchgrass scenario, be cause switchgrass requires less diese l than the crops it displaces.

3242.38

0.65

-2.96

-5.19 -6 -5 -4 -3 -2 -1 0 1 2 3Diesel Use (gal per thousand gal) Figure 2.6-6. Normalized Changes in Domestic Agricultural Diesel Use, 2022 (gallons of diesel per thousand etha nol equivalent gallons of biofuel)

Corn Ethanol Soy-Based Biodiesel Corn Stover Ethanol Switchgrass Ethanol

Figure 2.6-7 includes FASOM assumptions for average gasoline use by crop in 2022, for non-irrigated production without residue harvesting. The FASOM crop budgets do not include gasoline use for switchgrass production. Figure 2.6- 8 shows the change in domestic gasoline use per thousand gallons of biofuel production.

325Figure 2.6-7. FASOM Average Gasoline Use by Crop, 2022 Non-Irrigated, No Residue Harvesting (gallons per acre)

Corn Soybeans Switchgrass Wheat (hard Rice Sorghum Oats Hay Cotton red winter) (irrigated) Note: The range indicates the regions with the highest and lowest average gasoline use rates. 0 1 2 3 4 5Gasoline Use (gal per acre)

326Figure 2.6-8. Normalized Changes in Domestic Agricultural Gasoline Use, 2022 (gallons per thousand ethanol e quivalent gallons of biofuel)

-1.80 1.04

-0.29

-1.73 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5Gasoline Use (gal per thousand gal)

Corn Ethanol Soy-Based Biodiesel Corn Stover Ethanol Switchgrass Ethanol

FASOM crop budgets include electricity and natural gas use for irrigation water pumping. Rice and sugarbeets ar e the only crops assumed to use natural gas for water pumping (See Table 2.6-3). Therefore, overall natural gas u se in each scenario is dependent on changes in these crops. For the rest of the irrigated crops that have private energy use for water pumping, electricity is the assumed energy source, with the largest electri city consumption in the Great Plains region (See Figure 2.6-10).

Table 2.6-3. Natural Gas Usage by Crop, Irrigation Status and Region, 2022 (1000 cu ft/acre)
Crop CB GP LS NE PNWE PSW RM SC SE SW
Irrigated

Rice 23.3 NA NA NA NA 0.0 NA 20.1 NA 0.0 Sugarbeet 26.1 9.7 26.1 NA 3.8 0.0 0.0 NA NA 0.0

327Natural Gas Use (000 cu ft per 000 gal) 0

-1

-2

-3

-4

-5

-6 Figure 2.6-9. Normalized Change in Domestic Agricultural Natural Gas Use, 2022 (thousand cubic feet per thousand ethanol equivalent gallons)

Corn Ethanol Soy-Based Biodiesel Corn Stover Ethanol Switchgrass Ethanol

Figure 2.6-10. FASOM Electricity Use by Crop, 2022 Irrigated, No Residue Harvesting (kWh per acre)

0 2 4 6 8 10 12 14Electricity Use (kWh per acre)

Corn Soybeans Wheat, Hard Red Sorghum Oats Hay Cotton Winter Note: The range indicates the regions with the highest and lowest average gasoline use rates.

328Figure 2.6-11. Normalized Change in Domestic Agricultural Electricity, 2022 (kilowatt-hours per thousand ethanol equivalent gallons)

0.27

0.22

-0.02

-0.08 -0.10 0.00 0.10 0.20 0.30Electricity Use (kWh per thousand gal)

Corn Ethanol Soy-Based Biodiesel Corn Stover Ethanol Switchgrass Ethanol

Based on input data for each individual cr op and the associated costs of production and projected prices, the model predicts how the to tal U.S. agricultural sector will change with increased feedstocks used for biofuel production. The results for total ag ricultural sector inputs of the different fuel scenarios considered are shown in Table 2.6-4 through Table 2.6-7.

Table 2.6-4. Change in Domestic Agricultural Input s from Corn Ethanol Production, 2022

Units Corn Ethanol Only Case Control Case Difference Percent Change

Total N use Tons 13,579,165 13,656,663 77,497 0.57%
Total P2O5 use Tons 3,133,112 3,169,185 36,073 1.15%
Total K2O use Tons 4,071,676 4,099,299 27,624 0.68%
Total Lime Use Tons 11,010 11,017 6 0.06%
Herbicide Use Tons 208,026 210,343 2,317 1.11%
Pesticide Use Tons 41,232 41,168 -64 -0.15%
Total Diesel Fuel use gal 2,741,271,104 2,747,445,994 6,174,890 0.23%
Total Gasoline use gal 365,527,816 360,839,070 -4,688,746 -1.28% Total Electricity Use kWh 203,558,361 204,268,096 709,735 0.35%
Total Natural Gas Use Btu 6.9E+13 6.5E+13 -3.6E+12 -5.22%

329Table 2.6-5. Change in Domestic Agricultural Input s from Soybean Biodiesel Production, 2022

Units Soy-Based Biodiesel Only Case Control Case Difference Percent Change

Total N use Tons 13,754,243 13,656,663 -97,581 -0.71%
Total P2O5 use Tons 3,173,639 3,169,185 -4,454 -0.14%
Total K2O use Tons 4,116,977 4,099,299 -17,678 -0.43%
Total Lime Use Tons 11,056 11,017 -39 -0.35%
Herbicide Use Tons 210,670 210,343 -328 -0.16%
Pesticide Use Tons 41,551 41,168 -383 -0.92%
Total Diesel Fuel use gal 2,747,066,027 2,747,445,994 379,967 0.01%
Total Gasoline use gal 360,233,446 360,839,070 605,625 0.17% Total Electricity Use kWh 204,138,103 204,268,096 129,994 0.06%
Total Natural Gas Use Btu 6.9E+13 6.5E+13 -3.5E+12 -5.15%

Table 2.6-6. Change in Domestic Agricu ltural Inputs from Corn St over Ethanol Production, 2022

Units Corn Stover Only Ethanol Case Control Case Difference Percent Change

Total N use Tons 13,398,157 13,656,663 258,506 1.93%
Total P2O5 use Tons 3,040,127 3,169,185 129,057 4.25%
Total K2O use Tons 3,461,168 4,099,299 638,131 18.44%
Total Lime Use Tons 11,150 11,017 -134 -1.20%
Herbicide Use Tons 209,968 210,343 375 0.18%
Pesticide Use Tons 41,266 41,168 -97 -0.24%
Total Diesel Fuel use gal 2,769,975,459 2,747,445,994 -22,529,464 -0.81%
Total Gasoline use gal 363,019,719 360,839,070 -2,180,649 -0.60% Total Electricity Use kWh 204,417,097 204,268,096 -149,000 -0.07%
Total Natural Gas Use Btu 6.6E+13 6.5E+13 -7.2E+11 -1.09%

Table 2.6-7. Change in Domestic Agricu ltural Inputs from Switchgr ass Ethanol Production, 2022

Calc Units Control Case Switchgrass Ethanol Only Case Difference Difference as a % of Low Volume Case

Total N use Tons 13,656,663 13,789,870 133,207 0.98%
Total P2O5 use Tons 3,169,185 3,282,355 113,170 3.57%
Total K2O use Tons 4,099,299 4,167,733 68,434 1.67%
Total Lime Use Tons 11,017 10,525 -491 -4.46%
Herbicide Use Tons 210,343 205,014 -5,329 -2.53%
Pesticide Use Tons 41,168 39,478 -1,690 -4.11%
Total Diesel Fuel use gal 2,747,445,994 2,723,351,266 -24,094,728 -0.88%
Total Gasoline use gal 360,839,070 352,830,473 -8,008,597 -2.22% Total Electricity Use kWh 204,268,096 203,888,367 -379,729 -0.19%
Total Natural Gas Use Btu 6.5E+13 6.1E+13 -3.9E+12 -5.96%

330The amounts shown in Tables 2.6-4 through 2. 6-7 were combined with GREET defaults for GHG emissions from producti on of fertilizer and chemical s to calculate GHG emissions changes. Fuel use emissions included both the upstream emissions associated with production of the fuel as well as combustion emissions, also from GREET. Emissi ons from electricity production represented average U.S. grid electricity production.

In addition to the GHG emissions associated w ith fertilizer and chemical production, and fuel production and use, there are several other non-fossil fu el combustion related GHG sources of emissions from the agricultural sector that wo uld be impacted by the increased use of corn for ethanol and associated changes to the agricultu ral sector. FASOM pr ovides directly the GHG emissions from these additional sources.

2.6.2.2 Domestic Nitrous Oxide Emissions

An important GHG impact from the agricultura l sector is releases of nitrous oxide (N 2 O) emissions. N 2 O can be released from a number of diffe rent N-input sources including inorganic fertilizer, nitrogen fixing cr ops (e.g., soybeans), crop residues, and manure management. N 2 O can be released either directly or indirectly through N leachin g offsite. Figure 2.6-11 highlights some of the major sources of agricultural N 2 O emissions.

Figure 2.6-11. Agricultural Sources of N 2 O Emissions

This issue has become increas ingly important in the contex t of biofuels production based on a recent paper by Pa ul Crutzen et al. 523 The paper examines a top-down method for calculating N 2 O emissions and compares that to the standard IPCC bottom-up approach. The conclusion is that it is not appropriate to just consider the direct N 2 O releases but that all sources of N 2 O are important. The paper recognizes th at top-down estimates are not necessarily inconsistent with IPCC’s bottom-up range when a ll direct and indirect agricultural sources are considered, highlighting the importance of “indirect” N 2 O emissions.

331Therefore, for our modeling we capture all sources of N 2 O emissions shown in Figure 2.6-4. This section discusses dir ect and indirect emissions from synthetic fertilizer, N fixing crops, and crop residue. Livestoc k and residue burning emissions are discussed in other sections, as are international N 2 O emissions.

FASOM estimates direct and indi rect emissions from syntheti c fertilizer, N fixing crops, and crop residues based on the amount of fertilizer used and different regional factors to represent the percent of N applied that results in N 2 O emissions. This approach is consistent with IPCC guidelines for calculating N 2 O emissions from these sources.

In order to have a better representation of the N 2 O emissions from agricultural production, EPA is working with Colorado State University (CSU) to use the DAYCENT model to update the N 2 O factors in the FASOM model. CSU will obtain initial projections of land use trends from FASOM to simulate a suite of domest ic U.S. land use and crop management changes associated with potential biofuel produc tion between 2007 and 2030. CSU will use the DAYCENT model to simulate direct N 2 O emissions in croplands as well as the N losses associated with leaching, volatilization and su rface runoff and cropping pattern changes. Leaching, volatilization and su rface runoff losses will be used to estimate the indirect N 2 O emissions with IPCC default factors.

Separate DAYCENT simulations will be c onducted for each of the 63 agricultural regions included in the FASOM analysis. In each region, DAYCENT will simulate land use conversions from grassland and forestland, as we ll as changes in existing cropping rotations that are needed to meet the biofuel market demand according to initial FASO M results. Several scenarios may be addressed in the analysis.

Specifically, CSU will provide several key refinements for a re-analysis of land use, cropping trends and GHG emissions in the FASOM assessment, including:

• Direct N 2 O emissions based on DAYCENT simulati ons with an accounting of all N inputs to agricultural soils, in cluding mineral N fertilizer, organic amendments, symbiotic N fixation, asymbiotic N fixa tion, crop residue N, and mineralization of soil organic matter. CSU will provide the total emission rate on an acre basis for each simulated bioenergy crop in the 63 regions, and also a disaggregation by N source.

• Indirect N 2 O emissions on a per acre basis using results from DAYCENT simulations of volatilization, leaching and runoff of N from each bioenergy crop included in the analysis for the 63 regions, combined with IPCC factors for the N 2 O emission associated with the simulated N losses.

The analyses with updated N 2 O estimates are not yet complete, but we expect to incorporate updated emissions data into FASOM for future analyses. Below we describe the current procedures used in FASO M to generate estimates of N 2 O emissions.

The primary categories of direct N 2 O emissions are nitrogen fert ilizer and nitrogen-fixing crops. Changes in FASOM direct N 2 O emissions from croplands result from either changes in

332crop acreage or crop mix. To calculate direct N 2 O emissions from N fertilizer use, FASOM uses data on N fertilizer application per acre from the crop budgets. This value varies widely across crops, regions, and crop management activities. Emissions from synthetic fertilizer, manure applied as fertilizer, and non-manur e organic fertilizers are calculated based on IPCC guidance. N 2 O emissions are calculated similarly for organi c fertilizers. In FA SOM, the only N-fixing crops for which N 2 O emissions are calculated are soybeans and alfalfa hay. More details about current FASOM mode ling of direct N 2 O emissions are available in DRIA Chapter 5.

In addition to direct N 2 O emissions from croplands, ther e are several additional sources of indirect emissions, includi ng emissions from volatilization, leaching, crop residues, and residue burning. Some of the N applied to agricu ltural soils as fertilizer volatilizes, entering the atmosphere as ammonia and other oxides of nitrogen. The volatili zed N subsequently returns to soils through N deposition a nd then contributes to N 2 O emissions. After fertilizer application or heavy rain, large amounts of N may leach from the so il into drainage ditches, streams, rivers and eventually estuaries. Some of this N is em itted as nitrous oxide when the leached nitrogen fertilizer undergoes the process of nitrification or denitrif ication. There are also N 2 O emissions from crop residues that are incorporated into agricultural soils. Following IPCC guidance, N 2 O emissions are calculated as 1% of the N from crop residues that is incorpor ated into the soil. FASOM also assumes that a certain fraction of fi elds are burned each year , which results in N 2 O (and methane) emissions. These emissions are ca lculated using the IPCC default value, which assumes that on average 0.7% of N contai ned in the burned residue is emitted as N 2 O. In addition, methane emissions are calculated based on the average methane emissions per acre, but these emissions are typically quite small relative to the other emissions tr acked in FASOM. All FASOM calculations of N 2 O emissions are based on IPCC gu idance (See DRIA Chapter 5 for more details). Figure 2.6-12 summarizes FASOM average direct and indirect N2O emissions per acre by crop.

3330 100 200 300 400 500 600 700 800N2O Emissions (kgCO2eq per acre) Figure 2.6-12. FASOM Average N 2 O Emissions by Crop Non-Irrigated, No Residue Harvesting (kgCO 2- eq per acre)

Indirect -- Residue Burning

Indirect -- Crop Residues

Indirect -- Leaching

Indirect -- Volatilization

Direct -- N Fixing

Direct -- Fertilizer Application

Corn Soybeans Switchgrass Wheat (hard Rice Sorghum Oats Hay Cotton red winter) (irrigated)

Current N 2 O emission calculations from FASOM are from three main sources of emissions, fertilizer application (including direct emissions a nd indirect emissions from N leaching), Emissions from N fixing crops, and emi ssions associated with residues and N in the residue. Total N 2 O emissions for the different fuels an alyzed are presented in Table 2.6-8.

Table 2.6-8. Change in N 2 O Emissions in 2022 from Different Fuel Scenarios Source Units Corn Ethanol Only Case Soy-based Biodiesel Only Case Corn Stover Ethanol Only Case Switchgrass Ethanol Only Case

N Fert Application ‘000 tons CO2-eq. 442 -524 1,562 848 Emissions from N fixing crops ‘000 tons CO2-eq. -1,157 899 130 -2,033 Emissions from Crop residue ‘000 tons CO2-eq. -218 279 -33 -809 Domestic Net N 2 O Emissions ‘000 tons CO2-eq. -933 654 1,659 -1,994

The changes in N 2 O emissions from the different scenar ios reflect the crop pattern, and management practice changes be tween the different fuel volumes considered. For example, increased corn ethanol production resu lts in more corn and less soybean acres, this is reflected in more N 2 O emissions from increased nitrogen fertilizer and lower N 2 O emissions from N fixing crops. Switchgrass ethanol production results in similar land use changes and less N fixing crops. Soy-based biodiesel has the opposite eff ect, more soybean acres and less other crops. As discussed previously, these results do not cons ider any changes in crop inputs for example increased nitrogen fertilizer input to make up for lost soybean nitrogen fixing.

3342.6.2.3 Domestic Emissions from Grain Drying

Energy use for grain drying is calculated in FASOM based on assumptions that removing 10 percentage points of moisture from 100 bushels of grain requi res 17.5 gallons of propane and 9 kWh of electricity. Thus, energy use per acre is calculated as the numbe r of percentage points of moisture to be removed multiplied by the yi eld per acre and the energy use per percentage point and yield unit for each crop that is dried. Emissions are then calculated based on assumed emissions factors per unit of energy use by en ergy type. Table 2.6-9 shows the average emissions associated with grain drying that are used in FASOM. Drying rice is a relatively energy intensive process, as reflecte d in the grain drying GHG emissions per acre in Table 2.6-9. Emissions from grain drying are included in the overall domestic agricultural GHG emissions estimates.

Table 2.6-9. FASOM Average Carbon Dioxide Emissions from Grain Drying by Region (kgCO 2 -eq. / acre)
Crop CB GP LS NE PNWE PSW RM SC SE SW
Dryland

Corn 161.4 135.9 202.2 160.5 NA NA 66.1 24.5 43.8 15.2 Sorghum 99.4 22.3 NA 54.3 NA 17.7 NA NA NA NA Soybeans 26.0 7.0 24.1 14.3 NA NA NA NA NA NA
Wheat, Durham NA 5.1 23.4 NA NA NA NA NA NA NA Wheat, Hard Red Spring NA 6.7 25.4 NA 9.1 NA NA NA NA NA
Wheat, Hard Red Winter 51.3 11.1 51.6 34.5 NA 11.6 NA NA NA NA
Wheat, Soft White NA NA NA NA NA NA 11.6 NA NA NA Irrigated

Corn NA 185.1 NA NA 132.6 121.6 103.2 21.0 NA 30.7

Rice 1,216.6 NA NA NA NA 1,667.3 NA 1,254.8 NA 1,400.8 Sorghum NA 33.0 NA NA NA NA NA NA NA NA Soybeans NA 10.3 NA NA NA NA NA NA NA NA
Wheat, Durham NA 11.3 NA NA NA 21.0 NA NA NA NA Wheat, Hard Red Spring NA 10.2 NA NA NA NA 17.6 NA NA NA
Wheat, Hard Red Winter NA 15.4 NA NA NA 22.6 NA NA NA NA
Wheat, Soft White NA NA NA NA NA NA 18.3 NA NA NA

2.6.2.4 Domestic Rice Production Emissions

Another source of non-combus tion GHG emissions from the agricultural sector is CH 4 emissions from rice production. Most of the wo rld’s rice, and all rice in the United States, is grown on flooded fields. When fi elds are flooded, aerobic deco mposition of organic material gradually depletes most of the oxygen present in the soil, causing anaerobic soil conditions. Once the environment becomes anaerobic, CH 4 is produced through anaerobic decomposition of

335soil organic matter by methanogenic bacteria. Some of this CH 4 is transported from the submerged soil to the atmosphere primarily by diffu sive transport through the rice plants. Minor amounts of CH 4 also escape from the soil via diffusi on and bubbling through floodwaters.

FASOM assumes that all rice produced in the United States is grow n in flooded fields and has associated methane emissions. Although th ere are potentially changes in water and soil management practices that could be implem ented to reduce methane emissions, FASOM currently assumes that the only method available for reducing methane emissions from rice cultivation is to reduce rice acreage. Thus, ch anges in methane emissions from rice cultivation will result only from changes in the acreage planted to rice.

Methane emissions per acre are calculated based on regional emissi ons factors per acre calculated for each region based on 2001 data from the EPA GHG inventory for 1990–2003 (see Table 2.6-10). 524 The model then calculates emissions from rice production based on emissions factors for each region and the distribution of rice acreage in the model solution.

Table 2.6-10. FASOM Average Methane Em issions from Rice Cultivation by Region (kg CO 2 -eq. / acre)
Crop CB GP LS NE PNWE PSW RM SC SE SW
Irrigated

Rice 1,826.1 NA NA NA NA 1,783.4 NA 2,249.2 NA 4,375.0 Note: NA indicates not applicable, i.e ., those crops were not cultivated under that irrigation status in that FASOM region. In addition, there is no dryland rice or sugarcane production or irrigated hybrid poplar, switchgrass, or willow production in FASOM.

As with other sources of emissions differe nt management methods and other factors (such as soil type and amounts of fertilization) will impact CH 4 emissions from rice production. Rice methane emissions are projected to decrease due to a decrease in rice acres as shown in Table 2.6-11. Table 2.6-11. Percent Change in Domestic Rice Emission s in 2022 from Different Fuel Scenarios Corn Ethanol Soybean Biodiesel Corn Stover Ethanol Switchgrass Ethanol Rice Methane Emissions (‘000 tons CO2-eq.) -352 -355 -38 -457

2.6.2.5 Domestic Residue Burning Emissions

An additional minor source of GHG emissions fr om agricultural resi due burning is also included in this analysis. Fi eld burning of crop residues is no t considered a net source of CO 2 , because the carbon released to the atmosphere as CO 2 during burning is assumed to be reabsorbed during the next grow ing season. Crop residue burni ng is, however, a net source of CH 4 and N 2 O, which are released during combustion. Field burning is not a common method of agricultural residue disposal in the United Stat es. The primary crop types whose residues are typically burned in the United States are wheat, rice, sugarcane, corn, barley, soybeans, and

336peanuts. FASOM is predicting a minor reduction in GHG emissions from residue burning due to reductions in crop production with residues that are typically burned. Non-CO2 residue burning emissions factors by crop and region are included in DRIA Chapter 5.

2.6.2.3 Domestic Livestock Emissions

Livestock production and management also contribute signifi cant non-combustion GHG emissions from the agricultural sector. GHG em issions from livestock come from two main sources: enteric fermentation and manure ma nagement. Enteric fermentation produces CH 4 emissions as part of the normal digestive proc esses in animals. During digestion, microbes resident in an animal’s digestive system ferm ent food consumed by the animal. This microbial fermentation process, referred to as enteric fermentation, produces CH 4 as a by-product. The amount of CH 4 produced and excreted by an individua l animal depends primarily upon the animal’s digestive system, and the amount and type of feed it consumes. Ruminant animals (e.g., cattle, buffalo, sheep, goats, and camels) are the major emitters of CH 4 because of their unique digestive system.

The FASOM modeling reflects changes in live stock enteric fermentation emissions due to changes in livestock herds. For example, as more corn is used in producing ethanol the price of corn increases, driving ch anges in livestock production costs and demand. Enteric fermentation emissions from livestock are calcul ated based on the number of animals of each type and a calculated value of average emissi ons per head based on the 2001 emissions values by livestock type and the number of livestock in ea ch livestock category reported in the EPA GHG inventory report for 1990–2003. 525 Average emissions per head of livestock are listed in DRIA Chapter 5. There are emissions mitigation optio ns included within the FASOM model, but these options do not enter the market in th e absence of incentives for reducing CH 4 emissions. Thus, similar to the case of manure management, emissions from this source are currently affected only by the number of animals in each livestock catego ry. The FASOM model predicts reductions in livestock herds as shown in Table 2.6-12.

Table 2.6-12. Percent Change in Dom estic Livestock Herd by Scenario, 2022 Livestock Type Corn Ethanol Soy-based Biodiesel Corn Stover Ethanol Switchgrass Ethanol Dairy -0.52% 0.04% 0.03% -0.81% Beef -0.48% -0.23% -0.10% -0.48% Poultry -0.16% 0.00% 0.00% -0.98% Swine -1.63% -0.15% -0.26% -1.04%

Enteric fermentation emissions are impacted by type and quality of feed. As described in DRIA Chapter 5, the mix of feed types used w ill change based on the increased use of corn in ethanol and changes to corn pric es. This change in feed type is not reflected in enteric fermentation emissions shown here. The direction of this change would be likely be additional reductions in enteric fermentation emissions due to the fact that use of ethanol co-products DDGS as feed have been found to reduce enteric fermentation emissions. We plan to include these CH 4 reductions as part of th e final rule analysis.

337The management of livestock manure can produce anthropogenic CH 4 and N 2 O emissions. CH 4 is produced by the anaerobic decomposition of manure. N 2 O is produced as part of the nitrogen cycle th rough the nitrification and denitrific ation of the organic nitrogen in livestock manure and urine. The type of ma nure management methods will impact the amount of GHG emissions produced. FASOM calculate s these manure management emissions based on for emissions factors from the different types of livestock an d management methods as reported in the EPA GHG inventory report for 1990–2003. 526 . Manure management emissions are projected to be reduced as a result of lower livestock herd values.

2.6.2.6 Domestic Agriculture Sector Results (Excluding Land Use Change)

Table 2.6-13 provides an overview of th e total GHG emissions impacts from the domestic agricultural sector based on the result s of the FASOM modeling in terms of absolute changes between the GHG emissions from the di fferent volume scenarios considered. Results are also presented on a per mmBtu basis by dividing total GHG changes by the mmBtu change in volume of biofuel produced between the differe nt scenarios. Land use change impacts are discussed in Section 2.6.5.

Table 2.6-13. Domestic Agriculture GHG Emissi on Changes by Scenario, 2022

Corn Ethanol Soy-Based Biodiesel Corn Stover Ethanol Switchgrass Ethanol

Emission Source Tonnes CO2-eq. g/MMBtu CO2-eq. Tonnes CO2-eq. g/MMBtu CO2-eq. Tonnes CO2-eq. g/MMBtu CO2-eq. Tonnes CO2-eq. g/MMBtu CO2-eq.

On-Farm Combustion -218,314 -1,105 -228,655 -5,147 -299,049 -518 -578,771 -1,641
Fuel Production Upstream -29,726 -151 -31,032 -698 -62,305 -108 -108,720 -308 Farm Chemical Production / Transport Upstream 305,681 1,548 -296,291 -6,669 1,228,269 2,127 369,162 1,047
Livestock Change -1,061,886 -5,377 -181,679 -4,089 -90,825 -157 -1,047,699 -2,970
Rice Production Changes -352,374 -1,784 -354,897 -7,988 -37,899 -66 -456,840 -1,295 Fertilizer Application / Soil N2O Emissions -933,136 -4,725 654,440 14,730 1,658,579 2,872 -1,994,025 -5,653
Residue Burning 3,127 16 1,851 42 326 1 -34,647 -98

Total: -2,286,628 -11,579 -436,263 -9,820 2,397,095 4,150 -3,851,541 -10,920

As can be see in Table 2.6-13, the net GHG impact of increased biofuel production on the U.S. agriculture sector is in general a reduction in GHG emissions. This is driven primarily by indirect or secondary impacts such as decreases in energy use from crop shifting and reductions in livestock GHG emissions due to higher corn prices. The excepti on for this reduction is the corn stover ethanol case that s hows an increase in domestic agri cultural sector GHG emissions. This is mainly due to the fact that the corn stover case includes energy us e for residue harvesting and transport, and, because the use of stover for ethanol does not increase feed or food prices, less indirect impacts in comp arison with other feedstocks.

Overall the small impact in the domestic agricu ltural sector is due to the indirect effects and demand changes, specifically demand changes in U.S. exports. For example, the sources of corn used in ethanol production in the FASOM model are shown in Figure 2.6-13. Some of the additional corn comes from increased corn produ ction; however, the in crease in corn acres is

338mostly offset by reductions in ot her crop acres as shown in figure 2.6- 2. Some of the corn used for ethanol comes from decreased corn used fo r feed. As ethanol produces the co-product DDG, this partially offsets the use of corn directly as feed. The reducti on in livestock he rds also lowers the use of corn for feed.

Figure 2.6-13. Source of Corn for Ethanol

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Source for Change in Corn Usage in 2022

Other Use
Corn in Feed Corn Exports
Corn Production

1

However, as seen from Figure 2.6-13, one of the biggest sources of corn for ethanol production is projected to come from reductions in corn exports. Therefore, the domestic agricultural sector impacts are only a portion of the total impacts due to increased ethanol production in the U.S. The change in corn a nd other crop exports will have impacts on the international agricu ltural sector that need to be account ed for when determining lifecycle GHG impacts of biofuel produ ction in the U.S.

2.6.3 Export Comparison between FASOM and FAPRI

In order to estimate the impact on internat ional agricultural sector GHG emissions the FAPRI model was run with domes tic biofuel volume scenarios sim ilar to the analysis conducted with FASOM. The model provid ed changes in intern ational production of corn, soybeans, and other crops to replace lost U.S. exports.

339The impact on the international agricultural sector is highly dependent on the U.S. export assumptions. As we are using the FASOM mode l to represent domestic agricultural sector impacts with an assumed export picture, the inte rnational agricultural s ector impacts should be based on a consistent set of export assumptions. Therefore we worked with both models to build in a consistent set of assumptions in order to have an equivalent basis for modeling domestic and international impacts.

One of the key differences we worked to reco ncile was based on the different time frames considered in the models. FASOM is a long term econometric model, while FAPRI captures more short term impacts. Therefore, we wo rked to put some of the supply and demand elasticities on a more consistent basis, specifi cally the export response and livestock demand and feed use. We also coordinated on some of the key input assumptions in the two models as follows:

• Current crop yields and projected in creases for corn and soybeans based on USDA baseline projections. These drive the amount of land required and price and demand response of the different commodities in the different fuel scenarios.

• Ethanol yield and co-product use values, this impacts how much commodity is needed to produce a certain quantity of fu el as well as how co-products help to offset feed use and overall change in crop and livestock production.

• Reversion of CRP acres was limited such that 32 mill acres would remain in CRP. We also worked to have consistent set of assumptions for when and where CRP acres would be used across both models.

As a result, the total changes in projecte d export impacts shown in Figure 2.6-14 are relatively consistent across both models with the possible exception of impacts related to increased biodiesel production.

340Figure 2.6-14. Comparison of FASOM and FAPRI Projected Change in U.S. Exports by Scenario, 2022 (tons per million ethanol equivalent gallons)

Change in U.S. Exports per Million Gallons of Biofuel

-14000 -12000 -10000 -8000 -6000 -4000 -2000 0 2000 4000 6000 8000 FASOM FAPRI FASOM FAPRI FASOM FAPRI FASOM Switchgrass Ethanol Corn Ethanol Biodiesel (Ethanol Equivalent) Corn Residue EthanolTons per Million Gallons of Ethanol

Soybeans SoybeanOil SoybeanMeal Sorghum

Rice
Wheat
Cotton
Corn Barley

The differences in switchgrass exports are primarily driven by how the different models have considered switchgrass acr es. Currently, FAPRI does not specifically model cellulosic ethanol feedstocks, so CRP land was used as a proxy for switchgrass acres. FASOM specifically models switchgrass and how it competes for land w ith other crops. Therefore, FAPRI domestic crop competition and associated export changes are not as large as those predicted in FASOM.

Compared to other crops, FASOM and FAPRI differ more in their predicted impact on soybean export volumes. FASOM assumes more biodiesel will come from new soybean acres (but total domestic acres are relatively constant as other crops offset the increase in soybean acres). In comparison, FAPRI c ontains more types of oil, and has a more elastic demand in the soybean oil market. In addition, the FAPRI model allows for so me corn oil fractionation from DDGs, which can be used as a substitute for soybean oil.

For the analysis conduced and presented here we have used both the FASOM and FAPRI results as-is with no adjustment s. Because the impact on international land use could be significantly different if we had used the FASOM export prediction ra ther than the FAPRI prediction, we intend to further refine the mode ls with the goal of ha ving export response more closely aligned for the final rule.

341As the corn residue ethanol production has negligible export change impacts, it is assumed that there are no international emissions impacts associated with increased corn stover ethanol production in the U.S. However, we have modeled the im pact of increased production of Brazilian sugarcane ethanol for use in the U.S. market. The FAPRI m odel has been used to determine the international impacts of Brazilia n sugarcane ethanol production. The increase in Brazilian sugarcane ethanol pr oduction is assumed to have no impacts on domestic U.S. agriculture emissions.

2.6.4 International Agriculture

For this analysis we used the FAPRI model to estimate the impacts on international crop production due to changes in biofuel production. These results were used to generate GHG emissions from the internationa l agricultural sector, similar to what was done to determine domestic agricultural GHG emission changes.

2.6.4.1 International Crop Input and GHG Impacts

The FAPRI model does not di rectly provide an assessmen t of the GHG impacts of changes in international agricultural practices (e.g., changes in fertilizer load and energy use). However, it does predict changes in crop area and production by crop t ype and country. We, therefore, determined internati onal fertilizer and energy use ba sed on data collected by the Food and Agriculture Organization (FAO) of the United Nations and the International Energy Agency (IEA).

We took the FAPRI provided activity data on ch anges in crop acres, by crop and country, and multiplied by regional fertilizer use rate f actors (tons per acre) to determine the global impacts of biofuel production on fe rtilizer application. Fertilizer data was taken from the Food and Agricultural Organization's Fertistat data base, which reports fertilizer use by crop and country for a single y ear between 1988 and 2004. 527 Herbicide and pesticide use data was calculated in the same manner. We then used GREET factors for emissions from production of agricultural chemicals to estimate the upstream GHG impacts of fertilizer and chemical production to calculate total impacts for each fu el scenario. Table 2. 6-14 provides the total change in fertilizer and chemical use for the different fuel scenarios.

Table 2.6-14. International Change in Fertilizer and Chemical Use by Scenario, 2022 (tons) Input Corn Ethanol Only Case Soy-Based Biodiesel Only Case Switchgrass Ethanol Only Case Sugarcane Ethanol Only Case

N Application 80,492 3,627 18,167 134,842 P Application 47,663 9,495 12,774 112,862 K Application 38,606 8,640 7,549 155,311 Herbicide Application 230 57 72 524 Pesticide Application 382 58 111 663

Foreign agricultural energy use was calculated slightly differently. We collected data from IEA on total CO 2 emissions from agricultural fuel combustion by country. 528 We also

342collected IEA data on ag ricultural electricity and fuel use by country, which was combined with emissions factors to estimate c ountry-level GHG emissions from ag ricultural electricity and fuel use. Historical trends were used to projec t chemical and energy use in 2022. These total GHG emissions were only combustion related, so we scaled them to represent full lifecycle GHG emissions from fuel production, based on th e ratio of combustion to full lifecycle GHG emissions from U.S. fuel and electricity use. Country-level GHG emissions from agricultural energy use were then divided by the area of ag ricultural land in each country, from the FAOSTAT land area database to derive a per ac re GHG emissions factor from agricultural energy use by country. Our estimates use aver age energy consumption and GHG emissions per acre for all crops in each foreign country. We multiplied these agricultural energy consumption emissions factors by the country-l evel crop acreage changes proj ected by FAPRI to determine the change in GHG emissions from foreign agricultural energy u se for each fuel scenario. Table 2.6-15 provides the total change in agricultural energy use GHG emissions for the different fuel scenarios. EPA is organizing external expert re view of these calculations to be done before the final rule. Part of this review will be to look at how energy and fertilizer use will change as crop yields change.

Table 2.6-15. International Change in Agricultural En ergy Use GHG Emissions by Scenario, 2022 (MTCO 2 -eq.) Corn Ethanol Soybean Biodiesel Switchgrass Ethanol Brazilian Sugarcane Ethanol Agricultural Sector Energy Use GHG Emissions 618,311 109,552 205,926 338,743

2.6.4.2 International N 2 O Emissions

For international N 2 O emissions we considered both dir ect and indirect emissions from synthetic fertilizer application, crop residue N, and manure management. Manure management emissions are discussed in the following section. Direct and indirect em issions from synthetic fertilizer application and crop residues were calculated based on IPCC guidance as shown in Table 2.6-16. 529

343Table 2.6-16. Calculations of N 2 O Emissions from Synthetic Fertilizer and Crop Residues Direct N 2 O Emissions Direct Emissions (Overall Equation) Equation (N additions to soils fr om mineral fertilizer + N Emissions = additions to soils from crop residues) × EF EF for N additions from mineral fertilizer and crop residues = 0.01 kg N 2 O-N / kg N added N Additions from Mineral Fertilizers kg fertilizer N applied to soils (i.e., change in N additions to soils from mineral fertilizers = fertilizer N applications from Table 2.6-9) N Additions from Crop Residues above-ground residue dry matter × Crop Area × [N content of aboveground residues + ratio of belowground residues to harvested yield for crop N additions to soils from crop residues = × N content of belowground residues] where, Above-ground residue dry matter and N additions to soils from crop residues = Taken from IPCC default values by crop Indirect N2O Emissions Note that for indirect emissions, the ca lculation of N applied to soils from fe rtilizers or crop re sidues is the same as for direct emissions Indirect Emissions from Volatilization Equation N additions to soils from mineral fertilizers × N lost Emissions = through volatilization × EF N lost (from synthetic fertilizer additions) through volatilization = 0.1 (kg NH 3 –N + NOx–N) / kg N applied 0.010 kg N 2 O–N / (kg NH 3 –N + NOX–N EF for N lost through volatilization = volatilised) Indirect Emissions from Leaching/Runoff Equation (N additions to soils fr om mineral fertilizers + N additions to soils from crop residues) × N lost Emissions = through leaching or runoff × EF N lost through leaching/runoff (from all N sources) = 0.3 N losses by leaching or runoff / kg N addition EF for N lost through leaching/runoff = 0.0075 kg N20-N / kg N leaching or runoff

Based on the equations in Table 2.6-16 a nd the crop production changes projected by FAPRI, we estimated the total change in N 2 O emissions for each fuel scenarios, as shown in Table 2.6-17. As noted above, we are also wo rking with Colorado Stat e University to update these factors as part of the final rule analysis.

Table 2.6-17. International Change in N 2 O Emissions in 2022 from Different Fuel Scenarios (total metric tons N 2 O) Corn Ethanol Soybean Biodiesel Switchgrass Ethanol Brazilian Sugarcane Etanol Direct and Indirect N2O Emissions 1,901 104 446 2,642

3442.6.4.4 International Livestock Change GHG Impacts

Similar to domestic livestock impacts associ ated with an increase in biofuel production, internationally the FAPRI model predicts changes in livestock pr oduction due to changes in feed prices. The GHG impacts of these livestock changes, enteric fermentation and manure management GHG emissions, were included in our analysis. Unlike FASOM, the FAPRI model does not have GHG emissions built in and, therefore, livestock GHG impacts were based on activity data provided by the FA PRI model (e.g., number and t ype of livestock by country) multiplied by IPCC default factors for GHG emissions. Table 2.6-18 shows the changes in livestock predicted by the FAPR I model in 2022 for each of th e fuel scenarios considered.

Table 2.6-18. Foreign Livestock Changes by Region and Renewable Fuel, 2022 (head per million gallons)
Corn Ethanol Dairy Cattle Hog Sheep Poultry North America (Canada) -1.1 23.4 -117.7 0.0 268.0 Western Europe -0.4 -11.0 -3.8 0.0 280.7 Eastern Europe 0.0 7.0 -19.7 0.0 1349.7 Oceania 1.4 75.0 -1.6 13.5 513.3 Latin America -40.2 -144.4 13.8 0.0 792.8 Asia -17.8 368.8 -27.8 -268.6 565.0 Africa and Middle East -82.2 -14.3 0.0 0.0 -119.4 Indian Subcontinent 0.0 -11.9 0.0 0.0 10.0 Total -140.3 292.6 -156.9 -255.1 3660.2 Soy-Based Biodiesel Dairy Cattle Hog Sheep Poultry North America (Canada) 0.3 1.3 64.2 0.0 -130.6 Western Europe -0.2 16.5 -26.9 0.0 504.8 Eastern Europe 0.0 -1.5 8.5 0.0 -1017.0 Oceania 0.2 -4.7 -2.4 -37.9 -631.0 Latin America -4.1 -66.9 10.5 0.0 -4131.9 Asia 11.4 -27.4 27.4 137.7 -696.5 Africa and Middle East -12.0 6.4 0.0 0.0 -51.6 Indian Subcontinent 0.0 4.8 0.0 0.0 -625.7 Total -4.3 -71.4 81.3 99.8 -6779.6 Sugarcane Ethanol Dairy Cattle Hog Sheep Poultry North America (Canada) -0.2 2.7 -32.2 0.0 81.8 Western Europe -0.1 -2.1 -0.6 0.0 90.1 Eastern Europe 0.0 1.7 -8.3 0.0 417.5 Oceania 0.2 -4.7 0.2 1.8 26.7 Latin America -10.1 -40.7 -3.7 0.0 -1889.6 Asia -3.8 -32.2 -31.1 -66.7 -136.8 Africa and Middle East -13.0 0.0 0.0 0.0 4.4 Indian Subcontinent 0.0 26.3 0.0 0.0 -172.8 Total -27.0 -48.9 -75.8 -64.9 -1578.7 Switchgrass Ethanol Dairy Cattle Hog Sheep Poultry North America (Canada) 0.4 -2.7 37.3 0.0 -99.0 Western Europe 0.1 3.4 0.0 0.0 -57.8 Eastern Europe 0.0 -2.4 10.2 0.0 -435.4

345Oceania -0.5 -11. 1 0.4 -3.1 -108.6 Latin America 10.4 31.0 4.6 0.0 -81.9 Asia 6.7 1742.5 124.9 91.6 182.2 Africa and Middle East 21.4 2.3 0.0 0.0 90.1 Indian Subcontinent 0.0 5.5 0.0 0.0 12.0 Total 38.5 1768.5 177.3 88.5 -498.4

The enteric fermentation GHG impacts of livestock changes were calculated by applying regional default factors fo r enteric fermentation CH 4 emissions by livestock type. These factors are shown in Table 2.6-19.

Table 2.6-19. Enteric Ferm entation Emission Factors
Enteric Fermentation (kg CH4/head/year) Dairy Cattle Swine Sheep
North America 121 53 1.5 8 Western Europe 109 57 1.5 8 Eastern Europe 89 58 1.5 8
Oceania 81 60 1 5
Latin America 63 56 1 5
Asia 61 47 1 5
Africa and Middle East 40 31 1 5
Indian Subcontinent 51 27 1 5

Manure management GHG impacts of livestoc k changes for each fuel scenario were calculated by applying regional defau lt factors for manure management CH 4 and N 2 O emissions by livestock type. Manure management CH 4 emission factors are shown in Table 2.6-20. Manure management N 2 O values were based on default IPCC nitrogen produced per livestock type and IPCC default manure management practices by region.

Table 2.6-20. Manure Manageme nt Methane Emission Factors Manure Management (kg CH4/head - year) Dairy Cattle Swine Sheep Poultry
North America 78 2 23.5 0.28 0.02 Western Europe 51 15 15.5 0.28 0.02 Eastern Europe 27 13 6.5 0.28 0.02
Oceania 29 2 18 0.15 0.02
Latin America 1 1 1 0.15 0.02
Asia 18 1 4 0.15 0.02
Africa and Middle East 1.5 1 2 0.15 0.02
Indian Subcontinent 5 2 4 0.15 0.02

2.6.4.5 International Rice Production GHG Impacts

To estimate rice emission impacts internationa lly, we used the FAPRI model to predict changes in international rice produc tion as a result of the increase in biofuels demand in the U.S.

346Since FAPRI does not have GHG emissions factors built into the model, we applied IPCC default factors by country.

Calculating emissions from rice cultivati on, per the IPCC 2006 guidelines, requires the following data: area of rice harvested, an emissions factor, and planti ng to harvesting season length. Area of rice harvested by country was provided by the FAPRI resu lts. The default IPCC emission factors were used scaled for each croppi ng regime: irrigated, rainfed lowland, upland and deepwater by country. Rice cultivation season lengths were available from the International Rice Research Institute (IRRI). 530

2.6.4.6 International Crop Residue Burning

International crop residue burning, and specifi cally changes in residue burning emissions from changes in U.S. biofuel policy, were not sp ecifically included in this analysis. We did incorporate emissions from land clearing for cr op production as discussed in Section 2.6.5. Changes in crop production and acre s by country could lead to so me changes in international residue burning emissions and we will examine methods of incorporati ng these impacts for the final rule.

2.6.4.7 International Agriculture Sector Results (excluding land use change)

Table 2.6-19 provides an overview of th e total GHG emissions impacts from the international agricultural sector based on the result s of the FAPRI modeling in terms of absolute changes between the GHG emissions from the di fferent volume scenarios considered. Results are also presented on a per mmBtu basis by dividing total GHG changes by the mmBtu change in volume of biofuel produced between the differe nt scenarios. Land use change impacts are discussed in Section 2.6.5.

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348349Table 2.6-21.Foreign Agriculture GHG Emission Changes in 2022 from Different Fuel ScenariosCorn EthanolSoybean BiodieselSwitchgrass EthanolBrazilian SugarcaneEthanolEmission SourceTonnesCO2-eq.g/MMBtuCO2-eq.TonnesCO2-eq.g/MMBtuCO2-eq.TonnesCO2-eq.g/MMBtuCO2-eq.TonnesCO2-eq.g/MMBtuCO2-eq.Farm Energy Use Emissions618,3113,131109,5523,064205,926842338,7431,785Farm Chemical Production /Transport Upstream Emissions301,1481,52526,56774370,073287595,0783,136Livestock Changes GHG Emissions210,3091,065-75,984-2,1252,666,03610,906-189,238-997Rice Production GHG Emissions365,3221,85069,6451,948228,504935134,861711Fertilizer / Soil N2O Emissions589,3422,98432,256902138,339566819,0964,317Total:2,084,43210,555162,0364,5323,308,87913,5361,698,5408,952This page intentionally left blank.350351 2.6.5 Land Use Change GHG Impacts

Our lifecycle GHG estimates include emissions from domestic and international land use changes induced by increased renewable fuels consumption in the U.S. To estimate land use change GHG emissions we had to answer five key questions:

1. How much land is converted?
2. Where does land use change occur?
3. What types of land are converted?
4. What are the GHG emissions from that land conversion?
5. How do we account for the variable ti ming of land use change GHG releases?

This section describes our approa ch for answering these questions about land use change. We used FASOM to predict the total amount of cropland expansion in the U.S. FASOM was designed to simulate land use interactions to pred ict the types of land conve rted in the U.S. We used the FAPRI model to project international cropland expansion in response to increased U.S. biofuel consumption. The FAPRI model projects the area and amount of additional crop production, both by crop and count ry. FAPRI does not, however, project which types of land would be cleared to make room for additional cropland, or where within in each country or region cropland expansion would likely take place. To fill this information gap we used MODIS satellite data provided by Winrock International Incorpor ated (from now on referred to as Winrock), that shows recent trends in land use change from 2001 to 2004. To determine the GHG impacts of the projected land use changes we applied GHG emissions factors prepared by Winrock. We then used a net present value of GHG emissions to account for the variable time profile of land use change emissions.

2.6.5.1 How Much Land Is Converted

The main question regarding the amount of new land needed to meet an increasing demand for biofuels hinges on assumptions about the intensification of existing production versus expansion of production to other lands. This interaction is driven by the relative costs and returns associated with each op tion, but there are other factor s as described in the following sections.

2.6.5.1.1 Co-Product Use

One factor determining the amount of new crop acres required under an increased biofuel scenario is the treatment of co-products. For ex ample, distillers grains (DGs) are the major coproduct of dry mill ethanol production that is also used as animal feed. Therefore, using the DGs as an animal feed to replace the u se of corn tends to offset the lo ss of corn to ethanol production, and reduces the need to grow addi tional corn to feed animals. Along with the recent growth in corn ethanol production has been an expansion in the handlin g and use of DGs. FASOM and FAPRI use data about recent DGs usage to pred ict how DGs will affect agricultural markets as corn ethanol production expands even further, but there is still some uncertainty about how these co-products will be used in the future (e.g., whether it can be reformulated for higherincorporation into pork and poultry diets, whethe r it will be dried and shipped long distances, whether fractionation will become widespread).

Both our FASOM and FAPRI models account for the use of DGs in the agricultural sector. The FASOM and FAPRI mo dels both assume that a pound of co-product would displace roughly a pound of feed. DG replace ment is driven by how much DG can be included in animal feed, this so called inclusion rate differs by type of animal. The inclusion rate in turn impacts what percent of the DG produced will be used in which animal feed market. Finally the DG will substitute for different alternative feed products in different animal diets.

USDA reports that typical inclusion rates ar e 30-40 percent for beef cattle, 20-25 percent for dairy, 20 percent for hogs and 15 percent for poultry. 531 Furthermore, the same report indicates that a pound of DG will replace a pound of corn in beef rations, 0.45 pounds of corn in dairy rations, 0.85 pounds of corn in hog rations and 0.55 pounds of corn in poultry rations, with the remainder replacing soybean meal. Based on these assumptions and also that the majority of DG is used in the beef cattle market, FASO M assumes that one pound of DG will offset 0.9 pounds of corn and 0.1 pound of soybean meal.

The FAPRI model uses a slightly different set of assumptions for DG inclusion rates. 532 Beef cattle feed ration has the highest inclusion rate at 41% to 50%, the maximum inclusion rate for dairy follows at 26% to 30%, for hogs 20%, and for poultry 20%. Furthermore FAPRI assumes that DG will replace corn in beef and dairy cattle rations at 0.97 pound per pound, followed by pork at 0.89 pound per pound, a nd then poultry at 0.79 pound per pound. Displacement of soybean meal as a source of pr otein is 0.03 pound per pound of DG for beef and dairy cattle, 0.11 for pork, and 0.21 for poultry. FAPRI accounts for DG expor ts so that not all of the DG produced in the domestic ethanol market is used in the U.S. animal feed sector. Of the amount that is used in the U.S ., FAPRI assumes that the use of DG in animal feed is highest in the beef cattle sector (61% share), followed by th e dairy sector (21%), and then pork (9%) and poultry (9%) sectors. Based on all these assu mptions, FAPRI calculates that a pound of DG replaces 0.95 pounds of corn and 0.05 pounds of soybean meal.

A recent paper by Argonne National Laboratory 533 estimates that 1 pound of DGs can displace more then a pound of feed due to the higher nutritional va lue of DGs compared to corn. However, the Argonne replacement ratios do not take into account the dynamic least cost feed decisions faced by livestock producers. The actual use of DGs will depend on the maximum inclusion rates for each type of animal (based on the digestibility of DGs), the displacement ratio for each type of animal (based on DGs energy and protein content), and the adoption rate (based on the feed value relative to price).

Furthermore, as world vegetable oil prices in crease, dry mill ethanol producers will have an incentive to extract the corn oi l from the DGs. This step changes the nutritional content of the DGs, which results in different replacement rat es than currently used in FAPRI or FASOM or described by Argonne.

We plan to evaluate and incorporate a least co st feed rationing appro ach for the final rule in both the FASOM and FAPRI modeling systems. This will include assumptions about

352inclusion rates and displacement ratios, including considering a dditional replaced feed inputs such as urea as a protein source.

2.6.5.1.2 Crop Yields

Assumptions about yields and how they may change over time can also influence land use change predictions. Domestic yields were based on USDA projections, extrapolated to 2022. In 2022 we estimate that the U.S. average corn yield will be approximately 180 bushels/acre (a 1.6% annual increase consistent with recent trends) and average U.S. soybean yields will be approximately 50 bushels per ac re (a 0.4% annual increase). R RRRRRR Using the FASOM model, we conducted a sensitivity analysis on the impact of higher and lower yields in the U.S. Details on this scenario are included in DRIA Chapter 5.1. International yields changes are also based on historic trends. The FAPRI m odel contains projected yields a nd yield growth rates that are generally lower in other countries compared to the U.S. For example, corn yields in Argentina are assumed to be approximately 140 bu/acre in 2022 (a 0.8% annual increase). Corn yields in Brazil are assumed to be 68 bu/acre in 2022 (a 1.1% annual increase).

The FASOM and FAPRI models currently do not take into account changes in productivity as crop production shif ts to marginal acres or the im pact of price-induced yield changes on land use change. We would expect th ese two factors to work in opposite directions, and therefore serve as offsetting im pacts. Marginal acres in fully developed agricultural systems are expected to have lower yields, because most productive acres are already under cultivation. This may not be the case in developing systems where prime agricultural lands are not currently in full production due to, for example, lack of supporting infrastructure. Changes in agricultural inputs (e.g., fertilizer, pesticides ) can also change crop yields pe r acre. Higher commodity prices might provide an incentive to increase production on existing acres. If the costs of increasing productivity on existing land were minimal relative to the value of the increased production, then agricultural landowners would presumably adopt these productivity-enhancing actions under the reference case. Although it is reasonable to assume a trend wherein some productivityenhancing practices may become profitable if co mmodity prices are high enough, it is not clear that farmers would find significan t increases in production per acre profitable. If crop yields either domestically or internati onal are significantly impacted by higher commodity prices, this could affect our assessment of land use impacts and the resulting GHG emissions due to increased biofuel demand in the U.S. Howeve r, as described in Chapter 5, the change in commodity prices associated with the increase in U.S. biofuel as a result of the EISA mandates are relatively small and likely not large enough to induce very large yield changes.

2.6.5.1.3 Land Conversion Costs

The assumed cost associated with different t ypes of land conversion can also play a key role in determining how much land will be conver ted. In FASOM, the decision to convert land from pasture or forest to cropl and is based on whether the landow ner can increase the net present value of expected retu rns through conversion (inc luding any costs of conv ersion). Among other things, the decision to co nvert land depends on regi onal yields, costs, and other factors affecting

RRRRRRR Note that these assumptions ab out exogenous yield increases were applied in every scenario.

353profitability and on the returns to alternative land uses. In other words, FASOM assumes that land conversion is based on maximizing prof its rather than minimizing costs.

FASOM allows acres to convert between diffe rent types of crops without incurring conversion costs. Crop land is allowed to move from the pasture land inventory into the crop land category and backwards; however, there ar e replacement costs for forage as well as conversion costs of moving between land uses. Conversion costs from pasture to crop are expected to be generally lower than conversi on costs from forest to crop, because forest conversion requires an investment to clear stumps, level, and otherwise prepare the land for planting. Conversion cost will al so depend on the type of forest land converted and the value of the lumber or other products removed. Th e land quality factors su ch as topography or soil characteristics generally restrict some lands to only be in forest. Li kewise, growing conditions render some lands unsuitable for forest uses at all, particularly in the drie r plains areas and would thus only be suitable for some agricultural uses. For land moving from for est to agriculture, the net present value of land in agri culture must exceed returns to a rotation in forestry plus the future value of forested land by the investment co st to transfer land plus any hurdle cost (this term is currently set to zero). In both land transfer cases, the land moves between sectors until the markets equilibrate and the net present valu e plus the investment and market wedges are equal across the sectors for lands on the margin. Land movement does not occur if the differences in the land returns are less than the hurdle cost plus the land transformation investment costs.

FAPRI does not explicitly model land convers ion costs, but the in ternational production supply curves in the FAPR I model implicitly account for co nversion costs. FAPRI's supply curves are based on historical responses to price changes, which account for land conversion costs, based on expected future returns associated with land conversion. Thus, international land use changes projected by FAPRI are b ased on economic land-use decisions.

2.6.5.1.4 Total Area of Land Converted

We ran the FASOM and FAPRI models with the volume scenarios described in Table 2.3-1 to estimate biofuel induced crop expansi on domestically and intern ationally. The FASOM results were used to represent domestic land u se change and FAPRI results were used to determine international land use change.

FASOM domestic land use change was described in Section 2. 6.2.1. Table 2.6-22 shows the FASOM land use change results for th e different fuel scenarios considered.

Table 2.6-22. Domestic Crop Expansion by Scenario, 2022 Scenario Total Cropland Increase (million acres) Normalized Cropland Increase (acres per thousand ethanol equivalent gallons) Corn Ethanol 0.3 0.13 Soybean Biodiesel 0.2 0.34 Corn Stover Ethanol 0.0 0 Switchgrass Ethanol 1.3 0.28

354The FAPRI model predicted cr opland shifting internationall y due to changes in U.S. exports for the different fuel volume scenarios considered. Not all export losses are made up with production, as there ar e shifts in crops and de creases in demand that also occur. Figure 2.6­ 14 shows absolute intern ationally crop production pattern ch anges and Figure 2.6-14 shows per gallon crop pattern changes due to the changes in U.S. exports.

Figure 2.6-14. Foreign Cropland Changes by Scenario, 2022 (millions harvested acres)

International Cropland Use Change by Crop in 2022

-1.0 0.0 1.0 2.0 3.0 4.0 5.0WheatCottonCornSorghumBarleySoybeanRapeseedSunflowerPalmPeanutRiceSugarbeetSugarcaneTotalMillions of Acres

Corn Only Volume Scenario Biodiesel Only Volume Scenario Sugarcane Imports Only Volume Scenario Switchgrass Only Volume Scenario

355356Figure 2.6-15.Normalized Foreign Cropland Changes by Scenario, 2022(harvested acres per thousand gallons)International Cropland Use Change by Crop per Thousand Gallons of Biofuel in 2022-0.50.00.51.01.52.0 Wheat Cotton Corn Sorghum Barley Soybean Rapeseed Sunflower Palm Peanut Rice Sugarbeet Sugarcane Total

Acres per Thousand Gallons of Biofuel (Ethanol Equivalent)Corn Only Volume ScenarioBiodiesel Only Volume ScenarioSugarcane Imports Only Volume ScenarioSwitchgrass Only Volume ScenarioThis page intentionally left blank.357358 Table 2.6-23 shows the FAPRI land use change results for the different fuel scenarios considered.

Table 2.6-23. Foreign Cropland Increase by Scenario, 2022 Scenario Total Cropland Increase (million harvested acres) Normalized Cropland Increase (acre per thousand ethanol equivalent gallons) Corn Ethanol 4.4 1.68 Soybean Biodiesel 0.9 1.87 Switchgrass Ethanol 1.3 0.41 Brazilian Sugarcane Ethanol 3.7 1.48

The switchgrass ethanol scenario shows the l east acreage change per gallon. This is partly due to the fact that in 2022 switchgrass et hanol is assumed to have high yields per acre. For example, corn ethanol yields in 2022 are ass umed to be approximately 480 gal/acre, while switchgrass ethanol yields are as sumed to be approximately 5 80 gal/acre. The corn stover ethanol case is not assumed to have any land use change impacts. The models also show that there are significant land use change impacts asso ciated with increased sugarcane production in Brazil, and resultant changes in Brazilian commodity exports. Soybean biodiesel has lower gallons per acre yields than other fuels, appr oximately 75 gallons per acre, requiring more land per gallon of soy-based biodiesel production. However, biodiesel has a higher energy content then ethanol, so on a per gallon basis offsets more petroleum fu el, which decreases the land use impact per gallon of biodiesel production.

2.6.5.2 Where Does Land Use Change Occur

The first step in determining what domestic and international land will be converted due to biofuels production is to determine to what extent the increased dema nd for biofuel feedstock will be met through increased U.S. agricultu ral production or reductions in exports.

This question has several implications. Fo r example, U.S. agricu ltural production is typically more energy and input intensive, but has higher yields, than ag ricultural production in other parts of the world. This implies that increased production in the U.S. has higher input GHG emission impacts but lower land use change impacts compared to oversees production. In addition, the types of land where agriculture expands would be different in the U.S. as compared to other parts of the world.

As noted previously, EPA’s analysis reli es on FASOM predictions to represent changes in the U.S. agricultural sector, including land use, and on FAPRI to predict the resulting international agricultural sector impacts, incl uding the amount of additional cropland needed under different scenarios. The impact on the intern ational agricultural sector is highly dependent on U.S. agricultural commodity expo rt fluctuations resulting from increased biofuel production. As shown in Section 2.6.3, coordination of assumptions between FASOM and FAPRI has generated a consistent export picture respon se from both the FASOM an d FAPRI model for the majority of biofuel and feedstock scenarios considered.FASOM and FAPRI also show similar domestic land use change r esponses, with large shifts between crops. Figure 2.6-16 shows the comparison of FASOM and FAPRI domestic crop acre changes for the different fuel scenarios. Soy-based biodiesel production has the largest impact on U.S. exports per ethanol equivalent ga llon. For the corn a nd switchgrass ethanol, the models show the same type of response, with FAPRI showing slightly higher overall net crop acreage growth.

Figure 2.6-16. Comparison of FASOM and FAPRI Domest ic Crop Acre Changes by Scenario, 2022 (acres per thousand ethanol equivalent gallons)

Change in U.S. Acres per Thousand Gallons of Biofuel

-4 -3 -2 -1 0 1 2 3 4

FASOM FAPRI FASOM FAPRI FASOM FAPRI FASOM FAPRI

Corn Ethanol Only Volume Scenario SoyOil Biodiesel Only Volume Scenario Switchgrass Ethanol Only Volume Scenario Corn Stover Ethanol Only

Volume Scenario Sugarcane Ethanol Imports

Only Volume

ScenarioAcres per Thousand Gallons (Ethanol Equivalent) Other Crops (CARD) Other Crops (FASOM)

Wheat Switchgrass Sugarcane Sugarbeet Soybeans Sorghum

Rice
Oats Hay
Cotton Corn (Corn + Silage) Barley

Due to the wide range of carbon and biomass pr operties associated with land in different parts of the world, the location of crop conversion is also importan t to our lifecycle analysis. For example, an acre of forest in Southeast Asia st ores a much larger quant ity of carbon than an acre of forest in Northern Europe. The FAPRI m odel provides estimates of the acreage change by country and crop that result from a decrease in U. S. exports due to the increase in U.S. biofuel demand. These estimates are base d on historic responsiveness to changes in prices in other countries. Implicit in these supply curves are the costs associated with converting new land to crop production and the relative competitiveness of each country to increase production based on production costs, yields, and transp ortation costs. FAPRI also incl udes in its baseline projections estimates of future population growth, GDP growth , and other macroeconomic changes. FAPRI

359also takes into account the fact th at not all U.S. exports will need to be made up in international production, as there are small d ecreases in demand due to shifts in crop production and higher prices.

Figure 2.6-17 shows absolute acres of crop conversion, and Figure 2.6-18 includes normalized cropland changes for the different fuel scenarios. Our modeling predicts that Brazil will account for a large portion of internati onal crop expansion as a result of U.S. biofuel production and production of sugarc ane ethanol in Brazil. Howeve r, other countries have major crop expansion as well, includi ng India and Nigeria.

Figure 2.6-17. Regional Crop Expansion by Renewable Fuel, 2022 (thousand harvested acres)

Soy-Based Biodiesel Corn Ethanol Sugarcane Ethanol Switchgrass Ethanol 4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

-500 Argentina Brazil India China EU & Nigeria Other Paraguay Other Rest of S.E. Asia U.S. Foreign Russia Africa Latin World Total America

360Figure 2.6-18. Normalized Regional Crop Expansion by Renewable Fuel, 2022 (harvested acres per thousand gallons)

Soy-Based Biodiesel Corn Ethanol Sugarcane Ethanol Switchgrass Ethanol

0.0 0.5 1.0 1.5 2.0 2.5

Argentina Brazil India China EU & Nigeria Other Paraguay Other Rest of S.E. Asia U.S. World Russia Africa Latin World Total America

2.6.5.3 What Type of Land is Converted

2.6.5.3.1 Domestic Land Use Conversion

The FASOM model includes competition for land between agriculture, pasture, and forest. The interaction is based on providing the highest rate of return across the different land types. Therefore, domestically we have the abi lity to explicitly model what types of land would be converted to increased agriculture based on the rates of return for different land types for the 63 regions in FASOM. For this proposal we incorporated the agricu ltural component of the FASOM model, but not the forest ry component, so domestically all additional cr opland comes from pasture. As we incorporate the FASOM for estry model for the final rule we expect to see more interaction between the forestry and agriculture sectors, such that there may be conversion of forest to agriculture. While we do not know if forest will be converted to cropland, or the extent that this might occur, if domestic forest s were converted to cropl and, we would expect domestic GHG emissions to increase. On th e other hand, if more crop expansion occurs domestically we would expect that to offset some of the growth in foreign crop acreage. This work will be incorporated for our final rule.

2.6.5.3.2 International Land Use Conversion

Since FAPRI does not contain information on wh at type of land would be converted into cropland, we worked with Winrock to address this question. Using satellite data from 2001­

3612004, Winrock provided a breakdown of the types of land that have been converted into crop land for a number of key agricultu ral producing regions. We used this information about recent land use trends to estimate which types of land would be cleared in each region. Below we provide details about the data sources and methods used for these calculations.

2.6.5.3.2.1 International La nd Use Conversion Data Sources

Change in land use and land cover (LU/LC) w as estimated at the country and state level using MODIS satellite imagery (1 km resoluti on pixels) for the years 2001 and 2004 for all countries. MODIS imagery was chosen for this analysis due to its global coverage and homogenous classification scheme that allows di rect comparison across time and space. MODIS (Moderate Resolution Imaging Spectroradiometer) is a sensor that offers a unique combination of features: it detects a wide spectral range; it takes measurement s at three spat ial resolutions (levels of detail); it takes measurements all day, every day; and it has a wide field of view. MODIS is one of several sensors carried on bot h the Terra and Aqua satellites, managed by NASA. Terra was launched in December 1999 and Aqua was launched in May 2002. Both satellites complete a north to sout h orbit of the Earth in less than 2 days. The land cover dataset published by the USGS used in this analysis, MODIS/Terra Land Cover T ype Yearly L3 Global 1km SIN Grid (MOD12Q1) version 4, comes from the Terra satellite. S SSSSSS

The MOD12Q1 data are compiled from several sources to create a yearly global land cover map. Inputs to the M ODIS/Terra Land Cover Type Year ly L3 Global 1km SIN Grid include:

1. Land/water mask that restricts classi fication to land regions and shallow water regions 2. The MODIS Land Bands (1-7) 3. Spatial texture derived from Band 1 4. Directional reflectance information 5. MODIS Enhanced Vegetation Index (EVI) 6. Snow cover 7. Land surface temperature 8. Terrain elevation information

These data are composited over a 32-day cycle and values for land classification, change detection and mixture modeling are assigned by algorithms using decision tree and artificial neural network classifiers. Land cover classes are processed by continent. The 32 day products are used to produce the yearly globally-consistent, multi-temporal MOD12Q1 database on a 1­ km grid.

The algorithm development and validation effo rts for the Land Cover Product are based on a network of test sites devel oped to represent major global biom es and cover types. Sites for which temporal sequences of Thematic Mappe r (TM) and AVHRR data, coupled with fineresolution land cover and vegetation data, are available, espec ially in North and South America

SSSSSSS The official MOD12Q1 version 4 data were publicly released at the EDC DAAC on April 16, 2004. It can be found at http://edcdaac.usg s.gov/modis/mod12q1v4.asp

362(Belward 1999). According to the MODIS land team validation website , the accuracy of the IGBP land cover product, version 3, is 75-80 percent globally; 70-85 pe rcent by continental regions; and in individual classes ra nges from 60 in closed shrubland to 90 percent for barren or sparse land cover. T TTTTTT This analysis used version 4 of the MODIS/Terra Land Cover Type Yearly L3 Global 1km SIN Grid which is publishe d as being validated to Stage 1. A Stage 1 validation means that the product accuracy has been estimated using a small number of independent measurements obtained from select ed locations and time periods and groundtruth/field program effort. Ther e are no published reports as to th e accuracy of version 4 but it would be safe to assume it is no worse than version 3 and in all likelihood better.

Two tools are used by the MODIS land team to assess data quality; confusion matrixes and aggregations of confidence values. The confusion matrixes describe how well the training sites are classified when they are unknown by the classifier, a nd so provide information on the accuracy of the classification pro cess as applied to the training site database. The confidence values are generated by the classifier and indicate how well the pattern of spectral and temporal variation in annual observations of each pixel fits the examples of training data provided to the classifier. They may be treated as probabilities of correct classifi cation, given the input training data.

Within the classification process, errors are generated when the classification algorithm selects the wrong class. With resp ect to a particular class, errors of omission occur when pixels of that class are assigned wrong labels; errors of commission occur wh en other pixels are wrongly assigned the label of the class considered. These errors occur when the signal of a pixel is ambiguous, perhaps as a result of spectral mixi ng, or when the signal is produced by a cover type that is not accounted for in the training pr ocess. These errors are a normal part of the classification process. They can be minimized, but not voided entirely. Although they cannot be identified on a pixel-by-pixel basis due to processi ng constraints, they can be characterized in a statistical sense.

Although other global land cover products are available (suc h as the Global Land Cover 2000 dataset), the purpose of th is analysis was to analyze change in land use and cover over time. Thus we chose to use the MODIS imag ery because it adheres to a uniform 17-category IGBP classification scheme at both time periods (2001 and 2004), allowing for direct comparison over multiple regions and years.

MODIS data are published using the Intern ational Geosphere Bi osphere Programme (IGBP) global land cover categori es, a list of 17 cover classes (T able 2.6-23). The IGBP land cover list includes eleven classe s of natural vegetation, three classes of developed and mosaic lands, and three classes of non-vegetated lands. The natural vegetation units distinguish evergreen and deciduous, broadleaf and needle-leaf forests, mixed forests, where mixtures occur; closed shrublands and open shrublands; savannas and woody savannas; grasslands; and permanent wetlands of large areal extent. Th e three classes of deve loped and mosaic lands distinguish among croplands, urban and built-up lands, and cropla nd/natural vegetation mosaics. Classes of non-vegetated land cover units include snow and ice; barren land; and water bodies.

TTTTTTT The MODIS land team validation website can be accessed at http://landval.gsfc.nasa.gov/Prod uctStatus.php?ProductID=MOD12

363Table 2.6-23. Original IGBP and Modified Classificati on Schemes for MODIS Land Cover Imagery for the Years 2001 and 2004.

IGBP Land cover class Modified land cover class (this analysis) IGBP Class Explanation
Water Bodies Excluded

Evergreen Needleleaf Forests Forest Lands dominated by trees with a %canopy cover >60% and height >2m. Almost all trees remain green all year, canopy is never without green foliage.

Evergreen Broadleaf Forests Forest Lands dominated by trees with a %canopy cover >60% and height >2m. A;most all trees remain green all year. Canopy is never without green foliage.

Deciduous Needleleaf Forests Forest Lands dominated by trees with a % canopy cover >50% and height exceeding 2m. Consists of seasonal needleleaf tree communitieis with an annual cycle of leaf-on and leaf-off periods.

Deciduous Broadleaf Forests Forest Lands dominated by trees with a %canopy cover >60% and height exceeding 2m. Consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods.

Mixed Forests Forest Lands dominated by trees with a %canopy cover >60% and height >2m. Consists of tree communities with interspersed mixtures or mosaics of the other four forest cover types. None of the forest types exceeds 60% of landscape.

Closed Shrublands Shrubland Lands with woody vegetation <2m tall and with shrub canopy cover >60%. The shrub foliage can be either evergreen or deciduous.

Open Shrublands Shrubland Lands with woody vegetation <2m tall with shrub canopy cover between 10-60%. The shrub foliage can be either evergreen or deciduous.

Woody Savannas Savanna Lands with herbaceous and other understory systems and canopy cover of 30-60%. Height >2m.

Savannas Savanna Lands with herbaceous and other understory systems and canopy cover of 10-30%. Height >2m.

Grasslands Grassland Lands with herbaceous types of cover. Tree and shrub cover is less than 10%. Permanent Wetlands Excluded

Croplands Cropland Lands covered with temporary crops followed by harvest and a bare soil period (e.g., single and multiple cropping systems). Note that perennial woody crops will be classified as the appropriate forest or shrub land cover type. Urban and Built-Up Excluded Cropland/Natural Vegetation Mosaic Excluded Permanent Snow and Ice Excluded Barren or Sparsely Vegetated Excluded Unclassified Excluded

The IGBP land cover categories were not d esigned for use with MODIS specifically. Rather, the classification system was first publ ished with the IGBP Global Land Cover Map (DISCover v 1.0) in 1997. This dataset was derived at 1 km r esolution from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board several NOAA satellites and the 17 land cover classes are based on bi ophysical properties.

364We first reclassified the land use and land cover categories included in the original MODIS imagery into five general classes: cropl and, forest, grassland, savanna and shrubland (See Table 2.6-23). As the MODIS land cover maps used for this analysis are coarse resolution (1-km, equivalent to a pixel ar ea of 100 hectares) and classified in to broad land cover categories, the spectral characteristics of the finer classes may be similar to each other in many cases and thus land use conversions among them could be ambiguous. Other cate gories in the MODIS imagery that fell outside these five land cover classes (e.g., urban/built-up land) were excluded from the analysis. An example of the imagery before and after reclassification is shown for Argentina (top) and Brazil ( bottom) in Figure 2.6-18.

365Figure 2.6-18. Comparison of Original MODIS Classification (left) and Recl assification Used for This Analysis (right) for Two Sample Countries: Argentina (Top) and Brazil (Bottom).

Argentina Peru

Bolivia Colombia

P a r a g u a y y a n a

U

r u g u a y Argentina Peru

Bolivia Colombia

P a r a g u a y G u y a n a

U r u g u a y

After we had reclassified the two map pr oducts, change analyses were conducted by comparing the land cover map from 2001 with that for th e same location at 2004. A new dataset for each country, now referred to as land cha nge data, was produced that showed how the original land cover categories within each state and country changed, if at all, and into what new category the change occurred—often referre d to as a land cover change matrix.

366Comparing two products directly is not the best way to analyze change in LU/LC, as errors in the inte rpretation of the first map can be co mpounded when compared to a second map. The ideal way is to conduct a change analysis directly and interpret th e change. However, a direct change analysis requires a major effort to not only perform the change detection but to also interpret what the change is (from what to wh at). A comparison of the two LU/LC maps to obtain change is not the most desirable approach but it is the only approa ch available given the products that exist. Furthermore, very few countries have LU/LC products that have been prepared using change detection techniques fo r full change detection in all LU/LC classes.

Even with the best av ailable imaging technology, we found that the areas of some of the LU/LC classes and their changes ar e somewhat ambiguous. In an attempt to shed light on the causes of these ambiguities, we first compared the area of forest cover for the 2004 MODIS product with that given in the FAO FRA 2005 report (See Table 2.6-24. 534 Several anomalies in the two data sets are evident:

-- All countries report greater area of forest in th e FRA 2005 report than we obtained from the MODIS product, except for the three Southeast Asian countries of Indonesia, Malaysia, and Philippines.

-- The greater area of forest reported by FRA 2005 is to be expected as FAO requires countries to report area of all wooded lands w ith a canopy cover of 10% or higher, whereas the MODIS product defines forests w ith a canopy cover greater than 50-60%. For example, FRA 2005 gives almost 478 million ha of forest for Brazil –this includes not only the Amazonian and Atlantic forests, but also a range of woody savanna/shrublands that have a canopy cover of >10%. In our analysis, these woody savannas an d shrublands are purportedly reported as a separate class, resulting in an area of forest of about 406 million ha. The reasons for the greater forest cover reported under FRA 2005 versus th e MODIS estimate for the other countries are likely to be the same as for Brazil.

-- For Indonesia, Malaysia, and the Philippines, the MODIS product results in considerably greater forest area than reported by FRA 2005. This suggests that the large areas of plantation, perennial tree crops (e.g. rubber and palm oil plantations), and the woody formations are classed as forest unde r the MODIS algorithm.

367Table 2.6-24. Country-Level MODIS Forest Area Cover Data and Forest Cover Reported in FAO FRA 2005 Country MODIS 2004 Forest Area (ha) FAO FRA2005 Forest Area (ha) Argentina 23,566,147 33,021,000 Brazil 402,472,52+ 477,698,000 China 149,007,347 197,290,000 India 31,807,808 67,701,000 Indonesia 150,652,300 88,495,000 Malaysia 28,309,936 20,890,000 Philippines 15,952,945 7,162,000

There are other ambiguities in the MODIS data. For some countries forest area appeared to increase (Brazil, China, Malays ia) or show little change (Indon esia, Philippines) in the period 2001-2004, and the rest had a loss in forest cover as is often expected. However, Table 2.6-25 shows the net change, and just beca use total area appears to change little or even increase it does not mean that forests are not being cleared. Th e value of the MODIS data is that transitions between all LU/LC classes can be estimated for the period 2001-2004. For example, Table 2.6­ 25 shows that 341,186 hectares of land were conver ted from forest to cropland in Brazil from 2001 to 2004.

Table 2.6-25. Area of Main LU/LC Classes in Braz il in 2001 and 2004 Based on MODIS Data

BRAZIL 2004

2001 Cropland Forest Grassland Mixed Savanna Shrub Total Hectares

2001 Cropland 9,477,369 249,901 919,366 4,346,228 8,529,974 116,682 23,639,519 Forest 341,186 379,059,490 1,408,111 1,500,199 15,947,827 1,136,045 399,392,858 Grassland 1,472,851 1,453,441 9,152,627 2,576,107 22,384,406 1,456,720 38,496,152 Mixed 4,918,777 1,813,684 2,064,084 21,433,207 25,956,386 211,126 56,397,265 Savanna 6,004,819 18,614,978 12,295,754 21,595,617 214,505,969 4,698,914 277,716,052 Shrub 304,374 1,281,032 2,190,966 440,100 16,752,860 7,296,200 28,265,531 Total Hectares 2004 22,519,377 402,472,526 28,030,907 51,891,458 304,077,422 14,915,687 823,907,377

MODIS also provides intra-coun try LU/LC changes by state or administrative unit. For example, using MODIS data Table 2.6-26 shows where forest to cropland conversion took place in Brazil between 2001 and 2004.

368Table 2.6-26. MODIS Forest to Crop Conversion in Brazil , 2001 to 2004

Country State Share of Forest to Crop Conversion Brazil Acre 0.0% Alagoas 2.0% Amapa 0.6% Amazonas 0.1% Bahia 3.5% Ceara 0.2% Distrito Federal 0.0% Espirito Santo 0.7% Goias 4.1% Maranhao 1.1% Mato Grosso 3.2% Mato Grosso Do Sul 11.5% Minas Gerais 4.1%
Para 1.6% Paraiba 0.3%
Parana 31.2% Pernambuco 1.5% Piaui 0.1% Rio De Janeiro 0.2% Rio Grande Do Norte 0.1% Rio Grande Do Sul 15.4% Rondonia 0.0% Roraima 0.1% Santa Catarina 2.9% Sao Paulo 14.5% Sergipe 0.8% Tocantins 0.2% All Brazil 100.0%

2.6.5.3.2.2 Calculating the Type and Location of Land Use Change

We used the MODIS LU/LC data described a bove to estimate what types of land would be cleared to make room fo r cropland expansion, and wher e within countries that land conversion would occur. The steps in this calc ulation are described below, with Brazil as the primary example.

2.6.5.3.2.2.1 What Type of Land is Converted to Cr opland by Region

Table 2.6-27 shows the percentage of land type converted to cr opland according to the MODIS data. We used these percentages to calcu late a weighted average of the types of land converted into cropland. For exam ple, if FAPRI predicts that one additional hectare of cropland

369will be brought into production in Brazil, we used the Winrock data to estimate that 4% on average of that hectare will come from forest, 18% of that hectare will come from grassland, 74% of that land will come from Savanna, and 4% will come from shrubland.

Table 2.6-27. MODIS Types of Land Converted to Cropland by Country COUNTRY Forest Grassland Savanna Shrub

Argentina 8% 40% 45% 8%

Brazil 4% 18% 74% 4% China 17% 38% 23% 21%

EU 27% 16% 36% 21% India 7% 7% 33% 53% Indonesia 34% 5% 58% 4% Malaysia 74% 3% 19% 3% Nigeria 4% 56% 36% 4% Philippines 49% 5% 44% 3% South Africa 10% 22% 53% 15%

2.6.5.3.2.2.2 In Which States or Administrative Regions Does the Land Conversion Occur

We also used the MODIS data to estimate where the land conversions would occur within countries or regions. Table 2.6- 24 shows where in Brazil cropland was added, and what type of land was cleared in each state. This is important information because an acre of forest in the State of Amazonas, for example, sequesters substa ntially more carbon than an acre of forest in Sao Paulo. As you can see in Table 2.6-28, recen t forest to cropland co nversion has been far more common in Sao Paulo than Amazonas. Therefore, an estimate of land use change emissions in Brazil that does not weight forest to cropland conversion by the States where it would likely occur would tend to overestimate emissions per acre of crop expansion.

370Table 2.6-28. MODIS Land Conversion to Cropland in Br azil by Land Type and State, 2001 to 2004 (percent of land conversion type by state)

Start Category Forest Grassland Savanna Shrub End Category Crop Crop Crop Crop Country State Brazil Acre 0.0% 0.0% 0.0% 0.0% Alagoas 2.0% 2.3% 0.8% 1.1% Amapa 0.6% 0.0% 0.0% 0.1% Amazonas 0.1% 0.0% 0.0% 0.0% Bahia 3.5% 13.4% 7.6% 24.9% Ceara 0.2% 0.4% 0.3% 1.5% Distrito Federal 0.0% 0.2% 0.2% 0.2% Espirito Santo 0.7% 1.3% 1.2% 0.4% Goias 4.1% 7.5% 11.5% 6.6% Maranhao 1.1% 0.9% 0.6% 1.4% Mato Grosso 3.2% 12.0% 8.9% 10.2% Mato Grosso Do Sul 11.5% 11.6% 9.0% 3.6% Minas Gerais 4.1% 7.6% 15.7% 10.5%
Para 1.6% 0.2% 0.2% 0.1% Paraiba 0.3% 2.3% 0.9% 3.9%
Parana 31.2% 10.5% 8.4% 5.7% Pernambuco 1.5% 3.6% 1.6% 5.2% Piaui 0.1% 0.3% 0.3% 1.4% Rio De Janeiro 0.2% 0.8% 2.8% 2.6% Rio Grande Do Norte 0.1% 1.5% 0.6% 2.5% Rio Grande Do Sul 15.4% 6.9% 6.7% 2.7% Rondonia 0.0% 0.0% 0.0% 0.0% Roraima 0.1% 0.3% 0.1% 1.0% Santa Catarina 2.9% 0.7% 0.5% 0.9% Sao Paulo 14.5% 13.4% 21.0% 11.6% Sergipe 0.8% 1.7% 0.4% 1.1% Tocantins 0.2% 0.5% 0.6% 0.7% All Brazil 100.0% 100.0% 100.0% 100.0%

We used the data summarized in Tables 2. 6-27 and 2.6-28 to estimate where and what types of land in Brazil would be cleared per hect are of cropland expansion. This calculation can be summarized with the following formula:

Land clearing per acre crop expa nsion by land-type and State = CE L * CE LS , where: CE L = share of crop expansion from land-type L ; CE LS = share of crop expansion from land-type L in State S ; L = forest, shrubland, savanna, or grassland S = one of the 26 Brazilian States

371Using this calculation we estimated the land use changes per hectare of cropland expansion in Brazil shown in Tabl e 2.6-29. For example, in Brazil 4% of crop expansion occurs on shrubland and 25% of shrub to crop conversion occu rred in the state of Bahia. Therefore, for each hectare of crop expansion in Brazil, we proj ected that approximately 0.01 hectares of shrub would be converted to cropland in Bahia. We re peated this calculation fo r all land types and for each of the ten countries that we had MODIS data for.

Table 2.6-29. Projected Crop Expansion in Brazil by Land Type and State (acres of conversion per a cre of crop expansion)

Start Category Forest Grassland Savanna Shrub End Category Crop Crop Crop Crop Country State Brazil Acre 0.00 0.00 0.00 0.00 Alagoas 0.00 0.00 0.01 0.00 Amapa 0.00 0.00 0.00 0.00 Amazonas 0.00 0.00 0.00 0.00 Bahia 0.00 0.02 0.06 0.01 Ceara 0.00 0.00 0.00 0.00 Distrito Federal 0.00 0.00 0.00 0.00 Espirito Santo 0.00 0.00 0.01 0.00 Goias 0.00 0.01 0.08 0.00 Maranhao 0.00 0.00 0.00 0.00 Mato Grosso 0.00 0.02 0.07 0.00 Mato Grosso Do Sul 0.00 0.02 0.07 0.00 Minas Gerais 0.00 0.01 0.12 0.00
Para 0.00 0.00 0.00 0.00 Paraiba 0.00 0.00 0.01 0.00
Parana 0.01 0.02 0.06 0.00 Pernambuco 0.00 0.01 0.01 0.00 Piaui 0.00 0.00 0.00 0.00 Rio De Janeiro 0.00 0.00 0.02 0.00 Rio Grande Do Norte 0.00 0.00 0.00 0.00 Rio Grande Do Sul 0.01 0.01 0.05 0.00 Rondonia 0.00 0.00 0.00 0.00 Roraima 0.00 0.00 0.00 0.00 Santa Catarina 0.00 0.00 0.00 0.00 Sao Paulo 0.01 0.02 0.16 0.00 Sergipe 0.00 0.00 0.00 0.00 Tocantins 0.00 0.00 0.00 0.00 All Brazil 0.04 0.18 0.74 0.04

3722.6.3.2.2.3. Land Use Cha nge Beyond Cropland Expansion

As described above, we used the FAPRI m odel to project how much cropland expansion would occur in each country, and MODIS satellit e data provided by Winrock to estimate where and what types of land would be cleared within each country or region to make room for new cropland. Table 2.6-27 shows that in many countries cropland has b een added in areas that were previously categorized as grassland or savanna. In consultation with Winrock we determined that lands categorized as grassla nd or savanna, according to IG PB the classification scheme, could be used as pasture for livestock grazing. Therefore, if we used the land use change estimates in Table 2.6-29 as our end point, we would project significant losses of actively managed pasture. However, it may be unreasona ble to project pasture losses unless we also project comparable overall dec lines in livestock production an d/or increases in pasture intensification. To resolve this issue and to estimate overall land use changes as accurately as possible, we added a certain amount of “pasture replacement” land use change to our total land use change estimates.

We assumed that actively managed pasture c onverted to cropland would have to be replaced with new pasture acreage, thereby captu ring some of the domino effects associated with converting fertile land into crop production. We used land cover information from the GTAP database to estimate the proporti on of grassland and savanna that is actively managed as pasture in each country (see Table 2.6-30). Accordi ng to the GTAP datab ase 64% of grassland and savanna is Brazil is actively managed for livestock grazing. We applied this data to our land use change estimates, which implies that, on average, 64% of the grassland and savanna converted to crop production in Brazil would previously have b een used for livestock grazing. Therefore, since recent trends indicate that 92% of Braz ilian crop expansion would occur on grasslands or savanna (see Table 2.6-27), we projected that approximately 59% of crop expansion would occur on land previously used as pasture.

Table 2.6-30. Grassland and Savanna Actively Manage d for Livestock Grazing by Region, 2000-2001

COUNTRY Unmanaged Savanna and Grassland (hectares) Managed Pasture (hectares) Percent of Savanna and Grassland Managed for Pasture Argentina Brazil China European Union India Indonesia Malaysia Nigeria Phillipines South Africa 77,716,560 101,991,992

42,534,708 14,529,446 12,806,170

7,704,545 0 24716916 0 19132926 96,452,176 181,046,752 286,370,272

64,005,559 11,798,180

2,462,572

292,900 18762928

131,755 77293112 55.4% 64.0% 87.1% 81.5% 48.0% 24.2% 100.0%

43.2% 100.0%

80.2% SOURCE: GTAP Land Cover Data (2000-2001)

Figure 2.6-18 is a graphical representation of our Brazil pasture replacement estimates. In order to replace pasture displaced by crop expa nsion, we assumed that forest and shrubland would be cleared to make room for new pasture areas. We used the MODIS satellite data to estimate the shares of forest and sh rubland that would then be clea red and converted to pasture to

373replace the pasture converted to crops. We used the state-by-sta te distribution of each of these conversions to estimate where in Brazil the pastur e replacement would occur. For example, we used the MODIS data’s state-by- state breakdown of where forest was converted to savanna in Brazil to estimate where forest would likely be converted to savanna to replace some of the lost pasture. The per hectare emi ssions factors shown in Figure 2. 6-18 are non-discounted emissions over 80 years for each type of land conversion. Notice that in Brazil, average GHG emissions are greater for projected forest to grassland conversion than projected forest to cropland projection, because recent trends suggest that fo rest to grassland conversion tends to occur in regions with very carbon-rich forests. These em issions factors are discussed in detail below.

Figure 2.6-18. Brazil Land Use Change Flow Chart with Cropland Expansion and Managed Pasture Replacement

Table 2.6-31 provides a comple te summary of projected land use change in Brazil per hectare of crop expansion. Noti ce that the land use changes under step one (cropland expansion) are exactly the same as in Table 2.6-29. The diff erence is that the complete summary of land use change in Brazil includ es the pasture replacement step. We used the same methodology to calculate land use changes for all ten countries for which MODIS satellite data was provided.

374Table 2.6-31. Summary of Brazil Land Use Change with Managed Pasture Replacement (acres of land conversion per acre of crop expansion)

Step One: Crop Expansion Step Two: Pasture Replacement Replace Grassland Replace Savanna Start Category Forest Grassland Savanna Shrub Forest Shrub Forest Shrub End Category Crop Crop Crop Crop Grassland Grassland Savanna Savanna Country State Brazil Acre 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Alagoas 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 Amapa 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Amazonas 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Bahia 0.00 0.02 0.06 0.01 0.00 0.02 0.02 0.06
Ceara 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03
Distrito Federal 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Espirito Santo 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00
Goias 0.00 0.01 0.08 0.00 0.00 0.00 0.00 0.01
Maranhao 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.01
Mato Grosso 0.00 0.02 0.07 0.00 0.01 0.00 0.04 0.01 Mato Grosso Do Sul 0.00 0.02 0.07 0.00 0.00 0.00 0.01 0.00
Minas Gerais 0.00 0.01 0.12 0.00 0.00 0.01 0.01 0.02

Para 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00
Paraiba 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.02

Parana 0.01 0.02 0.06 0.00 0.00 0.00 0.02 0.00
Pernambuco 0.00 0.01 0.01 0.00 0.00 0.01 0.00 0.02
Piaui 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01
Rio De Janeiro 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 Rio Grande Do Norte 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.02
Rio Grande Do Sul 0.01 0.01 0.05 0.00 0.01 0.00 0.01 0.00
Rondonia 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00
Roraima 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01
Santa Catarina 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00
Sao Paulo 0.01 0.02 0.16 0.00 0.00 0.00 0.01 0.00 Sergipe 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Tocantins 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 All Brazil 0.04 0.18 0.74 0.04 0.05 0.07 0.23 0.24

EPA is reviewing parameters used to es timate pasture replacement, including further research and peer-review of these issues. For exam ple, we plan to look at studies about whether it is more or less likely for pasture to be c onverted to crop production th an grassland or savanna that is not already used for graz ing. Grazing tends to occur near agricultural activity, suggesting that crop expansion would be more likely to occu r on pasture. On the other hand, there is an extra opportunity cost associated with convert ing pasture to crop production, so farmers may prefer to expand crop production on to lands not already used for gr azing. More detailed regional land use studies and economic modeling wi ll help to answer these questions.

375Intensification of grazing lands also has th e potential to change the impacts of crop expansion, and to reduce the overall land use foot print of agriculture. For example, in Brazil cattle per hectare increased by 10% from 2001- 2005, and one estimate suggests almost 50 million acres of pastureland could become available due to more efficient use of land for cattle. This could reduce economic pressures to expand li vestock grazing into unused lands, such as forests. We also plan more research to distinguis h between grazing intens ification that would occur regardless of biofuel production, and in tensification induced by biofuel production.

The amount of lost pasture that would be re placed and the types of land that would be cleared to replace it are also important. Based on recent land use trends, we assumed that forest and shrubland would be cleared to replace lost pasture. We did not, therefore, include the possibility that grassland and savanna that is no t currently used for pasture could be put into grazing activities. Section 2.8 includes sensitiv ity analysis of the pasture replacement assumptions used in our analysis, which indicates that this is an important factor in land use change projections for lifecycle GHG analysis.

To calculate the total area of land use cha nges in Brazil, we simply took the product of the total area of crop expansion projected by FAPRI and the land u se changes per hectare summarized in Table 2.6-31. For an increase in U.S. corn ethanol consumption from 12.4 to 15 billion gallons in 2022 (i.e. the difference betw een our Corn Only and Control Case), FAPRI projected 747,000 hectares of crop expansion in Brazil. A summary of total 2022 Brazil land use changes associated with this corn ethanol scenario is provided in Table 2.6-32. Land use change summaries for all ten countries are available in spreadsheets on the public docket for this proposed rulemaking.

376Table 2.6-32. Projected Brazil Land Use Changes in Corn Ethanol Only Scenario, 2022 (acres)

Step One: Crop Expansion Step Two: Pasture Replacement Replace Grassland Replace Savanna Start Category Forest Grassland Savanna Shrub Forest Shrub Forest Shrub End Category Crop Crop Crop Crop Grassland Grassland Savanna Savanna Country State Brazil Acre 0 8 39 0 74 4 2,407 5 Alagoas 630 3,156 4,399 316 220 258 717 891 Amapa 174 39 205 32 696 389 2,069 317

Amazonas 16 24 16 8 2,539 781 2,088 256

Bahia 1,107 18,206 41,789 6,966 2,877 13,174 16,595 44,016

Ceara 55 607 1,563 418 49 2,789 755 19,584

Distrito Federal 0 266 1,108 64 12 96 35 314 Espirito Santo 217 1,770 6,839 104 90 44 1,456 411

Goias 1,278 10,095 63,275 1,835 253 1,112 1,718 4,273

Maranhao 338 1,230 3,548 397 737 1,163 18,568 4,969

Mato Grosso 996 16,252 49,333 2,863 5,217 3,122 26,670 6,371

Mato Grosso Do Sul 3,608 15,717 49,598 1,001 2,268 964 6,867 2,190

Minas Gerais 1,296 10,269 86,582 2,926 1,514 3,990 7,921 14,424

Para 505 284 1,182 32 2,597 1,211 24,164 928

Paraiba 102 3,108 5,146 1,103 37 3,708 307 13,367

Parana 9,786 14,225 46,476 1,598 1,648 253 12,628 1,463

Pernambuco 486 4,834 8,689 1,444 237 4,204 1,763 18,257

Piaui 39 391 1,880 402 149 1,802 4,810 10,111

Rio De Janeiro 59 1,093 15,414 726 88 102 697 429 Rio Grande Do Norte 31 2,091 3,205 698 12 4,137 57 16,006

Rio Grande Do Sul 4,837 9,379 36,900 760 4,692 2,424 10,941 3,516

Rondonia 8 54 124 8 1,301 313 9,243 93

Roraima 24 422 670 268 1,751 2,568 1,735 6,962

Santa Catarina 898 896 2,819 251 1,742 235 7,967 1,326

Sao Paulo 4,559 18,143 115,807 3,256 984 444 6,097 2,288 Sergipe 251 2,271 2,399 315 257 278 473 360

Tocantins 78 613 3,189 201 1,853 3,177 3,509 7,829 All Brazil 31,375 135,441 552,194 27,990 33,895 52,740 172,259 180,954

It is important to note that our analysis for the proposed ru le treats all acres of crop expansion the same in an individu al country. This implies that an additional acre of corn in Brazil would result in the same la nd conversion as an additional acre of soybeans or sugarcane in Brazil. However, it is possible that different types of crop expansion would occur in different regions and thus have different impacts on la nd conversion. Current areas where corn and soybeans are grown in Brazil are largely in the same regions in the south, however, there are areas further north where producti on of soybeans occur as shown in Figures 2.6-19 and 2.6-20,

377indicating that different types of crop production in Brazil would potentially have different impacts.

Figure 2.6-19. Corn Production in Brazil

378Figure 2.6-20. Soybean Production in Brazil

The impact of this assumption on overall result s is somewhat mitigated by the fact that each fuel scenario in the U.S., e.g., corn ethano l and soybean biodiesel wi ll result in a mix of crop export changes and a mix of crop expansion in ternationally. For example, increased corn ethanol production in the U.S. resu lts in reduced exports of not ju st corn, but other crops as well, due to crop shifting in the U.S. agricultural sector. Also the M ODIS satellite data is based on a mix of crop expansion in different countries such that it is already reflectin g crop expansion in a mix of different crops. However, this is an area that will be examined for the final rule analysis.

2.6.5.3.3 GTAP Approach

GTAP is an economy-wide general equilibrium model that was originally developed for addressing agricultural trade issues among countri es. The databases and ve rsions of the model are widely used internationally by a large modeling community. 535 Since its inception in 1993, GTAP has rapidly become a common "language" for many of those conducting global economic analysis. For example, the WTO and the Worl d Bank co-sponsored two conferences on the socalled Millennium Round of Multilat eral Trade talks in Geneva. Here, virtually all of the quantitative, global economic an alyses were based on the GTAP fr amework. Over the past few years, a version of the model was develope d to explicitly model global competition among different land types (e.g., forest, agricultural la nd, pasture) and different qualities of land based on the relative value of the alternative land-uses. More recently, it was modified to include biofuel substitutes for gasoline and diesel. In simulating land use changes due to biofuels production, GTAP explicitly models land-use co nversion decisions, as well as land management intensification. For example, it allows for price-induced yield changes (e.g., farmers can

379reallocate inputs to increase yields when commod ity prices are high) and considers the marginal productivity of additional land (e.g., expansion of crop land onto lower quality land as a result of the increased use of biofuels). Most importantly, in contrast to other models, GTAP is designed with the framework of predicting the amount and types of land needed in a region to meet demands for both food and fuel production. The GT AP framework also allo ws predictions to be made about the types of land available in th e region to meet the needed demands, since it explicitly represents different la nd types within the model.

The global modeling of land-use competiti on and land management decisions is relatively new, and evolving. 536 GTAP is currently adding cellulosic biofuel feedstocks. In addition, GTAP does not currently contain unman aged land, which could be a major factor driving current GTAP land use projections and is a signifi cant potential source of GHG emissions. We will continue to consider using GTAP as it develops a more comprehensive treatment of biofuel pa thways and land supply.

Our proposal is therefore based on the FAPRI/Winrock estim ates. Additional refinements will be made to the GTAP model betw een the proposal and final rule and we plan to include information about important developmen ts in the docket as it becomes available.

2.6.5.4 What Are the GHG Emissions Associat ed with Different Types of Land Conversion

2.6.5.4.1 Domestic Land Use Change Emissions

Domestic land use change GHG emissions are based on outputs of the FASOM model. As we are just using the agricu ltural portion of the FASOM model for this analysis the land use change GHG emissions are limited to changes in land use for existing crop and pasture land. Some of that crop land could curr ently be fallow and some of the pasture land could currently be unused. However, no new crop or pasture la nd (beyond some CRP land due to legislative changes in the program) are added compared to current levels. Thus FASOM only models shifts in the use of this land.

Changes in the agricultural sector due to incr eased crops used for biofuels have impacts on land use change in a number of ways. FASOM explicitly models change in soil carbon from increased crop production acres and from differ ent types of crop production. FASOM also models changes in soil carbon from convert ing non-crop land into crop production. Land converted to crop land could include pasture land.

Land conversion GHG emissions in FASO M include above ground carbon and soil carbon changes from land conversion and crop pattern changes. The FASOM land conversion factors are based on factors for different cr ops, management practices, and land conversion effects, e.g., converting pasture to crop pr oduction. Therefore, FA SOM soil carbon changes capture not only converting new la nd into crop but also changes from existing cropland due to changing management practices or cropping patter ns. As part of the ongoing work with Colorado State University we are in the proc ess of updating the soil and land conversion factors in FASOM based on new DAYCENT model runs. We plant to in clude these updates as part of the analysis for the final rule.

3802.6.5.4.2 International Land Use Change Emissions

Land use change emissions factors were calculated by the non- profit organization Winrock International following 2006 IPCC AFOL U Guidelines. Winrock’s staff is highly regarded for their years of experience and acco mplishment in this field, including their work with the IPCC to develop the AFOL U Guidelines. For the proposed rule, emissions factors were estimated for ten countries including Argentina, Brazil, China, India, Indonesia, Malaysia, Mexico, Philippines, EU and South Africa. Emissi ons factors were based on the initial change in biomass stocks as well as chang es in soil carbon stocks, non-CO 2 emissions from biomass burning due to land clearing, and fo rgone forest sequestration. In general, emission factors for all land use types were highest in the Southeast Asian countr ies of Malaysia, Indonesia and Philippines and lowest in Argentina and Nigeria. This section describes the methods used to estimate GHG emissions from international land use change.

2.6.5.4.2.1 Data sources used to estima te land use change GHG emissions factors

Forest Carbon Stocks : To calculate changes in biomass carbon stocks due to land use change, data were compiled at the state level to estimate carbon stocks of various land use types. Average forest carbon stocks per administrative un it in each country were extracted from spatial data layers developed for various regions of the world. Esti mates represent both above- and belowground biomass. Data sources for each region are summarized below:

Argentina : A global map of forest carbon stocks developed by Gibbs et al. (2007) was used to estimate forest carbon stocks in Argentina.

Asia : A map developed by Gibbs and Brown (2007) was used to estimate forest carbon stocks in the year 2000 in the Asian countri es of Indonesia, Malaysia and Philippines.

Mexico : A map developed by Harris and Brown (unpublished results) was used to estimate forest carbon stocks in Mexico. The map was developed using similar methods to the products developed for Asia and Africa by Gibbs and Brown (2007, 2008), where potential biomass maps were developed based on various climatic and biophysical factors and were then degraded based on population density for the year 2000 to estimate actual forest biomass. The biomass estimates were then expanded to account for roots based on factors given in Cairns et al. (1997) and then converte d to carbon stocks (carbon = biomass*0.5).

South America : A map of aboveground forest biomass was obtained from Saatchi et al. (200x), an example of which is shown in Fi gure 2.6-21. The biomass estimates were first expanded to account for roots based on factor s given in Cairns et al (1997) and then converted to carbon stocks (carbon = biomass*0.5).

381Figure 2.6-21. Example of a Spatial Data Base for Forest Carbon Stocks

Soil Carbon Emissions: Soil carbon emissions associated with land use change in different regions around the world we re estimated using global soil carbon maps. A digital soil carbon map of the world is available from th e U.S. Department of Agriculture, Natural Resources Conservation Service, s ee Figure 2.6-22. This map is based on a reclassification of the FAO-UNESCO Soil Map of the World combined with a soil climate map. The map shows little variation for soil C in the tropics with most areas showing a range in soil carbon of 40-80 t C/ha (4-8 Kg C/m2). The soil organic carbon map shows the distributi on of the soil organic carbon to 30 cm depth. 537 This data layer was chosen becau se land use change (e.g., tilling for cropland conversion) typically affects only the upper layers of the soil profile.

382a The GIS data product has higher resolution than indicated here in the map (1 kg m -2 = 10 t C ha -1 ).

Cropland Carbon Stocks : Carbon stocks on annual cropland were estimated as 5 metric Figure 2.6-22. Estimates of So il Organic Carbon to 30cm Depth a

tons of carbon per hectare (MT C ha -1 ), based on Table 5.9 of the IPCC AFOLU. In Malaysia, carbon stocks for oil palm and rubber were estim ated using values from Table 5.3 of the IPCC AFOLU (68 and 89 MT C ha -1 , respectively).

Grassland, Savanna and Shrubland Carbon Stocks : Above- and belowground biomass values for grassland, savanna and shrublands were obtained from a variety of sources, see Table 10-11. Biomass estimates were obtained from de Castro and Kauffman (1998) for the Brazilian vegetation gradient that spans campo limpo ( pure grassland), campo sujo (a savanna with a sparse presence of shrubs), campo cerrado (a domi nance of shrubs with scattered trees and a grass understory), cerrado sensu stricto (a dominance of trees with scattered shrubs and a grass understory) and cerradão (a closed canopy forest) (Couti nho 1978, Eiten 1972, Goodland & Pollard 1973). Shrubland carbon stocks were es timated from the average of biomass values reported for cerrado aberto and cerrado denso. Sa vanna carbon stocks were estimated from the biomass value reported for campo sujo. Grasslan d carbon stocks were estimated from the biomass value reported for campo limpo.

Estimating carbon stocks in other countries for the grassland-savanna-shrubland continuum was slightly more problematic. For all countries except Brazil (explained in the paragraph above), grassland carbon stocks were estimated for each administrative unit as the default value given in Table 6.4 of the IPCC AFOLU, where values are given as a function of temperature and moisture regime.

Very few carbon stock estimates were availabl e for savanna and shrubland outside of Brazil. Therefore, carbon stocks of these land cover types were estimated using a proportional approach based on the Brazil dataset, which indica tes an increasing trend in carbon stocks from grassland Æ savanna Æ shrubland of 10.9 to 19.7 to 37.3 MT C ha -1 , respectively, translating into a ratio of 1 to 1.8 to 3.4. These ratios we re applied to other countries for estimating carbon stocks in savanna and shrubland based on the estimated carbon stocks of grassland within each country. One exception to the ratio approach wa s for the Southeast Asian countries of Indonesia, Malaysia and Philippines, where a shrubland biomass value of 85 MT ha -1 (42 MT C ha -1 ) was

383applied based on actual data reported in Grace et al. 2006 for a savanna woodland in Klong Hoi Khong, Thailand.

Table 2.6-33. Estimated Carbon Stocks (MT C ha -1 ) for Grassland, Savanna and Shrubland in Each Country Analyzed a Country Grassland Savanna Shrubland

Brazil 10.9 19.7 37.3
India 4.1 – 7.6 7.4 – 13.6 13.9 – 25.7

Malaysia 7.6 13.6 42.0
Indonesia 7.6 13.6 42.0 Philippines 7.6 13.6 42.0
China 7.6 13.6 25.7 Argentina 2.7 – 6.4 5.1 – 11.5 9.7 – 21.7

Mexico 4.1 - 7.6 7.4 – 13.6 13.9 – 25.7

a ranges are reported for countries in which carbon stocks varied by state

2.6.5.4.2.2 Methods for Land Use Cha nge GHG Emissions Factors Estimation

For each state in each country, emission fact ors were calculated for various land cover conversions using data tables and equations from the 2006 IPCC AFOLU Guidelines and from other global data sets. Emission factors accoun t for changes in above- and belowground biomass carbon stocks, soil carbon stocks and lost forest sequestration. Non-CO 2 emissions were also accounted for, including methane (CH 4 ) and nitrous oxide (N 2 O) emissions resulting from land clearing with fire and CH 4 emissions resulting from rice cultivation.

Changes in biomass carbon stocks : Initial changes in biomass carbon stocks on land converted to another land category (e.g., from forest to cropland) we re calculated based on Equation 2.16 in the IPCC AFOLU:

384Δ C CONVERSION = 

## ∑ ( B AFTER − B BEFORE ) • CF

i i i where: Δ C CONVERSION = initial change in biomass carbon stocks on land converted to another land category, tonnes C ha -1 yr -1
B AFTER i = biomass stocks on land type I im mediately after the conversion, tonnes d.m. ha -1
B BEFORE i = biomass stocks on land type I be fore the conversion, tonnes d.m. ha -1
CF = carbon fraction of dry ma tter, tonne C (tonnes d.m.) -1
i = type of land use converted to another land-use category

Initial changes in biomass carbon stocks reflect gross emissions rather than net emissions; harvested wood products, including long-term storage and retirement, were not considered in the analysis. Based on consultation with Winrock we determined that including IPCC default GHG credits for harvested wood products (HWPs) would have an insi gnificant impact on our estimates of land use change emissions, as there is lim ited evidence that trees cleared from converted forestland is converted to durable wood pr oducts. The 2006 IPCC Guidelines for National Greenhouse Gas Inventories also states that (p 12.8) the HW P contribution can be reported as zero if the annual change in carbon in HWP stocks is judged insignificant. Even if we assumed that forestland cleared from Brazil had 10m3 of timber/ha, which is likely an upper bound for many of the forests being cleared, this would translate to about 8tCO2/ha, or less than 2% of the total emissions from converting forest to cropland. However, we intend to analyze the impact of wood product credits for the final rule.

Winrock provided emissions factors for la nd conversion to pere nnial crops, but only emissions factors for conversion to annual crops were used for th e proposed rule analysis. We intend to differentiate between annual and perennial cropland expansion for the final rule.

Changes in soil carbon stocks : Changes in soil carbon st ocks on land converted to cropland were calculated based on Sec tion 5.3.3.4 of the IPCC AFOLU.

Soil carbon stocks after convers ion to cropland were based on specific soil stock change factors for land use, management and inputs (F LU , F MG , F I , respectively) listed in Table 5.10 of the IPCC AFOLU. (Relevant factors are listed in Table 2.6-34 of this report). Stock change factors were selected for each land cover type (before and after conversion) and multiplied by reference soil carbon stocks. Following the IPCC AFOLU guidelin es, the total difference in carbon stocks before and after conversion was aver aged over 20 years. Thus the average annual change in soil carbon stocks due to land use conversion we re calculated as:

385( SOC Re f • F LU

, before • F MG

, before • F I

, before )

− ( SOC Re f • F LU

, after • F MG

, after • F I

, after ) Δ SOC = 20

where: Δ SOC = average annual change in carbon stocks in top 30 cm of soil; t C ha -1 yr -1
SOC Re f = reference carbon stocks in top 30 cm of soil; t C ha -1
F

LU = land use factor before or after conversion F

MG = management factor be fore or after conversion F

I = input factor befo re or after conversion

As default values for stock change factors ( F LU , F MG , F I ) are all one for forest soils and non-degraded grassland soils, soil carbon stocks were assumed to remain unchanged for all conversion types (conversion to shrubland, savanna, perennial cropland) except conversion to cropland. Full tillage and medium inputs were a ssumed in all scenarios of cropland conversion. Consistent with IPPC default gui delines, soil carbon stock cha nges were spread equally over 20
years.

Table 2.6-34.

Relative Stock Change Factors (F LU , F MG , F I ) for Different Management Activities Based on Tables 5.5 and 5.10 in 2006 IPCC AFOLU Factor value type Level Temperature Regime Moisture Regime IPCC default

Land use (F LU ) Native forest orgrassland (nondegraded) All All 1

Dry 0.8 Temperate/Boreal Moist 0.69
Land use (F LU ) Long-term cultivated Tropical Dry 0.58
Moist/Wet 0.48
Tropical montane n/a 0.64

Land use (F LU ) Paddy rice All All 1.1

Land use (F LU ) Perennial/tree crop All All 1
Tillage (F MG ) Full All All 1

Input (F I ) Medium All All 1

Peat soil drainage: Winrock provided emissions factor s for peat drainage for countries where that is likely to occur, such as Indonesia, and Malaysia. However, emissions from peat soil were not included in our analysis for the proposed rule. Below is a description of the

386methodology Winrock used to provid e factors for peat drainage, wh ich we intend to use for the final rule. For countries containing peat (o rganic) soils, emission factors were chosen that accounted for annual losses of CO 2 due to peat drainage. Emission fact ors were based on a regional dataset from Southeast Asia that correlates p eat drainage depth with annual peat CO 2 emissions. Emission factors were estimated using this region al dataset rather than basing them on climate type (Table 5.6 of the IPCC AFOLU) because all countries included in the analysis that contained peat soils were located in Southeas t Asia. Thus the use of regional data was appropriate. The emission factor for drained organic soils was estimated based on the following equation (from Hoo ijer et al. 2006):

Y = 0.91 X where: Y = CO 2 emissions from drained peat, MT CO 2 ha -1 yr -1
X

= drainage depth, cm

Drainage depth was assumed as 80 centimeter s, which is the minimum likely peat drainage estimate for conversion to croplands cited in Hooijer et al. (2006) . Thus the annual emission factor from drained peat soil s was estimated as 80 x 0.91 = 72.8 MT CO 2 ha -1 yr -1

. Emission factors for draining peat soils were estimated for land use conversions originating from forest and grassland only, based on the assumption that in Southeast Asia, the only land cover types on peat are intact or degraded peat swamp forest or already drained and deforested peatland that has revegetated to Imperata grasslands.

Lost forest sequestration : Over time, trees sequester car bon dioxide from the atmosphere and accumulate carbon as biomass. When forests ar e converted to another land use, the trees that would have continued to grow are cut down a nd no longer act as carbon sinks. Therefore, each year after a land use conversion ha s occurred the carbon that would have been sequestered in the growing trees is “lost”. In an avoided emissi ons scenario, carbon benefits can be generated not only from preventing the immediate loss of bi omass carbon stocks, but also from allowing carbon to accumulate over time in the vegeta tion that would have been cleared.

Average annual biomass accumulation rates are given in Table 4.9 of the 2006 GL AFOLU and are specified according to ecological zone and continen t. These values were used to estimate the lost biomass accumulati on of forests that were converted to another land use (Table 2.6-36). Average values were used where a rang e of values is listed. Lost carbon accumulation was then assumed equal to the biomass accumu lation value multiplied by the carbon fraction in dry matter (IPCC default = 0.47).

The area of forest in each state was st ratified by FAO ecozone and continent, and appropriate values from Table 2.6- 30 were applied to calculate an average area-weighted value. As biomass accumulation rates in Table 4.9 of th e 2006 GL AFOLU are separated into rates for forests less than and greater than a 20 year threshold, in this anal ysis all forests that were being converted were assumed to be >20 years in ag e. The average annual foregone sequestration is calculated as:

387Δ C lostseq = 

# ∑ ( Cseq LUbefore i

, − Cseq LUafter i

, )

i where:

lostseq -1 Δ C = change in carbon sequestration in aboveground biomass; MT C ha -1 yr Cseq LUbefore i

, = annual sequestration in aboveground biomass before conversion; MT C ha 1 -1

yr Cseq LUafter i

, = annual sequestration in abovegr ound biomass after conversion MT C ha -1 yr -1 i = type of land use converte d to another land use category.

Studies have estimated that new fore sts grow for 90 years to over 120 years. U UUUUUU More recent estimates suggest that old growth forests accumulate carbon for up to 800
V VVVVVV years. For this analysis we assumed that fo regone sequestration would continue for 80 years, which is within the range supported by the scientific lite rature and the 2006 IPCC guidelines. Section 2.8 presents a sensitivity analysis of the length of foregone sequestration assumed.

UUUUUUU See Greenhouse Gas Mitigation Pote ntial in U.S. Forestry and Agriculture, EPA Document 430-R-05-006 for a discussion of the time required for forests to reach carbon saturation.
VVVVVVV Luyassert, S et al., 2008. Old-growth forests as global carbon sinks. Nature 455: 213-215. Link: http://www.nature.com/nature/journal/v455/n7210/abs/nature07276.html

388Table 2.6-35. Aboveground Net Biomass Growth in Natural Forests From Table 4.9 in IPCC AFOLU

A boveground biomass Ecological growth (tonnes d.m. Domain Zone Continent ha-1 yr-1) Africa 3.1 South America 3.1 Tropical rain Asia (continental) 2.2 forest Asia (insular) 3.4 Africa 1.3 Tropical moist South America 2.0

deciduous Asia (continental) 2.0

forest Asia (insular) 3.0 Africa 1.8
Tropical Tropical dry South America 1.0 Asia (continental) 1.5 forest Asia (insular) 2.0 Africa 0.9 South America 1.0 Tropical Asia (continental) 1.3 shrubland Asia (insular) 1.0 Africa 1.0 - 1.5 Tropical South America 0.4 - 1.4 mountain Asia (continental) 0.5 - 1.0 systems Asia (insular) 1.0 - 3.0 North and South America 2.0 Subtropical Asia (continental) 2.0 humid forest Asia (insular) 3.0 Africa 1.8 North and South America 1.0 Subtropical dry Asia (continental) 1.5 forest Asia (insular) 2.0 Sub-tropical Africa 0.9 North and South America 1.0 Subtropical Asia (continental) 1.3

steppe Asia (insular) 1.0 Africa 0.2 - 1.6 Subtropical North and South America 0.4 - 1.4 mountain Asia (continental) 1.0 - 2.2 systems Asia (insular) 1.0 Temperate continental Asia, Europe, North forest America 0.5 - 7.5

Temperate Temperate mountain systems Asia, Europe, North America 0.5 - 6.0 Boreal coniferous Asia, Europe, North forest America 0.1 - 2.1 Boreal tundra Asia, Europe, North woodland America 0.4 Boreal Boreal mountain systems Asia, Europe, North America 1.0 - 1.1

Non-CO 2 emissions from land clearing : In countries where fire is used commonly as a land clearing practice for conve rsion to agriculture, non-CO 2 emissions were estimated using

389emission factors in Table 2.5 and Equation 2.27 of the IPCC AFOLU. Fire for land clearing was assumed to occur in all countries included in the analysis except China and Argentina.

Non-CO 2 emissions from land clearing with fire were estimated as:

L fire = A • M B • C f • G ef • 10
− 3

Where: L fire = amount of greenhouse gas emissions from fire, MT of each GHG (i.e.,

CH 4 , N 2 O) A = area burnt, ha M B = mass of fuel available for combustion, MT ha -1

. C f = combustion factor, dimensionless G ef = emission factor, g kg -1 dry matter burnt

The mass of fuel available for combustion was conservatively assumed to be equal to the above- and belowground biomass only; dead wood and litter pools were not included in the fuel load estimates.

IPCC defaults were used for the forest com bustion factor. Values from de Castro and Kaufmann (1998) were used for clearing other la nd cover types (grassland, savanna, shrubland). Combustion factors and emission f actors used in this analysis are summarized in Table 2.6-36 and 2.6-37 respectively.

Table 2.6-36. Combustion Factor Values (Proportion of Pre-Fire Fuel Biomass Consumed) for Fires in a Range of Vegetation Types Vegetation Type Subcategory Value

Primary tropical forest (slash and burn) Primary tropical forest 0.32 Primary open tropical forest 0.45 Primary tropical moist forest 0.5 Primary tropical dry forest

- All primary tropical forests 0.36 Shrubland 0.43

Savanna 0.84 Grassland 0.92

390Table 2.6-37. Emission Factors (g kg -1 Dry Matter Burnt) Used in This Analysis for Biomass Burning From Table 2.5 of IPCC AFOLU Category CO 2 N 2 O Savanna and grassland 1613 0.21 Tropical forest 1580 0.2 Extra tropical forest 1569 0.26

2.6.5.4.2.3 Integrated Emissions Factors for Land Use Change

In summary, emissions factors were estimated for each state for each land use conversion as metric tonnes CO2 equivalent. All non-CO2 estimates were convert ed using IPCC SAR GWP factors:

EF = Δ C + Δ C + Δ C + E + E LUC biomass

, LUC soil

, LUC lost

_ seq

, LUC fire LUC rice

, LUC

, where: EF i = emission factor for converting one hectare of land to land use LUC ; Δ C biomass

, LUC = change in biomass carbon stocks as a result of land use change LUC ; Δ C soil

, LUC = change in soil carbon stocks (top 30 cm) as a result of land use change LUC; Δ C lost

_ seq LUC

, = lost forest sequestration resulting from land use change (if applicable); E fire

, LUC = non-CO2 GHG emission associated w ith land clearing with fire (if applicable);

The per hectare emissions factors used for th e proposed rule are presented in Table 2.6­ 34. The emissions factors in Table 2.6-38 are weighted by where land conversions are likely to take place within each country, based on the M ODIS satellite data discussed above. For example, the forest to cropland emissions fact or for Brazil is weighted by where forest was converted to cropland in Brazil from 2001 to 2004. The MODIS satellite data suggests that in Brazil forest to grassland convers ion tends to occur in regions w ith more carbon-rich forests than the areas that have more forest to cropland conve rsion. This explains why, for example, the Brazil per hectare emissions factor for forest to gr assland conversion is greater than the factor for forest to cropland conversion. The emissions f actors in Table 2.6-34 are undiscounted emissions over 80 years.

As depicted in Figure 2.6-14 for Brazil, the per hectare emissions factors in Table 2.6-38 were weighted by the shares of land types convert ed to cropland, and by the types of land cleared to replace pasture converted to cropland. With the weighting approach discussed above, we derived land use change emissi ons factors per hectare of crop land expansion. Table 2.6-39 includes the country-level weight ed average emissions factors (W AEFs) per hectare of cropland expansion that were used for th e proposed rule central estimate . The WAEFs in Table 2.6-39 are undiscounted emissions over 80 years.

391Table 2.6-38. Regional Land Conversion GHG Emissions Factors Undiscounted Emissions Over 80 Years (MTCO2-eq. / acre)
Country / Region To From
Forest Grassland Savanna Shrub Crop 115 16 17 33 Argentina Grassland 136 15
Savanna 129 9 Crop 258 45 60 82 Brazil Grassland 257 39
Savanna 240 26 Crop 237 23 34 49 China Grassland 186 27
Savanna 240 27 Crop 318 28 38 47 European Union Grassland 216 22
Savanna 181 14 Crop 247 26 26 38 India Grassland 249 249
Savanna 236 12 Crop 432 43 51 94 Indonesia Grassland 455 51
Savanna 415 42 Crop 473 50 57 103 Malaysia Grassland 457 51
Savanna 438 42 Crop 96 18 23 78 Nigeria Grassland 92 58
Savanna 112 28 Crop 402 34 44 88 Philippines Grassland 371 51
Savanna 359 42 Crop 111 18 36 79 South Africa Grassland 109 58
Savanna 86 47

392Table 2.6-39. Regional Weighted Average Emissions Factors (WAEF) (MTCO2-eq. per acre of crop expansion) Country / Region Crop Expansion Pasture Replacement Total WAEF Argentina 25 34 60 Brazil 66 76 142 China 68 45 113 European Union 106 41 147 India 48 33 81 Indonesia 182 60 242 Malaysia 367 93 461 Nigeria 25 31 56 Philippines 219 170 389 South Africa 47 32 79

2.6.5.4.2.3.4 What Are the Total GHG Emissions Associated With International Land Use Change

To determine the GHG impacts of the land use changes we applied GHG emissions factors based on data prepared by Winrock to th e land use changes projected by the combination of FAPRI and satellite data. Ta ble 2.6-39 shows our emissions fact ors for ten countries. The ten countries we have emissions factors for account for a majority of foreign cropland expansion projected by FAPRI. Table 2.6- 40 shows the share of projected cr opland expansion in these ten countries in the FAPRI fuel-specifi c scenarios. We are in the pr ocess of acquiring data for more countries to be used for the final rule.

Table 2.6-40. Share of Projected Foreign Crop Expansion Covered by Proposed Rule Winrock/MODIS Data Fuel-Specific Scenario Share of 2022 Crop Expansion Covered Corn Ethanol 63% Soy-Based Biodiesel 87% Imported Sugarcane Ethanol 53% Switchgrass Ethanol 57% Note: Winrock/MODIS data covers Argentina, Brazil, Chin a, EU, India, Indonesia, Malaysia, Nigeria, Philippines and South Africa

We used a weighted average emissions fact or (WAEF) from the ten countries we have Winrock data for to estimate the GHG emissions from cropland expansion in countries not covered by Winrock data. The country-level shar es of crop expansion varied by fuel-specific scenario, so the WAEF applied to missing countries also varied by scenario. Table 2.6-41 shows the WAEF used for missing countries for each scenario. The WAEF for missing countries is highest for the soy biodiesel scenario because FAPRI projects that U.S. soy biodiesel consumption encourages crop expansion in tropical countries with very large emissions factors for crop expansion, such as Malaysia and Philippi nes. Crop expansion in these South East Asian

393oil palm exporting countries is la rge in the soy biodiesel scenario, because oil palm is a substitute for soy oil.

Table 2.6-41. Weighted Average Emissions Factors (W AEFs) Applied to Countries without Winrock/MODIS Data Fuel-Specific Scenario Regions Covered by Winrock/MODIS Corn Ethanol Soy-Based Biodiesel Sugarcane Ethanol Switchgrass Ethanol

Share of

crop expansion by region Argentina 4% 2% 13% 7%
Brazil 27% 64% 33% 17%
China 10% 2% 7% 13%
E.U. 6% 1% -3% 5% India 24% 5% 24% 31% Indonesia 5% 1% 6% 3% Malaysia 0% 0% 6% 0% Nigeria 20% 27% 12% 21% Philippines 3% 0% 2% 2% South Africa 3% -1% 1% 1% WAEF applied to missing regions (MTCO2-eq. / acre crop expansion) 114 117 132 104

Figure 2.6-23 shows results for 2022 tota l undiscounted land use change GHG emissions over 80 years per million Btu of renewable fuel.

Figure 2.6-23. Total Land Use Chan ge GHG Emissions by Scenario Undiscounted GHG Emissions Over 80 Years (MTCO 2 -eq. / mmBtu)

2.53 3.25

2.26

0.56

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50GHG Emissions (MTCO2-eq. / mmBtu)

Corn Ethanol Soy-Based Biodiesel Sugarcane Ethanol Switchgrass Ethanol

3942.6.5.5 Timing of GHG Emissions

When comparing the lifecycle GHG emissions associated with biofuels to those associated with gasoline or diesel emissions, it is critical to take into consideration the time profile associated with each fuel’s GHG’s emissi ons stream. With gasoline, a majority of the GHG emissions associated with extraction, conversi on, and combustion are likely to be released over a short period of time (i.e., annually) as crude oil is converte d into gasoline or diesel fuel which quickly pass to market.

In contrast, the GHG emissions from the produc tion of a typical biofuel (e.g., corn-based ethanol) may continue to occur over a long period of time. As with petroleum based fuels, GHG emissions are associated with the conversion and combustion of biofuels in every year they are produced. In addition, GHG emissi ons could be released through tim e if new acres are needed to produce corn or other crops for biofuels. Th e GHG emissions associated with converting land into crop production would accumulate over time with the largest release o ccurring in the first few years due to clearing with fire or biomass decay. After the land is converted, moderate amounts of soil carbon would continue to be released for approximately 20 years. Furthermore, there would be foregone sequestra tion associated with the fact that the forest would have continued to sequester carbon had it not been cleared for approximately 80 years (See Figure 2.6­ 24).

Figure 2.6-24. Timing of International Land Use Change Emissions in Corn Ethanol Scenario (gCO2-eq. / mmBtu)

International Land Use Change Emissions 1,200,000

1,000,000GHG Emissions (gCO2-eq / mmBtu) 800,000

600,000

400,000

200,000

0 Year 0 Emissions: - Clearing with fire - Biomass decay - Soil carbon emissions - Foregone sequestration

Year 1-19 Emissions: - Soil carbon emissions - Foregone sequestration Year 20-79 Emissions: - Foregone sequestration2022203020402050

While biomass feedstocks grown each year on new cropland can be converted to biofuels that offer an annual GHG benefit relative to the petroleum product they replace, these benefits may be small compared to the upfront rel ease of GHG emission. Depending on the specific

395biofuel in question, it can take many years for the benefits of the biofuel to make up for the large initial releases of car bon that result from land conversi on (e.g., the payback period).

As required by EISA, our analysis must demonstrate whether biofuels reduce GHG emissions by the required amount relative to the 2005 petroleum baseline. A payback period alone cannot answer that question. Since the payb ack period is not sufficient for our analysis, we have developed methods for capturing the stream of emissions and benefits over time. For our analytical purposes, it is important for us to determine how the time prof iles of emission releases of different fuels compare. It is useful to have a unitary metric that allows for a direct comparison of biofuels compared to gasoline or diesel, which requires an accounting system for GHG emissions over time. When considering the time profile of GHG emissions, the two assumptions that have a significant impact on th e determination of whether a biofuel meets the emissions reduction threshold includ e: 1) the time period considered and 2) the discount rate we apply to future emissions. The preamble to the proposed rule presents results using a 100-year time horizon and a 2% discount rate, as well as results with a 30-year time horizon and a 0% discount rate. The preamble includes a discussi on of the important factors to consider when choosing a time horizon and discount rate. In Section 2.8, we present lifecycle GHG results using a variety of time hori zons and discount rates.

2.6.6 Feedstock Transport

The GHG impacts of transporting biofuel feedstoc k from the field to the biofuel facility and transporting co-products from the biofuel facili ty to the point of use were included in this analysis. The GREET default of tr uck transportation of 50 miles wa s used to represent corn and soybean transportation from farm to plant. Th is includes 10 miles from farm to stacks and 40 miles from stacks to plant. Transportation a ssumptions for DGs transport were 14% shipped by rail 800 miles, 2% shipped by barge 520 miles, and 86% shipped by truck 50 miles. The percent shipped by mode was from data provided by USDA and based on Association of American Railroads, Army Corps of Engin eers, Commodity Freight Statistic s, and industry estimates. The distances DGs were shipped were based on GR EET defaults for other commodities shipped by those transportation modes. Default GREET assump tions were also used for cellulosic ethanol feedstock transport. Crop residue s, switchgrass and forest wastes were all assumed to be shipped by truck from point of producti on to plant. Crop residue di stance shipped was 30 miles, switchgrass distance was 40 miles, and forest waste was 75 miles.

The GHG emissions from transport of feed stock and co-product are based on GREET default emission factors for each type of vehicle including capacity, fuel economy, and type of fuel used.

As part of this rulemaking analysis we have conducted a more detaile d analysis of biofuel production locations and transportation distances a nd modes of transport used in the criteria pollutant emissions inventory ca lculations described in DRIA Chapter 1.6 and for the cost analysis of this rule describe d in DRIA Chapter 4.2. Given the timing of when the current analysis was completed we were not able to in corporate this updated tr ansportation information into our lifecycle analysis but plan to do that for the final rule.

396Furthermore, the transportation modes and distances assumed for corn and DGs do not account for the secondary or indirect transportati on impacts. For example, decreases in exports will reduce overall domestic agricultural commodity transport and emissions but will increase transportation of commodities internationally. We plan to consider these secondary transportation impacts in our final rule analysis.

2.6.7 Biofuel Production

The GHG emissions associated with the proce ssing of renewable fuels is dependent on a number of assumptions and varies significantl y based on a number of key variables. For example, for corn ethanol the ethanol yield impact s the total amount of corn used and associated agricultural sector GHG emissions. The amount of DGs and other co-p roducts produced will also impact the agricultural sector emissions in terms of being used as a feed replacement. Finally the energy used by the ethanol plant will result in GHG emissi ons, both from producing the fuel used and through direct combustion emissions. The following sections outline the assumptions used to model biofuel production for different feedstocks and fuel pathways.

In each case, the GHG emissions from rene wable fuel production are calculated by multiplying the Btus of the different types of energy inputs by emissions factors for combustion of those fuel sources. The emission factors for the different fuel types are from GREET and are based primarily on assumed carbon contents of the different process fuels. The emissions from producing electricity are also taken from GREET and represent av erage U.S. grid electricity production emissions. The emissions from combus tion of biomass fuel source are not assumed to increase net atmospheric CO 2 levels. Therefore, CO 2 emissions from biomass combustion as a process fuel source are not included in the li fecycle GHG inventory of the biofuel production plant.

2.6.7.1 Corn Ethanol

The two basic methods for producing ethanol from corn are dry milling and wet milling. In the dry milling process, th e entire corn kernel is ground an d 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.

For this analysis the amount of corn us ed for ethanol production as modeled by the FASOM and FAPRI models was based on yield assumptions built into those two models. Assumptions were ethanol yields of 2.71 gallons per bushel for dry mill plants and 2.5 gallons per bushel for wet mill plants (yields represents pure ethanol).

As mentioned above, in traditional lifecy cle analyses, the energy consumed and emissions generated by a renewable fuel plant must be allocated not only to the renewable, but also to each of the by-products. However, for co rn ethanol production, this analysis accounts for

397the DGs and other co-products use directly in the FASOM and FAPRI agricultural sector modeling described above. DGs are considered a partial replacement for corn and other animal feed and thus reduce the need to make up fo r the corn production that went into ethanol production. Since FASOM take s the production and use of DGs into account, no further allocation was needed at the ethanol plant and all plant emissions are accounted for here.

In terms of the energy used at renewable fuel facilities, there is a lot of variation between plants based on the process type (e.g., wet vs. dry m illing) and the type of fuel used (e.g., coal vs. natural gas). There can also be variation between the same type of plants using the same fuel source based on the age of the plan t and types of processes included, etc. For our analysis we considered different pathways for corn ethanol production. Our focus was to differentiate between facilities based on the key differences between plants, namely the type of plant and the type of fuel used. One other key difference we modeled between plants was the treatment of the co-products DGs. One of the main energy driver s of ethanol production is drying of the DGs. Plants that are co-located with feedlots have th e ability to provide the co -product without drying. This has a big enough impact on overall results th at we defined a specific category for wet vs. dry co-product. One additional factor that appears to have a significant impact on GHG emissions is corn oil fractionati on from DGs. Therefore, this category is also broken out as a separate category in the following section. See DRIA Chapter 1. 4 for a discussion of corn oil fractionation.

Furthermore, as our analysis was based on a future timeframe, we modeled future plant energy use to represent plants that would be bui lt to meet requirements of increased ethanol production, as opposed to current or historic data on energy used in ethanol production. The energy use at dry mill plants was based on A SPEN models developed by USDA and updated to reflect changes in technology out to 2022 as de scribed in DRIA Chapter 4.1. The modeling provided energy use for the different types of dry mill ethanol plants as shown in Table 2.6-41.

398Table 2.6-41. Energy Use at Ethanol Plants (Btu/gal)

Type Technology NG Use Coal Use Biomass Use Purchased Elec Corn Ethanol – Dry Mill NG Near Term Technology (dry DDGS) 33,032 2,660 2022 Base Plant (dry DDGS) 28,660 2,251 2022 Base Plant w/ CHP (dry DDGS) 30,898 512 2022 Base Plant w/ CHP and Fractionation (dry DDGS) 22,014 714 2022 Base Plant w/ CHP, Fractionation and Membrane Seperation (dry DDGS) 17,994 714 2022 Base Plant w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (dry DDGS) 13,408 714 2022 Base Plant (wet DGS) 17,081 2,251 2022 Base Plant w/ CHP (wet DGS) 19,320 512 2022 Base Plant w/ CHP and Fractionation (wet DGS) 15,594 714 2022 Base Plant w/ CHP, Fractionation and Membrane Seperation (wet DGS) 11,574 714 2022 Base Plant w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (wet DGS) 8,841 714 Corn Ethanol – Dry Mill Coal 2022 Base Plant (dry DDGS) 35,824 2,694 2022 Base Plant w/ CHP (dry DDGS) 39,407 205 2022 Base Plant w/ CHP and Fractionation (dry DDGS) 28,301 447 2022 Base Plant w/ CHP, Fractionation and Membrane Seperation (dry DDGS) 23,276 447 2022 Base Plant w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (dry DDGS) 17,545 447 2022 Base Plant (wet DGS) 21,351 2,694 2022 Base Plant w/ CHP (wet DGS) 24,934 205 2022 Base Plant w/ CHP and Fractionation (wet DGS) 20,277 447 2022 Base Plant w/ CHP, Fractionation and Membrane Seperation (wet DGS) 15,252 447 2022 Base Plant w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (wet DGS) 11,836 447 Corn Ethanol – Dry Mill Biomass 2022 Base Plant (dry DDGS) 35,824 2,694 2022 Base Plant w/ CHP (dry DDGS) 39,407 205 2022 Base Plant w/ CHP and Fractionation (dry DDGS) 28,301 447 2022 Base Plant w/ CHP, Fractionation and Membrane Seperation (dry DDGS) 23,276 447 2022 Base Plant w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (dry DDGS) 17,545 447 2022 Base Plant (wet DGS) 21,351 2,694 2022 Base Plant w/ CHP (wet DGS) 24,934 205 2022 Base Plant w/ CHP and Fractionation (wet DGS) 20,277 447 2022 Base Plant w/ CHP, Fractionation and Membrane Seperation (wet DGS) 15,252 447 2022 Base Plant w/ CHP, Fractionation, Membrane Seperation, and Raw Starch Hydrolysis (wet DGS) 11,836 447 Corn Ethanol – Wet Mill Plant with NG 45,950 Plant with coal 45,950 Plant with biomass 45,950

3992.6.7.2 Biodiesel

Two scenarios for biodiesel production were co nsidered, 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 US DA and used by EPA in the cost modeling of soybean oil biodiesel including cr ushing, as discussed in Chap ter 4. Soybean crushing was modeled assuming yields of 11.2 lbs soybean oil/bu soybeans and energy use of 14,532 Btu of natural gas and 2,740 Btu of purchased electr icity per gallon of bi odiesel produced.

Similar to the case with corn ethanol co-products, we analyze the aggregate GHG emissions from soybean crushing and transesterif ication that occur as a result of increased demand for a particular biofuel. Therefore, any increase in soybean meal or soybean oil produced as a result of larger biodiesel volumes would take into account GHG emissions reductions from a decrease in the production of ot her feed and vegetable oil substitutes from our FASOM modeling.

Biodiesel is produced through the transest erification process where vegetable oil (triglyceride) is reacted with an alcohol (e.g., methanol) and a catalyst (e.g., sodium hydroxide) to produce biodiesel and glycerin. Soybean biodi esel transesterification was modeled assuming yields of one kilogram of biodiesel from a kil ogram of soybean oil and energy use of 5,559 Btu of natural gas and 340 Btu of purchased elec tricity per gallon of biodiesel produced.

For the yellow grease case, no s oybean agriculture emissions or energy use was included. Soybean crushing natural gas us e was included as a surrogate for yellow grease processing (purification, water removal, etc.). Also, due to additional processing requirements, the energy use associated with producing biodiesel from yellow grease is higher than for soybean oil biodiesel production. The energy use for yellow grease biodiesel production was assumed to be 1.7 times the energy used for soybean oil biodie sel and yields of 0.94 kilograms of biodiesel from a kilogram of yellow grease.

GHG emissions from other biod iesel production raw material inputs were also included in the analysis. HCl, methanol, NaOCH3 and sodium hydroxide are used in the production of biodiesel and GHG emissions from producing the ra w material inputs were also added to the model. Table 2.6-42 shows the values that were used to convert raw material inputs into GHG emissions used in the anlaysis.

Table 2.6-42. Lifecycle Factors for Biodiese l Raw Material Production

Factor Unit Methanol Sodium Methoxide Sodium Hydroxide HCl
CO 2 g/g 0.401 0.966 0.923 1.011
CH 4 g/g 0.003 0.002 0 0
N 2 O g/g 3.9E-06 2.5E-06 0 0 Total Energy Btu/g 19.05 24.10 9.67 9.35

4002.6.7.3 Cellulosic Ethanol

Cellulosic ethanol can be produced through two main types of production processes, either fermentation or gasification. The ferm entation option is mainly used for homogeneous feedstock sources like farmed trees, switchgrass and corn stover whereas the gasification option is recommended for more heterogeneous sources like forestry waste.

As discussed in Chapter 1, we have worked with NREL to generate models of cellulosic ethanol production. NREL has estimates for plants that were assumed to be in operation in the near-future (2010) and future (2015 and 2022). Cu rrently NREL has generate d results for a corn stover ethanol fermentation plant. We have us ed this to represent both corn stover and switchgrass ethanol production. The modeling assumes 78,378 Bt u of biomass and 118 Btu of diesel fuel is required per gall on of ethanol produced. Process en ergy is assumed to be generated through the unfermentable portion (mainly ligni n) of incoming biomass being burned for electricity production. The process is assumed to generate 8,053 Btu of excess electricity per gallon of ethanol produced.

The benefit of electricity generation is the possibility of lowering greenhouse gas emissions by offsetting other forms of electricity pr oduction. This is captur ed in our analysis by assuming that the excess electricity produced by the ethanol plant will offset U.S. grid electricity production. Therefore, GHG emissi ons from U.S. grid electricity are calculated for the amount of excess electricity produced based on GREET defaults for electr icity production and subtracted from the lifecycle results of ce llulosic ethanol production.

We are also examining ways to incorporat e this excess electricity co-product offset evaluation in our secondary energy sector modeling with the NEMS model. This would be more consistent with our treatment of corn ethanol and biodiesel co-produc ts where we expand the system boundaries to include the specific use of the co-products in an economic sector model. We will be examining this approach for or final rule analysis.

2.6.7.4 Brazilian Sugarcane Ethanol

Processing energy use for sugarcane ethanol wa s modeled similar to cellulosic ethanol in that it can produce excess electricity from burni ng bagasse. The assumpti on was that Brazilian sugarcane ethanol would produce 1.78 kWh of excess electricity per gallon of ethanol produced. The excess electricity from ethanol production was assumed to offset Brazilian grid electricity to calculate GHG emissions savings.

2.6.8 Fuel Transport

The default values in GREET for ethanol transportation and modes were used to represent transport of ethanol fr om the plant to the retail location. These default values are shown in Table 2.6-42.

401Table 2.6-42. GREET Ethanol 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

Typically if ethanol facilities are located within 100-200 miles of a terminal, trucking is preferred. Rail and barge are used for longer di stances. Pipelines are not currently used to transport ethanol and are not project ed to play a role in ethanol transport in the future time frame considered.

For biodiesel transport, the values assumed in the RFS1 analysis were used to represent biodiesel transport. The assumptions were 22% of biodiesel shipped by barge and 78% shipped by rail. The GREET default distances for biodiesel ra il and barge transport as well as terminal to station assumptions are consistent with ethanol transportation and distribution assumptions and were used in this analysis.

As with feedstock transport we have conducted a more detailed analysis of fuel transport and distribution impacts for use in criteria pollu tant inventories (see DRIA Chapter 1.6) and for our cost analysis described in DR IA Chapter 4.2. Due to the timing of this analysis we were not able to incorporate the results in our lifecycle calculation but plan to do that for the final rule.



### 2.7 Tailpipe Combustion

Combustion CO 2 emissions for ethanol, biomass-ba sed diesel, petroleum diesel and gasoline were based on the carbon content of the fuel. However, over the full lifecycle of the fuel, the CO 2 emitted from biomass-based fuels combustion does not increase atmospheric CO 2 concentrations, assuming the biogenic carbon emitted is offset by the uptake of CO 2 resulting from the growth of new biomass. As a result, CO 2 emissions from biomass-based fuels combustion are not included in their lifecycle em issions results. Net carbon fluxes from changes in biogenic carbon reservoirs in wooded or crop lands are accounted for separately in the land use change analysis as outlined in the agricultural sector modeling above.

When calculating combustion GHG emissions, however, the CH 4 and N 2 O emitted during biomass-based fuels combustion are included in the analysis. Unlike CO 2 emissions, the combustion of biomass-based fuels does result in net additions of CH 4 and N 2 O to the atmosphere. Therefore, combustion CH 4 and N 2 O emissions are include d in the lifecycle GHG emissions results for biomass-based fuels.

Combustion related CH 4 and N 2 O emissions for both biomass-based fuels and petroleumbased fuels are based on EPA MOVES model results. The values used are s hown in Table 2.7-1. CO2 emissions from biofuels are shown for illustrative reasons, but as mentioned above are not

402included in the analysis because they are assu med to be offset by carbon uptake from plant growth.

Table 2.7-1. Tailpipe Combustion Emissions for Bio and Petroleum Based Fuels
CO2 CH4 N2O Fuel Type (g/mmBtu) (g/mmBtu) (g/mmBtu)
Ethanol 75,250 269 611
Biodiesel 81,044 11 689

Gasoline 77,567 67 1,659
Diesel Fuel 78,761 11 689



### 2.8 Fuel-Specific Lifecycle Green house Gas Emissions Results

2.8.1 Renewable Fuel Payback Periods and Lifecycle GHG Emissions Results

In this section we present detailed lifecycl e GHG analysis results, in cluding the results of sensitivity and scenario analyses on key assump tions. As discussed above, to implement the EISA the crucial result that determines which re newable fuel pathways qualify for RFS2 credits is the percent reduction in lifecycle GHG emissions compared to the average lifecycle greenhouse gas emissions for gasoline or diesel sold or distributed as tran sportation fuel in 2005. To compare lifecycle GHG emissions from renewabl e fuels and petroleum, we present the grams of CO2-equivalent emissions per Btu of fuel produced (gCO2eq/mmBtu). The previous sections in this Chapter discussed our methodology fo r calculating lifecycle GHG emissions for each component of the renewable fuel lifecycle, and for the 2005 petroleum baseline. In this Section we present and compare the GHG emissions result s for each of these components in the fuel lifecycle. We also discuss how key assump tions can change the GHG emissions from each component of the fuel lifecycle, and how they influence the final GHG percent reduction estimates.

In addition to estimating GHG emissions at every stage of the fuel lifecycle, EPA’s task in this rulemaking is to integrat e the GHG emissions estimates from all stages of the lifecycle in order to estimate lifecycle GHG percent reductions for each renewable fuel pathway. We have considered a number of ways to meet this challenge, and have identified several key methodological issues that can in fluence whether a particular re newable fuel pathway meets the thresholds set forth in the EISA. For example, one issue that deserves attention is the timing of lifecycle GHG emissions.

Section 2.6.5.5 explained that the lifecycle GHG emissions associated with biofuels can vary over time. Clearing forests, grasslands, and othe r types of land that sequester carbon, for crop production can results in GHG emissions for many years. As depicted in Figure 2.6-19, this type of land conversion produces large immedi ate GHG emissions, followed by a lesser stream of emissions that can last for many years. Biomass feedstocks grow n annually on new cropland can be converted to biofuels that offer a GHG benefit relative to th e petroleum product they

4032005 Gasoline Baseline

Corn Ethanol Lifecycle GHG Emissions (2022 NG Dry Mill)

Cumulative Net Corn Ethanol Life Cycl e GHG Emissions (2002 NG Dry Mill)

Year 0 Land Use •Clearing with fire •Biomass decay

•Soil carbon emissions •Foregone forest sequestration replace, but these benefits may be small compared to the upfront GHG emissions associated with land clearing to expand crop production. Dependi ng on the specific biofuel in question, it can take many years for the benefits of the biofuel to make up for th e large initial re leases of carbon that result from land conversi on (e.g., the payback period).

The payback period calculation, presented graphi cally in Figure 2.8-1, represents the time it takes for the emissions savings from the produc tion of biofuels to e qual the potentially large initial emissions from land use ch anges. Although we do not believ e it is appropriate to use the payback period for RFS2 compliance purposes, this calculation helps to i llustrate the importance of the time dimension of renewa ble fuel lifecycle GHG emissions.

Figure 2.8-1. Corn Ethanol Payback Period

-200,000 0 200,000 400,000 600,000 800,000 1,000,000 1,200,0002022203020402050GHG Emissions (gCO2-eq / mmBtu)

Years 1-19 Land Use

•Soil carbon emissions •Foregone forest sequestration Years 20-79 Land Use •Foregone forest sequestration

Payback ~33 years -200,000 0 200,000 400,000 600,000 800,000 1,000,000 1,200,0002022203020402050GHG Emissions (gCO2-eq / mmBtu) 2005 Gasoline Baseline

Corn Ethanol Lifecycle GHG Emissions (2022 NG Dry Mill)

Cumulative Net Corn Ethanol Life Cycl e GHG Emissions (2002 NG Dry Mill)

Year 0 Land Use •Clearing with fire •Biomass decay

•Soil carbon emissions •Foregone forest sequestration

Years 1-19 Land Use

•Soil carbon emissions •Foregone forest sequestration Years 20-79 Land Use •Foregone forest sequestration

Payback ~33 years

Figure 2.8-1 shows the lifecycle GHG emissions from corn ethanol produced with a 2022 technology natural gas fired dry mill, and from th e 2005 gasoline baseline. In the first year, in this case 2022, corn ethanol lifecycle GHG emissi ons are more than ten times greater than the gasoline it replaces. However, corn ethanol ha s ongoing GHG benefits in ev ery subsequent year. It takes approximately 33 years for the annual GHG benefits of corn ethanol compared to gasoline to pay back the initial GHG releases from land clearing. This te lls us that unless we analyze the lifecycle GHG emissions of corn ethanol over more than 33 years, corn ethanol from this pathway will not achieve a reduction compared to gasoline. As we extend our analysis beyond 33 years we will see increas ing GHG reductions associated w ith the use of corn ethanol.

As discussed in Section 2.6.5.5, economic th eory suggests that in general consumers have a time preference for benefits received today vers us receiving them in the future. Therefore, future benefits are often valued at a discounted rate. If we apply this concept to GHG emissions, the GHG reduction benefits achieved by corn ethanol in future years carries less weight than earlier emissions. Figure 2.8-2 il lustrates that the payback peri od is significantly longer if we use a two percent or thre e percent discount rate.

4042005 Gasoline Baseline

Corn Ethanol Lifecycle GHG Emissions (2022 NG Dry Mill)

Cumulative Net Corn Ethanol Life Cycle GHG Emissions (2002 NG Dry Mill), r=0%

Cumulative Net Corn Ethanol Lifecycle GHG Emissions (2022 NG Dry Mill), r 2%

Cumulative Net Corn Ethanol Lifecycle GHG Emissions (2022 NG Dry Mill), r 3% Figure 2.8-2. Corn Ethanol Payback Period with Different Discount Rates

-100,000 100,000 300,000 500,000 700,000 900,000 1,100,000202220302040205020602070208020902100GHG Emissions (gCO2-eq / mmBtu) =

=

Note: Annual emissions discounted 3%

-100,000 100,000 300,000 500,000 700,000 900,000 1,100,000202220302040205020602070208020902100GHG Emissions (gCO2-eq / mmBtu) 2005 Gasoline Baseline

Corn Ethanol Lifecycle GHG Emissions (2022 NG Dry Mill)

Cumulative Net Corn Ethanol Life Cycle GHG Emissions (2002 NG Dry Mill), r=0%

Cumulative Net Corn Ethanol Lifecycle GHG Emissions (2022 NG Dry Mill), r=2%

Cumulative Net Corn Ethanol Lifecycle GHG Emissions (2022 NG Dry Mill), r=3%

Note: Annual emissions discounted 3%

r = 0%Pa y back

~ 33 y ears r = 2% P a y back ~4 7 y ear s r = 3% P a y back

~ 69 y ear s

The payback period concept helps to dem onstrate the importance of the choice of a discount rate and time horizon for this analysis. These factors are so im portant because of the variation in GHG emissions from renewable fuels over time, and the contrasting steady annual emissions from the petroleum baseline. It is difficult to compare these two very different emissions profiles unless we put them on an equa l footing. To do this, we compared the net present value (NPV) of renewable fuels emission s and the NPV of emissions from the petroleum baseline. Using NPVs to compare streams of cost s and benefits is a well established practice for cost-benefit analysis. Similarly, a net present value appr oach is commonly used to assess GHG emissions. W WWWWWW The NPV of lifecycle GHG emissions is the sum of all terms:

GHG t / (1 + r)^ t , where

t = the year, where year 0 is the first year of analysis r = the discount rate GHG t = the net flow of GHG emissions in year t

In the preamble for the proposed rule, we highlig ht results that use a discount rate of two percent (i.e., r=2%), with a 100 year time horizon (i.e. T=100), and a 0% discount rate (i.e., r=0%) with a 30 year time horizon (T=30). Fi gure 2.8-3 includes lifecycle GHG results using a 100-year time horizon and a 2% discount rate for a typical dry mill corn ethanol facility fired with natural gas.

WWWWWWW See for example The Stern Review of the Economics of Climate Change, October 2006, http://www.occ.gov.uk/activities/stern.htm

405Figure 2.8-3. Corn Ethanol Lifecycle GHG Results (NPV, r=2%, T=100 years)GHG Emissions (gCO2-eq / mmBtu) 5,000,000

4,000,000

3,000,000

2,000,000

1,000,000

0

-1,000,000 -16%

2005 Gasoline Baseline 2022 Base Dry Mill NG Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O

Other (fuel and feedstock transport)

Domestic Soil Carbon

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

Net Emissions

The NPV of gasoline GHG emissions over 100 y ears is presented on the left side of Figure 2.8-3. The chart shows that with a 2 pe rcent discount rate the 10 0-year NPV of gasoline lifecycle GHG emissions is 4,240,674 gCO2eq/mmBtu. Tailpipe emissions make up over 80% of the lifecycle GHG emissions from gasoline. The rest of gasoline’s lifecycle GHG emissions occur during the fuel production process, which includes both upstream and downstream production processes.

The lifecycle GHG emissions from corn ethano l appear on the right- hand side of Figure 2.8-3. With a 2 percent discount rate the 100 ye ar net present value of lifecycle GHG emissions from corn produced in a typical natural gas fired dry mill is 3,563,294 gCO2eq/mmBtu. This represents a 677,379 gCO2eq/mmBtu benefit compared to the gasoli ne baseline, or a 16 percent lifecycle GHG reduction.

As discussed above in Section 2.7, tailpipe emissions are almost negligible for corn ethanol, because the carbon emitted upon ethanol co mbustion was sequestered when the corn was growing in the field. The result is a ne t carbon emission of zero, and small emissions of non-CO2 GHGs.

406Fuel production accounts for almost 40% of th e net emissions from corn ethanol. The fuel production emissions presented in Figure 2. 8-3 correspond with a typical dry mill corn ethanol production facility that uses natural ga s and electricity for en ergy, and produces dried DGs. The ethanol production facility in this example has the projec ted efficiency of a typical dry mill in 2022. Emissions associated with transp ortation and distribution of feedstocks to the ethanol plant, and ethanol to the end consumer, account for almost 5 percent of corn ethanol’s net lifecycle GHG emissions.

As discussed in detail above, corn production requires land, fertiliz er, pesticides, energy use, and other inputs that produce GHG emissions . However, using more corn for ethanol has other significant indirect impacts that ripple through the domestic agricultural sector. Many of the secondary agricultura l sector impacts have significant GHG emissions implications. For example, as corn prices increase, corn-inten sive livestock producti on tends to decrease and methane emissions from cattle also drop. As Figure 2.8-3 shows, we estimated that using more corn for ethanol results in net reductions of GHG emissions from domestic livestock, domestic farm inputs and nitrous oxide, and domestic rice methane emissions. Domestic soil carbon emissions increase modestly, but the net effect is a reduction in GHG emissions from domestic agriculture. Foreign ag riculture is also a significant sour ce of GHG emissions. As a result of using more U.S. corn for ethanol, we project th at crop and livestock pro duction will increase in foreign countries, with corresponding GHG emissions..

Figure 2.8-4 includes NPV lifecycle GHG resu lts for four types of corn ethanol production facilities, and fo r ethanol made from othe r types of feedstocks.

Figure 2.8-4. Ethanol Lifecycle GHG Results (NPV, r=2%, T=100 years)

6,000,000

-115%

-128% -44% -39% 13%

-39%* -16%

2005 Gasoline Baseline 2022 Base Dry Mill NG 2022 Best Case Dry Mill NG 2022 Base Dry Mill Coal 2022 Base Dry Mill Biomass Sugarcane Ethanol Switchgrass Ethanol Corn Stover Ethanol

CORN ETHANOL ADVANCED ETHANOL CELLULOSIC ETHANOL Fuel Production

International Land Use Change 5,000,000 International Farm Inputs and Fert N2O Other (fuel and feedstock 4,000,000 transport)
Domestic Soil Carbon

3,000,000 International Livestock

International Rice Methane
2,000,000 Tailpipe

Domestic Rice Methane 1,000,000

Domestic Farm Inputs and Fert N2O

0 Domestic Livestock

20% Reduction Threshold
-1,000,000 50% Reduction Threshold

60% Reduction Threshold -2,000,000

Net EmissionsGHG Emissions (gCO2-eq / mmBtu)

Note: “Best case” plants include CHP, fractionation, memb rane separation and raw starch hydrolysis technologies

407A “best case” corn ethanol plant fired with natural gas achieves a 39% reduction compared to gasoline. The best case plant is equipped with advanced t echnologies that can be deployed in tandem: combined heat and power (CHP), fractionation, membrane separation and raw starch hydrolysis. Details about these fuel production technologies are discussed in Section 2.6.7. The results in Figure 2.8-4 suggest that the energy efficiency of future corn production facilities could be an important determinant of which production pathways meet the thresholds specified in the EISA.

Figure 2.8-4 shows that imported sugar cane ethanol achieves a 44 percent reduction compared to the gasoline baseline, which is close to the 50 percent reduction threshold for advanced renewable fuels and exceeds the minimum adjusted threshold of 40 percent. Sugarcane ethanol has lower lifecycle GHG emissions than corn ethanol for a number of reasons. Converting sugarcane to ethanol is less energy intensive than the conversion process with corn. Standard sugarcane ethanol pr oduction facilitie s burn bagasse X XXXXXX for energy, which shrinks their carbon footprints. Interna tional land use change emissions associated with sugarcane ethanol production are about 10 pe rcent less than intern ational land use change emissions from corn ethanol. Sugarcane ethanol al so has benefits compared to corn ethanol in the international rice methane, international livestoc k, and other emissions categories.

The cellulosic ethanol pathways presented in Figure 2.8-4, corn stover and switchgrass ethanol, both achieve greater than 100 percent lifecycle GHG reductions compared to gasoline. In fact, as the figure shows, producing ethanol from these pathways creates a net abatement of GHG emissions. A major source of the emissions mitigation in the cellu losic ethanol lifecycle occurs at the fuel production stage. The cellulo se and lignin in switchgrass and corn residue are separated with an enzymatic process. The cellu lose is fermented to produce ethanol, leaving lignin as a by-product which can be burned for heat and electricity. Our process modeling suggests that by burning lignin, cellulosic ethanol production f acilities utilizing an enzymatic conversion process will generate mo re electricity than they need. These facilities will be net exporters of electricity to the grid, offsetting an average mix of grid electricity. Switchgrass ethanol has modest impacts on in ternational land use and crop pr oduction, because it competes with other crops for land.

In our results ethanol from switchgrass achieves the la rgest lifecycle GHG reductions compared to petroleum. The results include a la rge decrease in foreign livestock emissions as a result of switchgrass ethanol production. However, th is result may be an artifact of the imperfect way that switchgrass was repr esented in the FAPRI model. Y YYYYYY As we refine this estimate for the final rule, we might see a different ou tcome for international livestock emissions. Switchgrass land use change emissions are rough ly one-fifth as larg e as land use change emissions from corn ethanol because switchgrass can be produced on less fertile lands, thus displacing less commodity crop production. However, this estimate is also subject to change as we refine our modeling of switc hgrass with the FAPRI model.

XXXXXXX Bagasse is the biomass remaining after sugarcane stalks are crushed.
YYYYYYY When we conducted our analysis for the proposed rule the FAPRI model did not explicitly include switchgrass cultivation. As a proxy for switchgrass we modeled an increase in domestic land enrolled in the Conservation Reserve Program (CRP).

408Biodiesel Lifecycle GHG Results (NPV, r=2%, T=100 years)

5,000,000 Fuel Production

International Land Use Change

3,000,000 4,000,000mmBtu) -22% International Farm Inputs and Fert N2O

Other (fuel and feedstock transport)

Domestic Soil CarbonO2-eq /
International Livestock

2,000,000s (gC International Rice Methane

Tailpipe

0 1,000,000GHG Emission

-80% Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

50% Reduction Threshold

-1,000,000 Net Emissions Corn stover is a waste product that does not displace land from commodity crop production. In fact, using stove r for ethanol creates a market for corn residue, making corn farming a more profitable enterprise. Corn resi due is a source of nitrogen, so removing it from the field requires additional fe rtilizer application, which is w hy we see a modest increase in emissions from domestic farm inputs. On the other hand, farmers must employ no-till farming practices in order to harvest stover and sell it to ethanol. The spread of no-till farming has soil carbon benefits that show-up in Figure 2.8-4.

Biodiesel lifecycle GHG emissions results are pr esented in Figure 2.8-5. Over a 100-year time horizon soy-based biodiesel achieves a 22% lifecycle GHG reduction, and biodiesel derived from waste grease achieves a larg er lifecycle GHG reduction. We did not include upstream or indirect impacts in the waste grease biodiesel lifecycle. Upstream emissions were fully attributed to the first use of th e oil; not to the residual waste grease. Although indirect impacts are not included in the results presented here, we intend to conduct a closer study of the potential for indirect impacts from using waste grease for biodiesel, instead of usi ng it for other purposes. This will include further review of waste grease supply and demand projections.

Figure 2.8-5.

2005 Diesel Baseline Soy Biodiesel Waste Grease

Figure 2.8-6 illustrates the effect of the choice of discount rate on lifecycle GHG results. As the discount rate increases , the NPV of non-land use change emissions decrease. This happens because a higher discount rate diminishes the relative valuation of emissions that occur

409in the future. On the other hand, the NPV of emissions from of land use changes are less affected by the choice of discount rate. This is because, as displayed in Figure 2.8-1, a large share of international land use change emissions occur in the first year. Therefore, for renewable fuels that include international land use cha nge emissions, increasing the discount rate will reduce their lifecycle GHG percent reduction compared to petroleum. In Figure 2.8-6, going from a 0% to 7% discount rate changes the lifecycle GHG emissi ons of biodiesel compared to average diesel from a 48% reduction to a 51% increase.

Figure 2.8-6 Biodiesel Lifecycle GHG Result s with Different Discount Rates (NPV, r=0%, 2%, 3%, 7%, T=100 years)GHG Emissions (gCO2-eq / mmBtu) 12,000,000

10,000,000

8,000,000

6,000,000

4,000,000

2,000,000

0 Non-Land Use Change Land Use Change Net Emissions

-48%

2005 Diesel

Baseline Soy-Based Biodiesel

0% DISCOUNT RATE -22%

2005 Diesel

Baseline Soy-Based Biodiesel

2% DISCOUNT RATE -7%

2005 Diesel

Baseline Soy-Based Biodiesel

3% DISCOUNT RATE 51%

2005 Diesel

Baseline Soy-Based Biodiesel

7% DISCOUNT RATE

When lifecycle emissions vary over time—as th ey do for biofuels that induce indirect land use change—the choice of tim e horizon is essential. Figure 2.8-7 includes corn ethanol lifecycle GHG results using a 30-year time horizon and a 0% discount rate. This figure is similar to Figure 2.8-4 except for the time horizon. For corn ethanol produced in a typical natural gas fired dry mill, decreasing the time horizon from 100 to 30 years and shifting the discount rate from 2% to 0% changes the lifecycle GHG results from a 16 percent decr ease to an 5 percent increase compared to gasoline.

410-18% +34%

-18% +5%

-1,000,000 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000GHG Emissions (gCO2-eq / mmBtu) Figure 2.8-7. Corn Ethanol Lifecycle GHG Results (NPV, r=0%, T=30 years)

Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O
Other (fuel and feedstock transport)

Domestic Soil Carbon

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

20% Reduction Threshold

Net Emissions

2005 Gasoline 2022 Base Dry Mill 2022 Best Case Dry 2022 Dry Mill Coal 2022 Dry Mill

Baseline NG Mill NG Biomass

Figure 2.8-8 includes lifecycle GHG results for corn ethanol produced in a natural gasfired dry mill over a continuum of time horizons. The horizontal axis is the choice of time horizon. As discussed above, our results indicate that the payback period for a standard corn ethanol pathway is approximately 33 years. With a zero percent discount (the blue line in Figure 2.8-8) corn ethanol increases GHG emissions by 5 percent over 30 years, and reduces emissions by 17 percent and 36 percent over 50 and 100 years re spectively. With higher discount rates, it takes longer for the future benefits of corn ethanol production to payb ack earlier land clearing emissions. When we use a discount rate greate r than zero, future be nefits are discounted, causing the curves in Figure 10- 8 to flatten out over time.

411Figure 2.8-8. Lifecycle GHG Results for Corn Ethano l Produced in a Natural Gas Dry Mill (Percent Change from Gasoline with Diff erent Discount Rates and Time Horizons)

-36% 18%

7%

-17% 5%

-2%

-16% 25%

-5%

-40% -20%

0% 20% 40% 60% 80% 100%Lifecycle GHG Percent Change From Gasoline Baseline Corn Ethanol Base Plant, 0% Discount Rate

Corn Ethanol Base Plant, 2% Discount Rate

Corn Ethanol Base Plant, 3% Discount Rate

30, 50 & 100 Year Results, r=0%

30, 50 & 100 Year Results, r=2%

30, 50 & 100 Year Results, r=3%2022204020702100

2.8.2 Land Use Change Sensitivity Results

In our results, emissions from land use cha nges are a significant source of the lifecycle GHG emissions for renewable fuels produced with f eedstocks that can be used in food products or grown on fertile land. As discussed above, we used a number of agricultural and economic models to estimate the land use impacts of these fuels. We used the FASOM model to estimate GHG emissions from land use changes in response to renewable fuel production. With the FAPRI model we projected which foreign countries would expand crop production as a result of the RFS2 program. We then used satellite da ta from 2001 to 2004 to project where cropland is likely to be added within forei gn countries, and which types of la nd are cleared to make room for new crop areas. Emissions factors developed by the non-profit Winrock International, using 2006 IPPC guidelines, were applied to estimate the GHG emissions associated with foreign land use change. This approach for estimating la nd use change GHG emissions used the best available tools. However, even with the best available tools, projecting how a U.S. policy will effect international land use change is a task wi th inherent uncertainty. For this reason we identified key parameters within our land us e change projections a nd conducted selective sensitivity analysis.

2.8.2.1 Types of Land Converted to Cropland Sensitivity Analyses

One of the most important questions that determine the magnitude of land use change GHG emissions is what types of land are cleared wh en new cropland is added. This question is significant because clearing forest to expand cr opland produces more GHG emissions per acre

412than clearing grassland, or ot her types of land that sequester less carbon. For example, we estimate that in Brazil an averag e acre of forest contains almost 10 times more carbon than an average acre of grassland. To address uncertainty, we conducted sensitiv ity analyses where we varied the types of land that are cleared inte rnationally as a result of biofuel production.

A common input from stakeholders has been th eir belief that in many countries, such as Brazil, there is ample abandoned cropland, idle grassland and degraded pasture to accommodate crop expansion induced by biofuel production. The MODIS satellite data s uggests that if recent trends persist, crop expansion would occur on land previously inha bited by a variety of ecosystem types. However, future economic conditions or government policies could dictate that cropland expansion would occur excl usively on idle grassland. We did a sensitivity analysis that assumes that all new cropland occurs on idle grassland, which also implies no pasture replacement is necessary. Z ZZZZZZ Figure 2.8-9 shows that th e assumption that all cropland expansion occurs on idle grassland has a dramatic impact on GHG emissions per acre. This assumption has a particularly large impact on the weighted average emissions factors for South East Asian countries that have a large portion of forest to cropland c onversion in our central estimates for the proposed rule.

Figure 2.8-9. GHG Emissions per Acre of Cropland Expansion (NPV, r=2%, T=100 years)
Primary Estimate Forest Only Grassland Only 500GHG Emissions per Acre (MTCO2eq / mmBtu)
450

400

350

300

250

200

150

100

50

0 Argentina Brazil China E.U. India Indonesia Malaysia Nigeria Philippines South Africa

Tables 2.8-1 and 2.8-2 show how EPA’ s lifecycle GHG emission estimates vary by renewable fuel. Table 2.8-1 includes results using a 100-year time horizon and a 2% discount rate. Table 2.8-2 includes re sults using a 30-year time horizon and a 0% discount rate. Our primary estimates include GHG emissions from forest and grassland clearing (proportionally estimated based on MODIS satellite data) resulti ng from the use of agricultural products for

ZZZZZZZ See Section 2.6 for a discu ssion of pasture replacement

413biofuel production. A AAAAAAA The tables also show how th e percent reduction estimates would vary if we assumed that all new cropland woul d come from forest, or all from grasslands.

Table 2.8-1. Lifecycle GHG Results 100-Year Time Horizon wi th 2% Discount Rate

GHG Emissions Reductions Relative to the 2005 Petroleum Baseline Land Use Change Forest Only Primary Estimate Grassland Only Corn Ethanol (Natural Gas Dry Mill) +20% -16% -48% Corn Ethanol (Coal Dry Mill) +48% +13% -19% Corn Ethanol (Biomass Dry Mill) -4% -39% -71% Soy-Based Biodiesel +15% -22% -65% Waste Grease Biodiesel -80% -80% -80% Sugarcane Ethanol -23% -44% -70% Swtichgrass Ethanol -118% -128% -135% Corn Stover Ethanol -115% -115% -115%

Table 2.8-2. Lifecycle GHG Results 30-Year Time Horizon with0% Discount Rate
GHG Emissions Reductions Relative to the 2005 Petroleum Baseline Land Use Change Forest Only Primary Estimate Grassland Only Corn Ethanol (Natural Gas Dry Mill) +49% +5% -38%
Corn Ethanol (Coal Dry Mill) +78% +34% -9% Corn Ethanol (Biomass Dry Mill) +26% -18% -61%
Soy-Based Biodiesel +49% +4% -54%
Waste Grease Biodiesel -80% -80% -80% Sugarcane Ethanol -4% -26% -60% Swtichgrass Ethanol -113% -124% -134%
Corn Stover Ethanol -116% -116% -116%

As discussed in the preamble, in the case of sugarcane production in Brazil, a number of researchers argue that expanded sugarcane production is likely to occur in significant part through the use of degraded or abandoned pastur e land without additional land use impact in Brazil. B BBBBBBB If these researchers are correct, then we estimate that sugarcane ethanol produced in Brazil would produce a 64% lifecy cle GHG emissions reduction compared to gasoline with a 100-year time horizon and a 2% discount rate, or a 52% lifecycle GHG reduction with a 30-year time horizon and a 0% discount rate.

AAAAAAAA Per acre land use change emissions factors were estimated by Winrock International Inc., following 2006 Intergovernmental Panel on Climate Change guidelines
BBBBBBBB See for example “Mitigation of GHG emissions using sugarcane bio-ethanol - Working Paper” by Isaias C. Macedo and Joaquim E. A. Seabra, and “Prospects of th e Sugarcane Expansion in Brazil: Impacts on Direct and Indirect Land Use Changes – Working Paper” by Andre Nassar et al, both received by EPA October 13, 2008

4142.8.2.2 Foregone Forest Sequestra tion Sensitivity Analysis

Section 2.6 discussed our primary approach for estimating foregone forest sequestration when forests are converted to cropland. As disc ussed in that section, foregone sequestration was estimated using average annual biomass accumulation rates for forests greater than 20 years old given in Table 4.9 of the 2006 GL AFOLU accordi ng to ecological zone and continent. These values were used to estimate the lost bioma ss accumulation of forests that were converted to another land use. Foregone sequestration was as sumed to continue at a constant rate for 80 years. Studies have estimated that new forests grow for 90 years to over 120 years. C CCCCCCC More recent estimates suggest that old growth forests accu mulate carbon for up to 800 years. 538 Our proposed estimate that foregone sequestration continues for 80 years is within the range supported by the scientific literature and the 2006 IPCC guidelines. However, there are other reasonable approaches that c ould have been used to estimate foregone sequestration.

Given the wide range of estimates about how long forests continue to accumulate carbon it would not be unreasonable to assume that we should account for more than 80 years of lost sequestration. On the other hand, if we expect that very old forests would be converted to cropland, it would also be reason able to assume a shorter timeframe for lost sequestration, or no lost sequestration at all. Fi gure 2.8-10 shows how using differ ent assumptions about the length of foregone sequestration affects weighted aver age emissions factors for forest to cropland conversion in Brazil.

CCCCCCCC See Greenhouse Gas Mitigation Pote ntial in U.S. Forestry and Agriculture, EPA Document 430-R-05-006 for a discussion of the time required for forests to reach carbon saturation.

415Figure 2.8-10. Weighted Average Emissions per Acre of Fo rest to Cropland Conversion in Brazil Different Lengths of Foregone Sequestration (NPV, r=2%, T=100 years)

157 165

103

65

0 20 40 60 80 100 120 140 160 180MTCO2-eq / Acre Forest to Cropland Conversion

80 Years of Foregone 100 Years of Foregone 20 Years of Foregone No Foregone Sequestration Sequestration Sequestration Sequestration

Figure 2.8-11 includes lifecycle GHG results for soy-based biodies el with different assumptions about the length of foregone fore st sequestration. The length of foregone sequestration is an important part of international land use change emissions, but even if we assume no foregone sequestration, our results sugge st that soy-based biod iesel does not achieve a 40 percent lifecycle GHG reduction comp ared to average diesel fuel.

416Figure 2.8-11. Soy Biodiesel Lifecycle GHG Results – Foregon e Sequestration Length Sensitivity (NPV, r=2%, T=100 years)GHG Emissions (gCO2-eq / mmBtu) 5,000,000

4,000,000

3,000,000

2,000,000

1,000,000

0

-1,000,000 -37% -31% -21% -22%

2005 Diesel

Baseline 80 Years Foregone Sequestration 100 Years Foregone Sequestration 20 Years Foregone Sequestration No Foregone Sequestration

SOY-BASED BIODIESEL Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O

Other (fuel and feedstock transport)

Domestic Soil Carbon

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

50% Reduction Threshold

Net Emissions

As discussed in detail in S ection 2.6.5, annual foregone se questration was calculated as:

lostseq C Δ =

, ( LUbefore i

Cseq 

# ∑

, ) LUafter i Cseq

−
i where:

lostseq Δ C = change in carbon sequestrati on in aboveground biomass t C ha -1 yr -1

-1 Cseq = annual sequestration in abovegroun d biomass before conversion t C ha -1 yr LUbefore i

, Cseq = annual sequestration in abovegroun d biomass after conversion t C ha -1 yr -1 LUafter i

, i = type of land use converted to another land use category.

Table 4.9 of the 2006 GL AFOLU was used to estimate the annual sequestration in aboveground forest biomass. The 2006 GL AF OLU provides a sequestration range for typical forest types by continent and ecological zone. Th e area of forest in each state was stratified by FAO ecozone and continent. This calculation is consistent with 2006 IPCC guidelines, but there are other reasonable methodologies available to estimate foregone sequestration. For example, foregone sequestration could be estimated as a percentage of a forest’s initial aboveground biomass. As explained in Section 2.6, we used regional biomass maps to estimate forest carbon stocks in each state. Instead of using Ta ble 4.9 of the 2006 GL AFOLU to estimate annual foregone sequestration, other approaches th at were recommended by Winrock included calculating annual lost sequestrati on as one or two percent of th e initial biomass in cleared forests. Figure 2.8-12 compares lifecycle GHG re sults for soy-based biodiesel using our primary estimate of foregone sequestration, and two other estimates that use one percent and two percent of initial forest biomass.

417GHG Emissions (gCO2-eq / mmBtu) 5,000,000

4,000,000

3,000,000

2,000,000

1,000,000

0

-1,000,000 Figure 2.8-12 Soy-Based Biodiesel Results Different Foregone Sequestration Calculation Methods (NPV, r=2%, T=100 years)

-37% -18% -27% -22%

2005 Diesel

Baseline Primary Foregone Sequestration

Method Foregone Seuqestration =

1% of Initial Aboveground

Biomass Foregone Seuqestration =

2% of Initial Aboveground

Biomass No Foregone Sequestration

SOY-BASED BIODIESEL Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O

Other (fuel and feedstock transport)

Domestic Soil Carbon

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

50% Reduction Threshold

Net Emissions

2.8.2.3 Soil Carbon Emissi ons Sensitivity Analysis

Land in annual crop production holds less so il carbon than lands with undisturbed root systems. A digital soil carbon map of the worl d available from USDA was used to estimate soil carbon by region. 539 Carbon stocks on annual cropland were estimated as 5 metric tons of carbon per hectare based on Table 5.9 of the IP CC AFOLU. Changes in soil carbon stocks on land converted to cropland were calculated ba sed on Section 5.3.3.4 of the IPCC AFOLU. Following the IPCC guidelines, the total differen ce in soil carbon stocks before and after conversion was averaged over 20 years. (See Se ction 2.6 for details about methods used to estimate changes in soil carbon stocks).

During the proposed rule making process, EPA received feedback from stakeholders that soil carbon emissions could occur more rapidly th an 20 years. To gauge the importance of the timing of soil carbon emissions on our lifecycle, we calculated re sults assuming that soil carbon emissions occur immediately when land is plow ed in preparation fo r crop production. The timing of soil carbon emissions could influence overall lifecycle GHG results, because, using a NPV approach, near term emissions are weighted more heavily than emissions further into the future. Figure 2.8-13 shows th at the timing of soil carbon emi ssions has a modest impact on lifecycle GHG results for sugarcane ethanol.

4184,500,000 Figure 2.8-13. Sugarcane Ethanol Results Soil Carbon Emissions Sensitivity (NPV, r=2%, T=100 years)

Fuel Production

4,000,000 International Land Use Change

3,500,000 International Farm Inputs and Fert N2O

3,000,000 Other (fuel and feedstock transport)

Domestic Soil Carbon

2,500,000 International Livestock

2,000,000 International Rice Methane

1,500,000 Tailpipe

1,000,000 Domestic Rice Methane

500,000 Domestic Farm Inputs and Fert N2O

0 Domestic Livestock

-500,000 50% Reduction Threshold

Net EmissionsGHG Emissions (gCO2-eq / mmBtu)

-44% -42%

2005 Gasoline Baseline Immediate Soil C Emissions Soil C Emissions Over 20 Years

SUGARCANE ETHANOL

2.8.2.4 Pasture Replacemen t Sensitivity Analysis

We used a two step methodology to project bi ofuel induced land use change (See Section 2.6 for a detailed description). The first step in cludes land use changes associated with cropland expansion. The second step includes land shif ting to replace managed pastures that were converted to cropland during the first step. To illustrate the importance of the pasture replacement concept, Figures 2.8-14 and 2. 8-15 bound our primary corn ethanol lifecycle GHG results with results that do not include pasture replacement, and results th at replace all grassland and savanna (not just managed past ureland) converted to cropland. It is clear that interactions between crop expansion and pasturelands can ha ve a substantial impact on overall lifecycle GHG estimates. For the final rule we intend to c onduct additional analysis of the secondary land use effects of converting pa sture to crop production.

419GHG Emissions (gCO2-eq / mmBtu) Figure 2.8-14. Corn Ethanol Results Pasture Replacement Sensitivity (NPV, r=2%, T=100 years)

5,000,000

4,000,000

3,000,000

2,000,000

1,000,000

0

-1,000,000 -16% -2%

-34%

2005 Gasoline Baseline Managed Pasture Replacement 100% Pasture Replacement No Pasture Replacement

CORN ETHANOL - 2022 NG DRY MILL Fuel Production

International Land Use Change

International Farm Inputs and Fert N2O

Other (fuel and feedstock transport)

Domestic Soil Carbon

International Livestock

International Rice Methane

Tailpipe

Domestic Rice Methane

Domestic Farm Inputs and Fert N2O

Domestic Livestock

20% Reduction Threshold

Net Emissions

420GHG Emissions (gCO2-eq / mmBtu) Figure 2.8-15. Corn Ethanol Results Pasture Replacement Sensitivity (NPV, r=0%, T=30 years)

4,500,000

-19% +24%

+5%

2005 Gasoline Baseline Managed Pasture Replacement 100% Pasture Replacement No Pasture Replacement

CORN ETHANOL - 2022 NG DRY MILL Fuel Production

4,000,000 International Land Use Change

3,500,000 International Farm Inputs and Fert N2O

3,000,000 Other (fuel and feedstock transport)

2,500,000 Domestic Soil Carbon

International Livestock 2,000,000

International Rice Methane 1,500,000

Tailpipe 1,000,000

Domestic Rice Methane

500,000

Domestic Farm Inputs and Fert 0 N2O

Domestic Livestock -500,000

20% Reduction Threshold -1,000,000

Net Emissions

2.8.2.5 Year of Analysis Sensitivity Results

The EISA mandates increasing vo lumes of U.S. renewable fuel volumes, culminating in 36 billion gallons renewable fuel use in 2022. To estimate the full fuel-specific GHG impacts of the EISA mandates we used 2022 as the primary y ear for our analysis. To address whether the GHG emissions vary significantly over time, we also analyzed GHG emissions estimates in 2012 and 2017. As shown in Table VI.C.2-3, the GHG em issions reductions are fairly consistent over the time period of our analysis. There may be countervailing forces that stabilize land use change emissions over this period. Crop yields increase over time (therefore reducing land use pressure), but there is also more production of other renewable fuels that require land for feedstock production (therefore increasing land use pressure). The results for 2012 and 2017 used 2022 domestic soil carbon emissions imp acts, because of issues with FASOM’s methodology for soil carbon projections. D DDDDDDD

DDDDDDDD As discussed in Section 2.6, we are updating FASOM’s domestic soil emissions estimates for the final rule, which should allow for distinct soil carbon estimates in 2012, 2017 and 2022.

421GHG Emissions (gCO2-eq / mmBtu) Figure 2.8-16. Corn Ethanol Results – Year of Analysis Sensitivity (NPV, r=2%, T=100 years)

-16% -13% -16%

2005 Gasoline Baseline 2022 2017 2012

CORN ETHANOL - NG DRY MILL Fuel Production

International Land Use Change

4,000,000 5,000,000

International Farm Inputs and Fert N2O
Other (fuel and feedstock transport)
3,000,000 Domestic Soil Carbon

International Livestock

2,000,000 International Rice Methane

Tailpipe

1,000,000 Domestic Rice Methane

Domestic Farm Inputs and Fert N2O 0 Domestic Livestock

20% Reduction Threshold

-1,000,000 Net Emissions

2.8.2.6 Fuel Volumes Mode led Sensitivity Analysis

The renewable fuel volume scenarios used fo r the proposed rule analysis are described above in Section 2.3.2. We modeled the differe nce between domestic fuel consumption without EISA and domestic fuel consumption with EISA . For corn ethanol, this meant modeling the difference between 12.4 and 15 billion gallons of domestic corn ethanol consumption in 2022. This approach allowed us to isolate the incremen tal impacts of the revisions to the RFS program. For the reasons discussed in the preamble for th e proposed rule, we beli eve this is the most relevant analytical approach for RFS2 implementa tion. However, we also tested the lifecycle GHG impacts of a larger change in corn ethano l volumes. For this sensitivity analysis we estimated lifecycle GHG impacts for an increase in ethanol consumption from 8.7 to 15 billion gallons in 2022.

Figure 2.8-176 includes lifecycle GHG estimates with a lower corn ethanol volume in the reference case. The internati onal land use emissions were very close between the two volume scenarios. In the lower volume reference case (i.e., 8.7 billion gallons in 2022), GHG emissions from domestic farm inputs such as fertilizer did not decrease as much as they did in our primary reference case. The largest di fference between the scenarios is that overall livestock GHG emissions increased when we used a lower refere nce case volume, which is counter to the overall livestock response in our primary estimate. We intend to explore the sensitivity of the fuel volumes modeled in more depth for the final rule.

422GHG Emissions (gCO2-eq / mmBtu) Figure 2.8-17. Corn Ethanol Results Fuel Volumes Modeled Sensitivity (NPV, r=2%, T=100 years)

5,000,000

-16% -6% Fuel Production

International Land Use Change

4,000,000 International Farm Inputs and Fert N2O

Other (fuel and feedstock transport) 3,000,000 Domestic Soil Carbon

International Livestock

2,000,000 International Rice Methane

Tailpipe

1,000,000 Domestic Rice Methane

Domestic Farm Inputs and Fert N2O 0
Domestic Livestock

20% Reduction Threshold -1,000,000 2005 Gasoline Baseline 12.4 to 15 Bgal in 2022 8.7 to 15 Bgal in 2022 Net Emissions
CORN ETHANOL - NG DRY MILL

2.8.3 Fuel-Specific Results Conclusions

In addition to providing lifecycle GHG emissi ons estimates, EPA’s lifecycle analysis for the proposed rule highlights the importance of several key elements:

• The time horizon for analysis;

• The choice of discount rate;

• The types of land cleared to make room for cropland expansion;

• Interactions between cropland expansion and pasture area; and

• The size of renewable fuel volume increases.

The proposed rule is an important opportunity for EPA to solicit feedback on these and other issues that will improve and solidify our analysis before final rulemaking. For more information about our lifecycle GHG analysis, we have placed spreadsheets w ith all of our calculations and results on the docket for this rulemaking.

423

### 2.9 Overall Lifecycle Greenhouse Gas Emissions Results of Rulemaking Volume

Our analysis of the overall GHG emission im pacts of this proposed rulemaking was performed in parallel with the lifecycle analys is performed to devel op the individual fuel thresholds described in previous sections. Th e same system boundaries apply such that this analysis includes the effects of three main areas: a) emissions related to th e production of biofuels, including the growing of feedstock (corn, soybeans, etc.) with associated domestic and international land use change impacts, transport of feedstock to fuel production plants, fuel production, and distribution of fi nished fuel; b) emissions rela ted to the extraction, production and distribution of petroleum gasoline and diesel fu el that is replaced by use of biofuels; and c) difference in tailpipe combustion of the renewabl e and petroleum based fuels. As discussed in the previous section we will be updating our lifecyc le approach for the final rule and there are some areas that we were not able to quantify at this time, such as secondary impacts in the energy sector. We are working to include this for our final rule analysis.

Consistent with the fuel vol ume feasibility analysis and criteria pollutant emissions evaluation, our analysis of the GHG impacts of this proposed rulemaking was conducted by comparing the difference between a 2022 referen ce case and a 2022 control case with volumes of renewable fuels meeting the RFS2 mandate. Si milar to what was done to calculate lifecycle thresholds for individual fuels we considered th e change in 2022 of these two volume scenarios of renewable fuels to determine overall GHG imp acts of the rule. The reference case for the GHG emission comparisons was taken from the AEO 2007 projected rene wable fuel production levels for 2022 prior to enactment of EISA. This scenario provided a point of comparison for assessing the impacts of the RFS2 standard vol umes on GHG emissions. We ran these multi fuel scenarios through our FASOM a nd FAPRI models and applied the Winrock land use change assumptions to determine to overall GHG impacts of producing this increase in renewable fuels.

The main differences between this overall im pacts analysis and the analysis conducted to develop the threshold values for the individual fuels were that we analyzed the total change in renewable fuels in one scenario as opposed to looking at indi vidual fuel impacts. When analyzing the impact of the total 36 billion gall ons of renewable fuel, we also took into account the agricultural sector interactions necessary to produce the full complement of feedstock. Figure 2.9-1 shows the different scenarios considered in this analysis.

424Figure 2.9-1. Fuel Volume Scenario Considered to Determine GHG Impact of Proposed Rulemaking

Biofuel Volumes in 2022

0 5 10 15 20 25 30 35 40

Reference Case Primary Volume Scenario Reference Case Primary Volume Scenario

FASOM FAPRIBillions of Gallons (Ethanol Equivalent) Imports FAPRI Imports FASOM Forestry
MSW
Cellulosic FAPRI
Cellulosic FASOM
Other Fats / Oils Soybean Oil
Other Grains
Corn

As discussed in Chapter 5, certain types of fuels and feedstocks are not specifically captured in the models, for example MSW and forest ry feedstocks are not explicitly modeled in FASOM while cellulosic ethanol in general is not explicitly modeled in FAPRI. Also, imports are not explicitly modeled in FASOM but are included in FAPRI modeling.

We also considered a mix of plant types a nd configurations for the 2022 renewable fuel production representing the mix of plants we project to be in operation in 2022. The following table shows the types of plants considered in the analysis for the references and control cases.

425Table 2.9-1. Types of Plants Considered in 2022

Plant Type Technology Volu
Reference Case me of Fuel (
Control

Case Bgal)

Difference

Corn Ethanol – Dry Mill Plant with NG (dry DG) Plant with NG (wet DG) Plant with coal (dry DG) Plant with coal (wet DG) Plant with biomass(dry DG) Plant with biomass (wet DG) 7.19

2.62

1.11

0.11
0.09

0.12 8.77
3.25

1.17

0.10
0.19

0.14 1.58

0.63

0.06

-0.01
0.09

0.01 Corn Ethanol – Wet Mill Plant with NG
Plant with coal 0.00

1.30 0.16

1.23 0.16

-0.06 Cellulosic Ethanol – Enzymatic Switchgrass feedstock & lignin used as fuel Corn stover feedstock & lignin used as fuel 0.00

0.25 1.28

8.80 1.28

8.55 Cellulosic Ethanol – Thermochemical Forest waste feedstock 0.00 5.92 5.92

Biodiesel Soybean oil feedstock Yellow grease / tallow feedstock 0.37

0.01 0.79

0.02 0.41

0.01 Renewable Diesel Yellow grease / tallow feedstock 0.00 0.38 0.38 Imports Brazilian Sugarcane Ethanol 0.64 3.14 2.50

The upstream feedstock production and processing impacts for each of the different fuel technologies were modeled based on the same a ssumptions used in determining the per fuel lifecycle GHG results described in previous sections.

For this overall impacts analysis we also used a different petroleum baseline fuel that is offset from renewable fuel use. The lifecycle th reshold values are required by EISA to be based on a 2005 petroleum fuel baseline. For this anal ysis of the overall im pacts of the rule we considered the crude oil and finished pr oduct that would be replaced in 2022.

Displaced petroleum product analysis was cons istent with work performed for the energy security analysis described in Chapter 15. For this analysis we consider that 25% of displaced gasoline will be imported gasoline and 0% of displaced diesel fuel will be imported diesel fuel.

For the types of gasoline displaced we assume 65% of the displaced gasoline will be conventional gasoline and 35% will be RFG ble ndstock gasoline. We assume 100% of the displaced diesel fuel will be low sulfur diesel fuel.

In order to come up with GHG emissions for average crude oil used in producing gasoline and diesel fuel in 2022 we assumed 7.6% would be from tar sands and 3.8% would be from Venezuelan heavy crude. The basi s for this was EIA projections for 2022 540 . EIA projects that roughly 64% of total Canadian crude oil production will be oil sa nd production in 2022, and that roughly 40% of total Ven ezuelan crude oil production will be heavy crude production in 2022. EIA also has assumptions on how much crude oil will be imported into the U.S. from

426Canada and Venezuela in 2022. We assumed the percentage of this imported Canadian and Venezuelan crude oil that would be oil sands and heavy oil was the same percentage of total production that is unconventional crude in thos e countries (~64% for Canada and ~41% for Venezuela). Based on the percent of Canadian and Venezuelan imports to total crude oil projected in 2022, oil sands repres ented 7.6% and heavy oil repres ented 3.8% of total crude oil

use.

For this analysis we did not assume any efficiency improvements at the petroleum refining portion of the gasoline and diesel fuel lifecycle. Theref ore the same refining energy use and emissions was assumed that was used to repr esent the 2005 petroleum fuel baseline. One the one hand this may be overestimating energy use and emission from petroleum refining, however, this also does not factor in recent regulations th at might increase energy use and emissions, such as increased desulfuriz ation of both gasoline and diesel fuel.

Furthermore, the tailpipe emissions changes were determined based on the specific volumes and blends of fuel considered as oppos ed to looking at only the difference between the renewable fuel and petroleum fuel replaced. For highway vehicles, the impact of this rule on Methane (CH4) and Nitrous Oxide (N2O) emissions is primarily du e to vehicles switching from gasoline to E85 fuel. Based on available data, we projected no change in N2O or CH4 emissions from highway vehicles that switched from conve ntional gasoline to E10. For diesel highway vehicles, emissions of N20 and CH4 are almost one hundred times less than emissions from gasoline vehicles, 541 thus diesels were omitted from this analysis.

E85 effects for this analysis based on avai lable emission data from EPA’s certification program, testing performed by EPA as part of th e Mobile Source Air Toxic (MSAT) rulemaking, and a recent study performed by Environment Cana da [insert reference]. This analysis found that on a per-vehicle basis using E85 resulted in a 56 percent d ecrease in N2O emissions and a 123 percent increase in CH4 emissions.

To estimate the inventory-wide impact, we used MOVES to model CH4 and N20 for highway gasoline vehicles using reference case fuels. Because MOVES does not vary CH4 and N20 emissions by temperature or by gasoline fuel properties, the model was run at the annual, national level. The control case was modeled by computing an E85 factor for each model year and vehicle type that was the product of the proportion of FFV fuel that is E85 and the proportion of FFVS in that model year, as described in Chapter 1. FFV use was assumed to be limited to light duty cars and light duty trucks. We multiplied the appropriate E85 factor by the emissions for that model year and then computed a weighted average of E85 and E10 emissions for both CH4 and N2O. In order to compare the results in a meaningful way, we also computed the CO2 equivalent by multiplying the tons for ea ch pollutant by the Global Warming Potential (310 for N2O, 21 for CH4 542 ) and summing the products. The re sults are summarized in Table 2.21 below.

427Table 2.9-2. Tailpipe Nitrous Ox ide and Methane Emissions in 2022

Pollutant Reference Case Tons Control Case Tons Percent Change LDGV & N2O 31,447 29,191 -7% LDGT CH4 50,683 61,853 22% CO2 equiv. 10,812,803 10,348,003 -4% All Gasoline N2O 33,997 31,741 -7% Highway CH4 55,277 66,447 20% Vehicles CO2 equiv. 11,699,809 11,235,009 -4%

Given these many differences, it is clearly not possible to simply add up the individual lifecycle results determined in Section 2.8 multi plied by their respective volumes to assess the overall rule impacts. The two analyses are sepa rate in that the overall rule impacts capture interactions between the different fuels but can not be broken out into per fuels impacts, while the threshold values represent impacts of specific fu els but do not account for all the interactions.

For example, when we consider the combined impact of the different fuel volumes the overall land use change is greater than when considering fuel i ndependently, as shown in Table 2.9-3.

Table 2.9-3. Comparison of International Land Use Change (‘000 Acres) Considering Only Change in Soybean Based Biodiesel Fuel Volumes Considering Only Change in Corn Ethanol Fuel Volumes Considering Only Change in Brazilian Sugarcane Ethanol Fuel Volumes Considering Change of all Fuel Volumes Combined Land Use Change 880 4,400 3,684 9,867

The primary reason for the difference in acre change between the sum of the individual fuel scenarios and the combined fuel scenarios is that when looking at in dividual fuels there is some interaction between different crops (e.g., corn replacing soybeans), but with combined volume scenario when all mandates need to be met there is less opportunity for crop replacement (e.g., both corn and soybean acres needed) and therefore more land is required.

Overall rule impacts were determined for the different componen ts of the lifecycle analysis as described in previous sections. The domestic agricultural sector impacts include changes in energy use GHG emissions and fertili zer / soil N2O emissions as well as changes in livestock and rice production GHG emissions.

Our analysis indicates that overall domestic agriculture emissions would increase. There is a relatively small increase in total domestic crop acres however, there are additional inputs required due to the removal of crop residues. Th e assumption is that removal will require more inputs to make up for lost residue nutrients. These additional inputs result in GHG emissions

428from production and from N2O releases from applic ation. This effect is somewhat offset by reductions due to lower livestock production. These results are depende nt on our agricultural sector input and emission assu mptions that are being updated for the final rule (e.g., N2O emission factor work).

As with domestic agriculture impacts, the international agricultural sector impacts include changes in energy use GHG emissions a nd fertilizer / soil N2O emissions as well as changes in livestock and rice production GHG emissions.

Increased crop production internationally result ed in increased fertilizer and fuel use emissions, however, these are offset by reduc tions in livestock GHG emissions. Higher commodity crop prices internationa lly are resulting in lower livest ock production internationally.

We estimate the largest overall agricultural sector impact is an increase in land use change impacts, reflecting the shift of crop pr oduction both domestically and internationally to meet the biofuel demand in the U.S., and land us e change emissions associated with converting land into crop production.

Domestic soil carbon change impacts are negative reflecting th e soil carbon changes associated with crop shifting and production. Th ere are several factors impacting this result, switchgrass has a large benefit in terms of soil carbon and ca rbon sequestration. Residue removal is greater on low and no till cropping mana gement practices, and larger removal rates shift to some of these practices and result in increased soil ca rbon sequestration. However, land use change impacts are dominated by the internat ional acreage and land use change increase.

Other portions of the biofuel lifecycle incl ude fuel production and feedstock and fuel transport. We project reductio ns in GHG emissions from the renewable fuel production portion of the lifecycle due to the generation of electricity along with the increased production of cellulosic ethanol. A large portion of the ce llulosic ethanol production is assumed to be produced through the enzymatic process which also produces excess electricity to be sold to the grid. This excess electricity is offsetting grid electricity produc tion and results in GHG benefits. Cellulosic biofuels contribute by far the most to the total emission reductions due to both their superior per gallon emission reductio ns and the large volume of these fuels anticipated to be used by 2022. Process emission reductions are part ially offset by both feedstock and fuel transportation emissions.

CO2 produced in the combusti on of biofuels is offset by the uptake of CO2 in the biomass crop used to produce the fu el, resulting in a significant net reduction of CO2 compared to fossil fuel tailpipe combustion. Net carbon fl uxes from changes in biogenic carbon reservoirs in wooded or crop lands associated with land us e change are accounted for in the domestic and international agriculture impacts shown in upstrea m impacts. In addition we assume biofuel use is offsetting petroleum fuel production which al so results in GHG emi ssions reductions from reduced upstream emissions of petroleum fuel production (crude oil extr action and transport, refining, transport).

429The results of the individual lifecycle stag e results can be summed to determine the overall GHG impact of the proposed rulemaking. As discussed in previ ous sections on lifecycle GHG thresholds there is an initial one time release from land conversion and smaller ongoing releases but there are also ongoing benefits of using renewable fuels over time replacing petroleum fuel use. Figure 2.9-2 shows the GHG emissions impact of the change in fuel volumes considered over time.

Figure 2.9-2. GHG Impacts over time

-4,500,000,000 -4,000,000,000 -3,500,000,000 -3,000,000,000 -2,500,000,000 -2,000,000,000 -1,500,000,000 -1,000,000,000

-500,000,000 0 500,000,000 1,000,000,000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Time Since Initial Land Use Change (years)Net Total GHG Impacts (MT CO2-eq.)

Land Use Change Impacts Ongoing Benefits (w/o Land Use Change)
Net Cumulative Total Emissions Over Time

Based on the volume scenario considered the one time land use change impact results in 448 million metric tons of CO2- eq. emissions. There are however, based on the biofuel use replacing petroleum fuels, annual reduction be nefits of 161 million metric tons of CO2-eq. emissions. This results in a less than two year payback period before the ongoing benefits of the biofuels use offsets the initial land use impacts.

The timing of the impact of land use change and ongoing renewable fu els benefits were discussed in the previous lifecycle re sults section. The issue is sli ghtly different for this analysis since we are considering absolute tons of emi ssions and not determining a threshold comparison to petroleum fuels. However the results can be presented in a similar ma nner to our individual fuels analysis in that we can determine net bene fits over time with different discount rates and over a different time frame for consideration.

430If we consider a 2% discount rate and 100 years of benefits (i.e., 161 million tons of GHG emissions reductions per year) we get an estimated discounted NPV reduction in GHG emissions of 6.8 billion tons of GHG emissions . Assuming a 0% discount rate over 30 years would result in an estimate of 4.5 billion tons of discounted GHG emission reductions.

Furthermore, for the calculations of the monetized GHG benefits we calculate an annualized NPV GHG reduction. This annualized value is based on converting a lump sum present value into its an nualized equivalent. For this analysis we convert the NPV results for the 100 year 2% discount rate into an annualized stream such that the NPV of the annualized emissions will equal the NPV of the emission str eam over 100 years with a 2% discount rate. This results in an annualized emission re duction of 160 million metr ic tons of CO2-eq. emissions. With a 30 year time horizon and 0% discount rate the annua lized emission reduction is 150 million metric tons CO2-eq.



### 2.10 Effects of GHG Emission Re ductions and Changes in Global Temperature and Sea Level

2.10.1 Introduction

The reductions in CO 2 and other GHGs associated with the proposal will affect climate change projections. Because GHGs mix well in the atmosphere and have long atmospheric lifetimes, changes in GHG emissions will affect fu ture climate for decades to centuries. One common indicator of climate change is global mean surface temperature. This section estimates the response in global mean surf ace temperature and sea level rise projections to the estimated net global GHG emissions reductions associated with the Proposed Rulemaking (See Section 2.9) for the estimated net reductions in global emissions over time by GHG).

2.10.2 Estimated Projected Reductions in Global Mean Surface Temperatures

EPA estimated changes in projected global mean surface temperatures to 2100 using the MiniCAM (Mini Climate Assessment Model) integrated assessment model E EEEEEEE coupled with the MAGICC (Model for the Assessment of Gree nhouse-gas Induced Climate Change) simple

EEEEEEEE MiniCAM is a long-term, global integrated assessment model of energy, economy, agriculture and land use, that considers the sources of emissions of a suite of greenhouse gases (GHG's), emitted in 14 globally disaggregated global regions (i.e., U.S., Western Europe, China), the fate of emissions to the atmosphere, and the consequences of changing concentrations of greenhouse related gases for cl imate change. MiniCAM begins with a representation of demographic and economic developments in each region an d combines these with assumptions about technology development to describe an internally consistent representation of energy, agriculture, land-use, and economic developments that in turn shape global emissions. Brenkert A, S. Smith, S. Kim, and H. Pitcher, 2003: Model Documentation for the MiniCAM. PNNL-14337, Pacific Northwest National Laboratory, Richland, Washington. For a recent report and detailed description and discussion of MiniCAM, see Clarke, L., J. Edmonds, H. Jacoby, H.Pitcher, J. Reilly, R. Richels, 2007. Scenarios of Greenhouse Gas Emissions and Atmospheric Concentrations. Subreport 2.1A of Synthesis and Assessment Product 2.1 by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. Department of Energy, Office of Biological & Environmental Research, Washington, DC., USA, 154 pp.

431climate model. F FFFFFFF MiniCAM was used to create the gl obally and temporally consistent set of climate relevant variables required for running MAGICC. MAGI CC was then used to estimate the change in the global mean surface te mperature over time. Given the magnitude of the estimated emissions reductions associated wi th the proposed rule, a simple climate model such as MAGICC is reasonable for estimating the climate response.

EPA applied the estimated annual GHG emissions changes for the proposal to the MiniCAM U.S. Climate Change Science Prog ram (CCSP) Synthesis and Assessment Product baseline emissions. Specifically, the CO 2 , N 2 O, and CH 4 annual emission changes from 2022­ 2121 from Section 2.9 were applied as net redu ctions to the MiniCAM CCSP global baseline net emissions for each GHG. Post-2121, we assumed no change in emissions from the baseline. This assumption is more conservative than al lowing the emissions reductions to continue.

Table 2.10-1 provides our estimated reductions in projected global mean surface temperatures and sea level associated with the pr oposed increase in renewable fuels in 2022. To capture some of the uncertainty in the climate system, we estimated the changes in projected temperatures and sea level across the most curre nt Intergovernmental Panel on Climate Change (IPCC) range of climate sens itivities, 1.5°C to 6.0°C. G GGGGGGG To illustrate the time profile of the estimated reductions in projected global mean surface temperatures and sea level, we have also provided Figures 2.10-1 and 2.10-2.

FFFFFFFF MAGICC consists of a suite of coupled gas-cycle, cl imate and ice-melt models integrated into a single framework. The framework allows the user to determin e changes in GHG concentrations, global-mean surface air temperature and sea-level resulting from anthropogenic emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), reactive gases (e.g., CO, NOx, VOCs), the halocarbons (e.g. HCFCs, HFCs, PFCs) and sulfur dioxide (SO2). MAGICC emulates the global-mean temper ature responses of more sophisticated coupled Atmosphere/Ocean General Circulation Models (AOGCMs) with high accuracy. Wigley, T.M.L. and Raper, S.C.B. 1992. Implications for Climate and Sea-Level of Revised IPCC Emissions Scenarios Nature 357, 293-300. Raper, S.C.B., Wigley T.M.L. and Warrick R. A. 1996. in Sea-Level Rise and Coas tal Subsidence: Causes, Consequences and Strategies J.D. Milliman, B.U. Haq, Eds., Kluwer Academic Publishers, Do rdrecht, The Nether lands, pp. 11-45. Wigley, T.M.L. and Raper, S.C.B. 2002. Reasons for larger warming projections in the IPCC Third Assessment Report J. Climate 15, 2945-2952.
GGGGGGGG In IPCC reports, equilibrium climate sensitivity refers to the equilibrium change in the annual mean global surface temperature following a doubling of the atmospheric equivalent carbo n dioxide concentration. The IPCC states that climate sensitivity is “likely” to be in the range of 2°C to 4.5°C and described 3°C as a "best estimate." The IPCC goes on to note that climate sensitivity is “very unlikely” to be less than 1.5°C and “values substantially higher than 4.5°C cannot be excluded.” IPCC WGI, 2007, Climate Change 2007 - The Physical Science Basis, Contribution of Working Group I to the Fourth Asse ssment Report of the IPCC, http://www.ipcc.ch/.

432Table 2.10-1. Estimated Reductions in Projected Global Mean Surface Temperature and Global Mean Sea Level from Baseline in 2030, 2050, 2100, and 2200 for the Proposed Standard in 2022

Climate sensitivity

1.5 2 3 4.5 6 Change in global mean surface temperatures (degrees Celsius)

2030 0.000 0.000 -0.001 -0.001 -0.001

2050 -0.001 -0.002 -0.002 -0.002 -0.003

2100 -0.003 -0.004 -0.005 -0.006 -0.007
2200 -0.003 -0.004 -0.006 -0.008 -0.009 Change in global mean se a level rise (centimeters)

2030 -0.002 -0.002 -0.003 -0.003 -0.003

2050 -0.012 -0.014 -0.017 -0.020 -0.022

2100 -0.045 -0.052 -0.063 -0.074 -0.082
2200 -0.077 -0.091 -0.114 -0.143 -0.172

The results in Table 2.10-1 and Figures 2. 10-1 and 2.10-2 show small, but detectable reductions in the global mean surface temperature and sea level rise projections across all climate sensitivities. Overall, the reductions are sma ll relative to the IPCC’s “best estimate” temperature increases by 2100 of 1.8oC to 4.0oC. H HHHHHHH Although IPCC does not issue “best estimate” sea level rise projections, the model-based range across SRES scenarios is 18 to 59 cm by 2099. I IIIIIII Both figures illustrate that the overall emissions reductions can decrease projected annual temperature and sea level for all climate sensti tivities. This means that the distribution of potential temperatures in any part icular year is shifting down. Ho wever, the shift is not uniform. The magnitude of the decrease is larger for higher climate sensitivities. Thus, the probability of a higher temperature or sea level in any year is lowered more than the probability of a lower temperature or sea level. For instance, in 2100, the reduction in projected temperature for climate sensitivities of 3 and 6 is approximately 65% and 140% greater than the reduction for a climate sensitivity of 1.5. This difference grows over time, to approximately 80% and 185% by 2200. The same pattern appears in the reductions in the sea level rise projections. J JJJJJJJ Also noteworthy in Figures 2.10-1 and 2. 10-2, is that the size of the d ecreases grows over time due to the cumulative effect of a lower stock of GH Gs in the atmosphere (i.e., concentrations). K KKKKKKK

HHHHHHHH IPCC WGI, 2007. The baseline increases by 2100 from our MiniCAM-MAGICC runs are 2°C to 5°C for global mean surface temperature and 35 to 74 centimeters for global mean sea level.
IIIIIIII “Because understanding of some important effects drivin g sea level rise is too lim ited, this report does not assess the likelihood, nor provide a best estimate or an upper bound for sea level rise.” IPCC Synthesis Report, p. 45
JJJJJJJJ In 2100, the reduction in projected sea level rise for climate sensitivities of 3 and 6 is approximately 40% and 80% greater than the reduction for a climate sensitivity of 1.5. This difference grows over time, to approximately 50% and 120% by 2200.
KKKKKKKK For global average temperature afte r 2100, the growth in the size of th e decrease noticeable slows. This is because the emissions changes associated with the policy were onl y estimated for 100 years. Note that even with emissions reductions stopping after 100 years, there contin ues to be a decrease in proj ected temperatures due to reduced inertia in the climate system from the earlier emissi ons reductions. However, unlike temperature, after 2100,

433Degrees Celsius The bottom line is that the risk of climate cha nge is being lowered, as the probabilities of any level of temperature increase and sea level rise are reduced and the probabilities of the largest temperature increases and sea level rise are reduced even more. For the Final Rulemaking, we hope to more explicitly estima te the shapes of the distributions and the estimated shifts in the shapes in response to the Rulemakings.

Figure 2.10-1 Estimated Projected Reductions in Gl obal Mean Surface Temperatures across Climate Sensitivities (CS) for the Proposed Standard in 2022

0.000199020002010202020302040205020602070208020902100211021202130214021502160217021802190220022102220223022402250

-0.002

-0.004

-0.006

-0.012 -0.010 -0.008 CS = 1.5

CS = 2.0

CS = 3.0

CS = 4.5

CS = 6.0

the size of the decrease in sea level rise increases as the pr ojected reduction in warming has a continued effect on ice melt and ocean thermal expansion.

434Figure 2.10-2 Estimated Projected Reductions in Global Me an Sea Level across Climate Sensitivities (CS) for the Proposed Standard in 2022

0.000199020002010202020302040205020602070208020902100211021202130214021502160217021802190220022102220223022402250Centimeters -0.050

-0.100

-0.150

-0.200

-0.250 CS = 1.5

CS = 2.0

CS = 3.0

CS = 4.5

CS = 6.0

435

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

In addition to the GHG impacts laid out in Ch apter 2, we project that today’s proposal will affect emissions of “criteria” pollutants (t hose pollutants for which a National Ambient Air Quality Standard has been established), criteria pollutant precursors, and air toxics. Changes in these emissions would derive from the direct effect of renewable fuels on the tailpipe and evaporative emissions of vehi cles and off-road equipment; and increased renewable fuel production and distribution including the effect of decreases in the produc tion and distribution of gasoline and diesel displaced by renewable fuel . For today’s proposal we have focused on estimating the change in mass emissions for these pollutants across the entire U.S. in 2022, when the program is fully implemented; for the final rule we plan to conduct full-scale air quality modeling and health impact assessment as well. This chapter presents national emission impacts for nitrogen oxides (NOx), volatile orga nic compounds (VOC), carbon monoxide (CO), particulate matter 10 microns in diameter and less (PM10), particulate matter 2.5 microns in diameter and less (PM2.5), sulfur dioxide (S O2), ammonia (NH3),, benzene, 1,3-butadiene, acrolein, formaldehyde, acetaldehyde, naphthalene, and ethanol. This chapter also presents the methodology for developing these estimates and discusses air quality and health impacts qualitatively.



### 3.1 Methodology for Calculating Non-GHG Emission Impacts

Our analysis focused on the projected impact of today’s proposed action in 2022, the first full year of implementation. The impacts of th e program were quantified as the difference in mass emissions between the proposed volumes a nd several reference cases, as presented in Section 3.2. In order to allow assessment of total emission impacts of mandated renewable volumes, the main reference case presented in this analysis was the RFS1 mandate volume of 7.5 billion gallons of renewable fuel (6.7 billion gallons ethanol). We are also presenting impacts relative to the roughly 14 billion gallons of renewable fuel s projected by the Department of Energy (DOE) Annual Energy Outlook (AEO) 2007 (summarized in Table V.A.1-1 of the Preamble to today's proposal) to show the impact of today’s action increm ental to the projected renewable market pre-EISA. Finally, we also pres ent impacts relative to the pre-RFS1 baseline of roughly 4 billion gallons renewable fuel. Due to time constraints, for the proposed rule we did not perform separate modeling runs for each refe rence case. Rather, we focused our modeling on the impacts between the AEO re ference case and control case, and scaled our estimates of national emissions based on the volumes of ethanol. For the final rule inventories and air quality work we intend to model two of these reference cases - RFS1 mandate and AEO.

Our analysis of non-GHG emissions impacts was comprised of a) an analysis of direct impacts on motor vehicles, off-road equipment and other sources from burning (or evaporating) renewable fuels in place of petroleum-based fu els; and b) the emissions impacts from the production and distribution of rene wable fuels. These analyses are discussed separately in Sections 3.1.1 and 3.1.2.

4363.1.1 Impact on Non-GHG Emission s from Motor Vehicles and Equipment

Today’s proposed volumes of renewable fuel will directly affect emissions from most mobile source categories, and for this analysis we have quantified the effects on exhaust and evaporative emissions of gasoline-fueled vehicl es and equipment including passenger cars, light trucks, heavy trucks, motorcycles and off-road sources such as lawn mowers, recreational boats and all-terrain vehicles. We have also estim ated the impact of ethanol on emissions from portable fuel containers, and increased refueling emissions due to higher volatility of ethanolblended fuel and increased refueling events due to lower energy content of biofuels. The emissions impacts of biodiesel were also es timated on heavy-duty dies el vehicles, assuming additional biodiesel would be burned by on-road sources.

A considerable source of uncertainty in estimating the emission impacts of renewable fuels is the effect ethanol blends will have on emissions of cars and light trucks. Under today’s proposed action every gasoline vehicle and piece of equipment would be fueled on at least E10, and with the volumes of E85 pr ojected necessary to meet toda y’s mandate, we estimate that, on average, flexible fueled vehicles with access to E85 would need to be using E85 about 74 percent of the time. The impacts of E10 emissions have been studied for years, but current data on newer technology vehicles is not definitive on its impact on NOx and exhaust VOC emissions. Data on E85 is limited, and the results show large variability of emission effects in some pollutants. EPA is in the midst of a large-scale emission testing program to improve our understanding of fuel effects on Ti er 2 cars and trucks, and we expect our analysis for the final rule to reflect the results of this program. 543 In the meantime, in order to help bound the uncertainty of exhaust fuel effects, we calculate d emissions impacts for two vehicle fuel effects scenarios as detailed in Section 3.1.1.2.

EPA is in the process of updating its mobile source emission platform to the Motor Vehicle Emissions Simulator (MOVES). For the an alysis of gasoline-fueled highway vehicles, a pre-draft version of MOVES was used to genera te national inventories based on the fuel supply scenarios determined for the R FS2. Although the model is not complete, we decided to use a pre-release version of MOVES fo r this analysis to begin to reflect significant updates in emissions from MOBILE6. As the other mob ile source categories in MOVES were still under development at the time of this analysis, onroa d diesel vehicles and all off-road equipment emissions were calculated with the National Mob ile Inventory Model (NMIM) model, a platform which generates emission inventories base d on EPA’s MOBILE6 and NONROAD models.

The development of vehicle and equipment emission impact estimates for today’s proposal required: a) developi ng fuel supply inputs at the co unty level for the 2005 base year and 2022 reference and control cases which accounted for the projected change in fuel properties due to today’s proposed action; b) developing individual vehicle fuel effects; and c) running MOVES and NMIM to produce raw inventory estimates and post-processing these results as needed to account for different baselines, to apply “off model” corrections, or to estimate impacts not accounted for in the models. Each of these steps are detailed in the following sections

4373.1.1.1 Fuel Inputs

As inputs to our emissions modeling, we de veloped a detailed profile of fuels for each modeling case. We prepared county-level databases of fuel properties and fuel market shares for the 2005 base case and the 2022 AEO reference case and 2022 control case. These county-level databases were applied in both NMIM and MOVE S for consistency in fuel inputs across the different mobile source categories.

The 2005 base case fuel properties were derive d from 2005 historical data. These data included national summer and winter fuels surveys, studies that tracked the total amount of ethanol produced for use in gasoline each year, and Reformulated Gasoline (RFG) measurements. Additional data were available on the fuel properties of all gasoline produced and imported annually by refiners, and on the di stribution of gasoline to and from Petroleum Administration for Defense Districts (PADDs). Wher e survey data was available, it was used to determine a county's fuel properties for summer and winter. Where survey data was not available, fuel properties were set to equal the average fuel pr operties in that PADD. Special adjustments were made to some counties to acco unt for local gasoline volatility control programs and winter oxygenated gasoline programs.

For the 2022 reference and control cases, the 2005 base case fuel properties were adjusted to account for implementation of other fuel regulations and to account for increased ethanol use. There is a greater percentage of ethanol in the 2 022 AEO reference case than in the 2005 base case because methyl tert-butyl ethe r (MTBE) has been replaced with ethanol and because of AEO projected growth in ethanol pro duction for 2022. For this analysis, ethanol was allocated to the state and county level based on the economics of distribu tion and blending, as well as other factors. Refer to Section 1.7.1 of this document for details. The 2022 control case models the requirements of EISA, in which appr oximately 34 bgal of ethanol are present in the fuel supply for 2022. The control case models this by assigning E10 gasoline to all U.S. counties, and E85 based on the economics of distribution, blending a nd other factors.

In general, future fuel properties in both th e reference and control cases were adjusted to account for widespread increases in ethanol under the assumption th at, while historically ethanol has been splash blended in conventional gaso line (CG), it will be match-blended (i.e., the changes associated with ethanol addition will be accounted for by refiners when producing the base gasoline) by 2022. We believe this is reasonable given that there will be a large (and thus more geographically predictable) volume of ethanol used in gaso line, and that certain property changes that take place when ethanol is blended (such as octane increase) could be financially beneficial to refiners if able to be assumed when producing the ba se gasoline. Thus, we adjusted aromatics, olefins, T50, and T90 fuel parameters by first backing out the effects of any existing oxygenate (by reverse dilution), and then re-adjus ting the properties for ethanol blends based on refinery modeling done for the RFS1 rulemaking that projected how gasoline properties were likely to change given widespread use of ethanol. Table 3.1-1 shows the adjustment factors used per volume percent ethanol blended. Reid Vapor Pressure (RVP) was increased 1.0 psi wherever ethanol was present in conventional gaso line unless there was a state constraint.

438Table 3.1-1. 2022 Adjustments for Etha nol Added to Conventional Gasoline Aromatics (vol%) Olefins (vol%) E200 (vol%) E300 (vol%) Additive change per vol% ethanol added -0.464 0.019 0.909 0.063

For Reformulated Gasoline (RFG) areas, re finers already account for the blending of ethanol when producing the base gasoline, and therefore the properties are not predicted to change in the same ways as for conventional ga soline (CG). We used refinery modeling results for each PADD (produced along with those above fo r CG) to project the properties of fuel in RFG areas accounting for increased ethanol in th e national gasoline pool. RFG properties used in the reference and control cas es in 2022 are shown here in Table 3.1-2. The 2022 reference and control cases also incorporate reductions in gasoline sulfur resu lting from Tier 2 regulations. It should be noted that fuel ben zene levels presented in this ta ble are higher than what they should be, given impacts of the 2007 mobile sour ce air toxics (MSAT) rule, which mandates a 0.62% fuel benzene standard. 544 For the final rule, we will use more recent refinery modeling, which provides better estimates of benzene leve ls with implementation of the MSAT rule.

Table 3.1-2. 2022 Reformulated Gasoline Properties by PADD PADD RVP (psi) Aromatics (vol%) Benzene (vol%) Olefins (vol%) E200 E300

Summer 1 7.0 20.1 0.70 13.9 56.0 83.5 2 7.1 17.9 0.76 17.3 54.1 81.8 3 7.0 20.0 0.76 14.1 52.0 87.5 5 6.8 22.5 0.61 5.7 54.5 86.2 Winter 1 12.8 19.6 0.71 15.3 60.8 83.2 2 13.0 17.8 0.64 12.3 63.9 79.5 3 11.8 21.2 0.64 12.6 53.4 87.5 5 12.6 23.7 0.61 5.7 58.2 86.2

For the control case, we also modeled flexible -fueled vehicles running on E85. For most emissions, these effects were modeled using fuel adjustments as described in Section 3.1.1.2, below. However, in MOVES, vapor venting em issions and sulfate emissions are calculated directly from the RVP and sulfur level of the fu el. These properties were derived for E85 fuels from the properties of the same county's E10 fuel in the control case, with the RVP adjusted to account for the lower vapor pressure of E85. 545 and the sulfur level adjusted to 15% of that of the original E10 fuel in that county. The predicted sulfur adjustment underestimates the actual sulfur content of the fuel because it neglects the sulf ur content of ethanol (about 1.5 ppm) and it does not account for any refinery increases in sulfur in the base gasoline; thus the benefit attributed to sulfate reductions in the E85 case may be overestimated.

For each of the modeled scenarios, fuel info rmation was input into an NMIM database and used for NMIM runs. For MOVES runs, th e NMIM databases were converted into MOVES

439databases using a conversion program. To redu ce time needed for MOVES runs, we reduced the size of the MOVES fuel database by processing the database with a "binner" program that grouped fuels with similar properties and assigne d each group to a single fuel formulation identification number and a si ngle set of fuel properties.

3.1.1.2 Effect of Fuels on Non-GHG Em issions from Vehicles & Equipment

The average effect of renewable fuels on an individual vehicle basis, based on available research, are the foundation of the emission impact assessment. This section contains discussion of the effects used in the emission impact assessment for E10 on gasoline vehicles and equipment, for E85 on gasoline vehicles, and for biodiesel.

3.1.1.2.1 On-road Gasoline Vehicle E10 Effects

3.1.1.2.1.1 Exhaust Emissions

A 10 percent ethanol blend can affect exhaus t emissions from vehicles and off-road equipment. A comprehensive analysis of E10 impacts on exhaust emissions was undertaken for the RFS1 rule, as documented in Chapter 3 of the RFS1 Regulatory Impact Analysis. 546 This analysis considered previous EPA work in coming up with a so-called “Predictive Model” to assess California’s request for an oxygenate waiver in 2000, as well as test data from several test programs conducted by the auto trade associatio ns (AAM/AIAM), ExxonMobil, Toyota, and the Mexican Petroleum Institute. This assessment concluded that for Tier 1 and later vehicles (nominally model year 1996 and later, comprising th e majority of the fleet in 2022) there was not enough consistency across these stud ies to confidently predict the impact of oxygenated fuel on exhaust HC and NOx emissions. As a result the RFS1 analysis carried forward two sets of fuel effects: a “primary” analysis assuming no effect of oxygen on non-methane hydrocarbon (NMHC) and NOx emissions from Tier 1 and late r vehicles, and a “sensitivity” analysis which applied EPA’s Predictive Model effect s to Tier 1 and later vehicles.

We are the midst of a large scale testing e ffort, scheduled for completion in 2009, aimed at quantifying the effects of several fuel parame ters, including ethanol an d other fuel properties impacted by the blending of ethanol into gaso line, on exhaust and evap orative emissions from Tier 2 vehicles. 547 It is our expectation that data from this program will be used in our analysis for the final rule. For this proposal we are analyzing two ethanol effect scenarios: “less sensitive” based on the “primary” case used in RFS1, and “more sensitive” based on the RFS1 “sensitivity” case. While the effects of E10 on individual vehicles will vary depending on properties of the fuel (e.g., RVP, distillation, a nd aromatic content), Table 3.1-3 demonstrates the effects used for conventional and reformulat ed gasoline based on the fuel properties derived from Tables 3.1-1 and 3.1-2. The effects s hown for NOx, HC and toxics were applied to only Tier 0 vehicles (mid 1990’s and older) in the “les s sensitive” case, and to all vehicles in the “more sensitive” case.

440Table 3.1-3. Exhaust Effect of E10 Relative to E0 a Pollutant Source CG RFG Exhaust HC (VOC) EPA Predictive -7.4% -9.7% NOx Models 7.7% 7.3% CO b MOBILE6.2 -11% / -19% -36% Exhaust Benzene EPA Predictive -24.9% -38.9% Formaldehyde and Complex 6.7% 2.3% Acetaldehyde Models 156.8% 173.7% 1,3-Butadiene -13.2% 6.1%

a Assumes summer (July) conditions b The first figure shown applies to normal emitters; the second applies to high emitters.

3.1.1.2.1.2 Evaporative Emissions

3.1.1.2.1.2.1 Permeation

E10 affects evaporative emissions from gasolin e vehicles due to th e increased volatility of E10 blends, the increased permeation of fuel vapors through tanks and hoses, and the increased vapor emissions due to the lower molecula r weight of E10. Each of these effects were modeled using the draft MOVES model, which separates permeation emissions from vapor venting emissions to allow better accounti ng for these different processes.

Permeation effects were developed from C oordinating Research Council's (CRC) E-65
program 548 , which measured evaporative emissions fr om ten fuel systems that were removed from the vehicles on E0 and E5.7 fuels; fuel syst ems were removed to ensure that all evaporative emissions measured were from permeation of th e fuel through the different components of the fuel system. For this analysis, we estimated the effect by calculating the percent increase in average emissions from all vehicles between E0 and E5.7 fuels over the 65 to 105 degree Fahrenheit diurnal test. This va lue was 46 percent. In order to estimate the effect at E10 we simply multiplied this result by 1.75 (10/5.7), re sulting in a 79 percent increase applied to cars and light trucks from all model years.

This approach heavily weights the emission contribution of older vehicles in the test program, and, in conjunction with lower emissi on rates for vehicles certified to Enhanced Evaporative and later standards in MOVES, ma y underestimate the impact of E10 on permeation from newer vehicles. The limited data on vehicl es certified to Enhanced Evaporative standards showed an increase of 232 percent, versus 46 percent for all vehicles. We expect CRC to release a significant body of new E-77 program pe rmeation data on later model vehicles before the end of 2008. We plan to revisit our permea tion emissions estimates when these results are available.

3.1.1.2.1.2.2 Tank Vapor Venting

In MOVES, tank vapor venting (TVV) emissi ons from controlled vehicles are modeled as a function of uncontrolled vapor generation (TVG) , which is affected by both ethanol content and RVP according to the formula (Reddy, 1989):

441B * RVP CT x CT 2 

### TVG = Ae ( e

− e )
where A, B, and C are coefficient dependent on etha nol level as described in the table 3.1-4, and T x and T 2 are the temperatures at the start and en d point of the vapor generation period.

Table 3.1-4. Tank Vapor Vent ing Coefficients (at sea level) E0 E10 Constant A 0.0082 0.0088 B 0.2357 0.2056 C 0.0409 0.0430

In MOVES, the relationship between TVV and TVG is a function of vehicle model year, temperature rise, and whether RVP is held constant or increased with the additional of ethanol. For example, for the 1978-1995 model year group ove r a temperature rise of 65 to 105 degrees F, the increase in tank vapor venting em issions is 6 percent for E10 rela tive to E0 with no change in RVP. However, the same scenario a 1.0 psi incr ease results in an incr ease in tank vapor venting emissions of 46 percent. The national impacts pr esented in this analysis reflect a composite effect of the mix of vehicle model years in 2022, hourly temperature differentials by county, and county-specific fuel property changes with the addition of ethanol.

3.1.1.2.2 On-road Gasoline vehicle E85 effects

As discussed in Section 1.7.1, because ethano l blended into E10 everywhere in the U.S. would account for less than half of the mandated ethanol volumes, we project that nearly 30 billion gallons of E85 will be required to fulfill the mandate. E85 is by no means a new fuel - automakers have been producing flex fuel vehicl es (FFVs) capable of operating on E85 since the early 1990s. Nonetheless, there are relatively few sources of data available comparing the emissions of non-oxygenated (E0) and high-level (E 85) ethanol blended fuels either for older (Tier 0 and 1), and particularly for current technology (i.e. Tier 2) vehicles. Understanding the emissions effects of E85 on the la tter group of vehicles is of part icular importance since Tier 2 FFVs will dominate the FFV fleet in future years. For this reason, we focused our analysis for this proposed rulemaking on newer t echnology (i.e. LEV and later) vehicles L LLLLLLL

. For this analysis we identified three recent data sources that investigate the effects of E85 549, 550, 551 on current technology (i.e. Ti er 2 and similar) vehicles M MMMMMMM

. Two of these sources are test programs conduc ted by Southwest Research Inst itute and Environment Canada, and the third is EPA certification data. This section briefly descri bes each data source and highlights the key findings, and explains how thes e data were used to generate E85 effects.

In 2006, Southwest Research Institute (SwRI) conducted a study for EPA on three model year 2005 Tier 2 FFVs (bins 5 and 8) operating on several gasoline and ethanol blends. This

LLLLLLLL For a discussion of the effect of E85 on older technology vehicles, see Chapter 3 of the RFS RIA available here: http://www.epa.gov/otaq/renewablefuels/420r07004chap3.pdf
MMMMMMMM EPA is aware of several test programs, either plan ned or underway, by CRC and others that may provide additional test data for future fuel effects modeling and rulemaking support.

442study was primarily focused on the impacts of fu el ethanol content and reduced ambient test temperature (tests were conducte d at 75oF and 20oF) on VOC and PM emissions. Multiple fuel blends were evaluated in this program, although for this analysis we will focus only on E0 and E85 emissions at 75o F. At this test temperature, Tier 2 certification fuel was used as the nonoxygenated test fuel (E0) as well as the base gasoline for the spla sh-blended E10 and E85 fuels. Additionally, EPA certification “cold CO” winter time gasoline was used for reduced ambient temperature (20 oF) testing – used alone (E0) an d as the base fuel for wintertime E10 and E70 blends. This base gasoline has a higher RVP th an its summertime equivalent, which is necessary to ensure proper fuel vaporization at lower ambi ent temperatures. Repeat tests were conducted for the 20oF tests on the winter fuel blends, but no repeats were run for 75oF testing. In addition to the regulated pollutants, SwRI measured CO 2, CH4, benzene, 1,3-butadiene, naphthalene, acetaldehyde, acrolein, and ethanol. This study saw reductions in PM 2.5, benzene, and 1,3­ butadiene of 55% - 70% with E85 relative to E0 . HC emissions increased while NOx and CO decreased. Emissions of methane, formal dehyde, and acetaldehyde were found to increase significantly with E85 use. Table 3.1-6 summarizes the average percent change in emissions with E85 vs. E0. This table also compares the findings of this dataset with the other two programs described below.

Environment Canada released a report in 2005 in which an NLEV and an interim nonTier 2 vehicle were tested on Tier 2 certificati on fuel and a commercially available E85 blend. Repeat tests were conducted in this study so that each vehicl e was tested three times on each fuel. The pollutants measured include NMOG, NMHC, CO, NOx, CO2, CH4, N20, benzene, 1,3-butadiene, acetaldehyde, formaldehyde, acrolei n, and ethanol, among others. The results, summarized in Table 3.1-6 showed statistically significant reductions in CO and NOx (-48% and -40%, respectively) when sw itching from E0 to E85. E85 caused non-methane organic gases (NMOG) emissions to increase in one vehicle and d ecrease in the other. Toxics reductions were of a similar order of magnitude as the vehicl es tested in the SwRI study discussed above

EPA’s Certification and Fuel Economy In formation System (CFEIS) database was accessed to identify data from five model year 2006 Tier 2 vehicles (bins 5, 8, and 9) tested on both E85 and Tier 2 certification gasoline. The E85 blend tested here was 85% denatured ethanol splash blended with 15% Ti er 2 certification gasoline. E ach vehicle was only tested once on each fuel. Weighted FTP results were reporte d for the regulated pollutants (except PM) as well as CO2, acetaldehyde, and ethanol (forma ldehyde, acetaldehyde, and ethanol were only measured for tests where E85 was used; theref ore these are expressed as fractions of NMOG here). This data indicates that E85 causes a slight increase in NMOG emissions, a slight decrease in NOx and CO2, and significant reducti ons in CO. The average percent change in each pollutant for these vehicl es when operated on E85 is shown in Table 3.1-5, below.

443Table 3.1-5. Effect of E85 on LEV and Later Per-mile Exhaust Emissions Relative to Conventional Gasoline: Percent change separated by data source EPA - CFEIS EPA - SwRI Env. Canada NMOG 10% 87% 5% CO -34% -15% -48% NOx -3% -42% -40% Benzene NA -61% -65% 1,3 Butadiene NA -66% -74% Acetaldehyde 12% of NMOG 5600% 3121% Formaldehyde 2% of NMOG 116% 98% Acrolein (E85 mg/mile emissions) NA 0.023 0.010 Unburned Ethanol (E85 mg/mile emissions) 28.3 (55% of NMOG) 25.4 (33% of NMOG) 34.6 (48% of NMOG) PM 2.5 NA -68% NA

Viewed independently, each study provides onl y limited insight on the effects of E85 on emissions relative to E0. Table 3.1-6 shows that while changes in some pollutants compare reasonably well between studies, others can vary widely. This makes it difficult to determine quantitative trends in emissions, since calculating an average percent change in emissions across all three studies does nothing to a ddress the variability of the test data. Without this assessment of variability there is no way to estimate the st atistical significance of the reported values. Only the Environment Canada conducted th e repeat tests necessary to asse ss the test-to-te st variability of a given vehicle, and none of the studies test ed enough vehicles to confidently state that their findings can be applied to the Tier 2 FFV fleet as a whole. In this case it is desirable to combine the test data into a single data set and apply a statistical modeli ng technique that allows one to estimate the average effect of E85 observed across the vehicles tested and to assess whether those changes are statistically significant.

Combining the data from these three studies re sults in a dataset that is still quite small (only 26 unique tests) and is by no means ideal. Th is clearly illustrates the need for additional testing in this area. In the absence of such da ta, we feel that combining the data is warranted since the majority of the vehi cles employ similar emissions c ontrol technology and the base gasoline used in each study was Tier 2 certificatio n fuel. In the SwRI and EPA CFEIS datasets, the E85 fuels were splash blended from denatured ethanol and Tier 2 gasoline. In the case of Environment Canada, their E85 was purchased fro m a commercial distributor, so its physical properties are slightly different than the sp lash blended E85 fuels. However, when approximately 82% of the fuel is a single compo und (ethanol), the variab ility in the remaining fuel properties is small enough to consider the E85 fuels from all three data sources to be substantially similar for the purposes of this anal ysis. In other words, no distinction was made between the E0 and E85 fuels used in study A vs. the E0 and E85 fuels used in study B.

444The fact that Environment Canada tested non-Tier 2 vehicles is noteworthy. The 2004 Chrysler Sebring was an interim non-Tier 2 bi n 8 vehicle. Despite its name, however, the standard is equivalent to the fi nal Tier 2 bin 8 FTP standards in all areas but the full useful life (120K miles vs. 100K miles). In f act this vehicle was cleaner than required by the standard, with observed emissions on E0 at the level of a Tier 2 bin 7 vehicle. The second vehicle tested by Environment Canada was a 2002 Dodge Caravan cer tified to the NLEV LEV LDT level. The standards at this certification level are considerably more relaxed than Tier 2 levels for some pollutants but not others. While these vehicl es share the same NMOG certification standard (0.100 g/mi), the CO standard is roughly 30% hi gher and the NOx standard nearly 4 times higher than the Tier 2 bin 8 level. As a result of this difference in standards, the Caravan emitted about 20% more CO and 2.5 times more NOx than th e Sebring. NMOG emissi ons were nearly the same for both vehicles with non-oxygenated gaso line. On a relative scale, both vehicles experienced similar percent changes in emissi ons between E0 and E85. The Sebring emitted more ethanol with E85 than did the Caravan, resulting in a higher E85 NMOG emissions factor for that vehicle. For the purposes of this anal ysis, we did not differen tiate either of these vehicles from the Tier 2 vehicles tested in the other studies.

The fact that tests were conducted in different laboratories is of little consequence since we are attempting to quantify the average effect of E85 across individual vehicles, not necessarily comparing the results (in absolute terms) of one vehi cle to another. Therefore any laboratory bias is implicitly in cluded in an individual vehicle’ s test results and does not confound the relative effects of fuel on emissions for that vehicle.

The statistical software package SPSS (version 9.0.1) was used to create a univariate linear model using the natural log of a given pollutant (CO 2 , NO X , etc) as the dependent variable, the fuel terms as a fixed variable, and the vehicle term as a random variable. This is a so-called mixed model since it includes both fixed and random variables N NNNNNNN . Only the first order (linear) fuel and vehicle terms were considered in this model since three fuel levels would have been needed to assess nonlinear fuel effects. The model also excluded the vehicle by fuel interaction term. This approach was taken since our primary focus is to identify the main effect of fuel on the natural log of emissions (the depe ndent variable). Including the vehicle by fuel interaction would have told us something abou t the sensitivity of each individual vehicle to changes in fuel, but for this analysis we are more concerned with the average effect of fuel across all vehicles in the dataset. Therefore we rem oved the interaction from the model since including it on such a small dataset would confound the main fuel effect and inappropriately constrain the model.

The results of the statistical analysis are summarized in Table 3.1-6. Statistically significant results are shown in bold.

NNNNNNNN A sensitivity analysis was performed designating the ve hicle term as a fixed variable (a fixed-effect model) but the results were identical to those with th e mixed model so only these results are presented here.

445Table 3.1-6: Combined E 85 Effects over 3 Programs Pollutant E85 change from E0 NMOG 8.6% CO -38.7% NOx -20.9% Toxics (mg/mile) Benzene -59.6% 1,3 Butadiene -61.2% Acetaldehyde 3739.8% Formaldehyde 62.3% Acrolein (mg/mile emission w/ E85) Unburned Ethanol (mg/mile emission w/ E85) PM 2.5 -68.2%

Bold = statistically significant at 95% CI

As shown in the table above, the effect of E85 on emissions was significant for all pollutants of interest except for NOx and NM OG. For NOx, the p-value is 0.83 and for NMOG the p-value is 0.55, meaning these results ar e only significant at the 17% and 45% confidence levels (respectively). While relatively speaking, NOx is more statistically significant than NMOG, for our purposes they bot h can only be used qualitative ly. Directionally, the NMOG increase and NOx decrease are consistent with the observations of each individual study and agree with basic engineering judgment. The va riability in the magnit ude of these changes, however, is what weakens the stat istical significance. Had additional observations been made, these results may have become more statistically significant. For our “more sensitive” fuel effects case, only those effects show n to be statistically significant were applied in the analysis. No E85 effects were applied for the “less sensitive” case.

The “more sensitive” case also included a 50 percent reduction in evaporative emissions with use of E85 based on results from one ve hicle from CRC’s E-65 evaporative permeation program. For the final rule we expect to verify the change in evaporative emissions with E85 based on results from ongoing EPA and CRC testing.

Data from the analyses discussed above, and an additional dataset from a 1995 test program conducted by EPA’s Office of Research a nd Development, were used to develop inputs for MOVES, in order to model E85 impacts on ai r toxics inventories. Since MOBILE6 does not model air toxics for E85, ratios were develope d to apply to E85 hydrocarbon or PM mass (Table 3.1-7). The exhaust ratios for all pollutants except naphthalene were obtained from data on seven vehicles from the 1995 test program in EP A’s Office of Research and Development, along with the previously discussed 2007 test pr ogram at Southwest Research Institute, 552 and the 2005 test program at Environment Canada. 553, 554 The data from the ORD test program is unpublished, but is available in the docket for th is rule. Naphthalene inputs for were derived from estimates from E10 values based on dilution of fuel with ethanol. The only source of data on evaporative emissions were results of hot soak tests from the Auto/Oil Air Quality Improvement Research Program. 555

446Table 3.1-7. Toxic to THC/PM Ratios used for E85 Fuel in MOVES Pollutant Exhaust/Ratio Type Evaporative/Ratio Type

Benzene 0.0036/THC 0.0054/THC 1,3-Butadiene 0.0005/THC N.A. Acetaldehyde 0.0673/THC N.A. Formaldehyde 0.0093/THC N.A.
Acrolein 0.0002/THC N.A.
Ethanol 0.3316/THC 0.6123/THC Naphthalene 0.0126/PM 0.00006/THC

3.1.1.2.2 Spark-Ignited Off-Road Engines

Effects of E10 relative to E0 on exhaust as well as fuel ta nk and hose permeation emissions from gasoline-fueled off-road engi nes are contained in EPA’s NONROAD model, based on limited data. The effects on exhaust HC, NOx, and CO are shown in Table 3.1-8. Effects on tank and hose permeation emissions vary by equipment type and were recently updated to reflect new information on uncontrolled emissions and their control due to recently finalized new standards. 556 For most small spark-ignition engines and recreational marine engines in 2022 E10 is estimated to double th e tank and hose permeation emissions. There can also be increases in diurnal and refueling emissions with E10 if the fuel volatility of the blend is allowed to be greater than E0. These volatility effects are accounted for in the NMIM model that has the county-specific fuel properties that were used to generate the emission inventory impacts for this proposal presente d below in Section 3.2.

Table 3.1-8. Exhaust Effect of Ethanol (E10) on Spark-Ignited Gasoline Emissions 4 stroke 2 stroke HC exhaust -15.75% -2.1% NOx +40.25% +65.1% CO -21.7% -22.75%

EPA, DOE and the California Air Resources Bo ard (ARB) are in the midst of additional testing of off-road engines that will be used to update our analysis for the final rule.

3.1.1.2.4 Biodiesel Effects on Diesel Emissions

As discussed in Appendix A to this RIA, we investigated the emission impacts on NOx, PM, HC, and CO of 20 volume percent biodiese l fuels on emissions fr om heavy-duty diesel vehicles. 557 Average NOx emissions were found to incr ease 2.2 percent, while PM, HC, and CO were found to decrease 15.6 percent, 13.8 percen t, and 14.1 percent, respectively, for all test cycles run on 20 volume percent soybean-based biodi esel fuel (Table 3.1-9). These results are generally consistent with the exhaust emission impacts for heavy-duty, in-use diesel engines

447found in our previous work on this subject. 558 For our estimate of biodiesel impacts on toxics we applied the HC emission change from Table 3.1-9 to toxic emissions.

Table 3.1-9. BD20 Emission Impacts Percent change in emissions

NOx PM HC CO +2.2% -15.6% -13.8% -14.1%

3.1.1.3 Non-GHG Emission Im pact Scenarios Analyzed

As discussed in Section 3.1.1.2.1, two sets of fuel effects scenarios were analyzed in the RFS1 rule in order to bound the uncertainty in ethanol effects on on-road gasoline engines. For today’s proposal we have carried forward these scenarios, but have expanded the sensitivity case to consider the impacts of E85 as well. Fuel effects cases differ only in how on-road gasoline exhaust emissions are modeled. For all othe r sources discussed above – on-road gasoline evaporative and refueling emissi ons, off-road spark ignition (SI), portable fuel containers, and heavy-duty diesel emissions – the same fuel e ffects, as summarized in the preceding sections, were used. The differences between the fu el effect cases are summarized below:

1) “Less Sensitive”: No exhaust VOC or NOx em ission impact on Tier 1 and later vehicles due to E10, and no impact due to E85. This was the “primary” case analyzed in the RFS1 rule, and results in only pre-Tier 1 vehicles having an effect from E10. 2) “More Sensitive”: VOC and NOx E10 emi ssion impacts based on test data on newer technology vehicles, as presente d in Table 3.1-3, and the statistically significant E85 effects that were presented in Table 3.1-6.

3.1.1.4 Non-GHG Emission Impact Calculation Methodology

3.1.1.4.1 On-Road Gasoline

Emissions from gasoline highway vehicles were generated with a preliminary draft version of EPA's new MOVES model. This mode l is currently being pr epared for release in draft form in early 2009. Although we applied a preliminary pre-draft ve rsion of the model and we anticipate changes between th e version used for the NPRM an alysis and the version to be released publicly (and th e final rule version), our analysis does reflect sign ificant updates in gasoline vehicle emissions from MOBILE6. Exha ust emission rates for HC, CO and NOx were developed based on an analysis of state inspectio n/maintenance and roadside remote sensing data from millions of vehicles. 559 Emissions of particulate matter are based on EPA's recent Kansas City gasoline PM study. 560, 561 Evaporative emission rates have been updated based on extensive evaporative testing conducted by EPA and the Coordinating Res earch Council (CRC) since the release of MOBILE6, including i nvestigations quantifying the e ffects of ethanol on permeation

448emissions. 562 For this assessment of toxics, MOVES applies toxic ratios from the MOBILE6.2 model to updated MOVES HC estimates within the model. 563

Details of how MOVES was configured and r un are documented in a report contained in the docket. 564 To summarize, a separate MOVES run was configured for each year (2005 and 2022) and fuel effect case (“less sensitive” and “m ore sensitive”) for the reference case (AEO reference) and control case. Each of these ru ns required a unique “run specification” file and bundle of input databases to allow modeling of differences in anal ysis year, fuel supplies, and fuel effects. For example, for the 2005 base ye ar run, MOVES was configured to run with the estimates of fuel formulations and market shar es by county in 2005, usi ng the inputs developed as discussed in Section 3.1.1.1, and two sets of fuel effects to reflect the “less sensitive” and “more sensitive” cases. Reference and control case runs in 2022 we re run with estimates of fuel formulations and market shares for both cases by county as we project in 2022, again with the two fuel effects cases. MOVES was only run for the AEO reference case, so results relative to other reference cases (i.e. RFS1 mandate or ba seline) were based on sc aling the differences between the AEO reference and control cases. VMT used to model onroad emissions was that developed for the Office of Air Quality Pl anning and Standards's 2002 Version 3 Modeling Platform 565 and used in the recently published Locomotive-Marine Rule. 566

MOVES allows different levels of pre-ag gregation depending on the level of resolution needed. For regional inventory applications, the finest level of aggregation the model can run is by county for each hour of the day, which maximizes the influence of inputs such as county-level fuel effects, hourly temperatures and activity pa tterns; however, since runn ing the model at this level for the entire nation over multiple years a nd scenarios would be time prohibitive, the model was run at a higher level of aggr egation to reduce run time. Exhaust emissions were run at the state/month aggregation for all ca ses, meaning that county-level inputs were aggregated to a single state average before being processed into MOVES, and hourly inputs were aggregated into an average monthly value. Likewise evaporativ e emissions were run at the state/hour level of aggregation, since evaporative emissions are requir ed to be run at the hourly level. For further time savings runs were performed in January a nd July only and weighted to approximate an entire year – for most pollutants we simply used the number of days in each month to extrapolate to the full year (approximately 50/50), however due to the higher temperature sensitivity of PM, care was taken to develop weigh ting factors that better approxim ated a yearly total based on a month-by-month run (January/July weightings of approximately 30 /70). While aggregation does lose some resolution in the overall emission results, test runs indicated that emissions differences are within a few percent of fu lly disaggregated runs and acceptable for estimating the emission impacts of the control programs.

MOVES categorizes vehicles according to use ty pe; for this analysis the majority of the focus was on light vehicles, which would in clude the MOVES use types Passenger Car, Passenger Truck and Light Commercial Truck. To account for larger ga soline vehicles which will also have emissions affected by the change in gasoline formulations, we also ran additional MOVES use types: school buses, single-unit truc ks, and motor homes. For the purposes of this rulemaking, the number of gasoline-powered refu se trucks, transit buses, intercity buses and combination trucks was determined to be negligible, and they were omitted from the MOVES

449runs to reduce run time. Because the PM10 cal culation inputs for MOVES were incomplete, PM 10 was calculated outside the model as PM2.5 times a correction factor of 1.086.

Because at the time of this analysis the MOVES module for automating the calculation of E85 emissions from flexible fueled vehicles (FFVs) was not complete, we calculated the emission impact of E85 use by running MOVES for all E85 and for all E10 and then weighted the emissions in a post-processing step. To run MOVES for "all E85" we created a special set of MOVES input files that essentially set all gasolin e vehicles to run on E85. We created MOVES fuel supply and fuel adjustment tables that applied multiplicative E85 fuel adjustments from Table 3.1-7 to all gasoline vehicle emissions. O OOOOOOO Because sulfate and vapor venting emissions are calculated using fuel properties (sulfur level and RVP) rather than fuel adjustments, we also created a specific MOVES table of E85 fuel properties as described in Section 3.1.1.1 In a post-processing step, we calculated a weighted average of the "all E85" results and the 2022 control case, sensitivity analysis results (called "all E10" results here). We chose to use the "sensitivity" results for consiste ncy with its premise that modern vehicles are responsive to changes in fuel ch aracteristics. The all E85 an d all E10 results were weighted together by state, model year, a nd vehicle type using a weighting factor that was the product of the FFV fraction and the E85 market share, wher e FFV fraction is the fraction of that vehicle type and model year that are projected to be E85 flexible-fueled vehicles, and the E85 marketshare is the state fraction of FFV energy us e that we project will be provided by E85. These fractions were generated using the assumpti ons described in the sections in Chapter 1, Section 1.7.1 pertaining to Primary FFV Growth Assumptions and Projected Growth in E85 Access. We performed this calculation for passenger cars and trucks and light commercial trucks only since the number of heavy-duty vehi cles using E85 is expected to be small.

Toxic emissions are in development for MOVE S; for this analysis some post-processing was required to generate complete inventory es timates. Specific toxi c:hydrocarbon ratios by fuel formulation, vehicle class and model year were developed from a series of MOBILE6 runs and fed into MOVES, which applied these ratios to HC emissions to produce emissions of benzene, acetaldehyde, 1-3 butadiene, formaldehyde and acrolein for all of these cases analyzed. Napthelene from heavy-duty vehicl es was ratioed to PM 10 in M OVES. For light-duty vehicles, naphthalene emssions were calculated as th e sum of PM 2.5 elemental carbon and PM 2.5 organic carbon emissions times a ratio of 0.088. Aggregate ratios from the running emissions were also applied to start emissi ons to develop overall toxic emi ssion inventories. E85 emissions were calculated in MOVES using the factors in Table 3-1.7.

3.1.1.4.2 Off-Road Gasoline

Emissions from nonroad gasoline equipment were developed by running the National Mobile Inventory Model (NMIM), a consolid ated emissions modeling system for EPA’s MOBILE6 and NONROAD models. 567 The key feature of NMIM is a national county database (NCD), which includes county-level informati on on temperatures, fuel properties, equipment populations, etc. NMIM runs MOBILE6 and NONROAD based on information in the NCD.

OOOOOOOO The MOVES fuel adjustment table developed for this analysis contained all E85 fuel effects from Table 3.1-9, including the not statistically significant NOx and NMHC results; however, only results pollutants identified as statistically significant in Table 3.1-9 are reported in the sensitivity case inventory results

450The NCD used to produce these inventories was updated as part of the 2005 National Emission Inventory (NEI) process. 568 The NCD also included the 2005 and 2022 fuels described in Section 3.1.1.2. The version of the NONROAD Model used included the effects of the recently signed Final Rule: Control of Emissions of Air Pollution from New Nonroad Spark-Ignition Engines, Equipment, and Vessels. 569 It is also capable of modeli ng the effects of gasoline blends containing 10 percent or less of ethanol.

Emissions from onroad and nonroad diesel e quipment were also developed by running NMIM (see above), using the same NCD and vers ion of the NONROAD Mode l described above. The version of MOBILE was MOBILE6.2. Dies el fuels are less fully characterized than gasoline, since the only property used by MOBILE and NONROAD is fuel sulfur.

Toxic emissions for off-road reference cas es were taken directly from NMIM. Inventories for the control case were develope d by applying ratios of the aggregate MOVES toxic exhaust, evaporative and refueling emi ssions for on-road gasoline for control versus reference case, to refere nce case toxic emissions for off-road from NMIM.

3.1.1.4.3 On-Road Diesel

Today’s proposal requires an increase in biodiesel to 0.81 billion gallons by 2022. As it is likely this will be consumed in a variety of blend levels (e.g. 20 percent, 5 percent, 2 percent) by light-duty diesel vehicles and off-road diesel equipment as well as heavy-duty diesel vehicles, we assumed for this analysis that the effects of biodiesel on emissions ar e linear with biodiesel concentration as demonstrated by Sze, et al, 570 and that impacts can be analyzed assuming all biodiesel is blended as BD20. We applied the BD20 effects discussed in Section 3.1.1.2.4 to baseline heavy-duty emissions generated by NM IM, as MOVES heavy-duty diesel estimates were not available in time for this analysis. Biodiesel impacts were using the following formula:

Biodiesel Impact P = Base HD Emissions P * Effect P * (Increase in BD20 Volume / Total Diesel Volume)

Where:

P = pollutant Effect = Percent change with BD20 blend from Section 3.1.1.3.3 Increase in B20 Volume= Change in B20 volume from 2022 reference case to control case in billion gallons of B20 blend (ie, change in gallons of biodiesel * 5) Total Volume = Total Highway Diesel Volume in 2022 in billion gallons

Toxic effects were calculated using the HC effects from Table 3.1-9

3.1.1.4.4 Portable Fuel Containers

There are several sources of emissions asso ciated with portable fuel containers (PFC) used for gasoline. These sources include vapor displacement and spillage while refueling the gas can at the pump, spillage during transport, perm eation and evaporation from the gas can during transport and storage, and vapor displacement a nd spillage while refueling equipment. As the

451calculation of emissions for re fueling non-road equipment incl udes spillage and some vapor displacement, these impacts are not included here.

As part of the 2007 regulation controlling em issions of hazardous pollutants from mobile sources (MSAT2 rule), EPA promulgated requireme nts to control VOC emissions from gas cans. The methodology used to develop emission inventories for gas cans is described in the regulatory impact analysis for the rule and in an accompanying technical support document. 571, 572

Based on the MSAT work, we generated tw o sets of hypothetical nationwide annual estimates of PFC VOC emissions, for calendar y ears 2017 and 2030, based on all E0 and all E10. Interpolation can be used to estimate PFC VOC em issions for the reference cases. Proportions of national E0 and E10 fuel use were calculated fr om the 2022 reference and policy scenarios. The reference case featured a mix of 89.1% E10 a nd 10.9% E0, while the policy case featured 100% E10. While E85 is used in flexible fueled highway vehicles, it is unlikely to be used in the near future in non-road equipment, and is therefore un likely to be stored or dispensed from PFCs.

MSATs found in liquid gasoline will be pr esent as a component of VOC emissions. These MSATs include benzene and naphthalene. Ethanol is present as well in VOC emissions from ethanol blends. Inventories for these pollu tants were estimated by the application of toxic to VOC ratios.

For benzene emissions from all sources except permeation, the following formula was used to calculate toxic to VOC ratios:

PFC Benzene Emissions = ⎛ Re fueling Benzene LDGV ⎞ PFC VOC Emissions × ⎜ ⎜ ⎟ ⎟ × 0.36 ⎝ Re fueling VOC LDGV ⎠

where the ratio of refueling benzene to VOC was estimated using average nationwide fuel properties for zero and 10 percen t ethanol gasoline from refinery modeling, done for RFS rule, and applied to EPA’s Complex M odel for reformulated gasoline. 573, 574 The 0.36 multiplier corrects for the difference in the percentage of gasoline in refueling emissions at 90o F, the temperature assumed for the algorithm in the Comp lex Model, versus a mo re typical lower fuel temperature of 60 o F for gas cans. The basis of this adjustment is discussed in more detail in the regulatory impact analysis for the Mobile Source Air Toxics Rule. An additional adjustment factor is applied to th e ratio for permeation emissions, based on a recent study 575 that suggests that the ratio of ben zene from permeation to total VOC fr om permeation is about 1.77 times higher than the ratio associated with evapor ation, according to the following formula:

PFC Benzene Emissions = ⎛ Re fueling Benzene ⎞ PFC VOC Emissions × ⎜ ⎜ LDGV ⎟ ⎟ × 0.36 x 1.77 ⎝ Re fueling VOC LDGV ⎠

452The resulting ratios for 0% and 10% ethanol did not differ at the fi fth decimal place, and were 0.0135 for all sources except for permeation, and 0.00239 for permeation. Thus, impacts of this rule on benzene emissions are due to th e overall impact of RVP changes on total VOC emissions.

A naphthalene to VOC ratio was estimated using the following formula:

PFC Naphthalene Emissions = ⎛ Evaporative Naphthalene LDGV ⎞ PFC VOC Emissions × ⎜ ⎜ ⎟ ⎟ × 0.0054 ⎝ Evaporative VOC LDGV ⎠

An evaporative naphthalene to VOC ratio for light-duty gasoline vehicles of 0.0004 was obtained from analyses done for the Mobile Source Air Toxics Ru le, and did not vary by fuel type. The 0.0054 adjustment was based on a recent analysis of average nationwide percentage of naphthalene in gasoline vapor fr om gasoline distribution with an RVP of 10 psi at 60 degrees Fahrenheit. 576, 577 The resulting ratio applied to PFC emissions was 0.0000022.

For E10 fuel, we assumed 16.74 percent of the evaporative emissions were ethanol (SPECIATE profile 1301) 578 and 33.34 percent of permeation emissions were ethanol. 579

3.1.1.4.5 Refueling Emissions

Refueling emissions were ca lculated by NMIM, based on MOBILE6 refueling module. Emissions are impacted by the increase in RVP due to ethanol, and also because the reduced energy density of ethanol would require more f illups. NMIM directly provides the emission increase due to increased RVP for the areas allowing the 1.0 psi waiver, so no additional processing was required to estimate RVP effects on refueling.

In order to estimate the emission impact of th e increase in refueling events, we developed ton per gallon refueling emission factors based on NMIM by dividing tota l refueling emissions from NMIM for each case by the number of gallons consumed in the AEO case. The ton per gallon emission factors were then applied to the total volume in gallons in each case. Fuel volumes for the control case, RFS 1 base, RFS 1 mandate and AE0 cases are listed in Table 3.1­ 10. Our estimates of total gallons were calcul ated from energy balance, reflecting the various number of gallons needed to consume the same en ergy. We assume the number of trips to the pump will increase in proportion to the incr eased gallons estimated for the rule.

Table 3.1-10. Gasoline Volumes (Billion Gallons)

RFS 1 Base RFS 1 Mandate AEO2007 Control Case

E0 77.95 107.51 16.03 0 E10 67.00 36.40 131.00 124.6 E85 0.00 0.00 0.11 29.3 Total Gallons 138.25 143.91 147.14 153.9

4533.1.2 Impact on Non- GHG Emissions from Fuel Production and Distribution

In addition to the effects of increased renewable fuel use on emissions from the vehicles and equipment that use the fuels, as discussed above, there are sh ifts in the fuel production and transport/distribution methods th at can have substantial impact s on emissions. These "upstream" emissions are associated with al l stages of biofuel production and distribution, including biomass production (agriculture, forestry), fertilizer and pesticide pro duction and transport, biomass transport, biomass refining (corn or cellulosic ethanol production f acilities), biofuel transport to blending/distribution terminals, and distribution of finished fuels to retail outlets. Additionally, changes in agricultural economics associated wi th increased biomass pr oduction can result in shifts in related agricultural production, such as livestock.

This section describes the changes in upstream emission sources and related emission rates connected with the renewable fuel use. The emission inventory impacts resulting from these changes are described in Section 3.2. This section is divided into two major sub-sections, the first covering emissions of cr iteria pollutants, thei r precursors, and ammonia, and the second covering non-criteria air toxic emissions and ethanol. The speci fic air toxics covered are: benzene, acetaldehyde, formaldehyde, 1,3-butadiene, acrolein, and naphthalene.

3.1.2.1 Upstream Criteria Pollutants

3.1.2.1.1 Agricultural Sector

Introduction

In prior EPA estimates, such as the RFS1 rule, changes in agricultural emissions were based solely on the increases in bushels of corn (and soybeans for biodies el), and the necessary acreage to produce those additional bushels. Given the greater pressure on farmland use likely in the 2022 timeframe for this proposed rule (15 billi on gallons of corn ethanol) compared to the 2012 assessment for RFS1 (6.7 or 9.6 billion gallons of ethanol depending on scenario), additional factors have been added to the agricultura l analysis, such as likely shifts of acreage to corn from certain other crops as corn prices increase.

The number of acres of cropland for corn, soy, and all other principle crops were estimated using the FASOM agriculture and forestry model, as described in Section 2.1 of this document. We are using the change in total ac res of planted cropland to estimate changes in certain agricultural emissions, such as tillage dust and crop residue burning, that are not directly calculated by FASOM. Another substantial s ource of agricultural emissions (especially ammonia and methane) is livestock. Changes in livestock-related emissions are estimated based on the change in head counts of cattle, sw ine, and poultry predicted by FASOM.

The impacts relative to the two RFS1 referen ce cases (3.64 and 6.7 billion gallons) rely only on applying ethanol volume pr oportions to the modeling result s of the AEO reference case (13.2 billion gallons). Due to th e complex interactions involved in projections in the agricultural modeling, we did not attempt to adjust the agricu ltural inputs of the AEO reference case for the other two reference cases. So th e fertilizer and pesticide quantit ies, livestock counts, and total

454agricultural acres were the same for all three refe rence cases. The agricultural modeling that had been done for the RFS1 rule itself was much simp ler and inconsistent with the new modeling, so it would be inappropriate to use those estimates. Thus, we plan to conduc t additional agricultural modeling specifically for the RFS1 mandate case prior to finalizing this rule.

3.1.2.1.1.2 VOC/NOx/CO/SOx/PM2.5

Criteria pollutants related to agricultural opera tions come from five major sources: farm equipment (mainly diesel engine emissions), fertilizer producti on and application, pesticide production and application, burni ng of crop residue, and fugiti ve dust from field tilling and related activities.

Agricultural Equipment Emissions

Changes in farm equipment emissions were estimated by multiplying an average fuelbased emission factor for diesel or gasoline farm equipment by the change in farm fuel consumption predicted by FASOM. The emission factors for each pollutant in units of grams emitted per million BTU of fuel burned were calculated from EPA NONROAD2005 nationwide modeling outputs for 2022 (pollutant tons emitted, gallons of fuel consumed) for each year of interest. The diesel emissions include all agricu ltural diesel equipment, which are dominated by agricultural tractors, while the gasoline emissions include only the limited number of larger agricultural gasoline-fueled equipment, such as tractors, combines, balers, swathers, and irrigation sets. The fuel energy contents (lower heating value) used for the unit conversions were 115,000 BTU/gallon for gasoline and 130,000 BTU/ga llon for diesel. For comparison, the corresponding 2020 emission factors from GREET ar e shown, where available. Most of the differences between NONROAD and GREET are sma ll and are likely attributable to the difference between 2020 and 2022 values. A nd although the gasoline equipment emission factors for VOC and CO from NONROAD are much greater than those used in GREET, this does not have much impact on emission inventorie s due to the small numb er of gasoline-fueled equipment used in agriculture relative to diesel equipment.

455Table 3.1-11. Agricultural Equipment Emission Factors (grams per mmBTU of fuel burned)

Pollutant Diesel Gasoline NONROAD GREET NONROAD GREET NOx 306 298 204 208
VOC 30.55 34.87 355.53 52.30
PM10 21.12 22.67 7.49 9.07 PM2.5 20.49 20.41 6.89 8.34
CO 130 136 10,067 204
Benzene 0.62

-- 11.90

-- Ethanol 0.00

-- 0.00

-- 1,3-Butadiene 0.057

-- 1.90

-- Acetaldehyde 1.62

-- 1.63

-- Formaldehyde 3.61

-- 3.17

-- Naphthalene 0.027

-- 0.66

-- Acrolein 0.09

-- 0.14

-- SO2 0.44

-- 15.88

-- NH3 0.68

-- 1.01

--

Fertilizer and Pesticide Production

The manufacturing processes for agricultural fertilizer and pesticides generate a variety of pollutants. The agricultural inputs from GREET provide emission factors in grams of pollutant per ton of nutrient for various types of fertilizers, herbicides, and insecticide, as shown in Table 3.1-13. These emission factors were multiplied by the changes in fertilizer and pesticide use predicted by FASOM, as shown in Table 3.1-12, to give projected changes in nationwide agricultural fe rtilizer and pesticide production emissions.

Table 3.1-12. Changes in Agricultural Chemical U se Relative to AEO Reference Case

Nitrogen (average) Phosphate (P2O5) Potash (K2O) Limestone (CaCO3) Herbicides Pesticides

Annual Short Tons 322,430 211,988 657,066 -239 -11,240 -781
Percentage 2.42% 7.17% 19.09% -2.12% -5.07% -1.86%

456Table 3.1-13. Agricultural Chemical Production & Transport Emission Factors (grams per ton of nutrient)

Pollutant Nitrogen (average) Phosphate (P2O5) Potash (K2O) Limestone (CaCO3) Herbicides Pesticides

NOx 1,605 4,484 734 573 19,371 21,628
VOC 2,761 240 40.7 56.8 1,575 2,040
PM10 454 1,551 148 506 10,840 11,746 PM2.5 262 1,018 74.5 167 4,869 5,479
CO 2,595 790 129 186 5,417 6,872
Benzene 0.00 0.00 0.00 0.00 3.21 4.16 Ethanol 0.00 0.00 0.00 0.00 0.00 0.00 1,3-Butadiene 0.000 0.000 0.000 0.000 0.576 0.745 Acetaldehyde 0.018 0.001 0.000 0.000 0.082 0.106 Formaldehyde 20.75 1.55 0.19 0.41 18.11 23.44 Naphthalene 0.033 0.117 0.010 0.039 114.4 124.0 Acrolein 0.009 0.001 0.000 0.000 0.024 0.031
SO2 703 53,299 321 701 11,300 12,895
NH3 0.00 0.00 0.00 0.00 0.00 0.00

Until the 1990s it was reasonable to assume th at all fertilizers and pesticides used on domestic agriculture were produced within the U.S. This has b een less true in recent years as more agricultural chemicals, especially fertil izers, are being imported from countries with a greater availability of natural gas at lower co sts. For greenhouse gase s the location of these emissions is of less importance, but for criteria pollutants and toxi cs it is important to reduce the estimated impacts by the percen tage of production and transpor tation occurring ou tside of the U.S. Using data from USDA 580, 581 the percentages applied from domestic sources are shown in Table 3.1-14. After applying these percentages to the produc tion and initial transportation portions of the GREET emission factors, the unadj usted final (domestic) transportation portion of the GREET emission factors was added back in. Since the relative emissions from production versus transportation vary by po llutant, the net adjustments to the GREET emission factors also vary by pollutant, as shown in the second Section of Table 3.1-14. To calculate an overall factor for nitrogen fertilizers, the proportions from GREET were used: 70.7% ammonia, 21.1% urea, and 8.2% ammonium nitrate. The pesticide adjustment does not vary by pollutant because virtually all of the pesticide emissions come from actual prod uction rather than transportation/distribution.

457Table 3.1-14. Domestic Fractions of Fertilizer and Pesticide Production Applied to Crops Nitrogen Fertilizers Potash Phosphate Pesticides

Domestic Fraction of Production 50% 20% 94% 76%

Net Adjustment to Production, Transportation & Distribution Emission Factor from GREET VOC 50.63% 94.85% 53.62% 76%

CO 52.47% 94.92% 54.37% 76% NOx 73.34% 94.92% 65.55% 76% PM10 52.64% 94.12% 24.06% 76% PM2.5 53.67% 94.14% 28.31% 76%

SOx 60.48% 94.02% 33.66% 76%

Fertilizer and Pesticide Application

In addition to the agricultural equipment em issions mentioned above, the application of fertilizer and pesticides (herbici des, insecticides, fungicides, etc. ) to agricultural fields causes the release of certain types of pollutants into the ai r. For nitrogen fertil izers the only pollutant considered to be significant is ammonia (NH3), the estimation of which is covered in Section 3.1.3.1.1.2. Pesticide application emissions are mainly VOC and vari ous individual organic compounds, most notably benzene and acrolein. A di scussion of the toxic pollutant emissions as a fraction of VOC is presented in Section 3. 1.3.2.2, but the resulti ng emission factors and inventory impacts are shown here in Table 3.1-15. There are also potential toxi city concerns with volatilization of the pesticide active ingredient s, and this is discussed in Section 3.4 of this document.

The basis of the pesticide a pplication emissions for this analysis was the 2002 NEI areasource inventory. The ton per year emissions da ta from the NEI was used with USDA pesticide application data for 2002 (or the nearest year for which data were collected) to generate an overall average estimate of the pesticide applicati on emissions per ton of pesticide applied. This ratio of pollutant tons (for VOC, benzene, and acrolein) per ton of pes ticide applied was then multiplied by the change in total pesticide tons used (including herbicides) as projected by FASOM and shown in Table 3.1-12 to give th e projected change in nationwide agricultural pesticide application emi ssions in Table 3.1-15.

458Table 3.1-15. Herbicide and Pesticide Application Emission Factors and Impacts Relative to AEO Reference Case

Pollutant Emission Factor Impact (tons per ton applied) (annual short tons)
VOC 0.543 -6,531
Benzene 0.142 -1,707 Acrolein 0.0036 -43.1

Agricultural Residue Burning Emissions

One source of air polluti on related to crop farming is the burning of crop residues. This practice is one of the methods that is used to cl ear fields between crop cycles so that the old crop residue does not build up and cl og or otherwise hinder the tilling of the fields in preparation for new crop planting. This practice is mainly used for grassy crops like wheat, rye, and barley, but in some areas it is also used for corn and other crops.

Crop residue burning produces substantial emi ssions of CO2, VOC, CO, NOx, as well as ammonia and toxic pollutants such as benzene, formaldehyde, 1,3-butadiene, and acrolein.

The use of crop residue burning is quite vari able from area to ar ea and among individual farmers, since there are alte rnative methods to deal with crop residue, including use of conservation tillage methods and equipment that allows planting through th e residue. In some locations and time periods crop residue burning has been prohibited by law, due to the possible health effects in near by residential areas.

Although the agricultu ral burning data in the NEI does not currently cover all states where crop residue burning occurs, the data available was used to generate a rough overall estimate of the average crop burning emissions per acre of planted crops. This was done using 2002 crop acreage data from USDA/NASS for th e counties covered by the NEI agricultural burning data. The overall ratio of pollutant tons (for each pollutant) per total acre farmed was then multiplied by the change in total planted acres projected by FASOM to give a projected change in nationwide agricultural burning emissions. These values are shown below in combination with the agricultural dust emissions in Table 3.1-12.

Agricultural Dust Emissions

Soil and related dust particles (e.g., fertilizer, pesticide, manure) be come airborne as a result of field tillage and anim al grazing/foraging, especially in drier areas of the country. Some of this dust is in a size range th at is a concern for human health and welfare. The NEI includes estimates of these particulate emissions by county.

As done for agricultural burning emissions, th e agricultural dust data from the 2002 NEI was used to generate an estimate of the average fugitive dust emissions per acre of planted crops

459for the crop related dust, and per head of cattle for the dust related to cattle. This was done using 2002 nationwide crop acreage and livestock inve ntory data from USDA/NA SS. The calculated pollutant tons (for PM) per total acre farmed wa s then multiplied by the change in total planted acres projected by FASOM to give a projected change in nationwide crop related dust emissions. And the calculated PM tons per head of cattle was multiplied by the change in cattle inventory projected by FASOM to give a pr ojected change in nationwide livestock related dust emissions.

The emission factors and inventory impacts of agricultural burning and fugitive dust from crop related acvities and livestock are shown in Table 3.1-16 and 3.1-17. The ton per year impacts for the crop-related emissions are ba sed on a modeled increase of 2,327,000 farmed acres (0.79%) in 2022 relative to the AEO refere nce case. The changes in fugitive dust from livestock operations are based on the head count changes shown in Table 3.1-19.

Table 3.1-16. 2022 Crop-related Burning and Dust Emission Impacts Relative to AEO Reference Case

Pollutant Emission Factors Inventory Impacts (Tons per thousand acres farmed) (annual short tons)

NOx 0.191 443.8
VOC 0.535 1,245
PM10 7.62 17,723
PM2.5 1.71 3,974
CO 6.13 14,255
Benzene 0.017 38.97 Ethanol 0 0 1,3-Butadiene 0.0073 17.06 Acetaldehyde 0.032 75.46 Formaldehyde 0.07 173.46 Naphthalene unknown unknown Acrolein 0.013 30.01
SO2 0.059 136.6
NH3 0.071 165.5

460Table 3.1-17. 2022 Livestock-related Dust Emission Impacts Relative to AEO Reference Case

Pollutant Beef Cattle Dust Emissions Dairy Cattle Dust Emissions (kg/head/year) (annual short tons) (kg/head/year) (annual short tons) PM10 0.888 -0.945 0.172 -0.008
PM2.5 0.089 -0.095 0.017 -0.001

3.1.2.1.1.2 Ammonia (NH3)

The two primary sources of ammonia emissions on farms are fertilizer application and livestock waste. Fertilizer application emi ssions were estimated using an average emission factor of 57,428.71 grams per ton of fertilizer nitr ogen applied for all forms of nitrogen, which is a weighted average of the standard EPA emission f actors that are used to generate the NEI. The weightings for each type of fertilizer come fr om USDA Economic Research Service data for 2006. The individual emission factors, weighti ngs, and resulting average emission factor are shown in Table 3.1-18. This average emission factor was multiplied by the nitrogen application quantities generated by the FASOM model for each scenario.

Table 3.1-18. Fertilizer Ammonia Emission Factors Fertilizer Type SCC Emission Factor (lbs NH3/Ton Nitrogen) USDA 2006 all crops Weighting Anhydrous Ammonia 2801700001 24 15.46% Aqua Ammonia 2801700002 24 1.61% Nitrogen Solutions 2801700003 61 40.88% Urea 2801700004 364 21.73% Ammonium Nitrate 2801700005 49 3.9% Ammonium Sulfate 2801700006 194 4.93% Ammonium Thiosulfate 2801700007 64 Other Straight Nitrogen 2801700008 61 11.49% Ammonium Phosphates 2801700009 97 N-P-K 2801700010 97

avg lbs/ton 126.61 avg grams/ton 57428.71

Changes in ammonia emissions from livesto ck waste were estimated using emission factors (kg/head/year) multiplied by the change in animal head counts predicted by FASOM. The ammonia emission factors and livestock head changes used in this analysis, along with resulting ammonia inventory impacts are shown in Table 3.1-19. This analysis was limited to these four types of livestock because they are the ones specifically modeled by FASOM.

461Table 3.1-19. Livestock Ammonia Emission Impacts Relative to AEO Reference Case
Livestock Type kg NH3 per head
per year a Head count change (million head) Percent change Change in NH3 emissions (annual short tons)
Beef Cattle 9 -1.065 -1.40% -9.58 Dairy Cattle 25 -0.047 -0.60% -1.18 Swine 5 -2.553 -2.15% -12.76 Poultry 0.22 -90.14 -0.97% -19.83

a Source: EPA/600/R-02-017, "Review of Emission Factors and Methodologies to Estimate Ammonia Emissions From Animal Waste Handling," April 2002.

Although it is a minor source of ammonia compar ed to fertilizer and livestock emissions described below, changes in farm equipment ammonia emissions were estimated by multiplying an average fuel-based emission factor for diesel or gasoline farm equipment by the change in farm fuel consumption predicted by FASOM. Th e ammonia emission factors in units of grams emitted per million BTU of fuel burned were calculated from the default ammonia emission factors used in the EPA NMIM model: 116 mg per gallon of gasoline burned; and 88.3 mg per gallon of diesel fuel burned.

3.1.2.1.2 Biofuel Production

Emissions from the production of biofuels include the emissions from the production facility itself as well as the emissions from pr oduction and transport of the biomass and any other fuels used by the biofuel plant, such as natural gas, coal, and electricity. The biomass feedstock production emissions are discu ssed above in the section on agricultural emissions. The calculation of emissions from corn ethanol, cellu losic ethanol, and biodi esel plants, including feedstock transport, was done using the basi c methodology of the GREET model. But some updates and enhancements were made to GREET, including updated feedstock energy requirements and estimates of excess electricity available for sale from new cellulosic ethanol plants, based on modeling by the National Re newable Energy Laboratory (NREL).

The facility emission factors used are shown in Table 3.1-20. The dry mill corn ethanol plant emission factors were developed for the R FS1 rule based on an average of state supplied data (RFS1 final rule RIA Table 3.4-2, ba sed on C. Jackson memo Docket EPA-HQ-OAR-2005­ 0161, April 6, 2007). The dry mill PM2.5 factor s and all the wet mill emission factors come from GREET 1.7. Emission factor s for cellulosic ethanol plants, biodiesel, and renewable diesel production were taken from GREET (version 1.8 a dded renewable diesel). Additional analyses are still being conducted to de termine potential decreased pl ant energy requirements and emission rates that may be feasible by 2022.

462Table 3.1-20. Biofuel Production Plant Emission Factors in 2022 (grams per gallon produced) Biofuel Plant Type VOC CO NOx PM10 PM2.5 SOx NH3 Corn Ethanol, Dry Mill NG 4.000 1.900 5.500 2.200 0.265 7.000 0.000 Corn Ethanol, Dry Mill NG (wet DGS) 4.000 1.900 5.500 2.200 0.222 7.000 0.000 Corn Ethanol, Dry Mill Coal 4.000 1.900 5.500 2.200 1.884 7.000 0.000 Corn Ethanol, Dry Mill Biomass 4.000 1.900 5.500 2.200 0.421 7.000 0.000 Corn Ethanol, Dry Mill Biomass (wet DGS) 4.000 1.900 5.500 2.200 0.313 7.000 0.000 Corn Ethanol, Wet Mill NG 2.330 1.039 1.677 0.998 0.288 0.012 0.000 Corn Ethanol, Wet Mill Coal 2.334 3.501 4.857 4.532 1.984 4.595 0.000 Cellulosic Ethanol (switchgrass or corn stover, enzymatic) 1.937 11.722 16. 806 2.792 1.116 0.625 0.000 Cellulosic Ethanol (forest waste, thermochemical) 0.363 5. 154 7.427 0.854 0.435 0.271 0.000 Biodiesel, Soybean oil 0.040 0.454 0.733 0.062 0.062 0.005 0.000 Renewable Diesel, Soybean Oil 0.029 0.329 0.530 0.045 0.045 0.004 0.000

3.1.2.1.3 Crude Oil Production/Transport/Refining

The estimate of emissions associated with production of gasoline and diesel fuel from crude oil is based on emission factors in th e GREET model. The act ual calculation of the emission inventory impacts of the decreased ga soline and diesel produc tion is done in EPA's spreadsheet model for upstream emission impacts. This model uses the decreased volumes of the crude based fuels and the various crude production and tr ansport emission factors from GREET to estimate the net emissions impact, whic h is shown below in Section 3.2 (see the displaced gasoline line of Table 3.2-5).

3.1.2.1.4 Finished Fuel Transport and Distribution

Transfer and Storage Evaporative Emissions from Gasoline, Gasoline/Ethanol Blends, and Ethanol -- VOC emissions are produced by transfer and storage activities associated with distribution of gasoline, gasoline/ ethanol blends, and et hanol. These are referred to as Stage I emissions. 582 Stage I distribution begins at the point the fuel leaves the production facility and ends when it is loaded into the storage tanks at dispensing facilities. There are five types of facilities that make up this distribution chain for gasoline. Bulk gasoline terminals are large storage facilities that receive gasoline directly from the refineries via pipelines, barges, or tankers (o r are collocated at refineries). Gasoline from the bulk terminal storage tanks is loaded into car go tanks (tank trucks or railca rs) for distribution to smaller intermediate storage facilities (bulk plants), or directly to gasoline dispensing facilities (retail public service stations and privat e service stations). When ethanol is blended into gasoline it usually occurs in the pipes which supply the tank trucks.

463There are two types of pipeline facilities found at various intervals along gasoline distribution pipelines: pipeline breakout stations a nd pipeline pumping stations. Pipeline breakout stations receive gasoline via pipelines, store it in stor age tanks, and re-inject it into pipelines as needed to meet the demand from downstream facilities. Pipeline pumping stations are located along the entire length of a pipeline at about 40 m ile intervals. Their purpose is to provide the extra ‘‘push’’ need ed to move the product throug h the pipeline. They do not normally have gasoline storage capability.

Bulk plants are intermediate storage and di stribution facilities that normally receive gasoline or gasoline/ethanol blends from bulk terminals via tank tr ucks or railcars. Gasoline and gasoline/ethanol blends from bulk plants are subsequently loaded into tank trucks for transport to local dispensing facilities.

Gasoline and gasoline/ethanol blend dispensing facilities include bot h retail public outlets and private dispensing operations such as rental car agencies, fleet vehicl e refueling centers, and various government motor pool facilities. Di spensing facilities receive gasoline and gasoline/ethanol blends via tank trucks from bulk terminals or bulk plants. Inventory estimates for this source category only include the delivery of gasoline at di spensing facilities and does not include the vehicle or equi pment refueling activities.

Emission factors (EFs) for gasoline were based on inventory estimates from the 2002 NEI. 583 We used these data to develop E0 gasoline emission factors even though the 2002 emissions included the E10 that was in the fuel pool at that time. In 2002 this was still a relatively small proportion of gasoline consumpti on, so it should not s ubstantially affect the national E0 estimates. Since etha nol is blended with gasoline at bulk terminals to produce E10 and E85 at the point fuel is loaded into tank trucks, we assumed bulk terminal emissions were associated with unblended gasoline. We then divi ded emissions into a refi nery to bulk terminal component and a bulk terminal to dispensing facili ty component. Total nationwide emissions for these two components were divi ded by the energy content of the total volume of gasoline distributed in 1999, to obtain th e emission factor in g/mmBTU. Total volume of gasoline was based on gasoline sales as reported by the Energy Information Administration. 584 These emission factors are provided in Table 3.1-21.

We also developed emission factors for Stage 1 emissions of E10 and E85 subsequent to blending at bulk terminals. Th ese emission factors were calc ulated by applying adjustment factors to the gasoline EF. The adjustment f actors for E10 and E85 were based on an algorithm from the 1994 On-Board Refueling Vapor Recovery Rule 585 :

EF (g/gal) = exp[-1.2798 - 0.0049( Δ T) + 0.0203(Td) + 0.1315(RVP)] (1)

where delta T is the difference in temperature be tween the fuel in the tank and the fuel being dispensed, and Td is the temperature of the gaso line being dispensed. We assumed delta T is zero, temperature of the fuel being dispensed averages 60 degrees over the year, and that the RVP of conventional gasolin e is 8.7 psi, 10% ethanol is 9.7, a nd 85% ethanol is 6.2. Using these assumptions, the adjustment factor is +14% fo r E10 and -30% for E85. Emission factors in grams per million BTU of fuel transf erred are given in Table 3.1-21.

464In addition to these Stage I emissions for ga soline and gasoline/ethanol blends, transport of ethanol to bulk terminals al so results in evaporative emi ssions of ethanol, a VOC. These emissions are estimated based on an adjustment to the gasoline transport VOC emissions to account for the much lower vapor pressure (appr oximately 3 psi at 100F for denatured ethanol versus 9 psi for gasoline) and molecular weight (48.7 for denatured ethanol versus approximately 72 for gasoline vapor). The net factor is 0.23 x gasoline evap VOC, resulting in the value shown for E100 in Table 3.1-21:

3.56 g/mmBTU of ethanol = 0.23 x gasoline VOC per-gall on EF / ethanol energy content = 0.23 x (10.2137 g/mmBTU x 115000 BTU/gal) / 76000 BTU/gal

Note that this very simplified methodology does not attempt to account for differences between ethanol and gasoline transport modes, dist ances, or transfer methods in movement of the fuel from production facility to the bulk distri bution terminal. We pl an to do a more thorough analysis of this for the final rule.

Significant evaporative emissi ons are not expected from storage and transport of biodiesel fuel due to its low volatility.

Table 3.1-21. VOC Emission Factors for Gasoline and Gasoline/Ethanol Blend Storage and Transf er Emissions (Stage 1) Process Blend EF(g/mmBTU) Refinery to Bulk Terminal E0 14.94 Refinery to Bulk Terminal E100 3.56 Bulk Terminal to Pump E0 27.79 Bulk Terminal to Pump E10 32.74 Bulk Terminal to Pump E85 25.93

Combustion Emissions from Transport and Distribution of Fuels and Feedstocks -- Emissions are produced by the vehicles and engi nes used to transport feedstocks such as crude oil, corn, and cellulosic biomass to fuel producti on facilities, as well as transpor t/distribution of the finished fuels from the production plants to distribution terminals and reta il outlets. For example, corn would be transported from farms and grain faciliti es to ethanol plants by truck and possibly rail. The finished ethanol would be transported from there to bulk distri bution terminals by truck, rail, or barge, and distribution from te rminal to retail outlet is by truc k. The emission factors for the year 2022 in Table 3.1-22 take into account the mix of newer better controlled engines (including trucks meeting the standards for 2008 and later engines 586 and engines meeting the 2008 locomotive/marine diesel engine rule 587 ), as well as any remaining older engines subject to less stringent standards. The truck EFs are given in terms of grams per vehicle mile traveled, while the other EFs are in grams per million BTU of fuel burned by the engine. Emission factors for these engines are taken from the rulemaking analyses.

465To estimate the net emission rates for the as sumed mix of transport modes for each fuel type, these emission factors were incorpor ated into a modified version of GREET 588 , since GREET 1.7 and 1.8 retained emissi on factors based only on earlier regulations. Thus, the miles traveled and quantities of fuel burned are those used by GREET for each transport mode and fuel being transported. For the final rule air quality an alysis we will have a more detailed analysis of miles and fuel volumes transported by mode within each county.

Table 3.1-22. 2022 Criteria Emissions from Fuel and Feedstock Transport/Distribution

Transport Mode Year VOC CO NOx PM10 PM2.5 Class 2B HD Diesel Trucks (g /mile) 2005 0.282 1.303 3.594 0.163 0.139 2022 (2020) 0.137 0.205 0.483 0.033 0.019 Medium HD DIesel Trucks (g /mile) 2005 0.653 2.482 8.297 0.309 0.271 2022 (2020) 0.289 0.417 1.243 0.053 0.035 Locomotive (g/mmBTU of fuel bur ned) 2005 84.733 212.861 1620.376 51.575 50.028

Barge (avg of C1 & C2 vessels) 589 2022 (2020)
2005 34.070 26.761 203.984 237.513 815.271 1276.901 19.015 47.923 18.445 46.485 (g/mmBTU of fuel burned) 2022 (2020) 15.527 188.994 676.097 22.017 21.356 Ocean Tanker (C3 vessels) 2005 79.943 181.746 2177.881 181.410 166.930 (g/mmBTU of fuel burned) 2022 (2020) 79.891 181.526 2054.819 181.118 166.666

3.1.2.2 Upstream Air Toxics

3.1.2.2.1 Upstream Air Toxics Reference Case

Air toxic emissions are associat ed with a variety of upstream processes. These processes include production of agricultural pesticides and fertilizers, as we ll as their application, operation of petroleum refineries, operati on of ethanol and biodiesel pr oduction facilities, operation of electrical production fac ilities which supply power to these facilities, and distribution of agricultural pesticides and fertilizers, feedstocks, gasoline, gasoline/ethanol and biodiesel blends.

Although a large number of compounds which are considered air toxics could be impacted by this rule, we focused on those which were identified as national and regional-scale cancer and noncancer risk drivers in the 1999 NATA 590 and were also likely to be significantly impacted by this rule. These compounds in clude benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein. Naphthalene impacts we re included for petroleum refineries, since it is a significant emission product fo r those facilities. Ethanol impacts were also included in our analyses because of health concerns (Section 3.4. 5) and its role as an acetaldehyde precursor.

2002 air toxic emissions for stationary sources, other than for fires, were obtained from the 2002 National Emissions Inventory (NEI), version 3. Future year emissions of benzene, 1,3­ butadiene, formaldehyde, acetaldehyde, and acrolein we re estimated for sectors only, rather than individual sources. These sect ors included non-EGU (electric generating unit) point sources,

466EGU point sources, the nonpoint storage and tr ansfer subsector, and other nonpoint sources. Emissions were estimated by applying future year to 2002 VOC ratios to 2002 air toxic estimates. Air toxics from fires were estima ted by applying toxics-to- VOC ratios to the VOC emissions from a fire inventory developed for air quality modeling. 2002 and future year ethanol emissions were estimated by speciating the VOC estimates. This was done using the Sparse Matrix Operator Kernel Emissi ons (SMOKE) modeling system, vers ion 2.3. More details on the methods and data used to devel op these inventories are found in a memo included in the docket for this rule. 591

Air toxic emission estimates for agricultura l equipment (mainly diesel agricultural tractors) were obtained from the EPA NMIM mode l, as described for criteria pollutants in Section 3.1.2.1.1.1.

3.1.2.2.2 Upstream Air Toxics Control Cases

As described below, we developed emission f actors for several air toxics using the most recent available data. These emission factors we re used with estimates of changes in fuel volumes and associated energy outputs to estimate inventory changes associated with the proposed program. In general, emission fact ors are expressed as grams per million BTU (g/mmBTU) of energy produced or distributed as part of the process. Underlying data are available in the docket for the rule.

Agricultural Pesticides and Fertilizers – The estimation of air toxic emissions from production and application of pesticides and fertilizers was done using toxic fractions of the corresponding VOC emissions described in Section 3.1.3.1.1.1. Table 3.1-23 shows the toxic fractions, which were calculated from the 2002 NEI inventories fo r VOC and each of the listed toxic pollutants. All the pollutants except acrolein from pesticide a pplication are based on nationwide inventories. California was the only state that reported ac rolein emissions associated with pesticide application, so the 0.66% valu e shown in the table represents the sum of acrolein emissions divided by the sum of VOC emissions from pesticid e application for all counties in California in 2002. The fertilizer and pesticid e application data come from queries of the NEI area source inventories for SCCs like "28017*" (for fertilizer application) and SCCs like "246180*" or like "246185*" (for pesticide application).

The production and blending data for fertilizer and pesticides come from queries of the NEI point source data that were submitted by 40 st ates and Puerto Rico for the following MACT codes 0911 - Pesticide Active Ingredient Production 0960 - Agricultural Chemicals and Pesticides Manufacturing 1410 - Phosphate Fertilizers Production The data for these codes was compiled for the fo llowing four categories: Fertilizer production (F), Fertilizer mixing blending (FMB), Pestic ide production (P), and Pe sticide mixing blending (PMB).

467Table 3.1-23. Air Toxic Fractions of VOC for Fertilizers and Pesticides Fertilizer Production & Blending Pesticide Production & Blending Fertilizer Application Pesticide Application

1,3-Butadiene

-­ 0.0003653

-­ -­ Acetaldehyde 6.530 E-06 5.198 E-05

-­ -­ Acrolein 3.320 E-06 1.513 E-05

-­ 0.0066 Benzene

-­ 0.002038

-­ 0.2615 Ethanol

-­ -­ -­ -­ Formaldehyde 0.007517 0.011494

-­ -­

Petroleum Refineries – Total nationwide emissions of air toxics for 153 U. S. petroleum refineries in 2002 were obtained from data collected as part of a risk and technology review (RTR) for EPA’s proposed rule, “National Emission Standards fo r Hazardous Air Pollutants From Petroleum Refineries.” 592 These emissions were divi ded by BTUs of energy produced by those refineries in 2002 to obtai n emission factors in g/mmBTU. Thus the resultant emission factors represent 2002 technology and emission sta ndards. Energy output estimates included all refinery products, such as conventional and refo rmulated gasoline, avia tion gasoline, jet fuel, kerosene, distillate fuel oil, residual fuel oil, petrochemical feedstocks, naphthas, lubricants, and other miscellaneous products. Energy output was estimated by multiplying volume of each product supplied 593 by its heating value in BTUs per gallon.

Resultant emission factors are provided belo w in Table 3.1-25, along with those for ethanol and electric ity production.

Ethanol Production Facilities – There are a number of processes at ethanol production facilities that result in emissions of air toxics. These processes include fermenta tion, distillation of the resultant mash, and drying of spent wet grain to produce animal feed. Emissions of air toxics vary tremendously from facility to facility due to a variety of factors, and it is difficult to determine how differences in the production proc esses individually impact emissions. Numerous production facilities have commen ced operation in the last few years. To develop emission factors we used the most recent available inventory for benzene, formaldehyde, acetaldehyde, and acrolein, from calendar year 2005. Th ese data were obtained from two sources:

1) 2005 NEI State submittals for SCCs associated with ethanol production facilities 2) the 2005 Toxics Release Inventory (TRI)

2005 NEI data submittals were obtained from EPA’s Office of Air Quality Planning and Standards. These data are include d in the docket for the rule. A dditional data for facilities not included in these submittals were obtained from the 2005 TRI ( http://www.epa.gov/triexplor er/list-chem ical-hap.htm ). Where emissions data were not available for a facility, the facility was excluded from subsequent calculations.

Only a few facilities reported very low emi ssions of 1,3-butadiene, and the rest reported no emissions, so emissions of this pollutant from ethanol production facil ities were assumed to

468be insignificant. Almost all of the data were from wet mill plants running on natural gas, so it was not possible to develop separate emission factors for wet and dry mill plants, or those running on coal or natural gas.

Energy output for each facility was estimat ed by multiplying production capacity by the heating value for ethanol. Since data on actual production by facility were not available, all plants were assumed to operate at capacity. Es timates of production capacity were obtained from data collected by the Renewable Fuels Association ( http://www.ethanolrfa.org/industry/locations/ ). For some major et hanol producers production capacity was not available for specific facilities.

Data for facilities where both emissions and production capacity were available were used to estimate nationwide emission rates in g/mmBTU. Table 3.1-24 lists the number of ethanol production facilities with emissions data for various ai r toxics, as well as production capacity estimates.

Table 3.1-24. Number of Facilities with Emission Inventory Data

by Pollutant and Production Capacity Estimates Pollutant No. of Facilities with Emissions Data and Production Capacity Estimates. Benzene 30 Formaldehyde 35 Acetaldehyde 50 Acrolein 22

An emission factor for ethanol was estimated using data collected in Minnesota from 16 facilities, all of which were dry mill plants. 594 Since most ethanol emissions occur during fermentation, and new production of ethanol is likely to occur at dr y mill facilities, these data are likely to provide representative estimates of futu re year increases in ethanol emissions under the control scenarios modeled. The resultant emissi on factors for ethanol pr oduction facilities are provided in Table 3.1-25.

Distillers’ grains with solubles (DGS) is a co-product of dry mill corn ethanol production that can be used as animal feed. Corn oil remaining in the DGS can be extracted and sold for commercial uses, such as biodies el production, at a relatively hi gh value compared to the DGS itself. The oil can be extracted by gravimetric me thods or by extraction with n-hexane, which is a potentially important toxic emission associat ed with increased ethanol production. Capital costs for solvent extraction are higher, but so are yields.

Corn oil for food grade use is produced by a process wherein corn is separated into component parts, prior to fermentation, with the starch heavy dehulled-degermed corn portion fed to the ethanol plant and the co rn germ fed to a hexane-based co rn oil extraction facility. This process is capital intensive and must be designe d into the plant. We expect the food grade extraction process to be less widespread than commercial grade processes for these reasons.

469VeraSun recently submitted an application to the Iowa Department of Natural Resources (IDNR) to add a facility for solv ent extraction of corn oil to an ethanol plant in Fort Dodge Iowa. 595 In this application, Verasun proposed to control particulate matter emissions from the process using a baghouse, and to minimize VOC emissions through good design and operating processes. Verasun estimated that this plant, with an annual DDGS capacity of 455,000 tons of DDGS per year, would produce 305 tons of VOCs pe r year, with n-hexane emissions of 295 tons per year. PM 10 emissions would be about 13 tons.

EPA used the Verasun application data to de velop an estimate of potential nationwide nhexane emissions from ethanol plants nationwid e. EPA estimates that about 40% of ethanol production will have corn oil extr action by 2022; thus, we assumed that about half of this would be from solvent extraction and 20% of dry mill plan ts would employ this process. It is likely a number of plants will use gravimetric recovery, since it can be easily retrofitted to any size plant at modest capital cost. First, we developed emission rates per ton of DDGS production. Then we developed an estimate of DDGS produced nationwide, using i ndustry characterization estimates of 13.67 billion gallons of dry mill ethanol production in 2022, and 0.00334 tons DDGS per gallon of ethanol produced by dry mills. 596 Multiplying the emission rate from the Verasun application by total production of DDGS , EPA estimates these facilities could emit about 9,000 tons of n-hexane na tionwide. However, given the very limited data on emissions from such facilities and the nascent nature of th is process at ethanol pr oduction facilities, such estimates should be regarded as highly uncertain.

Biodiesel Production Facilities -- To estimate emission factors for biodiesel productio n facilities, we identified air toxic emission data for individual facilities developed for the 2005 NEI. Unfortunately, only toxics data for two existing biodiesel facilities could be found. These data were used to develop toxic to VOC ratios, then applied to VOC emissi on factors for biodiesel plants obtained from GREET, with modifications to add energy used in crushing soybeans. VOC emission rates vary by feedstock. Toxic to VOC ratios, VOC emission rates, and resultant toxic emission rates in grams per gallon are give in Table 3.1-26.

Transportation and Distribution of Gasoline, Ethanol , Gasoline/Ethanol Ble nds and Biodiesel -- Air toxic emissions associated w ith distributing fuel and fuel blends come from two sources. The first source is evaporative, spillage and permeation emissions from storage and transfer activities, and the second source is emissions from vehicles and pipeline pumps used to transport the fuels. Since a pipeline system does not exist for ethanol, increased ethanol use is likely to increase toxic emissions from vehicles used to transport it, while a corresponding decrease in gasoline distribution would decrease any emi ssions related to pipeline pumping.

Storage and transfer activities result in evaporative emissi ons of benzene and ethanol from gasoline, ethanol, and gaso line/ethanol blends. Evaporative emissions from biodiesel fuel are not expected to be significant. Emissions of ethanol occur both during transport of ethanol from production facilities to bulk terminals, and af ter blending, at bulk terminals. In addition, emission factors for benzene must be estimated sepa rately for fuel before and after blending. As previously discussed, we assumed bulk termin al emissions were associated with unblended gasoline. We then divided emissions into a re finery to bulk terminal component and a bulk terminal to dispensing facility component. Be nzene emission factors for gasoline transport from

470refinery to bulk terminals were weighted by th e fraction of 2002 VOC emissions for this part of the process, whereas emission factors for E0 ga soline, E10 gasoline, and E85 were weighted by the fraction of 2002 VOC from the bulk terminal to the pump. Benzene emission rates from these activities also vary with the year being m odeled, since phase-in of the recently finalized Mobile Source Air Toxics Rule will substantially reduce the amount of benzene in gasoline beginning in 2011. 597 Thus, one set of emission factors were developed for 2002, and a separate set of emission factors for the reference case in that year. The reference case also includes impacts of the 2007 renewable fuels standard. 598 Thus, the reference case already reflects ethanol volumes mandated by RFS1.

The emission factors used for 2002 were derive d from the estimated gasoline distribution inventory for benzene in 1999, estimated for the Mobile Source Air Toxics Rule. 599 Total nationwide emissions were divi ded by the energy content of the total volume of gasoline distributed in 1999, to obtain th e emission factor in g/mmBTU. Total volume of gasoline was based on gasoline sales as reported by the Energy Information Administration. 600 To estimate the energy content, sales of fuel types (c onventional, Federal reformulated, California reformulated) were multiplied by their respective heating values.

The emission factors used for the reference case in 2022 were derived from an estimated gasoline distribution inventory for that year. This inventory estimate was calculated by linear interpolation of 2020 and 2030 inventories from the Mobile Source Air Toxics Rule. Total nationwide emissions were divi ded by the energy content of the total volume of gasoline projected for 2022 by the Energy Information Administration. 601 To estimate the energy content, the projected gasoline volume was multiplied by the heating value for low-sulfur gasoline (115,000 BTU/gallon).

We assumed that in order to attain the fuel benzene standard for gasoline promulgated in the Mobile Source Air Toxics Rule , E10 would have the same fuel benzene content per gallon as E0 . However, for E10 the E0 emission factor was adjusted to account for the lower energy content of E10 relative to E0. For E85, the E0 emission factor was adjusted to account for 66% lower benzene emissions per gallon, as well as the lower energy content of E85.

The emission factors for benzene are provided in Table 3.1-27.

To estimate ethanol emissions associated with the distribution of E10 and E85, ethanol to benzene emission ratios were applied to benzene estimates. The ratios were 14.8 for E10 and 112.8 for E85. The ratio for E10 was obtained from the profile for composite evaporative emissions from U. S. EPA’s SPECIATE database, profile 1301. 602 The ratio for E85 was obtained from analyses of evapor ative emissions from three vehi cles tested as part of the Auto/Oil program in the early 1990’s. 603 These emission factors are reported in Table 3.1-27.

471Table 3.1-25. Air Toxic Em ission Factors for Petrol eum Refineries, Ethanol Refineries, and Electricity Production (g/m mBTU of fuel or electricity produced)

Pollutant Petroleum Refinery Ethanol refinery Electricity Production 1,3-butadiene 0.0014 N. A. 0.0001 Acetaldehyde 0.0002 3.0585 0.0297 Acrolein 0.0001 0.1323 0.0115 Benzene 0.0264 0.0998 0.0443 Ethanol 0.0000 21.6858 Formaldehyde 0.0042 0.5263 0.0629 Naphthalene 0.0029

Table 3.1-26. Air Toxic Emi ssion Factors for Biodiesel Production Facilities (g/gallon produced)

Pollutant Toxic/VOC Ratio Biodiesel Soybean Oil EF (g/gal) Biodiesel Yellow Grease/tallow (g/gal) Renewable Biodiesel Soybean Oil (g/gal) VOC 0.040 0.042 0.029
Benzene 7.4x10 -7 3.0x10 -8 3.1x10 -8 2.1x10 -8
1,3-Butadiene 0 0 0 0 Formaldehyde 3.5x10 -5 1.4x10 -6 1.5x10 -6 1.0x10 -6 Acetaldehyde 5.6x10 -6 2.3x10 -7 2.4x10 -7 1.6x10 -7

Acrolein 4.8x10 -6 1.9x10 -7 2.0x10 -7 1.4x10 -7

Ethanol 0 0 0 0 Naphthalene 6.3x10 -7 2.5x10 -8 2.6x10 -8 1.8x10 -8

472Table 3.1-27. Air Toxic Evaporative Emi ssion Factors for Gasoline, Ethanol, and Blend Transport and Distribution (g/mmBTU of fuel transported)

Pollutant Process Year Fuel EF (g/mmBTU) Benzene Refinery to Bulk Terminal Refinery to Bulk Terminal Bulk Terminal to Pump Bulk Terminal to Pump Bulk Terminal to Pump Bulk Terminal to Pump 2002

2022

2002

2022

2022

2022 E0

E0

E0

E0

E10

E85 0.0488

0.0270

0.0908

0.0502

0.0519

0.0228

Ethanol Bulk Terminal to Pump 2022 E10 1.8591

Bulk Terminal to Pump 2022 E85 7.1432

As mentioned previously, etha nol vapor emissions during tran sport from the ethanol plant to the bulk terminal are based on an adjustme nt to the gasoline transport VOC emissions to account for the much lower vapor pressure and molecular weight.

There are also toxic emissions associated wi th combustion of fuels used in transport and distribution of feedstocks and fu els. The emission factors for th ese are shown in Table 3.1-28 as fractions of exhaust VOC, or PM10 for exha ust naphthalene. The VOC and PM10 emission factors that these fractions are applied to are presented above in Table 3.1-22. The locomotive, marine distillate, and residual boiler estimates come from a 2005 EPA report. 604 The heavy-duty diesel truck emission fractions come from a 2002 report docu menting the toxics module of EPA's MOBILE6.2 model, 605 and the pipeline values come from the EPA AP-42 document. 606

473Table 3.1-28. Toxic Fractions of Exhaust VOC (or frac tion of PM10 for exhaust naphthalene) (grams toxics per gram of VOC or PM10)

Mode Source 1,3­ Butadiene Acetaldehyde Acrolein Benzene Formaldehyde Naphthalene

Rail Diesel Locomotive 0.003246519 0.018786 0.0031238 0.002587511 0.04328653 0.0018716

Barge Marine Diesel -- Distillate 0.00061 0.074298 0.0035 0.020344 0.1496 0.0018716 Ocean Tanker Residual Boiler 0 0.003858 0 0.000165354 0.02645669 0.0025885

Truck HD Diesel Trucks 0.00061 0.0288 0.0035 0.0105 0.0782 0.00128892

Pipeline Natural Gas Turbines 0 0.019048 0.0030476 0.005714286 0.33809524 Gasoline Farm Equip HD Gasoline Trucks 0.088005387



### 3.2. Non-GHG Emission Impact Results

The reference case emission inventories used fo r this proposed rule are based on different sources depending on sector. For stationary/area sources and aircraft we used the 2002 National Emissions Inventory (NEI), Version 3, including the NEI projections for 2020. The development of these inventories is documented in the November 27, 2007, memo titled, "Approach for Developing 2002 andFuture Year National Emission Summaries," from Madeleine Strum to Docket EPA-HQ-OAR2007-0491. That memo summarizes the methodologi es and additional reference documents for criteria air pollutants (CAP) and mo bile source air toxics (MSATs). For onroad mobile sources we used a special version of the MOVES model that estimates emissions from light-duty and heavy-duty gasoline vehicles, except for motorcycles. For other onroad vehicles including diesel vehicles and motorcycles, we relied on the MOBILE6.2 model as run using the NMIM platform with county spec ific fuel properties a nd temperatures. Most nonroad equipment was modeled with NONROAD2005d using NMIM, which is a version of the NONROAD that includes the benefits of the two nonroad regulations published in 2008 (the locomotive and marine diesel rule and the sma ll spark-ignition and recreational marine engine rule). Inventories for locomotives and commercial marine vessels are not covered by the NONROAD model, and they have been updated since the 2002 NEI was published. Thus we used the more recent inventories published in the regulatory impact analyses of their respective recent rulemakings. Locomotives and C1/C2 comme rcial marine vessel inventories come from the spring 2008 final rule, and the C3 commerc ial marine emission inventory is from the Advance Notice of Proposed Rulemaki ng (ANPRM) published in late 2007.

474Table 3.2-1 shows the total 2022 mobile a nd non-mobile source inventory projections that were used as the basis for the impact percentages shown above in Table 3.2-1 through 3.2-4. The mobile source values in this table use the inventory values of the AEO reference case.

Table 3.2-1. 2022 Reference Case Emissions by Sector
VOC CO NOx PM10 PM2.5 SO2 NH3
Onroad Gasoline 909,960 26,250,531 1,333,977 48,472 44,633 34,031 390,486

Onroad Diesel 140,854 243,820 501,660 29,894 16,533 4,352 11,426 Nonroad Gasoline 1,434,660 14,718,809 228,632 54,432 50,077 1,422 1,112

Other Nonroad a 276,526 1,608,678 3,379,781 232,711 212,053 1,027,440 3,035 Stationary/Area 8,740,057 11,049,239 5,773,927 3,194,610 3,047,714 7,864,681 3,839,925

Total 11,502,057 53,871,078 11,217,977 3,560,119 3,371,010 8,931,926 4,245,983

Table 3.2-1 continued Benzene Ethanol 1,3­ Butadiene Acetaldehyde Formaldehyde Naphthalene Acrolein

Onroad Gasoline 25,363 43,875 3,282 6,623 7,866 4,037 384

Onroad Diesel 1,749 0 958 3,857 10,589 20 513 Nonroad Gasoline 26,193 71,433 4,935 4,033 7,245 713 436

Other Nonroad a 3,815 12 939 9,549 22,355 24 1,021 Stationary/Area 111,337 462,566 1,847 13,118 23,846 9,404 3,412

Total 168,457 577,885 11,961 37,181 71,901 14,198 5,766

a Nonroad diesel, LPG, CNG engines and all locomotive, aircraft, and commercial marine

Our projected overall emission impacts for the less sensitive and more sensitive cases are shown in Table 3.2-2 and 3.2-3 for 2022, showing the expected emission changes for the U.S. in that year, and the percent contribut ion of this impact relative to the total U.S. inventory across all sectors relative to each of the reference cases.

475Table 3.2-2. RFS2 “Less Sensitive” Case Emission Impact s in 2022 Relative to Each Reference Case

Pollutant RFS1 Base RFS1 Mandate AEO2007

Annual Short Tons % of Total US Inventory Annual Short Tons % of Total US Inventory Annual Short Tons % of Total US Inventory NOx 312,400 2.8% 274,982 2.5% 195,735 1.7%
HC 112,401 1.0% 72,362 0.6% -8,193 -0.07%
PM10 50,305 1.4% 37,147 1.0% 9,276 0.3%
PM2.5 14,321 0.4% 11,452 0.3% 5,376 0.16%
CO -2,344,646 -4.4% -1,669,872 -3.1% -240,943 -0.4%
Benzene -2,791 -1.7% -2,507 -1.5% -1,894 -1.1%
Ethanol 210,680 36.5% 169,929 29.4% 83,761 14.5% 1,3-Butadiene 344 2.9% 255 2.1% 65 0.5% Acetaldehyde 12,516 33.7% 10,369 27.9% 5,822 15.7% Formaldehyde 1,647 2.3% 1,348 1.9% 714 1.0% Naphthalene 5 0.03% 3 0.02% -1 -0.01%
Acrolein 290 5.0% 252 4.4% 174 3.0%
SO2 28,770 0.3% 4,461 0.05% -47,030 -0.5%
NH3 -27,161 -0.6% -27,161 -0.6% -27,161 -0.6%

Table 3.2-3. RFS2 “More Sensitive” Case Emission Impact s in 2022 Relative to Each Reference Case

Pollutant RFS1 Base RFS1 Mandate AEO2007

Annual Short Tons % of Total US Inventory Annual Short Tons % of Total US Inventory Annual Short Tons % of Total US Inventory NOx 402,795 3.6% 341,028 3.0% 210,217 1.9%
HC 100,313 0.9% 63,530 0.6% -15,948 -0.14%
PM10 46,193 1.3% 33,035 0.9% 5,164 0.15%
PM2.5 10,535 0.3% 7,666 0.2% 1,589 0.05%
CO -3,779,572 -7.0% -3,104,798 -5.8% -1,675,869 -3.1%
Benzene -5,962 -3.5% -5,494 -3.3% -4,489 -2.7%
Ethanol 228,563 39.6% 187,926 32.5% 105,264 18.2% 1,3-Butadiene -212 -1.8% -282 -2.4% -430 -3.6% Acetaldehyde 16,375 44.0% 14,278 38.4% 9,839 26.5% Formaldehyde 3,373 4.7% 3,124 4.3% 2,596 3.6% Naphthalene -175 -1.2% -178 -1.3% -187 -1.3%
Acrolein 253 4.4% 218 3.8% 143 2.5%
SO2 28,770 0.3% 4,461 0.05% -47,030 -0.5%
NH3 -27,161 -0.6% -27,161 -0.6% -27,161 -0.6%

476Fuel production and distribu tion emission impacts of th e proposed program were estimated in conjunction with the development of life cycle GHG emission impacts, and the GHG emission inventories discussed in chapter 2. These emissions are calculated according to the breakdowns of agriculture, feedstock transport, fuel production, and fuel distribution; the basic calculation is a function of fuel volumes in the analysis year and the emission factors associated with each process or subprocess. Additionally, the emission impact of displaced petroleum is estimated, using the same domestic/import shares discussed in chapter 2.

In general the basis for this life cycle evalua tion was the analysis conducted as part of the Renewable Fuel Standard (RFS1) rulemaking, bu t enhanced significantly. While our approach for the RFS1 was to rely heavily on the “Gr eenhouse Gases, Regulated Emissions, and Energy Use in Transportation” (GREET) model, deve loped by the Department of Energy’s Argonne National Laboratory (ANL), we are now able to take advantage of additional information and models to significantly strengthen and expand our an alysis for this proposed rule. In particular, the modeling of the agriculture sector wa s greatly expanded beyond the RFS1 analysis, employing economic and agriculture models to consider factors such as land-use impact, agricultural burning, fert ilizer, pesticide use, livestock, cr op allocation, and crop exports.

Other updates and enhancements to the GREET model assumptions include updated emission factors for NOx, CO, and SO2 from new cellulosic ethanol plant modeling by the National Renewable Energy Labor atory (NREL), and updated fuel and feedstock transport emission factors that account for recent EPA emi ssion standards and modeling, such as the Tier 4 diesel truck standards publishe d in 2004 and the locomotive and commercial marine standards finalized in 2008. Emission fact ors for new corn ethanol plants continue to use the values developed for the RFS1 rule, which were based on data submitted by states for dry mill plants. There are no new standards planned at this tim e that would offer any additional control of emissions from corn or cellulosi c ethanol plants. In addition, GREET does not include air toxics or ethanol. Thus emission fact ors for ethanol and the following ai r toxics were added: benzene, 1,3-butadiene, formaldehyde, acetaldehyde, acrolein and naphthalene.

Results of these calculations relative to each of the referenc e cases for 2022 are shown in Table 3.2-4 for the criteria pollutants, ammonia, et hanol and individual air toxic pollutants. It should be noted that the impacts relative to the two RFS1 reference cases (3.64 and 6.7 billion gallons) rely on applying etha nol volume proportions to the modeling results of the AEO reference case (13.2 billion gallons ). Due to the complex interactions involved in projections in the agricultural modeling, we cannot say how reliable this simple proportional methodology is, and the modeling that had been done for the RFS1 rule itself was much simpler and inconsistent with the new modeling. Thus, we plan to c onduct additional agricultu ral modeling specifically for the RFS1 mandate case prior to finalizing this rule.

The fuel production and di stribution impacts of the proposed program on VOC are mainly due to increases in emissions connected with biofuel production, countered by decreases in emissions associated with ga soline production and distribution as ethanol displaces some of the gasoline. Increases in NOx, PM2.5, and SO x are driven by combustion emissions from the substantial increase in corn and cellulosic ethanol production. Etha nol plants (corn and cellulosic) tend to have greater combustion emi ssions relative to petrol eum refineries on a per477British thermal unit (BTU) of fuel produced basis. Increases in SOx emissions are primarily due to corn ethanol production. Ammonia emissions are expected to decrease substantially due to lower livestock counts, which more than offset s increased ammonia from fertilizer use.

Ethanol vapor and most air toxic emissi ons associated with fuel production and distribution are projected to increase. Relative to the US total reference case emissions with RFS1 mandate ethanol volumes, increases of 10 - 20 percent for acetaldehyde and ethanol vapor are especially significant because they are driv en directly by the increased ethanol production and distribution. Formaldehyde a nd acrolein increases are smaller, on the order of 1 - 5 percent. Benzene emissions are estimated to decrease by 1 percen t due to decreased gasoline use. There are also very small increases in 1,3-butadiene and decreases in naphthale ne relative to the US total emissions.

Table 3.2-4. Fuel Production and Distri bution Impacts in 2022 Relati ve to Each Reference Case

Pollutant RFS1 Base RFS1 Mandate AEO2007

Annual Short Tons % of Total US Inventory Annual Short Tons % of Total US Inventory Annual Short Tons % of Total US Inventory NOx 241,041 2.1% 222,732 2.0% 183,951 1.6%
HC 77,295 0.7% 46,702 0.4% -17,501 -0.2%
PM10 50,482 1.4% 37,324 1.1% 9,453 0.3%
PM2.5 14,419 0.4% 11,550 0.3% 5,473 0.16%
CO 186,559 0.3% 179,855 0.3% 165,656 -0.5%
Benzene -1,670 -1.0% -1,686 -1.0% -1,719 -1.0%
Ethanol 115,187 19.9% 100,134 17.3% 68,379 11.8% 1,3-Butadiene 16 0.13% 16 0.14% 17 0.14% Acetaldehyde 7,460 20.1% 6,680 18.0% 5,029 13.5% Formaldehyde 877 1.2% 800 1.1% 638 0.9% Naphthalene -6 -0.04% -5 -0.04% -4 -0.03%
Acrolein 278 4.8% 244 4.2% 174 3.0%
SO2 28,770 0.3% 4,461 0.05% -47,030 -0.5%
NH3 -27,161 -0.6% -27,161 -0.6% -27,161 -0.6%

A breakout of these upstream emissions by where they occur in the production/distribution chain is show n in Table 3.2-5. The displaced gasoline line of this table refers to the impacts of decr easing the petroleum based gasolin e and diesel fuel production as some of the needed energy is replaced with ethanol and biodiesel fuels.

478Table 3.2-5. Emission Inventory Impacts by Fuel Production/D istribution Segment Relative to the AEO Reference Case (annual short tons)

VOC CO NOx PM10 PM2.5 SO2 NH3 Agriculture -4,619 15,449 -8,454 17,074 4,108 12,773 -27,161
Biofuel Feedstock Transport 534 786 2,201 348 149 917 0

Biofuel Production 32,278 159,188 217,975 5,372 6,150 -37,629 0 Biofuel Transport & Distribution -2,217 3,610 12,755 470 355 1,147 0
Displaced gasoline -43,477 -13,377 -40,526 -13,812 -5,288 -24,238 0
Total Upstream -17,501 165,656 183,951 9,453 5,473 -47,030 -27,161

Table 3.2-5 continued Benzene Ethanol 1,3­ Butadiene Acetaldehyde Formaldehyde Naphthalene b Acrolein
Agriculture -1,665 0 17.71 75 179 -1.25 -13 Biofuel Feedstock Transport 6 0 0.33 15 42 0.45 2

Biofuel Production 156 37,856 -0.01 4,986 835 0.01 191

Biofuel Transport & Distribution -89 30,523 1.78 17 40 0.84 2

Displaced gasoline -127 0 -3.22 -64 -458 -4.21 -8

Total Upstream -1,719 68,379 16.59 5,029 638 -4.17 174

Tables 3.2-6 and 3.2-7 summ arize the vehicle and equipm ent emission impacts in 2022 for the “less sensitive” and “ more sensitive ” cases, including the biodie sel impacts. Table 3.2-8 shows that the biodiesel contributio n to these impacts is quite small. While the three fuel effect scenarios were only modeled for passenger cars and trucks, these totals reflect the net emissions from all mobile sources, including passenger cars and trucks, heavy duty tr ucks, off-road sources and portable fuel containers, us ing the same emissions in all three cases for the non-passenger car/truck categories. A full description of th e basis of these vehicle and equipment emission impacts is given in Section 3.1.1 of this document.

479Table 3.2-6. 2022 Vehicle and Equipment Emission Impacts by Fuel Type for the “Less Sensitive” Case Re lative to Each Reference Case

Pollutant RFS1 Base RFS1 Mandate AEO2007

Annual Short Tons % of Total US Inventory Annual Short Tons % of Total US Inventory Annual Short Tons % of Total US Inventory NOx 71,359 0.6% 52,250 0.5% 11,784 0.11%
HC 35,106 0.3% 25,659 0.2% 9,308 0.08%
PM10 -177 0.00% -177 0.00% -177 0.00%
PM2.5 -98 0.00% -98 0.00% -98 0.00%
CO -2,531,205 -4.7% -1,849,728 -3.4% -406,599 -0.8%
Benzene -1,122 -0.7%

- 821 -0.5% -174 -0.1%
Ethanol 95,493 16.5% 69,795 12.1% 15,383 2.7% 1,3-Butadiene 328 2.7% 238 2.0% 48 0.4% Acetaldehyde 5,057 13.6% 3,689 9.9% 793 2.1% Formaldehyde 771 1.1% 548 0.8% 76 0.11% Naphthalene 10 0.07% 8 0.05% 3 0.02%
Acrolein 12 0.2% 8 0.14% -0.4 -0.01%
SO2 0 0.0% 0 0.0% 0 0.0%
NH3 0 0.0% 0 0.0% 0 0.0%

480Table 3.2-7. 2022 Vehicle and Equipment Emission Impacts by Fuel Type for the “More Sensitive” Case Relative to Each Reference Case

Pollutant RFS1 Base RFS1 Mandate AEO2007

Annual Short Tons % of Total US Inventory Annual Short Tons % of Total US Inventory Annual Short Tons % of Total US Inventory NOx 161,754 1.4% 118,295 1.1% 26,266 0.2%
HC 23,018 0.2% 16,828 0.15% 1,553 0.01%
PM10 -4,289 -0.12% -4,289 -0.12% -4,289 -0.12%
PM2.5 -3,884 -0.12% -3,884 -0.12% -3,884 -0.12%
CO -3,966,131 -7.4% -3,284,654 -6.1% -1,841,524 -3.4%
Benzene -4,293 -2.6% -3,808 -2.3% -2,770 -1.6%
Ethanol 113,376 19.6% 87,792 15.2% 36,886 6.4% 1,3-Butadiene -228 -1.9% -298 2.5% -446 -3.7% Acetaldehyde 8,915 24.0% 7,598 20.4% 4,809 12.9% Formaldehyde 2,497 3.5% 2,324 3.2% 1,958 2.7% Naphthalene -170 -1.2% -172 -1.2% -182 -1.3%
Acrolein -25 -0.4% -27 -0.5% -31 -0.5%
SO2 0 0.0% 0 0.0% 0 0.0%
NH3 0 0.0% 0 0.0% 0 0.0%

Table 3.2-8. 2022 Biodiesel Emission Impacts Relative to All Reference Cases (these impacts are included in Tables 3.2-5 and 3.2-6)

Pollutant Biodiesel Impacts
Annual Short Tons NOx 418
HC -753
PM10 -177
PM2.5 -98
CO -1,275
Benzene -9.4
Ethanol 0.0 1,3-Butadiene -5.1 Acetaldehyde -21 Formaldehyde -57 Naphthalene -0.12
Acrolein -2.7
SO2 0.0
NH3 0.0

481Table 3.2-9 shows a breakout of the relative impacts of this proposal on the various types of vehicle and equipment emissions for the more sensitive case relative to the AEO reference case. The gasoline vehicle exhaust emission values were generated by MOVES, while the NMIM model was used to generate the other vehicle and equipment emission impacts. The impacts on portable fuel container emissions were estimated using an analysis of available data, adjusted for the ethanol and ga soline fuel volumes in this proposal. The methods used are described above in Section 3.1.1. The subs tantial CO and PM reduc tions from light-duty vehicles are due to the effects of E85. Evaporative and refue ling vapor emissions only include VOC, benzene, and naphthalene.

Table 3.2-9. Vehicle and Equipment Emi ssion Inventory Impacts by Source Type for the “More Sensitive” Case Relative to the AEO Reference Case (annual short tons)
VOC a CO NOx PM10 PM2.5 SO2 NH3 Light-duty gasoline vehicle exhaust -1,945 -1,418,363 14,258 -4,112 -3,786 0 0 Light-duty gasoline vehicle evap -2,549 n/a b n/a n/a n/a n/a n/a Light-duty gasoline vehicle refueling 4,476 n/a n/a n/a n/a n/a n/a Heavy-duty gasoline vehicle exhaust -141 839 1,060 0 0 0 0

Heavy-duty gasoline vehicle
evap 44 n/a n/a n/a n/a n/a n/a

Heavy-duty gasoline vehicle refueling 977 n/a n/a n/a n/a n/a n/a

Nonroad gasoline equipment exhaust -6,810 -422,725 10,530 0 0 0 0 Nonroad gasoline equipment evap 7,216 n/a n/a n/a n/a n/a n/a Nonroad gasoline equipment refueling 6,609 n/a n/a n/a n/a n/a n/a

Portable fuel containers 1,037 n/a n/a n/a n/a n/a n/a

Onroad diesel vehicles -753 -1,275 418 -177 -98 0 0

482Table 3.2-9 continued Benzene Ethanol 1,3­ Butadiene Acetaldehyde Formaldehyde Naphthalene b Acrolein

Light-duty gasoline vehicle exhaust -2,758 19,850 -474 4,489 1,948 -180 -29 Light-duty gasoline vehicle evap 27 7,981 n/a n/a n/a -4 n/a Light-duty gasoline vehicle refueling 15 1,034 n/a n/a n/a 2 n/a Heavy-duty gasoline vehicle exhaust -19 109 0.46 33 4.6 0 0

Heavy-duty gasoline vehicle
evap 0.39 77 n/a n/a n/a 0.02 n/a

Heavy-duty gasoline vehicle refueling 3 214 n/a n/a n/a 0.11 n/a

Nonroad gasoline equipment exhaust -156 3,071 32 308 63 0 1.2 Nonroad gasoline equipment evap 128 4,937 n/a n/a n/a 0 n/a Nonroad gasoline equipment refueling 108 1,645 n/a n/a n/a 0 n/a

Portable fuel containers -0.30 646 n/a n/a n/a 0.13 n/a

Onroad diesel vehicles -9 0 -5 -21 -57 -0.12 -3

a "VOC" values shown are actually THC for onroad gasoline exhaust and evaporative emissions.
b n/a = Not applicable

Table 3.2-10 shows the relative impacts of various types of renewable fuels on the basis of tons per million BTUs of renewable fuel consumed. These values include all vehicle/equipment as well as upstream fu el production/distribution impacts.

483Table 3.2-10. Emission Invent ory Impacts (tons per mmBTU) by Type of Renewable Fuel Relative to the AEO Reference Case
VOC CO NOx PM10 PM2.5 SO2 NH3
Ethanol from domestic corn (except coal) 32.50 -1,131 57.45 41.72 3.62 111.30 -18.85

Ethanol from domestic corn (coal) -72.28 -1,038 -12.47 332.23 79.56 -131.38 -11.85

Ethanol from domestic cellulosic -9.32 -1,035 131.30 -0.40 1.69 -55.03 -18.88

Ethanol from imported
sugarcane -23.82 -1,174 -30.37 -10.54 -5.33 -13.71 0.29

Biodiesel -37.11 -15.20 5.02 4.97 0.35 10.78 -11.04

Renewable Diesel -24.37 -3.85 -10.63 -2.69 -0.82 -4.85 0.00
Ethanol from domestic corn (except coal) -0.94 43.35 0.0120 3.42 0.50 -0.0025 0.64

Ethanol from domestic corn (coal) -0.60 43.35 0.0073 3.40 0.44 -0.0022 0.46

Ethanol from domestic cellulosic -0.94 47.27 0.0119 3.67 0.51 -0.0026 0.63

Ethanol from imported
sugarcane 0.12 19.39 -0.0008 -0.0272 -0.25 -0.0020 0.505

Biodiesel -0.61 2.89E-07 0.0057 0.0095 -0.15 -0.0026 0.289

Renewable Diesel -0.072 0 -0.0012 -0.0273 -0.25 -0.0022 -0.0035



### 3.3 Air Quality Impacts

Section 3.2 of this DRIA presents the proj ected emissions changes due to the proposed rule. Once the emissions changes are projected, th e next step is to look at how the ambient air quality would be impacted by those emissions cha nges. This section presents information about ambient air quality, including disc ussion of the methodology that we will use to perform ambient air quality modeling for the final rule. Section 3. 4 describes the health effects associated with criteria and air toxic pollutan ts. Section 5.4 describes the methodology for calcu lating monetized benefits of this rule due to reductions in advers e health effects associat ed with PM2.5 and ozone.

4843.3.1 Air Quality Modeling

Air quality models use mathematical and numer ical techniques to simulate the physical and chemical processes that affect air pollutants as they disperse and react in the atmosphere. Based on inputs of meteorological data and source information, these models are designed to characterize primary pollutants that are emitted directly into the atmosphere and secondary pollutants that are formed as a result of complex chemical reactions within the atmosphere. Photochemical air quality models have become widely recognized and routinely utilized tools for regulatory analysis by assessing the effectiveness of control strategi es. These models are applied at multiple spatial scales - local, regional, national, and global. Section 3.3.1.2 of this DRIA provides more detail on the photochemical mode l, the Community Multi-scale Air Quality (CMAQ) model, which will be utilized for the final rule analysis.

3.3.1.1 Current and Projected Ambient Levels of Pollutants

Although the purpose of this pr oposal is to implement the renewable fuel requirements established by the Energy Independence and Secu rity Act (EISA) of 2007, this proposed rule would also impact emissions of criteria and air toxic pollutants. This section describes current and recently projected ambient levels of ozone, PM and some air toxics without the standards being proposed in this rule.

3.3.1.1.1 Particulate Matter (PM 2.5 and PM 10 )

As of December 16, 2008, approximately 88 m illion people live in the 39 areas that are designated as nonattainment for the 1997 PM 2.5 National Ambient Air Quality Standard (NAAQS). These PM 2.5 nonattainment areas are comprised of 208 full or partial counties. The 1997 PM 2.5 NAAQS was recently revi sed and the 2006 24-hour PM 2.5 NAAQS became effective on December 18, 2006. Area designations for the 2006 24-hour PM 2.5 NAAQS are expected to be promulgated in 2009 and become effective 90 da ys after publication in the Federal Register.

As of December 16, 2008, approximately 26 m illion people live in the 47 areas that are designated as nonattainment for the PM 10 NAAQS. There are 40 full or partial counties that make up the PM 10 nonattainment areas.

EPA has already adopted ma ny mobile source emission c ontrol programs that are expected to reduce ambient PM levels. Thes e control programs include the Marine SparkIgnition and Small Spark-Igniti on Engine rule (73 FR 59034, Oc tober 8, 2008), Locomotive and Marine Compression-Ignition Engine Rule (73 FR 25098, May 6, 2008), the Clean Air Nonroad Diesel rule (69 FR 38957, June 29, 2004), the Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur C ontrol Requirements (66 FR 5002, Jan. 18, 2001) and the Tier 2 Motor Vehicle Emissions Standards and Gasolin e Sulfur Control Requirements (65 FR 6698, Feb. 10, 2000). As a result of these programs, the number of areas that fail to meet the PM 2.5 NAAQS in the future is expected to decrease. This decrease is shown in the PM air quality analyses that were performed for the Marine Spark-Ignition a nd Small Spark-Ignition Engine

485rule. P PPPPPPP That analysis projected that there would be 11 counties ( down from 208 counties currently designated as nonattainment), where 24 million people are projected to live, with PM 2.5 design values at or above the 2006 annual PM 2.5 standard in 2020.

3.3.1.1.2 Ozone

As of December 16, 2008, approximately 132 m illion people live in the 57 areas that are designated as nonattainment for the 1997 8-hour ozone NAAQS. There are 293 full or partial counties that make up the 8-hour ozone nonattainment areas. The 1997 ozone NAAQS was recently revised and the 2008 ozone NAAQS was finalized on March 12, 2008. Table 3.3-2 presents the number of counties in areas cu rrently designated as nonattainment for the 1997 ozone NAAQS as well as the number of additional counties that have monitored data that is greater than the 2008 ozone NAAQS.

Table 3.3-2. Counties with Design Values Greater than the 2008 Ozone NAAQS Based on 2004-2006 Air Quality Data Number of Counties Population a 1997 Ozone Standard: counties within the 57 areas currently designa ted as nonattainment 293 131,977,890

2008 Ozone Standard: additional counties that would not meet the 2008 NAAQS b 74 15,984,135

Total 367 147,962,025

Notes:

a Population numbers are from 2000 census data.
b Attainment designations for the 2008 ozone NAAQS have not yet been made. Nonattainment for the 2008 Ozone NAAQS will be based on three years of air quality data fr om later years. Also, the county numbers in the table include only the counties with monitors violating the 200 8 Ozone NAAQS. The numbers in this table may be an underestimate of the number of counties and populations th at will eventually be included in areas with multiple counties designated nonattainment.

EPA has already adopted ma ny mobile source emission c ontrol programs that are expected to reduce ambient ozone levels. Thes e control programs incl ude the Marine SparkIgnition and Small Spark-Igniti on Engine rule (73 FR 59034, Oc tober 8, 2008), Locomotive and Marine Compression-Ignition Engine Standard s (73 FR 25098, May 6, 2008), the Clean Air Nonroad Diesel rule (69 FR 38957, June 29, 2004), the Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requireme nts (66 FR 5002, Jan. 18, 2001) and the Tier 2 Motor Vehicle Em issions Standards and Gasoline Sulfur Control Requirements (65 FR 6698, Feb. 10, 2000). As a result of these pr ograms, the number of areas that fail to meet the 8-hour ozone NAAQS in the future is expected to decrease. This decrease in areas that fail to meet the 8-hour ozone NAAQS is shown in the oz one air quality analyses that were performed for the Marine Spark-Ignition and Small Spark-Ignition Engine rule. Q QQQQQQQ That analysis projected that there would be 8 counties (down from 293 coun ties currently designated as

PPPPPPPP Details on the air quality modeling are provided in the RIA for the Marine Spark-Ignition and Small SparkIgnition Engine final rule, included in the docket for this proposed rule. QQQQQQQQ Details on the air quality modeling are provided in the RIA for the Marine Spark-Ignition and Small Spark-Ignition Engine final rule, included in the docket for this proposed rule.

486nonattainment), where 22 million people are projected to live, with 8-hour ozone design values at or above 85 ppb in 2020.

3.3.1.1.3 Air Toxics

According to the National Air Toxic Assessm ent (NATA) for 1999, mobile sources were responsible for 44 percent of outdoor toxic emissi ons and almost 50 percent of the cancer risk. Benzene is the largest co ntributor to cancer risk of all 133 pollutants quantitatively assessed in the 1999 NATA. Mobile sources were responsible for 68 percent of benzen e emissions in 1999. Over the years, EPA has implemented a number of mobile source and fuel controls resulting in VOC reductions, which also reduce benzene and ot her air toxic emissions. Recent fuel and vehicle standards were implemente d to specifically control benzen e emissions, and it is expected that overall levels of air toxics will decrease through 2020, afte r which concentrations start to increase slowly due to expected incr eases in vehicle and engine activity. 607

However, as presented in Section 3.2 of this DRIA, there would be a substantial increase in emissions of ethanol and acetaldehyde from the proposed fuels standards. This suggests a likely increase in ambient levels of acetalde hyde. Formaldehyde and acr olein emissions would also increase somewhat, while emissions of benzene and 1,3-butadiene would decrease as a result of the proposed standards. Full-scale photochemical modeli ng is necessary to provide the needed spatial and temporal detail to more comp letely and accurately estimate the changes in ambient levels of both criteria and non-criteria pollutants. The plans for full-scale photochemical air quality modeling are discussed imme diately below in Section 3.3.1.2.

3.3.1.2 Community Multi-scale Ai r Quality (CMAQ) Modeling Plans

Full-scale photochemical air qual ity modeling is necessary to accurately project levels of PM 2.5 , ozone and air toxics. For the final rule, a national-scale air qualit y modeling analysis will be performed to analyze the impacts of the standards on PM 2.5 , ozone, and selected air toxics. The length of time needed to prepare necessary inventory and model update s precluded us from performing air quality modeling for this proposal.

The atmospheric chemistry related to ambient concentrations of PM 2.5 , ozone and air toxics is very complex, and making predictions based solely on emissions changes is extremely difficult. Localized changes in VOC and NOx emissions, as well as direct PM and PM precursors may impact concentrations of PM a nd ozone. Air quality modeling will allow us to account for both changes in the spatial distributio n of PM and PM precursors, and changes in VOC speciation which could impact secondary PM formation. For example, reductions in aromatics in gasoline might re duce ambient PM concentratio ns by reducing secondary PM formation. Section 3.3.3 of this DRIA contai ns more information on aromatics and secondary aerosol formation.

In addition, air quality modeling would accoun t for changes in fuel type and spatial distribution of fuels that woul d change emissions of ozone pr ecursor species and thus could affect ozone concentrations. Section 3.3.2 of th is DRIA provides more detail on the atmospheric chemistry and potential changes in ozone forma tion due to increased us age of ethanol fuels.

487For the final rule, EPA intends to use a 2005- based CMAQ modeling platform as the tool for the air quality modeling. The CMAQ modeling system is a comprehensive three-dimensional grid-based Eulerian air quality model designed to estimate the formation and fate of oxidant precursors, primary and secondary PM concen trations and deposition, and air toxics, over regional and urban spatial scales (e.g., over the contiguous U.S.). 608, 609, 610 The CMAQ model is a well-known and well-respected tool and is comm only used for both regulatory analyses, for instance the recent ozone NAAQS rule 611 , and in developing attain ment demonstration State Implementation Plans. The CMAQ model (version 4.6) was peer-reviewed in February of 2007 for EPA as reported in “Third Peer Review of CMAQ Model.” 612

CMAQ includes many science modules that simulate the emission, production, decay, deposition and transport of organi c and inorganic gas-pha se and particle-phase pollutants in the atmosphere. We intend to use the most rece nt CMAQ version (v4.7) which was officially released by EPA’s Office of Research and Deve lopment (ORD) in Decemb er 2008, and reflects updates to earlier versions in a number of areas to improve the underlying science. These include (1) enhanced secondary organic aerosol (SOA) mechan ism to include chemistry of isoprene, sesquiterpene, and ag ed in-cloud biogenic SOA in addi tion to terpene; (2) improved vertical convective mixing; (3) improved heterogeneous reaction i nvolving nitrate formation; and (4) an updated gas-phase chemistry mechanism, Carbon Bond 05 (CB05), with extensions to model explicit concentrations of air toxic species as well as chlorine and mercury. This mechanism, CB05-toxics, also com putes concentrations of specie s that are involved in aqueous chemistry and that are precursors to aerosols. Section 3.3.3 of the DRIA for this proposal discusses SOA formation and details about th e improvements made to the SOA mechanism within this recent release of CMAQ.

As shown in Figure 3.3-3 the CMAQ modeling domain covers the continental U.S. and portions of Canada and Mexico. There are two 12 x 12 km horiz ontal grid resolution modeling domains, an Eastern US domain ( outlined in red) and a Western US domain (outlined in blue). The modeling domain contains 14 vertical layers with the top of the modeling domain at about 16,200 meters, or 100 millibars (mb).

488Figure 3.3-3. CMAQ 12-km Eastern and Western US Modeling Domains

The key inputs to the CMAQ model include emissions from anthropogenic and biogenic sources, meteorological data, and initial and boundary conditi ons. The CMAQ meteorological input files will be derived from a simulation of the Pennsylvania State University / National Center for Atmospheric Research Mesoscale Model 613 for the entire year of 2005. This model, commonly referred to as MM5, is a limited-ar ea, nonhydrostatic, terrain- following system that solves for the full set of physical and ther modynamic equations which govern atmospheric motions. The meteorology for the national 36 km grid and the 12 km Eastern and Western U.S. grids will be developed by EPA and described in more detail within the Air Quality Modeling Technical Support Document for the final rule. 614 The meteorological outputs from MM5 will be processed to create m odel-ready inputs for CMAQ usi ng the Meteorology-Chemistry Interface Processor (MCIP) version 3.4 to deri ve the specific inputs to CMAQ, for example: horizontal wind components (i.e., speed and direction), temperatur e, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical layer. 615

The lateral boundary and initia l species concentrations wi ll be provided by a threedimensional global atmospheric chem istry model, the GEOS-CHEM model. 616 The global GEOS-CHEM model simulates atmospheric ch emical and physical processes driven by assimilated meteorological observations from the NASA’s Goddard Earth Observing System

489(GEOS). This model will be run for 2005 with a grid resolution of 2 degree x 2.5 degree (latitude-longitude) and 30 vertical layers. Th e predictions will be used to provide one-way dynamic boundary conditions and an initial c oncentration field for the 36 km CMAQ simulations. The future base conditions from the 36 km coarse grid modeling will be used as the initial/boundary state for all subse quent 12 km finer grid modeling.

3.3.2 Atmospheric Reactions Associ ated with Ethanol in Gasoline

Ethanol, molecular formula CH 3 CH 2 OH, is a volatile organic hydrocarbon and is present in the gas-phase in the atmosphere. 617 Complete combustion of et hanol in fuel produces carbon dioxide (CO 2 ) and water (H 2 O).

Incomplete combustion results in the produc tion of other air pollutants, such as acetaldehyde and other aldehydes, a nd the release of unburned ethanol . Ethanol is also present in evaporative emissions. In the atmosphere , ethanol from unburned fuel and evaporative emissions can undergo photodegradation to fo rm aldehydes (acetaldehyde and formaldehyde) and peroxyacetyl nitrate (PAN), and also plays a role in gr ound level ozone formation.

Based on kinetic data for molecular reactions , the only important chemical loss process for ethanol (and other alc ohols) is reaction with the hydroxyl radical (·OH). 618 This reaction produces acetaldehyde (CH 3 CHO) with a 90% yield. 619 The lifetime of ethanol in the atmosphere can be calculated from the rate co efficient, k, and due to reaction with the OH radical, occurs on the order of hours in polluted urban areas or several da ys in unpolluted areas. (All rate coefficients are listed at 298 K and, if a pplicable, 1 bar of air.)

-1 620 CH 3 CH 2 OH + ·OH → CH 3 C·HOH + H 2 O k = 3.2 x 10 -12 cm 3 molecule -1 s CH 3 C·HOH + O 2 → CH 3 CHO + HO 2
Reactions of ethanol (and othe r alcohols) with nitrate radi cal, ozone, and photolysis are slow and of negligible im portance in the atmosphere.

3.3.2.1 Aldehydes

3.3.2.1.1 Acetaldehyde

Acetaldehyde is the main photodegradation produ ct of ethanol (see Section 3.3.2 of this DRIA) as well as other precurs or hydrocarbons. Acetaldehyde is also a product of fuel combustion. In the atmosphere, acetalde hyde can react with the OH radical and O 2 to form the acetyl peroxy radical [CH 3 C(O)OO·]. R RRRRRRR This radical species can then further react with nitric oxide (NO), to produce formaldehyde (HCHO), or with n itrogen dioxide (NO 2 ), to produce PAN [CH 3 C(O)OONO 2 ]. An overview of these reactions and the corresponding reaction rates are provided below.

RRRRRRRR Acetaldehyde is not the only sour ce of acetyl peroxy radicals in the atmosphere. For example, dicarbonyl compounds (methylglyoxal, bi acetyl, and others) also form acetyl radi cals, which can further react to form peroxyacetyl nitrate (PAN).

490-1 621 CH 3 CHO + ·OH → CH 3 C·O + H 2 O k = 1.5 x 10 -11 cm 3 molecule -1 s CH 3 C·O + O 2 + M → CH 3 C(O)OO· + M

-1 622 CH 3 C(O)OO· + NO → CH 3 C(O)O· + NO 2 k = 2.0 x 10 -11 cm 3 molecule -1 s CH 3 C(O)O· → ·CH 3 + CO 2 ·CH 3 + O 2 + M → CH 3 OO· + M CH 3 OO· + NO → CH 3 O· + NO 2 CH 3 O· + O 2 → HCHO + HO 2

-1 623 CH 3 C(O)OO· + NO 2 + M → CH 3 C(O)OONO 2 + M k = 1.0 x 10 -11 cm 3 molecule -1 s

Acetaldehyde can also photolyze (h ν ), which predominantly produces ·CH 3 and HCO:

CH 3 CHO + h ν → ·CH 3 + HCO λ = 240-380 nm 624

As described above, ·CH 3 is oxidized in the atmosphere to produce formaldehyde (HCHO).

3.3.2.1.2 Formaldehyde

Formaldehyde is the photodegradation product of acetaldehyde (DRIA Section 3.3.2.1.1 above) as well as other precursor hydrocarbons. Formaldehyde is also a product of hydrocarbon combustion. In the atmosphere, formaldehyde undergoes photolysis and reaction with the OH radical, NO 3 radical, and ozone, and the resulting life times are ~4 hours, 1.2 days, 83 days, and >4.5 years, respectively. S SSSSSSS Formaldehyde is removed ma inly by photolysis whereas the higher aldehydes, those with tw o or more carbons such as ac etaldehyde, react predominantly with OH radicals. The photolysis of formaldehyde is important since it is a source of additional radicals.

HCHO + h ν → H + HCO λ = 240-360 nm 625

3.3.2.2 Peroxyacetyl Nitrate (PAN)

PAN is a reservoir and carrier of NO x . PAN is the product of acetyl radicals reacting with NO 2 in the atmosphere. One source of PAN is the photooxidation of acetaldehyde (see Section 3.3.2.1.1 of this DRIA), but any hydroc arbon having a methyl group has the potential for forming acetyl radicals and therefore PAN. T TTTTTTT PAN can undergo thermal decomposition with a lifetime of approximately 1 hour at 298K or 148 days at 250K.

-1 626 CH 3 C(O)OONO 2 + M → CH 3 C(O)OO· + NO 2 + M k = 3.3 x 10 -4 s

SSSSSSSS Lifetime calculated using the following: for photolysis, with overhead sun (at noontime during the summer); for OH radical reactions, a 12-hour daytime average of 2.0 x 10 6 molecule cm -3 ; for NO 3 radical reactions, a 12-hour nighttime average of 5 x 10 8 molecule cm -3 ; and for ozone, a 24-hour average of 7 x 10 11 molecule cm -3

. TTTTTTTT Many aromatic hydrocarbons, particularly those present in high percentages in gasoline (toluene, m-, o-, pxylene, and 1,3,5-, 1,2,4-trimethylbenzene), form methyl glyoxal and biacetyl, which are al so strong generators of acetyl radicals (Smith, D.F., T.E. Kleindienst, C.D. McIver (1999) Primary product dist ribution from th e reaction of OH with m-, p-xylene and 1,2,4- and 1,3,5-Trimethylbenzene. J. Atmos. Chem., 34: 339- 364.).

491This temperature sensitivity allows PAN to be stable enough at low temperatures to be transported long distances befo re decomposing to release NO 2 . NO 2 can then participate in ozone formation in regions remote from the original NO x source. 627

3.3.2.3 Ozone Formation

The series of reactions described above pl ays a role in the formation of ground level ozone. In the photodegradation r eactions, NO is converted to NO 2 (see OH radical reaction of acetaldehyde in Section 3.3.2.1.1 of this DRIA ). In the thermal decomposition of PAN, NO 2 is released. In both cases, NO 2 further photolyzes to produce ozone (O 3 ).

NO 2 + h ν → NO + O( 3 P) λ = 300-800 nm 628 O( 3 P) + O 2 + M → O 3 + M

3.3.2.4 Chemical Mechanisms in Modeling

The RFS2 rule is presenting inventories for ethanol and six air to xics -- benzene, 1,3­ butadiene, formaldehyde, acetaldehyde, acrolein, and naphthalene. All of these compounds are explicit model specie s in the CMAQv4.7 model with carbon bond 5 (CB05) mechanisms. 629 However, CMAQ also requires inve ntories for a large number of ot her air toxics and precursor pollutants. Inventories for these other pollu tants will be developed either by running the National Mobile Inventory Model (NMIM) or by application of speciation profiles to VOC.

Emissions of benzene, 1,3-butadiene, formaldehyde, acetaldehyde, acrolein, and naphthalene for light duty gasolin e vehicles will be generated using the Motor Vehicle Emissions Simulator (MOVES). Ethanol emissions for air quality modeling will be based on speciation of VOC using different ethanol profiles (E0, E10, and E85). Light duty gasoline vehicle air toxic emissions not modeled by MOVES will be based on NMIM outputs, as will emissions of mobile source air toxic pollutants from other vehicle classes and nonroad engines. All other pollutants will be modeled by application of speciation profiles to VOC. MOVES, NMIM, and the speciation profiles which will be used all account for impacts of ethanol on emissions.

CMAQ has the capability to track and quantify primary emissions of acetaldehyde separately from acetaldehyde formed by secondary reactions. In determin ing how much of the acetaldehyde in ambient air is from primary (e mitted) versus seconda ry (produced in the atmosphere) sources, CMAQ would account for th e degradation of primary emissions of acetaldehyde (lessening the impact of these emissi ons on total acetaldehyde) plus the formation of secondary acetaldehyde from other precursors.

The CB05 mechanisms also have a species U UUUUUUU representing higher molecular weight aldehydes, those with three or mo re carbons. These aldehydes form, among other products, acetaldehyde. To the extent that thes e higher molecular weightaldehydes decrease or increase in the exhaust with in creased use of ethanol, the amount of acetaldehyde formed in the ambient air is affected. The amount of acetalde hyde, as well as formaldehyde, formed in the

UUUUUUUU The word species represents a technique used to lump several similar compounds into a single ‘bin’ for streamlining the numerical calcula tions performed by the model.

492ambient air as well as emitted in the exhaust (the latter being accounted for in emission inventories) would also be affected by any changes in alkane s and alkenes due to the addition of ethanol to fuels (i.e., decreas es in alkanes and alkenes w ould cause some decrease of acetaldehyde, and to a larger extent, formaldehyde ). As mentioned above, the exhaust level of these aldehydes could be affected by use of etha nol which would also be incorporated into the modeling.

The addition of ethanol to fuels has been shown to contribute to PAN formation and therefore ozone formation (discussed below in DRIA Section 3.3.2. 5), and these compounds would be quantified by CMAQ. Additionally , other aromatic hydrocarbons (AHC) and hydrocarbons are considered becaus e any increase in acetyl pe roxy radicals due to ethanol increases might be counterbalanced by a decrease in radicals resulting from decreases in AHC and other hydrocarbons.

CMAQ includes 63 inorganic reactions to account for the cycling of all relevant oxidized nitrogen species and cycling of radi cals, including the termination of NO 2 and formation of nitric acid (HNO 3 ) without PAN formation.

-1 630 NO 2 + ·OH + M → HNO 3 + M k = 1.19 x 10 -11 cm 3 molecule -1 s

The CB05 mechanism also includes more than 90 organic reactions th at include alternate pathways for the formation of acetyl peroxy radical, such as by reaction of methylglyoxal, which is also formed from reactions of AHC. Alternate reactions of acetyl peroxy radical, such as oxidation of NO to form NO 2 , which again leads to ozone formation, are also included.

It is unclear whether ozone and other atmospheric oxidants (such as OH radical) would increase or decrease with the increased usage of ethanol fuels, or where the largest impacts would occur. The decreases in hydrocarbons other than ethanol in fuels, especially the AHC, would counteract the effects of increased acetaldehyde exhaust em issions to some extent, since AHC contribute to ozone formation. NOx increases or decreases would also have a critical effect on ozone. Moreover, changes in CO em issions would also im pact regional background ozone. Modeling the ethanol fuels exhaust and evaporative emissions and the complex chemistry they undergo in the ambient atmos phere would allow a better assessment of the impacts of the changes in fuel composition.

3.3.2.5 Ambient Monitoring and Modeling Data

Ambient monitoring and modeling studies in the US and abroad quantify the relative concentrations of ethanol emissions and the pot ential air quality impact s of using ethanol in fuels.

3.3.2.5.1 US Studies

In the late 1980s Denver, Colorado adopted the first regulation in the country that required the use of oxygenated fuels to reduce ca rbon monoxide (CO) emissions. From the time the regulation was first adopted, the fuels used sh ifted from methyl tertia ry butyl ether (MTBE)

493and gasoline to largely ethanol-ble nded fuels. By the winter of 1996-1997, nearly all of the fuel was blended with ethanol at 10% by volume. The effect of using oxygenated fuels on formaldehyde and acetaldehyde concentrations were monitored from the winter of 1987-1988 (95% of fuel blended with MTBE) through the winter of 1996-1997 (nearly all fuel blended with ethanol). 631 Results showed no clear effect on ambi ent concentrations of formaldehyde and acetaldehyde through ten winters of oxygenated fuels use. It was hypothesized that the photochemical destruction of these com pounds suppressed the emissions effect.

Albuquerque, NM is another location that mandated the use of oxygenates in the wintertime to reduce CO emissions. A field stud y was conducted in Albuquerque to determine the atmospheric impacts of the use of ethanol fuels. 632 Atmospheric concentrations of ozone, NO x , CO, PAN, aldehydes, and organic acids were measured in the summer of 1993, before the use of ethanol fuels, and in the winters of 1994 and 1995, during the use of 10% ethanol fuel (>99%). Results showed increased levels of PAN and aldehydes in winter. Seasonal differences were not accounted for. The aut hors noted that the daytime temper atures were fairly comparable for the summer and winter study periods so it appeared that the signi ficantly higher winter values, despite the much lower photochemical reac tivity in winter, were primarily due to local production of PAN. For acetaldehyde, winter valu es were about twice as high as the summer values. These acetaldehyde levels anti-correlated with PAN levels, indicating a primary source of aldehydes in the winter.

Grosjean et al (2002) conducted monitoring studies in various California cities and measured daily maximum PAN concentrations ranging from 0.2 to 6.9 ppb. 633 Peroxypropionyl nitrate (PPN, a compound similar to PAN) concentrations were measured at lower levels and ranged from 0.33-1.04 ppb. This study conclude d that aromatics and alkene compounds are responsible for significant PAN formation with ethanol and acetaldehyd e having a minor role.

A modeling analysis performed by the Calif ornia Air Resources Board suggests PAN formation with increased use of ethanol, but the increase was somewhat minimal. 634 Acetaldehyde and ethanol concentrations were shown to incr ease for the ethanol-blended fuels by 1 to 6% and 37 to 121%, respectively. The changes in acetaldehyde and ethanol concentrations were predicted to lead to a 1 to 3% increase in PAN concentration, similar to the 3% increase in PAN concentration predicted for the non-oxygenated gasoline. Benzene (0 to ­ 9%), formaldehyde (-1 to -2%), and NO 2 (0 to 1%) concentrations stayed the same or decreased, and changes in 1,3-butadiene (-2 to 2%) a nd PPN (-1 to 4%) were similarly modest. Consequently, a sensitivity analysis of PAN form ation indicated that in creases in acetaldehyde and ethanol emissions would not lead to substa ntially higher PAN concen trations, presumably because other VOCs are larger c ontributors to PAN formation.

Another air quality modeling st udy investigated the impacts of widespread usage of E85 in Los Angeles and the US in 2020. 635 Overall results showed increases in acetaldehyde and formaldehyde and decreases in 1,3-butadiene and benzene in Los Angeles and the US. Sources of acetaldehyde included direct emissions a nd to a larger degree photooxidation of unburned ethanol. Results of this mode ling study also showed increases in unburned ethanol, PAN, and ozone for a future E85 scenario.

494The US studies discussed here are winter studies and the lack of summer studies makes it difficult to quantify the magnitude of air quality impacts of ethanol fuel usage, which are expected to be greatest during the summer.

3.3.2.5.2 Brazilian Studies

The following studies investigate changes in ambient concentrations of several air pollutants that result from the us e of ethanol fuels in Brazil. These studies are not directly relevant to the US due to differences such as vehicles, fuels, and climate; therefore, the EPA could not draw upon these conclusions to assess th e magnitude of the impact of ethanol fuels on air quality. However, these studies do provi de useful information on potential directional changes in pollutant levels with widespread ethanol use.

Brazil is the only count ry in the world where a nationw ide, large-scale alcohol fuel program has been implemented. In 1997, approximately 4 million automobiles ran on neat ethanol and approximately nine million autom obiles ran on a 22% ethanol-blended gasoline mixture. 636

In Salvador, Bahia, Brazil, ambient levels of formaldehyde and acetaldehyde and their relationship with vehicular fl eet composition was evaluated. 637 The measured concentrations for formaldehyde and acetaldehyde ranged from 0.20 to 88 ppbv and from 0.40 to 93 ppbv, respectively. The ratio of formaldehyde to aceta ldehyde revealed the relationship of vehicular fleet composition to ambient levels. In locati ons where ethanol-fueled vehicular emissions dominated, the ratio decreased, versus locati ons where diesel-fueled vehicles dominated. Sampling in rural areas showed no relations hip between formaldehyde and acetaldehyde.

Acetaldehyde and formaldehyde concentrations were measured in the winter of 1999 in Sao Paulo, Brazil. 638 Ambient levels of th ese carbonyls were similar. Higher average mixing ratios of acetaldehyde and formaldehyde were found in the morning (18.9 and 17.2 ppbv) than midday (9.5 and 11.8 ppbv) and evening (7.2 an d 10.2 ppbv). In the morning, direct emission from vehicles seemed to be the main primary source, whereas at midday and evening these compounds appeared to result mainly from photochemistry.

A survey of volatile organic compounds in ar eas impacted by heavy traffic, including a tunnel, was obtained for Sao Paulo. 639 Researchers found the ambient air was dominated by ethanol (414 ppbv) with el evated methanol and 1- and 2-propa nol. These levels were well above those measurements available for US cities, pa rticularly Los Angeles, CA. The overall data trend also showed levels of C 4 -C 9 n -aldehydes to be approximately 10 times higher than in Los Angeles. They conclude that the use of alc ohol-based fuels is the primary source for these differences since alcohol comprise s about 40% of the mobile fuel by volume compared to 3% in Los Angeles. Also, the single-ring aromatic hydrocarbons (2.6 ppbv benzene, 9.0 ppbv toluene, 4.6 ppbv m,p -xylene) and the C 4 -C 11 n -alkanes were similar or slightly elevated in concentration compared to Los Angeles.

A study in Rio de Janeiro, Brazil some y ears ago measured and modeled ambient PAN concentrations. 640 The measurements were as high as 5 ppb over a 200 day period, but typically

495below 1 ppb, at one site; at another site, as high as 3 ppb, but again generally below 1 ppb. Modeling estimates were as high as 3 ppb for PAN and 1 ppb for PPN. This study concluded that with increased use of ethanol in fuel s there would be increas es in ambient PAN.

More recent monitoring studies in Brazil m easured daily maximum PAN concentrations ranging from 0.19 to 6.67 ppb. 641 Also, PPN was measured at lower levels of 0.06 to 0.72 ppb. During the 41 days of these measurements, PAN levels accounted for a large fraction of the ambient NO x . This study concluded that aromatics and alkene compounds are responsible for significant PAN formation with ethanol and acetaldehyde having a minor role.

Speciated ambient carbonyls have also been measured in Rio de Janeiro. 642 The most abundant carbonyls were formaldehyde (9.3 ppb) and acetaldehyde (9.0 ppb). They also examined the ambient acetaldehyde to formaldehyde concentration ratio in Brazilian cities since mid-1980 in the context of changes in Brazil’s reliance on ethanol as a vehicle fuel. They showed that this ratio has begun to decrease in re cent years due to fleet turnover and decrease in ethanol-fueled vehicles. Ethanol-fueled vehicl es are being replaced by lower-emitting models that run on a gasolin e-ethanol blend.

Using an empirical kinetic modeling a pproach, researchers simulated ozone, formaldehyde, and acetaldehyde concentrations for the urban downtown area of Rio de Janeiro. 643 The simulated ozone peak was in good agr eement with monitoring results. Modeling results also showed that acet aldehyde and formaldehyde concentr ations were highest in early morning, reaching a maximum which coincided with peak vehicular traffic. Additionally, they confirmed monitoring evidence that the high acetaldehyde to form aldehyde ratios were due to the use of alcohol-based fuels.

These studies modeled and meas ured ambient concentrations of several compounds that result from the use of ethanol fu els. However, the direct impacts of ethanol fuel usage on air quality in Brazil could not be evaluated since th ere were no ambient data available prior to the use of ethanol fuel.

3.3.3 Aromatics and SOA Formation

Particulate matter (PM) is an air pollutant consisting of a complex mixture of particles that can be solid, liquid or both, are suspended in the air, and consist of organic and inorganic substances. These particles vary in size, compos ition and origin. The major PM components are sulfate, nitrates, ammonia, carbon, crustal material and water.

Primary PM is emitted directly into the atmosphere from anthropogenic and biogenic processes. Primary PM includes PM from mob ile sources and, on a mass basis, is largely carbonaceous in nature. Various studies have shown that mobile sources are a major source of primary PM in urban areas over ma ny portions of the United States. 644, 645, 646, 647, 648, 649, 650 Primary PM that is carbonaceous is also re ferred to as primary organic aerosol (POA).

Secondary PM is formed in the atmosphere from chemical transformations of gases. Secondary products include sulfates, nitrates an d organics. Secondary organic aerosol (SOA)

496results when products of atmo spheric transformation or photo oxidation of a volatile organic compound (VOC) form or partition to the par ticle phase. Current research suggests SOA contributes significantly to ambi ent organic aerosol (OA) concen trations, and in southeast and midwest states may make up more than 50% (alth ough the contribution varies from area to area) of the organic fraction of PM 2.5 during the summer (but less in the winter). 651, 652 A wide range of laboratory studies conducted ove r the past twenty years show that anthropogenic aromatic hydrocarbons and long-chained al kanes, along with biogenic is oprene, monoterpenes, and sesquiterpenes, contribute to SOA formation. 653 Based on these laboratory results, SOA chemical mechanisms have been developed and in tegrated into air quality models such as the Community Multiscale Air Quality (CMAQ) m odel and have been used to predict OA concentrations. 654 Studies have also show n that alkanes could be important anthropogenic SOA contributors. 655, 656, 657, 658

Over the past 10 years, ambient OA concentrat ions have been routinely measured in the U.S. and some of these data have been used to determine, by employing source/receptor methods, the contributions of the major OA sour ces, including biomass burning and vehicular gasoline and diesel exhaust. Since mobile s ources are a significant source of VOC emissions, currently accounting for approximately 50% of anthropogenic VOC, 659 mobile sources are also an important source of SOA.

Toluene is an important c ontributor to anthropogenic SOA, although anthropogenic SOA is a small portion of all SOA. Other aromatic compounds contribute as we ll, but the extent of their contribution has not been quantified. Mobile sources are the most significant contributor to ambient toluene concentrations as shown by analyses done fo r the 1999 National Air Toxics Assessment (NATA) 660 and the Mobile Source Air Toxics (MSAT) Rule. 661 MSAT rule analyses indicate that onroad and nonroad mobile sources accounted for 68% (2.22 μg/m 3 ) of the total average nati onwide ambient concentrati on of toluene in 1999 (3.24

 μg/m 3 ).

The amount of toluene in gasoline influences the amount of toluene emitted in vehicle exhaust and evaporative emissions. In turn, levels of toluene and other ar omatics in gasoline are potentially influenced by the amount of ethanol blended into the fuel. Due to the high octane quality of ethanol, it greatly reduces the need for and levels of other high-octane components such as aromatics including toluene (which is the major aromatic compound in gasoline) when ethanol is used as a component of gasoline. Since toluene cont ributes to SOA and the toluene level of gasoline is decreasing, it is important to assess the eff ect of these reductions on ambient PM. To do this, EPA plans to run air quality models to assess the overall role of toluene’s contribution to ambient PM and the benefits th at would accrue from PM reductions associated with gasoline/ethanol blends having lower aromatic levels.

There is also the potential for ethanol to di rectly form SOA or affect SOA formation indirectly through changes in the radical popu lations from increasi ng ethanol exhausts. However, there is a lack of evid ence for both. Scientists at th e U.S. EPA’s Office of Research and Development’s National Exposure Research Laboratory recently directed a couple of experiments to investigate et hanol’s SOA form ing potential. 662 The experiments were conducted under conditions where peroxy radical reactions w ould predominate (irradiations performed in the absence of NOx and OH produced from the photolysis of hydrogen peroxide). This was the

497most likely scenario under which SOA formation could occur, since a highly oxygenated C4 organic would be potentially made. As expected, no SOA was produced. From these experiments, the upper limit for the aerosol yi eld would have been less than 0.01% based on scanning mobility particle sizer data. Given the expected negati ve result, this data was not published.

In general, a review of the literature s hows limited data on SOA concentrations, largely due to the lack of analytical methods for iden tifying and determining the concentrations of the highly polar organic compounds th at make up SOA. The most widely applied method of estimating total ambient SOA concentrations is the EC tracer method using ambient data and estimates of the OC/EC ratio in primary source emissions. 663, 664 SOA concentrations have also been estimated using OM (organic mass) to OC (organic carbon) ratios, which can indicate that SOA formation has occurred, or by subtracting the s ource/receptor-based total POA from the measured OC concentration. 665 Such methods, however, may not be quantitatively accurate and provide no information on the contribution of individual biogenic and anthropogenic SOA sources, which is critical information needed to assess the impact of specific sources and the associated health risk. In pa rticular, the contributions of an thropogenic SOA sources, including those of aromatic precursors, are required to determine exposures and risks associated with replacing fossil fuels with biofuels.

Upon release into the atmosphere, numer ous VOC compounds can react with free radicals in the atmosphere to form SOA. While th is has been investigated in the laboratory, there is relatively little informati on available on the speci fic chemical composition of SOA compounds themselves from specific VOC precursors. Th is absence of compositional data has largely prevented the identification of aromatically-deriv ed SOA in ambient samples which, in turn, has prevented observation-based measurements of the aromatic and other SOA contributions to ambient PM levels.

As a first step in determining the ambient SOA concentrations, EPA has developed a tracer-based method to estim ate such concentrations. 666, 667 The method is based on using mass fractions of SOA tracer compounds , measured in smog chamber-generated SOA samples, to convert ambient concentrations of SOA tracer co mpounds to ambient SOA concentrations. This method consists of irradiating the SOA precursor of interest in a smog chamber in the presence of NO x , collecting the SOA produced on filters, and th en analyzing the samples for highly polar compounds using advanced analy tical chemistry methods. Empl oying this method, candidate tracers have been identified for several VOC compounds which are emitted in significant quantities and known to produce SOA in the at mosphere. Some of these SOA-forming compounds include toluene, a variet y of monoterpenes, isoprene, and β -caryophyllene; the latter three of which are emitted by vegetation. Smog ch amber work can also be used to investigate SOA chemical formation mechanisms. 668, 669, 670, 671

Although these concentra tions are only estimates, due to the assumption that the mass fractions of the smog chamber SOA samples using th ese tracers are equal to those in the ambient atmosphere, there are presently no other means av ailable for estimating the SOA concentrations originating from individual SOA precursors. Among the tracer compounds observed in ambient PM 2.5 samples are two tracer compounds that have been identified in smog chamber aromatic

498SOA samples. 672 To date, these aromatic tracer co mpounds have been identified, in the laboratory, for toluene and m -xylene SOA. Additional work is underway by the EPA to determine whether these tracers are also formed by benzene and other alkylbenzenes (including

o -xylene, p -xylene, 1,2,4-trimethylbe nzene, and ethylbenzene).

One caveat regarding this work is that a large number of VOCs emitted into the atmosphere, which have the potential to form SOA, have not yet been studied in this way. It is possible that these unstudied co mpounds produce SOA species whic h are being used as tracers for other VOCs. This means that the presen t work could overestimate the amount of SOA formed in the atmosphere by the VOCs studied to date. This approach may also estimate entire hydrocarbon classes (e.g. all methylsubstituted-monoaromatics or all monoterpenes) and not individual precursor hydrocarbons. Thus the tracers could be br oadly representative and not indicative of individual precursors. This is st ill unknown. Also, anthropogenic precursors play a role in formation of atmospheric radicals and aerosol acidity, and thes e factors influence SOA formation from biogenic hydrocarbons. This an thropogenic and biogenic interaction, important to EPA and others, needs further study. The issu e of SOA formation from aromatic precursors is an important one to which EPA and othe rs are paying significant attention.

For benzene, smog chamber studies show that benzene forms SOA possibly through reactions with NO x . Early smog chamber work suggests be nzene might be relatively inert in forming SOA, although this st udy may not be conclusive. 673 However, more recent work shows that benzene does form SOA in smog chambers. 674 This new smog chamber work shows that benzene can be oxidized in the presence of NO x to form SOA with maximum mass of SOA being 8-25% of the mass of benzene. As menti oned above, work is needed to determine if a tracer compound can be found for benzene SOA wh ich might indicate how much of ambient SOA comes from benzene.

The aromatic tracer compounds and their mass fractions have also been used to estimate monthly ambient aromatic SOA concentrations from March 2004 to Februa ry 2005 in five U.S. Midwestern cities. 675 The annual tracer-based SOA concen tration estimates were 0.15, 0.18, 0.13, 0.15, and 0.19 μ g carbon/m 3 for Bondville, IL, East St. Louis, IL, Northbrook, IL, Cincinnati, OH and Detroit, MI, respectively, with the highest concentrat ions occurring in the summer. On average, the aromatic SOA c oncentrations made up 17 % of the total SOA concentration. Thus, this work suggests that we are finding ambient PM levels on an annual basis of about 0.15 μ g/m 3 associated with present toluene le vels in the ambient air in these Midwest cities. Based on preliminary analysis of recent laboratory experiments, it appears the toluene tracer could also be formed duri ng photooxidation of some of the xylenes. 676

Over the past decade a variety of modeli ng studies have been conducted to predict ambient SOA levels, with most studies focusing on the contributions of biogenic monoterpenes and anthropogenic aromatic hydrocarbons. More recently, modelers have begun to include the contribution of the isoprene SOA to ambient OC concentrations. 677 In general, the studies have been limited to comparing the sum of the P OA and SOA concentrations with ambient OC concentrations. The general consensus in the at mospheric chemistry community appears to be that monoterpene contributions, which are cl early significant, and the somewhat smaller aromatic contributions, are insufficient to account for observed ambient SOA levels. Part of this

499gap has been filled recently by SOA predictions fo r isoprene. Furthermore, the identification in ambient SOA of a tracer co mpound for the sesquiterpene β -caryophyllene, 678 coupled with the high sesquiterpene SOA yields measured in the laboratory, 679 suggests this class of hydrocarbons should be included in SOA chemical mechanisms . In addition, recent data on SOA formation from aromatic hydrocarbons suggest their cont ributions could be larger than previously thought. 680

Based on laboratory experiments, research ers have proposed a new aromatic SOA chemical mechanism that takes into account bot h the impact of NOx on SOA formation and the contribution of benzene to SOA formation. EPA has updated the SOA treatment in CMAQ by using this new mechanism, along with (1) labo ratory-determined thermodynamic enthalpies of vaporization and SOA-to-secondary organic car bon ratios, (2) SOA contributions from sesquiterpenes and isoprene, (3) an elementary treatment of acid catalyzed SOA from isoprene, (4) a simple treatment of SOA oligomerization, and (5) SOA contributions from dicarbonyl compounds (glyoxyl, methylglyoxyl) that dissolv e in cloud water and are oxidized to form oligomers.

This new SOA mechanism was first eval uated by comparing simple box model predictions with field measurements, and ini tial comparisons indicated the mechanism might underpredict the ambient SOA data. This underprediction might be due to potential shortcomings in the tracer cont ribution method as well as difficulties associated with using the laboratory-based Odum model approach to predict ambient SOA concentrations. An underprediction in ambient SOA data might result in an underprediction of SOA benefits that might result from decreasing aromat ic content due to increasing etha nol content in gasoline. On the other hand, aldehydes produced from the oxidation of ethanol c ould increase the SOA formed in cloud water, but only two aldehydes (glyoxal and methylgl yoxal) are considered in the current SOA treatment. More fundamental resear ch will be required to further enhance the SOA mechanism. Recently, the EPA awarded seven grants totaling $3.8 million to leading SOA researchers for additiona l mechanistic research.

The new SOA mechanism has been integrated into CMAQ version 4.7 and has undergone internal EPA evaluation. The SOA predicti ons have been compared with ambient OC concentrations measured at PM 2.5 monitoring sites across the c ountry, CMAQ version 4.7 with the new SOA chemical mechanism will be used in the final rule. Public release of this version of the model occurred in December 2008.



### 3.4 Health Effects

In this section we will discuss the health and environmental effects associated with particulate matter, ozone, carbon monoxide and air toxics. The renewable fuel requirements established by the Energy Independence and Secur ity Act (EISA) of 2007 will impact emissions of criteria and air toxic pollutants.

3.4.1 Health Effects of Particulate Matter

500As stated in EPA’s Part iculate Matter Air Quality Criteria Document (PM AQCD), available scientific findings “dem onstrate well that human health outcomes are associated with ambient PM.” V VVVVVVV We are relying on the data and conclusions in the PM AQCD and PM Staff Paper, which reflects EPA’s analysis of policy-relevant scienc e from the PM AQCD, regarding the health effects asso ciated with particulate matter. 681, 682 We also present additional recent studies published after the cut-off date for the PM AQCD. W WWWWWWW 683 Taken together this information supports the conclusion that PM -related emissions are associated with adverse health effects.

3.4.1.1 Short-term Exposure Mortality and Morbidity Studies

As discussed in the PM AQCD, short-term exposure to PM 2.5 is associated with mortality from cardiopulmonary diseases (PM AQCD, p. 8-305), hospitalization and emergency department visits for cardiopulmonary diseases (PM AQCD, p. 9-93), increased respiratory symptoms (PM AQCD, p. 9-46), decreased lung function (PM AQC D Table 8-34) and physiological changes or biomarkers for cardiac changes (PM AQCD, Section 8.3.1.3.4). In addition, the PM AQCD describes a limited body of new evidence from epidemiologic studies for potential relationships between short term e xposure to PM and health endpoints such as low birth weight, preterm birth, and neonatal and infant mortality. (PM AQCD, Section 8.3.4).

Among the studies of effects from short-term exposure to PM 2.5 , several specifically address the contribution of mob ile sources to short-term PM 2.5 effects on daily mortality. These studies indicate that there are statistically significant associat ions between mortality and PM related to mobile source emissions (PM AQCD, p.8-85). The analyses incorporate source apportionment tools into daily mortality studie s and are briefly mentioned here. Analyses incorporating source apportionment by factor analysis with daily time-series studies of daily death indicated a relationship between mobile source PM 2.5 and mortality. 684, 685 Another recent study in 14 U.S. cities examined the effect of PM 10 exposures on daily hospital admissions for cardiovascular disease. This study found that the effect of PM 10 was significantly greater in areas with a larger proportion of PM 10 coming from motor vehicles, indicating that PM 10 from these sources may have a greater e ffect on the toxicity of ambient PM 10 when compared with other sources. 686 These studies provide evidence that PM-related emissions, specifically from mobile sources, are associated with adverse health effects.

3.4.1.2 Long-term Exposure Mo rtality and Morbidity Studies

VVVVVVVV Personal exposure includes contributions from many different types of particles, from many sources, and in many different environments. Total personal exposure to PM includes both ambient and nonambient components; and both components may contribute to adverse health effects.

WWWWWWWW These additional studies are included in the 2006 Provisional Assessment of Recent Studies on Health Effects of Particulate Matter Exposure. The provisional assessment did not and could not (given a very short timeframe) undergo the extensive critical review by EPA, CASAC, and the public, as did the PM AQCD. The provisional assessment found that the “new” studies ex pand the scientific information and provide important insights on the relationship between PM exposure and health effects of PM. The provisional assessment also found that “new” studies generally strengthen the evidence that acute and chronic ex posure to fine particles and acute exposure to thoracic coarse particles are associated with health effects.

501Long-term exposure to elevated ambient PM 2.5 is associated with mortality from cardiopulmonary diseases and l ung cancer (PM AQCD, p. 8-307), and effects on the respiratory system such as decreased lung function or the development of chronic respiratory disease (PM AQCD, pp. 8-313, 8-314). Of specific importance to this pr oposal, the PM AQCD also notes that the PM components of gasoline and dies el engine exhaust represent one class of hypothesized likely important contri butors to the observed ambient PM-related increases in lung cancer incidence and mortality (PM AQCD, p. 8-318).

The PM AQCD and PM Staff Paper emphasize the results of two long-term studies, the Six Cities and American Cancer Society (ACS) prospective cohort studies, based on several factors – the inclusion of measur ed PM data, the fact that the study populations were similar to the general population, and the fact that these studies have unde rgone extensive reanalysis (PM AQCD, p. 8-306, Staff Paper, p.3-18). 687, 688, 689 These studies indicate that there are significant associations for all-cause, car diopulmonary, and lung cancer mort ality with long-term exposure to PM 2.5 . One analysis of a subset of the ACS co hort data, which was published after the PM AQCD was finalized but in time for the 2006 Provisi onal Assessment, found a larger association than had previously been reported between long-term PM 2.5 exposure and mortality in the Los Angeles area using a new exposure estimati on method that accounted for variations in concentration within the city. 690

As discussed in the PM AQCD, the morbidity studies that combine the features of crosssectional and cohort studies provide the best evidence for chronic exposur e effects. Long-term studies evaluating the effect of ambient PM on children’s development have shown some evidence indicating effects of PM 2.5 and/or PM 10 on reduced lung function growth (PM AQCD, Section 8.3.3.2.3). In another recent publica tion included in the 2006 Provisional Assessment, investigators in southern Calif ornia reported the results of a cross-sectional study of outdoor PM 2.5 and measures of atherosclero sis in the Los Angeles basin. 691 The study found significant associations between ambient residential PM 2.5 and carotid intima-media thickness (CIMT), an indicator of subclinical athero sclerosis, an underlying factor in cardiovascular disease.

3.4.1.3 Roadway-Related PM Exposure and Health Studies

A recent body of studies examines traffic -related PM exposures and adverse health effects. However, note that the near-road envi ronment is influenced by both gasoline sparkignition (SI) and diesel vehicles, as well as re-ent rained road dust and brake and tire wear. One study was done in North Carolina looking at concentrations of PM 2.5 inside police cars and corresponding physiological changes in the police personnel driving the cars. The authors report significant elevations in markers of cardiac risk associated with concentrations of PM 2.5 inside police cars on North Carolina state highways. 692 Other studies have found associations between traffic-generated particle concen trations at residences and adve rse effects, including all-cause mortality, infant respiratory symptoms, and reduced cognitive functional development. 693, 694, 695, 696 There are other pollutants present in the near roadway environment, including air toxics which are discussed in Sectio n 3.4.5, and it is important to note that current studies do not identify a si ngle pollutant that is most associated with adverse health effects. Additional information on near-roadway health e ffects can be found in the recent Mobile Source Air Toxics rule (72 FR 8428, February 26, 2007).

5023.4.2 Health Effects of Ozone

Exposure to ambient ozone contributes to a wide range of adverse health effects. X XXXXXXX These health effects are well documente d and are critically assessed in the EPA ozone air quality criteria documen t (ozone AQCD) and EPA staff paper. 697, 698 We are relying on the data and conclusi ons in the ozone AQCD and staff paper, regarding the health effects associated with ozone exposure.

Ozone-related health effects include lung f unction decrements, respiratory symptoms, aggravation of asthma, increased hospital a nd emergency room vis its, increased asthma medication usage, and a variety of other respirat ory effects. Cellular-level effects, such as inflammation of lungs, have been documented as we ll. In addition, there is suggestive evidence of a contribution of ozone to cardiovascular-related morbidity and highly suggestive evidence that short-term ozone exposure directly or indirectly contributes to non-accidental and cardiopulmonary-related mortality, but additional research is n eeded to clarify the underlying mechanisms causing these effects. In a r ecent report on the estimation of ozone-related premature mortality published by the National Research Council (NRC), a panel of experts and reviewers concluded that short- term exposure to ambient ozone is likely to contribute to premature deaths and that ozone-re lated mortality should be include d in estimates of the health benefits of reducing ozone exposure. 699 People who appear to be more susceptible to effects associated with exposure to ozone include childre n, asthmatics and the elderly. Those with greater exposures to ozone, for instance due to time spent outdoors (e.g., children and outdoor workers), are also of concern.

Based on a large number of scientific studi es, EPA has identified several key health effects associated with exposure to levels of ozone found today in many areas of the country. Short-term (1 to 3 hours) and prolonged e xposures (6 to 8 hours) to ambient ozone concentrations have been linked to lung functio n decrements, respiratory symptoms, increased hospital admissions and emergency room visits for respiratory problems. 700, 701, 702, 703, 704, 705 Repeated exposure to ozone can increase suscep tibility to respirat ory infection and lung inflammation and can aggravate preexisting respiratory diseases, such as asthma. 706, 707, 708, 709, 710 Repeated exposure to sufficient c oncentrations of ozone can also cause inflammation of the lung, impairment of lung defense mechanisms, and possi bly irreversible changes in lung structure, which over time could affect premature aging of the lungs and/or the development of chronic respiratory illnesses, such as emphysema and chronic bronchitis. 711, 712, 713, 714

Children and adults who are outdoors and act ive during the summer months, such as construction workers, are among those most at risk of elevated ozone exposures. 715 Children and outdoor workers tend to have hi gher ozone exposure because they typically are active outside, working, playing and exercising, during times of day and seasons (e.g., the summer) when ozone levels are highest. 716 For example, summer camp studies in the Eastern United States and Southeastern Canada have reported statistica lly significant reductions in lung function in

XXXXXXXX Human exposure to ozone varies over time due to changes in ambient ozone concentration and because people move between locations which have notable different ozone concentrations. Also, the amount of ozone delivered to the lung is not only influenced by the ambient concentrations but also by the individuals breathing route and rate.

503children who are active outdoors. 717, 718, 719, 720, 721, 722, 723, 724 Further, children are more at risk of experiencing health effects from ozone exposure than adults because their respiratory systems are still developing. These individuals (as well as people with respiratory illnesses, such as asthma, especially asthmatic children) can experience reduce d lung function and increased respiratory symptoms, such as chest pain and coug h, when exposed to relatively low ozone levels during prolonged periods of moderate exertion. 725, 726, 727, 728

3.4.3 Health Effects of Carbon Monoxide

We are relying on the data and conclusions in the EPA Air Quality Criteria Document for CO (CO Criteria Document), which was publis hed in 2000, regarding the health effects associated with CO exposure. Y YYYYYYY 729 Carbon monoxide enters the bloodstream through the lungs and forms carboxyhemoglobin (COHb), a com pound that inhibits the blood’s capacity to carry oxygen to organs and tissues. 730, 731 Carbon monoxide has long been known to have substantial adverse effects on human health, including toxic effects on blood and tissues, and effects on organ functions. Although there are effective compensatory increases in blood flow to the brain, at some concentra tions of COHb somewhere above 20 percent, these compensations fail to maintain sufficient oxygen delivery, and metabolism declines. 732 The subsequent hypoxia in brain tissue then produces behavioral effect s, including decrements in continuous performance and reaction time. 733

Carbon monoxide has been linke d to increased risk for people with heart disease, reduced visual perception, cognitive functions and aerobic capacity, and possible fetal effects. 734 Persons with heart disease are especially sensitive to CO poisoning and may experience chest pain if they breathe the gas while exercising. 735 Infants, elderly persons, a nd individuals with respiratory diseases are also particularly sensitive. Carbon monoxide can affect healthy individuals, impairing exercise capacity, visual perception, manual dexterity, learning functions, and ability to perform complex tasks. 736

Several epidemiological studies have show n a link between CO and premature morbidity (including angina, congestiv e heart failure, and other cardiovascul ar diseases). Several studies in the United States and Canada have also reporte d an association between ambient CO exposures and frequency of cardiovascular hospital admissi ons, especially for c ongestive heart failure (CHF). An association between ambient CO exposure and mortal ity has also been reported in epidemiological studies, though not as consistently or specifically as with CHF admissions. EPA reviewed these studies as part of the CO Cr iteria Document review process and noted the possibility that the average ambient CO levels used as exposure indi ces in the epidemiology studies may be surrogates for ambient air mixe s impacted by combustion sources and/or other constituent toxic components of such mixes. More research will be needed to better clarify CO’s role. 737

3.4.4 Environmental Effects of Criteria Pollutants

YYYYYYYY The NAAQS review process is underway fo r CO and the CO Integrated Science Assessment is scheduled to be completed in January 2010.

504In this section we discuss some of the e nvironmental effects of PM and its precursors such as visibility impairment, atmospheric depos ition, and materials damage and soiling, as well as environmental effects associated with the pr esence of ozone in the ambient air, such as impacts on plants, includi ng trees, agronomic crops and urban ornamentals.

3.4.4.1 Visibility Impairment

Visibility can be defined as the degree to which the atmosphere is transparent to visible light. 738 Visibility impairment manifests in two prin cipal ways: as local visibility impairment and as regional haze. 739 Local visibility impairment may take the form of a localized plume, a band or layer of discoloration appearing well ab ove the terrain as a result of complex local meteorological conditions. Alternatively, local vi sibility impairment may manifest as an urban haze, sometimes referred to as a “brown cloud.” This urban haze is largely caused by emissions from multiple sources in the urban area and is no t typically attributable to only one nearby source or to long-range transport. The second type of visibility impairment, regional haze, usually results from multiple pollution sources spread ov er a large geographic region. Regional haze can impair visibility over large regions and across states.

Visibility is important because it has direct significance to people’s enjoyment of daily activities in all parts of the country. Individuals value good visibility for the well-being it provides them directly, where they live and work and in places where they enjoy recreational opportunities. Visibility is also highly valued in significant natura l areas such as national parks and wilderness areas, and special emphasis is gi ven to protecting visibility in these areas.

Fine particles are the major cause of reduced visibility in parts of the United States. To address the welfare effects of PM on visibility, EPA sets secondary PM 2.5 standards which work in conjunction with the regi onal haze program. The s econdary (welfare-based) PM 2.5 NAAQS is equal to the suite of primary (health-based) PM 2.5 NAAQS. The regiona l haze rule (64 FR 35714, July 1999) was put in place to protect the vi sibility in mandatory class I federal areas. These areas are defined in Section 162 of the Act as those national parks exceeding 6,000 acres, wilderness areas and memorial parks exceeding 5,000 acres, and all inte rnational parks which were in existence on August 7, 1977. Visibility is impaired in both PM 2.5 nonattainment areas and mandatory class I federal areas.

3.4.4.2 Particulate Matter Deposition

Particulate matter contributes to adverse e ffects on vegetation and ecosystems, and to soiling and materials damage. These welfare e ffects result predominately from exposure to excess amounts of specific chemical species, rega rdless of their source or predominant form (particle, gas or liquid). Refl ecting this fact, the PM AQCD conc ludes that regardless of size fractions, particles containing nitr ates and sulfates have the greatest potential for widespread environmental significance, while effects are also related to othe r chemical constituents found in ambient PM, such as trace metals and organics. The following characterizations of the nature of these welfare effects are based on the informa tion contained in the PM AQCD and PM Staff Paper. 740, 741

5053.4.4.2.1 Deposition of Nitrates and Sulfates

Nitrogen and sulfur interacti ons in the environment are highly complex. Both are essential, and sometimes limiting, nutrients needed for growth and productivity. Excesses of nitrogen or sulfur can lead to acidificati on, nutrient enrichment, and eutrophication. 742

The process of acidification a ffects both freshwater aquatic and terrestrial ecosystems. Acid deposition causes acidification of sensitive surface waters. The effects of acid deposition on aquatic systems depend largely upon the ability of the ecosystem to ne utralize the additional acid. As acidity increases, alum inum leached from soils and sediments, flows into lakes and streams and can be toxic to both terrestrial and aquatic biota. The lower pH concentrations and higher aluminum levels resulting from acidifica tion make it difficult for some fish and other aquatic organisms to survive, grow, and reprodu ce. Research on effects of acid deposition on forest ecosystems has come to focus increasingly on the biogeochemical processes that affect uptake, retention, and cycling of nutrients within these ecosyste ms. Decreases in available base cations from soils are at least partly attributab le to acid deposition. Ba se cation depletion is a cause for concern because of the role these ions play in acid neutralizat ion and, because calcium, magnesium and potassium are essential nutrient s for plant growth and physiology. Changes in the relative proportions of th ese nutrients, especially in comparison with aluminum concentrations, have been associat ed with declining forest health.

At current ambient levels, risks to vegetation from short-term exposures to dry deposited particulate nitrate or sulfate are low. However, when found in acid or acidifying deposition, such particles do have the poten tial to cause direct leaf injury. Sp ecifically, the responses of forest trees to acid precipitatio n (rain, snow) include accelerated we athering of leaf cuticular surfaces, increased permeability of leaf surfaces to toxic ma terials, water, and disease agents; increased leaching of nutrients from foliage; and altere d reproductive processes—all which serve to weaken trees so that they are more susceptible to other stresses (e.g., extreme weather, pests, pathogens). Acid deposition with levels of acidity associated with the leaf effects described above are currently found in some locations in the eastern U.S. 743 Even higher concentrations of acidity can be present in occu lt depositions (e.g., fog, mist or clouds) which more frequently impacts higher elevations. Thus, the risk of leaf injury occurring from acid deposition in some areas of the eastern U.S

. is high. Nitrogen deposition has also been shown to impact ecosystems in the western U.S. A study conducted in th e Columbia River Gorge National Scenic Area (CRGNSA), located along a portion of the Ore gon/Washington border, in dicates that lichen communities in the CRGNSA have shifted to a hi gher proportion of nitrophilous species and the nitrogen content of lichen tissue is elevated. 744 Lichens are sensitive indicators of nitrogen deposition effects to terrestrial ecosystems and the lichen studies in the Columbia River Gorge clearly show that ecological eff ects from air pollution are occurring.

Some of the most significant detrimental eff ects associated with excess reactive nitrogen deposition are those associated with a syndrome known as nitrogen saturation. These effects include: (1) decreased productivity, increased mortality, and/or shifts in plant community composition, often leading to decreased biodi versity in many natural habitats wherever atmospheric reactive nitrogen deposition increases significantly above background and critical thresholds are exceeded; (2) leaching of excess nitr ate and associated base cations from soils into

506streams, lakes, and rivers, and mobilization of soil aluminum; and (3) fluctuation of ecosystem processes such as nutrient and energy cycles through changes in the functioning and species composition of beneficial soil organisms. 745

In the U.S. numerous forests now show severe symptoms of nitrogen saturation. These forests include: the northern hardwoods a nd mixed conifer forests in the Adirondack and Catskill Mountains of New York; the red spru ce forests at Whitetop Mountain, Virginia, and Great Smoky Mountains National Park, North Caro lina; mixed hardwood watersheds at Fernow Experimental Forest in West Virginia; Amer ican beech forests in Great Smoky Mountains National Park, Tennessee; mixed conifer forests a nd chaparral watersheds in southern California and the southwestern Sierra Nevada in Central California; the alpine tundra/subalpine conifer forests of the Colorado Front Range; and red alder forests in the Cascade Mountains in Washington.

Excess nutrient inputs into aquatic ecosystems (i.e. streams, rivers, lakes, estuaries or oceans) either from direct atmospheric deposit ion, surface runoff, or leaching from nitrogen saturated soils into ground or surface waters can contribute to conditions of severe water oxygen depletion; eutrophication and alg ae blooms; altered fish distribu tions, catches, and physiological states; loss of biodiversity; habi tat degradation; and increases in the incidence of disease.

Atmospheric deposition of nitrogen is a si gnificant source of to tal nitrogen to many estuaries in the United States. The amount of nitr ogen entering estuarie s that is ultimately attributable to atmospheric deposition is not we ll-defined. On an annual basis, atmospheric nitrogen deposition may contribut e significantly to the total nitr ogen load, depending on the size and location of the watershed. In addition, epis odic nitrogen inputs, wh ich may be ecologically important, may play a more important role than indicated by the annual av erage concentrations. Estuaries in the U.S. that suffe r from nitrogen enrichment ofte n experience a condition known as eutrophication. Symptoms of eutrophication in clude changes in the dominant species of phytoplankton, low levels of oxygen in the water co lumn, fish and shellfish kills, outbreaks of toxic alga, and other population changes wh ich can cascade throughout the food web. In addition, increased phytoplankton growth in the water column and on surfaces can attenuate light causing declines in submerged aqua tic vegetation, which serves as an important habitat for many estuarine fish and shellfish species.

Severe and persistent eutr ophication often directly impacts human activities. For example, losses in the nation’s fi shery resources may be directly caused by fish kills associated with low dissolved oxygen and toxic blooms. De clines in tourism occur when low dissolved oxygen causes noxious smells and floating mats of algal blooms create unfavorable aesthetic conditions. Risks to human health increase when the toxins from algal blooms accumulate in edible fish and shellfish, and when toxins beco me airborne, causing respiratory problems due to inhalation. According to a NOAA report, more than half of the nation’s estuaries have moderate to high expressions of at least one of these symp toms – an indication that eutrophication is well developed in more than half of U.S. estuaries. 746

3.4.4.2.2 Materials Damage and Soiling

507The effects of the deposition of atmosphe ric pollution, including ambient PM, on materials are related to both physi cal damage and impaired aesthetic qualities. The deposition of PM (especially sulfates and nitr ates) can physically aff ect materials, adding to the effects of natural weathering processes, by potentially promoting or acceler ating the corrosion of metals, by degrading paints, and by deteri orating building materials such as concrete and limestone. Only chemically active fine part icles or hygroscopic coarse partic les contribute to these physical effects. In addition, the deposit ion of ambient PM can reduce the aesthetic appeal of buildings and culturally important articles through soiling. Particles consisting primarily of carbonaceous compounds cause soiling of comm only used building materials and culturally important items such as statues and works of art.

3.4.4.3 Impacts of Ozone on Vegetation

The Air Quality Criteria Document for Ozone and related Photochemical Oxidants notes that “ozone affects vegetation throughout the United States, impairing crops, native vegetation, and ecosystems more than any other ai r pollutant. Like carbon dioxide (CO 2 ) and other gaseous substances, ozone enters plant ti ssues primarily through apertures (s tomata) in leaves in a process called “uptake”. 747 Once sufficient levels of ozone, a hi ghly reactive substance, (or its reaction products) reaches the interior of plant cells, it can inhibit or damage essential cellular components and functions, including enzyme activities, lipids, and cellular membranes, disrupting the plant's osmotic (i.e., water) balance and energy utilization patterns. 748, 749 This damage is commonly manife sted as visible foliar injury, such as chlorotic or necrotic spots, increased leaf senescence (accelerated leaf ag ing) and/or reduced photosynthesis. All these effects reduce a plant’s capacity to form carbohydrates, which are the primary form of energy used by plants. 750 With fewer resources available, the plant reallocates existing resources away from root growth and storage, above ground growth or yield, and reproductive processes, toward leaf repair and maintenance. St udies have shown that plants stre ssed in these ways may exhibit a general loss of vigor, which can l ead to secondary impacts that m odify plants' responses to other environmental factors. Specifically, plants may become more sensitive to other air pollutants, more susceptible to disease, insect attack , harsh weather (e.g., dr ought, frost) and other environmental stresses. Furthermore, there is evidence that ozone can interfere with the formation of mycorrhiza, essentia l symbiotic fungi associated with the roots of most terrestrial plants, by reducing the amount of carbon available for transfer from the host to the symbiont. 751, 752

Ozone can produce both acute and chronic in jury in sensitive species depending on the concentration level and the duration of the exposur e. Ozone effects also tend to accumulate over the growing season of the plant, so that even lower concentrations experienced for a longer duration have the potential to create chronic stress on sensitive vegetation. Not all plants, however, are equally sensitive to ozone. Much of the variation in sensitivity between individual plants or whole species is related to the plant’s ability to regulate the extent of gas exchange via leaf stomata (e.g., avoidance of O 3 uptake through closure of stomata). 753, 754, 755 Other resistance mechanisms may involve the intercellular produ ction of detoxifying substances. Several biochemical substances capable of detoxifying ozone have been reported to occur in plants including the antioxidants ascorbat e and glutathione. After injuries have occurred, plants may be capable of repairing the damage to a limited extent. 756

508Because of the differing sens itivities among plants to oz one, ozone pollution can also exert a selective pressure that leads to changes in plant commun ity composition. Given the range of plant sensitivities and the fact that numerous other environmental factors modify plant uptake and response to ozone, it is not possible to identify threshol d values above which ozone is consistently toxic for all plants. The next few paragraphs present additional information on ozone damage to trees, ecosystems, agronomic crops and urban ornamentals.

Ozone also has been conclusively shown to cause discernible injury to forest trees. 757, 758 In terms of forest productivity and ecosystem diversity, ozone may be the pollutant with the greatest potential for regional-scale forest impacts. Studies have demonstr ated repeatedly that ozone concentrations commonly obs erved in polluted areas can ha ve substantial impacts on plant function. 759, 760

Because plants are at the cen ter of the food web in many ecosy stems, changes to the plant community can affect associated organisms and eco systems (including the suitability of habitats that support threatened or e ndangered species and be low ground organisms living in the root zone). Ozone impacts at the community and ecosystem level vary widely depending upon numerous factors, including con centration and temporal variation of tropos pheric ozone, species composition, soil properties and climatic factors. 761 In most instances, responses to chronic or recurrent exposure in forested ecosystems are subtle and not observable for many years. These injuries can cause stand-level forest decline in sensitive ecosystems. 762, 763, 764 It is not yet possible to predict ecosystem responses to ozone with much certainty; however, considerable knowledge of potential ecosystem responses has been acquired through long-term observations in highly damaged forests in the United States.

Laboratory and field experime nts have also shown reductions in yields for agronomic crops exposed to ozone, includi ng vegetables (e.g., lettuce) an d field crops (e.g., cotton and wheat). The most extensive field experi ments, conducted under the National Crop Loss Assessment Network (NCLAN) examined 15 speci es and numerous cultivars. The NCLAN results show that “several economically importa nt crop species are sens itive to ozone levels typical of those found in the Unites States.” 765 In addition, economic studies have shown reduced economic benefits as a result of pred icted reductions in crop yi elds associated with observed ozone levels. 766, 767, 768

Urban ornamentals represent an additional vegetation category likely to experience some degree of negative effects associated with exposure to ambient ozone levels. It is estimated that more than $20 billion (1990 dolla rs) are spent annually on landscaping using ornamentals, both by private property owners/tenants and by govern mental units responsible for public areas. 769 This is therefore a potentially costly environmenta l effect. However, in the absence of adequate exposure-response functions and economic damage functions for the potential range of effects relevant to these types of vegetation, no dire ct quantitative analysis has been conducted.

3.4.5 Health Effects of Air Toxics

Motor vehicle emissions contribut e to ambient levels of air toxics known or suspected as human or animal carcinogens, or that have noncan cer health effects. The population experiences

509an elevated risk of cancer and other noncancer health effects from exposure to air toxics. 770 These compounds include, but are not limited to, benzene, 1,3-butadiene, formaldehyde, acetaldehyde, acrolein, naphthale ne, ethanol and PAN. These compounds, except acetaldehyde, ethanol and PAN, were identifie d as national or regional risk drivers in the 1999 National-Scale Air Toxics Assessment (NATA) and have significant inventor y contributions from mobile

sources.

According to NATA for 1999, mobile sources we re responsible for 44 percent of outdoor toxic emissions and almost 50 percent of the cancer risk. Benzene is the largest contributor to cancer risk of all 133 pollutant s quantitatively assessed in th e 1999 NATA and mobile sources were responsible for 68 percent of benzene emi ssions in 1999. In response, EPA has recently finalized vehicle and fuel controls that address this public health risk. 771

Noncancer health effects can result from chronic, Z ZZZZZZZ subchronic, A AAAAAAAA or acute B BBBBBBBB inhalation exposures to air toxics, and in clude neurological, ca rdiovascular, liver, kidney, and respiratory effects as well as effects on the immune and reproductive systems. According to the 1999 NATA, nearly the entire U.S. population was exposed to an average concentration of air toxics that has the potential for adverse noncancer respiratory health effects. This will continue to be the cas e in 2030, even though toxics concentrations will be lower. Mobile sources were responsible for 74 percent of the noncancer (respiratory) risk from outdoor air toxics in 1999. The majority of this risk was from exposure to acrolein. The confidence in the RfC for acrolein is medium and confiden ce in NATA estimates of population noncancer hazard from ambient exposure to this pollutant is low. 772, 773

The NATA modeling framework has a number of limitations which prevent its use as the sole basis for setting regulatory standards. These limitations and uncerta inties are discussed on the 1999 NATA website. 774 Even so, this modeling framework is very useful in identifying air toxic pollutants and sources of greatest concern, setting regulatory priorities, and informing the decision making process.

Benzene: The EPA’s IRIS database list s benzene as a known human carcinogen (causing leukemia) by all routes of exposure, and conclude s that exposure is associated with additional health effects, including geneti c changes in both humans and anim als and increased proliferation of bone marrow cells in mice. 775, 776, 777 EPA states in its IRIS databa se that data indicate a causal relationship between benzene exposure and acute lymphocytic leukemia and suggest a relationship between benzene exposure and ch ronic non-lymphocytic leukemia and chronic lymphocytic leukemia. The International Ag ency for Research on Carcinogens (IARC) has determined that benzene is a human carcinogen and the U.S. Department of Health and Human Services (DHHS) has characterized benzene as a known human carcinogen. 778, 779

ZZZZZZZZ Chronic exposure is defined in the glossary of the Integrated Risk Information (IRIS) database (http://www.epa.gov/iris) as repeated exposure by the oral, dermal, or inhalation route for more than approximately 10% of the life span in humans (more than approximately 90 days to 2 years in typically used laboratory animal species).
AAAAAAAAA Defined in the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an organism.

BBBBBBBBB Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less.

510A number of adverse noncancer health effects including blood disorders, such as preleukemia and aplastic anemia, have also been associated with long-term exposure to benzene. 780, 781 The most sensitive noncancer effect observed in humans, based on current data, is the depression of the abso lute lymphocyte count in blood. 782, 783 In addition, recent work, including studies sponsored by the Health Eff ects Institute (HEI), provides evidence that biochemical responses are occurring at lower le vels of benzene exposure than previously known. 784, 785, 786, 7 87 EPA’s IRIS program has not ye t evaluated these new data.

1,3-Butadiene: EPA has characterized 1, 3-butadiene as carci nogenic to humans by inhalation. 788, 789 The IARC has determined that 1,3- butadiene is a human carcinogen and the U.S. DHHS has characterized 1,3- butadiene as a known human carcinogen. 790, 791 There are numerous studies consistently demonstrating that 1,3-butadiene is metabolized into genotoxic metabolites by experimental animals and humans. The specific mechanisms of 1,3-butadieneinduced carcinogenesis are unknown; however, the scientific evid ence strongly suggests that the carcinogenic effects are mediated by genotoxic metabolites. Animal data suggest that females may be more sensitive than males for cancer effects associated with 1,3-butadiene exposure; there are insufficient data in humans from which to draw conclusions about sensitive subpopulations. 1,3-butadiene also causes a variet y of reproductive and de velopmental effects in mice; no human data on these effects are availabl e. The most sensitive effect was ovarian atrophy observed in a lifetime bioassay of female mice. 792

Formaldehyde: Since 1987, EPA has classi fied formaldehyde as a probable human carcinogen based on evidence in humans and in rats, mice, hamsters, and monkeys. 793 EPA is currently reviewing recently pub lished epidemiological data. Fo r instance, research conducted by the National Cancer Institute (NCI) found an increased risk of nasopharyngeal cancer and lymphohematopoietic malignancies such as leukemia among workers exposed to formaldehyde. 794, 795 NCI is currently performing an update of these studies. A recent National Institute of Occupational Safe ty and Health (NIOSH) study of garment workers also found increased risk of death due to leukem ia among workers exposed to formaldehyde. 796 Extended follow-up of a cohort of British chemical work ers did not find evidence of an increase in nasopharyngeal or lymphohematopoi etic cancers, but a continuing st atistically significant excess in lung cancers was reported. 797

In the past 15 years there has been substa ntial research on the inhalation dosimetry for formaldehyde in rodents and primates by the CIIT Centers for Health Re search (formerly the Chemical Industry Institute of Toxicology), with a focus on use of rodent data for refinement of the quantitative cancer dose-response assessment. 798, 799, 800 CIIT’s risk assessment of formaldehyde incorporated mechanistic and dosimetric information on formaldehyde.

Based on the developments of the last decad e, in 2004, the working group of the IARC concluded that formaldehyde is carcinogenic to humans (Group 1), on the basis of sufficient evidence in humans and sufficient evidence in expe rimental animals - a higher classification than previous IARC evaluations. Afte r reviewing the currently available epidemiological evidence, the IARC (2006) characterized the human evid ence for formaldehyde carcinogenicity as “sufficient,” based upon the data on nasopharyngeal cancers; the epid emiologic evidence on leukemia was characterized as “strong.” 801 EPA is reviewing the recent work cited above from

511the NCI and NIOSH, as well as the analysis by th e CIIT Centers for Health Research and other studies, as part of a reassessment of the hum an hazard and dose-response associated with formaldehyde.

Formaldehyde exposure also causes a range of noncancer health effects, including irritation of the eyes (burning and watering of the eyes), nose and throat. Effects from repeated exposure in humans include resp iratory tract irritation, chroni c bronchitis and nasal epithelial lesions such as metaplasia and loss of cilia. Animal studies suggest th at formaldehyde may also cause airway inflammation – including eosinophil infi ltration into the airways. There are several studies that suggest that formal dehyde may increase the risk of asthma – particularly in the 802, 803 young.

Acetaldehyde: Acetaldehyde is classified in EPA’s IRIS database as a probable human carcinogen, based on nasal tumors in rats, and is considered toxic by th e inhalation, oral, and intravenous routes. 804 Acetaldehyde is reasonably anticip ated to be a human carcinogen by the U.S. DHHS in the 11 th Report on Carcinogens and is classified as possibly carcinogenic to humans (Group 2B) by the IARC. 805, 806 EPA is currently conducting a reassessment of cancer risk from inhalation exposure to acetaldehyde.

The primary noncancer effects of exposure to acetaldehyde vapors in clude irritation of the eyes, skin, and respiratory tract. 807 In short-term (4 week) rat studies, degeneration of olfactory epithelium was obs erved at various concentra tion levels of acetaldehyde 808, 809 exposure. Data from these studies were used by EPA to develop an inhalation reference concentration. Some asthmatics have been shown to be a sensitive subpopulation to decrements in functional expiratory volume (FEV1 test) and bronchoconstriction upon acetaldehyde inhalation. 810 The agency is currently conducting a r eassessment of the he alth hazards from inhalation exposure to acetaldehyde.

Acrolein: EPA determined in 2003 that the human carcinogenic potential of acrolein could not be determined because the available data were inadequate. No information was available on the carcinogenic e ffects of acrolein in humans a nd the animal data provided inadequate evidence of carcinogenicity. 811 The IARC determined in 1995 that acrolein was not classifiable as to its ca rcinogenicity in humans. 812

Acrolein is extremely acrid and irritating to humans when inhaled, with acute exposure resulting in upper respiratory tr act irritation, mucus hypersecre tion and congestion. Levels considerably lower than 1 ppm (2.3 mg/m 3 ) elicit subjective complaints of eye and nasal irritation and a decrease in the respiratory rate. 813, 814 Lesions to the lungs and upper respiratory tract of rats, rabbits, and hamste rs have been observed after s ubchronic exposure to acrolein. Based on animal data, individuals with compro mised respiratory function (e.g., emphysema, asthma) are expected to be at in creased risk of developing adverse responses to strong respiratory irritants such as acrolein. This was demons trated in mice with allergic airway-disease by comparison to non-diseased mice in a study of the acute respiratory irrita nt effects of acrolein. 815

EPA is currently in the process of conducting an assessment of acute exposure effects for acrolein. The intense irritancy of this carbonyl has been demonstr ated during controlled tests in

512human subjects, who suffer intolerable eye and na sal mucosal sensory reactions within minutes of exposure. 816

Naphthalene: Naphthalene is found in small quantities in gasoline and diesel fuels. Naphthalene emissions have been measured in larger quantities in both gasoline and diesel exhaust compared with evaporative emissions from mobile sources, indicating it is primarily a product of combustion. EPA released an exte rnal review draft of a reassessment of the inhalation carcinogenicity of naphthalene base d on a number of recent animal carcinogenicity studies. 817 The draft reassessment comp leted external peer review. 818 Based on external peer review comments received, additional analyses are being undertaken. This external review draft does not represent official agency opinion and wa s released solely for the purposes of external peer review and public comment. Once EPA eval uates public and peer re viewer comments, the document will be revised. The National Toxicology Program listed naphthalene as "reasonably anticipated to be a human carcinogen" in 2004 on the basis of bioassays reporting clear evidence of carcinogenicity in rats and some evidence of carcinogenicity in mice. 819 California EPA has released a new risk assessment for naphthalene , and the IARC has reevaluated naphthalene and re-classified it as Group 2B: possibly carcinogenic to humans. 820 Naphthalene also causes a number of chronic non-cancer effects in animals, including abnormal cell changes and growth in respiratory and nasal tissues. 821

Ethanol: EPA is conducting an assessment of the cancer and noncancer effects of exposure to ethanol, a compound whic h is not currently listed in EPA’s IRIS. A description of these effects will be presented, as required by Section 1505 of EPAct, in a Report to Congress on public health, air quality and water resource impacts of fuel additives. We expect to release that report in 2009.

Extensive data are available regarding adverse health effects associated with the ingestion of ethanol while data on inhalati on exposure effects are sparse. As part of the IRIS assessment, pharmacokinetic models are being evaluated as a means of extrapolating across species (animal to human) and across exposure routes (oral to inhala tion) to better characterize the health hazards and dose-response relationships for low levels of ethanol exposure in the environment.

The IARC has classified “alcoholic bevera ges” as carcinogenic to humans based on sufficient evidence that malignant tumors of the mouth, pharynx, larynx, esophagus, and liver are causally related to the consum ption of alcoholic beverages. 822 The U.S. DHHS in the 11 th Report on Carcinogens also identified “a lcoholic beverages” as a kn own human carcinogen (they have not evaluated the cancer risks specifically from e xposure to ethanol), with evidence for cancer of the mouth, pharynx, larynx, esophagus, liver and breast. 823 There are no studies reporting carcinogenic effects from inhalation of ethanol. EPA is currently evaluating the available human and animal cancer data to identify which cancer ty pe(s) are the most relevant to an assessment of risk to humans from a low-level oral and inhalation exposure to ethanol.

Noncancer health effects data are available fr om animal studies as well as epidemiologic studies. The epidemiologic data are obtained from studies of alc oholic beverage consumption. Effects include neurological impa irment, developmental effects, cardiovascular effects, immune system depression, and effects on the liv er, pancreas and reproductive system. 824 There is

513evidence that children prenatally exposed via mo thers’ ingestion of al coholic beverages during pregnancy are at increased risk of hyperactivity and attention deficits, impaired motor coordination, a lack of regulation of social behavior or poor psychos ocial functioning, and deficits in cognition, mathematical ability, verbal fluency, and spatial
memory. 825, 826, 827, 828, 829, 830, 831, 832 In some people, genetic factor s influencing the metabolism of ethanol can lead to differences in internal levels of ethanol and may render some subpopulations more susceptible to risks from the effects of ethanol.

N-Hexane: N-Hexane is asso ciated with polyneuropathy in humans. Effects observed in rodents include nasal lesions as well as neurotoxic effects. EPA has developed a reference concentration of 700 μ g/m 3 from a study of peripheral neuropathy. 833 There is inadequate data to assess its carcinogenic potential.

Peroxyacetyl nitrate (PAN): PAN has not b een evaluated by EPA’s IRIS program. Information regarding the potential carcinogenicity of PAN is limited. As noted in the EPA air quality criteria document for oz one and related photochemical oxidants, cytogenetic studies indicate that PAN is not a potent mutagen, cl astogen (a compound that can cause breaks in chromosomes), or DNA-damaging agent in mammalian cells either in vivo or in vitro. Some studies suggest that PAN may be a weak bacter ial mutagen at high con centrations much higher than exist in present urban atmospheres. 834

Effects of ground-level smog cau sing intense eye ir ritation have been attributed to photochemical oxidants, including PAN. 835 Animal toxicological information on the inhalation effects of the non-ozone oxidants has been limite d to a few studies on PAN. Acute exposure to levels of PAN can cause changes in lung morphol ogy, behavioral modifications, weight loss, and susceptibility to pulmonary infections. Huma n exposure studies indicate minor pulmonary function effects at high PAN con centrations, but large inter-individual variability precludes definitive conclusions. 836

Pesticides: There are potential toxicity conc erns with volatilization of pesticide active ingredients, 837 in addition to concerns with contam ination of foods and drinking water. Furthermore, raising acreage under corn produc tion may increase the quantity of pesticide products in use. As the domestic corn s upply grows between the years of 2005 and 2022, the percentage of corn used for et hanol production in the US is e xpected to increase, though the agricultural impacts of this shifting of crop pr oduction domestically are antic ipated to be small. Whether there is the potential for adverse human h ealth effects from any increase in pesticide use associated with increased corn production domestically warrants further assessment. Additional information on pesticides and health effects is included in Section 6.1 of this DRIA.

Other Air Toxics: In addition to the co mpounds described above, other compounds in gaseous hydrocarbon and PM emissions from vehi cles will be affected by today’s proposed action. Mobile source air toxic compounds th at will potentially be impacted include ethylbenzene, polycyclic organic matter, propionaldehyde, toluene, and xylene. Information regarding the health effects of these compounds can be found in EPA’s IRIS database. 838

5143.4.6 Environmental Effects of Air Toxics

Fuel combustion emissions contribute to ambi ent levels of pollutant s that contribute to adverse effects on vegetation. P AN is a well-established phytotoxicant causing visible injury to leaves that can appear as metallic glazing on the lower surface of l eaves with some leafy vegetables exhibiting particular sens itivity (e.g., spinach, lettuce, chard). 839, 840, 841 PAN has been demonstrated to inhibit photosynt hetic and non-photosynthetic proces ses in plants and retard the growth of young navel orange trees. 842, 843 In addition to its oxidizi ng capability, PAN contributes nitrogen to forests and other vegetation via uptak e as well as dry and wet deposition to surfaces. As noted above in Section 3.4.2.2, nitrogen de position can lead to saturation of terrestrial ecosystems and research is needed to unders tand the impacts of excess nitrogen deposition experienced in some areas of the c ountry on water quality and ecosystems. 844

Volatile organic compounds (VOCs), some of which are considered air toxics, have long been suspected to play a role in vegetation damage. 845 In laboratory experi ments, a wide range of tolerance to VOCs has been observed. 846 Decreases in harvested seed pod weight have been reported for the more sensitive plants, and so me studies have reported effects on seed germination, flowering and fruit ripening. E ffects of individual VOC s or their role in conjunction with other stressors (e.g., acidification, drought, te mperature extremes) have not been well studied. In a recen t study of a mixture of VOCs including ethanol and toluene on herbaceous plants, significant effects on seed pr oduction, leaf water content and photosynthetic efficiency were reported for some plant species. 847

Research suggests an adverse impact of ve hicle exhaust on plants, which has in some cases been attributed to aromatic com pounds and in other cases to nitrogen oxides. 848, 849, 8 50 The impacts of VOCs on plant reproduction may have long-term implications for biodiversity and survival of native species near major roadways. Most of the studies of the impacts of VOCs on vegetation have focused on short-term exposur e and few studies have focused on long-term effects of VOCs on vegetation and the potential for metabolites of these compounds to affect herbivores or insects.

515

## Chapter 4: Impacts on Co st of Renewable Fuels, Gasoline, and Diesel

4.1 Renewable Fuel Production Costs

4.1.1 Ethanol Production Costs

4.1.1.1 Corn Ethanol

Corn ethanol costs for our work were es timated 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. 851 The USDA model considers a 40 MMgal/y r corn plant producing ethanol with a primary co-product of distillers dried grains with sol ubles (DDGS). The et hanol yield used in the model is 2.76 gallons per bushel with 2.0% ga soline 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 technol ogy, yields, and capital estimates, we made some modifications to allow estimation of cost s for ethanol plants of different sizes and operating under different energy and feedstock pr ices. We believe that these updates, in combination with the industry and supplier surveys done by US DA in developing the model, result a reasonable estimate for pr ojected ethanol production costs.

We estimate an average corn ethanol pr oduction cost of $1.43 per gallon in 2022 (2006 dollars) for the policy case. The co st of ethanol production is most se nsitive to the prices of corn and the primary co-product, DDGS. Utilities, capital, a nd labor expenses also have an impact, although to a lesser extent . Corn feedstock minus DDGS sale credit represents about 57% of the final per-gallon cost, while util ities, facility, and labor comprise about 22%, 11%, and 4%, respectively. For this work, we used corn a nd DDGS price projections generated by the Forestry and Agricultural Sector Optimization Model, which is described in Section 5.1.1 of this RIA. Figure 4.1-1 shows the cost breakdown for producti on of a gallon of ethanol . Note that this production model does not account for the cost to ship the DDGS. Those costs are external and are expected to increase the price of DDGS an end user located far from the plant.

516Figure 4.1-1. Cost Breakdown of Natural Gas Dry Mi ll Corn Ethanol Production (2006$).

-0.46 0.81 0.31 0.16 0.09 0.06

-$0.50 -$0.25 $0.00 $0.25 $0.50 $0.75 $1.00 $1.25 $1.50$/gal Labor
Chemicals, enzymes
Facilty cost, maint

Utilities

Corn minus DDGS

DDGS sale

Corn
cost
Corn price $3.34/bu DDGS price $139.78/ton Natural gas price $6.21/MMBtu

Electricity price $0.059/kWh

The effect of plant scaling on producti on cost can be estimated by applying an engineering scaling factor to all plant equipment. In past rule makings involving modifications to refineries we have used a material scaling factor of 0.65. This f actor is applied as an exponent to the ratio of the new size to the original size, th e result of which is then multiplied by the original capital cost. The fact that this figure is less than 1.0 reflects the per-u nit or per-gallon savings that is often realized when pr ocesses are scaled up. However, there is information suggesting that a general factor may be considerably highe r for ethanol plants. A factor of 0.84 was put forth in a recent publication on dry mill ethanol production. 852 Using this larg er factor, we find that the change in per-gallon production cost due to economies of scale is very small over the range of typical plant sizes, on the order of $0.02 between 40 and 100 MMgal/yr. Thus, in computing production costs for this rulemaking we chose to ignore effects of any changes average plant size. Capital cost for a 40 million gal/yr facility is estimated at $58.1 million (2006$) based on the USDA production model.

We considered the per-gallon cost impact of coal, biomass, and biogas as a process energy source rather than natural gas. Our i ndustry characterization a nd forecast work suggests that ethanol production in the time frame of our analysis will use a mix of feedstocks for process energy. We assumed that on average, coal a nd biomass combustion systems would have the capital cost due to similarities in feed, ash ha ndling, and emission controls; the same argument can be made for use of biogas combustion rela tive to natural gas co mbustion (excluding the digesters or other source). Ta bles 4.1-2 and 4.1-3 show the br eakdown of fuel sources and their cost impacts in cents per gallon. The weighted average cent per gallon change to baseline for ethanol production is given at the bottom of Table 4.1-3.

517Table 4.1-2. Breakdown of fuel types u sed in estimating production cost of corn ethanol Fuel Type Total by Plant Type Plant Type Biomass Coal Natural Gas Biogas All Fuels Coal/Biomass Boiler 11% 0%

- - 11% Coal/Biomass Boiler + CHP 10% 4%

- - 14% Natural Gas Boiler

- - 49% 14% 63% Natural Gas Boiler + CHP

- - 12%

- 12% Total by Fuel Type 21% 4% 61% 14% 100%

Table 4.1-3. Breakdown of cost impacts by fu el type used in estimating pro duction cost of corn ethanol Fuel Type Total by Plant Type Plant Type Biomass a Coal Natural Gas Biogas b All Fuels Coal/Biomass Boiler -$0.02 -$0.02

- - - Coal/Biomass Boiler + CHP +$0.14 +$0.14

- - - Natural Gas Boiler

- - baseline +$0.00

- Natural Gas Boiler + CHP

- - +$0.16

- - Total by Fuel Type

- - - - $0.04

Table 4.1.3 shows that we made the assump tion that biomass firing has the same overall cost impact on ethanol production as coal firing. One reason for th is is that our analysis of biomass feedstock costs suggests a range of $72 per ton in future years which is comparable to the cost of coal supplied to non-electric-power industries after tran sportation is included. 853 Wood and stover biomass has on average approximate ly 85% of the energy content of coal on a mass basis, varying by type of biomass and coal, again suggesting that they are comparable on an energy per mass basis. 854 Thus, it is plausible that some ethanol producers near biomass sources (such as the Midwest and Southeast) may have a cost incentive to transition from coal to biomass for process heat. A second reason the assumption of biomass replacing coal in ethanol plants a desire for ethanol plant owners to comply with or hedge agai nst potential fossil carbon emission limits for stationary sources and/ or transportation fuel producers.

Similarly, we made the assumption that bi ogas combustion for pro cess heat would have the same cost impact on ethanol production as natural gas combustion. Use of biogas is somewhat different from biomass in that it w ould require some capital investment for on-site anaerobic digesters and related feedstock and ga s handling equipment. However, we anticipate the digester feedstock itself woul d have very low or no cost, thus it is reasonable to assume that the ongoing operating costs besides capital would be considerably less than purchasing natural gas. As with biomass combus tion, most plants utilizing bi ogas would take advantage of situations such as co-location with feedlots or MSW facilities where suitable biomass resources are available.

Energy prices used in the model were taken from EIA Annual Energy Outlook 2008, and are shown in Table 4.1-4. 855 Gasoline denaturant was assumed to cost $2.05 per gallon in all cases, and coal was assumed to be $1.94/MMBtu in all cases. The impact of coal firing and CHP were figured as changes from baseline. Table 4.1-5 shows the feedstock prices used for the reference and low-yield agricultural cases. The lo w-yield case refers to a reduction of per-acre

518corn yields by approximately 12 %, which has a higher impact on prices than the AEO high oil

case.

Table 4.1-4. Energy Prices Used for Ethanol Cost Modeling for 2022 (2006$) EIA Price Case Crude Oil Natural Gas Electricity $/bbl $/MMBtu $/kWh Reference 53 6.21 0.059 High oil 92 7.21 0.061

Table 4.1-5. Feedstock Prices Used for Ethano l Cost Modeling for 2022 (2006$)
AEO Price Case Agricultural Case Corn DDGS $/bu $/ton Reference Reference 3.34 139.78 Reference Low yield 3.84 160.32 High Reference 3.42 141.68

4.1.1.2 Cellulosic Ethanol

Crop Residue Costs

We could have used any of the crop residues as an example feedstock in the following discussion, since similar logistics apply to all of them. We chose to use corn stover, e.g., the stalks, leaves, and cobs that remain following gr ain harvest, since it is likely to represent a significant portion of cellulosic f eedstocks in the future. Since there is no equipment specifically designed to harvest corn, the system we describe below uses combines, mowers, rakes, balers, and bale haulers already in use fo r harvesting hay or straw. Differe nces in stalk or stem diameter and density, bale density, moisture content, m achine field speeds and efficiencies are a few things that make it relatively more difficult to ha rvest stover than hay or straw. One of the main concerns is that the density of the large stove r bales, whether round or rectangular, can be as little as one-half that of similar dimension hay bales, which usually translates into higher transportation costs. 856

Most biomass feedstocks must be harveste d, stored, and transported to a processing facility before they can be converted into etha nol. At present, there are no commercial sized cellulosic ethanol plants in the U.S. Like wise, there are no commercially proven, fullyintegrated feedstock supply systems dedicated to providing any of the crop residues or other feedstocks to ethanol facilities of any size. We emphasize ‘integrated feedstock supply systems’ because logistically the delivery of a feedstock to a processing fac ility will require the planning, executing, and controlling of several different, closely integrated operations, e.g., feedstock harvesting, gathering, storing, and moving by road and rail. Ap art from the large numbers and wide variety of equipment, these operations wi ll require professional and technical support services and personnel such as office space, staff, and office equipment such as computers and printers. Also, engineers, light- and heavy- duty equipment operators , vehicle maintenance

519personnel and repair and storage fa cilities for tractors, rakes, ba lers, loaders, and trucks and trailers, as well as transportation infra structure planning and management.

Ordinarily, to determine the operational sufficiency and efficiency of such a system, we would ‘analyze’ it. We would fi rst break it down into its compone nt parts or essential features and then study them, e.g., how much they cost, and how and/or whether th ey operate efficiently within the ‘system.’ However, no such system currently exists. Therefore, we ‘synthesized’ a feedstock supply system in orde r to analyze it. We used a Purdue University, School of Industrial Engineering simulation study of corn st over logistics from satellite storage to an ethanol plant, to set up our feedstock harvesti ng and gathering operation. Purdue’s notional cellulosic ethanol plant was to be constructed next to an actual existing corn grain plant in northern Indiana. They used discrete event simulation software and GIS tools to study the transportation logistics a ssociated with supplying the conversi on facility directly from satellite storage. They identified 785,200 available ac res out of 848,453 potential acres, on 2,052 actual farms, of 200-, 400-, or 800-acres, in 12 north ern Indiana counties wi thin 50-miles of the production facility (they disregarde d fields or farms of less than 200-acres). We reproduced their original table as Table. 4.1-6.

Table 4.1-6. Feedstock Availability at Variou s Distances From South Bend, IN

Average Farm Size
Average Actual Cumulative 200 400 800 Available Acres Cumulative Acres bales/ day bales/day /farm set Distance County Acres Acres Acres
12 St Joseph 69 69 74 51 35 63 63 4,956 4,956 25 Elkhart 150 219 90 47 23 55 118 4,328 10,257 Marshall 110 52 41 76 194 5,928 30 La Porte 113 332 113 73 70 108 302 8,453 8,453 35 Starke 60 392 34 39 41 55 357 4,328 4,328

40 Kosciusk

o 93 485 108 66 43 82 439 6,461 6,461

45 Lagrange 132 617 53 47 20 45 485 3,560 9,049 Fulton 86 66 43 70 555 5,489 50 Porter 225 842 76 59 34 66 621 5,175 18,224 Noble 115 47 21 59 679 4,595 Pulaski 39 72 64 108 787 8,453

842 998 619 435 787 61,726 61,728

We initially assumed that the stover had been harvested (square bales), gathered, fieldtransported, and stored at se ven-satellite storage areas lo cated near the corn fields. 857 However, upon further study, we determined the counties that Purdue combined into each of the farm sets, weren’t anywhere near each other. In reality, it would have been far too costly to gather all the bales from the Porter, Noble, and Pulaski countie s into one site, because these three counties are actually separated by other counties. Rather than try to construct se ven satellite storage sites, we constructed a site at the center of each county. This was done in order to estimate the cost to collect the bales from all the fields in each count y. We determined that the distance from the center of each of the Porter, N oble, and Pulaski counties, as well as the other two so-called farm-sets, to the ethanol plant was about equal, so regardless of whether we treat them as single sites, the transport costs for the bales to the et hanol plant will be the same. We ‘synthesized’ the feedstock system to harvest, gather, field tran sport, and store stover bales at the 11-notional

520satellite storage units rather than the seven farm-s et units used in the Purdue transportation model (our study was not done in conj unction with the Purdue study; rather we used their information/data as the basis from which to sy nthesize our notional operation). The format we chose to analyze was to shred, rake, square-bale, gather, field-side, and then load and haul the bales to satellite storage; then, as needed, haul the bales to the processing plant.

Rather than guess at how such a system s hould look and function, we carefully studied several similar systems that were put forward by various agricultural and biological experts. 858 ,

859 , 860 , 8 61, 862, 863, 8 64, 865, 866 We used the American Society of Agricultural and Biological Engineers (ASABE), 2007 Standards, Engineering Practic es, and Data as the primary source for our equipment capital and operating cost estimates. It has a machinery management section, ASE EP496.3, FEB2006, devoted to providing helpful information in making management decisions involving machine power requirements, capacities, cost, selection, and replacement, as well section ASAE D497.5 FEB2006, with data whic h includes representative values of farm machinery operating parameters, to aid managers , planners, and designers in estimating the performance of field machines. These data ar e intended for use with ASAE EP496 (some data are also presented in equation form for easier use with computers, etc.). 867 We used these sections along with other examples by other experts to estimate the machinery capital and operating costs for our analysis. 868 , 869 , 8 70, 871, 872 We were able to get some machinery purchase prices from vendors whose identities are confiden tial. We reduced the equipment listed price by 10% to determine the purchase price, a standard in dustry estimating practice. Otherwise, most of the data used to calculate machinery costs were generated with equations and appropriate data from the ASABE 2007 Standards. We used the equipment, list and purchase prices, along with their power and size estimates, w ith the suggested data and equations, mentioned previously, to calculate the lifetime hours and year s, annual use, field efficiency, salvage value, fuel and oil use and cost (we obtained vendor quotes for oil cost), capital charge, repairs, insurance, housing, taxes, and labor. We compared our data , where appropriate, w ith the USDA 2006 Price Summary, published July 2007. 873 We also compared our result s with those generated by the experts we listed earlie r in this paragraph.

The Purdue study was based on supplyi ng a 100-million gallon per year ethanol production facility, which they assumed would convert the stover-to-ethanol at 72-gallons per ton; they assumed that the on-farm stover yield was 2-dry tons per acre, as did we. We used National Agricultural Statistical Service (NASS) data to determine the actual corn grain yield in 2005 (the data year for the Purdue study) for the counties studied in the simulation. We determined how much corn each county produced and from that how much stover was produced, 2,455,058 tons or 3.12 dry tons per acre, with an assumed harvest index (HI) of 1:1 (see Table. 4.1.1.2.2.) 874 HI is based on the assumption that, for a single corn plant, half of the above ground dry matter is made up of stover and the other half is made up of gr ain. This is a fairly common assumption, although more than one group of resear chers has found that this 1:1 ratio may not be the most accurate under some conditions. When considering above ground dry matter before and after full grain physiological maturation, they found th at a stover to grain ra tio of 0.8 to 1 may be more realistic especially when grain moisture is between 18 and 31 percent. 875

521Table 4.1-7. Data Comparison: NASS Indiana Data with Purdue Data and with EPA Calculations

USDA-NASS – Counties in State of Indiana - 2005 Purdue Model Year – 2005

Planted Harvested Yield Production Model Available

acres Prorated Production wet tons dry tons

County acres x 10 3 acres x 10 3 bu/

acre bu x 10 6 acres x 10 3 acres x 10 3 bu x 10 6 x 10 3 x 10 3
St Joseph 71 70 147 10.2 69 65 9.5 266 225
Elkhart 60 53 142 7.6 150 52 7.4 206 174
Marshall 94 89 150 13.4 0 87 13.1 366 309
La Porte 117 112 137 15.3 113 104 14.2 398 336 Starke 61 60 137 8.2 60 56 7.7 214 181
Kosciusko 102 100 149 14.9 93 93 13.8 386 326 Lagrange 55 51 113 5.8 132 45 5.0 141 119

Fulton 90 89 159 14.1 0 78 12.3 345 291
Porter 68 67 137 9.2 225 61 8.4 234 198 Noble 65 62 142 8.8 0 57 8.0 225 190

Pulaski 103 101 152 15.3 0 92 13.9 390 330

855 842 787 3,172 2,455

However, a professor of agri cultural engineering at the Un iversity of Wisconsin found that several researchers, going back to 1973 re ported a grain mass fractio n of 45% to 55% of total corn crop DM yield. On average, the vari ations seem to confirm the common rule of thumb of one unit mass of stover for a unit mass of grain. However, differences among harvesting methods, stages of maturity, a nd harvest dates can no doubt lead to much of the variation the researchers found in this estimate. His research in dicates that the ratio of grain dry mass to total mass increased from about 38% in late August to about 59% on in mid-October, during a recent harvest. Therefore, the stover to total ratio declined from 62% to 41%. During the typical harvest period in the Upper Midwest when grain moisture is between 20% and 30%, the ratio of stover to total dry mass was less than 45% and averaged 43%. These results are similar to those found by others. 876 Mainly, because we have no information upon which to base a reason to use something different, we chose to us e the 1:1 ratio for corn stover to corn grain. We also assumed 56-lbs per wet bushel (15.5% moisture) and 47.3-lbs per dry bushel, for the corn grain, to make our stover yield calculations. 877 Table 4.1-8 summarizes the gene ral operating parameters for our study.

522Table 4.1-8. Operating Parameters EtOH Operating Year 350-days/yr

On Stream Factor 0.96 Hours per year 8,400-hr/yr EtOH Production Rate 100,000,000-gal/yr EtOH Yield 72-gal/dry ton Feedstock Required
per year 1,388,889-dry tons/yr per day 3,968-dry tons/day Expected Dry Matter Loss 11.8%

Feedstock Harvested 1,574,400-dry tons/yr Feedstock Yield 2-dry tons/acre Harvest Period

Days, 50-days Hours per Day 16-hr/day Harvest Hours 800-hr Format: Shred/Rake, Bale - Lg. Sq. - 3'x4'x8' Field Side - Self-Propelled Wagon Satellite Storage – Pole Barn on Concrete Transport to EtOH Facility – Truck & Trailer

At 72 gallons per dry ton, the processing plan t would require 1,388,889 dry tons of stover per year. However, we believe storage and transportation losses can be significant and should be taken into account. If, as stated in the report, they harves ted 2-tons per acre, they actually harvested 1,574,400 tons of stover, or 64% of th e 2,455,058 tons of available dry stover. The quantity of stover used versus the quanti ty harvested represents an 11.8% loss. C CCCCCCCC Thus, we assumed that with an 11% loss, we would need to harvest 4,498- dry tons of stover per day, which by the time it reaches the plant will actually equal 3,968-dry tons – the amount required per day at the ethanol plant. During a 350-day production year, 1.574-million tons of stover would have be stored in about 3.499-million, 900- lb bales (Purdue study bale weight), at the various satellite storage areas. For this study, we assumed all the loss took place between the satellite storage areas and the ethanol plant, rather than guess what the loss es would be at various points within the harvest/transport scheme.

In the following analysis, we did not account fo r the extra time or equipment that would be necessary for inevitable break-downs. Nor was time and equipment factored in for driving between fields and for weather delays. Stover suppliers face several, in some cases, difficult problems. At best, the actual harvest period is nearly always too shor t; winter weather can suddenly set in, which in some cases may complete ly stop a potential stover or straw harvest. Once the grain is harvested and the stalks are mowe d, stover usually must be left in the field for three or four days to dry to below 20% moisture before it’s baled, otherwise spoilage or rot as well as spontaneous combustion are possible. If it rains, additional time is required for drying and muddy roads and fields can be badly damaged and the field-soil compacted by the increased

CCCCCCCCC We indicated in a previous section that there may be as little as 25% to 50% stover actually available; however, since we didn’t have the computer software and database Purdue used and therefore couldn’t rerun the simulation, we chose to use the data we had.

523heavy-weight harvest and transpor t equipment traffic. One expert commented that, “If there’s a rainy harvest, you might as well fo rget about it. Also, the longer th e wet material is left in the field, there’s more of a chance for micr obes to eat away at the hemicellulose.” 878 Also, delayed baling raises the chances of dry matter loss. The stover needs to field-dry, so the stover harvest can’t actually begin for at least a fe w days after the grain harvest starts. But, once it begins it can continue until either it’s finished or until wint er weather stops it. For reasons that weren’t explained in the report, the researchers at Purdue chose 50-days for the ha rvest period, which at their 16-hr per day schedule, provided a total of 800-hrs to complete the harvest and store the stover. In this harvest format (800-hrs), most of the machinery will be stored for the balance of the year. Crop harvest schedules in the Mi dwest and upper-Midwest are determined by the length of the growing season, the time of year when the crops are mature enough to harvest, and the time when winter weather sets in. Under ordinary conditions, farmers use their harvesting machinery during just a few weeks each year. During the past few years, as machinery costs have risen, many farmers have turned to custom harvesters, that move into an area and harvest several farms. A farmer must always weigh th e differences in the custom rates and what it would cost him to own the equipment and complete the harvest himself, but then store most of the harvesting machinery for the rest of the year . In the South, winters are milder and it’s possible to harvest some crops all year long. In such cases, feedstocks could conceivably be harvested and shipped to a convers ion facility on an ‘a s needed’ basis; storage costs could be saved and machinery would be used all year long . However, an important factor, when it comes to harvest machinery, is the us able-life of the equipment. The more hours used each year, the more often the machine will need to be replaced. A machine la sts only so many hours, whether it’s used 800-hrs per year or 8, 000-hours per year. We obviously could have arbitrarily chosen some longer period, but in order to maintain at least some consistency with the Purdue study, we chose to use the 50-day schedule for our study. This short period means we must harvest and store a full year’s invent ory within a few weeks.

Mow, Shred, Rake: Modern corn combines strip most of the leaves from a corn stalk, but leave up to about half of the stalk standing when they cut it off just below the bottom ear. In the combine, the corn grain is stripped from the cob, a nd the top part of the stalk, the leaves, and the cobs are subsequently discharge out the spreader at the rear of the machine. According to a group of researchers, at the time of grain harvest, of th e total stover dry mass, 16% resides in the cob, 7% in the husk, 16% in the leaves, and 60% in the stalk fractions. Of the stalk dry mass, roughly 45% is found in the bottom one-quarter a nd 80% in bottom one-half of the stalk. If stover yield is to be maximized, harvesting system s must be developed that allow the bottom half of the stalk to be fully harvested 879 We summarized the costs to shred and rake in Table 4.1-9

524Table 4.1-9. Corn Stover Shredding & Raking Operation

Tractor – 245-hp FlailShredder -30’ Tractor 75-hp MFWD Wheeled V-Rake - 20 ft Equipment Factors Purchase price $ 144,502 28,733 59,383 3,660 Useful life yrs 11.3 3.1 11.3 15 Discounted Salvage value $ 11,736 0.00 7,861

Annual use hr 800 800 800 800 Fixed Costs $/hr Depreciation and intere st 25.47 14.51 10.06 1.87 Taxes Insurance Housing (TH I) $/hr 3.97 0.79 1.63 0.10 Total Fixed Costs $/hr 29.44 15.30 11.69 1.97 Variable Costs Repairs and maintenance $/hr 19.62 14.05 8.06 1.63 Fuel consumption gal/hr 9.6 8 5.6 Fuel and lubrication $/hr 23.82 19.87 13.81 4.27 Operating Interest 1.70 1.21 0.82 0.21

Labor $/hr 15.91 15.91 15.91 4.00 Total Variable Cost $/hr 61.05 51.04 38.60 10.11 Total Costs Total $/hr 90.49 66.34 50.29 12.08 Equipment capacity MT/hr 32.7 13.27 Total $/ton 4.80 4.70

It will likely be necessary to flail-cut or mo w the standing-stalks, and then rake and bale the windrows. We estimated that it cost about $4.80 per ton of stover for shredding and about $4.70 per ton for raking.

Bale: As previously discussed, large square bales will likely be the bale-format for this system, although large round bales c ould be used. There are curre ntly more round balers than square balers in use, mainly because large sq uare balers are more expensive. However, gathering, stacking, and transporti ng large, round bales is much less efficient. It is difficult to stack round bales more than about three high, since they tend to deform rather badly, during even short storage periods; square bale stacks can be stacked up to five or six bales high, which translates into a more efficient use of storage ar ea as well as more stable stacks that are far less prone to deformation over extended storage periods . Although large round bales tend to weather better out in the open, for the reas ons just stated as well as those given in the Purdue report, we used large square bales in this analysis. Ta ble 4.1-10 summarizes th e cost of the baling operation.

525Table 4.1-10. Corn Stover Baling Operation
Tractor - 275-hp Lg. Sq. Baler - 3' x 4' x 8' Equipment Factors Purchase price $ 147,102 110,723

Useful life yrs 11.3 3.8 Discounted Salvage value $ 7,553 32,455

Annual use hr 800 800 Fixed Costs $/hr Depreciation and interest 25.93 36.74 Taxes Insurance Housing (THI) $/hr 4.05 3.04 Total Fixed Costs $/hr 29.98 39.78 Variable Costs Repairs and maintenance $/hr 19.98 45.11 Fuel consumption gal/hr 10 8 Fuel and lubrication $/hr 24.84 19.87 Operating Interest 1.71 2.38 Labor $/hr 15.91 10.66 Total Variable Cost $/hr 62.44 78.02 Total Costs Total $/hr 92.42 117.80 Equipment capacity DMT/hr 19.4 Total $/ton 10.87

Another, sometimes overlooked i ssue, is that when stover is shredded, raked, and baled dirt tends to be to picked-up with the bales, whic h must be removed prior to processing. Dirt can usually be traced back to dirt clods left by the producer’s cult ivation practices and when the pickup on the harvester is set too low in an attempt to pickup as much stover as possible. Some dirt sticks to the corn stover if the stalks are left on the ground and run over by equipment; occasionally roots, attached to the short, cut- off stalks that are left following the shredding operation may be tipped out of the ground by harves t equipment and end up in the bales. Exact amounts of dirt aren’t easily meas ured, but are thought to be in th e 2% to 5% range. Areas that contain excessive weeds should be avoided, if possible. Some w eed types also contribute to additional moisture and dirt and these are thought to be major fact ors in stover deterioration and overall shrinkage. Furthermore, dirt that’s ca rried through the harvesting process causes severe equipment wear; grinders, mills, valves, pipes, conveyors, and other associated equipment fail much sooner, which translates into increased mainte nance costs and reduced on-stream operating time. We estimated that baling cost about $10.87 per ton. 880 , 881 ,

Bale Pick-Up & Field Side: It is important to remove the stover bales from off the fields. Few farmers will tolerate bales left for long period s on their fields, especially if there is a chance spring planting will be negatively affected. Nor do we expect farmers will allow random piles of bales left at field edges, for retrieval over the winter and sp ring months. Aside from the likelihood that trucks and other equipment woul d get stuck in muddy ro ads and fields, thus slowing down deliveries and running up operating co sts, farmers would have little tolerance for torn-up roads and fields. This may not be a big problem, if the farmer/grower intends to plow a field in the spring. However, it could be hi ghly problematic for a farmer/grower who “no till” farms and would be forced to repair ruts and ho les in fields and roads before Spring planting. Apart from this, dry matter losses from bales, le ft out in the open on dirt, can be as high as 10%

526to 20%. At harvest time, the bales, regardless of format, must be picked up and hauled either to a satellite storage site for intermediate storage or hauled directly to the processing plant.

Several variables must be taken into account for bale pickup and the field-side haul operation that could easily affect the cost. Because the exact location on a field where a bale lands as it falls from a baler varies, at a minimum, according to stover yield and harvest efficiency, there is no easy or accurate method for predicting the exact location of each bale on the field, either relative to each other or to th e field edges or entry. The distance between bales and the potential variability in the area, shape, and relative dimensions of each field add to the difficulty of estimating bale pickup costs. If it was possible to somehow tag each bale with GPS coordinates as it fell to the ground, theoretically the coordinates co uld be used in some type of ‘bale retrieval’ program to optimize the time and pickup distance traveled. 8 82, 883

For this study, we used the theoretical stover density on the field, the speed and width of the harvester to estimate the distance between bale s. We used a spreadsheet with these data to position the bales in a variety of patterns on a notional rectangula r 100-acre field. We devised three or four drive-patterns in which the bale s could be retrieved, by using simple visual inspection. We calculated the time to pick up the bales using each pattern and the average speed of the self-propelled bale-wagon. There were a few variables for which we couldn’t adjust our numbers because we simply had no way of knowing their effect. For example, if the field was furrowed, it seemed that the less time spent dr iving across the furrows, at, as we assumed a slower speed than could be travel ed along the furrows, the more e fficient would be the pick up. Table 4.1-11 summarizes the information we used for our calculations.

Table 4.1-11. Bale Pickup and Field-Side

Ft. Between Each Bale 490 Pickup One 10-Bale Load - Ft/Load 4,901 Bales/Acre 4.4 Bales/Load 10 Tons/Load 4.5 Loader Speed - mph 7.5 Field Size - Acres 200 400 800

Number of Loads per Field Size 89 178 356 Bales/Field Size - Total 889 1,778 3,556 Miles Traveled per Load 1.74 2.05 2.51 Tons/Hour 26 22 18 Cost per Ton – Pickup & Field-Side $2.82 $3.31 $ 4.05

In any case, using our basic assumptions, the ti me it took to retrieve a 10-bale load didn’t vary significantly for any of our plots



## . Table 4.1-12 presents the operating data for the bale
wagon.

527Table 4.1-12 Self-Propelled Bale-Wagon
Bale Wagon Equipment Factors Purchase price $ 153,716 Useful life yrs 18.1 Discounted Salvage value 6,013 Annual use hr 800 Fixed Costs $/hr Depreciation and Interest $/hr 20.70 Taxes Insurance Housing ( THI) $/hr 4.23 Total Fixed Costs $/hr 24.93 Variable Costs Repairs and Maintenance $/hr 12.96 Fuel Consumption gal/hr 7 Fuel and Lubrication $/hr 17.39 Operating Interest 1.21 Labor $/hr 15.91 Total Variable Cost $/hr 47.47 Total Costs $/hr 72.40 Total $/hr 72.40 Equipment capacity DMT/hr 23 Total $/ton 3.15

Since the fields were no smaller than 200-acres, we piled the bales at one of the corners, which we assumed as the field-entry. We calcula ted the cost to haul bales for each of the 200-, 400-, and 800-acre fields. We a ssumed the 20-ft rake made 148-pa sses across the 200-acre field; 209-passes across the 400-ft field; and 295-passes across the 800-ft field. We used the number of windrows the rake left to determine the num ber of passes the baler would make. The baler dropped a bale every 490-ft; to co llect a 10-bale load the loader would need to travel 4,900-ft. We estimated that the average distance every bale w ould need to travel to the corner of the field, e.g., the field entry, would be the distance from the field center to the corner (field-entry); we assumed the fields were square. The loader woul d need to travel that distance and then return empty, for the next load. Therefore, each load er would travel 4,900-ft to pickup the load, and then an additional 4,174-ft for the 200-acre field; 5,903-ft for the 400-acre field; and 8,348-ft for the 800-acre field to haul to the field edge (corner) and return. We chose to pick up 10-bales per load with a self-propelle d bale wagon with an average speed of about 10-mph. We assumed the bales would be picked up, transported, and dumped at the field-edge at a cost of $2.82-per ton for the 200-acre fields; $3.31-per ton for the 400- acre fields; and $4.05-per ton for the 800-acre fields

. We weighted the DM hauled for each fiel d size by the total tons recovered from each size, to arrive at 23-weighted dry tons/hr hauled for $3.15-per ton

Haul to Satellite Storage: Theoretically, we could store all the bales at the ethanol plant. If so, we would need to move 3,148,800-bales or about 101-loads per hour for 16 hours each day during the 50-day harvest. It would require 50- stacks, each, eight-bales wide by 5-bales high, by 1,577-bales (12,615-ft.) long, with 51 x 20-ft aprons and isles, between each stack and along two-sides of the entire area, pl us a 20-ft apron, across the entir e front and rear. The area would total ~33-million square feet or 1.19-square miles.

528We stored the bales at satellite facilities near the center of each county. As previously discussed, the Purdue study es tablished ‘farm-sets,’ but did not describe how they were configured and how bales were to be hauled to each storage site, nor did the study specify exactly where each storage site was. Rather , they only estimated the distance between each storage area and the ethanol plan t. The following table summariz es the cost factors used to estimate the costs to haul the bales to satellite storage.

Table 4.1-13. Haul - Field Side to Satellite Storage High-Speed

Tractor Bale Wagon 2­ Telescopic Handlers Equipment Factors Purchase price $ 133,865 23,851 130,106 Useful life yrs 15 15 12.5 Discounted Salvage Value $ 10,782 4,803 6,490 Annual use hr 800 800 800 Fixed Costs $/hr Depreciation and Interest $/hr 19.62 3.34 21.48 Taxes Insurance Housing(THI) $/hr 3.68 0.66 3.58 Total Fixed Costs $/hr 23.30 4.00 25.06 Variable Costs Repairs and Maintenance $/hr 13.63 1.94 15.90 Fuel Consumption gal/hr 6 0 2 Fuel and Lubrication $/hr 14.90 0 9.94 Operating Interest $/hr 1.13 0.09 1.02 Labor $/hr 15.91 7.95 31.82
Total Variable Cost $/hr 45.57 9.98 58.68

Total Costs $/hr 68.87 13.98 83.74 Total $/hr 166.59 Equipment capacity DMT/hr 9.3 Total $/ton 17.91

We assumed that a telescopic loader would lo ad the bales at the field-edges onto 20-bale, 2-axle, 30-ft long wagons, each pulled by a high-speed tractor to the storage area located at the center of each county, where they were unloade d by another telescopic loader and the bales stacked for temporary storage. Several variable s make the cost of this operation difficult to estimate. A cursory inspection of the general out line/shape of many of the counties in the study reveals that they are by no means square. Howe ver, to make our calculations manageable, we assumed they were in order to determine the average distance each bale would need to be transported to its respective stor age area in each county. We es timated that the average distance any load would travel from any position in the coun ties, e.g., from the furthest to the nearest, would be equal to one-half the distance from the co rner of the county to it s center. We used the published area of each county, from which we de termined the distance from one-corner to the center; that distance equaled the trip to the st orage area and the return. We multiplied each by a 30% winding factor (rather than a straight-l ine drive, this accounts for turns and other meanderings). 884 We estimated the operation would cost about $17.91 per ton.

529Satellite Storage: We assumed each storage unit would consist of a concrete slab with open sides and pole-supported tin ro of. Smooth paved surfaces are safer and make work easier. Gravel and dirt do not stick to the bottoms of the bales. If winter (wet, muddy) access is necessary, this cost should be included in the overall costs for storage; particularly from the highway to the stack. Beyond the need to keep the area around the stack accessible and clean, the could be problems with local authorities, if trucks leav ing the property carry significant quantities of mud onto a public highway. Ordinari ly, in an agricultural area a certain amount of mud is expected to be left on highways during wet weather. However, at the truck and trailer volumes we’re anticipating, the amount be ing tracked onto highway s and possibly through municipalities, would increase rapi dly. We used 1% of construc tion costs for upkeep, and 2% of the construction cost for the storage unit to cove r the cost of access. Th ese are incurred costs within the overall maintenance of stored stac ks of biomass. These costs are essentially insignificant in the overall storage costs, are subject to great fl uctuations due to weather and equipment availability, and, therefore, were ro lled up into a percentage of the overall storage costs of stacked bales. The following table summ arizes our storage area c onstruction cost factors and costs.

Table 4.1-14. Satellite Storage Construction & Main tenance Costs – For Each of 11-Areas Land Rent $/acre/yr 100 Land Preparation $/acre 30,000 Construction $/sq ft 3.75 Upkeep – 1% of Construction $/t 0.91

Access – 2% of Construction $/t 1.81 Depreciation Period yrs 12

Number of Bales Bale Storage

Area sq ft Bale Storage

Area

acres Number of Bales per site Tons Stover per site Supply days Total Storage

Cost $/dry ton St Joseph 280,889 2,943,716 280,889 280,889 126,400 28.1 1,123,463 Elkhart 245,333 2,571,093 245,333 245,333 110,400 24.5 981,252 Marshall 336,000 3,521,280 336,000 336,000 151,200 33.6 1,343,889 La Porte 479,111 5,021,084 479,111 479,111 215,600 47.9 1,916,286 Starke 245,333 2,571,093 245,333 245,333 110,400 24.5 981,252 Kosciusko 366,222 3,838,009 366,222 366,222 164,800 36.6 1,464,768 Lagrange 201,778 2,114,631 201,778 201,778 908,00 20.2 807,044 Fulton 311,111 3,260,444 311,111 311,111 140,000 31.1 1,244,342 Porter 293,333 3,074,133 293,333 293,333 132,000 29.3 1,173,236 Noble 260,444 2,729,458 260,444 260,444 117,200 26.1 1,041,692 Pulaski 479,111 5,021,084 479,111 479,111 215,600 47.9 1,916,286 350 Total Cost $/ton 8.89

With well-paved surfaces, equipment can be maneuvered regardless of weather, and surfaces can be sloped to enhance drainage. We also assumed the bales would be stored in multiple stacks, 8-bales wide and 5-bales high, and long enough to accommodate the number of bales we expect; there would 20-ft aprons along the outside of th e stacks and 20-ft isles between stacks for stacking, stack management , and for general and fire safety. 885 One researcher

530determined that the economics of si ze, in the current situation, did not really apply. In order to determine a cost per square foot, we assume d all 3,148,800 bales would be stored on a single slab/pole barn. Stacked five-hi gh, it would require 758-acres or 1.18 sq. miles of concrete and roof. We estimated that storag e would cost about $8.89 per ton. 886 We used this as the cost per ton for the satellite storage as well as for storage at the plant.

We estimated that to shred, rake, bale, field side, construct satellite storage, and haul to satellite storage plus pay the farmer/grower $10/t and replace the nutrients for $11.81/ton, it would cost about $72.20 per dry ton. 887 888

We used 11-telehandlers at the storage areas. (However, once the harvest is complete the telehandlers being used for loadi ng and unloading bale wagons dur ing the field-side to storage area operation could possibly be pr essed into transport – load, unl oad service (see Table 4.1-15). If so, the cost to use the telehandlers could be reduced from $3.28 to $3.08 per ton.) Plus 11­ telehandlers at the plant to load and unload tr ucks and trailers that deliver the stover from satellite storage to the plant; extra telehandler time at the plant will be used to move feedstock as needed. The following table summarizes the cost associated with the transportation. The following table summarizes the cost associated with the transportation

Table 4.1-15. Haul From Satellite Storage to Plant
Class 8 Truck 53-ft Flatbed Trailer 22-Telescopic Handlers Equipment Factors Purchase price $ 103,839 42,173 130,106 Useful life yrs 20 22 13 Discounted Salvage Value $ 4,025 931 6,490 Annual use hr 5,600 5,600 800 Fixed Costs $/hr Depreciation and Interest $/hr 1.91 0.74 236.28 Taxes Insurance Housing(THI) $/hr 0.41 0.71 39.38 Total Fixed Costs $/hr 2.31 1.45 275.66 Variable Costs Repairs and Maintenance $/hr 1.13 3.16 174.90 Fuel Consumption gal/hr 9 0 2 Fuel and Lubrication $/hr 33.95 0 109.34 Operating Interest $/hr 0.37 0.14 11.22 Labor $/hr 17.46 350.02 Total Variable Cost $/hr 52.91 2.31 $645.48 Total Costs $/hr 55.22 3.76 921.14 Total $/hr 58.97 921.14 Equipment capacity DMT/hr 4.5 281 Total, each $/ton 13.10 3.28 Total $/ton 16.38

To transport the bales to the ethanol plant, we calculated the ton- weighted average triptime to be 4.09 hr. We plan to ship 4,497-tons of stover to the plant on each of the 350-operating days. At 17.5-tons per load, we anticipate there will be about 256-loads per day. Using the 4.09­ ton weighted trip time, we estimated that it woul d require 63-trucks and tr ailers to haul 4 x 17.5­

531ton per day. The cost of trans portation plus loading and unloadi ng is estimated to be $16.38 per dry ton. The following table summarizes the tota l cost to harvest, bale, field-side, haul to satellite storage, store, and ha ul to the plant. The following table summarizes all the costs.

Table 4.1-16. Ag Residue Cost Summary Farm-Set Size, acres 200 400 800 Total Tons Tons per Farm-Set, t 756,4 39 476,402 341,559 1,574,400
Farmer/Grower $/t 10.00 10.00 10.00 Nutrient Replace $/t 11.81 11.81 11.81 Shred $/t 4.80 4.80 4.80 Rake $/t 4.70 4.70 4.70 Bale $/t 10.84 10.84 10.84 Haul – Edge $/t 2.82 3.31 4.05 Total Farm Edge Cost $/t 44.97 45.46 46.20 Haul – SS $/t 17.91 17.91 17.91 Storage $/t 8.89 8.89 8.89 Haul to Ethanol Plant $/t 16.38 16.38 16.38 Field to Plant – Total $/t 43.18 43.18 43.18

Per Farm- Set – Total $/t 88.15 88.64 89.38 Avg. Total Cost $/t 88.71

The FASOM agricultural econometric model also estimated the farm edge price for corn stover. Like our analysis, the FASOM model acc ounted for harvesting, sh redding, raking, baling and hauling the corn stover to the farm edge, a nd replenishing the soil w ith nutrients. However, FASOM did not add on a $10 farmer payment. Adding on a $10 farmer payment, FASOM’s farm edge price for baled corn stover was $42. 70/ton. There was good agreement between our cost estimate and FASOM’s.

As mentioned previously, a supply system organized in the near-term could look something like the one we’ve just described, but there are several potential drawbacks;. a comment, which we can’t attribute for confidenti ality reasons was about the inefficiency of expending energy to package biomass just to expend more energy to unpackage this same biomass in order to meet the requir ements of the conversion process.

We anticipate that industry will address most of these drawback s by 2022 in order to improve the delivery and reduce the cost of feedst ocks. The current harvest-system is usually referred to as a multipass system: the corn grai n is first combined, and then the stover is shredded, raked, baled, and the bales hauled to th e field side. Each fiel d-pass adds to the final cost and further compacts the soil; soil compaction is especially critical if the soil is prone to compaction or in no-till situations. 889 Because the combine-spreader drops the stover on to the ground, not only are fewer cobs colle cted, but dirt, dirt clods, and other debris , including metal,

532are inevitably gathered up with the stover by the baler. 8 90, 891 Thus, extra effort and money must be expended to remove the debris before processi ng can begin, apart from the fact that dry matter is also lost during this oper ation. In their 2002 study report, NREL included a wash table to remove dirt and grit and had magnets to remove tramp iron, e.g., wire, etc. from the stover. 892 We discussed many of thes e issues previously.

According to a few sources, which for reason s of confidentiality, we can’t quote, there appears to be active interest in restructuring the system we just described to move the preprocessing (feedstock preparation) forward in the chain, away from the ethanol plant, and closer to the fields. Including th e issues highlighted in the prev ious paragraphs, a major concern has to do with the use of standard hay and fo rage equipment, for which the overall collection efficiency of stover (ratio of stover collected to the total above-ground st over excluding grain) using flail choppers, rakes, and balers was less than 30%. 893 Until now, most research has been based on a multipass system simila r to the one we synthesized. 894 , 895 In addition, the timeliness for collection (weather con cerns) and moisture content issues are major problems associated with a multipass corn stover harvest. 896

The restructuring efforts also include explori ng other methods to more efficiently gather the stover that avoids the need to pick it up from the ground, e.g., gather or catch it before it hits the ground. 897 In one early case, a baler was hitched directly onto a combine, to capture the combine effluent and square-bale it. The problem was that there was a strict need to limit moisture to under 20% if bales are to be stor ed, plus the extra equi pment slowed the grain harvest. 898 Ideally, the stover harvest system should be capable of harvesting stover at any level of moisture even while the grain is being harvested. All the cobs would be collected, the stover wouldn’t touch the ground and a controlled amount of residue would be left to meet any conservation requirements (we believe finding a way to leave the correct amount of residue behind will be diffi cult, and should be a top priority).

A modification of the system we previously described, would be to use a mobile tub grinder that could be towed from one satellite storage area to the next. A telehandler would feed the grinder to directly fill trucks for transpor t to the production facili ty. The ‘walking floor,’ rear-dump, or belly-dump trailers would unload the ground-up stover into silos or tanks at the facility. These silos or tanks could be sized to pr ovide as much feed surge capacity as the facility required to maintain continuous operation. 899

Again, ideally, the corn stover harvest should be reduced to a single-pass operation during which the amount of residue left on the field will be less a function of harvest efficiency and more a function of the farmer/grower and th e harvesting company being able to determine how much residue must be left to maintain soil health. In reality, most of the equipment doesn’t actually exists that could perform some of the operations we will describe. Nevertheless, we believe this reflects some of the forward thinking that is currently taking place. For example, a combine designed specifically for the job must still be constructed. A single-pass harvester would cut the whole stalk a few inches above th e soil, leaving some st alk anchored to the ground. It would pull the entire plant, e.g., stalks , leaves, and cobs with grain into the combine, where they are mixed into a single, clean, grain a nd stover stream. It would then blow the entire stream into tractor-pulled grain-carts that run along-side the harvester. It is important to be able

533to change full carts for empties without stopping the harv ester. As a cart is filled, it is pulled from beneath the discharge tube, as an empty cart is pulled under it. The fu ll cart is hauled to the field side, where the harvested material is unloaded directly into bu lk ‘walking-floor’ semi trailers, for transport to a co-op or depot type elevator/facility. After the biomass stream is unloaded, equipment at the elevator/depot separa tes the stover from the grain, following which the stover is chopped, dried, and sent to tanks or silos for interm ediate storage. Currently, there are no simple methods for drying wet corn stover, ot her than to let it field-dry. However, if the single-pass harvest is to become a reality, the stover will need to be dried or else stored in much the same way silage is stored. 900 At harvest, corn grain has a moisture content of 25%, while at the same time, the stover ordinar ily ranges from 35% to well over 50% moisture. There have been studies to artificially dry corn stover as we ll as other biomass type s; there will likely be changes to the reported results of these and other studies , but, then we expect advancements and certainly changes in several part s of the feedstock supply system. 901, 902, 903 Given that these changes take place, the stover, would have flowab ility characteristics similar to small cereal grains, and could be moved by standard grai n loading and unloading systems into large corrugated steel bins (silos) for intermediate storag e. In this harvest format, the stover is handled by only two machines before it reaches the roadside and never hits the ground. Dry matter losses should be significantly reduced. 904

Harvesting, storing, and transporting a denser feedstock should offer significant savings. 905 Using this and other antic ipated improvements, it appear s possible that in the out years, e.g., by 2022, corn stover and other residu es could be commoditized, much as is the case with grain, and then purchased by a processor on an as-needed basis.

However, commoditization offers its own set of issues, among which are both tangible and non-tangible infrastructures. Although tangible infrastructure with regard to ethanol distribution are discussed in grea ter detail in Section 4.2, we believe the following comments fit within the context of our preced ing information. The impact of both feedstock and finished ethanol on rural road, highway and ra ilroad infrastructure is likely to be even greater than the current and anticipated impact of corn based ethanol. Raw ce llulosic feedstocks have lower levels of concentrated fermentable carbohydrat es and therefore require a greater mass of feedstock to produce an equivale nt level of ethanol. Thus, pub lic and private transportation infrastructure must move a greater volume of feedstock per gallon of ethanol produced. The magnitude of the impact will depend on the field density of feedstocks near the plant and whether feedstock densification will make it po ssible to ship more dense carbohydrate product to the cellulosic ethanol plant.

Intangible infrastructure is essentially abse nt for crop residue type cellulosic feedstocks. Intangible infrastructure includes such things as uniform grade and quali ty standards, market price discovery mechanisms, collateral warehous e receipts, regulatory structure and other marketing institutions. Grain market institutions have been developed and fine-tuned over the past century that give corn ethanol plants a decide d benefit. Daily price information, as well as a wealth of crop condition, and supply and dema nd information from a variety of public and private sources is available on corn grain. Well known institutions such Uniform Grade and Quality Standards, FGIS, Grain Warehouse Regul ations, Collateral Warehouse Receipts, Trade Associations, Non-Recourse Gove rnment Commodity Loans, and a set of futures markets that

534efficiently price grain over time and space are all readily available. This infrastructure is already in place, tested and readily accessible to corn grain ethanol producers. Although not highly visible and frequently taken for gran ted, it plays a critical role in efficient feedstock pricing, risk management, trading and financing. The cellulosic marketing infrastructure required for similar efficient commercial transactions will need to be established from top to bottom.

Pricing infrastructure is one of the most pres sing needs. Large daily volumes of corn and other grains are traded on well established excha nges with a great deal of confidence on the part of buyers and sellers that the other party will perf orm. Initially, it could be difficult, at best, to develop these infrastructure benefi ts for crop residues such as corn stover. Cellulosic feedstocks will be starting from a relatively small produc tion base with no pricing institutions in place. There are no existing grades and quality standa rds to underpin transact ions over distance and time. Nor are there any trade rules or established patterns for prompt and e fficient settlement of trade disputes between buyers and sellers. The ab sence of these factors doe s not mean that they won’t develop, but there could be a stressful transition period.

Also, there is no regulatory infrastructure to protect producers who wish to hold inventory after harvest in a public warehouse or handlers warehouse. This kind of infrastructure serves an important role in underpinning warehouse receipts a nd producer financing by creating a higher and more reliable collateral value for in ventory. Nor are there equivalents to the U.S. grain grades and quality standa rds or Federal Grain Inspection Service. While there are other ways these functions can be provided some type of commodity grades and standards will be necessary to permit trading. Anot her possibility would be to ha ve the production of cellulosic feedstocks and the production of ethanol vertically integrated in some fashion so that the responsibility for quality is internalized. 906

Energy Crops

Energy crops such as switchgrass and miscanthus would be harvested, baled, stored and transported very similar to crop residues. B ecause of their higher production density per acre, though, we would expect that the “farm gate” cost s to be slightly lower than crop residues (we estimate the costs to be about $1 per dry ton lo wer). Also, the higher production density would allow for fewer secondary storage facilities compar ed to crop residue and a shorter transportation distance. For example, we estimate that sw itchgrass would require less than 30 secondary storage facilities which would help to lower th e feedstock costs for a 100 million gallon per year plant compared to crop residues As a result the secondary storage and transportation costs are estimated to be $9 per ton lower th an crop residue such as corn stover. Thus, we estimate that cellulosic feedstock costs sourced from switchgrass would be about $77 per dry ton. Table 4.1­ 17 contains a summary of our feedstoc k cost estimate for switchgrass.

535Table 4.1-17. Switchgrass Cost Summary Farm-Set Size, acres 400 Tons per Farm-Set, t 1,891,000 Farmer/Grower $/t 10.00 Nutrient Replace $/t 11.81 Shred $/t 4.80 Rake $/t 3.95 Bale $/t 10.84 Haul – Edge $/t 2.81 Total Farm Edge Cost $/t 44.20 Haul – SS $/t 15.3 Storage $/t 7.89 Haul to Ethanol Plant $/t 9.76 Field to Plant – Total $/t 32.95

Per Farm- Set – Total $/t 77.15

Forestry Residue

Harvest and transport costs for woody biomass in its different forms vary due to tract size, tree species, volumes remove d, distance to the wood-using/st orage facility, terrain, road condition, and other many other cons iderations. There is a signifi cant variation in these factors within the United States, so timber harvest a nd delivery systems must be designed to meet constraints at the local level. Harvesting co sts also depend on the type of equipment used, season in which the operation occurs, along with a host of other factors. Much of the forest residue is already being harvested by logging operati ons, or is available from milling operations. However, the smaller branches and smaller trees proposed to be used for biofuel production are not collected for their lumber so they are normally left behind. Thus, this forest residue would simply have to be collected and transported out of the forest, although it w ould still have to be chipped before transport to the biofuel plant.

In general, most operators in the near future will chip at roadside in the forest, blowing the chips directly into a chip van. When the van is full it will be hauled to an end user's facility and a new van will be moved into position at th e chipper. The process might change in the future as baling systems become economically feasib le or as roll-off containers are proven as a way to handle logging slash. At present, most of the chipping for biomass production is done in connection with fuel-reduction tr eatments. This could change if the price of raw biomass increases to a point where it beco mes feasible to recover logging residues associated with normal commercial operations. The major problem asso ciated with collecting logging residues and biomass from small trees is handling the material in the forest before it gets to the chipper. Balers and roll-off containers o ffer some promise to reduce this cost. Whether the material is collected from a fuel-reduction treatment or a commercial logging operation, chips from residues

536will be dirty and will require screening or so me type of filtration at the end-user's facility. D DDDDDDDD

Results from a study in South Georgia show that under the right conditions, a small chipper can be added to a larg er operation to obtain additional chip production without adversely impacting roundwood production, and chips can be produced from limbs and tops of harvested trees at costs ranging from $11 pe r ton and up. Harvesting unde rstory (the layer formed by grasses, shrubs, and small trees under the canopy of larger trees and plants) for use in making fuel chips is about $1 per ton more expensive.

Per ton costs decrease as the volume chipped increases per ac re. Some estimates suggest that if no more than 10 loads of roundwood are produced before a load of chips is made, that chipper-modified system could break even. Cost projections suggest that removing only limbs and tops may be marginal in terms of cost sin ce one load of chips is produced for about every 15

loads of roundwood.

Instead of conducting our own detailed cost estimate for making forest residue chips available at the edge of the harvested forests, we instead relied upon the expertise of the U.S. forest service. The U.S. Forest Service provided us a cost curve for different categories of forest residue, including logging resi due, other removals (i.e., cl earing trees for new building construction), timberland trimmings (forest fire prevention strategy) and mill residues. 907 The data was provided to us on a county-by-county basi s. The national forest lands are omitted from consideration, and the urban forest residue is no t considered here, but in the section discussing MSW. The information was also provided at different price points. The quantities of forest residue is summarized by source type in Tables 4.1-18, 4.1-19 and 4.1-20. To avoid presenting a huge amount of data, we aggregated the county data by state, and we are presenting the data at specific price points: $30/dry ton, $45/dry ton and $70/dry ton.

DDDDDDDDD Personal Communication, Eini C. Lowell, Research Scientist, USDA Forest Service

537Table 4.1-18. Volume of Forest Residue Ava ilable for Producing Biofuel Biomass Available at $30/ton (dry tons) Logging Residue Other Removals Timberland Thinnings Primary Mill Residu e Total Quantity Alabama 2,405,083 507,240 867,038 7,117 3,786,478 Arizona 17,698 44,871 66,171 1,351 130,091 Arkansas 1,703,543 770,985 738,165 12,889 3,225,582 California 669,740 0 1,742,702 65,088 2,477,530 Colorado 18,405 14 0 2,302 20,721 Connecticut 8,391 30,678 20,929 3,949 63,948 Delaware 30,101 24,218 9,835 0 64,155 Florida 1,070,430 515,407 481,893 2,202 2,069,932 Georgia 3,113,907 993,262 1,107,254 45,138 5,259,561 Idaho 253,145 0 83,095 6,006 342,247 Illinois 278,202 235,178 230,863 18,523 762,766 Indiana 562,483 104,173 396,225 10,627 1,073,508 Iowa 112,098 55,160 97,983 159 265,400 Kansas 14,658 88,405 19,353 8,720 131,136 Kentucky 1,027,977 664,358 689,896 55,196 2,437,427 Louisiana 2,634,279 880,585 601,848 30,075 4,146,788 Maine 2,412,877 940 160,628 42,483 2,616,927 Maryland 181,443 830 81,988 17,067 281,327 Massachusetts 70,921 62,087 27,602 0 160,610 Michigan 758,926 244,952 655,280 13,763 1,672,922 Minnesota 697,614 662,985 265,424 26,878 1,652,900 Mississippi 3,097,069 710,142 850,688 95,138 4,753,038 Missouri 774,868 530,292 684,154 79,787 2,069,100 Montana 262,670 0 133,185 9,136 404,990 Nebraska 21,145 18,771 23,414 4,971 68,302 Nevada 29 105 0 0 134 New Hampshire 314,642 348 95,604 7,019 417,613 New Jersey 5,918 77 4,847 1,437 12,279 New Mexico 23,858 2,557 51,796 4,902 83,113 New York 734,006 109,342 326,672 27,390 1,197,410 North Carolina 2,026,330 1,259,265 1,121,627 12,811 4,420,033 North Dakota 2,906 15,202 7,644 265 26,017 Ohio 370,795 18,106 167,351 22,600 578,853 Oklahoma 347,738 197,587 106,086 495 651,906 Oregon 1,520,552 63 1,055,405 16,316 2,592,335 Pennsylvania 1,087,327 1,372 449,956 170,972 1,709,626 Rhode Island 1,769 45,721 5,600 389 53,478 South Carolina 1,429,102 696,577 603,700 1,051 2,730,431 South Dakota 13,944 28,873 5,986 2,294 51,096 Tennessee 633,412 489,840 847,812 187,583 2,158,647 Texas 1,233,553 436,927 371,437 3,021 2,044,938 Utah 5,946 0 19,817 4,437 30,200 Vermont 209,752 37,304 96,790 0 343,845 Virginia 1,483,346 813,600 873,740 39,366 3,210,052 Washington 1,282,288 44 1,850,958 21,446 3,154,736 West Virginia 976,712 49,428 323,306 118,779 1,468,225 Wisconsin 1,137,600 982,264 520,587 60,410 2,700,862 Wyoming 22,685 0 28,100 34,014 84,799 Total 37,061,885 12,330,137 18,970,435 1,295,560 69,658,018

538Table 4.1-19. Tons of Forest Residue Availa ble for Producing Biofuel Biomass Available at $45/ton (dry tons) Logging Residue Other Removals Timberland Thinnings Primary Mill Residue Total Quantity Alabama 2,405,083 507,240 1,012,090 7,117 3,931,530 Arizona 27,131 49,020 69,934 1,351 147,436 Arkansas 1,703,543 770,985 858,827 12,889 3,346,244 California 1,166,955 0 1,898,937 65,088 3,130,980 Colorado 20,112 22 61,238 2,302 83,674 Connecticut 8,601 32,190 20,929 3,949 65,670 Delaware 35,863 28,290 13,400 0 77,554 Florida 1,070,430 515,407 533,194 2,202 2,121,234 Georgia 3,113,907 993,262 1,288,591 45,138 5,440,898 Idaho 432,605 0 105,188 6,006 543,799 Illinois 278,305 235,178 230,863 18,523 762,869 Indiana 562,928 104,173 443,691 10,627 1,121,419 Iowa 112,100 55,215 99,102 159 266,576 Kansas 14,658 88,405 19,353 8,720 131,136 Kentucky 1,027,977 664,358 814,743 55,196 2,562,273 Louisiana 2,634,279 880,585 661,023 30,075 4,205,963 Maine 2,561,023 989 204,885 42,483 2,809,379 Maryland 189,159 842 81,988 17,067 289,054 Massachusetts 78,254 66,382 27,602 0 172,239 Michigan 783,465 257,201 820,603 13,763 1,875,033 Minnesota 717,037 683,787 319,980 26,878 1,747,683 Mississippi 3,097,069 710,142 935,870 95,138 4,838,219 Missouri 774,868 530,292 932,163 79,787 2,317,110 Montana 431,194 0 141,549 9,136 581,879 Nebraska 21,419 18,867 23,414 4,971 68,672 Nevada 44 142 0 0 186 New Hampshir e 331,037 395 115,132 7,019 453,583 New Jersey 6,368 79 4,847 1,437 12,731 New Mexico 34,478 2,575 53,724 4,902 95,679 New York 768,914 113,104 379,391 27,390 1,288,799 North Carolina 2,026,330 1,259,265 1,336,840 12,811 4,635,245 North Dakota 2,907 15,202 7,644 265 26,018 Ohio 372,045 18,139 177,144 22,600 589,927 Oklahoma 347,738 197,587 125,400 495 671,220 Oregon 2,502,187 68 1,095,253 16,316 3,613,824 Pennsylvania 1,092,836 1,386 604,355 170,972 1,869,549 Rhode Island 1,913 50,079 5,600 389 57,981 South Carolina 1,429,102 696,577 704,036 1,051 2,830,767 South Dakota 23,743 31,161 6,505 2,294 63,704 Tennessee 633,412 489,840 1,015,395 187,583 2,326,230 Texas 1,233,553 436,927 438,374 3,021 2,111,876 Utah 7,515 0 21,571 4,437 33,524 Vermont 217,084 38,363 107,673 0 363,120 Virginia 1,483,346 813,600 1,048,745 39,366 3,385,057 Washington 2,135,174 46 1,963,678 21,446 4,120,344 West Virginia 976,712 49,428 482,367 118,779 1,627,287 Wisconsin 1,153,876 998,604 654,054 60,410 2,866,943 Wyoming 36,327 0 36,405 34,014 106,745 Total 40,084,609 12,405,402 22,003,291 1,295,560 75,788,862

539Table 4.1-20. Tons of Forest Residue Availa ble for Producing Biofuels Biomass available at $70/ton (dry tons) Logging Residue Other Removals Timberland Thinnings Primary M ill Residu e Total Quantity Alabama 2,405,083 507,240 1,163,309 7,117 4,082,749 Arizona 27,131 49,020 77,357 1,351 154,859 Arkansas 1,703,543 770,985 984,188 12,889 3,471,605 California 1,166,955 0 2,001,231 65,088 3,233,274 Colorado 20,112 22 61,238 2,302 83,674 Connecticut 8,601 32,190 20,929 3,949 65,670 Delaware 35,863 28,290 13,400 0 77,554 Florida 1,070,430 515,407 664,706 2,202 2,252,745 Georgia 3,113,907 993,262 1,553,823 45,138 5,706,130 Idaho 432,605 0 123,852 6,006 562,463 Illinois 278,305 235,178 230,863 18,523 762,869 Indiana 562,928 104,173 443,691 10,627 1,121,419 Iowa 112,100 55,215 99,102 159 266,576 Kansas 14,658 88,405 19,353 8,720 131,136 Kentucky 1,027,977 664,358 927,808 55,196 2,675,339 Louisiana 2,634,279 880,585 750,104 30,075 4,295,044 Maine 2,561,023 989 332,233 42,483 2,936,728 Maryland 189,159 842 81,988 17,067 289,054 Massachusetts 78,254 66,382 27,602 0 172,239 Michigan 783,465 257,201 1,066,214 13,763 2,120,643 Minnesota 717,037 683,787 401,197 26,878 1,828,900 Mississippi 3,097,069 710,142 1,033,196 95,138 4,935,545 Missouri 774,868 530,292 1,287,857 79,787 2,672,803 Montana 431,194 0 166,045 9,136 606,375 Nebraska 21,419 18,867 23,414 4,971 68,672 Nevada 44 142 0 0 186 New Hampshire 331,037 395 116,195 7,019 454,646 New Jersey 6,368 79 4,847 1,437 12,731 New Mexico 34,478 2,575 64,375 4,902 106,330 New York 768,914 113,104 385,701 27,390 1,295,109 North Carolina 2,026,330 1,259,265 1,600,910 12,811 4,899,315 North Dakota 2,907 15,202 7,644 265 26,018 Ohio 372,045 18,139 177,144 22,600 589,927 Oklahoma 347,738 197,587 163,268 495 709,088 Oregon 2,502,187 68 1,133,187 16,316 3,651,758 Pennsylvania 1,092,836 1,386 680,995 170,972 1,946,189 Rhode Island 1,913 50,079 5,600 389 57,981 South Carolina 1,429,102 696,577 791,111 1,051 2,917,842 South Dakota 23,743 31,161 8,258 2,294 65,457 Tennessee 633,412 489,840 1,033,100 187,583 2,343,935 Texas 1,233,553 436,927 507,340 3,021 2,180,841 Utah 7,515 0 29,434 4,437 41,386 Vermont 217,084 38,363 142,210 0 397,658 Virginia 1,483,346 813,600 1,260,733 39,366 3,597,045 Washington 2,135,174 46 2,059,970 21,446 4,216,636 West Virginia 976,712 49,428 575,278 118,779 1,720,198 Wisconsin 1,153,876 998,604 841,550 60,410 3,054,440 Wyoming 36,327 0 43,195 34,014 113,536 Total 40,084,609 12,405,402 25,186,746 1,295,560 78,972,317

540The U.S. Forestry Service recommended th at we choose $45 per dr y ton as the price point for our cost analysis. 908 This seemed reasonable since this price point was roughly the same as farm gate crop residue di scussed above, and so we used this price point for our analysis Assuming that the wood chips woul d be ground further in the fiel d adds an additional $11 per dry ton to the feedstock cost.

Delivery of woody biomass from the harvesting site to a conversion facility, like delivery of more conventional forest products, accounts for a significant portion of the delivered cost. In fact, transportation of wood fibe r (including hauling within the forest) accounts for about 25 to 50 percent of the total delivered costs and highly depends on fuel prices, ha ul distance, material moisture content, and vehicle capacity and utilization. Also, beyond a certain distance, transportation becomes the limiting factor and the cost s become directly proportional to haul distance. E EEEEEEEE We used the Class 8 over-the-road haul ing costs estimated in Table 4.1-15 for estimating the hauling costs for fore st residue. As a feedstock, fore st residue is expected to be available at a higher density per ar ea than corn stover. Thus, its hauling costs would potentially be lower than those for corn stover because the forest residue would be hauled a shorter distance. However, forest residue is often located in more remote locations, so it may have to be hauled a further distance to reach the plant site. We antic ipate hauling forest residue to a plant will cost about $14 per ton. Totalling up the feedstock cost at the forest edge, the grinding cost and the hauling costs results in a total de livered price of $70 per dry ton.

Municipal Solid Waste

Million of tons of municipal solid waste (MSW) continue to be disposed of in landfills across the country, despite recent large gains in waste reduction and dive rsion. The biomass fraction of this total stream represents a potentially si gnificant resource for re newable energy (including electricity and biofuels). Because this waste ma terial is already being generated, collected and transported (it would solely need to be transported to a different location), its use is likely to be less expensive than other cellulosic feedstocks. One important difficulty facing those who plan to use MSW fractions for fuel production is that in many places, even today, MSW is a mixture of all types of wastes, including bi omaterials such as animal fats and grease, tin, iron, aluminum, and other metals, painted woods, plastics, and gla ss. Many of these materials can’t be used in biochemical and thermochemical ethanol pr oduction, and, in fact, would inflate the transportation costs, impede the operations at th e cellulosic ethanol plant and cause an expensive waste stream for biofuel producers.

Thus, accessing sorted MSW would likely be a requirement for firms planning on using MSW for producing cellulosic biofuels. In a conf idential conversation, a potential producer who plans to use MSW to produce etha nol indicated that their plant plans are based on the obtaining cellulosic biowaste which has al ready been sorted at the wast e source (e.g., at the curbside, where the refuse hauler pick s up waste already sorted by th e generating home-owner or business). For example, in a tract of homes, one refuse truck would pick up glass, plastic, and perhaps other types of waste destined for a sp ecific disposal depot, wh ereas a different truck

EEEEEEEEE Ashton, S.; B. Jackson; R. Schroeder. Cost Factors in Harvesting and Transporting Woody Biomass, 2007. Module 4: Introduction to Harvesting, Transportation, and Processing :: Fact Sheet 4.7

541would follow to pick up wood, pape r, and other cellulosic materials to be hauled to a depot that supplies an ethanol plant. Howe ver, only a small fraction of the MSW generated today is sorted at the curbside.

Another alternative would be to sort the waste ei ther at a sorting facility, or at the landfill, prior to dumping. There are two prominent options here. The first is that there is no sorting at the waste creation site, the home or business, and thus a single waste stream must be sorted at the facility. This operation would likely be done by ha nd or by automated equipment at the facility. To do so by hand is very labor intensive and some what slower than using an automated system. In most cases the ‘by-hand’ system produces a sli ghtly cleaner stream, but the high cost of labor usually makes the automated system more cost-effec tive. Perhaps the best approach for low cost and a clean stream is the combination of hand sorting with automated sorting.

The third option is a combination of the two wh ich requires that there is at least some sorting at the home or business which helps to pr event contamination of the waste material, but then the final sorting occurs downstream at a sorting site, or at the landfill.

We have little data and few estimates for the cost to sort MSW. One estimate we generated within our Office of Solid Waste for a combination of mechanically and manually sorting a single waste stream do wnstream of where the waste is generated puts the cost in the $20 to $30 per ton range. There is a risk, t hough, that the waste stream could still be contaminated and this would increase the cost of both transporting and using this material at the biofuel plant due to the toxic ash produced which would require dispos al at a toxic waste facility. If a less contaminated stream is desired it would probably require sorting at the generation site – the home or business - which would likely be mo re costly since many more people in society would then have to be involved and special trucks would need to be used. Also, widespread participation is difficult when a change in human behavior is required as some may not be so willing to participate. Offering incentives could help to speed the transition to curbside recycling (i.e., charging fee for nonsorted waste, or payi ng a small amount for sorted tree trimmings and construction and demolition waste). Assuming that curbside sorting is involved, at least in a minor way, total sorting costs might be in the $30 to $40 per ton range. We request comment on the costs incurred for sorting cellulosic material from the rest of MSW waste.

These sorting costs would be offset by the cost savings for not disposing of the waste material. Most landfills charge ti pping fees, the cost to dump a load of waste, a societal cost that would be avoided. In the United States, the national average nominal tipping fee increased fourfold from 1985 to 2000. The real tipping fee almost doubled, up from a national average (in 1997 dollars) of about $12 per ton in 1985 to just over $30 in 2000. Equally important, it is apparent that the tipping fee is much higher in densely populated regions. For example, in 2004, the trend differed by municipality level, from $9 a ton in Denver to $97 in Spokane. Statewide averages also varied widely, from $8 a ton in New Mexico to $75 in New Jersey. Tipping fees ranged from $21 to 98 per ton in 2006 for MS W and $18/ton to $120/ton for construction and demolition waste. It is likely that the tipping fees are highest for waste contaminated by toxic materials that require the disposal at Resource Conservation and Recovery Act (RCRA) certified toxic waste sites as opposed to a composting site. However, this same contaminated material would not be desirable to biofue l producers. Presuming that onl y the noncontaminated cellulosic

542waste (yard trimmings, building construction and demolition waste and some paper) is collected as feedstocks for biofuel plants, the handling a nd tipping fees are likely much lower, in the $30 per ton range. F FFFFFFFF

The avoidance of tipping fees , however, is a complex issue since landfills are generally not owned by municipalities anymore. Both la rge and small municipalities recognized their inability to handle the new and complex solid wast e regulations at a reasonable cost. Only 38 out of the 100 largest citi es own their own landfills. To deal with the solid waste, large private companies built massive amounts of landfill capacity . The economic incentive is for private landfill operators to fill their landfills with garbag e as early as possible to pay off their capital investment (landfill site) quickly. Also, the long er the landfill is operating the greater is its exposure to liability due to leakages and leach ing. Furthermore, landfills can more costeffectively manage the waste as the scale of the landfill is enlarged. As a result, there are fewer landfills and landfill owners, and an expansion of market share by large private waste management firms, thus decreasing the leverage an ethanol producer may have. G GGGGGGGG Hence, MSW-ethanol plants could be opposed by landf ill operators. This may also be true in the case of a waste-to-energy (WTE) facility, which burns as much garbage as possible to produce electricity. For sustainable operation, a certain amount of daily waste supply should be guaranteed. A MSW-ethanol plant may therefore be seen as an unwelcome competition to both landfill owners and WTE facilities. This competition may increase the cost of cellulosic biomass to the biofuel producers. We request comment on the costs av oided for diverting cellulosic material from landfills and WTE facilities.

Once the cellulosic biomass has been sorted fr om the rest of MSW, it would have to be transported to the biofuels plant. Transporting is different for MSW biomass compared to forest and crop residues. Forest and crop residues are collected from a forests and farms, which are both rural sites, and transported to the biofuel plant which likely is located at a rural site. The trucks which transport the forest and crop residues can be large over-the-road trucks which can average moderate speeds because of the lower amount of traffic that they experience. Conversely, MSW is being collected from th roughout the urban areas and would have to transported through those urban areas to the plan t site. If the cellulo sic biomass is being collected at curbside, it would likel y be collected in more conventional refuse trucks. If the plant is nearby, then the refuse trucks could transpor t the cellulosic biomass di rectly to the plant. However, if the plant is located far away from a portion of the urban area, then the refuse trucks would probably to be offloaded to more conventiona l over-the-road trucks w ith sizable trailers to make transport more cost-effective. We believe that in most cases, MSW will be transported by class 8, over-the-road trucks with operating characteristics lik e those summarized in Table 4.1­ 15. The trucks would likely need to pick up the sorted MSW at a MSW sorting facility and haul the MSW to the cellulosic biofuel plant. The ce llulosic biofuel plant w ould likely be located on the outskirts of the medium or large city, and so the MSW feedstock would have to be hauled through the urban area to reach the pl ant. Some of the trucks may not have to travel very far and would avoid most of the urban tra ffic since the MSW would be available on the same side of city

FFFFFFFFF A much more thorough analysis of tipping fees by waste type is planned for the final rulemaking analysis.
GGGGGGGGG Osamu Sakamoto, The Financial Feasibility Analysis of Municipal Solid Waste to Ethanol Conversion, Michigan State University, Plan B Master Research Paper in partial fulfillment of the requirement for the degree of Master of Science, Department of Agricultural Economics, 2004

543as the plant. However, another portion of the MSW may be located on th e opposite side of city from the plant and the trucks may have to e ndure significant traffic from time-to-time. We estimate that the cost to transport the cellulosi c biomass sourced from MSW to the biofuel plant would average $15 per ton.

Cellulosic biomass sourced from MSW can be generated year-round in many parts of the U.S. If a steady enough stream of this material is available, then seconda ry storage would not be necessary, thus avoiding the need to install se condary storage. We assumed that no secondary storage costs would be incurred for MSW-sourced cellulosic biomass.

The total costs for MSW-sourcedcellulosic biomass is estimat ed to be $30 -$40 per ton for sorting costs, $30 per ton for tipping costs av oided, $15 per ton for transportation costs and $11 per ton for grinding the cellulose to prepare it as a feedstock - resulting in a total feedstock cost of $26 to $36 per ton. In our cost analysis, we assumed an average cost of $31 per ton.

Table 4.1-21 below summarizes major cost components for each cellulosic feedstock..

Table 4.1-21. Summary of Cellulosic Feedstock Costs ($53/bbl crude oil costs) Ag Residue Switchgrass Forest Residue MSW 60% of Total

Feedstock 1% of total Feedstock 25% of Total Feedstock 14% of Total Feedstock Mowing, Raking, Baling, Hauling, Nutrients and Farmer Payment $43/ton Mowing, Raking, Baling, Hauling, Nutrients and Farmer Payment $42/ton Harvesting, Hauling to Forest Edge,

Chipping

$45/ton Sorting, Contaminant

Removal, Tipping Fees Avoided $0 – $10/ton

Hauling to Hauling to Grinding, Grinding, Secondary Secondary Hauling to Hauling to

Storage, Storage, Plant $25/ton Plant $26/ton Secondary Secondary

Storage, Storage, Hauling to Hauling to Plant $45/ton Plant $37/ton Total $88/ton Total $77/ton Total $70/ton Total Avg $31/ton

Weighting the cellulosic feedstock costs by th eir supply quantities results in an average cellulosic feedstock cost of $71 per ton which we us ed at the reference crud e oil price of $53/bbl.

We also estimated the feedstock costs assuming that crude oil is pr iced at $92/bbl. We adjusted the transportation cost s higher by 10 percent based on our analysis of how ethanol

544distribution costs would be affected by the higher crude oil price. We adjusted the farm gate costs upwards by about $2 per ton based on FASO M agricultural market modeling at the higher crude oil price. We also increased grindi ng costs upwards by 10 percent. Table 4.1-22 summarized our estimated feedstock costs at $93 per barrel crude oil prices.

Table 4.1-22. Summary of Cellulosic Feedstock Costs ($92/bbl crude oil costs) Ag Residue Switchgrass Forest Residue MSW 60% of Total

Feedstock 1% of total Feedstock 25% of Total Feedstock 14% of Total Feedstock Mowing, Raking, Baling, Hauling, Nutrients and Farmer Payment $45/ton Mowing, Raking, Baling, Hauling, Nutrients and Farmer Payment $42/ton Harvesting, Hauling to Forest Edge,

Chipping

$47/ton Sorting, Contaminant

Removal, Tipping Fees Avoided $3 – $13/ton

Hauling to Hauling to Grinding, Grinding, Secondary Secondary Hauling to Hauling to

Storage, Storage, Plant $28/ton Plant $28/ton Secondary Secondary

Storage, Storage, Hauling to Hauling to Plant $49/ton Plant $39/ton Total $94/ton Total $77/ton Total $75/ton Total Avg $36/ton

We estimate that at the crude oil price of $92 per barrel, the average feedstock cost increases to $76 per ton

Cellulosic Ethanol Production Costs

We contracted with the National Renewabl e Energy Laboratory (NREL) to estimate the
cost to convert corn st over into ethanol for the years 2010, 20 15, and 2022. It is of particular importance for the following discussion, to note the following: NREL used the same feedstock mass (772,168 dry tons of corn stover) in all three cases.

For the three cases, NREL assumed the feedstock, ‘as-needed,’ was hauled to the plant by trucks and trailers from satellit e storage, already shredded to th e appropriate size for processing, and free of dirt, iron, and other contaminates; in other words – process ready. The transport vehicles were unloaded into surge tanks, larg e enough to hold feedstock for three days of operation. The pretreatment a nd hydrolysis reactors are charge d from these feed surge tanks.

545The following is background information for our discussion of both operating and capital costs, some of which is also included in our br ief discussion of the proc ess flow description and capital equipment charges. The first step was to develop a set of process flow diagrams that set the arrangement of the equipment. Based on the desired production volume, these diagrams, were used within an ASPEN Plus4 © model to develop complete mass and energy balance. The model consists of 164 unit operati on blocks, 457 streams (247 materi al and 210 heat or work), 63 components, and 82 control blocks.

The overall model is thermodynamically rigor ous and uses physical properties for the feedstock and process chemicals included in th e ASPEN software as well as property data developed at NREL. The indivi dual unit models are also therm odynamically consistent and can be either rigorous (for example, the simulation of the distillation) or simple. The reactors could be modeled with kinetic expressions, but b ecause of the level of development of the experimental data, they were modeled as expe rimentally determined conversions of specific reactions. This type of model still satisfies the rigorous mass and energy balance. Other unit operations, such as liquid-solid separations, are typically modele d with fixed solids removal and liquid retention (in the solids st ream) data from vendor tests. H HHHHHHHH Using the process flow diagrams and the mass and energy balance info rmation, NREL estimated stream flows and conditions, along with the estimated quantities of raw materials and othe r process chemicals.

The following table presents NREL’s summary of each of the three y ear’s total project investment. For each year’s total project investment, NREL provided capital charge, which includes income tax, depreciation, and average return on investment , the cost of raw materials, waste handling charges, a nd by-product credits.

HHHHHHHHH A. Aden, M. Ruth, K. Ibsen, J. Jechura, K. Neeves, J. Sheehan, and B. Wallace National Renewable Energy Laboratory (NREL); L. Monta gue, A. Slayton, and J. Lukas Harris Group, Seattle, Washington, Ethanol Process Design and Economics Utilizi ng Co-Current Dilute Acid Prehydrolysis and Enzymatic Hydrolysis for Corn Stover ; June 2002; NREL is a U.S. Department of Energy Laboratory Operated by Midwest Research Institute • Battelle • Bechtel; Contract No. DE-AC36-99-GO10337

546Table 4.1-23. Summary of NREL’s Ca pital Charges and Operating Costs

Year Technology 2010 2015 2022

Plant Size MMgal/yr 56 69 71
Capital Cost $MM (TPI) 232 220 199
$MM/yr ¢/gal $MM/yr ¢/gal $MM/yr ¢/gal Capital Charge 10% after tax ROI 42 75 39 56 35 50

Fixed Costs 9 16 9 12 8 12

Feedstock Cost 84 46 51 35 50 35

Other Raw Matl. Costs 17 30 4 5 16 16

Enzyme Cost 18 32 7 10 5 8

Enzyme Nutrients 8 14 2 3 2 2

Electricity -6 -10 -7 -9 -12 -16

Waste Disposal 1 2 3 4 1 1

Total Costs 173 205 108 116 105 108

The quantities of all raw material, genera ted electricity, and produced wastes were determined using the ASPEN mass and energy balance model. These costs include: Feedstock – corn stover, CSL – purchased corn steep liquor (a nutrient); Cellulase – purchased cellulase
enzymes; Other Raw Materials – sulfuric acid, diammonium phosphate, make-up water, boiler feed water chemicals, cooling water chemicals. Waste Disposal – waste water chemicals, waste water polymers, ash disposal, gypsum disposal. Electricity – marketing and distribution of surplus electricity to the grid for credit.

We note that the percent change in total proj ect investment from y ear to year is not insignificant and reflects improvements in mechan ical process efficiencies among other general improvements in the process te chnology, including the automatic distributed process control system, all of which are off-set to some extent by increases in the real cost of the technology improvements, as well as those of constructions materials. We discuss capital costs following this discussion of operating costs.

The most notable reductions in NREL’s operating costs are in the price per dry ton of the corn stover feedstock and in th e cost of cellulase enzyme. NREL anticipates significant improvement in the efficiency of these enzymes, especially those that saccharify glucan to

547glucose and xylose oligomers I IIIIIIII generated during hydrolysis. They also expect improvement in the yeasts that ferment xylose. Accordi ng to the 2007 NREL – Stat e of Technology report, they anticipate that as a first step,,the rela tionship between corn stover hydrolysate conditioning and fermentation will be better defined and underst ood. Commercial cellulase preparations will continue to be analyzed for baseline performance (specific act ivity), and due to increased research efforts, cellulase function will be bett er understood, which shoul d lead to efficiency improvements. Integrated testing of whole slur ry and recycle options will also be conducted resulting in potential improveme nts in that area. Last, the efficacy of advanced enzyme preparations (including oligomerases and/or he micellulases) will continue to be tested in conjunction with alternative pr etreatment technologies. NREL expects that the cost of pretreatment will deminish, hydrolysis time will decrease, and the sugar (xylose and glucose) yields will increase. J JJJJJJJJ

As the process costs decline ove r time, the feedstock costs beco me a larger fraction of the overall costs. We also note that in the followi ng table that the cost of the feedstock makes up 50% of the total cost in 2010; 67% in 2015; and 68% in 2022. The reduction in feedstock cost, from $60 per dry ton in 2010, to $45.90 per dry ton in both 2015 and 2022 also has a significant effect on operating costs. In addition, NREL di d not include payments to the farmers/growers nor for soil nutrients (fertilizer, et c.) that were removed with the ha rvested corn stover. The cost of the cellulase enzyme is the next highest cont ributor, with percent redu ctions contributed to total cost that reduced from ~19% in 2010, to 13% in 2015 and 10% in 2022. It should be obvious that any reductions in these costs have significant effects on the total operating cost. The majority of research going forward will be focused on these two items, although some work will be done to reduce the cost of the others. Table 4.1-24 summarizes NREL’s operating costs for a biochemical cellulosic ethanol plant.

IIIIIIIII Xylan polymer chains, with considerably fewer residue numbers in the chain than were in the original xylan polymer; they were broken off the polymer as these shor t chains rather than as single molecule sugars. JJJJJJJJJ Andy Aden, National Renewable Energy Laboratory, Go lden, Colorado, Biochemical Production of Ethanol from Corn Stover: 2007 State of Technology Model, Technical Report NREL/TP-510-43205, Task No. BB07.2410; May 2008

548Table 4.1-24. Percent of the total operating cost fo r each actual operating cost item

2010 2015 2022

¢/gal % of Total ¢/gal % of Total ¢/gal % of Total

Feedstock 84 50 51 67 50 68 Biomass to

Boiler 0.0 0.0 0.0 0.0 0.0 0.0

CSL a 14 8 3 4 2 3 Cellulase 32 19 9 13 8 10 Other Raw Matl. Costs 31 18 5 7 16 22 Waste Disposal 2 1 4 5 2 2 Electricity -10 -6 -9 -12 -16 -22 Fixed Costs 16 10 12 16 12 16 168 76 72

a Corn steep liquor – provides nutrients for the enzymes.

The following table includes our adjustments to NREL’s variable or operating cost data for the three years studied. We note that th e two main differences between NREL’s and our estimates are in the feedstock costs and in the way we calculate capital charges. We adjusted NREL’s capital charges which were calculated usi ng a 10% after tax return on investment, to reflect a 7 percent before tax rate of return, whic h is the capital cost basis for our cost analyses. We also adjusted the NREL feedstock costs to t hose that we estimated in Table 4.1-21, which was $71.23 per ton. This significa nt difference between their and our feedstock cost estimates is due to our including payments to farmers/grower s plus covering the cost to replace nutrients (fertilizer, etc.) removed at the time the stover was harveste d. According to a personal communication, NREL used unpublis hed data from the Idaho Nati onal Laboratory that indicate feedstock costs will be significantly reduced between 2010 and 2015.

549Table 4.1-25. Adjusted Capital Charges and Operating Costs

Year Technology 2010 2015 2022

Plant Size MMgal/yr 56 69 71

Capital Cost $MM 232 220 199
$MM/yr ¢/gal $MM/yr ¢/gal $MM/yr ¢/gal Capital Cost 7% ROI before taxes 25 46 24 35 22 31

Fixed Costs 9 16 9 12 8 12

Feedstock Cost 55 99 55 79 55 77

Other Raw Matl. Costs 17 30 4 5 16 16

Enzyme Cost 18 32 7 10 5 8

Enzyme Nutrients 8 14 2 3 2 2

Electricity -6 -10 -7 -9 -12 -16

Waste Disposal 1 2 3 4 1 1

Total Costs 127 229 96 139 84 131

The changes in the minimum ethanol selli ng prices for the three years studied are partially due to the changes in ne cessary capital investments. In or der to determine capital costs, NREL developed specifications pieces of equipm ent that fall within different areas of a biochemical plant. A biochemical plant is di vided up into 8 different areas (Area 200 through Area 900)._ For each equipment specification, they developed individual purchased equipment and installation costs. Vendors supplied instal lation costs where possible; in other cases installation factors were used. Equipment co sts were obtained from vendor quotations when possible, especially for uncommon equipment such as pretreatment reactors. These costs reflect the base case for which the equipment was design ed. If process changes were made and the equipment size changed, the equipment is not genera lly re-costed, in detail. Rather, the cost was adjusted by scaling using the fo llowing exponential scaling expre ssion, [New Cost = Original Cost x (New Size/Original Size)^ exp]. They also scaled the size of equipment that was known to change linearly with a change in inlet flow. The scaling exponents (e xp) were obtained from vendor quotes, or from a standard reference, such as Garrett. K KKKKKKKK

KKKKKKKKK Garrett, D.E., Chemical Engineering Economics, Van Nostrand Reinhold, New York, 1989

550Installation costs were taken primarily from Delta-T, a pro cess consultant’s experience. Once the scaled, installed equipment costs (total installed capital costs) were determined, they applied overhead and contingency factors to determine a total plant investment cost. That cost, along with the plant operating e xpenses (generally developed fr om the ASPEN model) was used in a discounted cash flow analysis to determin e the cost of ethanol production, using a set discount rate. NREL use a discount rate of 10%, whereas we used 7% , a factor generally used in our financial calculations. For th is analysis, the minimum ethanol selling price was the primary value used to compare cases.

The total project investment was briefly discussed previously in our summary discussion of operating costs; we used NREL’s total projec t investment for our estimates. The following summarizes the capital expend itures that account for th at capital investment.

Area 200: Pretreatment and Hydrolysis . The equipment in this area consists of an assortment of pipe, pumps, tanks, tank-agitators, tank-mixers, coolers, 3-pneumapress filters, as well as three separate process tr ains, each of which includes a presteamer, a blow tank, and a reactor. The presteamer uses low-pressure steam to heat the feedstock to about 212 o F. It discharges the hot, saturated mix into a blow tank that serves as a seal between the presteamer and the hydrolysis reactor. The mi x is charged to the reactor and dilute sulfuric acid is added; the reactor operates at 191 psia and 547 o F. Most of the hemicellulose, e.g., primarily xylose, mannose, arabinose, and galactose are converted into sugars. Glu can in the hemicellulose and a small portion of the glucan in th e cellulose are converted to gl ucose. These conditions also solubilize some of the lignin in the feedstock and ‘expose’ the cellulose for subsequent enzymatic hydrolysis, in a downstream section. In addition, acetic acid is liberated from the hemicellulose hydrolysis. Degradation products of pentose sugars (primar ily furfural) and hexose sugars (primarily hydroxymethyl fu rfural (HMF)) are also formed.

Following the pretreatment reactor, the hydrol yzate liquid and solid s are flash cooled, which vaporizes a large amount of water, a portion of the acetic ac id, and much of the furfural and HMF, which can be toxic to downs tream fermentation microorganisms.

In addition to the flash removal of aldehydes, the solids are washed and filter-pressed to remove the liquid portion of the hydrolyzate, whic h contains sulfuric ac id. The liquid is then neutralized to pH 10 with ammonia and held unt il the gypsum precipitates and is filtered out. The hydrolyzate, which contains the hydrolyzed xylose sugars and some glucose sugars, is mixed back with dilution water and the filter cake, which contains the unhydrolyzed cellulose and is sent to saccharificati on and co-fermentation (Area 300)

An important issue on an industrial scale is accu rate pH control. By pH 11, as much as 30% of the glucose may be lost to HMF and other side reactions. Several factors increase the probability of overshooting pH e ndpoints during neutralization. The natural buffering capacity of hydrolyzates causes neutralizat ion reactions to be slow. Plus, measurements using pH membrane probes are affected by temperature an d the presence of dissolved organic compounds (sugars and lignin).

551Since we are handling the same mass of feed stock in each of the modeled years, we don’t expect the cost of the equipm ent for pretreatment and hydrolys is will change much over the 2010-2022 time period. The equipment costs fo r Area 200 – Pretreatment & Hydrolysis are 2010: $23-million; 2015: $22.7-million; and 20 22: $18.9-million. Neutralization and Conditioning costs were separated out from the Pretreatment & Hydrolysis costs, even though the NREL’s design report includes both cost centers in Area 200. Neutralization and Conditioning costs are as follows: 2010: $8.4-million; 2015: $9.4-million; and 2022: $7.7­ million. The combined cost for Area 200 is as follows: 2010: $31.4-million; 2015: $32.1­ million; and 2022: $26.6-million. In total, this area contributed about 23.5% to the total installed capital cost of the project in 2010; ab out 25.3% in 2015; an 23.2% in 2022

Area 300: Saccharification and Co-Fermentation . The equipment in this area consists of pumps, tanks, tank-agitators, cool ers, and heaters. Two different operations take place in this process area — the saccharification of the cellulo se to glucose using cellulase enzymes, and the fermentation to ethanol of that glucose plus the xylose and glucose sugars from the dilute acid pretreatment of hemicellulose from Area 200.

Glucan from the cellulose undergoes hydrol ysis or saccharification, at about 149 o F, prior to fermentation. This slightly higher temperat ure increases enzyme activity and reduces the time and amount of enzyme required for saccharificati on. Saccharification or cellulase enzymes, purchased from an enzyme manufacturer, and the diluted, detoxified hydrolyzate are continuously added to a train of five 1-million ga llon saccharification vessels; residence time is estimated to be 36-hours.

Cellulase enzyme is actually a ‘cocktail’ of enzymes, comprised of: (1) endoglucanases, which attack randomly along the cellulose fi ber to reduce polyme r size rapidly; (2) exoglucanases, which attack the ends of cellulose fibers, allowing it to hydrolyze highly crystalline cellulose; and (3) β -glucosidase, which hydrolyzes cel lobiose to glucose. Several bacteria and fungi naturally produ ce these enzymes, including bact eria in ruminant and termite guts and white rot fungus. The most common organi sm used to produce cellulase industrially is Trichoderma reesei . Genencor International and Novozymes Biotech are developing more cost effective cellulase enzymes. DOE is funding this important work, which should improve the economic viability of biomass conversion.

The recombinant Z. mobilis bacterium is used as the biocatalys t to ferment glucoses and xyloses to ethanol. Several rese arch institutions are genetically engineering strains, such as Z. mobilis , to treat additional sugars and identifying other naturally occurring organisms that metabolize hemicellulosic sugars.

The Z. mobilis must be ‘grown’ in increasingly higher volume stages. Initially, a small amount of saccharified slurry and nutrients are combined in a very small vessel with a seed inoculum, that’s been grown in the laboratory. This initial seed batch is us ed as the inoculum for the next larger size seed batch, and so on. This series of batch scale-ups continues until the last batch is large enough to support the actual production fermentation.

552Finally, the seed inoculum, nutrients (corn steep liquor) & (diammonium phosphate – a source of nitrogen for the yeast), and sacch arified slurry are cooled to about 106 o F and added to a train of five 1-million gallon continuous fermen tors. At this point, the process actually becomes a simultaneous saccharification and co-f ermentation (SSCF) process. Even though the temperature in the fermentation tanks has b een reduced to account for the ethanologen’s intolerance to heat, the enzymes do continue to h ydrolyze cellulose, albeit at a slightly reduced rate. The main byproduct, produced duri ng fermentation is carbon dioxide (CO 2 ), which is removed in a later process stage. The ethanol br oth called ‘beer’ is collected in a storage tank, called a beer well, before it’s pumped to distillation.

NREL anticipates significant capital savings for s accharification and co-fermentation between 2010 and 2015, with fewer between 2015 a nd 2022. We note that this area contributed 15.4% to the total installed capital cost in 2010, but only 8.8% in 2015 and 8.8% in 2022. The equipment costs for Area 300 – Saccharificati on and Fermentation are 2010: $20.5-million; 2015: $11.2-million; and 2022: $10.1-million.

Area 400 – In earlier studies, NR EL included plans to produce enzymes in Area 400. For the current studies, Area 400 has been remove d and enzymes will be purchased and grown on site under licensing agreements with enzyme suppliers.

Area 500 – Product, Solids, and Water Recovery (Distillation, Dehydr ation, Evaporation, and Solid-Liquid Separation ). The equipment in this area in cludes distillation and rectification columns, pumps, condensers and coolers, pum ps, pipe, filter-presses, and evaporators.

Beer, from the beer well in Area 300, is prehea ted and fed to a distillation column. The column overhead containing all the CO 2 and about 0.2% of the etha nol and a small quantity of water is sent to a scrubber, whic h recovers and recycles about 99% of the vented ethanol. In the tower bottoms, about 90% of the water has been removed and it contains approximately 0.7% of the total volume of ethanol fed to the tower. Over 99% of the total ethanol fed to the tower is removed as a 39.4% w/w mixture with water vapor through a side draw and fed directly to a rectification column for further ethanol enrichment. We discuss the distillation column bottoms in the evaporation and solid-liquid separation section of this area.

The rectification tower operating conditions are set to produce an overhead 92.5% w/w ethanol/water saturated vapor mixture. The tower bottoms are a 0.05% w/w ethanol/water mixture. In fact, only 0.1% of the total ethanol from the fermentation area is lost to the bottoms.

The rectification column ove rhead is superheated and fe d to one of two adsorption columns in a molecular sieve adsorption unit. The two columns operate alternately; while one bed is operated to remove water from the etha nol, the other is regene rated by passing a very small slipstream of pure ethanol vapor back thro ugh the loaded bed that strips the water off the adsorbent, while the column is under a vacuum. The mixture is condensed and returned to the rectification column feed stream . The adsorption column removes 95% of the water and a small quantity of ethanol. The 99.5% pure ethanol vapor is condensed, c ooled, and pumped to storage.

553Evaporation, and Solid-Liquid Separation : The beer column bottoms, with about 5.8% insoluble solids, are fed to the first effect eva porator, where 24% of the water in the feed is evaporated. The evaporator bo ttom slurry, cooled from ~243 o F to ~189 o F, is sent to a filterpress, from which the filtrate is returned to the second evaporator eff ect; the filter cake is not washed. In the second effect evaporator, 44% of the feed water is evaporated. The third effect evaporates 76% of the remaining water. The fi nal vapor is condensed and fed to a condensate drum. Of the total feed to the evaporation/sepa ration system, 10.5% remains as syrup, 11.5% is removed as a wet cake in the pressure filter, 17% is recycled back to the process as recycle water, and 61% is evaporated. The syrup from th e third evaporator bottoms is 60% water, e.g., the maximum dissolved solids level that can be ac hieved without rapid fouling of the evaporator; the flow of very low-pressure steam to the evaporator is set to achieve this level. This syrup is mixed with the cake from the filter-press and sent to the combustor for disposal. Air from the filter-press is used for combustion air.

The equipment costs for Area 500 – Product, So lids, and Water Recove ry (Distillation, Dehydration, Evaporation, and Solid-Liquid Separation) are for 2010: $23.4-million; 2015: $26.1-million; and 2022: $23.3-million. This area’ s contribution to the total installed capital cost in 2010 is 17.5%; in 2015, 20.6%; and in 2022, 20.3%. We believe that some of the increase from 2010 to 2015 has to do with the increased liquid flow due to the conversion improvements; larger pipe, pumps, tanks, etc may be necessary to handle the increased flow. The changes from 2015 to 2022 are not that signifi cant, as reflected by th e percent contribution of the area to total installed equipment cost.

Area 600 – Wastewater Treatment : The equipment in Area 600 consists mainly of aerobic and anaerobic digesters, digester agitators, tanks (basins), a biogas emergency flare, coolers, and pumps. The main pur pose of the wastewater treatmen t section is to reduce the plant makeup water requirement by recovering, treatin g and recycling as much process water as possible. The feed to the wastewater treatment s ection consists of: cond ensed pretreatment flash vapor, condensate from the hydrolyzate filter -press vent, boiler blow down, cooling tower blowdown, clean-in-place waste, and the non-recycled evaporator condensate. Rain and snow run-off, equipment washing, and other non-proc ess wastewater are assumed to flow to the municipal wastewater treatment sy stem. The stream is screened to remove large waste particles that are sent to a landfill; any remaining organi c matter is anaerobically and aerobically digested. Anaerobic digestion produces a methane rich (7 5%-methane, 25%-carbon dioxide) biogas that’s fed to the combustor. Aerobic digestion produces relativ ely clean water that’s recycled back to the process and sludge that’s bu rned in the combustor.

NREL didn’t expect much change over the 2010 to 2022 time period. In 2010 the installed capital cost for this area was $3.4-m illion; in 2015, $3.7-million; and in 2022 it was 3.1­ million. As important as this area is to the en tire operation its contribution to the total project installed capital cost is relatively minor. In 2010 the c ontribution was 2.5%; in 2015, 2.9%; and in 2022 it was 2.7%.

Area 700: Bulk Storage of Chemicals. This section of the plant stors chemicals in bulk for the process and for finished, fuel-grade ethanol. The feedstock feed surge tanks we discussed just prior to the discussion of Ar ea 200 are not included in this area. Process chemicals stored in

554this area include: corn steep liquo r (a nutrient), sulfuric acid, cellulase enzyme, gasoline (used as a denaturant finish the fu el grade ethanol), and wate r for fire suppression.

There is approximately five-d ays of SS316-stainless steel L LLLLLLLL tank sulfuric acid storage. Corn steep liquor (CSL), a nutrien t for fermentation seed growth and ethanol production, also has about five -days of SS304-stainless steel M MMMMMMMM storage; NREL expects the plant will require about three-25,000 gallon rail cars of CSL every three-days. There are seven-days of storage for di ammonium phosphate (DAP), delivered as pellets via rail car. Appropriate quantities of CSL and DAP are mi xed in a day-tank and used in Area 300 for fermentation seed production and ethanol produc tion. A producer that supplies cellulase enzymes is expected to set up an enzyme pr oduction operation either on site or on a nearby location. Liquid enzyme storage is set for f our-days in SS304 stainle ss steel tanks. The carbon steel fire-fighting water storage tanks provide about four-hours of operating time; the firewater pump delivers 2,500 gpm. Other pumps are sized per process requirements

There is seven-days of etha nol product storage in two 600, 000 gallon carbon steel tanks. Five percent gasoline (v/v), a denaturant, is added to the ethanol as it’s loaded for shipment to customers. The pumps in this section are genera lly sized to load a 10,000 gallon truck and trailer in about 15 min. to 20 min. maximum filling time. They can also be used to fill process day tanks.

The installed capital costs for bulk storage are, for 2010, $3.8-million; for 2015, $2.4­ million; and for 2022, $2.4-million. The contribution to total projec t installed capital costs are, for 2010, 2.8%; for 2015, 1.9%; and for 2022, 2.1%.

Area 800: Combustor, Bo iler, and Turbogenerator . The purpose of the combustor, boiler, and turbogenerator is to burn various by -product or waste streams to produce steam and to generate electricity. All of the feedstock lignin and some of the cellulose and hemicellulose are not hydrolyzed in Area 300.

As previously discussed, a high soluble, solids syrup is generated in Area 600 and anaerobic and aerobic digestion of the remaining wastewater produced biogas and a small quantity of biomass sludge which are burned to generate steam and produce electricity. This contributes to over-all plant ener gy self-sufficiency, reduces solid waste disposal costs, and generates additional revenue through sales of excess electricity. Because of heightened interest in using biomass, pulping wastes, and sewage sludge in place of fossil fuels, new methods are being developed to handle higher moisture feeds. Traditional methods include blending the wet feed with dry material or addi ng auxiliary fuel to maintain the combustion temperature. When the dry solids from the filter-press cake are combined with the high soluble, solids syrup, it helps

LLLLLLLLL SS316 is an improved version of SS304, with the ad dition of molybdenum and a slightly higher nickel content. The resultant composition of 316 gives it much increased corrosion resistance in many aggressiveenvironments. The molybdenum makes the steel more resistant to pitting and crevice corrosion in chloridecontaminated media, sea water and acid vapors. MMMMMMMMM SS304-stainless steel is the most versatile and the most widely used of all stainless steels. Itschemical composition, mechanical properties, weldability and corrosion/oxidation resistance provide the best allround performance stainless steel at relatively low cost.

555ensure a stable combustion bed temperature and improved boiler efficiency. In these studies NREL used a circulating fluidized bed combustor that is suitab le for varying feeds and feed characteristics; however, this flexibility makes the unit more expensive than a grate or pile combustor. A Lower Heating Value (LHV) of 2,000-2,500 BTU/lb is considered the minimum for maintaining combustion. N NNNNNNNN The combined feed to the combustor has a LHV of 4,179 Btu/lb. Thus, the total higher-heating value energy of the combined feed streams to the combustor is 706 MMBtu/hr. The solids contribute 59% of this energy an d the syrup contributes 37%. A baghouse removes particulates from th e combustion flue gas after it preheats the incoming combustion air and before it ’s discharged through the stack.

The boiler feed water (BFW) system includes a softener for makeup and condensate water, a deaerator to remove air and other non-condensables, surge tanks and pumps. The amount of water pretreatment necessary depends on the incoming water quality, metallurgy of the boiler, and the ratio of make up to condensate in the feed wate r. Pretreatment chemicals for pH control, scale removal, and oxygen removal ar e added. Treated well water used for makeup and condensate are softened, deaerated, preheated and mixed to provide BFW that’s converted to steam that’s superheated to 950 o F at 1,265 psia at the rate of 407,420 lb/hr. Support equipment includes BFW pumps, deaerator, automatic wate r pretreatment chemical injection, and condensate gathered from the vari ous heat exchangers in the process. Boiler efficiency, the percentage of the feed heat conve rted to steam heat, is estimated to be 68%. Boiler blowdown is 3% of steam production. The turbine e fficiency was estimated to be 85%.

The turbogenerator consisting of a multistage tu rbine with extraction ports, a generator, and condenser is used to generate electricity. After high pressure steam drives the multistage turbines, it is extracted at thr ee different conditions for injection into the pretreatment reactor and heat exchange in distillation a nd evaporation. Twenty-eight pe rcent of the steam is extracted from the turbine at 191 psia and 514°F, 60% at 65 psia and 327°F, and 3% at 25 psia and 239°F for process needs, as described. The remaining st eam (9%) is condensed at 1.5 psia with cooling water and returned to the BFW system. For th is design, a total of 30.4 megawatts (MW) of power is generated from the system. The process uses 11.7 MW, leaving 18.7 MW that is sold to the grid.

The installed capital cost for Area 800 are, for 2010, $45.5-million; for 2015, $46­ million; and for 2022, $43.3-million. This area’s cont ribution to the total installed capital cost is, for 2010, 34.1%; for 2015, 36.2%; and for 2022, 37.7%.

Area 900: Utilities . All utilities, except steam a nd electricity, necessary for the production of ethanol are accounted for in this area. The utilities provided include cooling water, chilled water, plant and instrume nt air, process water, and the clean-in-place (CIP) system. No chilled water is used in the plant; the required process temperatures can be achieved by cooling water year-round.

NNNNNNNNN Steam and Electricity Generation Options For the Biomass-To-Ethanol Process, NREL Subcontract ACO-8-18019-01, Reaction Engineering International, Salt Lake City, UT, March 16, 1998. http://www.ott.doe.gov/biofuels/process_engineering.html

556The plant and instrument air systems provide compressed air for air-driven equipment, instrument operation, for clean up, and the filte r-press units in the post-distillation dewatering. The process water system mixes fresh well water w ith treated wastewater and provides water at a constant pressure to the facility . Water is provided to seed pr oduction, boiler feed water, cooling tower make-up, the CIP system, and the scrubber. It is also mixed with recycle water for dilution before saccharification. Process water is also us ed throughout the facility for cleaning on an asneeded basis. The CIP system provides a solutio n that can be heated and includes cleaning and sterilization chemicals to sacchar ification and co-fermentation, s eed vessels, and the distillation system.

The installed capital costs for the utilities area are, for 2010, $5.6-million; for 2015, $5.5­ million; and for 2022, $6.1-million. This area’s contribution to the total project installed equipment costs are, for 2010, 4.2%; for 2015, 4.3%; and for 2022, 5.3%.

Table 4.1-26 summarizes the total projected capital costs for a biochemical cellulosic ethanol plant for the years 2010, 2015 and 2022.

557Table 4.1-26. Projected Capital Costs fo r a Biochemical Cellulosic Ethanol Plant ($million/yr) 2010 2015 2022 Total Capital Investment 133.5 127 114.8

Added Costs* 98.2 93.1 83.8 Total Project Investment 231.7 220.1 198.6

* Added costs include the following:

Warehouse: This is estimated to be 1.5% of total installed cost Site Development: This includes fencing, cu rbing, parking, lot, roads, well drainage, rail system, soil borings, and general paving. This factor a llows for minimum site development assuming a clear site, with no unusual problems such as ri ght-of-way, difficult land clearing, or unusual environmental problems, usually calculated as 9% of the installed cost of process equipment.

Prorateable Costs: This includes fringe benefits, burde ns, and insurance of the construction contractor, usually calculated as 10% of total installed cost. Field Expenses: This includes consumables, small tool equip. rental, field services, temporary construction facilities, and field construction supervision, usually calculated as 10% of total installed cost.

Home Office and Const.: This includes engineering pl us incidentals, purchasing, and construction, usually calculated as 25% of total installed cost. Project Contingency: These costs are small because of the detail included in the process design usually calculated as 3% of total installed cost.

Other Costs: This includes start-up and commissi oning costs; land, rights-of-way, permits, surveys, and fees; piling, soil compaction/dewate ring, unusual foundations; sales, use, and other taxes; freight, insurance in transit and import du ties on equipment, piping, steel, instrumentation, etc.; overtime pay during construc tion; field insurance; project team; transportation equipment, bulk shipping containers, plant vehicles, etc.; escal ation or inflation of costs over time; interest on construction loan. These other costs are usually calculated as 10% of total capital investment.

4.1.1.3 Imported Sugarcane Ethanol Costs

Our analysis of imported ethanol costs began w ith a literature search of recent estimates for production costs for sugar cane ethanol in Brazil. Since the liber alization of ethanol prices in Brazil, few cost estimation studies have been made and most of the cost analyses refer to the same study. 909 This study was carried out by the Brazi lian Ministry of Science and Technology (MC&T), based on 1990 data, and referred to a pr oduction cost of $0.87/gallon. Table 4.1-27 gives a breakdown of cost s based on this data.

558Table 4.1-27. Sugarcane Ethanol Pro duction Costs in Brazil, circa 1990 Average cost (US$ per gallon) Operating costs Labor Maintenance Chemicals Energy Other Interest payments on working capital Feedstock (cane) $0.64 $0.02 $0.02 $0.01 $0.01 $0.02 $0.08 $0.48 Fixed costs Capital at 12% depreciation rate Other $0.23 $0.19 $0.04 Total $0.87

Since then, there have been significant variat ions in exchange rates, costs of sugarcane and oil products, etc. For example, earlier estimates may underestimate crude and natural gas costs which influence the cost of feedstock as we ll as energy costs at the plant. Another possible difference in production cost estimat es is whether or not the estimat es are referring to hydrous or anhydrous ethanol. Costs for anhydrous ethanol (f or blending with gasoline) are typically several cents per gallon higher th an hydrous ethanol (for use in dedicated ethanol vehicles in Brazil). 910 It is not entirely clear from the majority of studies whether reported costs are for hydrous or anhydrous ethanol. Yet another differen ce could be the slate of products the plant is producing, for example, future plants may be dedi cated ethanol facilities while others involve the production of both sugar and ethano l in the same facility. Due to economies of scale, production costs are also typically smaller per gallon for larger facilities. Table 4.1-28 summarizes the various estimates reported by others. Production co sts range from as low as $0.57 per gallon of ethanol to as high as $1. 48 per gallon of ethanol.

559Table 4.1-28. Other Sugarcane Etha nol Production Cost Estimates Reference Cost (US$ per gallon) AgraFNP. 2007. Sugar and Ethanol in Brazil: A Study of the Brazilian Sugar Cane, Sugar and Ethanol Industries. $0.80-$1.07 per gallon (in 2006 $’s depending on region in Brazil), avg. is $0.78 per gallon for cane production cost and $0.13 per gallon for industrial costs IEA. 2004. Biofuels for Transport: An International Perspective. $0.87 per gallon (in 1990 $’s) references MC&T study; also reports recent production cost estimates for hydrous ethanol as low as $0.57 per gallon (at the prevailing exchange rate in Jan. 2004) USDA. 2006. The Economic Feasibility of Ethanol Production from Sugar in the United States. Avg. is $0.81 per gallon

Von Lampe, Martin. OECD. 2006. Working Party on Agricultural Policies and Markets: Agricultural Market Impacts of Future Growth in the Production of Biofuels. $0.83 per gallon

Brazil Institute. April 2007. The Global Dynamics of Biofuels: Potential Supply and Demand for Ethanol. Issue No. 3. $0.83 per gallon.

ESMAP. October 2005. Potential for Biofuels for Transport in Developing Countries. $.87-$1.10 per gallon

OECD, March 2008. ITF Round Tables No. 138. Biofuels: Linking Support to Performance. Avg. is $1.40 per gallon

Bain, R. December 2007. World Biofuels Assessment Worldwide Biomass Potential: Technology Characteri zations. NREL/MP510-42467. $1.04-$1.48 per gallon depending on size of plant, i.e. 100 MGY-4.6 MGY; Sugarcane feedstock costs $0.68 per gallon, Variable operating costs $0.27 per gallon, Fixed costs $0.02-0.13 per gallon, and Capital costs $0.07-0.40 per gallon Macedo, I.C. and L.A.H. Nogueira. 2005. “Biocombusíveis”. Cadernos NAE, No. 2. Núcleo de Assuntos Estratégicos da Presidência da República, Brasilia; As sited in OECD, op. sit. $0.79 per gallon in the Center-South Brazil

Kojima, M. and T. Johnson. 2006. “Potential for Biofuels for Transport in Developing Countries”. ESMAP Knowledge Exchange Series, No. 4.; As sited in OECD, op sit. $0.87-$1.09 per gallon

Smeets, E. 2008. The Sustainability of Brazilian Ethanol-An Assessment of the Possibilities of Ce rtified Production. Biomass and Bioenergy $1.18 assuming exchange rate of $1.20= 1 Euro

560The study by OECD (2008) entitled “Biofuel s: Linking Support to Performance”, appears to provide the most recent and detailed set of assumptions and production costs. As such, our estimate of sugarcane production costs primarily relies on the assumptions made for the study, which are shown in Table 4.1-29. The estimate assumes an ethanol-dedicated mill and is based off an internal rate of return of 12%, a debt/equity ratio of 50% with an 8% interest rate and a selling of surplus power at $57 per MWh.

Table 4.1-29. Cost of Production in a Standard Ethanol Project in Brazil Sugarcane Productivity 71.5 t/ha Sugarcane Consumption 2 million tons/year Harvesting days 167 Ethanol productivity 85 lit ers/ton (22.5 gal/ton) Ethanol Production 170 milli on liters/year (45 MGY) Surplus power produced 40 kWh/ton sugarcane Investment cost in mill USD 97 million Investment cost for sugarcane production USD 36 million O & M (Operating & Maintenance) costs $0.26/gal Sugarcane costs $0.64/gal Capital costs $0.49/gal Total production costs $1.40/gal

The estimate above is based on the costs of producing ethanol in Brazil on average, today. However, we are interested in how the costs of producing ethanol will change by the year 2022. Although various cost estimates exist, analysis of the cost trends ov er time shows that the cost of producing ethanol in Braz il has been steadily de clining due to efficiency improvements in cane production and ethanol conversion processe s. Between 1980 and 1998 (total span of 19 years) ethanol cost declined by approximately 30.8%. 911 This change in the cost of production over time in Brazil is known as the ethanol cost “Learning Curve”. See Figure 4.1-2.

Figure 4.1-2. Ethanol Cost “Learning Curve”

The change in ethanol costs will de pend on the likely productivity gains and technological innovations that can be made in the future. As the majority of learning has already occurred, it is likely that the dec line in ethanol costs wi ll be less drastic as the production process

561and cane practices have matured. In fact, there are few perspec tives for substantial efficiency gains with the sugarcane processi ng technology. Industrial efficiency gains are already at about 85% and are expected to increase to 90% in 2015. 912 Most of the productivity growth is expected to come from sugarcane production, where yields are expected to grow from the current 70 tons/ha, to 96 tons/ha in 2025. 913 Sugarcane quality is also e xpected to improve, with sucrose content growing from 14.5% to 17.3% in 2025. 914 All productivity gain s together could allow the increase in the production of ethanol from 6,000 liters/ha (at 85 liters/ton sugarcane) to 10,400 liters/ha (at 109 liters /ton sugarcane) in 2025. 915

Assuming that ethanol productiv ity increases to 100 liter s/ton by 2015 and 109 liters/ton by 2025, sugarcane costs are be expected to d ecrease to approximately $0.51/gal from $0.64/gal since less feedstock is needed to produce the sa me volume of ethanol using the estimates from Table 4.1-29, above. Table 4.1-30 shows the calculated decrease for the years 2005-2025. We assumed a linear decrease between data points for 2005, 2015, and 2025. Adding operating ($0.26/gal) and capital costs ($0.49 /gal) from Table 4.1-29, to a s ugarcane cost of $0.51/gal, total production costs are $1.26/gal in 2022.

Table 4.1-30. Estimated Decrease in Sugarcane Production Cost by 2022 Due to Increases in Ethanol Productivity

Sugarcane Production Cost $/gal liters/ton gal/ton
2005 0.64 85 22.46 2006 0.63 86.5 22.85 2007 0.62 88 23.25 2008 0.61 89.5 23.65 2009 0.60 91 24.04 2010 0.59 92.5 24.44 2011 0.58 94 24.83 2012 0.57 95.5 25.23 2013 0.56 97 25.63 2014 0.55 98.5 26.02
2015 0.54 100 26.42 2016 0.54 100.9 26.66 2017 0.53 101.8 26.90 2018 0.53 102.7 27.13 2019 0.53 103.6 27.37 2020 0.52 104.5 27.61 2021 0.52 105.4 27.85 2022 0.51 106.3 28.08 2023 0.51 107.2 28.32 2024 0.50 108.1 28.56
2025 0.50 109 28.80

Brazil sugarcane producers are also exp ected to move from burned cane manual harvesting to mechanical ha rvesting. See Figure 4.1-3. 916 As a result, large amounts of straw are

562expected to be available. Costs of mechanical harvesting are lower compared to manually harvesting, therefore, we would expect costs for sugarcane to decline as greater sugarcane producers move to mechanical harvesting. However, it is import to note that diesel use increases with mechanical harvesting, and with diesel fuel prices expected to increa se in the future, costs may be higher than expected. Therefore, we have not assumed any changes to harvesting costs due to the switchover from manual harves ting to mechanical harvesting.

Figure 4.1-3. Phase-out Schedule for Trash Burning Practices

As more straw is expected to be collected at future sugarcane ethanol facilities, there is greater potential for production of excess electricity. The production cost estimates in the OECD study assumes an excess of 40kWh per ton suga rcane, however, future sugarcane plants are expected to produce 135 kWh per ton sugarcane. 917 Assuming excess electr icity is sold for $57 per MWh, the production of an additional 95 kWh per ton would be equivalent to a credit of $0.22 per gallon ethanol produced. We did not include this potential additional credit from greater use of bagasse and straw in our estimate s at this time. Our cost estimates do include, however, the excess electricity produced from bagasse that is currently used today (40 kWh/ton).

It is also important to no te that ethanol production costs can increase if the costs of compliance with various sustainability criteria are taken into account. For instance, using organic or green cane production, adopting higher wages, etc. c ould increase production costs for sugarcane ethanol. 918 Such sustainability criteria could al so be applicable to other feedstocks, for example, those used in corn- or soy-based bi ofuel production. If these measures are adopted in the future, production costs will be higher than we have projected.

563In addition to production costs, there are also logistical a nd port costs. We used the report from AgraFNP to estimate such costs since it was the only resource that included both logistical and port costs. The total average logi stical and port cost fo r sugarcane ethanol is $0.19/gal and $0.09/gal, respectively, as shown in Table 4.1-31.

Table 4.1-31. Imported Ethanol Cost at Port in Brazil (2006 $’s)

Logistical Costs Port Cost Region US ($/gal) US ($/gal)
NE Sao Paulo 0.146 0.094 W Sao Paulo 0.204 0.094 SE Sao Paulo 0.100 0.094 S Sao Paulo 0.170 0.094 N Parana 0.232 0.094 S Goias 0.328 0.094 E Mato Grosso do sul 0.322 0.094 Triangulo mineiro 0.201 0.094 NE Cost 0.026 0.058 Sao Francisco Valley 0.188 0.058 Average 0.192 0.087

Total fuel costs must also include the cost to ship ethanol from Brazil to the U.S. In 2006, this cost was estimated to be a pproximately $0.15 per gallon of ethanol. 919 Costs were estimated as the difference between the unit valu e cost of insurance and freight (CIF) and the unit value customs price. The average cost to ship ethanol from Caribb ean countries (e.g. El Salvador, Jamaica, etc.) to the U.S. in 2006 was approximately $0.12 per gallon of ethanol. Although this may seem to be an advantage for Ca ribbean countries, it shoul d be noted that there would be some additional cost for shipping et hanol from Brazil to th e Caribbean country. Therefore, we assume all costs for shipping et hanol to be $0.15 per ga llon regardless of the country importing ethanol to the U.S.

The total imported ethanol fuel costs (at U.S. ports) over the time period of 2010 to 2022 are shown in Table 4.1-32. In 2022, the total suga rcane ethanol cost estima te prior to tariffs and taxes is $1.69/gallon. Direct Brazilian imports are also subject to an additional $0.54 per gallon tariff, whereas those imports arriving in the U.S. from Caribbean Basin In itiative (CBI) countries are exempt from the tariff. In addition, all imports are given an ad valorem tax of 2.5% for undenatured ethanol and a 1.9% ta x for denatured ethanol. We assumed an ad valorem tax of 2.5% for all ethanol. Thus, incl uding tariffs and ad valorem taxe s, the average cost of imported ethanol is shown in Table 4.1- 33 in the “Brazil Direct w/ Ta x & Tariff” and “CBI w/ Tax” columns for the years 2010-2022.

564Table 4.1-32. Average Imported Ethanol Costs Prior to Tariff and Taxes

Sugarcane Operating Capital Logistical Port Transport Cost Total Production Cost Cost Cost Cost from Port to US Cost Cost ($/gal) ($/gal) ($/gal) ($/gal) ($/gal) ($/gal) ($/gal) 2010 0.59 0.26 0.49 0.19 0.09 0.15 1.77 2011 0.58 0.26 0.49 0.19 0.09 0.15 1.76 2012 0.57 0.26 0.49 0.19 0.09 0.15 1.75 2013 0.56 0.26 0.49 0.19 0.09 0.15 1.74 2014 0.55 0.26 0.49 0.19 0.09 0.15 1.73 2015 0.54 0.26 0.49 0.19 0.09 0.15 1.72 2016 0.54 0.26 0.49 0.19 0.09 0.15 1.72 2017 0.53 0.26 0.49 0.19 0.09 0.15 1.71 2018 0.53 0.26 0.49 0.19 0.09 0.15 1.71 2019 0.53 0.26 0.49 0.19 0.09 0.15 1.71 2020 0.52 0.26 0.49 0.19 0.09 0.15 1.70 2021 0.52 0.26 0.49 0.19 0.09 0.15 1.70 2022 0.51 0.26 0.49 0.19 0.09 0.15 1.69 2023 0.51 0.26 0.49 0.19 0.09 0.15 1.69 2024 0.50 0.26 0.49 0.19 0.09 0.15 1.68 2025 0.50 0.26 0.49 0.19 0.09 0.15 1.68

Table 4.1-33. Average Imported Ethanol Costs

Brazil Direct ($/gal) Brazil Direct w/ Tax & Tariff ($/gal) CBI ($/gal) CBI w/ Tax ($/gal) 2010 1.77 2.35 1.77 1.81 2011 1.76 2.34 1.76 1.80 2012 1.75 2.33 1.75 1.79 2013 1.74 2.32 1.74 1.78 2014 1.73 2.32 1.73 1.78 2015 1.72 2.31 1.72 1.77 2016 1.72 2.30 1.72 1.76 2017 1.71 2.30 1.71 1.76 2018 1.71 2.29 1.71 1.75 2019 1.71 2.29 1.71 1.75 2020 1.70 2.28 1.70 1.74 2021 1.70 2.28 1.70 1.74 2022 1.69 2.27 1.69 1.73 2023 1.69 2.27 1.69 1.73 2024 1.68 2.27 1.68 1.73 2025 1.68 2.26 1.68 1.72

5654.1.2 Biodiesel Production Costs

Biodiesel production costs for th is rule were estimated usi ng two variations of a model generated by USDA for a 10 million gallon per year transesterification biodiesel plant. One version uses degummed soy oil as feedstock, an d a second version includ es acid pre-treatment steps required to utilize feedst ocks such as rendered fat and yellow grease, which have considerable free fatty acid c ontent. USDA used the SuperP ro Designer chemical process simulation software to build up a process flow with estimates of heat and material flowrates and equipment sizing. Outputs from this software we re then combined in a spreadsheet with capital, energy, labor, and feedstock co sts to generate a final estimat e of production cost. Additional details on the model are given in a 2006 publicat ion in Bioresource Technology, a peer-reviewed scientific journal. 920 At 10 million gallons per year, the modeled plant size is between the mean and median plant sizes (13 million and 5 million gal/yr, respectively) given in our industry characterization. Therefore, the model cost estima te is believed to be sufficiently accurate for our analyses and no further work was done to de termine the effect of scale on production cost.

Soy oil feedstock price was taken from the FASOM agricultural commodity modeling done for this proposal, described in more detail in section 5.1 of this DRIA. Distressed corn oil from ethanol plants was assumed to have a value 70% that of soy oil, consistent with historical yellow grease prices. A co-product stream produ ced by the model is crude glycerin valued at $0.03/lb. Table 4.1-34 shows the feedstock prices and resulting biodiesel price. Energy inputs included natural gas for steam generation, and elect ricity. The prices used for these were the same as those given in Table 4.1-4 in the corn ethanol production cost section.

Table 4.1-34. Biodiesel feedstock and pro duction costs used in this analysis, 2006$ Soy Oil Yellow Grease a Reference Case Feedstock $/lb $0.23 $0.16 Biodiesel $/gal $2.11 $1.99 Policy Case Feedstock $/lb $0.32 $0.22 Biodiesel $/gal $2.75 $2.47

a Includes corn oil extracted from thin stillage /DGS, rendered fats, recycled greases, etc.

Production cost for biodiesel is primarily a function of feedst ock price, with other process inputs, facility, labor, and en ergy comprising much smaller fractions. Table 4.1-35 shows the production cost allocation for the soy oil-to-biodiesel facili ty as modeled in the 2022 policy case.

Table 4.1-35. Production cost allocation fo r soy biodiesel derived from this analysis Cost Category Contribution to Cost Soy Oil 87% Other Materials a 5% Capital & Facility 4% Labor 3% Utilities 1%

a Includes acids, bases, methanol, catalyst

566To arrive at a final weig hted average for biodiesel pr oduction cost for the 2022 policy case, we weighted together the fraction of bi odiesel produced from vi rgin oil (660 million gallons of soy and commodity-grade corn oil) and yellow grease (150 million gallons, primarily extracted corn oil) to ar rive at $3.18/gal (2006$).

4.1.3 Renewable Diesel Production Costs

Renewable diesel can be produ ced using three processing op tions; 1.) a new standalone unit located in a refinery, 2.) co -processing in an existing refinery diesel hydrotreater and 3.) a standalone unit at a rendering plant (or anot her location outside of a refinery).

The renewable diesel production process convert s vegetable oils and animal fats into a diesel type fuel using thermal depolymerization, which is simila r to refinery hydro-treating to remove sulfur. The process uses hydrogen and catalyst to remove oxygen from the triglyceride molecules in the feedstocks oils via a decarboxylation and hyd ro-oxygenation reaction, yielding some light petroleum products and water as byproduc ts. The reactions also saturate the olefin bonds in the feedstock oils, converting them to paraffins. All of these reactions consume significant amounts of hydrogen. The extent of these reactions however, depends on the process conditions, as some of the carbon backbone of the oils can be cracked to naphtha and lighter products with higher severity. For our analysis though, we assume no su ch cracking and predict yields resulting in ninety nine pe rcent diesel fuel with the balance as propane and water.

We derive our production cost estimates fr om those presented by UOP and Eni’s at a 2007 industry conference to make renewable dies el in a grass roots standalone production process inside a refinery. O OOOOOOOO The process has a pre-treati ng unit that removes alkali and acidic producing compounds from fe ed streams, which removes the catalyst poisons. We also use the UOP engineering estimate to derive co sts for co-processing renewable diesel in an existing refinery’s diesel hydrotreater. For this, we assume that refiners will; 1) revamp their existing diesel hydrotreater to add capacity and 2) add a pre-treater to remove feedstock contaminants. Lastly, we derive costs for a stan dalone unit at a location outside a refinery at a rendering plant other facility, using a capit al cost estimate from Syntroleum Corp. P PPPPPPPP We assume that all of the renewable diesel produc tion will take place in PA DD 2, as feedstock shipping cost are reduced since most of the sour ces for feedstock supply are located primarily in the Midwest.

At the moment, we do not have a exact way of predicting the how industry will develop to build processing capacity using the three pr ocessing methods, as only a few known projects have been announced. We therefore base our estimate for future renewable processing capacity on current public project announcements and our production cost estimates, along with feedstock availability. In early 2007, Ty son and ConocoPhillips entered into a strategic alliance to produce 175 MM gallons per year of renewa ble diesel at Conoco’ s refineries located in the Midwest, either through co-processing or as a standalone unit. Tyson also announ ced this fall start of construction for a 75 MM gal/yr standalone plant at rendering plant in location in Geismar,

OOOOOOOOO A New Development in Renewable Fuels: Green Diesel, AM-07-10 Annual Meeting NPRA, March 18­ 20, 2007.
PPPPPPPPP From Securities and Exchange Commission Form 8-K for Syntroleum Corp, June 25 th 07.

567Louisiana. We assume that future capacity a dditions will mirror these announcements, and that a large fraction of the capacity for refinery inst allation will be produced using the co-processing method, as the production costs are lower than th ose for a new standalone unit in a refinery. Using this, we speculate that a bout 50% of renewable diesel be ing produced by the refinery coprocessing route, 17% from a new st and alone unit at a refinery a nd 33% at rendering plants or as a new site installation.

We derive an average processing cost to ma ke renewable diesel as 39.6 cents per gallon using yellow grease/animal fats, while processing cost from soy oil feed stock are 37.1 cents per gallon. Both of these cost s are the resulting average from using the processing industry capacities listed above, along with our cost estimate s. It is advantage ous, however to convert yellow grease and animal fats into renewable di esel, as the resulting di esel fuel has improved blending, cold flow, storage and shipping cap abilities over yellow gr ease converted via the biodiesel process. Additionally , since the process-related cost s for producing renewable diesel from yellow grease are considerab ly lower than for converting ye llow grease to biodiesel, we assume that all of our projected feedstock s upplies of yellow grease and animal fats will be converted using the renewable diesel process, yielding 375 million gallons per year in 2022. As such, we assume that future renewable proc essing capacity will mirror the volume produced from yellow grease. Considering the uncertainties with subsidies and that there is considerable capacity for processing soy oil in biodiesel plants, we assume that all soy oil will be processed in existing and announced biodiesel plants.

The specifics of our cost estimation are outlined in the sections that follow.

i. New Standalone Unit in Refinery

Capital costs:

We derived new standalone re newable diesel production co sts based on the UOP estimate for a new unit built inside an existing petroleum refinery. The total capital cost from the UOP estimate included ISBL and OSBL equipment outlays, which were $33.9 and $6.8 MM in 2005 dollars, respectively for an 8,000 BPSD unit. We adjusted the original UOP capital estimates, which were assumed to be in 2006 dollars to 2005 dollars using CPI. Additionally, for our analysis, we assumed that the process was designed to accommodate a throughput flow rate to maintain an average of 8000 BPSD per day on an annual basis. To get installed costs, we escalated the above total capital costs by 20 perc ent, so as to account for unknown contingencies for installation and adjusted the resulting costs for locating the process in PADD 2, multiplying by a 1.3 factor. We amortized the resulting cost, assuming 7% re turn on capital after taxes, depreciation, and amortization to derive a cost for capital of 5.9 c/gal. All of the economic factors used for amortizi ng the capital costs are summarized in Table 4.1-36.

568Table 4.1-36. Economic Factors Used in Deriving th e 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

Operating Costs:

For fixed costs, we assumed this is 7% of the total installed capital, resulting in a cost of 4.3 cents per gallon of renewable diesel fuel. For hydrogen operating costs, we used the UOP analysis and guidance from Conoco Philips Q QQQQQQQQ , to derive our estimate to make renewable diesel, assuming that the feedstock oils were not cracked to other products. The UOP paper presents a range of 1000 to 2000 SCF per barrel for converting the various feedstock to renewable diesel. We derived feedstock spec ific hydrogen requirements, weighting hydrogen needs for feedstock oxygen saturation, along with cost for hydrogen and UOP’s total variable operating cost. The resulting rene wable hydrogen needs are presente d in the Table 4.1-37 below.

Table 4.1-37. Feedstock Hydrogen De mands for Renewable Diesel (std. ft 3 /gallon) Feedstock Oil Hydrogen (std ft 3 ) Soy Oil 1410 Animal Grease and Yellow Grease 1590

Hydrogen costs were generated using a nnual projected hydrogen prices from Jacob’s along with the quantities listed a bove, resulting in a cost of 18. 0 and 21.0 c/gal for soy oil and yellow grease, respectively. All other remaini ng operating variable costs are estimated as 6.5 c/gal, based on the UOP paper. This figure represents cost for manpower, electricity, catalyst, utility requirements, etc.

The resulting total producti on costs are 34.5 and 37.4 cents per gallon for soy oil and yellow grease, respectively for new unit co-located in a refinery.

ii. Co-Processing in Existing Re finery Diesel Hydrotreater

Co-processing in a refinery appears to be th e most cost effectiv e, though feedstock pretreating equipment may be needed to remove impuri ties, so as to prevent degradation of refinery diesel hydrotreater catalyst. The advantage of co-processing in a hydrotreater, are that the capital cost are reduced as the existing diesel hydrotreater reactor, heat exchangers, pumps, process lines etc., can be used for the depolymerization reac tion. Since the depolymerization reaction is highly exothermic, though there is a limit on the amount of oil feedstock that can be routed through an existing diesel hydrotreat er. We derived our estimate for co-processing using the cost information form UOP’s standalone pr ocessing in the preceding section.

Capital costs:

QQQQQQQQQ Per EPA phone discussion July 23, 2007.

569We estimate that pre-treater capital costs will be 40% of the total capital cost of a standalone unit, per guidance from UOP. We assu me that this estimate includes capital costs to account for any needed refinery diesel hydrotreater modifica tions. The capital costs are amortized assuming 7% return on capital after taxe s, depreciation, and am ortization to derive a cost 2.7 cents.

Operating Costs:

For the fixed cost, we assume this is 8% of the total capital listed above, which equates to 1.9 c/gal. All other remaining operating costs were assumed to be the same as those listed for the UOP standalone unit in the preceding section. The resulting total opera ting costs are 26.1 and 29.2 c/gal for soy oil and yellow grease, respectively.

For co-processing in a refinery, the resul ting total production costs are 28.5 and 30.6 c/gal for soy oil and yellow grease, respectively.

iii. Standalone Unit at Rendering Plant

For a standalone unit, not instal led in a refinery, the capital cost are higher than a refinery new unit, as offsite and utility investments ar e needed to support th e renewable processing equipment. These units, therefore require capital for these additional items, since there are little existing infrastructure at the site such as build ings, roads, tanks, pipelines, etc. and utility processes for steam generation, cooling water, etc. For our estimation of these extra costs, we used the total installed capital cost from Syntroleum, which is for a grass roost unit and accounts for offsite items. For operating costs, we assume this will be the same as those in a UOP unit.

Capital costs:

Syntroleum estimated that th e total installed cap ital cost for new process is $135 MM for a 4800 BPSD unit, located in the South. This figure includes capital costs for offsite and utility investments. We estimated capital cost assuming 7% rate of return, after taxes and depreciation, resulting in 20.0 cents per gallon.

Operating Costs:

We estimated fixed costs as 6% of the to tal installed capital ou tlay listed above, which equates to 10.9 c/gal. All other remaining opera ting costs were assumed to be the same as those listed for the UOP standalone unit.

The resulting overall production costs are 55.2 and 58.3 c/ gal for soy oil and yellow grease for a standalone unit, respectively.

Table 4.1-38 summarizes the renewable processi ng costs outlined in this section. Note that these are per-gallon costs to be added to the cost of the feed stock. Also note that only the

570production cost from yellow grease is presented, si nce this is the feedstock we project will be used in our policy case.

Table 4.1-38. Summary of Renewabl e Diesel Processing Costs (2006$/gal) Installation Type Processing Cost from Yellow Grease Volume Share in Policy Case New unit at refinery 0.37 17% Existing unit at refinery 0.31 50% Stand-alone unit at rendering plant 0.58 33% Weighted average 0.41 100%

4.1.4 BTL Diesel Pro duction Costs

Biofuels-to-Liquids (BTL) processes, which are also thermochemical processes, convert biomass to liquid fuels via a syngas route. The primary product produced by this process is diesel fuel.

There are many steps involved in a BTL process which makes this a capital-intensive process. The first step, like all the cellulosic pr ocesses, requires that the feedstocks be processed to be dried and ground to a fine size. The second step is the syngas step, which thermochemically reacts the biomass to carbon monoxide and hydrogen. Since carbon monoxide production exceeds the stoichiometric ideal fraction of the mixture, a water shift reaction must be carried out to increase the rela tive balance of hydrogen. The syngas products must then be cleaned to facilitate the following Fischer-Tropsch reaction. The Fischer-T ropsch reaction reacts the syngas to a range of hydrocarbon compounds – a type of synthetic crude oil. This hydrocarbon mixture is then hydrocracked to ma ximize the production of hi gh cetane diesel fuel, although some low octane naphtha is also pr oduced. The many steps of the BTL process contribute to its high capita l cost. One report by Iowa State Univ ersity estimated that diesel fuel produced by a 35 million gallon per year cellulosic Fischer Tropsch plant would cost $2.37 per gallon. 921 This cost seems to be based on amortiz ing the capital costs using a 7 percent before tax rate of return which is the basis for our co st analyses. The report’s estimate for capital cost was $341 million. This cost was estimated in the year 2002, and adjusting this cost to a 2006 investment environment increases the cost to $48 4 million. When we amor tize this capital cost assuming a 7 percent rate of return before taxes, the yearly cost is $53 million, or about $1.51 per gallon of diesel fuel produced. The report estim ates the operating costs to be $1.48 per gallon, and adjusting the operating costs to reflect 2006 dollars increases the operating cost to $1.48. Adding the adjusted operating cost to the adjusted capital cost resu lts in a total cost of $2.99 per gallon. Almost $1.00 of the total operating cost is due to the feedstock costs, which seems to be assuming about $75 per dry ton of feedstock. After we adjusted to 2006 dollars, the report would estimate the feedstock to co st $84 per dry ton, which is hi gher than our average feedstock cost estimate of $71 per dry ton. Adjusting to an average estimated feedst ock cost of $71 per dry ton reduces the total cost to $2. 85 per gallon of diesel fuel.

Initially, the estimated cost of $2.85 per ga llon seems high relative to the projected cost for a year 2015 biochemical cellulosic plant, which is $1.39 per gallon of et hanol in 2006 dollars. However, ethanol provides about ha lf the energy content as Fischer Tr opsch diesel fuel. So if we double the biochemical cellulosic ethanol costs to $2.78 per diesel fuel-equivalent gallon, the

571estimated costs are very consistent between the two. The cellulosic bi ofuel tax subsidy favors the biochemical ethanol plant, t hough, because it is a per-gallon subsidy regardless of the energy content, and it therefore offsets twice as much cost as the BTL plant pr oducing diesel fuel. There is one more issue worth cons idering and that is the relative pr ice of diesel fuel to that of E10 or E85. Recently diesel fuel has been pri ced much higher than gasoline, and if this trend continues to hold, it would provide a better market for selling the B TL diesel fuel than for selling biochemical ethanol into the E85 market, which we believe will be a challenging pricing market for refiners.



### 4.2 Renewable Fuel Distribution Costs

Our analysis of the costs associated with di stributing the volumes of renewable fuels that we project would be used under RFS2 focuses on: 1) the capital cost of making the necessary upgrades to the fuel distribution in frastructure system directly rela ted to handling these fuels, and 2) the ongoing additional freight costs associated with shipping renewable fuels to the point where they are blended with petr oleum-based fuels. Our analys is considers distribution costs within the U.S. only. The costs associated with bringing ethanol imports to the U.S. are considered in the context of the cost of the imports themselves. R RRRRRRRR The following sections outline our estimates of the distribut ion costs for the additional volumes of ethanol, biodiesel, and renewable di esel fuel that we project would be used in resp onse to the RFS2 standards. There will be ancillary costs asso ciated with upgrading the basic rail, marine, and road transportation nets to handle the increase in freight volume due to th e RFS2. We have not sought to quantify these ancillary co sts because 1) the growth in frei ght traffic that is attributable to RFS2 represents a minimal fraction of the total anticipated increase in freight tonnage (approximately 2% by 2022, see Section 1.6.5 of this DRIA), and 2) we do not believe there is an adequate way to estimate such non-direct costs.

The biofuels used in response to the R FS2 standards would displace petroleum-based fuels that would otherwise be used. Thus, it w ould be appropriate to su btract the distribution costs for the displaced petroleum-based fuels from the distribution costs attr ibuted to the biofuels that replace these petroleum-base d fuels. However, we choose not to do so to for our primary analysis in order to help ensure a conservatively high estimate of biofue l distribution costs given the uncertainties in our analysis. S SSSSSSSS

A discussion of the changes that would be n eeded in the biofuels distribution system to accommodate the increased volumes of biofuels that we project would be used in response to the proposed RFS2 standards is contained in Section 1.6 of this DRIA. In th is chapter, we further detail the nature of these projected changes a nd estimate the associated costs. Distribution capital costs associated with the additional vol ume of ethanol, biodiesel, and renewable diesel that we project would be used by 2022 in respons e to the RFS2 standards relative to a 13 BGY 2022 reference case total 12.448 billion dollars in the U.S., 97% of whic h is attributed to ethanol.

4.2.1 Ethanol Distribution Costs

RRRRRRRRR The cost of imported ethanol is discussed in Section 4.1.1.3 of this DRIA.
SSSSSSSSS This was the approach taken in the RFS1 final rule.

572As discussed in the following sections, we estimate that the total capital costs in the U.S to support distribution of the additional volume of et hanol that would be used in response to the RFS2 standards would be 12.1 billion dollars by 2022. When amortized, this translates to 6.9 cents per gallon of additional ethanol attributed to the RFS2 standards. Amortization of capital costs was done over 15 years at a 7% annual cost of capital except in the case of the cost of tank trucks where a 10 year amortiza tion schedule was used. These co sts were calculated relative to the AEO 2007 baseline which projec ts that 13.2 BGY of ethanol w ould be used in 2022 absent the RFS2 standards. Under the RFS2 standards, we project that 34.1 BGY of ethanol would be used by 2022. Ethanol freight costs are estimat ed to be 11.3 cents per gallon on a national average basis. Thus, we estimate that total et hanol distribution costs would be 18.2 cents per gallon of ethanol attributed the RFS2 standards. The way in which we estimated these costs is detailed in the following sections.

As noted previously, we choos e not to subtract the distri bution costs for the petroleumbased fuels that would be displaced by the use of biofuels from our estimated biofuel distribution costs to help ensure a conservatively high es timate of biofuel dist ribution costs given the uncertainties in our analysis. We believe that the freight costs to ship petroleum-based fuels to the terminal are approximately 4 cents per gallon. If we were to subtract these costs from the estimated ethanol distribution costs, the result would be 14.2 cents per gallon.

We currently have a study underway through Oakridge National Labor atories (ORNL) to model the transportation of ethanol from producti on/import facilities to petroleum terminals. The ORNL model optimizes freight flows over the rail, marine, a nd road distribution net while addressing the use of multiple shipping modes. We plan to use the results of the ORNL study to adjust our ethanol freight and capital cost estimates for the final rule.

4.2.1.1 Capital Costs to Upgrade the Ethanol Distribution System

4.2.1.1.1 Summary of Ethanol Distribution Capital Costs

A summary of ethanol distri bution capital costs is contai ned in Table 4.2-1. Our estimation of these costs is detailed in the following sections.

573Table 4.2-1. Summary of Estimated Ethanol Distri bution Infrastructure Capital Costs*

Million $
Fixed Facilities

Marine Import Facilities 49 Ethanol Receipt Facilities at Rail Terminals Rail Car Handling and Miscellaneous Equipment 1,264 Ethanol Storage Tanks 354 Petroleum Terminals Rail Receipt Facilities 2,482 Ethanol Storage Tanks 1,611 Ethanol Blending and Miscellaneous Equipment 545

E85 Retail Facilities 2,957
Mobile Facilities

Rail Cars 2,938 Barges 183 Tank Trucks 223 Total Ethanol Distribution Capital Costs 12,066

* Relative to the AEO 2007 13.2 BGY 2022 reference case

4.2.1.1.2 Petroleum Terminal Ethanol Distribution Capital Costs

The facility upgrades that we project would be needed at petr oleum terminals to facilitate the distribution of the volume of ethanol that we project would be used in response to the RFS2 standards are discussed in Section 1.6.9 of this DRIA. Total capital costs at terminals by 2022 relative to the 13 BGY 2022 refere nce case are estimated at 4.637 b illion dollars. These capital costs are detailed in the following sections.

4.2.1.1.2.1 Capital Cost of Additiona l Ethanol Rail Receipt Capability at Petroleum Terminals

A discussed in Section 1.6.9 of this DRIA, we estimate that an additional 21 unit train and 148 manifest rail car receipt facilities woul d be built at petroleum terminals by 2022 to handle the additional volumes of ethanol that project would be transported to petroleum terminals via these modes (re lative to the 13 BGY referen ce case). Based on input from industry, we estimate the cost of installing etha nol rail receipt capability at 600 thousand dollars per rail car slot. This figure is meant to incl ude various ancillary costs associated with the necessary track and rail car handling facilities within the terminal gates. We believe that this is an upper bound regarding the potential costs of in stalling rail receipt capability at terminals, particularly with respect to un it train installations. We hope to receive additional input from industry with which to refine this estimate for the final rule. We assumed that ethanol unit train receipt facilities would need room for 100 rail ca rs, and that ethanol ma nifest rail car receipt facilities would accommodate 8 rail cars. Ethanol unit trains are typicall y composed of 70 to 100

574rail cars. Our assumption of an 8 ethanol mani fest rail car receipt faci lity is based on an evaluation of how much ethanol could be handled by such a facility on an annual basis. Assuming 60 deliveries per year (5 per month), tr anslates to an annual throughput at a manifest rail car receipt facility of approximately 14.2 m illion gallons per year, which we believe to be a reasonable volume for the terminals that would be served primarily by manifest rail. To accommodate contingencies associated with a dding ethanol receipt capability, we added 1 million dollars per unit train facility and 500 thousa nd dollars per manifest rail car facility. Thus, within the gates of the terminal, the cost of a unit train receipt facility is estimated at 61 million dollars and the cost of a manifest rail car receipt facility is estimated at 5.3 million dollars. This does not include the cost of linking the petroleum terminal to a rail line to the extent that such linkage does not already exist.

There is no comprehensive data on the number of terminals that have rail access and the logistical considerations in a dding rail access to terminals wher e proximity to a rail line makes this an option. Lacking such information, we as sumed that 50% of terminals that add ethanol rail receipt capability would already have rail access and 50% would n eed to install a rail spur to bring rail access to their facility. Based on input from industry, we estimate the cost of installing track for a rail spur at 2 million dollars per mile . We assumed that a 4 mile spur would be needed to connect the rail line to the petroleum terminal for unit train receipt and that a 2 mile long spur would be needed to connect the terminal for manifest rail car receipt. This is based on the premise that the length of unit trains may re quire more gentle curves and/or limit placement of the link to the rail line, wh ereas accommodating connections fo r the short manifest rail car shipments would be less demanding. Ancillary cost s associated with establishing a linkage to a rail line to allow rail receipt capability are estim ated at 4 million dollars per unit train facility. This is meant as a contingency to cover costs that we could not foresee such as the potential need to acquire additional land in certain cases. Again, this is based on the premise that accommodating a long unit train would present additional logistical challenges. The actual cost could be less. Based on the above, we estimate that the cost of providi ng linkage to a rail line would be 12 million dollars to allow unit train re ceipt capability and 4 million dollars to support manifest rail car receipt capab ility at a petroleum terminal.

Summing the above, we arrived at the estimates contained in Table 4.2-2 for the total cost of providing rail receipt capability at terminal s including the cost of providing rail access where needed.

575Table 4.2-2. Estimated Cost of Installing Ethanol Rail Receipt Capability at Petroleum Terminals

Cost Per Facility (million $) Unit Train Receipt Capability at a Terminal that Already had Rail Access 61

Unit Train Receipt Capability at a Terminal that Needed to Install New Rail Access 73

Manifest Rail Car Receipt Capability at a Terminal that Already had Rail Access 5.3

Manifest Rail Car Receipt Capability at a Terminal that Needed to Install New Rail Access 9.3

The total cost of the additional unit train receipt facilities (21) that we project would be installed at petroleum terminals by 2022 to suppor t the distribution of the additional volume of ethanol that would be used in response to the RFS2 standards (relative to the 13 BGY reference case) is estimated at 1.4 billion dollars. The tota l cost of the additional manifest rail car receipt facilities (148) that we project would be needed by 2022 to support the distribution of the additional volume of ethanol that would be used in response to the RFS2 standards (relative to the 13 BGY reference case) is estimated at 1.08 b illion dollars. These costs were amortized over 15 years at a 7% annual cost of capital to deri ve the cents-per-gallon distribution capital cost estimate in Section 4. 2.1 of this DRIA.

As discussed in Section 1.6.9 of this DRIA, there is considerable uncertainty regarding the extent to which additional rail receipt facili ties would be placed at petroleum terminals versus at rail terminals. This has implications not only with respect to whether the costs would be incurred by petroleum terminals or rail termin als but also the extent to which secondary transportation of ethanol is need ed to reach petroleum terminals and the extent to which ethanol storage and handling capability would be needed at rail terminals. Th e projected modes of transporting ethanol to petroleum terminals are di scussed in Section 1.6. 2 of this DRIA. The projected capital costs at rail terminals to facili tate the distribution of ethanol are discussed in Section 4.2.1.1.3 of this DRIA.

4.2.1.1.2.2 Capital Cost of Additional Etha nol Storage Tanks at Petroleum Terminals

A discussion of how we estimated the vol ume of new ethanol st orage that would be needed by 2022 as a result of the RFS2 standard s (relative to the 13 BGY 2022 reference case) is contained in Section 1.6.9 of this DRIA. Th e breakdown of the amount of new ethanol storage that we believe could be accomm odated through the retrofitting of existing tanks currently in gasoline service versus the amount that would re quire the construction of new storage tanks is also discussed in Section 1.6.9. We estimate th at a total of 97.3 million barrels of new ethanol storage would be needed at petroleum terminals due to the RFS2 standards, 65.2 million barrels of which would be existing gasoline storage tanks retrofitted to ethanol service and 32.1 million barrels of which would be new construction. As discussed in Section 4.2.1.1.3.2, we estimate

576that an additional 8.85 million barrels of new ethanol storage would also be needed at rail terminals, all of which would be satisfied with new construction. Based on information from industry with respect to the effect of increased steel prices on storage tanks costs, we estimate that the cost of constructing ne w ethanol storage tanks would be 40 dollars per barrel. The cost of installing an ethanol compa tible liner and making other modi fications to existing gasoline storage tanks to make them compatible for storing ethanol is estimated to be 5 dollars per barrel on average. Thus, we estimate that the total co st at petroleum terminal s of new ethanol storage would be 1.61 billion dollars, 1.28 billion dollars of which is attr ibuted to the construction of new tanks, and 326 million dollars of which is attr ibuted to the conversion of existing gasoline tanks to ethanol service. These costs were amortized over 15 years at a 7% annual cost of capital to derive the cents-per-gallon distribution capital cost estimate in Secti on 4.2.1 of this DRIA.

4.2.1.1.2.3 Capital Cost of Ethanol Blendi ng and Other Miscellaneous Equipment at Petroleum Terminals

As discussed in Section 1.6.9 of this DRIA , petroleum terminals would need to install additional equipment to blend ethanol with ga soline as well as making other miscellaneous upgrades to, piping, pumps, seals, and vapor recovery sy stems to ensure ethanol compatibility. As detailed in Section 1.6.9 of this DRIA, we pr ojected that 164 terminals would need to install ethanol blending equipment by 2022 as a result of the RFS2 standards (relative to the 13 BGY 2022 reference case). We further pr oject that 90% of all terminal s (931) would need to install E85 blending capability by 2022 as a result of th e RFS2 standards. The remaining terminals (132 out of a total of 1,063) were projected to have only E10 blending capability by 2022. We estimated that the terminals which would have in stalled E10 blending capability absent the RFS2 standards would be the ones that upgrade th eir ethanol blending capability to accommodate blending E85 as well as E10. We also estimat ed that the 164 terminal s that would not have received ethanol absent the RFS2 standards would need to upgrade their vapor recovery systems and make other miscellaneous changes to hand le ethanol blended fuel for the first time.

We estimated the costs of the changes need ed at petroleum by 2022 relative to the 13 BGY 2022 reference case based on input from te rminal operators and various other industry sources. The cost of upgrading th e vapor recovery systems at petr oleum terminals that would not have handled ethanol absent the RFS2 standards is estimated at 1 million dollars per terminal, and the miscellaneous costs associated with handling ethanol for the first time are estimated at 20,000 per terminal. This equates to a total of 176.3 million dollars for the 164 terminals that we project would not have handled ethanol absent th e RFS2 standards. We estimate that all 1,063 petroleum terminals would incur 300 thousand do llars per facility on average to upgrade their piping, electrical, and other miscellaneous equipm ent in order to handle the increased ethanol throughput that they would expe rience due to the RFS2 standa rds. This is based on our engineering judgment and contains substantial contingency costs. Actual costs could be less. This would total 318.9 million dollars by 2022.

In addition, the cost of installing E10 bl ending equipment is estimated to average 300 thousand dollars for the 164 terminal s that we estimate would not have installed ethanol blending capability in the absence of the RFS2 standards. The does not include the needed ethanol storage capability discussed in Section 4.2.1.1.2.2 of this DRIA and the miscellaneous piping and other

577work needed that is discussed above. Input fr om terminal operators in dicates that retrofitting E10 blending equipment to dispense E85 as well as E10 (apart from ethanol storage and delivery concerns) primarily involves changes to computer software. Based on th is input, we estimate that E85 blending equipment woul d cost 10 thousand dollars more than E10 blending equipment. We applied this 10 thousand dollar differential to new installations of E85 blending equipment as well as retrofits of existing E10 blending equipment. Based on this, we estimate the total cost of the ethanol blending equipment that would be needed at petroleu m terminals as a result of the RFS2 standards to be 58.5 million dollars.

Summing all of these estimated costs, we a rrived at an estimate of the total cost by 2022 for the ethanol blending and other miscellaneous equipment that would be needed at petroleum terminals as a result of the RFS2 standards of 544.7 million dollars. These costs were amortized over 15 years at a 7% annual cost of capital to derive the cents-per-gallon distribution capital cost estimate in Secti on 4.2.1 of this DRIA.

4.2.1.1.3 Rail Terminal Ethano l Distribution Capital Costs

The facility upgrades that we project would be needed at rail terminals to facilitate the distribution of the volume of etha nol that we project would be used in response to the RFS2 standards are discussed in Section 1.6.3 of this DRIA. Total capital costs at terminals by 2022 relative to the 13 BGY 2022 refere nce case are estimated at 1.618 b illion dollars. These capital costs are detailed in the following sections.

4.2.1.1.3.1 Capital Cost of Ethanol Unit Tr ain Receipt Capability at Rail Terminals

As discussed in Section 1.6.3 of this DRIA, we project that 21 ethanol unit train receipt facilities would be located at rail terminals to facilitate the delivery of ethanol to petroleum terminals. Consistent with the approach taken in the estimation of the cost of installing a unit train facility at a petroleum terminals (as discus sed in Section 4.2.1.1.2.1 of this DRIA), we estimated the cost of installing ethanol rail receipt capability at a rail terminal to be 600 thousand dollars per rail car slot. This tr anslates to 60 million dollars for a facility capable of receiving a 100 rail car unit train. In the case of rail terminals, we believe th is figure is sufficient to cover various ancillary costs associated with the necessary track and rail car handling facilities within the rail terminal gates. Given that rail termin als are already in the bus iness of handling large numbers of rail cars, we believe that there woul d be significantly less anc illary costs associated with handling ethanol rail cars themselves compared to a petroleum terminal. Thus, we believe that the 600 thousand per rail slot is also sufficient to cover what ever truck loading, piping, and other miscellaneous costs that would be incurred at rail terminals (with th e exception of the need for ethanol storage tanks that is discussed in the following section) . Nevertheless, we added a 1 million dollar per rail terminal contingency cost to help ensure that these ancillary costs are accounted for. Consistent with our estimate of the cost of unit train f acilities at petroleum terminals, we assumed the need to a ccommodate a 100 car ethanol unit train.

Based on the above, we project that the total cost of installing etha nol unit train receipt capability at a rail terminal to be 61 million dollars per facility. This totals 1.264 billion dollars for the 21 ethanol unit train rail receipt facilities th at we project would be in stalled as a result of

578the RFS2 standards by 2022 (relative to the 13 BGY 2022 reference case). These costs were amortized over 15 years at a 7% annual cost of capital to derive the cents-per-gallon distribution capital cost estimate in S ection 4.2.1 of this DRIA.

A discussed in Section 1.6.9 of this DRIA (and noted in Se ction 4.2.1.1.2.1), there is considerable uncertainty regarding the extent to which additional rail receipt facilities would be placed at petroleum terminals versus at rail terminals. To the exte nt that logistical constraints prevent the placement of some of the 21 additional ethanol unit trai n receipt facilities and/or 148 manifest rail car facilities that we project would be installed at petroleum terminals (see Section 4.2.1.1.2.1), we expect that additional unit train re ceipt facilities would be placed at rail terminals to compensate.

4.2.1.1.3.2 Capital Cost of Ethanol Storage Tanks at Rail Terminals

As discussed in Section 1.6.3 of this DRIA , we project that 17.7 million gallons of new ethanol storage would be needed at rail terminals to support unit trai n deliveries. This equates to a total of 371.7 million gallons or 8.85 million ba rrels of new ethanol storage tanks for the 21 ethanol unit train receipt facilites that we projec t would be installed to support use of the volume of ethanol that we estimate would be use in re sponse to the RFS2 standards (relative to the 13 BGY 2022 reference case). We used the same es timate of 40 dollars per barrel to construct new ethanol storage tanks that we used to estimate th e cost of new ethanol st orage tanks at petroleum terminals (in Section 4.2.1.1.2 of this DRIA). Thus, we estimate that the total cost of the additional ethanol storage tankage that would be needed due to the RFS2 standards at 354 million dollars. These costs were amortized over 15 years at a 7% annual cost of capital to derive the cents per gallon distri bution capital cost estimate in Section 4.2.1 of this DRIA. Should some of the ethanol rail receipt facilities that we project would be installed at petroleum terminals instead be installed at rail terminals, ethanol storage at additional rail terminals would be needed instead.

4.2.1.1.4 Import Facility Etha nol Distribution Capital Costs

As discussed in Section 1.6.4 of this DRIA , we estimate that a total of 28 port facilities would receive imported ethanol by 2022, 12 of which had not received ethanol before. We believe that all such port facilities also serve as petroleum terminals. Thus, the cost of additional ethanol storage, ethanol blendi ng equipment, and other miscellaneous equipment related to handling ethanol from the standpoint of terminal operations at such facilities is accounted for in the context of the costs at petr oleum terminals (see Section 4.2.1. 1.2 of this DRIA). However, there would be additional costs at the port facilities which had not received ethanol in the past. Input from industry indicates that offloading large marine transport containers of ethanol requires significantly upgraded vapory recovery equipment. Ba sed on this input, we estimated the cost of making the needed upgrades to vapor recovery equi pment at 2.5 million dollars per facility. We further estimated miscellaneous costs associated w ith delivery of ethanol into storage tanks from marine vessels at 1 million dollars per facility. This is meant to include new piping, pumps, various other fittings, and a con tingency cost. The actual cost could be significantly lower. Thus, we estimate that the total cost to prepar e for delivery of ethanol at a port that had not received ethanol before at 3.5 million dollars pe r facility. The total for the 12 ports that we

579project had received imported ethanol before is 42 million dollars. As discussed in Section 1.6.4, we are considering whether 2 additional ports th at have not received et hanol before should be added to the list of ports that we expect would receive ethanol imports by 2022. Including these 2 additional ports would increase the total cost for ports that ha d not received ethanol before to 49 million dollars. These costs were amortized over 15 years at a 7% annual cost of capital to derive the cents-per-gallo n distribution capital cost estimate in Section 4.2.1 of this DRIA.

4.2.1.1.4 Ethanol Rail Car Capital Costs

As discussed in Section 1.6.3 of this DRIA , we estimate that an additional 26,644 ethanol tank cars would be needed by 2022 to transport th e additional volume of et hanol that we project would be used to meet the RFS2 standards (rel ative to the 13 BGY 2022 re ference case). Based on input from industry, we estimat e that the cost of a new etha nol tank car of 30 thousand gallon nominal capacity at 90 thousand dolla rs. Thus, we estimate that the total cost of the ethanol rail tanks cars needed due to the RFS2 standards by 2022 would be 2.398 billi on dollars. These costs were amortized over 15 years at a 7% annual cost of capital to derive the cents-per-gallon distribution capital cost estimate in Section 4.2.1 of this DRIA.

4.2.1.1.5 Ethanol Barge Capital Costs

As discussed in Section 1.6.4, we estima te that an additional 131 10,000 barrel capacity barges would be needed by 2022 to transport the additional volume of ethanol that we project would be used to meet the RFS2 standards (rel ative to the 13 BGY 2022 re ference case). Based on input from industry, we estim ate the cost of a new 10,000 barre l barge capable of transporting ethanol at 1.4 million dollars. Thus, we estimate that the total cost of barges needed to transport ethanol due to the RFS2 standards by 2022 w ould be 183 million dollars. These costs were amortized over 15 years at a 7% annual cost of capital to derive the cents-per-gallon distribution capital cost estimate in S ection 4.2.1 of this DRIA.

We understand that the tank barge industry is trending towards the use of tank barges with a carrying capacity of 30,000 barrels which te nd to less expensive on a per-barrel basis than 10,000 barrel capacity barges. T hus, our assumed use of a 10,000 barrel capacity barges likely results in a conservatively high es timate of ethanol barge costs.

4.2.1.1.6 Ethanol Tank Truck Capital Costs

As discussed in Section 1.6.5 of this DR IA, we estimate that an additional 1,241 8,000 gallon capacity tank trucks would be needed by 2022 to transport the additional volume of ethanol that we project would be used to meet the RFS2 st andards (relative to the 13 BGY 2022 reference case). Based on input from industry, we estimate that the cost of a new 8,000 gallon tank truck capable of transpor ting ethanol at 180 thousand do llars. This is based on a 110 thousand dollar cost for the tractor and a 70 thousand dollar cost fo r the tank trailer. Thus, we estimate that the total cost of tank trucks needed to transport ethanol due to the RFS2 standards by 2022 would be 223 million dollars. These costs we re amortized over 10 years at a 7% annual cost of capital to derive the cents -per-gallon distribution capital cost estimate in Section 4.2.1 of

580this DRIA. We used a 10 year amortization sche dule for tank trucks as opposed to the 15 year schedule used for other capital equipment to reflect their likely shorter service life.

4.2.1.1.6 E85 Retail Facility Costs

As discussed in Section 1.6.10 of this DR IA, we estimate that an additional 24,250 E85 retail facilities would be needed by 2022 as a result of the RFS2 standards (relative to the 13 BGY 2022 reference case). We estimated that 40% of these new facilitie s would install 2 new E85 pumps with four nozzles. We assumed that no existing equipment would be retrofitted to meet this demand. On average, we estimate th e cost per E85 facility would be 122,000 dollars as detailed below. This cost estimate includ es the use of E85 compatible hardware and compliance with applicable regulations regarding the storing/dispensing fuels.

The following cost estimates are based on input from gasoline retailers and other parties with experience in the requirements and costs asso ciated with installing E85 retail equipment. The total cost of installing a tw o nozzle E85 dispenser is estimated at 23,000 dollars. This is composed of 17,000 dollars for the dispenser it self, 750 dollars for hanging hardware, 950 for refueling island hardware, 3,000 for installation, and a 1,300 dollar contingency cost. Hanging hardware costs are composed of 310 dollars for 2 nozzles, 135 dollars for 2 breakaway connections, 135 dollars for 2 swivel connecti ons, and 170 dollars for 2 hoses. Refueling hardware costs are composed of 450 dollars for the dispenser island, 250 for an island sump pump, and 250 dollars for bumper posts. Instal lation costs are composed of 1,500 dollars for concrete removal and replacement, and 1, 500 dollars for wiring and piping.

The cost of automatic tank level gauging equi pment is estimated at 6,500 dollars. It is estimated that 65% of retailers would insta ll automatic tank gauging (A TG) equipment and the remainder would rely on manual means of determ ining the amount of fuel remaining in their underground storage tank. Thus , the average cost per faci lity would be 4,225 for ATG equipment. We estimate the cost of instal ling a canopy addition to provide cover for an additional dispenser at 15,000. We estimated th at only 10% of facilities would need to install additional canopy coverage in order to accommodat e the new E85 retail dispenser. Thus, the average canopy cost per facility is estimated at 1,500 dollars. The cost of installing a new 8,000 underground E85 storage tank system is estimated at 84,000 dollars. The cost of connecting the tank to the dispenser(s) is included in this co st along with other mi scellaneous storage tank related costs.

We proposed that a level be pl aced near each E85 nozzle whic h states the E85 is for use only flex-fuel vehicles. T TTTTTTTT Based on input from industry, the cost of compliance with this proposal would be approximately 5 dollars per la bel. New labels would be needed on all E85 dispensers, which totals approximately 385,000 dollars for the 76,900 E85 nozzles that we project would be in operation by 2022 under the RFS2 standards.

Based on the above, we estimate the cost of installing a new single dispenser E85 facility would be 113,000 dollars and the cost for a 2 disp enser facility would be 136,000 dollars. Based on our projection that 40% of facili ties would install 2 dispensers, we estimate an average cost of

TTTTTTTTT See proposed regulatory Section 80.1469.

581122,000 per new E85 facility. Thus , the total cost of the 24,250 new E85 f acilities that we project would be needed by 2022 due to the RFS2 program would be 2.96 billion dollars. These costs were amortized over 15 year s at a 7% annual cost of capita l to derive the cents-per-gallon distribution capital cost estimate in Section 4.2.1 of this DRIA. As discussed in Section 1.6.10 of this DRIA, this cost estimate is based on our assessment that it would be sufficient for one in four retail facilities to offer E85 in 70% of the country.

As discussed in Section 1.6.10, we performed several sensitivity analyses to evaluate the potential that additional dispensers or retail facilities might be needed to facilitate the use of the volume of E85 that we project would be needed by 2022 to support compliance with the RFS2 standards. Under one scenario, we assumed th at 3 E85 dispensers would be needed at each facility that sells E85 in order to provide a less aggressive throughput pe r nozzle level. Under this scenario, the 4,500 E85 retail facilities that we es timate would be in place by 2022 absent the RFS2 requirements would need to in stall an additional 2 dispensers. U UUUUUUUU Based on the above, the cost to these facilities would be 46, 000 dollars. We also assumed that 50% of new E85 facilities would convert one of its gasoline storage tanks to E85 service in addition to installing a new E85 storage tank. V VVVVVVVV Based on these assumptions and the cost information discussed above, the cost per new E 85 facility would be 166, 000 dollars. The total cost under this scenario would rise to 4.24 billion dollars.

We evaluated 3 additional sensitivity cases under which the total number of E85 facilities needed was increased by varying our estimates regarding what fract ion of retailers would need to offer E85 in a given area to provi de reasonable access and the fract ion of the count ry that would need to have reasonable access to E85. In all cases, we maintained the assumption that 40% of new E85 retail facilities would install two dispen sers while the remainde r would install only one dispenser. Under the first such scenario, we assumed that one-in-t hree retail facilities in a given area would need to offer E85 to provide reasonable access, while maintaining our estimate that reasonable access would be needed in 70% of the county. Unde r this scenario , an additional 33,835 E85 retail facilities woul d be needed by 2022 relative to the 13 BGY 2022 reference case at a total cost of 4.13 billion dollars. Under the second such sc enario, we assumed that one-infour retail facilities in a gi ven area would need to offer E 85 to provide reasonable access consistent with our primary case, but that r easonable access would be needed in 100% of the county. Under this scenario, an additional 36,573 E 85 retail facilities wo uld be needed by 2022 relative to the 13 BGY 2022 at a total cost of 4.46 billion dollars. Under the third such scenario we assumed that one-in-three retail facilities in a given area would need to offer E85 to provide reasonable access and that reasona ble access would be needed in 100% of the county. Under this scenario, an additional 50,264 E85 retail facilit ies would be needed by 2022 relative to the 13 BGY 2022 at a total cost of 6.13 billion dollars.

4.2.1.2 Ethanol Freight Costs

Our estimation of the cost of transporting et hanol from production/im port facilities to the various locations where it would be used is based on the freight co st estimates developed for the

UUUUUUUUU As discussed in Section 1.6.10 of this DRIA we assumed that these facilities would install only a single E85 dispenser (with 2 nozzles) absent the RFS2 program.
VVVVVVVVV This cost includes all the necessary changes to ma ke the tank and associated equipment E85 compatible.

582RFS1 final rule 922 A comprehensive study of the infras tructure requirements for an expanded fuel ethanol industry conducted for th e Department of Energy (DOE) in 2002 provided the foundation for our estimate of the freight costs to handle the increased vol ume of ethanol needed under the RFS1 program. 923 As noted earlier, we currently have a study underway through Oakridge National Laboratories (ORNL) to model the tran sportation of ethanol from production/import facilities to petroleum terminal s. The ORNL model optimizes freight flows over the rail, marine, and road distribution net while addressing the use of multiple shipping modes. We plan to use the resu lts of the ORNL study to adjust our ethanol freight cost estimates for the final rule.

Our estimation of ethanol freight costs is ba sed on our evaluation of where ethanol would be produced/imported and where it would be used unde r the RFS2 standards. Section 1.5 of this DRIA contains a discussion of our projections regarding where ethanol would be produced and the points of entry for ethanol imports to satisfy the RFS2 requirements. Section 1.7 of this DRIA contains a discussion of where we project ethanol would be used under the proposed RFS2 program. Overall ethanol freight costs were minimized by first filling demand within a state with ethanol produced within or imported into that state and then reaching out to areas that had an excess production capacity compared to in-state demand. We developed ethanol freight costs on a state-by-state basis for ethanol that is produced within and imported into the state, and ethanol that is shipped in from PADD2. A higher freight cost was assessed to rural versus urban areas for ethanol brought in from out -of-state. This is based on the premise that ethanol shipped to rural areas from out-of-state w ould typically either first be de livered to a hub terminal in an urban area (typically by unit train) for further ship ment to terminals in rural areas or would be shipped directly to terminals in rural areas via manifest rail car. W WWWWWWWW

In-state shipment of ethanol was assumed to be done by tank truck. Such in-state ethanol freight costs were developed based on a review of shipping distances and topography with the state and the trucking costs developed for the RFS1 final rule. Ethanol freight costs from PADD 2 production facilities to meet out-of-state demand were also based on the estimates developed for the RFS1 final rule for such shipments. 924 We made several changes to the RFS1 freight cost estimates for the purpose of our RFS2 analysis. The freight cost estimates for shipment to rural areas in several states (California, Hawaii, and Mi chigan) were increased re lative to those in the RFS1 rule based on our reanalysis of the difficult ies in meeting all of the rural demand in these states. In part, this is based on the premise that ethanol would need to travel farther and more widely to satisfy the volume re quirements under the RFS2 standard s in comparison to the RFS1 standards. We also considered freight costs for the upper and lo wer peninsulas of the State of Michigan separately for the purpos es of our RFS2 analysis, wher eas the State of Michigan was treated as a whole under the RFS1 analysis.

Following is a discussion of our derivation of the freight costs for ethanol shipped from PADD2 production centers to meet out-of-st ate demand. The 2002 DOE study contains estimated ethanol freight costs for each of the 5 PADDs. These estimated costs are summarized in the following Table 4.2-3. 925 A map of the PADDs is contained in Figure 4.2-1.

WWWWWWWWW A discussion of our assessment of which areas are rural versus urban is contained in Section 1.7 of this DRIA.

583Table 4.2-3. Estimated Ethanol Freight Co sts 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

Figure 4.2-1. PAD District Definitions.

The Energy Information Administ ration (EIA) translated the cost estimates from the 2002 DOE study to a census division basis. 926 A summary of the resulti ng (EIA) ethanol distribution cost estimates are contained in the following Ta ble 4.2-4. A map of the census divisions is contained in Figure 4.2-2.

584Table 4.2-4. EIA Estimated Ethanol Freight Costs (Derived from the 2002 DOE Study)

Census Division Freight Cost (cents per gallon) From To 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 We st 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

585Figure 4.2-2. Census Divisions

We took the EIA projections and translated th em into state-by-state freight costs for shipment of the ethanol produced in the East and West North Cent ral Census Divisions (corresponding closely to PADD 2) . Distribution outside of PADD2 was assumed to be by rail, or marine vessel. In the case of Alaska and Ha waii, differences in ethanol delivery prices from the mainland were inferred from gasoline prices. A single average freight cost for each destination census division was generated by wei ghting together the 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% to refl ect the increased cost of transportation fuels used to ship ethanol between the 2002 DOE st udy and the publication of the RFS1 FRM, and then additional adjustments were applied to some individual states based on their position within the census division. We believe that these es timates reasonably correspond to the 53 dollar per barrel of crude case that we used in other areas of our analysis. We were unable to develop an estimate of how an increase in cr ude cost would affect ethanol fr eight costs for this proposal.

586Freight costs are correlate d to the cost of the transportation fuel used by the transportation modes employed. Transportation fuel costs represent a different frac tion of overall freight costs for each of the different modes (rail, barge, tank truc k). We were unable to obtain sufficient data with which to assess the effects on ethanol freigh t costs of increased crude costs for all of the modes employed. There are significant competitiv e issues with respect to how these costs are passed along to customers, and the extent to which increases in transportation fuel costs must be absorbed by the shipper as opposed to being passed along to the customer due to market conditions. We intend to pursue this issue further so that we can develop an estimate of ethanol freight costs under the 92 per barr el of crude case that was used in other areas of our analysis.

For some states, different freight costs for ethanol supplied to large hub terminals versus small satellite terminals were estimated. The r easoning behind this is that large shipments of ethanol shipped from the Midwest by barge, sh ip, and/or unit train will often be initially unloaded at hub terminals for furt her 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 would also incur a higher freight rate than ethanol shippe d to a larger-volume “hub” termin al. The largest adjustment was applied to the Rocky Mountain States since th ey 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 distribu tion costs. The estimated additional freight cost of shipping ethanol to satellite terminals versus hub terminals is contained in the following Table 4.2-5. All areas were not assigned both hub and sa tellite terminal ethanol distribution costs. For example, all shipment to of ethanol to the upper peninsula of the State of Michigan was assumed to be to smaller volume terminals, since there is insufficient population density in the area to support a hub terminal.

Table 4.2-5. Additional Freight Cost to Deliver Ethanol to a Satellite Terminal Compared to a Hub Terminal

States* cents per gallon OH, MI (lower peninsula) 2 AL, AR, CA, 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

* States not listed were not assigned separate hub and satellite freight costs.

We also made adjustments to the DOE study derived freight cost estimates to better account for the cost of shipping ethanol from the production plant to th e rail head / marine terminal either for larg e volume shipment by unit train or mari ne shipment to hub terminals, or for shipment at single car rates via multiple-product trains directly to satellite terminals. Chicago is a primary ethanol gathering point from producer s for further distribution. A 4 cent per gallon conveyance fee is charged to account for delivery of ethanol from the production plant gate to

587the Chicago Board of Trade delivery point for ta king ethanol. This incl udes train shipments, loading costs, and other miscellaneous fees. Ba sed on this information, we added 4 cents per gallon to our ethanol freight estimates.

We assumed that these freight costs do not in clude the costs associated with the recovery of capital for the distribution f acility changes that are necessary to accommodate the increased volume of ethanol. This may tend to overstate di stribution costs to some extent because some capital recovery may already 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 ra il car lease fees incorporate a capital recovery and a profit margin.

Our resulting estimates of the state-by-state ethanol freight costs in 2022 are contained in Table 4.2-6. Based on these estimates and our projections regarding wh ere ethanol would be produced/imported and used, we estimate that the national average ethanol freight cost would be 11.3 cents per gallon.

Ethanol freight costs will va ry from year-to-year base d on where ethanol would be shipped versus where it would be produced. As the volumes of ethanol used increase over time, more ethanol would be used in rural areas that have higher dist ribution costs. Therefore, we believe that average ethanol fr eight costs would increase over tim e. Thus, basing our evaluation of ethanol freight costs on the 2022 case should provide a conservati vely high estimate of freight costs for earlier years. A disc ussion of how we estimated the volumes of ethanol used in each state from in-state versus out -of-state production/imports and the estimation of volume that would be shipped to urban versus rural areas is contained in Secti on 1.7.1 of this DRIA.

588Table 4.2-6. 2022 Estimated Ethanol Freight Costs

Destination % of Use From In-State Supply

(vs. Out-ofState Supply) Freight Costfor InState Supply

(cpg) % of Out-ofState Supply

Shipped to Urban Areas

(vs. Rural

Areas) Freight Costfor Out-ofState Supply

Shipped to Urban Areas (cpg) Freight Costfor Out-ofState Supply

Shipped to Rural Areas (cpg) Alabama 68 4.4 46 11.2 15.2 Alaska 0 NA 53 45.4 50.5 Arizona 5 5.4 76 19.4 24.4 Arkansas 76 5.4 37 11.3 16.3 California 26 4.4 94 20.5 24.5 Colorado 22 5.4 64 14.4 19.4 Connecticut 18 4.4 100 15.4 NA Delaware 0 NA 100 15.4 NA District of Columbia 0 NA 100 15.4 NA

Florida 29 4.4 84 12.4 16.4 Georgia 44 5.4 68 15.4 19.4 Hawaii 15 5.4 60 40.5 44.5 Idaho 34 5.4 29 19.4 24.4 Illinois 100 4.4 NA NA NA Indiana 100 4.4 NA NA NA Iowa 100 3.4 NA NA NA Kansas 100 4.4 NA NA NA Kentucky 16 5.4 47 6.2 10.2 Louisiana 100 4.4 NA NA NA Maine 96 5.4 61 17.4 21.4 Maryland 0 NA 89 15.4 15.4 Massachusetts 18 4.4 100 15.4 NA Michigan –LP 69 5.4 68 6.2 10.2 Michigan –UP 0 NA 0 NA 10.2 Minnesota 100 4.4 NA NA NA Mississippi 38 4.4 27 10.2 14.2 Missouri 98 4.4 58 6 8 Montana 64 5.4 9 17.4 22.4 Nebraska 100 4.4 NA NA NA Nevada 6 5.4 77 20.4 25.4

589Table 4.2-6. (continued) 2022 Estimated Ethanol Freight Costs

Destination % of use from in-State Supply

(vs. Out-ofState Supply) Freight Cost for in-State Supply

(cpg) % of Out-ofState Supply

Shipped to Urban Areas

(vs. Rural

Areas) Freight Costfor out-ofstate supply to urban areas (cpg) Freight Costfor out-ofstate supply to rural areas (cpg)

New Hampshire 91 4.4 67 16.4 16.4 New Jersey 18 4.4 100 15.4 NA New Mexico 8 4.4 33 16.4 21.4 New York 28 5.4 85 15.4 19.4 North Carolina 40 4.4 61 15.4 19.4 North Dakota 100 5.4 NA NA NA Ohio 77 5.4 74 6.2 10.2 Oklahoma 100 4.4 NA NA NA Oregon 84 5.4 57 20.5 24.5 Pennsylvania 20 5.4 72 12.4 16.4 Rhode Island 18 4.4 100 15.4 NA South Carolina 45 4.4 49 15.4 19.4 South Dakota 100 5.4 NA NA NA Tennessee 34 5.4 58 6.2 10.2 Texas 39 5.4 88 14.3 19.3 Utah 0 NA 72 17.4 22.4 Vermont 0 NA 0 NA 16.4 Virginia 17 5.4 68 15.4 19.4 Washington 38 5.4 60 20.5 24.5 West Virginia 67 5.4 28 15.4 19.4 Wisconsin 100 4.4 NA NA NA Wyoming 8 5.4 0 NA 21.4

4.2.2 Biodiesel Distribution Costs

As discussed in the following sections, we estimate that the total capital costs to support distribution of the additional volum e of biodiesel that would be used in response to the RFS2 standards would be 381 million dollars by 2022. When amortized, this translates to 9.8 cents per gallon of additional biodiesel attributed to the RFS2 standards. Amortization of capital costs was done over 15 years at a 7% annual cost of capital except in the cas e of the cost of tank trucks were a 10 year amortization schedule was used. Th ese costs were calculat ed relative to the AEO 2007 baseline which projects that 380 MGY of biodi esel would be used in 2022 absent the RFS2 standards. Under the RFS2 standards, we proj ect that an additional 430 MGY of biodiesel would

590be used by 2022 for a total of 810 MGY. Biodiesel freight costs are estimated to be 9.3 cents per gallon on a national average basis. X XXXXXXXX Thus, we estimate that total biodiesel distribution costs would be 19.1 cents per gall on of biodiesel attributed the RFS2 standards. The way in which we estimated these costs is detailed in the following sections.

As noted previously, we choos e not to subtract the distri bution costs for the petroleumbased fuels that would be displaced by the use of biofuels from our estimated biofuel distribution costs to help ensure a conservatively high es timate of biofuel dist ribution costs given the uncertainties in our analysis. We believe that the freight costs to ship petroleum-based fuels to the terminal are approximately 4 cents per gallon. If we were to subtract these costs from the estimated biodiesel distribu tion costs, the result would be 15.1 cents per gallon.

4.2.2.1 Capital Costs to Upgrade th e Biodiesel Distribution System

4.2.1.1.1 Summary of Biodiesel Distribution Capital Costs

A summary of biodiesel distribution capital costs is contained in Table 4.2-7. Our estimation of these costs is detailed in the following sections.

Table 4.2-7. Summary of Estimated Biodiesel Distri bution Infrastructure Capital Costs*

Million $
Fixed Facilities Petroleum Terminals Biodiesel Storage Tanks 129 Biodiesel Blendi ng and Miscellaneous Equipment 192
Mobile Facilities Rail Cars 35

Barges 17 Tank Trucks 8
Total Biodiesel Distri bution Capital Costs 381

* Relative to the AEO 2007 380 MGY 2002 reference case.

4.2.1.1.2 Petroleum Terminal Biodi esel Distribution Capital Costs

The facility upgrades that we project would be needed at petr oleum terminals to facilitate the distribution of the increased volume of biodiesel in respon se to the RFS2 standards are discussed in Section 1.6.9 of this DRIA. Total capital costs at terminals by 2022 relative to the BGY 2022 reference case are estimated at 321 million dollars.

XXXXXXXXX The projected shorter transportation distances on aver age for biodiesel compared to ethanol resulted in a lower per gallon freight cost for biodiesel compared to ethanol (11.3 cpg).

591As discussed in Section 1.6.9 of this DR IA, we estimate that a total of 1.84 million barrels of new biodiesel storag e would be needed at petroleum terminals due to the RFS2 standards, all of which would need to be sati sfied by new construction. Based on information from industry with respect to th e effect of increased steel pric es on storage tanks costs, we estimate that the cost of constr ucting new biodiesel stor age tanks would be 70 dollars per barrel. This is considerably higher than the 40 per ba rrel cost we estimated for construction of new ethanol tanks for two reasons. Bi odiesel tanks need to be heated /insulated in colder climes. Biodiesel tanks tend to be of considerably smaller size compared to ethanol tanks. Both of these factors contribute significantly to the cost per ba rrel of constructing a ne w storage tank. We estimate that the total cost at petroleum terminals of new biodi esel storage tanks would be 129 million dollars.

As discussed in Section 1.6.9 of this DRIA , we projected that 200 terminals would need to install biodiesel blending equipment by 2022 as a result of the RFS2 sta ndards (relative to the 380 MGY 2022 reference case). Based on input from industry, we estimated that the cost of biodiesel blending equipment woul d be 400 thousand dollars per term inal. The cost of additional piping is estimated at 60,000 per terminal. Miscellaneous costs associated with receiving/blending/stori ng biodiesel for the first time are estimated at 500 thousand dollars per terminal. Estimated equipment costs for handlin g biodiesel are higher th an those for similar equipment designed to handle ethano l due to the need for insulated/heated equipment in colder climes.

Summing all of these estimated costs, we arri ved at an estimate of the total cost for the biodiesel blending, storage, and other misce llaneous equipment that would be needed at petroleum terminals as a result of the RFS2 standards by 2022 of 321.1 million dollars. These costs were amortized over 15 year s at a 7% annual cost of capita l to derive the cents-per-gallon distribution capital cost estimate in Section 4.2.2 of this DRIA.

4.2.1.1.3 Biodiesel Rail Car Capital Costs

As discussed in Section 1.6.3 of this DRIA , we estimate that an additional 353 biodiesel tank cars would be needed by 2022 to transport th e additional volume of et hanol that we project would be used to meet the RFS2 standards (r elative to the 380 MGY 2022 reference case). Based on input from industry, we estimate that the cost of a new biodiesel tank car of 25,600 gallon capacity at 99,000 dollars. The estimated cost for a biodiesel rail car is 10% higher than that of an ethanol rail car to ac commodate the need for insulated/heated tanks in colder climes. Thus, we estimate that the total cost of the biodiesel rail tanks cars ne eded due to the RFS2 standards by 2022 would be 35 million dollars. These costs were amortized over 15 years at a 7% annual cost of capital to de rive the cents-per-gallon distribu tion capital cost estimate in Section 4.2.2 of this DRIA.

4.2.1.1.4 Biodiesel Barge Capital Costs

As discussed in Section 1.6.4, we estima te that an additional 11 10,000 barrel capacity barges would be needed by 2022 to transport the a dditional volume of biodiesel that we project would be used to meet the RFS2 standards (r elative to the 380 MGY 2022 reference case).

592Based on input from industry, we estimate that the cost of a new 10,000 barrel barge capable of transporting biodiesel at 1.54 million dollars. This estimated cost is 10% higher than the cost of a barge designed to transport etha nol due to the need for an insulated/heated tank in colder climes. Thus, we estimate that the total cost of barges needed to trans port biodiesel due to the RFS2 standards by 2022 would be 16.6 million do llars. These costs were amortized over 15 years at a 7% annual cost of capital to derive the cents-per -gallon distribution capital cost estimate in Section 4. 2.2 of this DRIA.

While the tank barge industry is trending to wards the use of tank ba rges with a carrying capacity of 30,000 barrels (which tend to be less expensive on a per-barrel basis than 10,000 barrel capacity barges), we belie ve the assumed use of 10,000 barre l barges is appropriate for biodiesel transport due to the relatively smaller volumes of biodiesel that we project would be used.

4.2.1.1.5 Biodiesel Tank Truck Capital Costs

As discussed in Section 1.6.5 of this DR IA, we estimate that an additional 1,241 8,000 gallon capacity tank trucks would be needed by 2022 to transport the additional volume of biodiesel that we projec t would be used to meet the RFS2 standards (relative to the 380 MGY 2022 reference case). Based on input from industr y, we estimate that the cost of a new 8,000 gallon tank truck capable of tran sporting biodiesel at 198 thousand dollars. This is based on an 110,000 dollar cost for the tractor and an 88,000 thous and dollar cost for the tank trailer. This estimate is 25% higher than the cost of a tank trai ler designed to transport ethanol due to the need for an insulated/heated tank in colder climes. T hus, we estimate that the total cost of tank trucks needed to transport biodiesel due to the RFS2 standards by 2022 would be 7.8 million dollars. These costs were amortized over 10 years at a 7% annual cost of capital to derive the cents-pergallon distribution capital cost estimate in Section 4.2.2 of this DRIA . We used a 10 year amortization schedule for tank trucks as opposed to the 15 year schedule used for other capital equipment to reflect their likely shorter service life.

4.2.2.2 Biodiesel Freight Costs

The distribution of biodiesel from production plants to petroleum terminals where it would be blended with diesel fuel is discussed in Section 1.6.3 of the DRIA. Tank truck was the assumed method of shipment for distances of le ss than 300 miles. Where distances are beyond 300 miles, shipment by manifest rail was assumed to be the preferred option other than in cases on the East coast where there were apparent barge routes from pr oduction to demand centers. In no case was it assumed that biodiesel would need to be shipped by multiple modes prior to delivery at the petroleum terminal.

Our estimation of biodiesel freight costs is based on our evaluation of where biodiesel would be produced and where it would be used unde r the RFS2 standards. Section 1.5 of this DRIA contains a discussion of our projections regarding where biodiesel would be produced to satisfy the RFS2 requirements. Section 1.7 of this DRIA contains a discussion of where we project biodiesel would be used under the propos ed RFS2 program. Demand for biodiesel in each state was established based on current and an ticipated future state biodiesel mandates and

593the anticipated demand for 2% biodiesel in most heating oil used in the Northeast by 2022. Y YYYYYYYY Production volumes from each plant we re assigned to meet these demands based on minimizing overall dist ribution distances (and cost). The production volume that was not consumed in meeting the anticipated demand from state mandates and to meet heating oil demand was assumed to be used within trucking distance of the production plant to the extent possible while maintaining biodiesel blend concentrations below 5%. This may tend to result in a conservatively high estimate of biodiesel freight costs since bi odiesel blend levels could exceed 5% in some areas. The remaining volume needed to match our estimated production volume was assumed to be shipped via manifest rail car or barge to the nearest areas where diesel fuel use was not already saturated with biodiesel to the 5% level. A 1,000 mile shipping distance was selected to ensure that all biodiesel not used to satisfy a state mandate, otherwise used in state, or used for bio-heat could find a market. It is lik ely that some fraction w ould not need to travel quite at far. Therefore, this assumption is also likely to result in a conservatively high estimate of biodiesel fr eight costs.

Our estimates of the freight costs for shi pping biodiesel by tank tr uck are based on the ethanol tank truck freight costs that we developed for the RFS1 final rule. Z ZZZZZZZZ These ethanol transport costs were incr eased by 10% to account for the in creased cost associated with preventing fuel gelling during cold conditions. Th e cost of shipping biodiesel by truck when the trip (or multiple trips) could be completed in a day was estimated to range from 7 to 8 cents per gallon. Some long truck transports were assume d to be necessary (up to 300 miles), where a round trip could not be completed in a single day. In such cases, the need for an overnight layover was assumed to add 120 dollars to shipping costs, resulting in an estimated 9.5 cents per gallon freight cost.

Our estimate of the cost of shipping biodiesel by manifest rail cars is based on publicly available biodiesel freight tariff information from BNSF railway from February 2008. 927 We used the BNSF fuel surcharge schedule to adjust our freight cost estimates to match our 53 per barrel of crude scenario. Specific tariff inform ation was not available for source/destinations needed for our analysis. A minimum cost of 9 cents per gallon was assumed to accommodate loading, unloading, and rail car lease costs. Based on the BNSF tariff information, we estimated that every 100 miles of additional shipment by manifest rail car beyond 600 miles adds 1.4 cents per gallon to shipping cost. Thus, for the a ssumed 1,000 mile shipping distance for biodiesel used to meet miscellaneous demand (i.e. not used to meet state mandates or for bioheat) the cost to ship by manifest rail car was estimated at 15 cents per gallon. Ba rge shipping costs were assumed to be comparable to the cost of shipping by manifest rail. This will tend to overstate barge shipping costs, since we understand that ba rge freight costs tend to be significantly less than rail freight costs. However, given that on ly 1% of biodiesel is projected to me moved by barge, this will have only a minimal effect on our overall estimation of biodiesel freight costs. We intend to seek out additional information re garding the cost of shipping biodiesel by barge over the routes we have identified so that we can update our biodiesel fr eight cost estimate for the final rule. Shipping distances were estimate d based on a review of bi odiesel production plant locations, demand centers, and the rail/barge transportation net.

YYYYYYYYY When biodiesel is blending into heating oil, the product is commonly referred to as bioheat.
ZZZZZZZZZ See Section 4.2.1.2 of this DRIA.

594Similar to the situation regarding our estimation of ethanol freight cost s, we were not able to satisfactorily adjust our freight cost estimate s across all modes in orde r to estimate biodiesel freight costs under the 92 per barrel crude cost scenario used in ot her parts of our analysis. We intend to pursue this analysis further so that we can provide an estimate of the impact on biodiesel freight costs of ch anges in transportation fuel costs in the final rule.

Due to the favorable match up in the location of biodiesel plants and biodiesel demand centers, 86% of biodiesel was projected to be shipped by truck, 13% was estimated to be shipped by manifest rail car, and 1% was estimated to be shipped by barge. We project that approximately 44% of the biodiesel production volu me in 2022 would be used in the state where it was produced to meet state mandates, satisfy th e demand for bioheat, or to meet other in-state miscellaneous demand. The average cost of sh ipping this volume by tank truck is estimated to average 8 cents per gallon. Approximately 3% of biodiesel production volume is estimated to be shipped out-of-state by manifest rail car to meet miscellaneous biodiesel demand at an average freight cost of 15 cen ts per gallon.

Approximately 54% of biodiesel production is projected to be shipped out of state to satisfy state mandates or bioheat demand which coul d not be satisfied with in-state production. We assigned portions of the produc tion volumes from states that had already satisfied this demand to meet this demand in other states based on minimizing overall shipping distances (and costs). A freight cost estimate was derived fo r each source and destina tion pair based on the fraction of the volumes that would be shipped by each mode and the freight cost for each mode used given the shipping distance. On average the cost of shippi ng biodiesel from out-of-state to satisfy state biodiesel mandates or the demand for bioheat is estimated at 10 cents per gallon. By weighting the biodiesel volumes used to sati sfy the three demand categories by the respective freight cost to ship that volume we arrived at a national average biodiesel freight cost estimate of 9.3 cents per gallon. Biodiesel freight costs are summarized in Table 4.2-8.

Table 4.2-8. Estimated Biodiesel Freight Costs Biodiesel Demand Category Fraction of Biodiesel Production Freight Cost (cpg) Shipped In-State to Satisfy In-State Demand 43% 8 Shipped Out-of-State to Satisfy State Mandates and Demand for Bioheat 54% 10

Shipped Out-of-State to Satisfy Miscellaneous Demand 3% 15

Total (National Average) 100% 9.3

4.2.3 Renewable Diesel Distribution Costs

As discussed in Section 1.6.1.3, there are comparatively few additional costs associated with distributing renewable diesel fuel because the majority is projected to be produced at petroleum refineries and it can be shipped fungibly with petroleum-based diesel fuel. As discussed in Section 1.6.5 of th is DRIA, we estimate that 8 additional 8,000 gallon capacity tank trucks would be needed by 2022 to transport the a dditional volume of renewable diesel fuel that

595we project would be used to meet the RFS2 st andards from stand-alone production facilities to petroleum terminals (relative to the 0 MGY 2022 reference case). Based on input from industry, we estimate that the cost of a new 8,000 gall on tank truck capable of transporting renewable diesel fuel at 180 thousand dollars. This is based on a 110 thousand dollar cost for the tractor and 70 thousand dollar cost for the tank trailer. Thus, we estimate that the total cost of tank trucks needed to transport renewable diesel fu el due to the RFS2 standards by 2022 would be 1.3 million dollars. These costs were amortized over 10 years at a 7% annual cost of capital. This translates to 0.2 cents per gallon of the renewabl e diesel that would be produced at stand-alone facilities and hence need to be transported separately (125 mill ion gallons by 2022). We used a 10 year amortization schedule for tank trucks as opposed to the 15 year schedule used for other capital equipment to reflect thei r likely shorter service life.

As discussed in Section 1.6.4 of this DRIA, we estimate that renewable diesel fuel which does need to be transported separately from pe troleum-based diesel fuel (because it is produced at stand-alone facilities not loca ted at a refinery) would be shi pped by tank truck to a terminal facility where it would be blended directly with diesel fuel. The freight cost for this truck transport is estimated at 5 cents per gallon. Thus, the total cost of distributing re newable diesel fuel is estimated at 5.2 cents per gallon, and this only applies to the rene wable diesel that would be produced at stand-alone facilities.

As noted previously, we choose not to subtract the distributio n costs for the petroleum-based fuels that would be displaced by the use of biofuels from our es timated biofuel distribution costs to help ensure a conservatively high estimate of biofuel distributi on costs given the uncertainties in our analysis. We believe that the freight costs to ship petroleum-based fuels to the terminal are approximately 4 cents per gallon. If we we re to subtract these costs from the estimated renewable diesel distributi on costs, the result would be 1.2 cents per gallon.

4.2.4 Potential Fuel Retailer Costs to Faci litate the Use of Mid- Level Ethanol Blends

The potential changes that may be needed to th e fuel distribution system to facilitate the introduction of mid-level ethano l blends are discussed in Sec tion 1.6.11 of this DRIA. As discussed in Section 1.6.11, inve stigations are currently underway to evaluate the compatibility of existing retail fuel storage and dispensing equi pment with Mid-level etha nol blends. If suchequipment is found to compatible or to require only modest modification to accommodate midlevel ethanol blends, then the costs could be sm all. Even if some pieces of the above ground equipment are found to be incompatible with mi d-level ethanol blends, it may be possible to replace such equipment through normal attrition. For example, the “hanging hardware” on a fuel dispenser has a normal service lif e of only 3 to 5 years. Othe r components have longer service lives. The pump dispenser has an average serv ice life of 20 years, with up-scale marketers replacing their dispensers more frequently and lo wer tier marketers retaining their dispensers for as long as 40 years. Storage tank systems have a service life of approximately 40 years. Thus, depending on the type of equipment that might need to be replaced, waiting for its replacement through normal attrition could delay the widespr ead introduction of mid-level ethanol blends.

If the extensive changes are n eeded, and particularly if conc rete needs to be broken, the cost to introduce mid-level ethanol blends at retail could potential be large. To provide an upper

596bound on the potential, we used cost estimates that we compiled for E85 capable equipment. It may be possible that even if current equipment needs to be replaced in order to accommodate mid-level ethanol blends, that such equipmen t might not need to be manufactured to the specifications necessary to be E85 capable. If this were the case, the costs of such equipment may be lower than comparable E85-capable equipment.

We estimate that the cost of replacing the fuel-wetted components of a fuel dispenser with E85-capable equipment to be as much as $ 12,000 per dispenser. This includes the cost of the hanging hardware (at $750 for 2 nozzles, hoses, and break-away connections), the cost of the dispenser’s “hydraulic tree” (at $10,000), and installation costs ($1,250). A AAAAAAAAA Based on gasoline retailer survey data, we es timate that there is average of 3.9 gasoline dispensers at each gasoline retail facilities. 928 It was estimated that in 2007 there were 164, 292 gasoline retail facilities in the U.S. 929 Thus, we estimate that the co st of retrofitti ng all 641,000 gasoline pumps at $7.7 billion. The cost would be considerably higher if the complete underground storage tank would need to be replaced. The cost of replacing an ex isting storage tank is estimated at $84,000. Thus, the co st of replacing a single tank at each retail facility would result in an additional cost of $13.8 bill ion for a total cost of $21.5 billion. Many retail facilities have multiple storage tanks in order to keep pace with fuel sales. The replacement of such storage tanks would further increase the potential cost.

The introduction of a mid-level ethanol blend would not eliminate the need for the use of additional E85 (and additional E85 refueling faci lities) to facilitate the consumption of the projected ethanol volumes needed to comply with the RFS2 standards. B BBBBBBBBB However, it could delay and to some extent reduce the need for additional E85 refu eling facilities. To evaluate the potential effect on co sts of the reduced need for addi tional refueling facilities that might result from the introduction of a mid-level ethanol blend, we evaluated a scenario where reasonable access to E85 would only be needed in 50% of the nation. Under our primary E85 retail facility case, we assumed that reasonable access to E85 would be needed in 70% of the country, and that 40% of new E85 facilities ha ve 2 E85 pumps and the remainder would have 1 E85 pump. C CCCCCCCCC If we were to assume that to r easonable E85 availability in 50% of the nation while maintaining the same assumptions re garding the number of pumps per facility, the total cost of new E85 refueling f acilities would be 2 billion dolla rs. This is approximately one billion dollars less than under our primary case wh ere reasonable access is assumed to be needed in 70% of the country.

We evaluated a sensitivity case from our pr imary E85 retail facility cost estimate under which it was assumed that 3 pumps would be needed at each E85 retail facility to maintain the needed E85 throughput. Under this sensitivity case, if we to assume that reasonable E85 availability would only be needed in 50% of the country, the total co st of additional E85 refueling facilities needed due to the RFS2 st andards would be reduced by approximately 1.4 billion dollars.

AAAAAAAAAA The shell of fuel dispenser and the electronic components would remain unchanged.
BBBBBBBBBB A discussion of the projected ethanol use levels under the RFS2 standards is discussed in Section 1.7.2 of this DRIA.
CCCCCCCCCC See Section 4.2.1.1.6 of this DRIA for a discussion of E85 retail facility costs under the RFS2 program.

597598 

### 4.3 Reduced Refining Industry Costs

As renewable and alternative fuel use increa ses, the volume of petroleum-based products, such has gasoline and diesel fuel, would decrease. This reduction in finished refinery petroleum products is associated with a re duction in refinery industry costs. This cost reduction would essentially be the volume of fuel displaced multip lied by the cost for produc ing the fuel. There is also a reduction in capital costs which is importa nt because by not investing in new refinery capital, more resources are freed up to build plants that produce renewable and alternative fuels.

Although we conducted refinery modeling for estimating the cost of blending ethanol, we did not rely on the refinery model results for estimating the volume of displaced petroleum. Instead we conducted an energy ba lance around the increased use of renewable fuels, estimating the energy-equivalent volume of gasoline or dies el fuel displaced. This allowed us to more easily apply our best estimates for how much of the petrol eum would displace imports of finished products versus crude oil for our energy security analysis which is discussed in Chapter 15 of the DRIA.

As part of this analysis we accounted for the change in petroleum demanded by upstream processes related to addi tional production of the renewable fuel s as well as reduced production of petroleum fuels. For example, growing corn used for ethanol producti on requires the use of diesel fuel in tractors, which reduces the vol ume of petroleum displaced by the ethanol. Similarly, the refining of crude oil uses by-produ ct hydrocarbons for heati ng within the refinery, therefore the overall effect of reduced gasoline a nd diesel fuel consumption is actually greater because of the additional upstream effect. We used the lifecycle petroleum demand estimates provided for in GREET model to account for the ups tream consumption of petroleum for each of the renewable and alternative fuels, as well as for gasoline and diesel fuel. Although there may be some renewable fuel used for upstream en ergy, we assumed that this entire volume is petroleum because the volume of renewable and alte rnative fuels is fixed as described in Chapter 1 above.

For this proposed rule, we assumed that a portion of the gasoline displaced by ethanol is imported, while the other portion is produced fro m domestic refineries. The assumption we made is that one half of the et hanol market in the No rtheast, which comprises about half of the nation’s gasoline demand, would displace importe d gasoline or gasoli ne blend stocks. Therefore, to derive the portion of the new renewable fuels whic h would offset imports (and not impact domestic refinery production), we mu ltiplied the total volume of petroleum gasoline displaced by 50 percent to repr esent that portion of the ethanol which would be used in the Northeast, and 50 percent again to only account for that which woul d offset imports. The rest of the ethanol, including half of the ethanol presumed to be used in the Northeast, is presumed to offset domestic gasoline production. In the case of biodiesel a nd renewable diesel, all of it is presumed to offset domestic diesel fuel productio n. For ethanol, biodiesel and renewable diesel, the amount of petroleum fuel displaced is estimat ed based on the relative energy contents of the renewable fuels to the fuels which they are di splacing. The savings due to lower imported gasoline and diesel fuel is accounted for in the en ergy security analysis c ontained in Chapter 15.For estimating the reduced U.S. refinery costs, we multiplied the estimated volume of domestic gasoline and diesel fuel displaced by the wholesale price for each of these fuels, which are $1.57 per gallon for gasoline, and $1.61 per gallon for diesel fu el at $53/bbl crude oil, and $2.67 per gallon for gasoline, and $3.35 per gallon fo r diesel fuel at $92/bbl crude oil. For the volume of petroleum displaced upstream, we valued it using the wholesale diesel fuel price. Table 4.3-1 shows the net volumetric impact on th e petroleum portion of ga soline and diesel fuel demand, as well as the reduced refining costs for 2022.

Table 4.3-1. U.S. Refinery Cost Reduc tion for the RFS2 Fuel Program in 2022 Total Volume Displaced

(billion gallons) Refining Cost Reduction at $53/bbl crude oil price (billion dollars) Refining Cost Reduction at $92/bbl crude oil price (billion dollars) Upstream Petroleum -0.8 -$1.3 -$2.7 End Use Gasoline 10.4 $16.3 $27.7 Diesel Fuel 0.6 $0.9 $1.9 Total

- $15.9 $26.9



### 4.4 Overall Costs to Gasoline and Diesel Fuel

The previous sections of this chapter have presented estimates of the cost of producing and distributing ethanol, biodiesel and renewable di esel fuel. In this section, we summarize the results of refinery modeling conducted by Jacobs Consultancy under contract to EPA for using those biofuels. 930 Jacobs’s used the Haverly Linear Pr ogramming (LP) model to conduct the analysis. This model is widely used by the refi ning industry, consultants, engineering firms and government agencies to analyze refinery economi cs, refinery operations, fuel quality changes, refinery capital investments, environmental ch anges and demand changes. The Haverly model uses Jacobs’s Refining Process Technology Da tabase 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 fi xed operating costs, the va riable costs, and the total of all these costs. Becau se of the significant increase in crude oil prices in mid-2008, we estimated the costs of this program at two diffe rent crude oil prices. The low price is $53/bbl which represents the reference crude oil price estimated by the Energy Information Administration (EIA) for its 2008 Annual Energy Outlook (AEO). 931 The high price is $92/bbl which represents the high crude oil price esti mated by EIA for its 2008 AEO. We describe the methodology for adjusting the cost of the RFS2 standard further below in the methodology section.

4.4.1 Description of Refinery Mode ling Cases Modeled and Methodology

599The refinery modeling was set up to anal yze the volumes required by the RFS as described in Chapter 1. The primary renewable fuel modeled was ethanol in gasoline, and we considered a small amount of biodiesel and rene wable diesel as required under EISA. While the addition of ethanol was modeled wi th the refinery model to captur e the blending costs of using a large amount of additional ethanol, the projecte d additional volme of biodiesel and renewable diesel was not captured by the refinery model. Instead, the blending of biodiesel and renewable diesel was captured outside the refinery model, although we modeled a sl ightly lower the energyequivalent volume of diesel fuel based on the addition of the biodiesel and renewable diesel volume.

Jacobs conducted a Linear Programming modeling analysis of the refining industry for the various RFS scenarios using a model develo ped by Haverly’s LP technology. The modeling was set up to analyze the extent to which etha nol will be used in conventional gasoline and reformulated gasoline by region and the result ing effects on gasoline co mposition. The refining industry was modeled based on fi ve aggregate complex refining regions, representing Petroleum Administration for Defense Districts (PADD) 1, 2, 3, 4 & 5 together minus California, and California separately. All of the PADDS were modeled simultaneously together in the LP model which allowed the refinery model to most effi ciently rebalance the re gional gasoline production volumes in response to the addi tion of the renewable fuels.

The refinery modeling was conducted in three di stinct steps which involved a base case, a reference case and a control case.

4.4.1.1 Base Case

The first step involved the establishmen t of a 2004 base case which calibrated the refinery model against 2004 volumes, gasoline qualit y, and refinery capital in place. We chose 2004 because the following year, 2005, as well as the beginning of 2006, were affected by hurricanes and would not be representative of a typical year, and 2007 data was not yet available when we started the analysis. Refinery unit cap acities from the Oil and Gas Journal and Energy Information Administration (EIA), as well as re finery feedstock and product volumes from EIA data were entered into the refinery model. Th e refinery model was then run and the resulting gasoline quality compared and calibrated to actual gasoline quality data information from EPA’s Reformulated Gasoline data base.

4.4.1.2 Reference Case

The reference case is a business-as-usual case that serves as a reference to the control cases. Thus, the year of analysis for the referenc e case must be the same as that used for control cases. Because the RFS2 fuel standard beco mes fully implemented in 2022, we conducted our reference and control cases analysis in 2022. Two categories of adjustments were made to the base case refinery models to en able modeling the refini ng industry in 2022 for the reference case. First, the change in certain inputs assumptions such as pr oduct volumes and energy prices needed to be projected. U.S. re finery gasoline, diesel fuel and jet fuel demand were projected to grow to meet increased demand. These projecte d volumes were used for establishing finished product which then led to refinery production for each PADD to meet that increased demand.

600This projected growth in U.S. refinery production is entered into the refe rence case version of the LP refinery model.

The refinery modeling was conducted using th e projection of crude oil and product prices in 2022. Crude and other input prices were base d on Jacobs’ projection of refinery margins and crude prices in 2022 cases, which wa s also based on the historical price spreads of fuels between the PADDs, using information fr om EIA’s 2004 price information tables, Platts, and AEO 2006. The average price of crude oil was projected to be about $51 per barrel, although crude oil prices varied by PADD. For the reference case as well as for the control cases, we assumed the same crude oil and product prices. The crude oil pric es and summertime and wintertime prices for gasoline, diesel fuel and jet fuel are summarized in Table 4.4-1.

Table 4.4-1. Crude Oil and Finished Pr oduct Prices used in Refinery Modeling PADD 1 PADD 2 PADD 3 PADD 4/5 CA excl. California

Crude Oil ($/bbl) Yearround Average 53.6 53.0 50.6 51.8 50.2

Reformulated Gasoline (c/gal) Summer 175.2 173.5 170.8

- 186.7 Winter 161.4 158.7 155.3

- 173.7

Conventional Gasoline (c/gal) Summer 167.6 165.1 161.4 173.6

- Winter 154.0 151.8 148.5 160.4

-

Diesel Fuel (c/gal) Summer 156.6 157.6 154.6 166.6 164.6 Winter 162.6 163.6 160.6 172.6 170.6 Jet Fuel (c/gal) Summer 158.6 158.6 156.6 164.6 164.6 Winter 156.6 156.6 154.6 162.6 162.6

We also modeled the implementation of several new environmental programs that will have required changes in fuel quality by 2022, including the 30 ppm aver age gasoline sulfur standard, the 15 ppm cap standards on highway and nonroad diesel fuel, the Mobile Source Air Toxics (MSAT) 0.62 volume percent benzene st andard. Although there may still be a small amount high sulfur diesel fuel, we assumed that a ll distillate fuel would be ultra low sulfur in compliance with the 15 ppm cap standard. We modeled the implementation of the 2005 Energy bill, which by rescinding the RFG oxygenate standard, resulted in the discontinued use of MTBE, and a large increase in the amount of etha nol blended into reformulated gasoline. We modeled 13.2 billion gallons of ethanol in the gasoline pool (starch etha nol, cellulosic ethanol and sugar ethanol) and 0.3 billion gallons of biodi esel and renewable diesel fuel in the diesel pool for 2022, which is the “business-as-usual” volume as projected by AEO 2007. 932 However, we assessed the growth in energy demand usi ng AEO 2008 which modeled the EISA Corporate Average Fuel Economy (CAFE) standards in the re ference case because it will be phasing-in in parallel with the phase-in of the RFS2. 933 For the reference case, th e refinery model blended the ethanol as E10 as the refinery model did not find it economical to ble nd any ethanol as E85 (Although the refinery modeling did not capture t oday’s small volume of E85 in the reference

601case, not capturing this volum e does not appreciably impact the results of the refinery modeling.).

4.4.1.3 Control Case

The third step, or the control case, invol ved the modeling of the 34 billion gallons of ethanol and 1 billion gallons of biodiesel and renewable diesel to comply with the RFS requirements in 2022 when the proposed renewable fuels program is fully phased-in. The cost and other implications of this fully phased-in control case are co mpared to the reference case to assess the cost of the program. We also modeled two other cases which served as sensitivities to our primary control case. The first is a 100 per cent E10 case which modele d the saturation of the gasoline pool with 10 per cent ethanol, but did not require additional volumes of ethanol that would force the use of E85. The second case requ ired the same volumes of renewable fuels as our primary control case, except that ethanol was allowed to be blended at 20 percent. We ran these sensitivity cases to isolate aspects of the primary control case. We ran the E10 case to isolate the effects of E10 blending from that of E85 on gasoline fuel properties and costs. We ran the mid-ethanol blend sensitiv ity case to assess what might ha ppen to gasoline fuel properties and costs if a waiver were to be granted for mi d-ethanol blends as some have suggested. These cases are summarized below at the end of this section.

The principal change in renewa ble fuel volumes modeled, relative to the reference case, was a large increase in ethanol blended into gaso line. Table 4.4-2 belo w summarizes the volume of biofuels estimated to be used in the year 2022 for the reference case and the control
D DDDDDDDDD case.

Table 4.4-2. Summary of Reference and Control Case Volumes in 2022 (billion gallons) Reference Case Control Case Difference Corn Ethanol 12.3 15 2.7 Cellulosic Ethanol 0.2 16 15.8 Imported Ethanol 0.4 3.1 0.27 Biodiesel 0.27 0.70 0.43 Renewable Diesel 0 0.19 0.19 Total 13.5 36 22.5

The gasoline and diesel fuel product energy output for each control and sensitivity case modeled was maintained the same as that for the reference case. Maintaining constant energy output assumes that vehicle miles traveled woul d remain the same between the various cases despite any change in gasoline and diesel fuel pr ices caused by the use of renewable fuels. In reality the increased use of renewa ble fuels may result in changes to fuel prices to consumers, either directly as estimated in this section, or indirectly by affecting world oil prices as discussed in Chapter 15. However, our analysis was conducte d in parallel without th e ability to input the

DDDDDDDDDD The renewable fuels cost modeling assessed the impact of biodiesel fuel costs not including 0.11 billion gallons of corn oil made into biodiesel fuel, which is included in the volumes summary shown in Tables 1.2­ 1.

602results of the other analysis. Fu rthermore, it is difficult to predic t the impacts on fuel prices to consumers, especially in light of the federa l tax subsidies which we accounted for in our analysis, and the many and diverse state tax subsidies which we did not attempt to account for. Maintaining constant fuel produc t energy output captures the capital cost differences between the cases. Table 4.4-3 below summarizes the volume s of gasoline and diesel fuel used for the reference case, the primary contro l case and two sensitivity cases. E EEEEEEEEE

Table 4.4-3. Volumes of Gasoline and Diesel Fu el Used in Refinery Modeling (Billion Gallons/yr) Reference Case Primary Control Case Sensitivity Cases

13.2 Bgals Ethanol as E10 35 Bgals of E10 & E85 E10 35 Bgals of E10, E20

& E85 Gasoline Volume (Gasoline and Ethanol) 169.3 176.7 171.4 176.7

Diesel Volume (Diesel Only) 69.3 68.5 68.5 68.5

All the other environmental and ASTM fuel quality constraints modeled in the reference case described above are assumed to apply to the control case as well. Th e reference and control cases where modeled assuming that ethanol CG blends are entitl ed to the current 1.0 psi RVP waiver during the summer (i.e., for all 9.0 RVP and many low RVP control programs) so as to assess the impact on summertime butane removal. The crude oil and product prices for the control case were the same as the reference case. The capital investments made for the reference case are not assumed to be sunk when the refine ry model is assessing the economics for capital investments for the various control cases. Thus, the refinery model is free to optimize the capital investments made for each control case incrementa l to the base case. The control cases are run with capital costs evaluated at a 15 percent rate of return on invest ment (ROI) after taxes, but are then adjusted post-modeling to a 7 percent ROI before taxes.

4.4.1.4 Ethanol Blending and Prices

A special procedure was set up in the refine ry model to capture the costs of blending ethanol. Because ethanol is primarily produced in the Midwest, but distributed to the final terminals where it is blended with the gasoli ne (or gasoline blendstock for blending with ethanol), hypothetical terminals were set up in each PADD within the refinery model which would receive the shipped ethano l as well as the gasoline blends tock for blending with ethanol

EEEEEEEEEE The refinery modeling was run assuming about 20 billion gallons more gasoline demand and 8 billion gallons more diesel fuel demand than what AEO 2008 projects for the year 2022. This probably had only a very minor impact on the relative costs of the control cases co mpared to the reference case because the relative volume differences between the cases (considering the additions of renewable fuel s) were modeled as intended. The actual AEO 2008 gasoline and diesel fuel volumes were used as the basis to estimate the per-gallon costs.

603(also referred to as conventi onal blendstock for oxygenate bl ending (CBOB) for conventional gasoline and reformulated blendstock for oxyge nate blending (RBOB) for RFG and CARFG). The gasoline blendstock either comes from the sa me PADD where the terminal is located, or transferred from a different PADD. This refine ry modeling technique helps to more correctly estimate the distribution costs for both the ethanol and the gasoline. The refinery model assessed ethanol’s use in each PADD based on its price rela tive to CG and RFG, which is based on its production cost and distribution costs, and its blending economics. For the base case we assumed that ethanol would be splash blended into gasoline. But by 2022 we expect that most, if not all, of the ethanol will be octa ne match-blended for blending up E10.

The price of ethanol used in the refere nce case, the primary control case and the sensitivity cases was based on the 2004 yearly average price spread between regular conventional gasoline sold into the spot market in Houston and ethanol sold on the spot market on Chicago Board of Trade (CBOT). This was used to determine a Midwest ethanol production price. To derive ethanol prices for all othe r PADDs outside the Midwest, the Midwest ethanol production price was then adjusted for transportation costs to de liver ethanol from the Midwest to end use terminals (see Secti on 4.2 for additional details). This assumes that the Midwest ethanol market will continue to set the price for ethanol – a reasonable assumption considering the significant amount of corn and other biomass available in the Midwes t. The sales prices assigned to ethanol are summarized in Table 4.4-4.

Table 4.4-4. Ethanol Prices used in Refinery Modeling PADD 1 PADD 2 PADD 3 PADD 4/5 CA excl. California

Summer 159.9 153.3 163.2 168.3 168.5 Winter 152.5 145.9 155.8 160.9 161.1

After the refinery modeling was completed, the ethanol prices and the costs for each case were adjusted to reflect the ethanol production an d distribution costs descri bed above in Sections 4.1 and 4.2. The ethanol produc tion cost is the volume-weighted average for ethanol sourced from corn, cellulose and imports. The combined ethanol production cost and combined biodiesel and renewable diesel costs for 2022 for the refere nce crude oil price and the high crude oil price scenarios are shown in Table 4.4-5. The biodies el and renewable diesel fuel production costs are principally comprised of the feed stock costs from the FASOM agri cultural model, and this model estimated lower feedstock costs at the higher crude oil price. While this outcome initially seems unlikely, it could be possible based on change s in production levels of some agricultural products, so we opted to use the FASOM feedst ock costs and the lower production costs at the higher crude oil price.

604Table 4.4-5. Average Production Cost for Ethanol, Biodiesel and Renewable Diesel Fuel Crude Oil Price $53/bbl $92/bbl Production Cost (c/gal) Production Cost (c/gal) Volumetric Fraction Corn Ethanol 147 154 0.13 Cellulosic Ethanol 138 143 0.75 Imported Ethanol 169 171 0.12 Volume Weighted Cost 143 148 1.00

Biodiesel 270 268 0.69 Renewable Diesel Fuel 210 208 0.31 Volume Weighted Cost 251 249 1.00

The combined ethanol production and distribut ion costs and the biodi esel and renewable diesel production and dist ribution costs are summarized below in Table 4.4-6. We provided two sets of costs which represent th e projected costs at the $53/bbl crude oil price and at the $92/bbl crude oil price.

Table 4.4-6. Ethanol Costs used in th e Cost Analysis Post-Refinery Modeling Ethanol Costs Bi odiesel Renewable Diesel Region PADD 1 PADD 2 PADD 3 PADD 4/5 excl. CA CA U.S. U.S.

$53/bbl Crude Oil Price Production Cost 143 143 143 143 143 270 210

Distribution Cost 19.4 12.1 17.5 23.6 23.4 18.2 5.1

Total Cost 162.4 155.1 160.5 166.6 166.4 288.2 215.1 $92/bbl Crude Oil Price Production Cost 148 148 148 148 148 268 208

Distribution Cost 21.3 13.3 19.3 26.0 25.7 20 5.6

Total Cost 169.3 161.3 167.3 174 173.7 288.0 213.6

The ethanol production and dist ribution costs summarized in Ta ble 4.4-6 are different in value compared to the ethanol prices used in the refinery modeling summarized above in Table 4.4-4. To capture the social costs of the RFS 2 program, we adjusted the initial costs of the refinery modeling cost analysis using the ethano l production and distribution costs. This cost adjustment was made by multiplying the difference in ethanol cost or price between Tables 4.4-6 and 4.4-4 by the difference in ethanol volum e modeled between the control case and the reference case.

We also estimated the costs of the RFS 2 program taking into account the consumption

605subsidies for corn ethanol, cellulosic ethanol, bi odiesel and renewable diesel fuel. While these subsidies conceal large portions of the program costs, their economic effects deserve to be understood.

4.4.1.5 E85 Blending and Prices

We conducted the refinery modeling assuming th at E85 will be blended differently in the future compared to how it is blended today. Today E85 is blended at 85 percent by volume in the summer and at 70 percent by volume in the wi nter. Ethanol must be blended at less than 85 percent in the winter because of ethanol’s lo w blending Reid Vapor Pressure (RVP). Unlike when ethanol is blended at 10 pe rcent and causes a large vapor pressure increase, when ethanol is blended at 85 percent it blends much closer to its very low neat blendi ng RVP of 2.2. When ethanol is blended with gasoline at the terminal, the available gasoline blendstock must be used. This blendstock is either a conventional bl endstock for oxygenate blending (CBOB) or reformulated blendstock for oxygenate blending (RBOB) so that when this gasoline-like material blended with ethanol at 10 percent, the final blend complies with the local gasoline regulations. For example, reformulated gasoline (RFG) must comply with the hydrocarbon standard of the RFG program and therefore RFG tends to have an RVP of 6.8, and the RBOB that is blended with ethanol has an RVP of about 5.8. When this 5.8 RVP RBOB is blended with ethanol to make E85, its final RVP is estimated to be 5. 05. However, the RVP minimum specified in the E85 ASTM standard shows the summertime lower li mit of E85 is 5.5, thus, blending RBOB with 85 percent ethanol would not comply with th e ASTM lower RVP standard. Table 4.4-7 summarizes ethanol’s blending RVP, gasoline’s RVP and the final blend RVP for summertime RFG and CG gasoline, and compares the blends RVP values to that of the E85 ASTM RVP standards. 934

Table 4.4-7. Comparison of E85 RV P levels to the ASTM RVP Standard Summer E85 Winter E70 Gasoline RVP a CG 10.0 RFG & low RVP 6.8 ASTM Std CG/RFG 14 ASTM Std

Gasoline Blendstock RVP 9.0 5.8 13

Ethanol Blending RVP 4.9 4.9

- 6.5

Blend RVP 5.67 5.05 5.5 8.5 9.5

a Summertime CG is allowed a 1 psi waiver for bl ending with ethanol, however, RFG and some low RVP areas and wintertime CG/RFG do not receive 1 psi waivers.

Table 4.4-7 shows that summertime RFG and wintertim e gasoline cannot meet the ASTM RVP minimum standards based on blending et hanol with the locally available gasoline blendstock for blending up E10. For this reason, we ran the refinery model assuming that all E85 will also be blended with some butanes or pe ntanes (whichever is available from the nearest refineries) to bring E85 up to the maximum ASTM RVP standard, in addition to the CBOB or

606RBOB being supplied to the local area. The ma ximum ASTM E85 RVP standard is 8.5 in the summertime and 12.0 in the wintertime.

E85 is expected to be priced lower in the marketplace than E10 and even less relative to gasoline (E0) because of E85’s lower energy de nsity. E85 contains about 77,900 BTUs per gallon compared to E10 which contains about 11 1,300 BTUs per gallon. Thus, when consumers consider refueling their vehicle us ing E85, they will bypass using it un less if it is priced at parity with gasoline on an energy basis. Parity pric ing means that E85 would have to be priced 25 percent lower than E10. Assumi ng that E85 is priced 25 percent lower than E10 at retail to account for the energy content differences, the pricing disparity between et hanol and gasoline is even greater at the terminal. Table 4.4-8 summ arizes the pricing of E 85 at retail and at the terminal where ethanol is usually blended into gasoline blendstock to create the E85. Retail markup averages about 10 cents per gallon. 935 Federal and state taxes average 46 cents per gallon (although this varies signifi cantly by state), and transportation from the terminal to retail averages 3 cents per gallon. 936 Thus, if E10 gasoline is pri ced 163 cents per gallon at the terminal, it would be priced at 223 cents per ga llon at retail. Based on E85’s 25 percent lower energy density, E85 would have to be priced at 167 cents per gallon at retail to reflect its lower energy density. Using the same terminal to reta il costs/taxes, E85 would be priced at 107 cents per gallon at the terminal. All this is shown in Table 4.4-8. The bottom row of the table shows what ethanol (E100) would have to be price at for terminals to breakeven using ethanol in E85 (this assumes that E85’s gasoline blendstock is pr iced the same as E10 at the terminal). Based on ethanol’s energy content alone, ethanol would have to be discounted significantly compared to gasoline for refiners to find it cost-effective to use.

Table 4.4-8. E85 Pricing at Retail and at Terminals (cents per gallon) Price at Retail Retail Markup Average Federal and State Taxes Transportation terminal to retail Terminal Price

Gasoline E10 223 10 46 3 164 E85 167 10 46 3 108 Ethanol Breakeven Price - - - - 97

In addition to this effect of energy equiva lency, Section 1.7 above outlines the difficulty of using all this E85 because of the relatively low number of fuel flexible vehicles (FFVs) that will be available to consume the fuel. The relatively low number of FFVs means that the refueling rate of these vehicles will have to be very high. In the year 2022, we estimated in Section 1.7 that FFVs will have to refuel 74 pe rcent of the time to use the volume of ethanol required by the RFS2 standard. This E85 refuelin g level is even more severe considering that that it is unlikely that every service station would make the capital investments to make E85 available for sale. In Section 4.2, we estimate that one out of very four service stations will carry E85. Thus, E85 may have to be priced even lo wer than its fuel economy- adjusted price to entice FFV owners to refuel at a sta tion carrying E85. To estimate th e marginal lower price at which FFV owners would refuel at this high rate, we referenced an analysis based on a willingness to pay survey conducted by David Greene of the Oak Ridge National Laboratory. 937 The summary

607of this analysis is depicted in the Figure 4.4-1.

Figure 4.4-1



### Alte r n a t iv

e

F u e l Market S hare a s a Fu n c ti on of F u e l Av ailabilityailability and Pric e Advantage ( D a v id

L. G r eene, 1997, Figu re 6)

X $0. 21 / gal P ri c e A dv a nt age (1997$ )Refueling frequency given“reasonable access”

P e rcen t of st ati o ns off e ri ng E 8 5 i n areas o f the

c ount ry w i t h “ r eas onabl e acc e ss ”, i

. e. , 25%

Based on our estimates that E85 would have to be purchased 74 percent of the time and that one out of every f our service stations would carry E85, then Figure 4.4-1 estimates that E85 would have to be priced 21 cents per gallon lower than gasoline to match this availability and refueling scenario. This cost estimate is based in 1997 dollars. Ad justing this cost estimate to

6082006 dollars increases this esti mate to 26 cents per gallon. F FFFFFFFFF

There is one more factor which we believe could affect the price of E85. FFV owners who refuel on E85 will drive fewer miles before having to refuel compared to operating their vehicle on gasoline. The FFV drivers will therefore spend more time refueling their vehicle. As described above, FFV owners will drive 25 percent fewer miles per gallon and thus, will have to spend 25 percent more time refueling. We estim ate that each refueling event requires 6 minutes of time, and that a person’s time is worth an average of 30 dollars per hour. 938, 939, 940, 941 Finally we assumed that a typical refueling volume for a refueling event is 15 gallons. Using these assumptions, the increased refueling frequency is costing the average FFV owner 5 cents per gallon more to use E85. To account for this addi tional cost, E85 would have to be priced 5 cents per gallon lower to make refueling FFVs a br eakeven proposition. For our refinery modeling work, we reduced the refiner purchase price of E85 used in our refine ry modeling analysis by this additional 5 cents per gallon.

Table 4.4-9 summarizes the E85 refinery purch ase prices at terminals by PADD used in our refinery modeling work. Thes e prices represent the total of the energy content, the price adjustment for reduced fuel availability and th e cost for increased refueling events. The E85 prices should be compared to regular grade ga soline because the FFVs generally only require regular grade gasoline when operated on gasoline.

Table 4.4-9. Wholesale E85 Prices used within the Refinery Model
E85 used in Conventional

Gasoline Areas (c/gal) E85 used in Reformulated Gasoline Areas (c/gal) PADD 1 Summer Winter 76 67 81 72 PADD 2 Summer Winter 75 66 80 71 PADD 3 Summer Winter 74 65 79 70 PADDs 4/5 Summer Winter 84 75 80 81 CA Summer Winter -

- 86 77

While we used these E85 prices within the refinery model, they don’t necessarily represent the societal costs for using E85. The pricing to reflect reduced fuel availability, in particular, contains a signifi cant amount of transfer payments from the refining industry to consumers and other entities, and these transfer payments do not re present the true cost for using

FFFFFFFFFF If E85 is used to comply with the RFS2 standard as envisioned, and if E85 would have to be priced as low as we projected, refiners would probably have to find a way to recoup their financial losses for using E85. One way refiners could do that would be to price E10 gasoline somewhat higher to recoup the E85 financial loss. Since we are solely interested in estimating the social cost of the RFS2 program, we did not make this sort of adjustment to the price of E10.

609E85. G GGGGGGGGG

For estimating the program costs for using E85 (shown in Tables 4.4-12 and 4.4-13), we adjusted E85 price back up to 5 cents per gallon less than the gasoline price for each case (the additional time spent refueling is a true cost). We then used the relative energy density of the E85 to that of gasoline as reported by the refine ry model (see energy content values in Table 4.4­ 20) to account for the energy density costs for us ing E85. We preferred the energy content price cost made by the refinery model to our prel iminary E85 energy content price adjustments because the refinery model can also account for changes in gasoline energy content made when accommodating the blending of ethanol. We are not assuming that the price adjustment that we made to account for lower E85 availability account for any additional soci al cost. If the FFV driver would have to drive out of his way from time-to-time to find the E85, then there would be some extra cost associated with the lower availa bility. But most of the time FFV drivers would likely learn where to find E85 along the routes th at they normally frequent, thus, no additional effort and cost would be incurred for refueli ng on E85. Thus, we assume that the lower E85 price to account for reduced E85 availability is pu rely a transfer payment from the refiner to the FFV owner.

The assumption used here and throughout this proposed rulemaking is that ethanol’s fuel economy is directly proportional to its energy density and its concen tration in the fuel. Since the volumetric energy content of ethanol is appr oximately 33 percent le ss than conventional gasoline, we assumed this loss in fuel economy proportional to its concen tration in the fuel. Some studies have suggested, however, that ethanol’s decrease in fuel economy may be less than its relative decrease in volumetric energy content of the fuel. In other words, there is less of a fuel consumption decrease than what the lo wer energy density of et hanol would suggest. However, for a variety of reasons, we have reason to question the data from these studies. We therefore intend to investigate this issue more fo r the final rule as new data become available.

4.4.1.6 Other Adjustments to the Costs

As discussed above, we needed to adjust th e estimated program costs from costs based on $51/barrel crude oil, the crude oil price at wh ich the refinery model was run, to $53/bbl and $92/bbl, the crude oil costs that served as the basis for our cost analysis. To make these adjustments we estimated the wholesale gasoline a nd diesel fuel prices (w hich are the surrogate for the gasoline and diesel fuel production costs) at the adjusted crude oil price and compared these adjusted wholesale gasoline and diesel fu el adjusted prices to the baseline wholesale gasoline and diesel fuel prices. The baseline wholesale gasoline and diesel fuel prices, based on an average $51/bbl crude oil price, are summarized in Table 4.4-1. To adjust these wholesale prices, we needed to estimate how the price of crude oil will affect them. We conducted a regression between the annual average spot price of price of Western Te xas Intermediate crude oil and the annual average retail gasoline and diesel fuel prices for the years 2002 through 2006. 942, 943 This regression is reflected in Table 4.4-10 as Gasoline Retail Price = Crude Oil Price multiplied times 2.95 plus 65.7, or Gasoline Retail Price = Crude Oil Price x “X” + “Y.” The slope “X” and intercept “Y” for this equation are summarized in Table 4.4-10. The X and Y

GGGGGGGGGG The possibility for this potentially large transfer payment associated with using ethanol in E85 would cause refiners to encourage the development of a cellulo sic diesel fuel industry to avoid this E85 issue.

610factors for diesel fuel are also summarized in Table 4.4-10 as well. However, we needed to estimate the wholesale prices instea d of the retail prices, so we ad justed the equations to estimate the wholesale price using the Jaco b’s wholesale prices as the calib rating values. The regression, including the adjustment values to derive the wholesale prices equations are summarized in Table 4.4-10.

Table 4.4-10. Equations Used for Estimating Wholesale Average Gasoline and Diesel Fuel Prices* Equation for Retail Prices E quation for Wholesale Prices X Y X Adjusted Y Gasoline 2.95 65.7 2.95 +7.7 Diesel Fuel 3.46 41.5 3.46 -17.0
* The equation is used by multiplying the crude oil price ($/bbl) times the X and then adding Y to that product resulting in a gasoline or diesel fuel cost expressed in cents per gallon.

The equations were used to estimate the average wholesale gasoline and diesel fuel prices. These average wholesale gasoline and diesel fuel prices are summarized in Table 4.4-11.

Table 4.4-11 Average Gasoline and Diesel Fuel Wh olesale Prices by Crude Oil Price (c/gal) Crude Oil Price Gasoline Diesel Fuel 51 158 149 53 164 157 92 279 313

Table 4.4-11 shows the nationwide average co sts, but our cost analysis was conducted on a PADD basis, thus, it was necessary to estimate revised gasoline and diesel fuel wholesale prices in each PADD. This was accomplished by generating a ratio of the average wholesale gasoline and diesel fuel prices at the higher crude oil price rela tive to the average wholesale and applying this ratio to the gasoline price in each P ADD. It is important to note gasoline and diesel fuel pricing changes captured by our crude oil/gasoline and diesel fuel price relationship price model. Prior to 2005, diesel fuel was priced about the same as ga soline (+/- 5 cents per gallon on a yearly average). In 2005 and 2006, when crude o il was priced higher, diesel fuel was priced 13 cents per gallon higher than gaso line. Thus, the equation picks up this relatively higher diesel fuel price at the higher crude o il prices in 2005 and 2006 and projec ts an even greater relative higher price for diesel fuel at ou r high crude oil price of $92/bbl. A higher relative diesel fuel price at higher crude oil prices in the future may be appropriate for a couple reasons. The first reason is that from January to mid-October 2008, when crude oil prices were very high, diesel fuel averaged 51 cents per gallon higher than gaso line. While we cannot say for certain that this association would always hold true at higher crude oil prices , we do have a possible explanation for a possible relationship here. Higher crude oi l prices are likely to affect gasoline demand more than diesel fuel as more of the trips made by gasoline powered light-duty vehicles are discretionary. For example, peopl e may readily change their vacations plans at higher crude oil prices, while diesel fuel used to power trucks that bring f ood to markets would be expected to

611continue. Thus, as crude oil prices increase, gasoline consumption is likely more elastic resulting in greater reductions in gasoline demand compared to diesel fuel. We therefore believe that higher crude oil prices will tend towards rela tively higher diesel fuel prices compared to gasoline.

For other reasons, diesel fuel prices may tr end higher in the future relative to gasoline prices. Because EISA required that corporate average fuel economy (CAFE) standards be increased for light duty motor vehicles, over time light duty vehicl es, which are almost exclusively gasoline powered, will become more fu el efficient. This will cause gasoline demand to decrease, while diesel demand is projected to continue to increas e. A second reason why refinery gasoline production will decrease is that most of th e renewable fuel volume being produced to comply with the RFS is ethanol, and it will displace gasoline. This will contribute to the over supply of gasoline and the relative undersupply of diesel fuel , thus causing gasoline prices to be soft relative to diesel fuel prices.

Another adjustment we made to the costs di rectly estimated by the LP refinery cost model was to add additional cost for distributing gasoline from the refinery to the terminal. The refinery cost model assigned a low distribution costs to gasoline for moving the gasoline from the refinery to the terminal. We estimate that this distribution cost should be about 4 cents per gallon, but the refinery model only assigned 2.5 cents per gallon for this. Thus, we credited ethanol 1.5 cents per gallon for each gasoline-equi valent gallon of ethanol blended into each PADD’s gasoline, since this roughly corresponded to the volume of gasoline displaced by the ethanol.

4.4.2 Refinery Modeling Results

In this subsection, we summarize the results of the three refinery modeling cases that we conducted, the primary control case, and the sens itivity cases, which include the E10 case and the mid-ethanol blend case. Additional detail concerning the refinery modeling results is contained in Jacob’s refinery mode ling report contained in the docket.

4.4.2.1 Control Case

Tables 4.4-12 and 4.4-13 summarize the costs for the control case excluding federal ethanol, biodiesel and renewable diesel tax s ubsidies with crude oil priced at $53/bbl and $92/bbl, respectively. The costs are reported by different cost components as well as aggregated total and per-gallon costs. H HHHHHHHHH This estimate of costs reflect s the changes in gasoline that are occurring with the expanded use of ethanol, biodiesel and renewable diesel. The operating costs include the labor, utility a nd other operating costs and are a di rect output from the refinery model. These costs are adjusted to reflect et hanol’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 reduced capital costs. The costs associated wi th lower energy density gasoline are accounted for

HHHHHHHHHH EPA typically assesses social benefits and costs of a rulemaking. However, this analysis is more limited in its scope by sometimes examining the marginal production cost and the average production cost of ethanol and gasoline.

612using the fractional change in energy density shown in Table 4.4-20, multiplied times the wholesale price of gasoline. By excluding the fe deral ethanol, biodiesel and renewable diesel fuel consumption subsidies, we avoid the transf er payments caused by these subsidies that would hide a portion of the program’s costs. The vari able operating costs line item in Table 4.4-12 and 4.4-13 capture several different aspects of this category of costs. In addition to ethanol’s blending costs captured by the refinery model, this category of costs captures ethanol’s higher production and distribution cost. At $53 per barrel crude oil co sts, ethanol’s production and distribution costs are higher than the gasoline wholesale cost . Another item of the variable cost is the 5 cents per gallon refueling costs. Finally, this category of costs captures the 5 cents per E85 gallon cost which covers the cost of produci ng fuel flexible vehicles (FFVs) instead of nonFFVs.

Table 4.4-12. Summary of Control Case Costs (without Tax Subsidies) (million dollars/year and c/gal, except as noted; 2006 dollars, 7% ROI before taxes, $53/bbl crude oil price) Cost to Gasoline Cost to Diesel Fuel Refinery Capital Costs ($Billion) -7.89

- Amortized Refinery Capital Costs ($MM/yr) -842

- Fixed Operating Cost ($MM/yr) -232

- Variable Operating Cost ($MM/yr) 7,690 590 Lower Energy Density ($MM/yr) 10,340 187 Total Cost ($MM/yr) 16,960 777 Amortized Refinery Capital Costs (c/gal) -0.48

- Fixed Operating Cost (c/gal) -0.13

- Variable Operating Cost (c/gal) 5.66 0.66 Lower Energy Density (c/gal) 5.85 0.54 Total Cost Excluding Subsidies (c/gal) 10.91 1.20

Table 4.4-13. Summary of Control Case Costs (without Tax Subsidies) (million dollars/year and c/gal, except as noted; 2006 dollars, 7% ROI before taxes, $92/bbl crude oil price) Cost to Gasoline Cost to Diesel Fuel Amortized Refinery Capital Costs ($MM/yr) -842

- Fixed Operating Cost ($MM/yr) -232

- Variable Operating Cost ($MM/yr) -12,410 -419 Lower Energy Density ($MM/yr) 17,598 373 Total Cost ($MM/yr) 4,120 -46 Refinery Capital Costs (c/gal) -0.48

- Fixed Operating Cost (c/gal) -0.13

- Variable Operating Cost (c/gal) -6.70 -1.15 Lower Energy Density (c/gal) 9.95 1.08 Total Cost Excluding Subsidies (c/gal) 2.65 -0.07

Our analysis shows that when considering all the costs associated with these fuel changes resulting from the expanded use of ethanol, th at these various possible gasoline use scenarios will cost the U.S. nearly $17 billion per year in the year 2022 when crude oil is priced at $53 per barrel. Expressed as per-gallon costs, these fuel changes would cost the U.S. 10.91 cents per gallon of gasoline. The addition of biodiesel and renewabl e diesel fuel is estimated to cost about

613$780 million per year in the year 2022, or cost 1.20 cents per gallon.

At $92 per barrel crude o il, the addition of the ethanol is estimated to cost 4.1 billion per year in 2022, and cost 2.65 cents per gallon. The addition of biodi esel and renewable diesel fuelis estimated to reduce the cost of diesel fuel by 46 million dollars in 2022, and reduce the pergallon cost of diesel fuel by 0.07 cents per gallon.

Tables 4.4-12 and 4.4-13 express the total and per-gallon gasoline co sts for the RFS case with the federal ethanol, biodies el and renewable diesel subsidie s included with cr ude oil priced at $53/bbl and $92/bbl, respectively. The federa l tax subsidy is 45 cents per gallon for each gallon of new corn-based ethanol blended into gasoline and 101 cents per gallon for each gallon of cellulosic ethanol. Imported et hanol is also assumed to receive the 45 cents per gallon starch ethanol subsidy, although we a ssume that a greater volume of imported ethanol would be used than that which can flow through the Caribbean Ba sin, tariff free. Thus the 51 cents per gallon tariff would apply to that incremental volume of imported ethanol above the allowable Caribbean Basin initiative volume. We estimate that im ported ethanol would earn 23 cents per gallon net subsidy. The biodiesel subsidy is 100 cents per gallon, and the renewable diesel fuel subsidy is 50 cents per gallon. I IIIIIIIII The cost adjustment is estimated by multiplying the subsidy times the volume of new ethanol, biodies el and renewable diesel es timated to be used.

Table 4.4-14. Estimated Control Case Costs (Reflecting Tax Subsidies) (million dollars/year and cents/gallon; 2004 dollars, 7% ROI before taxes, $53/bbl crude oil price) Cost to Gasoline Co st to Diesel Fuel Total Cost ($MM/yr) 16,957 777 Federal Subsidy ($MM/yr) -17,699 -525 Revised Total Cost ($MM/yr) -742 252 Per-Gallon Cost Excluding Subsidies (c/gal) 10.91 1.20 Federal Subsidy (c/gal) -11.39 -0.81 Total Cost Including Subsidies (c/gal) -0.48 0.39

Table 4.4-15. Estimated Control Case Costs (Reflecting Tax Subsidies) (million dollars/year and cents/gallon; 2004 dollars, 7% ROI before taxes, $92/bbl crude oil price) Cost to Gasoline Cost to Diesel Fuel Total Cost ($MM/yr) 4,116 -46 Federal Subsidy ($MM/yr) -17,699 -535 Revised Total Cost ($MM/yr) -13,583 -571 Per-Gallon Cost Excluding Subsidies (c/gal) 2.65 -0.07 Federal Subsidy (c/gal) -11.39 -0.81 Total Cost Including Subsidies (c/gal) -8.74 -0.88

The cost including subsidies would represent ga soline and diesel fuel’s apparent cost as reflected to the fuel industry as a whole because the federal tax subsidie s tends to transfer a

IIIIIIIIII The recent economic stimulus law passed by Congress extended the biodiesel and renewable diesel fuel subsidies, and it also increased the re newable diesel fuel subsidy to 100 cents per gallon. However, the increase in renewable diesel fuel subsidy occurred too late to account for this change in our cost analysis.

614portion of the actual costs. Our analysis estimates that when th e oil price is $53/bbl, the fuel industry will see a 0.48 cent per gallon decrease in the apparent cost of producing gasoline, and a 0.39 increase in the apparent cost of producing diesel fuel. At th e higher oil price of $93/bbl, the apparent cost of producing gasoline is estimated to decrease by 8.7 cents per gallon, and the apparent cost of producing diesel fuel is expected to decrease by 0.88 cents per gallon. These costs would also represent the apparent cost to consumers “at the pump” if the full tax credit were passed along to the consumers. However, it is possible that only a portion of the tax subsidy will be passed along to the consumer (historically, this has been the case). Thus, the price impact at the pump may be somewhere betw een the values in Tables 4.4-10 and 4.4-11, and Tables 4.4-12 and 4.4-13. However, consum ers would also pay the full tax subsidy through higher taxes in addition to the values in Tables 4.4-12 and 4.4-13.

Table 4.4-14 summarizes the volumetric i nputs to refineries in each PADD for this control case. Because of the increased use of biofuels, petroleum inputs would be expected to decrease, and this is confirmed.

Table 4.4-16. Summary of the Total and Incremental Vo lumetric Refinery Inputs by PADD (thousand barrels/day)

PADD 1 PADD 2 PADD 3 PADD 4/5 CA Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case PADD Crude 1,525 0 3,317 -245 8,642 -458 1,726 -21 2,098 -39 GTL Naphtha 0 0 0 0 0 0 0 0 0 0 GTL Diesel 0 0 0 0 0 0 0 0 0 0 VGO HS 0 0 0 0 483 -139 0 0 4 -24 VGO LS 0 0 1 0 0 0 0 0 0 0 HS AR (A960) 0 0 0 0 0 0 0 0 0 0 LS AR (Alg) 200 -1 0 0 267 139 0 0 0 0 Normal Butane 31 5 61 6 112 0 38 1 40 0 Isobutane 14 4 22 0 44 -23 10 -4 14 -10 M T B E 0 0 0 0 0 0 0 0 0 0 Ethanol - E10 360 14 237 46 180 85 82 27 163 -8 Ethanol - E20 0 0 0 0 0 0 0 0 0 0 Ethanol - E85 475 475 174 174 370 370 67 67 118 118 Reformer Feed 0 0 0 0 0 0 0 0 0 0 Methanol 0 0 0 0 0 0 0 0 0 0 Natural Gas (FOE) 67 -12 145 4 557 -13 119 -3 175 2 Hydrogen (MSCF) 0 0 0 0 0 0 0 0 0 0 Pentanes Plus 0 0 32 0 52 0 19 0 0 0 Import CBOB 10% 277 277 0 0 0 0 0 0 0 0 Import CBOB 20% 0 0 0 0 0 0 0 0 0 0 Import RBOB 10% 123 -77 0 0 0 0 0 0 0 0 Import RBOB 20% 0 0 0 0 0 0 0 0 0 0 Import Alkylate 46 30 0 0 0 0 0 0 0 0 Import Raffinate 4 -44 0 0 0 0 4 4 46 0 Import Reformate 0 0 0 0 0 0 0 0 0 0 Import FCC Naphtha 0 0 0 0 0 0 0 0 0 0 Import Lt Naphtha 14 14 0 0 23 -7 0 0 1 0 Import Hvy Naph 0 0 0 0 42 0 0 0 0 0 Transfer Lt Naphtha 0 0 0 0 0 0 0 0 12 -12 Transfer Reformate 0 0 0 -5 0 0 0 0 0 0 Transfer Alkylate 60 0 6 3 0 0 0 0 60 0 Transfer FCC Naphtha 0 0 0 0 0 0 0 0 0 0 Transfer Raffinate 0 0 4 2 0 0 0 0 60 0 Transfer RBOB 10% 662 -34 79 29 0 0 0 0 0 0 Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0 Transfer CBOB 10% 1,300 2 421 -29 0 0 1 1 0 0 Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0 Isooctane 2 2 0 0 0 0 0 0 35 7 Isooctene 1 -10 0 -2 0 0 0 0 0 0

Table 4.4-16 shows that inputs of crude oil de creases substantially in most of the PADDs. In two of the PADDs, vacumn gas oil (VGO) inputs to refineries decreases, which is reasonable

615since VGO is a feedstock to fluidized catalytic cracker units, a large gasoline blendstock production unit within refineries.

Table 4.4-17 below summarizes the refinery output volumes and changes in refinery output volumes relative to th e reference case by PADD.

Table 4.4-17. Summary of Total and Incrementa l Refinery Outputs by PADD (thousand barrels/day)

PADD 1 PADD 2 PADD 3 PADD 4/5 CA Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Propane 33 -1 54 -10 145 0 26 -2 58 0 Propylene 19 0 43 0 245 0 2 0 12 0 Normal Butane 0 0 0 -4 30 -16 0 0 0 0 Isobutane 0 0 0 0 0 0 4 4 14 6 PC Naphtha 16 0 40 0 433 0 0 0 0 0 PC Gasoil 0 0 496 -60 158 0 0 0 0 0 CG Reg 0 -73 0 -552 0 -1,176 0 -246 0 0 C G P r e m 0 0 0 0 0

- 2 0

- 3 3 0 0 CG E10 Reg 1,916 436 1,667 495 1,069 928 671 220 115 3 CG E10 Prem 268 10 343 14 262 11 143 43 22 1 RFG E10 Reg 1,099 -322 273 -55 358 -105 0 0 1,218 -94 RFG E10 Prem 285 14 65 3 92 4 0 0 258 8 CG E20 Reg 0 0 0 0 0 0 0 0 0 0 RFG E20 Reg 0 0 0 0 0 0 0 0 0 0 E85 to CG 156 156 133 133 306 306 78 78 0 0 E85 to RFG 398 398 70 70 125 125 0 0 137 137 Transfer RBOB 10% 0 0 0 0 741 -5 0 0 0 0 Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0 Transfer CBOB 10% 0 0 0 0 1,722 -27 0 0 0 0 Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0 Jet/Kero A (450ppm) 27 0 115 0 1,363 0 380 0 300 0 X-Fer Diesel Rundown t 0 0 0 0 0 0 0 0 0 0 HSD Gr 76 (0.2%) 0 0 0 0 0 0 0 0 0 0 LSD Gr 74 (.05%) 0 0 0 0 0 0 0 0 0 0 ULSD (15 ppm) 565 13 648 -29 2,189 -34 515 4 0 0 CARB Diesel 0 0 0 0 0 0 0 0 354 -4 X-Fer C5's to Storage 0 0 0 0 0 0 0 0 0 0 1% Residual Fuel 0 0 0 0 0 0 0 0 0 0 Residual Fuel 86 16 57 0 251 -21 151 5 68 -5 Slurry 31 -1 80 -9 158 0 0 0 30 0 Asphalt & Wax 143 0 210 0 158 0 5 0 44 0 G a s o i l 0 0 9 0 0 0 0 0 1 0 0 Lubes 19 0 17 0 158 0 0 0 20 0 Benzene 11 0 11 0 51 0 0 0 0 0 Toluene 0 0 0 0 35 0 0 0 0 0 Xylenes 0 0 0 0 8 0 0 0 0 0 C u m e n e 0 0 0 0 0 0 0 0 0 0 Cyclohexane 0 0 0 0 0 0 0 0 0 0 Transfer Raffinate 0 0 0 0 64 2 0 0 0 0 Transfer Alkylate 0 0 0 0 126 3 0 0 0 0 Transfer Reformate 0 0 0 0 0 -5 0 0 0 0 Transfer FCC naphtha 0 0 0 0 0 0 0 0 0 0 Transfer Lt Naphtha 0 0 0 0 0 0 0 0 12 -12 Transfer Blendstock 0 0 0 0 0 0 0 0 0 0 Sulfur (STons) 1 0 4 0 16 -1 2 0 4 0 Coke (STon) 1 -1 12 -2 65 -4 7 0 19 -1

Table 4.4-18 summarizes the change in refinery unit capacities by PADD comparing the control case to the reference case.

616Table 4.4-18. Change in Refinery Unit Capacities by PADD relative to the Reference Case (thousand barrels/day) Refinery Unit PADD 1 PADD 2 PADD 3 PADD 4/5 CA U.S. Crude Tower 0 -421 -398 0 0 -818 Vacuum Tower 0 -188 -160 5 0 -343 Sats Gas Plant -29 0 -35 1 -4 -66 Unsats Gas Plant 0 -1 -53 0 -12 -66 F C C D e C 5 T o w e r 0 0 0 0 0 0 FCC 0 0 -175 0 -63 -238 FCC Splitter 0 -6 -97 0 -20 -123 Hydrocracker 0 0 0 -3 0 -3 H

- O i l U n i t 0 0 0 0 0 0 Delayed Coker 0 0 -91 0 0 -91 V i s b r e a k e r 4 8 0 0 0 0 4 8 Thermal Naphtha Splitter 0 -4 -11 0 -1 -16 CRU Reformer 0 0 0 0 0 0 S R U R e f o r m e r 0 0 0 0 3 3 B T X R e f o r m e r 0 1 0 0 0 1 C4 Isomerization 0 0 0 0 -4 -4 C 5 / C 6 I s o m e r i z a t i o n

- 1 0 0 0 0

- 1 H F A l k y l a t i o n 0 0 0 0 0 0 H2SO4 Alkylation -22 0 -51 -15 -26 -113 D i m e r s o l 0 0 0 0 0 0 Cat Pol y 9 0 0 0 0 9 I s o o c t a n e 0 0 0 0 0 0 DHT - Total 7 0 -93 3 0 -84 DHT 2nd RCT - Total 11 53 -69 -4 15 7 D H T A r o m S a t u r a t i o n 0 0 0 0 0 0 NHT - Total Fd -16 0 0 -2 0 -17 CGH - Generic 2 -48 25 8 0 -13 C G H - O l e f i n S a t ' n 0 0 0 0 0 0 FCCU Fd HDT 0 0 0 -34 60 26 L S R S p l i t t e r 0 1 6 0 0 0 1 6 L S R B z S a t u r a t o r 0 0 0 0 0 0 Reformate Saturator -9 0 -1 0 0 -9 Reformate Splitter -26 0 -2 0 0 -28 S D A 0 0 0 0 0 0 M T B E 0 0 0 0 0 0 T A M E 0 0 0 0 0 0 Hydrogen Plant - Total MSCF 7 34 36 -19 20 77 L u b e U n i t 0 0 0 0 0 0 Sulfur Plant 0 0 -1,402 -56 -96 -1,553 M e r o x J e t 0 0 0 0 0 0 M e r o x D i e s e l 0 0 0 0 0 0 B T X R e f o r m e r - T o w e r f e e d 0 0 0 0 0 0 B T X R e f o r m e r - E x t r a c t f e e d 1 1 0 0 0 1

Most of the capacity throughput changes are negative, reflec ting the decreased processing of crude oil and vacuum gas o il and decreased downstream refi ning units as projected by the refinery model. Of the negative throughput change s, the large reduced volume of the fluidized catalytic cracker unit is importa nt. As discussed above, the re finery model likely chose to decrease the fluidized catalytic cracker throughput to crack le ss heavy hydrocarbons to light hydrocarbons, producing less gasolin e material to offset the in creased volume of ethanol. Another important decrease in refinery unit th roughput is the alkylation unit. Because the

617alkylation unit is responsible fo r increasing octane, its decrease reflects the rebalancing by the refinery model to adjust for the large incr ease in the volume of hi gh octane ethanol.

These changes in refinery unit throughputs are associated with changes in capital investments. Table 4.4-19 summa rizes the projected change in capital investments between the control case and the reference case. This Table shows that incremental to the reference case, refiners are expected to reduce their capital in vestments by $7.9 billion co mpared to business as usual. Most of the reduction occurs in PADD s 2 and 3 where large volumes of ethanol, and other gasoline blendstocks are expe cted to enter the gasoline pool. Of course, this capital cost decrease is countered by the capital costs in curred to build new ethanol plants and to put into place the distribution system that the new et hanol requires (see Table 4.4-21 below).

618Table 4.4-19. Change in Refinery Unit Investments by PADD (million dollars/year) Refinery Unit PADD 1 PADD 2 PADD 3 PADD 4/5 CA U.S. Crude Tower 0 -995 -665 0 202 -1,660 Vacuum Tower 0 -527 -315 85 30 -756 Sats Gas Plant -112 0 -103 3 17 -249 Unsats Gas Plant 0 0 -260 0 0 -362 F C C D e C 5 T o w e r 0 0 0 0 0 0 FCC 0 0 -1,280 0 0 -2,066 FCC Splitter 0 -6 -47 0 0 -69 Hydrocracker 0 0 0 -65 0 -65 H

- O i l U n i t 0 0 0 0 0 0 Delayed Coker 0 0 -1,261 0 0 -1,261 V i s b r e a k e r 1 7 0 0 0 0 0 1 7 0 Thermal Naphtha Splitter 0 -5 -4 0 3 -11 CRU Reformer 0 0 0 0 0 0 SRU Reformer 0 0 0 0 32 72 B T X R e f o r m e r 0 1 1 2 0 0 1 3 C4 Isomerization 0 0 0 0 14 -23 C5/C6 Isomerization -64 0 0 0 0 -64 H F A l k y l a t i o n 0 0 0 0 0 0 H2SO4 Alkylation -372 0 -557 -227 10 -1,617 D i m e r s o l 0 0 0 0 0 0 Cat Pol y 7 2 0 0 0 0 7 2 I s o o c t a n e 0 0 0 0 0 0 DHT - Total 42 0 -689 17 32 -631 DHT 2nd RCT - Total 50 344 -313 -17 60 133 D H T A r o m S a t u r a t i o n 0 0 0 0 0 0 NHT - Total Fd -70 0 0 -6 0 -76 CGH - Generic 8 -200 101 26 0 -64 C G H - O l e f i n S a t ' n 0 0 0 0 0 0 FCCU Fd HDT -2 0 0 -349 60 443 L S R S p l i t t e r 0 7 0 0 8 7 L S R B z S a t u r a t o r 0 0 0 0 0 0 Reformate Saturator -39 0 -2 0 50 -41 Reformate Splitter -39 0 -1 0 150 -40 S D A 0 0 0 0 0 0 M T B E 0 0 0 0 0 0 T A M E 0 0 0 0 0 0 Hydrogen Plant - Total MSCF 19 118 95 -49 27 255 L u b e U n i t 0 0 0 0 5 6 0 Sulfur Plant 0 0 -3 0 437 -4 M e r o x J e t 0 0 0 0 0 0 M e r o x D i e s e l 0 0 0 0 0 0 B T X R e f o r m e r - T o w e r f e e d 0 1 0 0 0 1 B T X R e f o r m e r - E x t r a c t f e e d 3 3 0 0 0 6 Total -334 -1,250 -5,304 -582 0 -7,887

Table 4.4-19 essentially expr esses the change in refinery capacity input shown in Table 4.4-18, but expresses the changes in terms of dollars instead of thousands of barrels per day. The total reduced capital investment is 7.89 billion dollars.

Table 4.4-20 summarizes the gasoline volum e and qualities by diffe rent gasoline types for the control case, and also, for comparison, lists the same for the reference case.

619Table 4.4-20. Gasoline Volume, Quality and Energy Density by Gasoline Type at the PADD Terminal

RFG PADD 1 PADD 2 PADD 3 PADD 4/5 CA US Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control Total ('000 BPD) 1,692 1,384 391 338 551 450

- - 1,562 1,476 4,197 3,649 Ethanol ('000 BPD) 171 140 39 34 56 45

- - 158 149 424 369 RVP (psi) 10.7 10.6 10.4 11.3 9.7 10.6

- - 9.5 9.4 10.1 10.2 Sulfur (ppm) 23 24 23 23 20 22

- - 9 10 17 18 Octane (R+M/2) 88.1 88.3 88.0 88.2 88.0 88.2

- - 87.6 87.7 87.9 88.0 Aromatics (vol%) 19.9 19.9 20.0 19.6 19.5 19.5

- - 21.3 21.7 20.4 20.6 Benzene (vol%) 0.55 0.58 0.58 0.56 0.51 0.48

- - 0.51 0.52 0.53 0.54 Olefins (vol%) 11.7 11.6 10.3 10.4 8.7 11.1

- - 5.7 5.7 8.9 9.0 Oxygen (wt%) 3.7 3.7 3.7 3.7 3.7 3.7

- - 3.7 3.7 3.7 3.7 E200 (vol%) 55.1 55.4 59.7 58.8 54.3 54.0

- - 57.7 57.4 56.4 56.4 E300 (vol%) 93.9 95.1 84.3 95.2 87.4 95.2

- - 86.2 86.2 89.3 91.5 Energy (MMBtu/Bbl ) 4.954 4.948 4.920 4.905 4.983 4.961

- - 4.991 5.001 4.969 4.967 CG

Total ('000 BPD) 2,263 2,184 2,052 2,009 1,568 1,331 829 813 135 137 6,847 6,475 Ethanol ('000 BPD) 176 221 152 203 40 134 56 82 14 14 436 654 RVP (psi) 11.6 11.6 11.7 11.5 10.7 10.4 11.3 11.2 10.5 10.6 11.4 11.3 Sulfur (ppm) 24 24 23 24 24 22 27 28 25 26 24 24 Octane (R+M/2) 87.7 87.9 88.0 88.0 88.0 88.2 86.9 87.0 89.1 89.1 87.8 87.9 Aromatics (vol%) 24.2 22.4 24.4 22.5 27.2 22.3 19.4 17.3 28.4 28.5 24.4 21.9 Benzene (vol%) 0.61 0.53 0.64 0.52 0.59 0.50 1.16 1.04 0.60 0.56 0.68 0.58 Olefins (vol%) 13.9 14.7 11.1 11.0 14.9 11.9 6.9 7.4 17.1 15.1 12.5 12.0 Oxygen (wt%) 2.9 3.7 2.7 3.7 0.9 3.7 2.5 3.7 3.6 3.7 2.3 3.7 E200 (vol%) 55.4 55.6 55.7 57.8 46.5 55.6 59.6 63.2 55.8 55.4 54.0 57.3 E300 (vol%) 93.4 95.4 84.3 95.4 87.4 95.4 83.5 95.8 86.2 86.2 87.9 95.3 Energy (MMBtu/Bbl ) 4.980 4.947 4.965 4.931 5.069 4.951 5.011 4.962 4.969 4.983 4.999 4.946 E85

Total ('000 BPD)

- 554

- 202

- 431

- 78

- 137

- 1,402 Ethanol ('000 BPD)

- 475

- 174

- 370

- 67

- 118

- 1,204 RVP (psi)

- 10.1

- 10.8

- 11.1

- 12.0

- 11.7

- 10.8 Sulfur (ppm)

- 10

- 9

- 9

- 9

- 9

- 10 Octane (R+M/2)

- 107.7

- 107.8

- 107.9

- 107.9

- 107.9

- 107.8 Aromatics (vol%)

- 1.1

- 0.6

- 0.5

- 0.0

- 0.3

- 0.7 Benzene (vol%)

- 0.03

- 0.02

- 0.01

- 0.00

- 0.01

- 0.02 Olefins (vol%)

- 2.2

- 2.1

- 2.2

- 1.1

- 1.2

- 2.0 Oxygen (wt%)

- 30.5

- 30.6

- 30.7

- 30.8

- 30.7

- 30.6 E200 (vol%)

- 127.0

- 128.4

- 128.7

- 129.6

- 129.1

- 128.1 E300 (vol%)

- 95.6

- 96.1

- 96.2

- 96.6

- 96.5

- 96.0 Energy (MMBtu/Bbl )

- 3.652

- 3.630

- 3.623

- 3.596

- 3.607

- 3.632 All Mogas

Total ('000 BPD) 3,955 4,122 2,443 2,550 2,120 2,212 829 891 1,697 1,751 11,044 11,526 Ethanol ('000 BPD) 347 836 191 411 95 550 56 149 171 281 860 2,226 RVP (psi) 11.2 11.1 11.5 11.4 10.5 10.6 11.3 11.3 9.6 9.7 10.9 10.9 Sulfur (ppm) 24 22 23 22 23 19 27 26 10 11 22 20 Octane (R+M/2) 87.9 90.7 88.0 89.6 88.0 92.1 86.9 88.8 87.8 89.4 87.8 90.4 Aromatics (vol%) 22.4 18.7 23.7 20.4 25.2 17.5 19.4 15.8 21.8 20.6 22.9 18.9 Benzene (vol%) 0.58 0.48 0.63 0.48 0.57 0.40 1.16 0.95 0.52 0.48 0.62 0.50 Olefins (vol%) 12.9 12.0 10.9 10.2 13.3 9.8 6.9 6.9 6.6 6.1 11.1 9.9 Oxygen (wt%) 3.2 7.4 2.9 5.9 1.7 9.1 2.5 6.2 3.7 5.9 2.9 7.1 E200 (vol%) 55.3 65.1 56.4 63.6 48.5 69.5 59.6 69.0 57.5 62.8 54.9 65.6 E300 (vol%) 93.6 95.3 84.3 95.4 87.4 95.5 83.5 95.9 86.2 87.0 88.4 94.2 Energy (MMBtu/Bbl ) 4.969 4.773 4.958 4.824 5.046 4.695 5.011 4.843 4.989 4.890 4.988 4.792

Several very important differences are eviden t in Table 4.4-20. First, the energy content of the control case gasoline is much lower than th at of the reference case due to the addition of low energy content ethanol. A nother obvious change is the 4 volume percent decrease in aromatics content across the gasolin e pool. This change is caused by two factors. One factor is the increase in high octane ethano l in E10 which allows for decr eased production of high octane reformate, which is high in aromatics. Another factor is the blending of E85. The ethanol in E85 will dilute the aromatics contained in its gasoline blendstock. Another important change is the increase in the gasoline pool octane. Since the reference case gasoline pool was compliant in octane, the increased octane of the control cas e represents octane gi veaway. Because of

620ethanol’s very high octane and hi gh concentration in E-85, E85 c ontains a much higher octane level, and when averaged with the rest of the gasoline pool, re sults in a sizable increase the gasoline pool’s average octane leve l, or octane giveaway. Simila rly, the blending of E85 causes the decrease in gasoline pool benzene level.

The increased use of renewable and alternative fuels would require capital investments in corn and cellulosic ethanol plants , and renewable diesel fuel plants . In addition to producing the fuels, storage and distribution f acilities along the whole distributi on chain, including at retail, will have to be constructed for these new fuels. Conversely, as these renewable and alternative fuels are being produced, they supplant gasoline and diesel fuel demand which results in less new investments in refine ries compared to business-as-usual. In Table 4.4-21, we list the total incremental capital investments that we proj ect would be made for this proposed RFS2 rulemaking incremental to the reference case. These aggregate capital costs are estimated by deriving a total capital cost per gallon of product produced for each plant type and multiplying that capital cost per gallon factor by the total additional volume of that fuel supplied between the reference case and th e control case.

Table 4.4-21. Projected Total RFS Program U.S. Capital Investments (billion dollars) Plant Type Capital Costs Corn Ethanol 4.0 Cellulosic Ethanol 50.1 Ethanol Distribution 12.4 Bio/Renew Diesel Fuel Production and Distribution 0.25

Refining -7.9 Total 58.9

Table 4.4-21 shows that the total U.S. increm ental capital investments attributed to this program by 2022 is $58.9 billion. One contributi ng reason why the capital investments made for renewable fuels technologies is so much more than the decreas e in refining industry capital investments is that a large part of the decrease in petroleum gasoline supply was from reduced imports. In addition, renewable fuels technologie s are more capital intens ive per gallon of fuel produced than incremental increases in gasoli ne and diesel fuel pr oduction at refineries.

To understand entire costs of the program a nd their impacts on the U.S. economy, and to compare those costs to the benefits, we estimated the year-by-year costs fr om 2009, the first year of the program, to 2030. We first estimated re newable fuels volumes for each renewable fuels type based on the RFS2 volume standards and based on our projections of which renewable fuels would be used to comply with the standard. These volumes represent the increment between the RFS2 control case and the reference case. Base d on AEO 2008, we also estimated the overall gasoline and diesel fuel volumes . Table 4.4-22 below summarizes the projected year-by-year incremental renewable fuel, and total gasoline and diesel fuel volumes.

621Table 4.4-22. Summary of Year-by-Year Volumes (Billion Gallons/year)

Year Gasoline Volume Corn Ethanol Volume Cellulosic Ethanol Volume Imported Ethanol Volume Diesel Fuel Volume Biodiesel Volume Renewable Diesel Fuel Volume 2009 148.7 0.34 0.00 0.00 54.3 0.08 0.00 2010 149.8 1.04 0.00 0.00 55.8 0.23 0.01 2011 150.4 1.60 0.06 0.00 57.3 0.37 0.03 2012 151.4 2.12 0.25 0.00 58.2 0.62 0.04 2013 152.4 2.82 0.75 0.00 58.7 0.61 0.06 2014 153.2 3.39 1.50 0.16 59.3 0.69 0.07 2015 154.0 3.90 2.75 0.45 60.0 0.66 0.09 2016 154.7 3.84 4.00 0.80 60.7 0.54 0.10 2017 155.1 3.70 5.25 1.25 61.3 0.52 0.12 2018 155.1 3.51 6.75 1.71 61.9 0.51 0.13 2019 155.2 3.31 8.25 2.15 62.6 0.49 0.15 2020 154.8 3.17 10.25 2.10 63.4 0.47 0.16 2021 155.1 2.93 13.25 2.04 64.2 0.45 0.17 2022 155.4 2.71 15.75 2.50 65.0 0.43 0.19 2023 154.8 2.71 15.75 2.50 65.9 0.43 0.19 2024 154.3 2.71 15.75 2.50 66.8 0.43 0.19 2025 153.4 2.71 15.75 2.50 67.7 0.43 0.19 2026 153.2 2.71 15.75 2.50 68.8 0.43 0.19 2027 153.2 2.71 15.75 2.50 69.8 0.43 0.19 2028 153.3 2.71 15.75 2.50 70.9 0.43 0.19 2029 153.3 2.71 15.75 2.50 71.9 0.43 0.19 2030 153.7 2.71 15.75 2.50 73.0 0.43 0.19

Also for our year-by-year cost analysis, we needed year-by-year estimates of the production and distribution cost for each renewa ble fuel type. The feedstock costs were available for the years 2012, 2017 and 2022 from FA SOM for corn ethanol and soy oil, used for producing biodiesel, with crude oil priced at $53/bbl and $92/bbl. We entered those feedstock costs into the respective cost models and interpolated and ex trapolated the production costs for the years provided to develop production costs for the in-between years. For cellulosic ethanol, we assumed the same feedstock costs for all year s, but we adjusted the production costs based on our adjusted NREL production cost s, interpolating between the y ears for which we have specific cost data. Since the costs change so much for ce llulosic ethanol, the costs in any one year were based on the accumulated costs from the previous years. For example, the production costs in the year 2015 are a function of the production co sts times the new volume of cellulosic ethanol in 2015, plus the production costs times the volume of new cellulosic ethanol in 2014, and so on. This avoids underestimating the co sts in the later years. Importe d ethanol costs by year are from Section 4.1 above. Tables 4.4-23 and 4.4- 24 summarize the renewable fuels production and distribution costs by year along with the projected crude oil, gasoline and diesel fuel wholesale costs. Table 4.4-23 represents the projected cost s based on crude oil priced at $53 per barrel, and Table 4.4-24 presents the projected costs base d on crude oil priced at $92 per barrel.

622Table 4.4-23. Renewable Fuel Production a nd Distribution Costs Used in Year-by-Year Analysis ($53/bbl Crude Oil Case)

Year Crude Oil Price $/bbl Wholesale Gasoline Price Corn Ethanol Prod Cost Cellulosic Ethanol Prod Cost Imported Ethanol Prod Cost Wholesale Diesel Fuel Price Biodiesel Prod Cost Renewable Diesel Fuel Prod Cost 2009 68.3 204 155

- 178 220 270 210 2010 65.2 195 154 229 177 209 270 210 2011 62.7 188 153 211 176 200 270 210 2012 60.1 180 152 193 175 191 270 210 2013 57.4 172 151 175 174 182 273 212 2014 54.7 164 150 157 173 172 276 214 2015 52.0 156 149 139 172 163 279 217 2016 49.4 148 148 138 172 154 282 219 2017 49.5 149 147 137 171 154 285 221 2018 50.2 151 147 136 171 157 282 219 2019 50.9 153 147 134 171 159 279 217 2020 51.5 155 147 133 170 161 276 214 2021 52.1 156 147 132 170 163 273 212 2022 52.8 159 147 131 170 166 270 210 2023 53.8 161 147 131 170 169 270 210 2024 54.7 164 147 131 168 172 270 210 2025 55.7 167 147 131 168 176 270 210 2026 56.6 170 147 131 168 179 270 210 2027 55.8 167 147 131 168 176 270 210 2028 56.8 170 147 131 168 180 270 210 2029 57.9 174 147 131 168 184 270 210 2030 58.7 176 147 131 168 186 270 210

623Table 4.4-24. Renewable Fuel Production a nd Distribution Costs Used in Year-by-Year Analysis ($92/bbl Crude Oil Case)

Year Crude Oil Price $/bbl Wholesale Gasoline Price Corn Ethanol Prod Cost Cellulosic Ethanol Prod Cost Imported Ethanol Prod Cost Wholesale Diesel Fuel Price Biodiesel Prod Cost Renewable Diesel Fuel Prod Cost 2009 67.8 203 161

- 180 218 269 208 2010 69.2 207 160

- 179 223 269 208 2011 71.0 212 160 216 178 229 269 208 2012 73.1 218 160 203 177 236 269 208 2013 75.0 224 160 188 176 243 272 210 2014 76.8 229 160 175 175 249 274 212 2015 78.6 234 159 161 174 255 277 214 2016 80.4 240 159 155 174 261 279 216 2017 82.8 247 159 152 173 270 282 218 2018 84.2 251 158 149 173 274 279 216 2019 86.3 257 157 148 173 282 277 214 2020 88.3 263 156 146 172 289 275 212 2021 90.7 270 155 144 172 297 272 210 2022 91.8 273 154 143 172 301 270 209 2023 89.9 268 154 143 172 294 270 209 2024 91.2 272 154 143 170 299 270 209 2025 90.9 271 154 143 170 298 270 209 2026 91.8 273 154 143 170 301 270 209 2027 93.1 277 154 143 170 306 270 209 2028 94.4 281 154 143 170 310 270 209 2029 95.4 284 154 143 170 313 270 209 2030 96.4 287 154 143 170 317 270 209

Based on the volumes and renewable fuel s production and dist ribution costs, we estimated the program costs in years other than 2022 and summarized them in Tables 4.4-25 and 4.4-26. We started with the year 2022 costs as ou r basis. We then adjusted those costs using the volume and price relationship betwee n ethanol and gasoline to estimat e the costs in other years. We also calculated the total do llar amount of the subsidies base d on the volumes of renewable fuels and the subsidy that applies to each renewa ble fuel, and what the subsidized cost would be when the subsidies are applied.

624Table 4.4-25. Year-by-Year Annual Average and Per-Gallon Costs for Gasoline $53/bbl Crude Oil Price $92/bbl Crude Oil Price

Year Total Annual Cost $MM/yr Gasoline Cost c/gal Subsidy $MM/yr Subsidized Gasoline Cost c/gal Total Annual Cost $MM/yr Gasoline Cost c/gal Subsidy $MM/yr Subsidized Gasoline Cost c/gal 2009 -52 -0.03 153 -0.14 172 0.12 153 0.01 2010 37 0.03 468 -0.29 441 0.29 468 -0.02 2011 513 0.34 783 -0.18 540 0.36 783 -0.16 2012 1,363 0.90 1,208 0.10 536 0.35 1,208 -0.44 2013 2,244 1.47 2,028 0.14 705 0.46 2,028 -0.87 2014 3,738 2.44 3,116 0.41 1,451 0.95 3,116 -1.09 2015 5,761 3.74 4,733 0.67 2,268 1.47 4,733 -1.60 2016 7,508 4.85 6,073 0.93 2,700 1.74 6,073 -2.18 2017 8,910 5.75 7,326 1.02 2,916 1.88 7,326 -2.84 2018 10,384 6.70 8,794 1.03 3,268 2.11 8,794 -3.56 2019 11,747 7.57 10,248 0.97 3,319 2.14 10,248 -4.46 2020 13,031 8.42 12,245 0.51 3,154 2.04 12,245 -5.87 2021 14,998 9.67 15,207 -0.14 2,867 1.85 15,207 -7.96 2022 16,957 10.91 17,708 -0.48 2,962 1.91 17,708 -9.49 2023 16,584 10.71 16,488 0.06 3,517 2.27 16,488 -8.38 2024 16,259 10.54 16,488 -0.15 2,979 1.93 16,488 -8.76 2025 15,867 10.34 16,488 -0.40 3,079 2.01 16,488 -8.74 2026 15,501 10.12 16,488 -0.64 2,732 1.78 16,488 -8.98 2027 15,806 10.31 16,488 -0.44 2,231 1.46 16,488 -9.30 2028 15,427 10.07 16,488 -0.69 1,771 1.16 16,488 -9.60 2029 15,005 9.79 16,488 -0.97 1,372 0.89 16,488 -9.86 2030 14,725 9.58 16,488 -1.15 977 0.64 16,488 -10.09

625Table 4.4-26. Year-by-Year Annual Average and P er-Gallon Costs for Diesel Fuel $53/bbl Crude Oil Price $92/bbl Crude Oil Price

Year Total Annual Cost $MM/yr Diesel Fuel Cost c/gal Subsidy $MM/yr Subsidized Diesel Fuel Cost c/gal Total Annual Cost $MM/yr Diesel Fuel Cost c/gal Subsidy $MM/yr Subsidized Diesel Fuel Cost c/gal 2009 83 0.15 83 0.00 84 0.15 83 0.00 2010 260 0.47 235 0.04 226 0.41 235 -0.02 2011 460 0.80 387 0.13 350 0.61 387 -0.07 2012 814 1.40 640 0.30 533 0.92 640 -0.18 2013 890 1.52 639 0.43 505 0.86 639 -0.23 2014 1,103 1.86 729 0.63 549 0.93 729 -0.30 2015 1,155 1.93 707 0.75 503 0.84 707 -0.34 2016 1,051 1.73 596 0.75 396 0.65 596 -0.33 2017 1,048 1.71 583 0.76 348 0.57 583 -0.38 2018 1,001 1.62 571 0.69 296 0.48 571 -0.44 2019 952 1.52 558 0.63 229 0.37 558 -0.53 2020 906 1.43 546 0.57 164 0.26 546 -0.60 2021 864 1.35 533 0.51 93 0.14 533 -0.69 2022 817 1.26 521 0.46 49 0.08 521 -0.73 2023 798 1.21 521 0.42 87 0.13 521 -0.66 2024 781 1.17 521 0.39 61 0.09 521 -0.69 2025 761 1.12 521 0.35 67 0.10 521 -0.67 2026 742 1.08 521 0.32 49 0.07 521 -0.69 2027 758 1.09 521 0.34 23 0.03 521 -0.71 2028 739 1.04 521 0.31 0 0.00 521 -0.74 2029 717 1.00 521 0.27 -21 -0.03 521 -0.75 2030 703 0.96 521 0.25 -41 -0.06 521 -0.77

4.4.2.2 Sensitivity Cases

We also ran two sensitivity cases to the 2022 control case. The two sensitivity cases were: 1) ethanol use is increased beyond the re ference case until the gasoline pool is saturated with ethanol at 10 percent, and 2) ethanol, biodiesel and renewable diesel fuel are used at the same volumes as the primary year 2022 control cas e, however, the ethanol is primarily blended with gasoline at 20 percent which dramatically reduces the volume of E85 required. We ran sensitivity cases to isolate aspect s of the primary control case. We ran the E10 case to isolate the effects of E10 blending from thatof E85 on gasoline fuel properties and costs. We ran the midethanol blend sensitivity case to assess what might happen to gaso line fuel properties and costs if a waiver were to be granted for mid-et hanol blends as some have suggested.

6264.4.2.2.1 E10 Sensitivity Case

Tables 4.4-27 and 4.4-28 summarize the costs for the E10 sensitivity case ignoring federal ethanol tax subsidies and at two different crude oil prices. The costs are reported by different cost component as well as a ggregated total and the per-gallon costs. J JJJJJJJJJ This estimate of costs reflects the changes in gasoli ne that are occurring wi th the expanded use of ethanol. The operating co sts include the labor, utility and ot her operating costs and are a direct output from the refinery model. These costs are adjusted to reflect ethanol’s production cost plus distribution costs inst ead of the ethanol prices used in the refinery cost model. The fixed costs were assumed to be 3 percent of the capital co sts. The costs associated with lower energy density gasoline are accounted for using the frac tional change in energy density shown in Table 4.4-28 multiplied times the wholesale price of ga soline. By ignoring the federal ethanol tax subsidies in the table, we avoid the transfer pa yments caused by these subsidies that would hide a portion of the program’s costs.

Table 4.4-27. Summary of E10 Case Costs (wit hout Ethanol Tax Subsidies) (million dollars/year and c/gal, except as noted; 2004 dollars, 7% ROI before taxes, $53/bbl crude oil price) Cost to Gasoline Refinery Capital Costs ($Billion) -1.23 Amortized Refinery Capital Costs ($MM/yr) -131 Fixed Operating Cost ($MM/yr) -36 Variable Operating Cost ($MM/yr) 940 Lower Energy Density ($MM/yr) 1,930 Total Cost ($MM/yr) 2,704 Refinery Capital Costs (c/gal) -0.08 Fixed Operating Cost (c/gal) -0.02 Variable Operating Cost (c/gal) 0.76 Lower Energy Density (c/gal) 1.13 Total Cost Excluding Subsidies (c/gal) 1.79

JJJJJJJJJJ EPA typically assesses social benefits and costs of a rulemaking. However, this analysis is more limited in its scope by sometimes relying on the marginal production price which includes some market distortions , while other times relying on the average production cost for ethanol.

627Table 4.4-28. Summary of E10 Case Costs (wit hout Ethanol Tax Subsidies) (million dollars/year and c/gal, except as noted; 2004 dollars, 7% ROI before taxes, $92/bbl crude oil price) Cost to Gasoline Amortized Refinery Capital Costs ($MM/yr) -131 Fixed Operating Cost ($MM/yr) -36 Variable Operating Cost ($MM/yr) -1,348 Lower Energy Density ($MM/yr) 2,690 Total Cost ($MM/yr) 1,180 Refinery Capital Costs (c/gal) -0.08 Fixed Operating Cost (c/gal) -0.02 Variable Operating Cost (c/gal) -0.69 Lower Energy Density (c/gal) 1.57 Total Cost Excluding Subsidies (c/gal) 0.78

Our analysis shows that when considering all the costs associated with these fuel changes resulting from the expanded use of ethanol, that increased ethanol use up to 100 percent E10 will cost the U.S. $2.7 billion per year in the year 2022 when crude oil is pr iced at $53 per barrel. Expressed as per-gallon costs, th ese fuel changes would cost th e U.S. 1.79 cents per gallon of gasoline. At $92 per barrel crude oil, the a ddition of the ethanol up to 100 percent E10 is estimated to cost $1,180 million per year in 2022, and cost 0.78 cents per gallon. These cost impacts are for far lower than for the control cas e described above due to the far lower volumes of ethanol, but also the la ck of blending of E85.

Tables 4.4-29 and 4.4-30 expresses the to tal and per-gallon gasoline costs for the RFS case with the federal ethanol, biodiesel and rene wable diesel subsidies included with crude oil priced at $53/bbl and $9 2/bbl, respectively. The federal ta x subsidy is 45 cents per gallon for each gallon of new corn-based ethanol blended into gasoline and 101 cents per gallon for each gallon of cellulosic ethanol. The biodiesel s ubsidy is 100 cents per gallon, and the renewable diesel fuel subsidy is 50 cents per gallon. Th e cost adjustment is estimated by multiplying the subsidy times the volume of new et hanol, biodiesel and renewable dies el estimated to be used.

Table 4.4-29. Estimated E10 Case Cost (Reflecting Ethanol Tax Subsidies) (million dollars/year and cents/gallon; 2004 dollars, 7% ROI before taxes, $53/bbl crude oil price) Cost to Gasoline Total Cost ($MM/yr) 2,704 Federal Subsidy ($MM/yr) -3,491 Revised Total Cost ($MM/yr) -787 Per-Gallon Cost Excluding Subsidies (c/gal) 1.79 Federal Subsidy (c/gal) -2.32 Total Cost Including Subsidies (c/gal) -0.53

628Table 4.4-30. Estimated E10 Case Cost (Reflecting Ethanol Tax Subsidies) (million dollars/year and cents/gallon; 2004 dollars, 7% ROI before taxes, $92/bbl crude oil price) Cost to Gasoline Total Cost ($MM/yr) 1,179 Federal Subsidy ($MM/yr) -3,491 Revised Total Cost ($MM/yr) -2,312 Per-Gallon Cost Excluding Subsidies (c/gal) 0.78 Federal Subsidy (c/gal) -2.32 Total Cost Including Subsidies (c/gal) -1.54

The cost including subsidies may better represen t gasoline and diesel fuel’s apparent cost as reflected to the fuel industry as a whole a nd to consumers “at the pump” because the federal subsidies tends to transfer a porti on of the actual costs. Our analys is estimates that when the oil price is $53/bbl, the fuel industr y and consumers will see a 0.53 cent per gallon decrease in the apparent cost of producing gasoline. At the hi gher oil price of $92/bbl, the apparent cost of producing gasoline is estimated to decrease by 1.54 cents per gallon.

Table 4.4-31 summarizes the volumetric inputs to refineries in each PADD for this E10 control case.

629Table 4.4-31. Summary of the Total and Incremental Vo lumetric Refinery Inputs by PADD (thousand barrels/day)

PADD 1 PADD 2 PADD 3 PADD 4/5 CA Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case PADD Crude 1,525 0 3,444 -118 9,037 -63 1,750 3 2,138 1 GTL Naphtha 0 0 0 0 0 0 0 0 0 0 GTL Diesel 0 0 0 0 0 0 0 0 0 0 VGO HS 0 0 0 0 613 -8 0 0 28 -1 V G O L S 0 0 1 0 0 0 0 0 0 0 HS AR (A960) 0 0 0 0 0 0 0 0 0 0 LS AR (Alg) 205 4 0 0 137 8 0 0 0 0 Normal Butane 25 -1 45 -10 112 0 36 -1 40 0 Isobutane 9 0 23 2 62 -4 13 -1 23 0 M T B E 0 0 0 0 0 0 0 0 0 0 Ethanol - E10 404 58 250 59 217 121 87 31 172 0 Ethanol - E20 0 0 0 0 0 0 0 0 0 0 Ethanol - E85 0 0 0 0 0 0 0 0 0 0 R e f o r m e r F e e d 0 0 0 0 0 0 0 0 0 0 Methanol 0 0 0 0 0 0 0 0 0 0 N a t u r a l G a s ( F O E ) 8 0 1 1 4 2 2 5 7 6 6 1 2 2 1 1 7 2 0 Hydrogen (MSCF) 0 0 0 0 0 0 0 0 0 0 Pentanes Plus 0 0 32 0 52 0 19 0 0 0 Import CBOB 10% 421 421 0 0 0 0 0 0 0 0 Import CBOB 20% 0 0 0 0 0 0 0 0 0 0 Import RBOB 10% 187 -13 0 0 0 0 0 0 0 0 Import RBOB 20% 0 0 0 0 0 0 0 0 0 0 Import Alkylate 31 15 0 0 0 0 0 0 0 0 Import Raffinate 33 -15 0 0 0 0 0 0 46 0 Import Reformate 0 0 0 0 0 0 0 0 0 0 Import FCC Naphtha 0 0 0 0 0 0 0 0 0 0 Import Lt Naphtha 0 0 0 0 21 -9 0 0 1 0 Import Hvy Naph 0 0 0 0 42 0 0 0 0 0 Transfer Lt Naphtha 0 0 0 0 0 0 0 0 22 -2 Transfer Reformate 0 0 0 -5 0 0 0 0 0 0 Transfer Alkylate 60 0 10 7 0 0 0 0 60 0 Transfer FCC Naphth a 0 0 0 0 0 0 0 0 0 0 Transfer Raffinate 0 0 0 -2 0 0 0 0 60 0 Transfer RBOB 10% 734 38 79 29 0 0 0 0 0 0 Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0 Transfer CBOB 10% 1,300 2 421 -29 0 0 14 14 0 0 Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0 Isooctane 0 0 0 0 0 0 0 0 29 1 Isooctene 10 -1 2 0 0 0 0 0 0 0

Table 4.4-32 below summarizes the refinery output volumes and changes in refinery output volumes relative to th e reference case by PADD.

630Table 4.4-32. Summary of Total and Incrementa l Refinery Outputs by PADD (thousand barrels/day)

PADD 1 PADD 2 PADD 3 PADD 4/5 CA Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Propane 35 1 59 -5 139 -6 27 -1 59 1 Propylene 19 0 43 0 245 0 2 0 12 0 N o r m a l B u t a n e 0 0 3

- 1 4 7 2 0 0 0 0 Isobutane 0 0 0 0 0 0 0 0 10 1 PC Naphtha 16 0 40 0 433 0 0 0 0 0 PC Gasoil 0 0 527 -29 158 0 0 0 0 0 CG Reg 0 -73 0 -552 0 -1,176 0 -246 0 0 CG Prem 0 0 0 0 0 -2 0 -33 0 0 CG E10 Reg 1,935 455 1,745 573 1,334 1,192 723 273 114 2 CG E10 Prem 339 80 332 4 254 3 138 38 22 0 RFG E10 Reg 1,455 34 332 4 469 6 0 0 1,312 0 RFG E10 Prem 273 2 63 1 89 1 0 0 250 0 CG E20 Reg 0 0 0 0 0 0 0 0 0 0 RFG E20 Reg 0 0 0 0 0 0 0 0 0 0 E 8 5 t o C G 0 0 0 0 0 0 0 0 0 0 E85 to RFG 0 0 0 0 0 0 0 0 0 0 Transfer RBOB 10% 0 0 0 0 813 67 0 0 0 0 Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0 Transfer CBOB 10% 0 0 0 0 1,735 -13 0 0 0 0 Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0 Jet/Kero A (450ppm) 27 0 115 0 1,363 0 380 0 300 0 X-Fer Diesel Rundown 0 0 0 0 0 0 0 0 0 0 HSD Gr 76 (0.2%) 0 0 0 0 0 0 0 0 0 0 L S D G r 7 4 (

. 0 5 % ) 0 0 0 0 0 0 0 0 0 0 ULSD (15 ppm) 556 5 627 -50 2,210 -14 524 12 0 0 CARB Diesel 0 0 0 0 0 0 0 0 354 -4 X-Fer C5's to Storage 0 0 0 0 0 0 0 0 0 0 1% Residual Fuel 0 0 0 0 0 0 0 0 0 0 Residual Fuel 70 0 57 0 269 -3 149 4 74 2 Slurry 31 -1 87 -3 158 0 1 1 30 0 Asphalt & Wax 143 0 210 0 158 0 5 0 44 0 G a s o i l 0 0 9 0 0 0 0 0 1 0 0 Lubes 19 0 17 0 158 0 0 0 20 0 Benzene 11 0 11 0 51 0 0 0 0 0 Toluene 0 0 0 0 35 0 0 0 0 0 Xylenes 0 0 0 0 8 0 0 0 0 0 Cumene 0 0 0 0 0 0 0 0 0 0 Cyclohexane 0 0 0 0 0 0 0 0 0 0 Transfer Raffinate 0 0 0 0 60 -2 0 0 0 0 Transfer Alkylate 0 0 0 0 130 7 0 0 0 0 Transfer Reformate 0 0 0 0 0 -5 0 0 0 0 Transfer FCC naphtha 0 0 0 0 0 0 0 0 0 0 Transfer Lt Naphtha 0 0 0 0 0 0 0 0 22 -2 Transfer Blendstock 0 0 0 0 0 0 0 0 0 0 Sulfur (STons) 1 0 4 0 17 0 3 0 4 0 Coke (STon) 2 0 13 -1 68 -1 7 0 20 0

Table 4.4-34 summarizes the change in refinery unit throughputs by PADD comparing the E10 case to the reference case.

631Table 4.4-34. Change in Refinery Unit Capacities by PADD relative to the Reference Case (thousand barrels/day) Refinery Unit PADD 1 PADD 2 PADD 3 PADD 4/5 CA U.S. Crude Tower 0 -202 -59 0 0 -261 Vacuum Towe r 0

- 9 0

- 2 9 4 0

- 1 1 5 Sats Gas Plant 0 0 -10 2 0 -8 Unsats Gas Plant 0 -1 -6 0 0 -6 FCC DeC5 Tower 0 0 0 0 0 0 FCC 0 0 -18 0 -4 -22 FCC Splitte r 1

- 6

- 1 0 0

- 1

- 1 6 Hydrocracke r 0 0 0 3 0 3 H

- O i l U n i t 0 0 0 0 0 0 Delayed Coker 0 0 -11 0 0 -11 Visbreake r 0 0 0 0 0 0 Thermal Naphtha Splitter 0 -4 -1 0 0 -5 CRU Reforme r 0 0 0 0 0 0 S R U R e f o r m e r 0 0 0 0 3 3 B T X R e f o r m e r 0 0 0 0 0 0 C4 Isomerization 0 0 0 0 0 0 C5/C6 Isomerization 4 0 0 0 0 4 H F A l k y l a t i o n 0 0 0 0 0 0 H2SO4 Alkylation 0 0 -7 2 -5 -9 D i m e r s o l 0 0 0 0 0 0 Cat Pol y 0 0 0 0 0 0 Isooctane 0 0 0 0 0 0 DHT - Total 7 0 -12 11 0 6 DHT 2nd RCT - Total 6 -25 -11 9 0 -21 DHT Arom Saturation 0 0 0 0 0 0 NHT - Total Fd 1 0 0 -1 0 0 CGH - Generic -4 16 12 -15 0 8 CGH - Olefin Sat'n 0 0 0 0 0 0 FCCU Fd HDT 7 0 0 -29 0 -22 LSR Splitter 0 17 0 0 0 17 LSR Bz Saturator 0 0 0 0 0 0 Reformate Saturato r -2 0 0 0 0 -2 Reformate Splitter -6 0 -1 0 0 -7 S D A 0 0 0 0 0 0 M T B E 0 0 0 0 0 0 T A M E 0 0 0 0 0 0 Hydrogen Plant - Total MSCF 14 -16 107 17 -3 119 Lube Unit 0 0 0 0 0 0 Sulfur Plant 0 0 -98 -41 -5 -143 M e r o x J e t 0 0 0 0 0 0 M e r o x D i e s e l 0 0 0 0 0 0 B T X R e f o r m e r - T o w e r f e e d 0 0 0 0 0 0 BTX Reformer - Extract feed 0 0 0 0 0 0

632Table 4.4-35. Change in Refinery Unit Investments by PADD (million dollars/yr) Refinery Unit PADD 1 PADD 2 PADD 3 PADD 4/5 CA U.S. Crude Tower 0 -486 -62 0 0 -547 Vacuum Towe r 0 -219 -34 83 0 -170 Sats Gas Plant 0 0 -38 5 0 -34 Unsats Gas Plant 2 0 -52 0 0 -49 FCC DeC5 Tower 0 0 0 0 0 0 FCC 0 0 -225 0 -26 -251 FCC Splitte r 0

- 6

- 3 0 0

- 1 0 Hydrocracke r 0 0 0 4 9 0 4 9 H

- O i l U n i t 0 0 0 0 0 0 Delayed Coker 0 0 -121 0 0 -121 Visbreake r 0 0 0 0 0 0 Thermal Naphtha Splitter 0 -4 0 0 0 -5 CRU Reforme r 0 0 0 0 0 0 SRU Reformer 0 0 0 0 71 71 B T X R e f o r m e r 0 0 0 0 0 0 C4 Isomerization 0 0 0 0 0 0 C5/C6 Isomerization 26 0 0 0 0 26 H F A l k y l a t i o n 0 0 0 0 0 0 H2SO4 Alkylation 5 0 -57 20 -104 -136 D i m e r s o l 0 0 0 0 0 0 Cat Pol y 0 0 0 0 0 0 Isooctane 0 0 0 0 0 0 DHT - Total 45 0 -125 64 0 -15 DHT 2nd RCT - Total 28 -167 -34 40 0 -133 DHT Arom Saturation 0 0 0 0 0 0 NHT - Total Fd 4 0 0 -4 0 0 CGH - Generic -22 101 28 -58 0 49 CGH - Olefin Sat'n 0 0 0 0 0 0 FCCU Fd HDT 51 0 0 -314 0 -264 LSR Splitter 0 7 0 0 0 7 LSR Bz Saturator 0 0 0 0 0 0 Reformate Saturato r -9 0 -1 0 0 -10 Reformate Splitter -15 0 -1 0 0 -15 S D A 0 0 0 0 0 0 M T B E 0 0 0 0 0 0 T A M E 0 0 0 0 0 0 Hydrogen Plant - Total MSCF 38 -37 284 87 -40 332 Lube Unit 0 0 0 0 0 0 Sulfur Plant 0 0 0 0 0 0 M e r o x J e t 0 0 0 0 0 0 M e r o x D i e s e l 0 0 0 0 0 0 B T X R e f o r m e r - T o w e r f e e d 0 0 0 0 0 0 BTX Reformer - Extract feed 0 0 0 0 0 0 Total 153 -811 -440 -29 0 -1,226

These changes in refinery unit throughputs are associated with changes in capital investments. Table 4.4-35 summa rizes the projected change in capital investments between the reference case and the EIA control case. This Table shows that increm ental to the reference case, refiners are expected to reduce their capital investments by $1.2 billion compared to business as usual. Most of the reduction occurs in PADDs 2 and 3. This cap ital cost decrease is countered by the capital costs incurred to bui ld new ethanol plants and put into place the distribution system that the new ethanol requires.

633Table 4.4-36. Gasoline Volume, Quality and Energy Density by Gasoline Type at the PADD Terminal

PADD 1 PADD 2 PADD 3 PADD 4/5 CA US RFG Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control Total ('000 BPD) 1,692 1,728 391 396 551 558

- - 1,562 1,562 4,197 4,244 Ethanol ('000 BPD) 171 175 39 40 56 56

- - 158 158 424 429 RVP (psi) 10.7 10.8 10.4 10.6 9.7 9.8

- - 9.5 9.5 10.1 10.2 Sulfur (ppm) 23 24 23 24 20 22

- - 9 9 17 18 Octane (R+M/2) 88.1 88.1 88.0 88.0 88.0 88.0

- - 87.6 87.6 87.9 87.9 Aromatics (vol%) 19.9 19.9 20.0 20.0 19.5 19.5

- - 21.3 21.3 20.4 20.4 Benzene (vol%) 0.55 0.56 0.58 0.58 0.51 0.51

- - 0.51 0.51 0.53 0.54 Olefins (vol%) 11.7 12.2 10.3 10.6 8.7 9.3

- - 5.7 5.7 8.9 9.3 Oxygen (wt%) 3.7 3.7 3.7 3.7 3.7 3.7

- - 3.7 3.7 3.7 3.7 E200 (vol%) 55.1 55.3 59.7 57.8 54.3 53.9

- - 57.7 57.6 56.4 56.2 E300 (vol%) 93.9 93.9 84.3 82.9 87.4 85.8

- - 86.2 86.2 89.3 89.0 Energy (MMBtu/Bbl ) 4.954 4.950 4.920 4.939 4.983 4.976

- - 4.991 4.992 4.969 4.968 CG Total ('000 BPD) 2,263 2,274 2,052 2,078 1,568 1,588 829 1,588 135 136 6,847 6,937 Ethanol ('000 BPD) 176 230 152 210 40 160 56 160 14 14 436 701 RVP (psi) 11.6 11.8 11.7 11.5 10.7 10.7 11.3 10.7 10.5 10.6 11.4 11.4 Sulfur (ppm) 24 24 23 24 24 22 27 22 25 24 24 24 Octane (R+M/2) 87.7 88.1 88.0 88.0 88.0 88.0 86.9 88.0 89.1 89.2 87.8 87.9 Aromatics (vol%) 24.2 22.3 24.4 22.5 27.2 22.2 19.4 22.2 28.4 29.0 24.4 21.7 Benzene (vol%) 0.61 0.51 0.64 0.53 0.59 0.50 1.16 0.50 0.60 0.60 0.68 0.58 Olefins (vol%) 13.9 14.5 11.1 10.7 14.9 12.1 6.9 12.1 17.1 16.6 12.5 11.9 Oxygen (wt%) 2.9 3.7 2.7 3.7 0.9 3.7 2.5 3.7 3.6 3.6 2.3 3.7 E200 (vol%) 55.4 55.4 55.7 58.9 46.5 55.0 59.6 55.0 55.8 55.2 54.0 57.3 E300 (vol%) 93.4 93.9 84.3 82.9 87.4 85.8 83.5 85.8 86.2 86.2 87.9 88.1 Energy (MMBtu/Bbl ) 4.980 4.931 4.965 4.923 5.069 4.971 5.011 4.971 4.969 4.973 4.999 4.942 All Mogas Total ('000 BPD) 3,955 4,003 2,443 2,473 2,120 2,146 829 861 1,697 1,698 11,044 11,181 Ethanol ('000 BPD) 347 404 191 250 95 217 56 87 171 172 860 1,129 RVP (psi) 11.2 11.3 11.5 11.4 10.5 10.4 11.3 11.4 9.6 9.6 10.9 10.9 Sulfur (ppm) 24 24 23 24 23 22 27 28 10 10 22 22 Octane (R+M/2) 87.9 88.1 88.0 88.0 88.0 88.0 86.9 86.9 87.8 87.8 87.8 87.9 Aromatics (vol%) 22.4 21.3 23.7 22.1 25.2 21.5 19.4 16.3 21.8 21.9 22.9 21.2 Benzene (vol%) 0.58 0.53 0.63 0.54 0.57 0.50 1.16 1.04 0.52 0.52 0.62 0.57 Olefins (vol%) 12.9 13.5 10.9 10.6 13.3 11.4 6.9 6.7 6.6 6.6 11.1 10.9 Oxygen (wt%) 3.2 3.7 2.9 3.7 1.7 3.7 2.5 3.8 3.7 3.7 2.9 3.7 E200 (vol%) 55.3 55.3 56.4 58.7 48.5 54.7 59.6 63.3 57.5 57.4 54.9 56.9 E300 (vol%) 93.6 93.9 84.3 82.9 87.4 85.8 83.5 90.2 86.2 86.2 88.4 88.5 Energy (MMBtu/Bbl ) 4.969 4.939 4.958 4.926 5.046 4.972 5.011 4.957 4.989 4.990 4.988 4.952

4.4.2.2.2 Mid-Ethanol Bl end Sensitivity Case

For this case, the year 2022 control case volumes were run assuming that ethanol was mostly blended as E20. About 10 percent of the gasoline pool was required to remain E10 to provide gasoline that would be compatible w ith nonroad gasoline powered engines and legacy cars. Most of the remainder of the gasoline wa s blended with ethanol at 20 percent by volume until the gasoline pool was saturated, allowing for a small amount of E85 to achieve the necessary volume of ethanol to comply with the RFS2 standard.

Tables 4.4-37 and 4.4-38 summarize the cost s for the RFS case excluding federal ethanol, biodiesel and renewable fuels consumption subsidies with cr ude oil priced at $53/bbl and $92/bbl, respectively. The costs are reported by different cost component as well as aggregated total and the per-gallon costs. K KKKKKKKKK The operating costs include the labor, utility and other

634operating costs and are a direct output from the re finery model. These costs are adjusted to reflect ethanol’s producti on 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 us ing the fractional change in energy density shown in Table 4.4-37, multiplied times the wholesale price of gasoline.

Table 4.4-37. Summary of Mid-Ethanol Blend Case Costs (without Tax Subsidies) (million dollars/year and c/gal, except as noted; 2004 dollars, 7% ROI before taxes, $53/bbl crude oil price) Cost to Gasoline Cost to Diesel Fuel Refinery Capital Costs ($Billion) -7.30

- Amortized Refinery Capital Costs ($MM/yr) -780

- Fixed Operating Cost ($MM/yr) -215

- Variable Operating Cost ($MM/yr) 5,404 590 Lower Energy Density Gasoline ($MM/yr) 9,630 187 Total Cost ($MM/yr) 14,043 777 Refinery Capital Costs (c/gal) -0.44

- Fixed Operating Cost (c/gal) -0.12

- Vaiable Operating Cost (c/gal) 4.15 0.66 Lower Energy Density Gasoline (c/gal) 5.45 0.54 Total Cost Excluding Subsidies (c/gal) 9.04 1.20

Table 4.4-38. Summary of Mid-Ethanol Blend Case Costs (without Tax Subsidies) (million dollars/year and c/gal, except as noted; 2004 dollars, 7% ROI before taxes, $92/bbl crude oil price) Cost to Gasoline Cost to Diesel Fuel Amortized Refinery Capital Costs ($MM/yr) -780

- Fixed Operating Cost ($MM/yr) -215

- Variable Operating Cost ($MM/yr) -16,662 -419 Lower Energy Density Gasoline ($MM/yr) 16,390 373 Total Cost ($MM/yr) -1,267 -46 Refinery Capital Costs (c/gal) -0.44

- Fixed Operating Cost (c/gal) -0.12

- Variable Operating Cost (c/gal) -9.53 -1.15 Lower Energy Density Gasoline (c/gal) 9.28 1.08 Total Cost Excluding Subsidies (c/gal) -0.82 -0.07

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. $14.0 billion per year in the year 2022 when crude oil is pr iced at $53 per barrel. Expressed as per-gallon costs, th ese fuel changes would cost th e U.S. 9.04 cents per gallon of gasoline. The addition of biodies el and renewable diesel fuel is estimated to cost about $780 million per year in the year 2022, or cost 1.20 cents per gallon.

At $92 per barrel crude o il, the addition of the ethanol is es timated to decrease the cost of

KKKKKKKKKK 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 ga soline without accountin g for the effects of farm subsidies that tend to distort the market price of agricultural commodities.

635producing gasoline in 2022 by 1.3 billion per year , or 0.82 cents per gallon. The addition of biodiesel and renewable diesel fuel is estimated to reduce the cost of di esel fuel by 46 million dollars in 2022, and reduce th e per-gallon cost of diesel fuel by 0.07 cents per gallon.

Tables 4.4-39 and 4.4-40 expresses the to tal and per-gallon gasoline costs for the RFS case with the federal ethanol, biodiesel and rene wable diesel subsidies included with crude oil priced at $53/bbl and $9 2/bbl, respectively. The federal ta x subsidy is 45 cents per gallon for each gallon of new corn-based ethanol blended into gasoline and 101 cents per gallon for each gallon of cellulosic ethanol. The biodiesel s ubsidy is 100 cents per gallon, and the renewable diesel fuel subsidy is 50 cents per gallon. Th e cost adjustment is estimated by multiplying the subsidy times the volume of new et hanol, biodiesel and renewable dies el estimated to be used.

Table 4.4-39. Estimated Mid-Ethanol Blend Case Co sts (Reflecting Tax Subsidies) (million dollars/year and cents/gallon; 2004 dollars, 7% ROI before taxes, $52/bbl crude oil price) Gasoline Costs Diesel Fuel Costs Total Cost ($MM/yr) 11,561 777 Federal Subsidy ($MM/yr) -17,699 -525 Revised Total Cost ($MM/yr) -6,139 252 Per-Gallon Cost Excluding Subsidies (c/gal) 7.44 1.20 Federal Subsidy (c/gal) -11.39 -0.81 Total Cost Including Subsidies (c/gal) -3.95 0.39

Table 4.4-40. Estimated Mid-Ethanol Blend Case Co sts (Reflecting Tax Subsidies) (million dollars/year and cents/gallon; 2004 dollars, 7% ROI before taxes, $93/bbl crude oil price) Gasoline Costs Diesel Fuel Costs Total Cost ($MM/yr) -3,749 -46 Federal Subsidy ($MM/yr) -17,699 -535 Revised Total Cost ($MM/yr) -21,449 -571 Per-Gallon Cost Excluding Subsidies (c/gal) -2.41 -0.07 Federal Subsidy (c/gal) -11.39 -0.81 Total Cost Including Subsidies (c/gal) -13.80 -0.88

The cost including subsidies may better represen t gasoline and diesel fuel’s apparent cost as reflected to the fuel industry as a whole a nd to consumers “at the pump” because the federal subsidies tends to transfer a porti on of the actual costs. Our analys is estimates that when the oil price is $53/bbl, the fuel industr y and consumers will see a 3.95 cent per gallon decrease in the apparent cost of producing gasoline, and a 0.39 in crease in the apparent cost of producing diesel fuel. At the higher oil price of $93/bbl, the appa rent cost of producing ga soline is estimated to decrease by 13.8 cents per gallon, and the apparent cost of produci ng diesel fuel is expected to decrease by 0.88 cents per gallon.

Table 4.4-41 summarizes the volumetric i nputs to refineries in each PADD for this control case. Because of the increased use of biofuels, petroleum inputs would be expected to decrease, and this is confirmed.

636Table 4.4-41. Summary of the Total and Incremental Vo lumetric Refinery Inputs by PADD (thousand barrels/day)

PADD 1 PADD 2 PADD 3 PADD 4/5 CA Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case PADD Crude 1,525 0 3,316 -247 8,633 -467 1,703 -44 2,121 0 GTL Naphtha 0 0 0 0 0 0 0 0 0 0 GTL Diesel 0 0 0 0 0 0 0 0 0 0 VGO HS 0 0 0 0 551 -70 0 0 7 0 VGO LS 0 0 1 0 0 0 0 0 0 0 HS AR (A960) 0 0 0 0 0 0 0 0 0 0 LS AR (Alg) 199 -2 0 0 199 70 0 0 0 0 Normal Butane 28 2 38 -16 112 0 42 5 40 0 Isobutane 14 4 22 1 62 -5 14 0 0 0 MTBE 0 0 0 0 0 0 0 0 0 0 Ethanol - E10 62 -285 39 -152 34 -62 13 -42 27 0 Ethanol - E20 699 699 438 438 358 358 153 153 310 0 Ethanol - E85 0 0 0 0 94 94 0 0 0 0 Reformer Feed 0 0 0 0 0 0 0 0 0 0 Methanol 0 0 0 0 0 0 0 0 0 0 Natural Gas (FOE) 78 -1 143 3 570 1 122 0 176 0 Hydrogen (MSCF) 0 0 0 0 0 0 0 0 0 0 Pentanes Plus 0 0 0 -32 52 0 8 -10 0 0 Import CBOB 10% 277 277 0 0 0 0 0 0 0 0 Import CBOB 20% 0 0 0 0 0 0 0 0 0 0 Import RBOB 10% 123 -77 0 0 0 0 0 0 0 0 Import RBOB 20% 0 0 0 0 0 0 0 0 0 0 Import Alkylate 25 9 0 0 0 0 0 0 0 0 Import Raffinate 39 -9 0 0 0 0 0 0 46 0 Import Reformate 0 0 0 0 0 0 0 0 0 0 Import FCC Naphtha 0 0 0 0 0 0 0 0 0 0 Import Lt Naphtha 0 0 0 0 16 -14 0 0 1 0 Import Hvy Naph 0 0 0 0 42 0 0 0 0 0 Transfer Lt Naphtha 0 0 4 4 0 0 0 0 0 0 Transfer Reformate 0 0 0 -5 0 0 0 0 0 0 Transfer Alkylate 60 0 6 3 0 0 0 0 23 0 Transfer FCC Naphtha 0 0 0 0 0 0 0 0 0 0 Transfer Raffinate 0 0 0 -2 0 0 0 0 60 0 Transfer RBOB 10% 650 -47 79 29 0 0 0 0 0 0 Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0 Transfer CBOB 10% 1,300 2 421 -29 0 0 17 17 0 0 Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0 Isooctane 7 7 0 0 0 0 0 0 42 0 Isooctene 21 10 0 -2 8 8 0 0 5 0

637Table 4.4-42. Summary of Total and Incrementa l Refinery Outputs by PADD (thousand barrels/day)

PADD 1 PADD 2 PADD 3 PADD 4/5 CA Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Control Case Difference from Ref Case Propane 33 -1 53 -11 130 -15 22 -6 55 -3 Propylene 19 0 43 0 245 0 2 0 12 0 Normal Butane 0 0 6 2 45 -1 0 0 0 0 Isobutane 0 0 0 0 0 0 0 0 19 10 PC Naphtha 16 0 40 0 433 0 0 0 0 0 PC Gasoil 0 0 495 -60 158 0 0 0 0 0 CG Reg 0 -73 0 -552 0 -1,176 0 -246 0 0 CG Prem 0 0 0 0 0 -2 0 -33 0 0 CG E10 Reg 0 -1,480 0 -1,172 0 -141 0 -450 0 -113 CG E10 Prem 334 75 317 -11 240 -11 134 34 18 -4 RFG E10 Reg 0 -1,421 0 -328 0 -463 0 0 0 -1,312 RFG E10 Prem 276 5 65 3 92 4 0 0 253 3 CG E20 Reg 2,009 2,009 1,825 1,825 1,397 1,397 757 757 125 125 RFG E20 Reg 1,449 1,449 343 343 373 373 0 0 1,409 1,409 E85 to CG 0 0 0 0 0 0 0 0 0 0 E85 to RFG 0 0 0 0 110 110 0 0 0 0 Transfer RBOB 10% 0 0 0 0 729 -17 0 0 0 0 Transfer RBOB 20% 0 0 0 0 0 0 0 0 0 0 Transfer CBOB 10% 0 0 0 0 1,738 -11 0 0 0 0 Transfer CBOB 20% 0 0 0 0 0 0 0 0 0 0 Jet/Kero A (450ppm) 27 0 115 0 1,363 0 380 0 300 0 X-Fer Diesel Rundown 0 t 0 0 0 0 0 0 0 0 0 HSD Gr 76 (0.2%) 0 0 0 0 0 0 0 0 0 0 LSD Gr 74 (.05%) 0 0 0 0 0 0 0 0 0 0 ULSD (15 ppm) 559 8 650 -27 2,182 -41 526 14 0 0 CARB Diesel 0 0 0 0 0 0 0 0 354 -4 X-Fer C5's to Storage 0 0 0 0 0 0 0 0 41 41 1% Residual Fuel 0 0 0 0 0 0 0 0 0 0 Residual Fuel 83 12 57 0 244 -28 146 1 54 -18 Slurry 29 -3 80 -9 153 -5 0 0 30 0 Asphalt & Wax 143 0 210 0 158 0 5 0 44 0 Gasoil 0 0 9 0 0 0 0 0 10 0 Lubes 19 0 17 0 158 0 0 0 20 0 Benzene 11 0 11 0 51 0 0 0 0 0 Toluene 0 0 0 0 35 0 0 0 0 0 Xylenes 0 0 0 0 8 0 0 0 0 0 Cumene 0 0 0 0 0 0 0 0 0 0 Cyclohexane 0 0 0 0 0 0 0 0 0 0 Transfer Raffinate 0 0 0 0 60 -2 0 0 0 0 Transfer Alkylate 0 0 0 0 89 -34 0 0 0 0 Transfer Reformate 0 0 0 0 0 -5 0 0 0 0 Transfer FCC naphtha 0 0 0 0 0 0 0 0 0 0 Transfer Lt Naphtha 0 0 0 0 4 4 0 0 0 -24 Transfer Blendstock 0 0 0 0 0 0 0 0 0 0 Sulfur (STons) 1 0 4 0 16 -1 2 0 4 0 Coke (STon) 1 0 12 -2 64 -5 7 0 21 1
Table 4.4-43 summarizes the change in refinery unit throughputs by PADD comparing the mid-ethanol blend case to the reference case.

638Table 4.4-43. Change in Refinery Unit Capacities by PADD relative to the Reference Case (thousand barrels/day) Refinery Unit PADD 1 PADD 2 PADD 3 PADD 4/5 CA U.S. Crude Tower 0 -421 -467 -41 -1 -930 Vacuum Tower 0 -188 -200 -11 0 -399 Sats Gas Plant 9 0 -24 -22 -14 -51 Unsats Gas Plant 0 -1 -46 0 -12 -59 FCC DeC5 Tower 0 0 0 0 0 0 FCC 0 0 -151 0 -63 -214 FCC Splitter 0 -6 -82 0 -20 -108 Hydrocracker 0 0 0 -5 0 -5 H

- O i l U n i t 0 0 0 0 0 0 Delayed Coker 0 0 -91 0 0 -91 V i s b r e a k e r 0 0 0 0 0 0 Thermal Naphtha Splitter 0 -4 -11 0 1 -14 CRU Reformer 0 0 0 0 0 0 SRU Reformer 0 0 0 0 -30 -30 B T X R e f o r m e r 0 1 0 0 0 1 C4 Isomerization 0 0 0 0 -3 -3 C5/C6 Isomerization -16 0 0 0 0 -16 H F A l k y l a t i o n 0 0 0 0 0 0 H2SO4 Alkylation 27 0 -46 -8 -29 -56 D i m e r s o l 0 0 0 0 0 0 Cat Pol y 0 0 0 0 0 0 I s o o c t a n e 0 0 0 0 0 0 DHT - Total 10 0 -17 -1 0 -8 DHT 2nd RCT - Total 15 56 -17 -8 24 71 D H T A r o m S a t u r a t i o n 0 0 0 0 0 0 NHT - Total Fd 14 0 0 11 0 25 CGH - Generic 11 -49 -51 10 0 -79 C G H - O l e f i n S a t ' n 0 0 0 0 0 0 FCCU Fd HDT -1 0 0 -34 0 -35 LSR Splitter 0 17 0 0 0 17 L S R B z S a t u r a t o r 0 0 0 0 0 0 Reformate Saturator -16 -9 5 0 -50 -70 Reformate Splitter -48 -27 15 0 -150 -210 S D A 0 0 0 0 0 0 M T B E 0 0 0 0 0 0 T A M E 0 0 0 0 0 0 Hydrogen Plant - Total MSCF -2 81 253 17 83 432 Lube Unit 0 0 0 0 0 0 Sulfur Plant 0 0 -935 -98 -11 -1,044 M e r o x J e t 0 0 0 0 0 0 M e r o x D i e s e l 0 0 0 0 0 0 BTX Reformer - Tower feed 0 0 0 0 0 0 B T X R e f o r m e r - E x t r a c t f e e d 0 1 0 0 0 1

These changes in refinery unit throughputs are associated with changes in capital investments. Table 4.4-44 summa rizes the projected change in capital investments between the mid-ethanol blend case and the reference case.

639Table 4.4-44. Change in Refinery Unit Investments by PADD (million dollars/yr) Refinery Unit PADD 1 PADD 2 PADD 3 PADD 4/5 CA U.S. Crude Tower 0 -995 -739 -154 -1 -1,889 Vacuum Tower 0 -527 -423 -18 -1 -968 Sats Gas Plant 53 0 -63 -102 -68 -181 Unsats Gas Plant 1 0 -240 0 -101 -341 FCC DeC5 Tower 0 0 0 0 0 0 FCC 0 0 -1,032 0 -786 -1,818 FCC Splitter 0 -6 -38 0 -16 -60 Hydrocracker 0 0 0 -95 0 -95 H

- O i l U n i t 0 0 0 0 0 0 Delayed Coker 0 0 -1,261 0 0 -1,261 V i s b r e a k e r 0 0 0 0 0 0 Thermal Naphtha Splitter 0 -5 -4 0 1 -9 CRU Reformer 0 0 0 0 0 0 SRU Reformer 0 0 0 0 -180 -180 B T X R e f o r m e r 2 1 1 0 0 0 1 3 C4 Isomerization 0 0 0 0 -15 -15 C5/C6 Isomerization -198 0 0 0 0 -198 H F A l k y l a t i o n 0 0 0 0 0 0 H2SO4 Alkylation 443 0 -518 -99 -508 -682 D i m e r s o l 0 0 0 0 0 0 Cat Pol y 0 0 0 0 0 0 I s o o c t a n e 0 0 0 0 0 0 DHT - Total 63 0 -146 -6 0 -89 DHT 2nd RCT - Total 66 354 -104 -34 186 468 D H T A r o m S a t u r a t i o n 0 0 0 0 0 0 NHT - Total Fd 97 0 0 35 0 132 CGH - Generic 46 -203 -202 34 0 -325 C G H - O l e f i n S a t ' n 0 0 0 0 0 0 FCCU Fd HDT -9 0 0 -349 0 -358 L S R S p l i t t e r 0 7 0 0 0 7 L S R B z S a t u r a t o r 0 0 0 0 0 0 Reformate Saturator -96 -56 14 0 -318 -455 Reformate Splitter -56 -33 8 0 -166 -247 S D A 0 0 0 0 0 0 M T B E 0 0 0 0 0 0 T A M E 0 0 0 0 0 0 Hydrogen Plant - Total MSCF -5 266 607 84 289 1,241 Lube Unit 0 0 0 0 0 0 Sulfur Plant 0 0 -2 -1 0 -3 M e r o x J e t 0 0 0 0 0 0 M e r o x D i e s e l 0 0 0 0 0 0 BTX Reformer - Tower feed 0 1 0 0 0 1 B T X R e f o r m e r - E x t r a c t f e e d 0 3 0 0 0 4 Total 407 -1,183 -4,144 -703 0 -7,308

Table 4.4-44 shows that incremental to the re ference case, refiners are expected to reduce their capital investments by $7.3 b illion compared to business as usual. Most of the reduction occurs in PADDs 2 and 3 where large volumes of ethanol, and other gasoline blendstocks are expected to enter the gasoline poo l. Of course, this capital cost decrease is countered by capital costs incurred to build new ethanol plants and pu t into place the distribution system that the new ethanol requires.

640Table 4.4-45 summarizes the gasoline qualitie s for the mid-ethanol blend case and the reference case.

Table 4.4-45. Gasoline Volume, Quality and Energy Density by Gasoline Type at the PADD Terminal

PADD 1 PADD 2 PADD 3 PADD 4/5 CA US RFG Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control Ref Case Control Total ('000 BPD) 1,692 1,725 391 408 551 465

- - 1,562 1,661 4,197 4,260 Ethanol ('000 BPD) 171 321 39 76 56 85

- - 158 310 424 791 RVP (psi) 10.7 10.3 10.4 10.2 9.7 9.1

- - 9.5 9.3 10.1 9.8 Sulfur (ppm) 23 19 23 18 20 22

- - 9 9 17 15 Octane (R+M/2) 88.1 90.2 88.0 90.2 88.0 90.3

- - 87.6 88.3 87.9 89.5 Aromatics (vol%) 19.9 15.6 20.0 15.8 19.5 15.0

- - 21.3 19.1 20.4 16.9 Benzene (vol%) 0.55 0.46 0.58 0.42 0.51 0.44

- - 0.51 0.47 0.53 0.46 Olefins (vol%) 11.7 10.8 10.3 7.6 8.7 8.2

- - 5.7 5.7 8.9 8.2 Oxygen (wt%) 3.7 6.8 3.7 6.8 3.7 6.7

- - 3.7 6.8 3.7 6.8 E200 (vol%) 55.1 64.2 59.7 65.0 54.3 60.1

- - 57.7 62.1 56.4 63.0 E300 (vol%) 93.9 89.5 84.3 88.2 87.4 85.6

- - 86.2 86.2 89.3 87.6 Energy (MMBtu/Bbl) 4.954 4.806 4.920 4.784 4.983 4.848

- - 4.991 4.905 4.969 4.847 CG Total ('000 BPD) 2,263 2,344 2,052 2,142 1,568 1,637 829 891 135 143 6,847 7,156 Ethanol ('000 BPD) 176 440 152 401 40 306 56 166 14 27 436 1,340 RVP (psi) 11.6 11.5 11.7 11.3 10.7 10.4 11.3 11.1 10.5 10.2 11.4 11.1 Sulfur (ppm) 24 22 23 23 24 21 27 28 25 26 24 23 Octane (R+M/2) 87.7 90.2 88.0 90.1 88.0 90.1 86.9 87.2 89.1 90.3 87.8 89.8 Aromatics (vol%) 24.2 20.2 24.4 20.4 27.2 20.2 19.4 13.4 28.4 24.4 24.4 19.5 Benzene (vol%) 0.61 0.45 0.64 0.46 0.59 0.45 1.16 1.02 0.60 0.55 0.68 0.53 Olefins (vol%) 13.9 12.9 11.1 10.2 14.9 11.0 6.9 6.3 17.1 16.8 12.5 10.9 Oxygen (wt%) 2.9 6.9 2.7 6.8 0.9 6.8 2.5 6.9 3.6 6.8 2.3 6.8 E200 (vol%) 55.4 62.2 55.7 64.1 46.5 61.1 59.6 68.5 55.8 64.9 54.0 63.3 E300 (vol%) 93.4 84.9 84.3 86.3 87.4 84.9 83.5 90.9 86.2 83.8 87.9 86.0 Energy (MMBtu/Bbl) 4.980 4.813 4.965 4.809 5.069 4.845 5.011 4.811 4.969 4.841 4.999 4.819 E85 Total ('000 BPD)

- 0

- 0

- 110

- 0

- 0

- 110 Ethanol ('000 BPD)

- 0

- 0

- 94

- 0

- 0

- 95 RVP (psi)

- 10.5

- 10.5

- 12.2

- 10.3

- 10.4

- 12.2 Sulfur (ppm)

- 10

- 10

- 9

- 10

- 9

- 9 Octane (R+M/2)

- 107.8 - 107.8

- 108.0

- 107.6 - 107.7

- 108.0 Aromatics (vol%)

- 0.7

- 0.7

- 0.0

- 0.7

- 0.7

- 0.0 Benzene (vol%)

- 0.02

- 0.02

- 0.00

- 0.05

- 0.02

- 0.00 Olefins (vol%)

- 2.1

- 2.1

- 2.3

- 0.2

- 1.1

- 2.3 Oxygen (wt%)

- 30.6

- 30.6

- 30.8

- 30.6

- 30.6

- 30.8 E200 (vol%)

- 128.1 - 128.0

- 129.9

- 127.7 - 127.8

- 129.9 E300 (vol%)

- 96.1

- 96.0

- 96.6

- 96.0

- 96.1

- 96.6 Energy (MMBtu/Bbl)

- 3.635

- 3.636

- 3.598

- 3.635 - 3.637

- 3.598 All Mogas Total ('000 BPD) 3,955 4,069 2,443 2,550 2,120 2,212 829 891 1,697 1,804 11,044 11,526 Ethanol ('000 BPD) 347 760 191 477 95 485 56 167 171 337 860 2,226 RVP (psi) 11.2 11.0 11.5 11.2 10.5 10.2 11.3 11.1 9.6 9.4 10.9 10.6 Sulfur (ppm) 24 21 23 22 23 21 27 28 10 11 22 20 Octane (R+M/2) 87.9 90.2 88.0 90.1 88.0 91.0 86.9 87.2 87.8 88.5 87.8 89.8 Aromatics (vol%) 22.4 18.3 23.7 19.6 25.2 18.1 19.4 13.4 21.8 19.5 22.9 18.3 Benzene (vol%) 0.58 0.46 0.63 0.46 0.57 0.43 1.16 1.02 0.52 0.48 0.62 0.50 Olefins (vol%) 12.9 12.0 10.9 9.8 13.3 10.0 6.9 6.3 6.6 6.6 11.1 9.9 Oxygen (wt%) 3.2 6.9 2.9 6.8 1.7 8.0 2.5 6.9 3.7 6.8 2.9 7.1 E200 (vol%) 55.3 63.0 56.4 64.2 48.5 64.3 59.6 68.5 57.5 62.4 54.9 63.8 E300 (vol%) 93.6 86.8 84.3 86.6 87.4 85.6 83.5 90.9 86.2 86.0 88.4 86.7 Energy (MMBtu/Bbl) 4.969 4.810 4.958 4.805 5.046 4.784 5.011 4.811 4.989 4.900 4.988 4.818

641

## Chapter 5: Economic Impacts and Benefits

5.1 Agricultural Impacts

5.1.1 Models Utilized

As described in the Preamble, we used a su ite of tools to model the potential domestic and international impacts of this rulemaking on the agricultural sector. The Forest and Agricultural Sector Optimiza tion Model (FASOM), developed by Professor Bruce McCarl of Texas A&M University and othe rs, provides detailed informati on on domestic agricultural and greenhouse gas impacts of renewable fuels. The Food and Agricultural Polic y Research Institute (FAPRI) at Iowa State University and the University of Missouri-Columbia maintains a number of econometric models that are capable of providing detailed information on impacts on international agricultural markets from the wi der use of renewable fuels in the U.S.

FASOM is a long-term economic model of the U.S. agriculture sector that maximizes total revenues for producers wh ile meeting the demands of co nsumers. Using a number of inputs, FASOM determines which crops, livestock , and processed agricultural products would be produced in the U.S. In each model simulation, cr ops 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., fiel d crops, livestock, and biofuel products). FASOM also estimates prices using co sts associated with th e 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 pri ces and production, however, as it is designed to identify longterm trends.

The FASOM model also contains a forestry component. Running both the forestry and agriculture components of the m odel would show the interaction between these two sectors. However, the analysis for this proposal only s hows the results from the agriculture component with no interaction from the forestry sector, as the forestry component of the model is in the process of being updated. We plan to utilize a co mplete version of the m odel for our analysis in the final rule, where agricultural land use impacts also affect forestry land use, and cellulosic ethanol produced from the fore stry sector will affect cellu losic ethanol production in the agriculture sector.

FASOM uses supply and demand curves for the 11 major U.S. domestic regions, L LLLLLLLLL which are calibrated to historic pr ice and production da ta. FASOM also includes detailed supply and demand data for co rn, wheat, soybeans, rice and sorghum across 37

LLLLLLLLLL U.S. regions consist of the Pacific Northwest (Wes t and East), Pacific Southwest, Rocky Mountains, Great Plains, Southwest, South Central, Corn Belt, Lake States, Southeast, and the Northeast.

642foreign regions. M MMMMMMMMM FASOM contains transportation co sts to all regions and then uses all of this information to determ ine U.S. exports to the point wher e prices are then equated in all markets.

We chose to use FASOM to model the full potential impacts on the domestic agricultural sector given higher rene wable fuel volumes, in part becau se FASOM also provides detailed greenhouse gas information resulting from these ch anges (see Chapter 2 of this RIA for more information). FASOM is not able to mode l land use and agricultural sector changes internationally, however. Therefore, we ar e working with the FAPRI models to better understand international agricultural impacts. Additional details on the FASOM model are included in the docket. N NNNNNNNNN

The FAPRI models are econometric models covering many agricultural commodities. These models capture the biolog ical, technical, and economic relationships among key variables within a particular commodity and across commod ities. They are based on historical data analysis, current academic research, and a re liance on accepted economic, agronomic, and biological relationships in ag ricultural producti on and markets. The international modeling system includes international grains, oilseeds, et hanol, sugar, and livestock models. In general, for each commodity sector, the eco nomic relationship that supply equals demand is maintained by determining a market-clearing price for the co mmodity. In countries where domestic prices are not solved endogenously, these prices are mode led as a function of th e world price using a price transmission equation. Sin ce econometric models for each sect or can be linked, changes in one commodity sector will impact other sector s. Elasticity values for supply and demand responses are based on econometric analysis and on consensus estimates. Additional details on the FAPRI models are in cluded in the docket.

Agricultural and trade policies for each co mmodity in a country are included in the models to the extent that they affect the s upply and demand decisions of the economic agents. These policies include taxes on exports and imports , tariffs, tariff rate quotas, export subsidies, intervention prices, and set-aside rates. The FAPRI models assume that existing agricultural and trade policy variables will remain unchanged in the outlook period. O OOOOOOOOO

We recognize that there are inherent chal lenges in reconciling the results from two different models, however we believe that usi ng two models provides a more robust analysis than either model would be able to provide alone. Part of the difference in the model results is due to the fact that the two models have diffe rent time horizons. Since FASOM is a long term econometric model, short term spikes are smoothe d out over the five year reporting period. In comparison, the FAPRI model captu res annual fluctuations that may include short-term supply

MMMMMMMMMM 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, Re d 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.
NNNNNNNNNN Beach, Robert; McCarl, Bruce, Agricultural Impacts of the Energy Independence and Security Act: FASOM Results and Model Description , RTI International, October, 2008.
OOOOOOOOOO Technical Report: An Analysis of EPA Biofue l Scenarios with the FAPRI International Models , CARD Staff, November, 2008.

643and demand responses. Some of the discrepanc ies may be attributed to different underlying assumptions pertaining to elasticities of suppl y and demand for different commodities. These differences, in turn, affect projections of imports and exports, acreage shifting, and total consumption and production of various commodities. Some of the differences in results are described in more detail in the following secti ons. We will continue to work with the two models to isolate some of the ke y differences in assumptions and calibrate the models to a more consistent Reference Case in time for the Final Rulemaking.

5.1.2 Volumes

For the agricultural sector analysis using the FASOM and FAPRI models of the RFS2 biofuel volumes, we assumed 15 b illion gallons (Bgal) of corn ethanol would be produced for use as transportation fuel by 2022, an increase of 2.7 Bgal from the Reference Case. In addition, we modeled 1.0 Bgal per year of biodiesel use as fuel, an increase of 0.6 Bgal from the Reference Case, as well as an increase of 10 Bgal of cellulosic ethanol in 2022 from the agriculture sector. To satisfy the cellulosic ethanol requirements, the FASOM model was able to choose how much cellulosic etha nol was produced through the di fferent production pathways. This is purely based on the economics of each f eedstock, taking account the various harvesting and processing costs for each feedstock, and the in come the agriculture se ctor derives from each feedstock. In FASOM, this volume consists of 7.5 billion gallons of cellulosic ethanol coming from corn residue in 2022, 1.3 billion gallons from switchgrass and 1.4 billion gallons from sugarcane bagasse. The volumes for the individu al cellulosic ethanol f eedstocks were not set exogenously.

Given the short time frame for conducting this analysis, some of the projected sources of biofuels analyzed in the RFS2 proposal are not currently modeled in FASOM and FAPRI. For example, biodiesel from corn oil fractionation is not currently accounted for in FASOM. In addition, since FASOM is a domestic agricultural s ector model, it can’t be utilized to examine U.S. biofuel imports. Also, neither of the tw o models used for this analysis — FASOM or FAPRI — include biofuels derived from domestic municipal solid waste or from the U.S. forestry sector. Thus, for the RFS2 agricultura l sector analysis, these biofuel sources were estimated outside of the agricultural sector models (see Chapter 1). We estimate that approximately 2 billion gallons of cellulosic ethanol will be produced from municipal solid waste, and 4 billion gallons of cellulosic ethanol will be produced from the forestry sector in 2022.

All the results presented in this section are re lative to the AEO 2007 Reference Case renewable fuel volumes, which include 12.3 Bgal of grain-based ethanol, 0.4 Bgal of biodiesel, and 0.3 Bgal of cellulosic ethanol in 2022. Th e domestic figures are provided by FASOM and FAPRI, and all of the internati onal numbers are provided by FAPRI. For a more detailed set of results of the agricultural sector impacts of the RFS2 volumes, see the analytical reports submitted by FASOM and FAPRI in the docket of this rule.

644Table 5.1-1 Biofuel Volumes Modeled in 2022 (Billions of Gallons)

Biofuel Reference Case Control Case Change Corn Ethanol 12.3 15.0 2.7 Corn Residue Cellulosic Ethanol 0 7.5 7.5 Sugarcane Bagasse Cellulosic Ethanol 0.3 1.4 1.1 Switchgrass Cellulosic Ethanol 0 1.3 1.3 Other Ethanol 0 0.2 0.2 Biodiesel 0.4 1.0 0.6

5.1.3 Biofuel Feedstock Assumptions

In order to run a representative model of th e U.S. and international agriculture sectors to analyze the impact of the RFS2 biofuel volumes requirement, a series of assumptions and input parameters were set in order to achieve two majo r goals in our analysis. First, that the input assumptions are based on the most current da ta and projections av ailable in government, industry, and academia; and second, to ensure that the two agriculture models, FASOM and FAPRI, were analyzing similar real-world assumptions, and thus present a detailed and consistent worldwide analysis. Table 5.1-2 lists some of the key assumptions associated with the agriculture sector modeling, as well as the s ource for these assumptions . Additional details on the assumptions included in FASOM P PPPPPPPPP and FAPRI Q QQQQQQQQQ are included in the docket.

A series of information was gathered from and reviewed by other government agencies, such as USDA and DOE. These include corn and soybean yields, R RRRRRRRRR corn ethanol dry and wet mill plant processing energy use, corn et hanol yields, corn ethanol by-product use, and estimated corn stover yields. For other assumptio ns, we relied on external expertise, such as FAPRI; the Greenhouse gases, Regulated Emissions and Energy use in Transportation (GREET) model; S SSSSSSSSS the Assessment System for Popul ation Exposure Nationwide (ASPEN) model; T TTTTTTTTT and the Agriculture Resource Management Survey (ARMS). U UUUUUUUUU

PPPPPPPPPP Beach, Robert; McCarl, Bruce, Agricultural Impacts of the Energy Independence and Security Act: FASOM Results and Model Description , RTI International, October, 2008. QQQQQQQQQQ Technical Report: An Analysis of EPA Biofuel Scenarios with the FAPRI International Models , CARD staff, November, 2008.
RRRRRRRRRR USDA Agricultural Projections to 2017 (OCE-2008-1), February, 2008.
SSSSSSSSSS The GREET model is run by Argonne National Laboratory at the Department of Energy. GREET can simulate more than 100 fuel production pathways and more than 80 vehicle/fuel systems.
TTTTTTTTTT ASPEN is a computer simulation model used to estimate toxic air pollutant concentrations is called the Assessment System for Population Exposure Nationwide, or. This model is based on the EPA’s Industrial Source Complex Long Term model (ISCLT) which simulates the beha vior of the pollutants after they are emitted into the atmosphere. ASPEN uses estimates of toxic air pollutant emissions and meteorological data from National Weather Service Stations to estimate air toxics concentrations nationwide.
UUUUUUUUUU ARMS is sponsored by the Economic Research Service (ERS) and National Agricultural Statistics Service (NASS) at USDA, and provides observations of field-level farm practices, the economics of the farm business, and the characteristics of the American farm household.

645Projected crop yields, both domestically and in ternationally, constitute one of the most influential factors of this agricultural analysis. The yields presented in Table 5.1-2 are based on the USDA projections through 2017 (the last year of their baseline projections report) and then extrapolated out to 2022. The yields in this table represented the na tional weighted average yields, which vary by region. Th ese yields are based on historical averages for the region, which increase over time in both the AEO Reference case and the EISA C ontrol Case. FASOM assumes the rate of increase is (1.6% per year). For this proposal, we modeled the same yields in both the Reference Case and the Control Case , assuming there was no significant impact on yields due to commodity price changes resulting from the RFS2 volumes (i.e., no price-induced yield changes). In addition, we did not mode l changes in yields that might occur based on increased planting on marginal land or management practices (e.g., residue removal, corn after corn rotations) as a result of the RFS2 volumes.

For cellulosic biofuels from corn residues, th e current assumptions in FASOM for residue removal rates are based on the Graham et. al. paper V VVVVVVVVV and the Perlack et al study. W WWWWWWWWW This approach uses a maximum percent removal of residues on acres based on tillage practices. X XXXXXXXXX Other recent papers have raised the issue that a more relevant metric is a minimum amount of mass th at must remain on an acre of land to prevent runoff and maintain soil carbon levels. Y YYYYYYYYY However, we were unable to obtain national or regional estimates in time for the proposal. We intend to review th ese assumptions between proposal and final, and, if possi ble, update these assumptions as appropriate. In addition, we plan to review in more detail the sustainable removal rates for other crops su ch as wheat and rice residues. Z ZZZZZZZZZ, A AAAAAAAAAA

FASOM assumes fertilizer appl ication rates do not increase over time in proportion to the increase in yields, as shown in Table 5.1-2. This data is based on USDA data that shows fertilizer application rates per acre remaini ng relatively steady for the past 30 years, during which time corn yields have increased approximately 70%. B BBBBBBBBBB However, it may be reasonable to assume that there may be a corresp onding increase in fertili zer rates as crop yields increase into the future, and EPA requests comme nt on the relationship. In addition, when residues are removed from the field, some of the nutrients that ar e contained in the residue must be replaced through additional fertilizer use. For the proposal, we assumed that 7 additional pounds of nitrogen and 3.6 lbs of phosphorous must be applied per ton of residue removed. We will continue to review these assumptions for the final rule.

VVVVVVVVVV See also http://www.cpnrd.o rg/Harvesting%20Stover.pdf
WWWWWWWWWW Available at http://feedstockreview.ornl.gov/pdf/billion_ton_vision.pdf

. XXXXXXXXXX Many site specific factors associ ated with the sustainable removal of residue (e.g., crop type, soil type, soil fertility, slope, and climate) affect which geographic regions are suitable for crop residue removal. Detailed modeling of these factors was beyond the scope of this analysis.
YYYYYYYYYY Wilhelm et. al Corn Stover to Sustain Soil Organic Carbon Further Constrains Biomass Supply, Ag Journal (2007)
ZZZZZZZZZZ Kerstetter JD, Lyons JK. Logging and agricultural re sidue supply curves for the Pacific Northwest. Washington State University Energy Publication; 2001
AAAAAAAAAAA Banowetz et al. Assessment of straw biomass feed stock resources in the Pacific Northwest. Biomass and Bioenergy. In Press
BBBBBBBBBBB Data from the National Agriculture Statistics Service (NASS) at USDA: http://www.nass.usda.gov/

646Lastly, there is a limit to how many acres crops can be planted on. The Conservation Reserve Program (CRP) C CCCCCCCCCC is run by the Natural Resour ces Conservation Service at USDA. This program is designed to maintain Fe deral, State, and tribal environmental laws by making payments to farmers equivalent to the in come otherwise earned from developing the land enrolled in the program. In the 2008 Farm Bill, the number of acres enrolled in the CRP was given a minimum limit of 32 milli on acres. It is assumed that USDA will increase payments to match the rising costs of crops that could be grown on CRP acres through the 2022 time period for this analysis.

CCCCCCCCCCC See: http://www.nrcs.usda.gov/programs/CRP/

647648 Table 5.1-2. Agriculture Model Assumptions Assumption Notes Feedstock Production U.S. national average corn yields are approximately 170 bu/acre in 2017 and 180 bu/acre in 2022 (a 1.6% annual increase over the baseline year) Consistent with USDA projections (http://www.ers.usda.go v/publications/oce081/) U.S. national average soybean yields are approximately 50 bu/acre in 2022 (a 0.4% annual increase) International corn yields increasing over time, for example: Argentina ~140 bu/acre in 2022 (a 0.8% annual increase) Brazil ~68 bu/acre in 2022 (a 1.1% annual increase) FAPRI Models International soybean yields increasing over time, for example: Argentina ~48 bu/acre in 2022 (a 0.7% annual increase) Brazil ~50 bu/acre in 2022 (a 1.1% annual increase)

Corn residue removal rates of 50% are allowed for no till practices; 35% removal ra te allowed for reduced till practices (no removal from conventional till) Derived from Graham et. al., Agronomy Journal, 99:1–11 (2007). “Current a nd Potential U.S. Corn Stover Supplies.” and Perlack, R. D., L. L. Wright, A. F. Turhollow, R. L. Graham, B. J. Stokes, and D. C. Erbach. 2005. Biomass as Feedstock for a Bioenergy and Bioproducts Industry: the Technical Feasibility of a Billion-ton

Annual Supply. Report prepared for the U.S. Department of Energy and the U.S. Department of Agricutlure. The Conservation Reserv e Program (CRP) has a

minimum limit of 32 million acres enrolled in the program at any given time 2008 Farm Bill

USDA baseline assumptions Fertilizer Use U.S. nitrogen application rate in corn is approximately 136 lbs/acre in the corn belt in 2022

U.S. phosphorous application rate in corn is approximately 28 lbs/acre in the corn belt in 2022 Based on ARMS data, adjusted for differences in regions and irrigation practices

For U.S. assume higher yields require no increase in fertilizer use Based on USDA baseline assumptions

This holds for all farming rotations (e.g., corn / soybean and corn / corn) and land types (e.g., prime and marginal land); see below for stover removal impacts Nitrogen nutrient replacement application = 7 lbs/ton corn residue removed

Phosphorous nutrient replacement application = 3.6 lbs/ton corn residue removed These numbers come from the Argonne National Lab Report, Fuel Cycle Assessment of Selected

Bioethanol Production Pathways in the United States. (November 7, 2006). (Used and cited by GREET) Processing 1 bushel of corn produces 17 lbs of dried distillers grains (dry tons). 1 pound of DDG substitutes 0.9 pounds of corn and 0.1 pound of soybean meal feed www.ethanol.org

USDA baseline assumptions5.1.4 Economic Impacts – Domestic – Control Case

For this analysis, the FASOM model is util ized for all domestic agriculture impacts. Although the FAPRI models do not provide the same amount of de tail on GHG emissions for the domestic agriculture impacts as the FASOM model, FAPRI does estimate some of the same outputs, such as national crop ac res, prices, and exports. Add itionally, a common set of inputs were provided to both models, such as corn yi elds, energy prices, and the number of acres enrolled in the Conservation Reserve Program (CR P). In this chapter, we present both the FASOM and FAPRI results to demonstrate the ra nge of potential agricultural impacts. Though our primary domestic analysis relies upon the FA SOM model, as it contains more detail and scope beyond the FAPRI model for the U.S., this allows for a useful comparison between the two models, reinforces the accuracy of our dome stic analysis, and ensures consistency when analyzing the impacts of the RFS2 fuel volum e requirements on the domestic and international agriculture markets.

Commodity Prices

To satisfy the demands for increased biofue ls for the RFS2 propos al, the FASOM model predicts an increase in prices for the primary feedstocks of bi ofuels. The proposed increase in corn ethanol production results in an increase in U.S. corn pr ices of $0.15 per bushel (4.6%) above the Reference Case price of $3.19 per bushe l in 2022. Similarly, by 2022, the increase in demand for biodiesel production leads to an in crease of U.S. soybean prices, which would increase by $0.29 per bushel (2.9%) above the Refe rence Case price of $9.97 per bushel. One of the major feedstocks of cellulosi c ethanol is sugarcane bagasse, and as the demand for cellulosic ethanol increases with the RFS2 proposal, th e price of sugarcane in 2022 would increase $13.34/ton (41%) above the Reference Case price of $32.49 per ton. There are also indirect effects from the RFS2 proposal reflected in comm odity prices. For instance, corn is a major component in animal feed in the U.S., and as corn prices rise in 2022, beef prices would increase $0.93 per hundred pounds (1.4%), re lative to the Reference Case price of $67.72 per hundred pounds. Higher U.S. corn prices would have a dir ect 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 would also increase. Our analysis shows that la nd prices would increase by approximately 21% by 2022, relative to the Reference Case.

The FAPRI models also provide some domestic agriculture impacts in the U.S. In 2022, FAPRI predicts that U.S. co rn prices would increase $0. 22 per bushel (8.2 %) above the Reference Case to $2.91 per bushel. Soybean prices would increase $0. 42 per bushel (5.7%) above the Reference Case to $7.86 per bushel in 2022.

649Table 5.1-3. Change in U.S. Commodity Prices from the Reference Case (2006$) FASOM Model Commodity Change % Change Corn $0.15/bushel 4.6% Soybeans $0.29/bushel 2.9% Sugarcane $13.34/ton 41% Fed Beef $0.93/hundred pounds 1.4% FAPRI Model Commodity Change % Change Corn $0.22/bushel 8.2% Soybeans $0.42/bushel 5.7%

In FASOM, the increase in de mand for cellulosic ethanol also leads to the production of feedstocks in the Control Case that previously had no value in the Reference Case. This includes dedicated energy crops, such as sw itchgrass, as well as crop residues, such as corn stover. The price of switchgrass in 2022 is $30.18 per wet ton, and the farm gate feedstock price of corn residue is $32.74/wet ton in the Control Case. Th ese prices do not include the storage, handling, or delivery costs. We intend to update the costs assumptions (described in more detail in Section 4.1.1 of the DRIA) for the final rule. Since the FAPRI models do not explicitly model cellulosic ethanol production from agricu lture residues or dedicated en ergy crops, comparable price impacts are not available.

Commodity Use Changes

Changes in the consumption patterns of U.S. corn can be seen by the increasing percentage of corn used for ethanol. FASOM estimates that the amount of domestically produced corn used for ethanol in 2022 would increase to 33%, re lative to the 28% usage rate under the Reference Case. The rising price of corn and soybeans in the U.S. would have a direct impact on how corn is used. Higher domestic corn prices would lead to lower U.S. exports as the world markets shift to other sources of these products or expand the use of substitute grains. FASOM estimates that U.S. corn exports woul d drop 263 million bushels (-9.9%) to 2.4 billion bushels by 2022. In value terms, U.S. exports of corn would fall by $487 million (-5.7%) to $8 billion in 2022. FAPRI estimates that U.S. corn exports would decrease 288 million bushels (­ 7.6%) to 3.5 billion bushels in 2022. In value te rms, U.S. corn exports not change, maintaining its value of $10.2 billion.

U.S. exports of soybeans would also decrea se under this propos al. FASOM estimates that U.S. exports of soybeans would decrease 96.6 million bushels (-9.3%) to 943 million bushels in 2022. In value terms, U.S. e xports of soybeans would decrease by $691 million (­ 6.7%) to $9.7 billion in 2022. FAPRI predicts that U.S. s oybean exports would decrease 43 million bushels (-5.1%) to 802 million bushels and in value terms, U.S. soybean exports would increase by $19.4 million (0.3%) to $6.3 billion in 2022.

650Table 5.1-4. Reductions in U.S. Exports from the Reference Case in 2022 FASOM Model Exports Change % Change Corn in Bushels 263 million -9.9% Soybeans in Bushels 96.6 million -9.3% Total Value of Exports Change % Change Corn (2006$) -$487 million -5.7% Soybeans (2006$) -$691 million -6.7% FAPRI Model Exports Change % Change Corn in Bushels 288 million -7.6% Soybeans in Bushels 96.6 million -5.1% Total Value of Exports Change % Change Corn (2006$) $0 0% Soybeans (2006$) $19.4 million 0.3%

Higher U.S. demand for corn for ethanol produc tion would cause a decrease in the use of corn for U.S. livestock feed. Substitutes are availa ble for corn as a feedstock, and this market is price sensitive. Several ethanol processing byproducts could also be used to replace a portion of the corn used as feed, depending on the type of an imal. Distillers dried grains with solubles (DDGS) are a byproduct of dry mill ethanol production, and gluten meal and gluten feed are byproducts of wet milling ethanol production. Based on the assumption that 1 bushel of corn produces 17 lbs of DDGS, and that 1 pound of DDGS s ubstitutes for 0.9 lbs if corn and 0.1 lbs of soybean meal in animal feed (see Table 5.1-2), FASOM estimates that the use of these ethanol byproducts in feed would increase 19% to 30 m illion tons, compared to 25 million tons under the Reference Case in 2022.

Table 5.1-5. Percent Change in Ethanol Byproducts Use in Feed Relative to the Reference Case Category 2022 Ethanol Byproducts 19%

The EISA cellulosic ethanol requirements result in the production of residual agriculture products as well as dedicated energy crops. By 2022, FASOM estimates 90 million tons of corn residue are harvested for cellulosic ethanol producti on, as well as 18 million tons of switchgrass. Additionally, sugarcan e bagasse for cellulosic ethanol produ ction increases by 15.7 million tons to 19.7 million tons relative to the Reference Case.

Land Use Change

In order to satisfy the increased demand fo r corn ethanol, FASOM estimates an increase of 3.2 million acres, 3.9%, in harvested corn acres, above the 83.4 million acres harvested under

651the Reference Case. D DDDDDDDDDD Most of the new corn acres come from a reduction in existing crop acres, such as rice, wheat, and hay. Rice acres, in 2022, decrease s by 151 thousand acres (­ 3.8%) to 3.8 million acres in the Control Case. Wheat acres decrease by 676 thousand acres (­ 1.5%) to 43.6 million acres in 2022. Hay acres decrease by 625 thousand acres (-1.1%) to 54.2 million acres in 2022. Similarly, FAPRI estimates that harvested U.S. corn acres increase by 3.6 million acres, 4.2%, in 2022. See Table 5.1-6 for changes in crop acres in the FASOM model. As stated above, the FASOM m odel does not currently account for the impacts of the RFS2 biofuel volume requirements’ imp act on the forestry sector. We plan on using the forestry component in combination with the agriculture component to analyze the final rule.

Table 5.1-6. Change in Crop Acres Relative to the Reference Case in 2022 (millions of acres) Crop Change % Change Corn 3.2 3.9% Hay -0.6 -1.1% Rice -0.2 -3.8% Soybeans -0.4 -0.5% Sugarcane 0.7 55% Switchgrass 2.8 N/A Wheat 0.7 -1.5%

Though demand for biodiesel increases, the mode ls predict a fall in U.S. soybean acres harvested, assuming soybean-based biodies el meets the EISA GHG emission reduction thresholds. According to the FASOM model, harvested soybean acres would decrease by approximately 0.4 million acres (-0.5%) relative to the Reference Case acreage of 71.5 million acres in 2022. Despite the decrease in soybean acres, soybean oil pr oduction would increase by 0.4 million tons (4.0%) by 2022 over the Referen ce Case. Additionally, FASOM predicts that soybean oil exports would decrease 1.3 million t ons (-52%) in 2022 relative to the Reference Case.

Likewise, FAPRI estimates that the increa sed demand for biodiesel from soybean oil results chiefly in a reduction in so ybean oil exports, rather than an increase in acres harvested. Harvested soybean acres decrease by 0.9 millio n acres (-1.3%) to 69.1 million acres in 2022. FAPRI also estimates little change in soybean oil production, with a decrease of 32 million pounds (-0.1%) to 26.9 billion pounds in 2022. E xports in 2022 decrease by 2.9 billion pounds (-26.8%), to 7.9 bill ion pounds in 2022.

In the FASOM model, as the demand for ce llulosic ethanol increases, most of the production is derived from corn residue harvesting. FASOM projects that the least cost options for meeting the cellulosic ethanol mandate will be primarily residue based feedstocks. However, this assumption is highly dependent on the a ssumed densities associated with switchgrass production, as well as the sustaina ble removal rates associated with residue crops. If more mass

DDDDDDDDDDD FASOM estimates that total planted corn acres increa se to 93.8 million acres from the Reference Case level of 90.1 million acres in 2017. Total planted acres increases to 92.2 million acres from the Reference Case level of 89 million acres in 2022.

652653 is required on the land to prev ent erosion or soil carbon loss, ther e could be insufficient residue for cellulosic feedstocks. In addition, there are harvesting, transporting, and storing logistical issues that must be overcome be fore large-scale cellulosic biofue ls can be produced, as described in Chapter 1.

One of the primary crop residue feedstocks for cellulosic ethanol production is sugarcane bagasse. As demand for cellulosic ethanol increases, sugarcane acres increase by 0.7 millions acres (55%) to 1.9 million acres in 2022. In addi tion, some of the cellulo sic ethanol comes from switchgrass, which is not produced under the Reference Case. In the scenario analyzed, 2.8 million acres of switchgrass will be planted by 2022. As mentioned above, FAPRI does not explicitly model the production of cellulosic etha nol and the resulting im pacts on the agriculture sector. For both the Reference Case and the Control Case, we assume 32 million acres would remain in the Conservation Reserve Program (CRP) . Therefore, some of the new corn, soybean, and switchgrass acres may be indire ctly coming from former land that is not re-enrolled in the program. Figure 5.1-1 shows the ch ange in acres for all crops in the U.S. in 2022. Figure 5.1-2 shows the production levels of cellulo sic feedstocks by region in 2022.Figure 5.1-1. FASOM Model Estimated Change in U.S. Crop Acres Relative to the Reference Case in 2022 (millions of acres)

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0BarleyCornCottonWheatHayOatsRiceSilageSorghumSoybeansSugarbeetSugarcaneSwitchGrassOtherTotalMillions of Acres

654This page intentionally left blank.

655656Figure 5.1-2.Control Case Cellulosic Feedstock Production in the U.S. in 2022(thousands of acres)This page intentionally left blank.657658 As crop acres increase to meet the additional demand for corn and other crops for biofuel production, fertilizer use increases as a result. In 2022, FASOM estim ates that nitrog en fertilizer use in the U.S. agricultural sector would increase by 897 million pounds, 3.4% over the Reference Case level of 26.2 billion pounds, and th at phosphorous fertilizer use would increase by 496 million pounds, 8.6% higher than the Reference Case level of 5.8 billion pounds. The FAPRI model does not provide es timates for fertilizer use.

Table 5.1-7. Change in U.S. Fertilizer Use Relative to the Reference Case (millions of pounds)

Fertilizer Change %Change Nitrogen 897 3.4% Phosphorous 496 8.6%

Impact on U.S. Farm Income

The increase in renewable fuel production provides a significant increase in net farm income to the U.S. agricultural and livestock sect or. FASOM predicts that net U.S. farm income would increase by $7.1 billion dol lars (10.6%) in 2022 relative to the AEO 2007 Reference Case.

Impact on U.S. Food Prices

Due to higher commodity prices, FASOM estimates that U.S. food costs E EEEEEEEEEE would increase by $10 per person per year by 2022, relative to the Reference Case. F FFFFFFFFFF Total effective farm gate food costs woul d increase by $3.3 billion (0.2%) in 2022. G GGGGGGGGGG To put these changes in perspective, average U.S. per capita food expenditures in 2007 were $3,778 or approximately 10% of pe rsonal disposable income. Th e total amount spent on food in the U.S. in 2007 was $1.14 trillion dollars. H HHHHHHHHHH

5.1.5 Economic Impacts – Domestic – Sensitivities

EEEEEEEEEEE FASOM does not calculate changes in price to the consumer directly. The proxy for aggregate food price change is an indexed value of all food prices at the farm gate. It should be noted, however, that according to USDA, approximately 80 percent of consumer food expend itures are a result of handling after it leaves the farm (e.g., processing, packaging, storage, marketing, and distri bution). These costs consist of a complex set of variables, and do not necessarily change in proportion to an increase in farm gate costs. In fact, these intermediate steps can absorb price increases to some extent, suggesting that on ly a portion of farm gate price changes are typically reflected at the retail level. See http://www.ers.usda .gov/publications/foodreview/septdec00/FRsept00e.pdf.
FFFFFFFFFFF These estimates are based on U.S. Census population projections of 318 million people in 2017 and 330 million people in 2022. See http://www.cens us.gov/population/www/projections/natsum.html.
GGGGGGGGGGG Farm Gate food prices refer to the prices that farmers are paid for their commodities.
HHHHHHHHHHH See www.ers.usda.gov/Briefing/CPIFoodAndExpenditures/Data/table15.htm

.In the impact analysis volumes of the RFS2 volumes, a number of key assumptions were set in both in the Reference Case and the Contro l Case. Namely, energy prices in both cases are from the AEO 08 Reference Case, I IIIIIIIIII national average corn yields in 2022 reached 183 bu/acre, and the number of acres enrolled in CRP is 32 million acres in 2022. In order to demonstrate their influence, the FASOM model was used to analyze variations on these key assumptions and observe the impacts on corn pr ices, harvested acres, a nd exports. With each sensitivity run, only one variable is changed, such as energy prices, corn yields, or acres in CRP, for both the Reference Case and the Control Case. The impact of the RFS2 volumes is observed under each set of new circumstances. In the orig inal analysis, U.S. corn prices increase by $0.15/bu, 4.6%, to $3.34/bu; U.S. harvested corn acres increase by 3.2 million acres, 3.9%, to 86.6 million acres; and U.S. corn exports decrease by 263 million bushels, -9.9%, to 2.4 billion bushels in 2022.

The Reference Case and the Control Case contain energy prices from the AEO 08 Reference Case, in which gasoline prices are approximately $2.36 per gallon in 2022. As a sensitivity, the FASOM model ran a “high price case”, where all ot her variables remaining equal, energy prices are from the AEO 08 High Price Case, J JJJJJJJJJJ in which gasoline prices are approximately $3.28 per gallon in 2022. Higher en ergy prices lead to increased costs for crop production, leading to relatively higher prices. Under these circumstances, U.S. corn prices increase by approximately $0.15/bu, 4.6%, to $3.42/bu in 2022. Additionally, harvested U.S. corn acres increase by 2.7 million acres, 3.3%, to 85.3 million acres; and U.S. corn exports decrease by 278 million bushels (-11%) to 2.3 billion bushels in 2022.

Another key assumption examined here is th e estimated technological rate of progress for corn yields. Different projected corn yield values in a given y ear have large implications for domestic agricultural land use and the export commodity market. If yields are relatively low, more land is required to supply the amount of corn demanded for biofuel production, which would also lead to relatively lower corn exports and higher corn prices. C onversely, if yields are relatively high, less land is requir ed to meet the demand for corn ethanol, corn exports would be relatively higher, and corn prices would be relatively lower. In the original analysis, national average corn yields are approxima tely 183 bu/acre in 2022. In the “low yield case”, corn yields are equal to 163 bu/acre in 2022. Under this scenar io, implementation of the RFS2 volumes would lead to U.S. corn prices increasing by $0.34/bu (9.8%) to $3.84/bu in 2022. Also, U.S. corn acres increase by 1.1 million acres (1.3%) to 90.1 million acres and U.S. corn exports decrease by 425 million bushels (-20%) to 1.7 bil lion bushels in 2022. In the “high yield case”, corn yields are equal to 208 bu/acre in 2022. Under these circumstances, implementation of the RFS2 volumes would lead to and increase of U.S. corn prices by $0.07/bu (2.6%) to $2.95/bu, an increase in U.S. corn acres of 1.4 million acres (1.7%) to 82 million acres, and a decrease of U.S. corn exports of 493 million bushels (-13.3%) to 3.2 billion bushels.

The final key assumption varied here regards th e number of acres enrolled in CRP. In the original analysis, the number of acres in CRP were limited with a minimum of 32 million acres in accordance with the 2008 Farm Bill. As a se nsitivity analysis, a scenario was examined where there is no minimum restriction imposed in th e CRP program, and farmers may produce crops on

IIIIIIIIIII See: http://www.eia.doe.gov/oiaf/aeo/aeoref_tab.html
JJJJJJJJJJJ See: http://www.eia.doe.gov/oiaf/aeo/aeohighprice.html

659these acres if it proves to be economically bene ficial. With no limits on CRP acreage and more land becomes available for crop production, corn prices will be relatively lower. Under these circumstances, implementation of the RFS2 volum es would result in an increase in U.S. corn prices of $0.12/bu (3.7%) to $3.25/bu, an increase in U.S. corn acres of 2.6 million acres (3%) to 87.7 million acres, and a decrease of 299 million bushe ls of corn exports (-10.5%) to 2.5 billion bushels in 2022.

Figure 5.1-3. Change in U.S. Corn Prices in 2022 (2006$ per bushel)

Change in U.S. Corn Price in 2022 Relative to the Reference Case

$0.00 $0.05 $0.10 $0.15 $0.20 $0.25 $0.30 $0.35 $0.40

Control Case AEO 08 High Price Case Corn Yield - Low Case Corn Yield - High Case CRP - All Can Revert2006$ per bushel

660Figure 5.1-4. Change in U.S. Harvested Corn Acres in 2022 (millions of acres)

Change in U.S. Harvested Corn Acres in 2022 Relative to the Reference Case

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Control Case AEO 08 High Price Case Corn Yield - Low Case Corn Yield - High Case CRP - All Can RevertMillions of Acres

Figure 5.1-5. Change in U.S. Corn Exports in 2022 (billions of bushels)

Change in U.S. Corn Exports in 2022

Relative to the Reference Case

-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 Control Case AEO 08 High Price Case Corn Yield - Low Case Corn Yield - High Case CRP - All Can RevertBillions of Bushels

6615.1.6 Economic Impacts - International

The FAPRI models are utilized to assess the international impacts on trade, land use, and food consumption as a result of th e RFS2 biofuels requirement in the U.S. In the FAPRI models, links between the U.S. and inte rnational models are made th rough commodity prices and net trade equations. In general, fo r each commodity sector, the ec onomic relationship that quantity supplied equals quantity demanded is achieve d through a market-clearing price for the commodity. In other countries domestic prices are modeled as a functi on of the world price using a price transmission equation. Since econo metric models for each sector can be linked, changes in one commodity sector will impact the other sectors.

The model for each commodity consists of a number of countries/regions, including a rest-of-the-world aggregate to close the model. The models specify behavioral equations for production, use, stocks, and trade between countries/regions. The m odels solve for representative world prices by equating excess supply and dema nd across countries. Us ing price transmission equations, the domestic price for each country is linked with th e representative world price through exchange rates. It is through change s in world prices that change in worldwide commodity production and trade is determined.

Global Commodity Price Changes

As the RFS2 proposal alters various commodity production and prices in the U.S., it follows that the world price for these same commodities, and theref ore their production and trade, are affected as well. As the U.S. incr eases its use of corn fo r ethanol production, the FAPRI model estimates that it re duces corn net exports (as men tioned above), which results in the world price of corn increasing by $0.26/bu (7.5%) to $3.69/bu in 2022. Similarly, the decrease in U.S. net exports of soybeans ha s a corresponding effect on the world price of soybeans, which increases by $0.52/bu (5.6%) to $9.94/bu in 2022. As a primary feedstock for domestic biodiesel production, th e U.S. decreases in net exports of soybean oil to satisfy the increased demand for biodiesel , causing world soybean oil prices to increase by $120.93/ton (12.2%) to $1,116.43/ton in 2022.

World biofuel prices are also affected by the RFS2 proposal. As mentioned above in section 5.1.2, the U.S. satisfies a portion of th e increased demand for biofuels by increasing its imports of ethanol. The FAPRI model predicts that this increased demand on the world supply of ethanol leads to an increase its world price by $0.38/gallon ( 60.8%) to $1.00/gallon in 2022. Though the U.S. does not change it’s level of impor t or export of biodiesel, which is zero, the change in the price of biodiesel feedstocks, such as soybean o il, affects the world price of biodiesel, which increases by $0.10/gall on (2.11%) to $4.89/gallon in 2022.

662Table 5.1-8. Global Commodity Price Changes in 2022 (2006$ per unit)

Commodity Change (2006$ per Unit) % Change

Corn $0.26/bushel 7.5% Soybeans $0.52/bushel 5.6% Soybean Oil $120.93/ton 12.2% Ethanol $0.38/gallon 60.8% Biodiesel $0.10/gallon 2.11%

World Trade in Biofuels

As the U.S. increases its demand for increased use of biofuels, this will also lead to impacts on the world trade market for biofuels. With the RFS2 proposal, the U.S. will increase its imports of ethanol. EPA estimates that in 2022, the U.S. will increase net imports of ethanol by 2.5 billion gallons (388%) to 3.1 billion gallons. In response, the only net exporter of ethanol in the world, Brazil, increases production of ethanol and increases its net exports by 2.2 billion gallons (65.9%) to 5.4 billion gallons. However, since the U.S. demand for ethanol imports exceeds the increase in Brazilian net exports, other countries see a reduction in their net imports of ethanol. China decreases its net imports by 96 million gallons (-60.3%) to 64 million gallons, Canada decreases its net imports by 31 million gallons (-7.1%) to 412 million gallons, and the European Union decreases its net imports of ethanol by 40 million gallons (-7.4%) to 507 million gallons in 2022. Additionally, India decreases its net imports of ethanol by 57 million gallons (­ 12.7%) to 388 million gallons, Japan decreases ne t imports of ethanol by 10 million gallons (­ 2.6%) to 365 million gallons, and South Korea decreases net imports of ethanol by 5 million gallons (-2.9%) to 175 million gallons. The rest of the world decreases net imports of ethanol by 2 million gallons (-0.4%) to 477 million gallons in 2022.

663Figure 5.1-6. Ethanol Net Exports by Country in 2022 (billions of gallons)

Ethanol Net Exports in 2022

-4 -3 -2 -1 0 1 2 3 4 5 6BrazilChinaCanadaEuropean UnionIndiaJapanSouth KoreaUnited StatesRest of WorldTotal NetExportsBillions of Gallons

Reference Case Control Case

Though the U.S. does not import any biodiesel either in the AEO 2007 Reference Case nor the EISA Control Case, the decrease in soyb ean oil exports from the U.S. of 2.9 billion pounds (-27%) to 7.9 billion pounds, as estimated by the FAPRI model has implications in the biodiesel trade market. The decrease in U.S. soybean oil exports leads to an increase in the world price of soybean oil, causi ng other countries to increase their net exports of soybean oil. Argentina and Brazil increase their soybean oil net exports by 354 million pounds and 676 million pounds, respectively, in 2022. Since the wo rld price of soybean oil increases at a much larger rate (12.2%) than the world price of biodiesel (2.1%) due to the RFS2 proposal, it becomes relatively more profitable to increase ne t exports of soybean oi l for major producers. This results in less soybean oil being used in the production of bi odiesel and theref ore a decrease in biodiesel net exports. In 2022, Argentina d ecreases their biodiesel net exports by 8 million gallons (-3.0%) to 269 million gallons, and Br azil decreases their biodi esel net exports by 19 million gallons (-7.3%) to 240 million gallons. In response, the EU reduces their net imports of biodiesel by 26 million gallons (-5.2%) to 479 mill ion gallons and Japan reduces its net imports of biodiesel by 1 million gallons (-1.3%).

664Figure 5.1-7. Biodiesel Net Exports by Country in 2022 (billions of gallons)

Biodiesel Net Exports in 2022

-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4ArgentinaBrazilIndonesiaMalaysiaUnited StatesEuropeanUnionJapanROWTotal NetExportsBillions of Gallons

Reference Case Control Case

International Land Use Change

Changes to the global commodity trade mark ets and world commodity prices result in changes in international land use. The FAPRI model provides international change in crop acres as a result of the RFS2 proposal. Brazil has th e largest positiv e change in crop acres in 2022, followed by the U.S., Nigeria, Indi a, Paraguay, and China. As the U.S. increases its net imports of ethanol by 2.5 billion gallons in 2022, the major supplier of th is increase in ethanol is Brazil which produces ethanol from sugarcane. The FAPRI model estimates that Brazil crop acres increase by 3.1 million acres (2.0%) to 153.6 milli on acres relative to the Reference Case. The major crop contributing to this increase is sugarcane, which increases by 2.0 million acres (8.7%) in 2022 to 24.4 million acres. Total U.S. acres increase by 2.3 million acres (1.0%) in 2022 to 232.6 million acres. The major crop contributing to this increase is corn, which increases by 3.7 million acres (4.2%) to 90 million acres in order to satisfy the increased demand for corn ethanol. Though Brazil and the U.S. increase their crop acres to meet the increased demand for biofuel, other countries, such as Ni geria and India, respond by in creasing their major food crops: sorghum and rice, respectively. Nigeria has an increase in crop acres of 1.5 million acres (5.9%) to 27.3 million acres in 2022. This increase is entirely due to an increase in sorghum acres, which represents a 7.6% increase in sorghum acr es in 2022. India’s to tal crop acres increase by 1.0 million acres (0.3%) to 326 million acres in 2 022. Rice is the largest contributor to this increase, rising by 0.4 million acres (0.4%) in 2022, though soybeans, sorghum, and corn also

665increase by 0.25, 0.31, and 0.21 million acres, respectively. Total crop acres in Paraguay increase by 0.8 million acres (6.9%) to 12 million acr es. This increase is entirely due to the increase of soybean acres, which rise 6.9% in 2022. China’s total crop acres increase by 0.4 million acres (0.2%) to 257.8 million acres in 2022. The crop with the la rgest increase is corn, which rises by 0.7 million acres (1 .0%) 70.4 million acres in 2022.

Figure 5.1-8. Change in World Crop Acres By Country in 2022 (millions of acres)

14 Change in World Crop Acres in 2022

0 2 4 6 8 10 12

Brazil US Entire WorldMillions of Acres

Nigeria India Paraguay China Rest of World

International Crop Yields

When analyzing the change in international crop acres in response to the RFS2 proposal, it is important to note that diffe rent countries have different yi elds for each crop. The FAPRI model bases each country’s crop yield on historical trends and projects th is technical rate of progress into the future. Similar to the FASOM model, the yield for each crop is the same in both the Reference Case and the Control Case in that neither model has price induced crop yields. Table 5.1-9 lists the production and yields for the t op four producers of corn and soybeans in the Cont rol Case in 2022.

666Table 5.1-9. Corn and Soybean Control Case Production and Yields for the Top Four Producing Countries in 2022 Corn Country Production (billions of bushels) Yield (bushels/acre) U.S. 16.6 184 China 7.0 99 Brazil 2.5 68 EU 2.3 111 Soybeans Country Production (billions of bushels) Yield (bushels/acre) Brazil 3.8 50 U.S. 3.4 49 Argentina 2.4 48 China 0.6 30

World Food Consumption

The RFS2 mandate results in higher interna tional commodity prices , which would impact world food consumption. K KKKKKKKKKK As mentioned above, with the RFS2 proposal world corn prices would increase by 7.5% to $3.69 per bushe l in 2022, relative to the Reference Case. The impact on world soybean prices is somewhat smaller, increasi ng 5.6% to $9.94 per bushel in 2022. Since major agricultural commodity prices increase globally, it is anticipated that world consumption of food would decreas e. The FAPRI model indicates that world consumption of corn for food falls by 1.1 million metric tons (-0 .5%) in 2022 relative to the Reference Case. Similarly, the FAPRI mo del estimates that world consumpti on of wheat for food would decrease by 0.6 million metric tons (-0.1%) in 2022. Worl d consumption of oil for food (e.g., vegetable oils) decreases by 1.8 million metric tons (-1.4 %) by 2022. The model also estimates a small change in world meat consumption, decreasing by 0.3 million metric tons (-0.1%) in 2022. When considering all the food uses included in the model, world food consumption decreases by 0.9 million metric tons (-0.04%) by 2022. While FAPRI provides estimates of changes in world food consumption, estimating effects on global nut rition is beyond the scope of this analysis.

KKKKKKKKKKK The food commodities included in the FAPRI model include corn, wheat, sorghum, barley, soybeans, sugar, peanuts, oils, beef, pork, poultry, and dairy products.

667Table 5.1-10. Change in World Food Consumption Relative to the Reference Case (millions of metric tons) Category Change % Change Corn -1.1 -0.5% Wheat -0.6 -0.1% Vegetable Oils -1.8 -1.4% Meat -0.3 -0.1% Total Food -0.9 -0.4%



### 5.2 Petroleum and Energy Security Impacts

5.2.1 Impact on U.S. Petroleum Imports

In 2007, U.S. petroleum imports represented 19.5 percent of total U.S. imports of all goods and services. L LLLLLLLLLL In 2005, the United States im ported almost 60 percent of the petroleum it consumed. This compares roughly to 35 percent of petroleum from imports in 1975. M MMMMMMMMMM Transportation accounts for 70 percent of the U.S. petroleum consumption. It is clear that petroleum imports have a signifi cant impact on the U.S. economy. Diversifying transportation fuels in the U.S. is expected to lower U.S. petroleum imports. To estimate the impacts of this proposal on the U. S.’s dependence on imported oil, we calculate avoided U.S. expenditures on petroleum imports.

EPA analyzed two approaches to estimate th e reductions in U.S. pe troleum imports. The first approach utilizes a model of the U.S. energy sector, the Nationa l Energy Modeling System (NEMS), to quantify the type and volume of reduced petroleum imports based on supply and demand for specific fuels in a given year. The National Energy Modeling System (NEMS) is a computer-based, energy-economy modeling syst em of U.S. energy markets through the 2030 time period. NEMS projects U.S. production, imports, conversion, consumption, and prices of energy; subject to assumptions on world energy markets, resource availability and costs, behavioral and technolog ical choice criteria, cost and perf ormance characteristics of energy technologies, and demographics. NEMS is designed and implemented by the Energy Information Administration (EIA) of the U.S. Depa rtment of Energy (DOE). For this analysis, the NEMS model was run with the 2007 AEO levels of biofuels in the reference case compared with the biofuel volume RFS2 requirements.

Considering the regional nature of U.S. impor ts of petroleum imports, a second approach was utilized as well to estimat e the impacts of the RFS2 proposal on U.S. oil imports. This approach is labeled “Regional Ga soline Market” approach. This approach makes the assumption that one half of the ethanol market is in the Northeast portion of the U. S., which also comprises

LLLLLLLLLLL Bureau of Economic Affairs: “U.S. International Transactions, Fourth Quarter of 2007” by Elena L. Nguyen and Jessica Melton Hanson, April 2008.
MMMMMMMMMMM Davis, Stacy C.; Diegel, Susan W., Transportation Energy Data Book: 25 th Edition , Oak Ridge National Laboratory, U.S. Department of Energy, ORNL-6974, 2006.

668about half of the nation’s gasolin e demand. For this analysis, it is estimated that ethanol would displace imported gasoline or gasoline blend stocks in the Northeast, but not elsewhere in the country. Therefore, to derive the portion of the new renewable fuels which would offset U.S. petroleum imports (and not impact domestic refi nery production), we multiplied the total volume of petroleum fuel displaced by 50 percent to repres ent that portion of the ethanol which would be used in the Northeast, and 50 pe rcent again to only account for th at which would offset imports. The rest of the ethanol, including half of the etha nol presumed to be used in the Northeast, is presumed to offset domestic gasoline production, which ultimately offset s crude oil inputs at refineries. Biodiesel and renewable diesel are presumed to offset domestic diesel fuel production.

The results shown in Table 5.2-1-1 below refl ect the net lifecycle reductions in U.S. oil imports projected by NEMS. The net lifecycle re ductions include the upst ream petroleum used to produce renewable fuels, gaso line and diesel, as well as the petroleum directly used by endusers.

Table 5.2-1-1. Net Reductions in Oil Imports in 2022 (Using the NEMS Model) (millions of barrels per day) Category of Reduction 2022 Imports of Finished Petroleum Products 0.823 Imports of Crude Oil (0.007) Total Reduction 0.815 Percent Reduction 6.15

The NEMS model projects that for the y ear 2022 all of the reduction in petroleum imports comes out of finished petroleum produc ts. NEMS projects th at 91 percent of the reductions in 2022 come from reduced net imports of crude oil and finish ed petroleum products (as compared to a 9 percent reduction in domestic U.S. production).

The results shown in Table 5.2.1-2 below refl ect the net lifecycle reductions in U.S. oil imports projected by the use of the Regional Ga soline Market approach detailed above.

Table 5.2.1-2. Net Reductions in Oil Imports in 2022 (Using Regional Gaso line Market Approach) (millions of barrels per day) Category of Reduction 2022 Imports of Finished Petroleum Products 0.250 Imports of Crude Oil 0.637 Total Reduction 0.887 Percent Reduction 6.17%

669The Regional Gasoline Market approach projects that for 2022, 72% of the petroleum supply displacement (on a volume basis) comes out of reduced net crude oil imports, and 28% out of net imports of finished petroleum pr oducts (excluding biofue ls). Using our two approaches for projecting total petroleum im port reductions (the NEMS and the Regional Gasoline Market), we estimate that petroleum product imports will fall by between 0.815 to 0.887 million barrels per day in 2022 as a result of the RFS2 proposal.

Using the NEMS model, we also calculated the change in expenditures in both U.S. petroleum and ethanol imports w ith the RFS2 proposal and compared these with the U.S. trade position measured as U.S. net exports of all good s and services economy-wide. Changes in fuel expenditures were estimated by multiplying the changes in gasoline, diesel, and ethanol net imports by the respective AEO 2008 wholesale ga soline and distillate price forecasts, and ethanol price forecasts from the Food and Agricu ltural Policy Research Institute (FAPRI) for the specific analysis years. In Table 5.2.1-3, the net expenditures in reduced petroleum imports and increased ethanol imports are compared to the total value of U.S. net exports of goods and services for the whole economy for 2022. The U. S. net exports of goods and services estimates are taken from Energy Informa tion Administration’s Annual En ergy Outlook 2008. We project that avoided expenditures on impor ted petroleum products due to th is proposal would be roughly $16 billion in 2022. Relative to the 2022 projecti on, the total avoided expenditures on liquid transportation fuels are projected to be $12.4 billion with the RFS2 proposal.

Table 5.2.1-3. Changes in Expenditures on Transportation Fuel Net Imports (billions of 2006$) Category 2022 AEO Total Net Exports 16 Expenditures on Net Petroleum Imports (15.96) Expenditures on Net Ethanol and Biodiesel Imports 3.52 Net Expenditures on Transportation Fuel Imports (12.44)

5.2.2 Background on U.S. Energy Security

U.S. energy security is broadly define d as protecting the U.S. economy against circumstances that threaten significant short- and long-term increases in energy costs. Most discussion of U.S. energy security revolves around the topic of the economic costs of U.S. dependence on oil imports. The problem is that the U.S. relies on sources of imported oil from potentially unfriendly and unstable s ources. In addition, oil exporters have the ability to raise the price of oil by exerting monopoly pow er through the formation of a cartel, the Organization of Petroleum Exporting Countries (OPEC). Finally, these factors contribute to the vulnerability of the U.S. economy to episodic oil supply shocks a nd price spikes. In 2005, U.S. imports of crude oil were $232 billion (see Figure 5.2.2-1).

670Figure 5.2.2-1. U.S. Expenditures on Crude Oil

Source: Annual Energy Review 2006 and International Energy Review 2004. Year 2005 values are preliminary.

One effect of the RFS proposal is that it prom otes diversification of transportation fuels in the U.S. The result is that it reduces U.S. oil imports, which reduces both financial and strategic risks associated with a potential disruption in supply or a spike in the cost of a particular energy source. This reduction in risks is a meas ure of improved U.S. energy security. For the RFS2 proposal an “oil premium” approach is uti lized to identify those energy-security related impacts which are not reflected in the market pri ce of oil, and which are expected to change in response to an incremental change in the level of U.S. oil imports.

5.2.3 Methodology Used to Estimate U.S. Energy Security Benefits

In order to understand the en ergy security implications of reducing U.S. oil imports, EPA has worked with Oak Ridge Na tional Laboratory (ORNL), which has developed approaches for evaluating the social costs and energy security im plications of oil use. In a new study entitled

" The Energy Security Benefits of Reduced Oil Use, 2006-2015 ," produced for the Renewable Fuel Standard (RFS) Rule of 2007, 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. N NNNNNNNNNN, O OOOOOOOOOO A draft of this updated report wa s included as part of the record

NNNNNNNNNNN 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.
OOOOOOOOOOO 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.

671in the final RFS rulemaking, and the public was i nvited to analyze this Report and provide other perspectives for best quantifying energy security benefits. Since promulgation of the RFS rule, EPA has worked with ORNL to finalize the repo rt and quantify the energy security benefits associated with a reduction in imported oil. In addition, the Report has been the subject to an external peer review by six expert s from outside the U.S. government. P PPPPPPPPPP Updating the ORNL methodology to incorporate the comments from the Peer Reviewers, ORNL estimates that the total energy security benefits associated with a reduction of impor ted oil is $12.38/barrel, with a range of $6.88 - $18.52/barr el of imported oil reduced. Hi ghlights of the analysis are shown below.

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 a nd projected, suggest that the magnitude of the oil premium has changed. Sign ificant driving factors th at have been revised include: projected world oil pri ces, current and anticipated leve ls of OPEC production, U.S. import levels, the estimated respon siveness of regional o il supplies and demands to price, and the likelihood of oil supply disruptions (see Table 5.2.3-1). For this analysis, oil prices from the EIA's AEO 2007 were used. Using the "oil prem ium" approach, estimates of benefits of improved U.S. energy security from reduced U.S. oil imports due to increased availability and use of other transportation fuels are calculated.

Table 5.2.3-1. Market and Paramet er Changes Influencing Premium Estimate

Source: Leiby, Paul N. " The Energy Security Benefits of Reduced Oil Use, 2006-2015 ", Oak Ridge National Laboratory ORNL/TM-2007/028, July 2007.

PPPPPPPPPPP Leiby, Paul N. “Estimating the Energy Security Benefits of Reduced U.S. Oil Impor ts: Final Report”, ORNL/TM-2007/028, Oak Ridge National Laboratory, March 6, 2008.

672In conducting this analysis, ORNL cons idered 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 a ddition to the purchase price of petroleum itself. These are: (1) the higher costs for oil imports re sulting from the effect of U.S. import demand on the world oil price and OPEC market power (i .e., the "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 importe d oil to the U.S. (i.e., macroeconomic disruption/adjustment costs).

5.2.3.1 Effect of Oil Use on Long-Run Oil Pri ce, U.S. Import Costs, and Economic Output

The first component of the full economic co sts 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 pur chaser of foreign oil supplies, its purchases can affect the world oil price. This monopsony power means that in creases 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 benef it of decreasing U.S. oil purchases, due to the increased availability and use of other transportation fuels, is the potential decrease in the crude oil price paid for all crude oil purchased.

The demand or monopsony effect can be readily illustrated with an example. If the U.S. imports 10 million barrels per day at a world oil pr ice of $50 per barrel, its total daily bill for oil imports is $500 million. If a decrease in U.S. imports to 9 million barrels per day causes the world oil price to drop to $49 per barrel, the da ily U.S. oil import bill drops to $441 million (9 million barrels times $49 per barrel). While the world oil price only declines $1, the resulting decrease in oil purchase payments of $59 m illion per day ($500 million minus $441 million) is equivalent to an incremental benefit of $59 per barrel of oil imports re duced, or $10 more than the newly-decreased world price of $49 per barrel. This additional $10 per barrel “import cost premium” represents the incremental external be nefits to U.S. society as a whole for avoided import costs beyond the price paid oil purchases. This additional benefit arises only to the extent that reduction in U.S. oil imports affects the worl d oil price. ORNL estimates this component of the energy security benefit to be $7.65/barrel, with a range of $2.86 – $13.53/barel of imported oil reduced.

5.2.3.2 Short-Run Disruption Premium from Ex pected Costs of Sudde n Supply Disruptions

The second component of the oil import premiu m, the “disruption premium", arises from the effect of oil imports on the expected cost of disruptions. A sudden increase in oil prices triggered by a disruption in world oil supplies has two main effects: (1) it increases the costs of imports in the short run, further expanding the tr ansfer of U.S. wealth to foreign producers, and (2) it can lead to macroeconomic contraction, dislocation and GDP losses. ORNL estimates the composite estimate of these two factors that comprise the “disruption premium” to be $4.74/barrel, with a range of $2.17 – $7. 64/barrel of imported oil reduced.

5.2.3.2.1 Higher Costs of Oil Imports and Wealth Transfer during Shocks

673During oil price shocks, the higher price of imported oil causes increased payments for imports and a transfer of wealth from U.S. societ y to oil exporters. This increased claim on U.S. economic output is a loss to the U.S. that is separate from and additional to any reduction in economic output due to the shock. The increased wealth transfer during shocks is counted as a loss to the degree that the expect ed price increase is not anticip ated and internalized by oil

consumers.

5.2.3.2.2 Macroeconomic Costs: Potential Ou tput Loss and Dislocation/Adjustment Costs

Macroeconomic losses during price shocks reflect both aggregate output losses and allocative losses. The former are a reduction in the level of output that the U.S. economy can produce fully using its available resources; and th e latter stem from temporary dislocation and underutilization of available reso urces due to the shock, such as labor unemployment and idle plant capacity. The aggregate output effect, a re duction in “potential” economic output, will last so long as the price is elevated. It depends on the extent and duration of any disruption in the world supply of oil, since these factors determ ine the magnitude of the resulting increase in prices for petroleum products, as well as whether and how rapidly these pri ces return to their predisruption levels.

In addition to the aggregate contraction, there are “allocative” or “adjustment” costs associated with dislocated energy markets. Because supply disruptions and resulting price increases occur suddenly, empirical evidence shows they also impose additional costs on businesses and households which must adjust thei r use of petroleum and other productive factors more rapidly than if the same price increase had occurred gradually. Dislocational effects include the unemployment of workers and othe r resources during the time needed for their intersectoral or interregional reallocation, and pa uses in capital investment due to uncertainty. These adjustments temporarily reduce the level of economic output that can be achieved even below the “potential” output level that w ould ultimately be reached once the economy’s adaptation to higher petroleum prices was complete. The additional costs imposed on businesses and households for making these adjustments reflect their limited ability to adjust prices, output levels, and their use of energy, labor and other inputs quickly and smoothl y in response to rapid changes in prices for petroleum products.

Since future disruptions in foreign oil supplies are an uncer tain prospect, each of the disruption cost components must be weighted by the probability th at the supply of petroleum to the U.S. will actually be disrupted . Thus, the “expected value” of these costs – the product of the probability that a supply disruption will occur and the sum of costs from reduced economic output and the economy’s abrupt adjustment to shar ply higher petroleum pri ces – is the relevant measure of their magnitude. Further, when asse ssing the energy security value of a policy to reduce oil use, it is only the cha nge in the expected costs of di sruption that results from the policy that is relevant. The expected costs of disruption may change from lowering the normal (pre-disruption) level of domes tic petroleum use and imports, from any induced alteration in the likelihood or size of disruption, or from altering the short-run flexib ility (elasticity) of petroleum

use.

674In summary, the steps needed to calculate the disruption or security premium are first, determine the likelihood of an o il supply disruption in the fu ture; second, a ssess the likely impacts of a potential oil supply disruption on the world oil price; third, assess the impact of the oil price shock on the U.S. economy (in terms of import costs and macroeconomic losses); and fourth, determine how these costs change with oil imports. The value of price spike costs avoided by reducing oil imports becomes the o il security portion of the premium. ORNL estimates the composite value that comprises the “disruption premium” to be $4.74/barrel, with a range of $2.17 – $7.64/barrel of imported oil reduced.

We recognize that our current energy security analysis does not take into account riskshifting that might occur as the U.S. reduces its dependency on petroleum by increasing its use of biofuels. For example, our analysis did not take into account other energy security implications associated with biofuels, such as possible supply disruptions of corn-bas ed ethanol. Between the proposal and the final rulemaking, EPA will attemp t to broaden our energy security analysis to incorporate estimates of overall motor fuel suppl y and demand flexibility and reliability, and impacts of possible agricultural sector mark et disruptions (for example, a drought) for presentation in the final rule. Th e expanded analysis will also consider how the use of biofuels can alter short and long r un elasticity (flexibi lity) in the motor fuel mark et, with implications for robustness of the fuel system in the face of dive rse supply shocks. As part of this analysis, the Agency plans on analyzing those factors that can cau se shifts in the prices of biofuels, and the impact these factors have on the energy security estimate.

5.2.3.3 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. ener gy security policies. Th e two primary examples are maintaining a military presence to help secure a stable oil supply from potentially vulnerable regions of the world and maintaining the Strate gic Petroleum Reserve (SPR). The SPR is the largest stockpile of government-owned emergency crude oil in the world. Established in the aftermath of the 1973-74 oil embargo, the SPR provides the U.S. a response option should a disruption in commercial oil supplies threaten the U.S. economy. It also allows the U.S. to meet part of its International Ener gy Agency obligation to maintain emergency oil stocks, and it provides a national defense fuel reserve.

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. A ttempts to attribute some share of U.S. military costs to oil imports are further challenged by the need to estima te 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, hi storically these costs 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.

5.2.3.4 Modifications to Analysis Based Upon Peer Reviewer Comments

675The Agency commissioned ORNL to conduct a number of sensitivity analyses to address the comments of the Peer Reviewers. Based upon the Peer Reviewer comments, key parameters that influence the “oil import” premium were a ssessed. Since not all the comments were in agreement with each other, seve ral ranges of different parame ters were developed for the analyses. A summary of the results of th e analyses are shown in Table 5.2.3-2.

Three key parameters were varied in or der to assess their impacts on the oil import premium: (1) the response of OPEC supply, (2 ) the combined respons e of non-U.S., non-OPEC demand and supply and (3) the GDP response to a ch ange in the world oil as a result of reduced U.S. oil imports. The cases in Table 5.2.3- 2 used updated supply/demand elasticities for nonU.S./non-OPEC region after considering more recen t estimates than those used in 1997 study. As a result, the total market responsiveness is greater than previous ORNL estimates. Only small changes to the world oil pr ice are anticipated from a substa ntial reduction in U.S. demand, on average, about $0.70/bbl for every million-barrels-per-day reduction in demand.

In the ORNL framework, OPEC-behavior is tr eated parametrically, w ith a wide range of possible responses represented by a range of supply elasticities. Case One in the Table below refers to the AEO 2007 estimates of energy market trends and uses the elas ticity parameters from the original 1997 ORNL study. In Case Two, the OPEC supply elasticities range for 0.25 to 6 with a mean elasticity of 1.7. Case Three alters the distributi on of the OPEC supply elasticities so that the mean elasticity is 2. 2 instead of 1.7. With the more elastic OPEC oil supply in Case Three, the oil premium is lower. Alternativel y, a candidate rule for OPEC strategic response behavior, adapted from a lead article on what behavior maximi zes OPEC’s long run net revenue in a robust way, Q QQQQQQQQQQ would have OPEC responding to pr eserve its worldwide oil market share. This is presented as Case Seven. Applic ation of this rule instead of the range of OPEC supply responses used leads to an estimate of the oil import premium that is between Case Two and Case Three.

The second key parameter that was varied based upon Peer Revi ewer comments was nonOPEC, non-U.S. demand and supply responsiveness to a change in the U.S. oil import demand and, hence, the world oil price. In Case F our (see Table below), the mean non-U.S./non-OPEC demand and supply elasticities are taken to each be 0.3 in absolute value te rms. When combined together, the net elasticity of import demand fr om this region — the non-U.S./non-OPEC region is — roughly 1.6. Case Five takes the Case F our assumptions of a mo re elastic OPEC supply behavior and combines those a ssumptions with the 1.6 net elasticity of import demand for the non-U.S./non-OPEC region. Case Si x looks at the consequences of a yet higher net elasticity of import demand — 2.28 — for the non-U.S./non-OPEC region. The impact on the oil import premium is relatively modest.

Cases Eight and Nine cons ider a reduced GDP elasticity, the parameter which summarizes the sensitivity of GD P to oil price shocks. Severa l Reviewers suggested a lower estimate for this parameter. In response to their comments, a couple of cases were examined

QQQQQQQQQQQ Gately, Dermot 2004. "OPEC's Incentives for Faster Output Growth," The Energy Journal, 25(2):75-96, "What Oil Export Levels Should We Expect From OPEC?" The Energy Journal, 28(2):151-173, 2007.

676where the GDP elasticity was lowered to 0.032 in comparison to the original ORNL estimate of 0.0495. As anticipated, this change lowered th e oil import premium modestly. For example, compared with Case Four where OPEC supply is more elastic, lowering th e GDP elasticity with respect to the world oil price reduced the oil import premiu m by roughly $0.40/barrel. This is because the GDP-dislocation component is only about one-quarter of the total premium, and there are offsetting changes in ot her components. The last case examined, Case Nine, looks at the consequences for the oil import premium with a reduced elasticity of GDP if OPEC attempts to maintain its share of the world oil market.
Clearly there is an unavoidable degree of un certainty about the magnitude of marginal economic costs from the U.S. importation of petrol eum, and the size of the oil import premium. ORNL sought to reflect this with probabilistic ri sk analysis over key input factors, guided by the available literature and the best judgment of oil market experts. Cases shown in Table 5.2.3-2 explore some reasonable variati ons in the ranges of input assu mptions and the mean oil premium estimates vary in a fairly mode rate range between roughly $11 a nd $15/barrel of imported oil. On balance, Case Eight suggest ed a reasonable and cautious asse ssment of the premium value to ORNL, and is ORNL’s recommended case. This is based on a review of important driving factors, the numerical evaluati ons and simulations over major uncertainties, and taking into consideration the many comments and suggestio n from the Reviewers, the EPA and other Agencies. This recommended case, and the premiu m range resulting from 90% of the simulated outcomes, encompasses a wide array of perspectives and potential market outcomes in response to a reduction of U.S. imports.

This recommended case relies on the most r ecent available projecti ons of the U.S. and world oil market for the next ten years ba sed upon the AEO 2007 Reference Case. OPECbehavior is treated parametrica lly, with a wide range of possibl e responses represented by a wide range of supply elasticities, from small to quite large. This recommended case recognizes that the OPEC response is the most uncertain single el ement of this analysis. It could vary between inelastic defense of output levels, or market share, or could be highly elastic in defense of price, probably at the expense of longer run cartel pow er and discounted net profits. The balance between possible elastic a nd inelastic OPEC response is essentia lly even over a fairly wide range of elasticities. ORNL concluded that this is the best way to estimate OPEC behavior until greater progress can be made in synthesizing what insights ar e available from the evolving strategic game-theoretic and empi rical research on OPEC behavior , and advancing th at research. An alternative would have been to use OPEC st rategic response behavior to maximize long-run net revenue, which may well correspond to mark et-share preservation behavior (e.g., Case Seven), and a somewhat higher premium value.

Finally, ORNL’s recommended case uses a GDP elasticity range, the parameter which summarizes the sensitivity of GDP to oil price shocks, which is reduced compared to earlier estimates, and compared to the full range of histor ically-based estimates. This helps address the concerns of those who either ques tion the conclusions of past empirical estimates or expect that the impacts of oil shocks ma y well be declining. EPA reque sts comment on the ORNL oil security methodology outline above and the estim ates of the recommended “oil premium” estimate developed using the ORNL methodology.

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678679Table 5.2.3-2: Summary Results - Oil Import Premium Under Various Cases ($2006/BBL)ComponentStatistic1) AEO2007Base Outlook,1997 StudyElasticities2) AEO2007Base Outlook,Wider Range ofOPEC SupplyElasticities3) Case 2 withRevised WiderRange of OPECSupplyElasticities4) Case 2 withUpdated NonOPECSupply/DemandElasticities5) Case 4 plusRevised WiderRange of OPECSupplyElasticities6) Case 4Variant withWider, HigherRange of NonU.S./Non-OPECSupply/DemandElasticities7) Case 6 withApplied StrategicOPEC BehavioralRule: MaintainMarket Share8) Case 4 withReduced GDPElasticity9) Case 7 withReduced GDPElasticityMonopsony ComponentMean$5.42$9.99$7.95$7.57$6.35$6.27$9.11$7.65$9.17Range($3.51 - $7.98)($3.02 - $20.66)($3.04 - $18.79)($2.86 - $13.39)($2.82 - $12.72)($2.62 - $11.03)($6.51 - $12.09)($2.86 - $13.53)($6.54 - $12.21)Disruption Import CostsMean$2.30$2.27$2.28$1.87$1.89$1.88$1.84$2.14$2.10Range($0.5 - $4.54)($0.56 - $4.45)($0.64 - $4.49)($0.38 - $3.65)($0.37 - $3.74)($0.41 - $3.6)($0.43 - $3.56)($0.61 - $3.96)($0.6 - $3.86)Disruption Dislocation CostsMean$3.73$3.60$3.66$3.33$3.36$3.38$3.32$2.60$2.57Range($1.03 - $6.51)($1.01 - $6.4)($1.03 - $6.56)($0.9 - $6.07)($0.92 - $5.94)($0.9 - $5.97)($0.85 - $6.1)($0.88 - $4.71)($0.85- $4.74)Economic Disruption/AdjustmentCostsMean$6.03$5.87$5.94$5.20$5.25$5.26$5.16$4.74$4.68Range($2.86 - $9.75)($2.86 - $9.49)($2.82 - $9.59)($2.46 - $8.46)($2.53 - $8.39)($2.51 - $8.69)($2.47 - $8.51)($2.17 - $7.64)($2.19 - $7.57)Total Mid-PointMean$11.44$15.86$13.90$12.77$11.59$11.55$14.27$12.38$13.86Range($7.83 - $15.54)($7.89 - $26.7)($7.69 - $24.82)($7.07 - $19.08)($6.78 - $18.13)($6.61 - $16.78)($10.74 - $18.12)($6.88 - $18.52)($10.48 - $17.77)Total Premium, in $/GallonMean$0.27$0.38$0.33$0.30$0.28$0.28$0.34$0.29$0.33Price Reduction ($/MMBD)Mean$0.51$1.01$0.78$0.73$0.60$0.59$0.88$0.73$0.88Cases5) Revise Case 4, with OPEC behavior distributed over elasticities 0 to 6, so that 25% of response is inelastic (< 1.0), mode elasticity is 2.0 (mean elasticity is 2.2 rather than 1.76). Net elasticity of importdemand is -1.6 for the non-U.S./non-OPEC region.6) Alternative to Case 4 with expanded (and higher) range of non-U.S. supply/demand elasticities. Elasticity of non-U.S. demand = -0.3 to -0.7, triangular distribution with mode -0.4 mean -0.467, elasticity ofnon-U.S. supply = 0.2 to 0.6, mode 0.3 and mean 0.367. Implying (mode) net elasticity of import demand from non-U.S./non-OPECregions more than doubled to mode -1.92, mean -2.28.7) Applied Strategic OPEC Behavioral Rule to Case 6: Maintain Market Share (Gately 2004 paper best strategy). This rule implies that OPEC Supply elasticity matches that of all non-OPEC supply. As aresult non-U.S. elasticity of import demand ranges from -1.38 to -3.52, with mode -1.92, mean -2.28. Total elasticity of net import supply to U.S. then ranges from 4.8 to 8.6 (90% confidence interval).8) Variant on version Case 4, considered reduced GDP elasticity for future disruptions (range -0.01 to -0.054; midcase value -0.032; mean value is -0.032, reduced from mean value of -0.0495). OPECbehavior treated parametrically.9) Revise Case 7 (which applied Strategic OPEC Behavioral Rule toCase 6: Maintain Market Share (Gately 2004 paper best strategy)) with reduced GDP elasticity for future disruptions (range -0.01 to ­0.054; midcase value -0.032; mean value is -0.032, reduced from mean value of -0.0495).2) AEO2007 Base Outlook, with wider range of OPEC supplyelasticities, 0.25 to 6.0 anda mean elasticity of 1.76.3) Revise Case 2, with OPEC behavior distributed over elasticities 0 to 6, so that 25% of response is inelastic (< 1.0), mode elasticity is 2.0 (mean elasticity is 2.2 rather than 1.76)4) Updated Case 2 supply/demand elasticities for non-OPEC region with more recent estimates. Elasticity of non-U.S. demand -0.2 to -0.4, with mean and mode -0.3, non-U.S. Supply = 0.2 to 0.4, withmean and mode 0.3, implying (mode) net elasticity of import demand from non-U.S./non-OPEC regions ~doubled to -1.6.1) Based on AEO2007. Updated oil market outlook from AEO1994 Base Case to AEO2007 Base Case. Among other things, this means average crude price rises from $20.33 to $48.34. All elasticitiesmatch 1997 values. Non-U.S. elasticity of import demand = -0.876, parametric treatment of OPEC response with elasticity 1.0-5.0 (triangular distribution with mode 2.0).This page intentionally left blank.680681 5.2.4 Estimates of per Barrel and To tal U.S. Energy Security Benefits

Table 5.2.4-1 below summarizes ORNL’s estimate of the energy security benefits associated with the RFS2 proposal. The component s of the energy security benefit estimate are shown as well as the total U.S. energy security benefits associated with U.S. import reductions due to this proposed ru le, in Table 5.2.4-2.

Table 5.2.4-1. Energy Security Benefits ($/barrel in 2006$) Effect Updated ORNL Study Monopsony (best estimate) $7.65 (range) $3.35 – $12.45 Macroeconomic Disruption (best estimate) $4.74 (range) $2.64 – $6.96 Total (best estimate) $12.38 (range) $7.65 - $17.23

Total energy security benefits associated w ith the RFS2 proposal ar e calculated in Table 5.2.4-1 for the year 2022. These benefits are de rived from the estimated reductions in imports of finished petroleum products and crude oil us ing an energy security premium price of $12.38/barrel.

Table 5.2.4-2. Total Energy Security Benefits (billions of 2006$)
Year 2022 Benefits $3.7



### 5.3 Benefits of Reducing GHG Emissions

5.3.1 Introduction

The wider use of renewable fuels from th e RFS2 proposal results in reductions in greenhouse gas (GHG) emissi ons. Carbon dioxide (CO 2 ) and other GHGs mix well in the atmosphere, regardless of the locat ion of the source, with each un it of emissions affecting global regional climates; and therefore, influencing re gional biophysical and socioeconomic systems. The effects of changes in GHG emissions are felt for decades to centuries given the atmospheric lifetimes of GHGs. This section provides estimates for the marginal and total benefits that could be monetized for the projected GHG emissions reductions of this proposal. EPA requests comment on the approach utilized to estimate th e GHG benefits associated with the proposal.

5.3.2 Marginal GHG Benefits EstimatesThe projected net GHG emissions reductions associated with the proposal reflect an incremental change to projected total global emissi ons. Therefore, as shown in Section 2.4, the projected global climate signal will be small but discernable (i.e., it incrementally shifts the distribution of projected global mean surface temperatures to the left with the upper end shifting more than the lower end). Given that the climate response is projected to be marginal relative to the baseline climate, it is conceptually appropriate to use an approach that estimates the marginal value of changes in climate chan ge impacts over time as an estimate for the monetized marginal benefit of the GHG emissions redu ctions projected for this propos al. The marginal value of carbon is equal to the net present value of climate change im pacts over hundreds of years of one additional net global metric ton of GHGs emitted to the atmosphere at a particular point in time. This marginal value (i.e., cost) of carbon is some times referred to as the “social cost of carbon.”

Based on the global implications of GHGs a nd the economic principl es that follow, EPA has developed ranges of global marginal benefits estimates, as well as U.S. marginal estimates (Table 5.3-1). R RRRRRRRRRR It is important to note at the outs et that the estimates are incomplete since current methods are only able to reflect a partial accounting of the climate change impacts identified by the Intergovernmental Panel on Cl imate Change (IPCC; discussed more below). Also, domestic estimates omit impacts on the Un ited States resulting from climate change impacts in other countries (e.g., economic or na tional security impacts). The global estimates were developed from a survey analysis of the peer reviewed literature (i.e., meta analysis). U.S. estimates, and a consistent set of global estimates, were devel oped from a single model and are highly preliminary, under evaluati on, and likely to be revised. The single model used was the Climate Framework for Uncertainty, Negotiation, and Distribution (FUND; discussed below). It was necessary to develop the latte r set of estimates because the p eer reviewed literature does not provide regional (i.e., at the U.S. or China leve l) marginal benefits es timates, and, since the regional estimates are not directly comparable to the global meta analysis estimates, it was important to have a consistent set of regional and global estimates. Ranges of estimates are provided to capture some of the uncertainties associated with m odeling climate change impacts.

The range of estimates is wide due to the uncertainties relating to socio-economic futures, climate responsiveness, impacts modeling, as well as the choice of discount rate. S SSSSSSSSSS For instance, for 2007 emission reductions and a 2% consumption discount ra te the global meta analysis estimates range from $-3 to $159/t CO 2 , while the U.S. estimates range from $0 to $16/t CO 2 . For 2007 emission reductions and a 3% co nsumption discount rate, the global metaestimates range from $-4 to $106/t CO 2 , and the U.S. estimates range from $0 to $5/t
T TTTTTTTTTT CO 2
. The global meta analysis mean values for 2007 emission reductions are $68 and $40/t CO 2 for discount rates of 2% and 3% respec tively (in 2006 $) while the domestic mean

RRRRRRRRRRR For background on economic principles and the marginal benefit estimates, see Technical Support Document on Benefits of Reducing GHG Emissions , U.S. Environmental Protection Agency, June 12, 2008, www.regulations.gov (search phrase “Technical Support Document on Benefits of Reducing GHG Emissions”)
SSSSSSSSSSS A discount rate reflects the rate at which people trade-off a dollar received today versus a dollar received a year from now. People generally value benefits received today more than those received tomorrow. As a result, when making decisions today, they tend to discount the benefits they receive in the future. The actual discount rate depends on the decision-maker and the kind of investment. In this context, the decision-maker is the Federal Government and changing GHG emissions is an investment with an extremely long-run annual (non-constant) return. Therefore, social discounting and in tergenerational discount rates are appropriate.
TTTTTTTTTTT See Table 5.3-1 for global (FUND) estimat es consistent with the U.S. estimates.

682values from a single model are $4 and $1/t CO 2 for the same discount rates. The estimates for future year emission changes are likely to be higher as future marginal emissions increases are expected to produce larger incremental damages as physical and economic systems become more stressed as the magnitude of climate change increases. U UUUUUUUUUU

Table 5.3-1. Marginal GHG Benefits Estimates fo r Consumption Discount Rates of 2%, 3%, and 7% and Year of Emissions Change (all values are reported in 2006$/tCO 2 )

2% 3% 7%** Low Central High Low Central High Low Central High

Meta global 2007 -3 68 159 -4 40 106 n/a n/a n/a 2017 -2 91 213 -3 53 142 n/a n/a n/a 2022 -2 105 247 -2 62 165 n/a n/a n/a 2030 -1 134 314 -2 78 209 n/a n/a n/a 2040 -1 179 421 -1 105 281 n/a n/a n/a

FUND global 2007 -6 88 695 -6 17 132 -3 -1 5 2017 -4 118 934 -4 23 178 -2 -1 7 2022 -4 136 1083 -4 26 206 -2 -1 9 2030 -3 173 1372 -3 33 261 -1 0 11 2040 -2 232 1843 -2 44 351 -1 0 15

FUND domestic 2007 0 4 16 0 1 5 0 0 0 2017 0* 6 22 0* 1 7 0* 0* 0* 2022 0* 7 26 0* 2 9 0* 0* 0* 2030 0* 9 32 0* 2 11 0* 0* 0* 2040 0* 12 44 0* 3 15 0* 0* 0*

* These estimates, if explicitly estimated, may be greater than zero, especially in later years. They are currently reported as zero because the explicit estimates for an earlier year were zero and were grown at 3% per year. However, we do not anticipate that the explicit estimates for these later year s would be significantly above zero given the magnitude of the current central estim ates for discount rates of 2% and 3% and the effect of the high discount rate in the case of 7%. ** Except for illustrative purposes , the marginal benefits estimates in the peer reviewed literature do not use consumption discount rates as high as 7%.

The meta analysis ranges were deve loped from the Tol (2008) meta analysis. V VVVVVVVVVV The meta analysis range only includes global estimates generated by more recent peer reviewed studies (i.e., publishe d after 1995). In addition, the ranges only consider regional aggregations using simple summation and intergenerational consumption discount rates of approximately 2% and 3%. Discount rates of 2% and 3% are consistent with EPA and OMB guidance on intergenerati onal discount rates (EPA, 2000; OMB, 2003). W WWWWWWWWWW The estimated distributions of the meta global estimates are right skewed with long right tails, which is consistent with characterizations of low probability high impact damages, such as catastrophic even ts like collapse of the West Antarctic Ice

UUUUUUUUUUU The IPCC suggests an increase of 2-4% per year (IPCC WGII, 2007. Climate Change 2007 - Impacts, Adaptation and Vulnerability . Contribution of Working Group II to the Fourth Assessment Report of the IPCC, http://www.ipcc.ch/). For Table 5.3-1., we assumed the estimates increased at 3% per year. For the final rule, we anticipate that we will explicitly es timate FUND marginal benefits valu es for each emissions reduction year.
VVVVVVVVVVV Tol (2008) is an update of the Tol (2005) meta analysis. Tol (2005) was used in the IPCC Working Group II’s Fourth Assessment Report (IPCC WGII, 2007).
WWWWWWWWWWW OMB and EPA guidance on inter-generational discounting suggests using a low but positive discount rate if there are important intergenerational bene fits/costs. Consumption discount rates of 1% to 3% are given by OMB and 0.5% to 3% by EPA (OMB Circular A-4, 2003; EPA Guidelines for Preparing Economic Analyses, 2000).

683-36 0 36 72 108 144 180 2006$/tCO 2 ~2%
~3%Probability of$/tCO2 Sheet. X XXXXXXXXXX YYYYYYYYYYY ZZZZZZZZZZZ Figure 5.3-1 provides the estimated probability density functions by discount rate. Following To l (2008), we fit extrem e value Fisher-Tippett distributions because they better refl ected the right skewness of the data. A AAAAAAAAAAA Note that the x-axis of Figure 5.3-1 reflects that marginal value for emissions reductions circa 1995. As noted above, these values were grown to produce the estimates in Table 5.3-1. The central meta estimates in Table 5.3-1 are means, and the low and high are the 5 th and 95 th percentiles. Means are presented because, as a central statistic, they better represent the skewed shape of these distributions compared to medians.

Figure 5.3-1.

Probability Density Functions for Meta Analysis Global Marginal Benefit Estimates for a Marginal Emissions Reduction circa 1995 and Consumption Discount Rates of 2% and 3% (Developed from Tol, 2008, Meta Analysis)

-36 0 36 72 108 144 180 2006$/tCO 2 ~2%
~3%Probability of$/tCO2

The consistent domestic and global estimates were developed using the FUND integrated assessment model. B BBBBBBBBBBB The ranges were generated from sensitivity analysis where assumptions were varied with respect to climate sensitivity (1.5 to 6.0 degrees

XXXXXXXXXXX Webster, M., C. Forest, J.M. Reilly, M.H. Babike r, D.W. Kicklighter, M. Mayer, R.G. Prinn, M. Sarofim, A.P. Sokolov, P.H. Stone & C. Wang, 2003. Uncertainty Analysis of Climate Change and Policy Response, Climatic Change 61(3): 295-320.
YYYYYYYYYYY Weitzman, M., 2007, “The Stern Review of the Economics of Climate Change,” Journal of Economic Literature

. ZZZZZZZZZZZ Weitzman, M., 2007, “Structural Uncertainty and the Statistical Life in the Economics of Catastrophic Climate Change,” Working paper http ://econweb.fas.harvard.edu/faculty/weitz man/papers/ValStatLifeClimate.pdf.
AAAAAAAAAAAA For our meta analysis, the sample sizes were 13 and 10 for consumption discount rates of 2% and 3% respectively. A sample size of 10 or greater is considered sufficient for es timating an extreme value distribution (Carter, D. J. T. and P. G. Challenor, 1983. App lication of extreme value analysis to Weibull data, Quart. J. R. Met. Soc. 109, 429-433).
BBBBBBBBBBBB FUND is a spatially and temporally consistent framework across regions of the world (e.g., U.S., China), impacts sectors, and time. FUND explicitly models impacts sectors in 16 global regions. FUND is one of the few models in the world that explicitly models globa l and regional marginal benefits estimates. Numerous applications of FUND have been published in the peer reviewed literature dating back to 1997. See http://www.fnu.zmaw.d e/FUND.5679.0.html

.

684Celsius), C CCCCCCCCCCC the socio-economic and emissions baseline scenarios (the FUND default baseline and three baselines from the Inte rgovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios, SRES), D DDDDDDDDDDD and the consumption discount rates of approximately 2%, 3%, and 7%, where 2% and 3% are consistent with intergenerational discounting. E EEEEEEEEEEE Furthermore, the model was calibrated to the EPA value of a statistical life of $7.4 million (2006$). F FFFFFFFFFFF The FUND global estimates are the sum of the regional estimates within FUND. The FUND global and do mestic central values in Table 5.3-1 are weighted averages of the FUND estimates from the sensitivity analysis. The low and high values are the low and high estimates across the sensitiv ity runs. See Table 5.3-2 for the global and domestic marginal benefit estimates by baseline , climate sensitivity, a nd discount rate. The weighted averages were calculated using a clim ate sensitivity probability density function based on IPCC WGI (2007) and equal weights for the alternative socioeconomic baselines. G GGGGGGGGGGG

Table 5.3-2. Global and Domestic Marginal Benefit Es timates from FUND Modeling by Baseline, Climate Sensitivity (CS), and Discount Rate (DR) for an Additional Un it of Emissions in 2005 (2006$/t CO 2 )

CS = 1.5 deg C 2 3 4

. 5 6 Baseline DR = ~2% ~3% ~7% ~2% ~3% ~7% ~2% ~3% ~7% ~2% ~3% ~7% ~2% ~3% ~7%Global FUND

‐ $2

‐ $5

‐ $3 $9

‐ $2

‐ $3 $43 $9

‐ $1 $140 $35 $1 $365 $81 $5

A1b

‐ $6

‐ $5

‐ $3 $0

‐ $3

‐ $2 $16 $3

‐ $2 $54 $16 $0 $114 $37 $3
A2

‐ $2

‐ $6

‐ $3 $15

‐ $1

‐ $3 $68 $13

‐ $2 $240 $51 $1 $655 $125 $5
B2

‐ $4

‐ $5

‐ $3 $8

‐ $2

‐ $3 $43 $8

‐ $2 $145 $34 $1 $409 $83 $4Domestic FUND $0 $0 $0 $1 $0 $0 $3 $1 $0 $7 $2 $0 $11 $4 $0
A1b $0 $0 $0 $1 $0 $0 $2 $1 $0 $6 $2 $0 $10 $3 $0
A2 $0 $0 $0 $2 $0 $0 $4 $1 $0 $9 $3 $0 $15 $5 $0
B2 $0 $0 $0 $1 $0 $0 $3 $1 $0 $7 $2 $0 $11 $4 $0

CCCCCCCCCCCC In IPCC reports, equilibrium climate sensitivity re fers to the equilibrium change in the annual mean global surface temperature follo wing a doubling of the atmospheric equiva lent carbon dioxide concentration. The IPCC states that climate sensitivity is “likely” to be in the range of 2°C to 4.5°C and described 3°C as a "best estimate", which is the mode (or most likely) value. The IPCC goes on to note that climate sensitivity is “very unlikely” to be less than 1.5°C and “values substantially higher than 4.5°C cannot be excluded.” IPCC WGI, 2007, Climate Change 2007 - The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the IPCC, http://www.ipcc.ch/.
DDDDDDDDDDDD The IMAGE model SRES baseline data were used for the A1b, A2, and B2 scenarios (IPCC, 2000. Special Report on Emissions Scenarios . A special report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge). The A1b baseline represents a world focused on global economic growth with an incr easingly globalized economy, with a balanced energy technology portfolio between fossil fuel and non-fossil fuel supply and consumption, and diffusion of technological advances. The A2 baseline represents a world focused on regional economic growth with increasing inequality between regions as developed regions grow more quickly than developing regions. B2 represents a world focused on both global economic growth and environmental improvement.
EEEEEEEEEEEE The EPA guidance on intergenerational discounting st ates that “[e]conomic an alyses should present a sensitivity analysis of alternative discount rates, includin g discounting at two to three percent and seven percent as in the intra-generational case, as well as scenarios using rate s in the interval one-half to three percent as prescribed by optimal growth models.” (EPA, 2000)
FFFFFFFFFFFF This number may be updated to be consistent with recent EPA Office of Air and Radiation regulatory impact analyses that has used a value of $6.4 million (in 2006 real dollars).
GGGGGGGGGGGG The climate sensitivity probability density function was calibrated to the cumulative density functions in Chapter 10 of IPCC WGI (2007), and the IPCC’s formal definitions for climate sensitivity likelihood.

685Figures 5.3-2 through 5.3-5 provide the undiscounted global and domestic annual marginal benefits from our FUND modeling for an illustrative set of scenarios from our sensitivity analysis for an incremental emissions change in 2005. H HHHHHHHHHHH The figures illustrate the sensitivity of the global and domestic results to baseline and climate responsiveness. These streams of annual benefits were discoun ted following the common approach of Ramsey discounting, which incorporates changes in economic growth and the marginal utility of consumption over time. I IIIIIIIIIII We calibrated our discounting to produce U.S. consumption discount rates of approximately 2% and 3% in order to be cons istent with OMB and EPA intergenerational discounting guidance. The resultin g calibration produced 2 005 rates of 2.7% to 2.9% for the U.S., and 1.8% to 5.5% across region s. The rates vary by region and time period as regional annual growth in per capita consumption changes.

Figure 5.3-2. Undiscounted Global Annual Marginal Benefits from FUND Modeling for an Emissions Reduction in 2005 with the FUND Default Ba seline and Climate Sensitivities of 1.5, 2.0, 3.0, 4.5, and 6.0

$16

$14

$12

$10

$8

$6

$4

$2

$0

-$2 20 20 21 2 21 22

10

2020

20

30

20 5 60 20 80 20 00 21 2 30 21 50 21 70 18 00 22 20 22 4 50 22 70 22 9 40

20 70 20 90 21 10 21 40 21 60 21 90 22 10 22 30 22 60 22 80 22 0 0 0 0 0 CS6.0 FUND

CS4.5 FUND

CS3.0 FUND

CS2.0 FUND

CS1.5 FUND2006$/tCO2/year

HHHHHHHHHHHH Undiscounted annual benefits are not available fo r the meta analysis estimates. The FUND default year for our runs is 2005. FUND marginal benefit estimates for subsequent years in Table 5.3-1 were computed by growing the 2005 values at 3%/year as discussed above.
IIIIIIIIIIII A constant discount rate implies constant economic growth, which is unrealistic over long time horizons. Interest rates are a function of economic growth; and, incr easing (declining) economic growth implies an increasing (declining) discount rate. Furthermore, increasing uncer tainty in economic growth implies, among other things, increasing uncertainty in interest rates and declining certain ty equivalent rates of return s to capital (Hansen, A.C., 2006. “Do declining discount rates lead to time inconsistent economic advice?” Ecological Economics 60(1), 138­ 144.)

686Figure 5.3-3. Undiscounted Global Annual Marginal Benefits from FUND Modeling for an Emissions Reduction in 2005 with the SRES A1b, A2, B2, and FUND Default Baselines and a Climate Sensitivity of 3.0

$12

2010

2 0 2 0 2030

2040

2 050

2060

2 0 7 0 2080

2090

2 1 0 0 2110

2120

2130

2140

2 1 5 0 2160

2170

2 1 8 0 2190

2200

2210

2220

2 2 3 0 2240

2250

2 260

2270

2280

2290 CS3.0 FUND

CS3.0 A1b

CS3.0 A2

CS3.0 B2 $10

$8

$6

$4

$2

$02006$/tCO2

-$2

Figure 5.3-4. Undiscounted U.S. Annual Marginal Benefits from FUND Modeling for an Emissions Reduction in 2005 with the FUND Default Baseline and Climate Sensitivities of 1.5, 2.0, 3.0, 4.5, and 6.0

$0.3020102020203020402050206020702080209021002110212021302140215021602170218021902200221022202230224022502260227022802290 CS6.0 FUND

CS4.5 FUND

CS3.0 FUND

CS2.0 FUND

CS1.5 FUND2006$/tCO2 $0.25

$0.20

$0.15

$0.10

$0.05

$0.00

-$0.05

687Figure 5.3-5. Undiscounted U.S. Annual Marginal Benefits from FUND Modeling for an Emissions Reduction in 2005 with the SRES A1b, A2, B2, and FUND Default Baselines and a Climate Sensitivity of 3.0

$0.162006$/tCO2 $0.14

$0.12

$0.10

$0.08

$0.06

$0.04

$0.02

$0.00

-$0.0220102020203020402050206020702080209021002110212021302140215021602170218021902200221022202230224022502260227022802290 CS3.0 FUND
CS3.0 A1b
CS3.0 A2
CS3.0 B2

From Tables 5.3-1 and 5.3-2, we see that th e estimates are very sensitive to assumptions. The FUND sensitivity analysis shows that higher ma rginal benefits estimates are associated with higher climate sensitivities, lower economic growth per capita globally a nd regionally, and lower discount rates. In terms of the current monetized benefits, the domestic marginal benefits are a fraction of the global marginal benefits. Given uncertainties and omitted impacts, it is difficult to estimate the actual ratio of total domestic benefits to total global benefits. The estimates suggest that an emission reducti on will have direct benefits for current and future U.S. populations and large benefits for global populations. The lo ng-run and intergenerational impacts due to GHG emissions are evident in the di fference in results across discount rates. In the current modeling, there are substantial long-run benefits (b eyond the next two decades to over 100 years) and some near-term benefits as well as negative effect s (e.g., agricultural productivity and heating demand). High discount rates give less weig ht to the future benefits in the net present value calculations, and more wei ght to near-term effect s. Table 5.3-2 also illustrates that an additional unit of emissions is estimated to have a proportionally larger effect on the rest of the world compared to the U.S. if the climate and/or economic system turn out to be more sensitive (i.e., climate sensitivity of 6 degrees Celsius and A2 baseline respectively) to an increasing stock of GHGs in the atmosphere scenarios. Some of these points are discussed more below.

5.3.3 Discussion of Margin al GHG Benefits Estimates

This section briefly discusses important issues relevant to the marginal benefits estimates in Table 5.3-1. Because of the global nature of CO 2 and other greenhouse gases, estimating the marginal benefits required a global modeling framework w ith consistent integrated socioeconomics, emissions, climate change, and im pacts. Given uncertainties in socio-economic futures (e.g., population growth , economic growth, and technol ogy availability and diffusion), corresponding GHG emissions, climate responses to emissions changes, a nd the bio-physical and

688economic impacts associated with changes in cl imate, the quantified (p hysical and monetized) estimates of climate change are inherently uncertain.

The broad range of estimates in Table 5.3-1 re flects some of the uncertainty associated with estimating monetized marginal benefits of c limate change. The meta analysis range reflects differences in these assumptions as well as differe nces in the modeling of changes in climate and impacts considered and how they were modeled. EPA considers the meta analysis results to be more robust than the single model estimates in that the meta results reflect uncertainties in both models and assumptions.

The current state-of-the-art for estimating benefits is important to consider when evaluating policies. There are significant partially quantified and omitted impact categories not captured in the estimates provided above. The IPCC Working Group II (IPCC WGII; 2007) concluded that current estimates are “very likely” to be undere stimated because they do not include significant impacts that have yet to be monetized. J JJJJJJJJJJJ Current estimates do not capture many of the main reasons for conc ern about climate change, including non-market damages (e.g., species existence value and the valu e of having the option for future uses, such as for biomedical, tourism, or recreational purposes), the effects of climate variability, risks of potential extreme weather (e.g., droughts, heavy rains and wind), socially contingent effects (such as violent conflict or human itarian crisis), and thresholds (or tipping points) associated with species, ecosystems, and potential long-term catastrophic events. Some scientists believe that effects on factors such as arctic summer ice, Himalayan-Ti betan Glaciers, and the Greenland ice sheet are more sensitive to near-term GHG emissions change s, implying that there are nonlinearities in the effects attributable to GHG emissions. K KKKKKKKKKKK Underestimation is even more likely when one considers that the curren t trajectory for GHG emissions is higher than what is being considered in current integrated assessment modeling (such as that reflected in the modeling represented in Tol, 2008, and Clarke et al., 2006). When combined with current regional population and income trajectories that are more asymmetric than what is currently modeled, this implies greater climate change and vulnerability to climate change.

In Table 5.3-3, we provide a list of th e impacts categories currently modeled in the FUND model, and an initial, partial list of categori es of impacts that are currently not modeled in the FUND model and are thus not reflected in the FUND estimates. Table 5.3-4 provides an initial list of some more specific types of im pacts reported on by the IP CC but are not currently captured in FUND. L LLLLLLLLLLL A key challenge for many impact s sectors is data limitations (primarily physical data). Note that most of the omitted impacts are likely to lead to additional benefits in response to reductio ns in GHG emissions, including in ternational impacts that could affect domestic benefits (e.g., potential impact feedbacks to the United States, U.S. concern for international impacts, and international partic ipation). EPA is pla nning to develop a full assessment of what is not currently being captured in FUND for the final rule. In addition, EPA

JJJJJJJJJJJJ IPCC WGII, 2007. In the IPCC report, “very likely” was defined as a greater than 90% likelihood based on expert judgment.
KKKKKKKKKKKK Ramanathan and Feng, 2008. On avoiding dangerous anthropogenic interference with the climate system: Formidable challenges ahead. Proceedings of the National Academy of Sciences 105:143245-14250.
LLLLLLLLLLLL IPCC WGII (2007) Summary for Policy Makers and Chapter 14 North America.

689plans to quantify omitted impacts and update impacts currently represented to the extent possible for the final rule.

Table 5.3-3. Lists of Impact Categories Modeled and Omitted from Current FUND Modeling (see Table 5.3-4 for specific types of impacts omitted) Impacts currently modeled in FUND

• Agricultural production

• Forestry production

• Water resources

• Energy consumption for space cooling & heating

• Sea level rise dry land loss, wetland loss, and coastal protection costs

• Forced migration due to dry land loss

• Changes in human health (mortality, morbidity) associat ed with diarrhea incidence, vector-borne diseases, cardiovascular disorders, and respiratory disorders

• Hurricane damage

• Loss of ecosystems/biodiversity Examples of impacts omitted from current FUND modeling

• Catastrophic events (e.g., Antarctic ice sheet collapse)

• Risks from extreme weather (e.g., death, disease and economic damage from droughts, floods, and fires)

• Air quality degradation (e.g., increased ozone effects including premature mortality, forest damage)

• Increased infrastructure costs (e.g., water management systems, roads, bridges)

• Increased insurance costs

• Social and political unrest abroad that affects U.S. national security

• Damage to foreign economies that affects the U.S. economy

• Domestic valuation of international impacts

• Costs from uncertainty and changes in risk

• Arctic sea ice melt and global transportation & trade

690Table 5.3-4. Initial List of Specifi c Types of Impacts Not Current Captured in FUNDAgriculture Reduction in growing season (Sahel/southern Africa) Increase in growing season in moderate climates Impact of precipitation changes on agriculture Impact of weather variability on crop productionBiomes/ecosystems Reverse of carbon uptake, amplification of climate change Earlier timing of spring events/ longer growing season Poleward and upward shift in habitats/species migraiton Shifts in ranges of ocean life Increases in algae and zooplankton Range changes/earlier migration of fish in rivers Increase in ocean acidification/ impacts on coral reefs Ecosystem service disruption (e.g. loss of cold water fish habitat in the U.S.) Coral bleaching due to ocean warmingEnergy Energy production/infrastructure
Water temperature/supply impacts on energy productionForest Forest changes with longer fire seasons, longer burning fires, and increased burn area Disappearance of alpine habitat in the United States Tropical forest dieback in the AmazonGDP/economy National security/armed conflict Insurance costs with changes in extreme weather, flooding, and sea level rise Distributional effects within regions Societies that are highly dependent on climate-sensitive resources are highly vulnerable Extreme weather eventsHealth Increased deaths, injuries, infectious diseases, stress-related disorders with more frequent extreme weather Increased heavy rainfall increased risks to human health associated with waterborne disease, increased baterial counts Increases in malnutrition Health impacts due to interactions with air qualitySnow/glacier Chan g es in Arctic/Antarctic ecos y stems Enlar g ement and increased numbers of g lacial lakes/ increased floodin g Snow p ack in SE USTourism Changes in tourism revenues due to changes in ecosystems and weather events (e.g., wildfires

, drou g hts

, hurricances ) Arctic hunting/travel/mountain sportsWater River flooding Infrastructure, water supply Precipitation changes on water suppl y Increasing ground instability and avalanches Increased/earlier runoff in snow-fed rivers

The current estimates are also deterministic in that they do not account for the value people have for changes in risk associated with the lower likelihood of potential impacts associated with reductions in CO 2 and other GHG emissions (i.e., a risk premium). This is an issue that has concerned Weitzman and other economists. M MMMMMMMMMMM We plan to conduct a formal uncertainty analysis for the final rule to atte mpt to account for, to the extent possible, these and other changes in uncertainty.

MMMMMMMMMMMM See Webster et al., 20 03; Weitzman, M., 2007. http://econweb.fas.harvard.edu/faculty/weitzman/papers/ValStatLifeClimate.pdf.

691In part because monetized benefit estimates reflect a subset of climate change impacts, it is important in evaluating policies for reducing GHGs to consider the available scientific and other qualitative information on climate change impacts as well. A summary of potential U.S. climate change impacts based on the existing lite rature is provided in a report by the U.S. Climate Change Science Program, “Scientific Asse ssment of the Impacts of Climate Change in the United States,” released in May 2008. A similar scientific ove rview that discusses international as well as domestic impacts is pr ovided in an EPA techni cal support document that underwent interagency review and was finalized in June 2008. N NNNNNNNNNNN These reports contain information relevant to some of the om itted impacts categories in the tables above (e.g., in the EPA document, section 6f discusses ab rupt climate change; se ction 15(a) discusses national security), as well as on impacts that are monetized in this analysis.

There are two important points to keep in mi nd in applying the estimates in Table 5.3-1 and this kind of modeling. First, the estimates in Table 5.3-1 are not estimates of economically “optimal” marginal benefits (i.e., they are not as sociated with an emissions reduction level where marginal benefits equal marginal costs). The estimates in Table 5.3-1 are only relevant for incremental policies relative to the projected baselines (that do not reflect potential future climate policies) and there is substantial uncertainty a ssociated with the estimates themselves both in terms of what is being modeled and what is no t being modeled, with many uncertainties outside of observed variability. O OOOOOOOOOOO Furthermore, the estimates only represent the value of a marginal emissions change in a specific year, they do not account for potential affects of current reductions on the value of reductions in the futu re. These points are important for non-marginal emissions changes and estimating total benefits. For example, current estimates of marginal emissions changes do not model threshold impact s, variability changes, or changes in the likelihood of these impacts.

Second, because current marginal benefits es timates are incomplete and highly uncertain (with many uncertainties outside of observed va riability), we cannot us e them to identify an economically optimal (or economically efficient) standard, even for incremental changes in global GHG emissions, and it creates issues in applying positive net be nefit criteria. The uncertainty shifts attention to consideration of changes in risk and points to the importance of considering factors beyond monetized benefits and costs. In an uncertain situation such as that associated with climate, EPA typically recommends that analysis consider a range of benefit and cost estimates, and the potential implications of non-monetized and non-quantified benefits.

NNNNNNNNNNNN “Technical Support Document for the Endangermen t Analysis for Greenhouse Gas Emissions Under the Clean Air Act, Sixth Order Draft” Climate Chan ge Division, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.D., June 21, 2008
OOOOOOOOOOOO With projected increasing changes in climate, so me types of potential climate change impacts may occur suddenly or begin to increase at a much faster rate, rath er than increasing gradually or smoothly. In this case, there are likely to be jumps in the functioning of speci es and ecosystems, the frequency and intensity of extreme conditions (e.g., heavy rains, forest fires), and the occurre nce of catastrophic events (e.g., collapse of the West Antarctic Ice Sheet). As a result, different approaches are necessary for quantifying the benefits of “small” (incremental) versus “large” (non-incremental) reductions in global GHGs. Marginal benefits estimates, like those presented above, can be useful for estimating benefits for small changes in emissions. However, for large changes in emissions, a more comprehensive assessment of impact s would be needed to capture changes in economic and biophysical dynamics and feedbacks in response to the policy. Even small reductions in global GHG emissions are expected to reduce climate change ri sks, including catastrophic risks.

692Economic principles suggest that global be nefits should also be considered when evaluating alternative GHG reduction policies. GHG emissions are different from most air pollutants, due to their global and intergenerat ional externality implications. A ton of GHG emitted from any location or source can result in impacts throughout the globe and across multiple generations. Climate change can therefore be characterized as a global and intergenerational public good. T ypically, because the benefits and costs of most environmental regulations are predominantly domestic, EPA fo cuses on benefits that accrue to the U.S. population when quantifying the impacts of domestic regulation. However, OMB’s guidance for economic analysis of federal regulations also specifically allows for consideration of international effects. P PPPPPPPPPPP GHG have global and very long -run implications for climate, a global public good, and economic principles suggest that the full costs to society of emissions should be considered in order to identify the policy that maximizes the net benefits to society, i.e., achieves an efficien t outcome (Nordhaus, 2006). Q QQQQQQQQQQQ As such, estimates of global benefits more fully capture the value to society than domestic estimates and will result in higher global net benefits for GHG reductions when considered. R RRRRRRRRRRR

Furthermore, international effects of climate change may also affect domestic benefits directly and indirectly to the extent that U.S. citizens value international impacts (e.g., for tourism reasons, concerns for the existence of ecosystems, and/or conc ern for others); U.S. international interests are affected (e.g., risks to U.S. national security or the U.S. economy from potential disruptions in other nations); and/or domestic mitigation decisions affect the level of mitigation and emissions changes in general in other countries (i.e., the benefits realized in the U.S. will depend on domestic and international emissions changes). The economics literature also suggests that policies based on direct domes tic benefits will result in little appreciable reduction in global GHGs (e.g., Nordhaus, 1995). S SSSSSSSSSSS While these marginal benefits estimates are not comprehensive or economically optimal, the global estimates in Table 5.3-1 can help guide policies towards more efficient le vels of provision of th e public good since they internalize a larger portion of the full costs to society a unit of emi ssions — the global and intergenerational externalities.

A key challenge facing EPA is the appropriate discount rate over the longer timeframe relevant for GHGs. When considering climate cha nge investments, they should be compared to similar investments (via the discount rate). EPA typically discounts future costs or benefits back

PPPPPPPPPPPP OMB (2003), page 15. QQQQQQQQQQQQ Nordhaus, W., 2006, "Paul Samuelson and Global P ublic Goods,” in M. Szen berg, L. Ramrattan, and A. Gottesman (eds), Samuelsonian Economics, Oxford.
RRRRRRRRRRRR Both the United Kingdom and the European Commission followed these economic principles in consideration of the global social cost of carbon (SCC) for valuing the benefits of GHG emission reductions in regulatory impact assessm ents and cost-benefit analyses (Watkiss et al, 2006). Recently, the National Highway Traffic Safety Administration (NHTSA) issued the final Environmental Im pact Statement for their proposed rulemaking for average fuel economy standards for passenger cars and light trucks in which the preferred alternative is based on domestic marginal benefit estim ates for carbon dioxide reductions. See Average Fuel Economy Standards, Passenger Cars and Light Trucks, MY 2011-2015, Final Environmental Impact Statement http://www.nhtsa.dot.gov/portal/site/nhtsa/menuitem.43ac99aefa80569eea57529cdba046a0/
SSSSSSSSSSSS Nordhaus, William D. (1995). “Locational Competition and the Environment: Should Countries Harmonize Their Environmental Policies?” in Locational Competition in the World Economy, Symposium 1994, ed., Horst Siebert, J. C. B. Mohr (Paul Siebeck), Tuebingen, 1995.

693to the present using a discount rate, where the di scount rate represents how society trades off current consumption for future consumption. With the benefits of GHG emissions reductions distributed over a very long time horizon, benefit and cost comparisons are likely to be very sensitive to the discount rate. For policies with relatively short time horizons, up to 30 years or so (i.e., that affect the current generation of people), the analytic approach used by EPA is to use discount rates of 3% and 7% percent at a minimum. T TTTTTTTTTTT A 3% rate is consistent with what a typical consumer might expect in the way of a risk free market return. A 7% rate is an estimate of the average before-tax rate of retu rn to private capital in the U.S. economy.

However, what discount rates are appropriate for discounting social benefits and costs over the longer timeframe relevant for climate change policies? Changes in GHG emissions — both increases and reductions — are essentially long-run investme nts in changes in climate and the potential impacts from climate change. Inve stments in climate cha nge represent longer-term investments in infrastructure and technologies as sociated with mitigation, where there are annual returns of avoided impacts over a period of one hundred years and longer that affect multiple generations. Furthermore, there is a potential for significant impacts from climate change, where the exact timing and magnitude of these impacts are unknown. These factors imply an uncertain investment environment with uncertain ec onomic growth that va ries over time.

When there are important benefits or cost s that affect multiple generations of the population, EPA and OMB allow for low but positiv e consumption discount rates (e.g., 0.5% to 3% noted by the U.S. EPA, 1% to 3% by the OMB). U UUUUUUUUUUU In this multi-generation context, a three percent discount rate is consistent with observe d interest rates from long-term investments available to current generations (net of risk premiu ms) as well as current estimates of the impacts of climate change that reflect po tential impacts on consumers. A review of the literature indicates that rates of three percent or lower are more consistent with long-run uncertainty in economic growth and interest rates, considerations of issu es associated with the transfer of wealth between generations, and th e risk of high impact climate damages (which could reduce or reverse economic growth). Given the uncertain environment, analysis could also consider evaluating uncertainty in the discount rate (e.g., Newell and Pizer, 2001, 2003). V VVVVVVVVVVV WWWWWWWWWWWW

For the final rulemaking, we will be deve loping and updating the FUND model to the extent possible based on the late st research and peer reviewin g the estimates. To improve upon our estimates, we hope to evaluate several factors not currently captured in the proposed

TTTTTTTTTTTT EPA (U.S. Environmental Protection Agency), 2000. Guidelines for Preparing Economic Analyses. EPA 240-R-00-003. See also OMB (U.S. Office of Management and Budget), 2003. Circular A-4. September 17, 2003.
UUUUUUUUUUUU EPA (U.S. Environmental Protection Agency), 2000. Guidelines for Preparing Economic Analyses. EPA 240-R-00-003. See also OMB (U.S. Office of Management and Budget), 2003. Circular A-4. September 17, 2003. These documents are the guidance used when pr eparing economic analyses for all EPA rulemakings. VVVVVVVVVVVV Newell, R. and W. Pizer, 2001. Discounting the bene fits of climate change mitigation: How much do uncertain rates increase valuations? Pew Center on Global Climate Change, Washington, DC.
WWWWWWWWWWWW Newell, R. and W. Pizer, 2003. Discounting th e distant future: how much do uncertain rates increase valuations? Journal of Environmental Economics and Management 46: 52–71.

694estimates due to time constraints. For example, we will quantify additional impact categories where possible and provide a qualita tive evaluation of the implications of what is not monetized. We also plan to conduct an uncertainty analys is, consider complementary bottom-up analyses, and develop estimates of the margin al benefits associated with non-CO 2 GHGs relevant to the RFS2 proposal (e.g., CH 4 , N 2 O, and HFC-134a). X XXXXXXXXXXX

EPA solicits comment on the appropriatene ss of using U.S. and global values in quantifying the benefits of GHG re ductions and the appropriate appl ication of benefits estimates given the state of the art and ove rall uncertainties. We also se ek comment on our estimates of the global and U.S. marginal be nefits of GHG emissions reducti ons that EPA has developed, including the scientif ic and economic foundations, the methods employed in developing the estimates, the discount rate s considered, current and proposed fu ture consideration of uncertainty in the estimates, marginal benefits estimates for non-CO 2 GHG emissions reductions, and potential opportunities for impr oving the estimates. We are also interested in comments on methods for quantifying benefits for non-incr emental reductions in global GHG emissions.

5.3.4 Total Monetized GHG Benefits Estimates

As described in Section 2.3, annualized e quivalent GHG emissions reductions associated with the proposal in 2022 would be 160 million metric tons of CO 2 equivalent (MMT CO 2 eq) in with a 2% discount rate, and 155 and 136 MM CO 2 eq with discount rates of 3% and 7% respectively.

This section provides the monetized total GHG benefits estimates associated with the proposal in 2022. As discussed above in subs ection three, these estimates do not include significant impacts that ha ve yet to be monetized. Total monetized benefits in 2022 are calculated by multiplying the marginal benefits per metric ton of CO 2 in that year by the annualized equivalent emissions reductions. For th e final rulemaking, we plan to separate the emissions reductions by gas and use CO 2 and non-CO 2 marginal benefits estimates. Non- CO 2 GHGs have different climate and atmospheric im plications and therefore different marginal climate impacts.

Table 5.3-5 provides the estimated monetized GHG benefits of the proposal for 2022. The large range of values in the tables reflects some of the uncertainty ca ptured in the range of monetized marginal benefits estim ates presented in Table 5.3-1. Y YYYYYYYYYYY All values in this section are presented in 2006 real dollars.

XXXXXXXXXXXX Due to differences in atmospheric lifetime and radi ative forcing, the marginal benefit values of nonCO 2 GHG reductions and their growth rates over time will not be the same as the marginal benefits of CO 2 emissions reductions (IPCC WGII, 2007).
YYYYYYYYYYYY EPA notes, however, that the Nint h Circuit recently rejected an ap proach of assigning no monetized value to greenhouse gas reductions resulting from vehicu lar fuel economy. Center for Biodiversity v. NHTSA , F. 3d, (9 th Cir. 2007).

695Table 5.3-5. Monetized GHG Benefits of the Proposed Rule in 2022 (Billions of 2006$)

Marginal Benefit 2% 3% 7%

Meta global Low -$0.3 -$0.3 n/a Central $16.8 $9.6 n/a High $39.4 $25.5 n/a

FUND global Low -$0.6 -$0.6 -$0.3 Central $21.7 $4.0 -$0.1 High $172.8 $31.9 $1.2

FUND domestic Low $0.0 $0.0 $0.0 Central $1.1 $0.3 $0.0 High $4.1 $1.4 $0.0



### 5.4 Quantified and Monetized Co -pollutant Health and Environmental Impacts

This section describes EPA’s analysis of the co-pollutant health and environmental impacts that can be expected to occur as a result of this re newable fuels proposal throughout the period from initial implementation through 2030. GHG emissions are predominantly the byproduct of fossil fuel combustion processes th at also produce criteria and hazardous air pollutants. The fuels that are subject to the pr oposed standard are also significant sources of mobile source air pollution such as direct PM, NOx, VOCs and air toxics. The proposed standard would affect exhaust and evaporative emissions of thes e pollutants from vehicles and equipment. The proposal would also affect em issions from upstream sources such as fuel production, storage, and distribut ion and agricultural emissions. Any decrease or increase in ambient ozone, PM 2.5 , and air toxics associated with the pr oposal would impact human health in the form of avoided or incurred premature deaths and other serious human health effects, as well as other important public hea lth and welfare effects.

As can be seen in Chapter 3, we estimate that the proposal would lead to both increases and decreases in criteria and air toxic pollutant emissions. Making predictions about human health and welfare impacts based solely on emissions changes, however, is extremely difficult. Full-scale photochemical modeling is necessary to provide the needed spatia l and temporal detail to more completely and accurately estimate the changes in ambient levels of these pollutants. EPA typically quantifies and monetizes the PM - and ozone-related health and environmental impacts in its regulatory impact an alyses (RIAs) when possible. However, we were unable to do so in time for this proposal. EPA attempts to make emissions and air quality modeling decisions early in the analytical process so that we can complete the ph otochemical air quality modeling and use that data to inform the health and e nvironmental impacts analysis. Resource and time constraints precluded the Agency from completing this work in time for the proposal. EPA will, however, provide a complete characterization of the health and environmental impacts, both in terms of incidence and valuation, for the final rulemaking.

This section explains what PM- and ozone-related health and environmental impacts EPAwill quantify and monetize in the analysis for the final rules. EPA will base its analysis on peer696reviewed studies of air quality and health and welfare effects and peer-re viewed studies of the monetary values of public health and welfare impr ovements, and will be consistent with benefits analyses performed for the recent analysis of the proposed Ozone NAAQS and the final PM NAAQS analysis. Z ZZZZZZZZZZZ, A AAAAAAAAAAAA These methods will be described in detail in the RIA prepared for the final rule.

Though EPA is characterizing the changes in emissions associated with toxic pollutants, we will not be able to quantify or monetize the human health effects associated with air toxic pollutants for either the proposal or the final rule analyses. This is primarily because available tools and methods to assess air t oxics risk from mobile sources at the national scale are not adequate for extrapolation to benefits assessment. In addition to inherent limitations in the tools for national-scale modeling of air quality and exposu re, there is a lack of epidemiology data for air toxics in the general populati on. For a more comprehensive discussion of these limitations, please refer to the final Mob ile Source Air Toxics rule. B BBBBBBBBBBBB Please refer to Chapter 3 for more information about the air toxics em issions impacts associated with the proposed standard.

5.4.1 Human Health and Environmental Impacts

To model the ozone and PM air quality benefits of the final rules, EPA will use the Community Multiscale Air Quality (CMAQ) model (see Chapter 3 of the RIA for a description of the CMAQ model). The modeled ambient air quality data will serve as an input to the Environmental Benefits Mapping an d Analysis Program (BenMAP). C CCCCCCCCCCCC BenMAP is a computer program developed by EPA that integr ates a number of the modeling elements used in previous RIAs (e.g., interpol ation functions, population project ions, health impact functions, valuation functions, analysis and pooling met hods) to translate modeled air concentration estimates into health effects incidence esti mates and monetized benefits estimates.

Table 5.4.1-1 lists the co- pollutant health effect exposure-response functions (PM 2.5 and ozone) we will use to quantify the co-pollutant incidence impacts associated with the final standards.

ZZZZZZZZZZZZ U.S. Environmental Protection Agency. July 2007. Regulatory Impact Analysis of the Proposed Revisions to the National Ambient Air Quality Standards for Ground-Level Ozone. Prepared by: Office of Air and Radiation. EPA-452/R-07-008.
AAAAAAAAAAAAA U.S. Environmental Protection Agency. October 2 006. Final Regulatory Impact Analysis (RIA) for the Proposed National Ambient Air Quality Standards for Particulate Matter. Prepar ed by: Office of Air and Radiation.
BBBBBBBBBBBBB U.S. EPA. 2007. Control of Hazardous Air Pollutants from Mob ile Sources – Regulatory Impact Analysis. Assessment and Standards Division. Office of Transportation and Air Quality. EPA420R-07-002. February.
CCCCCCCCCCCCC Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ ecas/ benmodels.html.

697Table 5.4.1-1. Health Impact Functions Used in BenMAP to Estimate Impacts of PM 2.5 and Ozone Reductions

Endpoint Pollutant Study Study Population Premature Mortality Premature mortality – daily time series O3 Multi-city Bell et al (2004) – Non-accidental Huang et al (2005) - Cardiopulmonary Schwartz (2005) – Non-accidental Meta-analyses: Bell et al (2005) – All cause Ito et al (2005) – Non-accidental Levy et al (2005) – All cause All ages

Premature mortality —cohort study, all-cause PM 2.5 Pope et al. (2002) Laden et al. (2006) >29 years >25 years Premature mortality, total
exposures PM 2.5 Expert Elicitation (IEc, 2006) >24 years

Premature mortality — all-cause PM 2.5 Woodruff et al. (1997) Infant (<1 year) Chronic Illness Chronic Bronchitis PM 2.5 Abbey et al. (1995) >26 years Nonfatal heart attacks PM 2.5 Peters et al. (2001) Adults (>18 years) Hospital Admissions Respiratory O3 Pooled estimate: Schwartz (1995) - ICD 460-519 (all resp) Schwartz (1994a; 1994b) - ICD 480­ 486 (pneumonia) Moolgavkar et al. (1997) - ICD 480­ 487 (pneumonia) Schwartz (1994b) - ICD 491-492, 494­ 496 (COPD) Moolgavkar et al. (1997) – ICD 490­ 496 (COPD) >64 years

Burnett et al. (2001) <2 years PM 2.5 Pooled estimate: Moolgavkar (2003)—ICD 490-496 (COPD) Ito (2003)—ICD 490-496 (COPD) >64 years

PM 2.5 Moolgavkar (2000) – ICD 490-496 (COPD) 20-64 years

PM 2.5 Ito (2003) – ICD 480-486 (pneumonia) >64 years PM 2.5 Sheppard (2003) – ICD 493 (asthma) <65 years Cardiovascular PM 2.5 Pooled estimate: Moolgavkar (2003)—ICD 390-429 (all Cardiovascular) Ito (2003)—ICD 410-414, 427-428 (ischemic heart disease, dysrhythmia, heart failure) >64 years

PM 2.5 Moolgavkar (2000)—ICD 390-429 (all Cardiovascular) 20-64 years

Asthma-related ER visits O3 Pooled estimate: Jaffe et al (2003) Peel et al (2005) 5–34 years All ages All ages

698Endpoint Pollutant Study Study Population Wilson et al (2005) PM 2.5 Norris et al. (1999) 0-18 years Other Health Endpoints Acute bronchitis PM 2.5 Dockery et al.(1996) 8-12 years Upper respiratory symptoms PM 2.5 Pope et al. (1991) Asthmatics, 9-11
years Lower respiratory symptoms PM 2.5 Schwartz and Neas (2000) 7-14 years Asthma exacerbations PM 2.5 Pooled estimate: Ostro et al. (2001) (cough, wheeze and shortness of breath) Vedal et al. (1998) (cough) 6-18 years

Work loss days PM 2.5 Ostro (1987) 18-65 years School absence days O3 Pooled estimate: Gilliland et al. (2001) Chen et al. (2000) 5-17 years

Minor Restricted Activity Days (MRADs) O3 Ostro and Rothschild (1989) 18-65 years PM 2.5 Ostro and Rothschild (1989) 18-65 years

Endpoint Pollutant Study Study Population Premature Mortality Premature mortality – daily time series O3 Multi-city Bell et al (2004) – Non-accidental Huang et al (2005) - Cardiopulmonary Schwartz (2005) – Non-accidental Meta-analyses: Bell et al (2005) – All cause Ito et al (2005) – Non-accidental Levy et al (2005) – All cause All ages

Premature mortality —cohort study, all-cause PM 2.5 Pope et al. (2002) Laden et al. (2006) >29 years >25 years Premature mortality, total
exposures PM 2.5 Expert Elicitation (IEc, 2006) >24 years

Premature mortality — all-cause PM 2.5 Woodruff et al. (1997) Infant (<1 year) Chronic Illness Chronic Bronchitis PM 2.5 Abbey et al. (1995) >26 years Nonfatal heart attacks PM 2.5 Peters et al. (2001) Adults (>18 years) Hospital Admissions Respiratory O3 Pooled estimate: Schwartz (1995) - ICD 460-519 (all resp) Schwartz (1994a; 1994b) - ICD 480­ 486 (pneumonia) Moolgavkar et al. (1997) - ICD 480­ 487 (pneumonia) Schwartz (1994b) - ICD 491-492, 494­ 496 (COPD) Moolgavkar et al. (1997) – ICD 490­ 496 (COPD) >64 years

Burnett et al. (2001) <2 years PM 2.5 Pooled estimate: Moolgavkar (2003)—ICD 490-496 (COPD) Ito (2003)—ICD 490-496 (COPD) >64 years

699Endpoint Pollutant Study Study Population PM 2.5 Moolgavkar (2000) – ICD 490-496 (COPD) 20-64 years

PM 2.5 Ito (2003) – ICD 480-486 (pneumonia) >64 years PM 2.5 Sheppard (2003) – ICD 493 (asthma) <65 years Cardiovascular PM 2.5 Pooled estimate: Moolgavkar (2003)—ICD 390-429 (all Cardiovascular) Ito (2003)—ICD 410-414, 427-428 (ischemic heart disease, dysrhythmia, heart failure) >64 years

PM 2.5 Moolgavkar (2000)—ICD 390-429 (all Cardiovascular) 20-64 years

Asthma-related ER visits O3 Pooled estimate: Jaffe et al (2003) Peel et al (2005) Wilson et al (2005) 5–34 years All ages All ages

PM 2.5 Norris et al. (1999) 0-18 years Other Health Endpoints Acute bronchitis PM 2.5 Dockery et al.(1996) 8-12 years Upper respiratory symptoms PM 2.5 Pope et al. (1991) Asthmatics, 9-11
years Lower respiratory symptoms PM 2.5 Schwartz and Neas (2000) 7-14 years Asthma exacerbations PM 2.5 Pooled estimate: Ostro et al. (2001) (cough, wheeze and shortness of breath) Vedal et al. (1998) (cough) 6-18 years a

Work loss days PM 2.5 Ostro (1987) 18-65 years School absence days O3 Pooled estimate: Gilliland et al. (2001) Chen et al. (2000) 5-17 years b

Minor Restricted Activity Days (MRADs) O3 Ostro and Rothschild (1989) 18-65 years PM 2.5 Ostro and Rothschild (1989) 18-65 years

a The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998) study. Based on advice from the Science Advisory Boar d Health Effects Subcommittee (SAB-HES), we extended the applied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analys is in the Analytical Plan for EPA’s Second Prospective Analysis –Benefits an d Costs of the Clean Air Act, 1990—2020. EPA-SABCOUNCIL-ADV-04-004. See also National Research Council (N RC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press.
b Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on recent advice from the National Research Council and the EPA SAB-HES, we have calculated reductions in school absences for all school-aged children based on the biological similarity between children aged 5 to 17. 5.4.2

5.4.2 Monetized Impacts

Table 5.4.2-1 presents the monetary values we will apply to changes in the incidence of health and welfare effects associat ed with the final standards.

700Table 5.4.2-1. Valuation Metrics Used in BenMAP to Estimate Monetary Benefits

Valuation Endpoint Valuation Method (2000$) Premature mortality Assume d Mean VSL $5,500,000 Chronic Illness Chronic Bronchitis WTP: Average Severity $340,482 Myocardial Infarctions, Nonfatal Me dical Costs Over 5 Years. Varies by --- age and discount rate. Russell (1998) Medical Costs Over 5 Years. Varies by --- age and discount rate. Wittels (1990) Hospital Admissions Respiratory, Age 65+ COI: Medical Costs + Wage Lost $18,353 Respiratory, Ages 0-2 COI: Medical Costs $7,741 Chronic Lung Disease (less COI: Medical Costs + Wage Lost $12,378 Asthma) Pneumonia COI: Medical Costs + Wage Lost $14,693 Asthma COI: Medical Costs + Wage Lost $6,634 Cardiovascular COI: Medical Costs + Wage Lost (20- $22,778 64) COI: Medical Costs + Wage Lost (65- $21,191 99) ER Visits, Asthma COI: Smith et al. (1997) $312 COI: Standford et al. (1999) $261 Other Health Endpoints Acute Bronchitis WTP: 6 Day Illness, CV Studies $356 Upper Respiratory Symptoms WTP: 1 Day, CV Studies $25 Lower Respiratory Symptoms WTP: 1 Day, CV Studies $16 Asthma Exacerbation WTP: Bad Asthma Day, Rowe and $43 Chestnut (1986) Work Loss Days Median Daily Wage, County-Specific --- Minor Restricted Activity Days WTP: 1 Day, CV Studies $51 School Absence Days Median Daily Wage, Women 25+ $75

Worker Productivity Median Daily Wage, Outdoor --- Workers, County-Specific, Crocker and Horst (1981) Environmental Endpoints Recreational Visibility WTP: 86 Class I Areas

--- Source: Dollar amounts for each valuation method were extracted from BenMAP version 2.4.5.

5.4.3 Other Unquantified Health and Environmental Impacts

In addition to the co-pollutant health and e nvironmental impacts we will quantify for the analysis of the final standards (listed in the preceding sections), there are a number of other health and human welfare endpoints that we will not be able to quantify because of current limitations in the methods or available data. Thes e impacts are associated with emissions of air toxics (including benzene, 1, 3-butadiene, formaldehyde, acetald ehyde, acrolein, and ethanol), ambient ozone, and ambient PM 2.5 exposures. For example, we ha ve not quantified a number of known or suspected health effects linked with ozone and PM for wh ich appropriate health impact functions are not available or wh ich do not provide easily interpreta ble outcomes (i.e., changes in heart rate variability). A dditionally, we are currently unabl e to quantify a number of known welfare effects, including reduced acid and par ticulate deposition damage to cultural monuments

701and other materials, and environmen tal benefits due to reductions of impacts of eutrophication in coastal areas. For air to xics, the available tools and methods to assess risk from mobile sources at the national scale are not adequate for extrapol ation to benefits assess ment. In addition to inherent limitations in the tools for national-scale modeling of air toxics and exposure, there is a lack of epidemiology data for air toxics in the general population. Ta ble 5.4.3-1 lists these unquantified health and environmental impacts.

702Table 5.4.3-1. Unquantified and Non-Monetized Potential Effects

Pollutant/Effects Effects Not Incl uded in Analysis - Changes in: Ozone Health a Chronic respiratory damage Premature aging of the lungs Non-asthma respiratory emergency room visits Exposure to UVb (+/-) d
Ozone Welfare Yields for -commercial forests -some fruits and vegetables -non-commercial crops Damage to urban ornamental plants Impacts on recreational demand from damage d forest aesthetics Ecosystem functions Exposure to UVb (+/-)
PM Health b Premature mortality - short term exposures c Low birth weight Pulmonary function Chronic respiratory diseases other than chronic bronchitis Non-asthma respiratory emergency room visits Exposure to UVb (+/-) PM Welfare Residential and recreational visibility in non-Class I areas Soiling and materials damage Damage to ecosystem functions Exposure to UVb (+/-) Nitrogen and Sulfate Commercial forests due to acidic sulfate and nitrate deposition Deposition Welfare Commercial freshwater fishing due to acidic deposition Recreation in terrestrial ecosystems due to acidic deposition Existence values for curre ntly healthy ecosystems Commercial fishing, agriculture, and fo rests due to nitrogen deposition Recreation in estuarine ecosystems due to nitrogen deposition Ecosystem functions Passive fertilization CO Health Behavioral effects Hydrocarbon Cancer (benzene, 1,3-butadiene, form aldehyde, acetaldehyde, ethanol) (HC)/Toxics Health e Anemia (benzene) Disruption of production of blood components (benzene) Reduction in the number of blood platelets (benzene) Excessive bone marrow formation (benzene) Depression of lymphocyte counts (benzene) Reproductive and developmental effects (1,3-butadiene, ethanol) Irritation of eyes and mu cus membranes (formaldehyde) Respiratory irritation (formaldehyde) Asthma attacks in asth matics (formaldehyde) Asthma-like symptoms in non- asthmatics (formaldehyde) Irritation of the eyes, skin, and respiratory tract (acetaldehyde) Upper respiratory tract irritation and congestion (acrolein) HC/Toxics Welfare e Direct toxic effects to animals Bioaccumulation in the food chain Damage to ecosystem function Odor

a In addition to primary economic endpoints, there are a number of biological responses that have been associated with ozone health effects including in creased airway responsiveness to stim uli, inflammation in the lung, acute

703inflammation and respiratory cell damage, and increased susc eptibility to respiratory infection. The public health impact of these biological responses may be partly represented by our quantified endpoints. b In addition to primary economic endpoints, there are a number of biological responses that have been associated with PM health effects including mor phological changes and altered host de fense mechanisms. The public health impact of these biological responses may be partly represented by our quantified endpoints.

c While some of the effects of short-term exposures are likely to be captured in the estimates, there may be premature mortality due to short-term exposure to PM not captured in the cohort studies used in this analysis. However, the PM mortality results derived from the expe rt elicitation do take into account premature mortality effects of short term exposures. d May result in benefits or disbenefits.

e Many of the key hydrocarbons related to this rule are also hazardous air pollutants listed in the Clean Air Act. Please refer to Chapter 3 for more information about the health and welfare effects as sociated with air toxics.

While there will be impacts associated with air toxic pollutant emission changes that result from the final standards, we will not attempt to monetize thos e impacts. This is primarily because currently available tools and methods to a ssess air toxics risk from mobile sources at the national scale are not adequate for extrapolation to incidence estimations or benefits assessment. The best suite of tools and methods currently available for assessment at the national scale are those used in the National-Scale Air Toxics Assessment (NATA). The EPA Science Advisory Board specifically commented in their review of the 1996 NATA that these tools were not yet ready for use in a national-scale benefits anal ysis, because they did not consider the full distribution of exposure a nd risk, or address sub- chronic health effects. D DDDDDDDDDDDD While EPA has since improved the tools, there remain critical limitations for estimating incidence and assessing benefits of reducing m obile source air toxics. EPA con tinues to work to address these limitations; however, we do not anticipate having methods and tools available for national-scale application in time for the analysis of the final ru les. Please refer to the final Mobile Source Air Toxics Rule RIA for more discussion. E EEEEEEEEEEEE

DDDDDDDDDDDDD Science Advisory Board. 2001. NATA – Evaluating the National-Scale Air Toxics Assessment for 1996 – an SAB Advisory. http://w ww.epa.gov/ttn/atw/sab/sabrev.html.
EEEEEEEEEEEEE U.S. EPA. 2007. Control of Hazardous Air Pollutants from Mob ile Sources – Regulatory Impact Analysis. Assessment and Standards Division. Office of Transportation and Air Quality. EPA420R-07-002. February.

704705 

## Chapter 6: Impacts on Water

6.1 Feedstock Production and Water Quality

As the production and price of corn and othe r biofuel feedstocks increase, there may be substantial impacts to both water quality a nd water quantity. To analyze the impact, EPA focused on corn production for several reasons. Corn acres have increased dramatically, 20 percent in 2007 from 2006, an increase of over 15 million additional corn acres for a total of 93.6 million acres. Over two-thirds of the new corn acres came from soybean production. 944 Most of the remaining acres came from the conversion to corn from cotton. Although corn acres declined seven percent to 87.3 million acres in 2008 due to strong prices for other commodities including soybeans, total corn acres remained the s econd highest since 1946. 945

There are three major pathways for contaminants to reach water from agricultural lands: runoff from the land’s surface, subsurface tile drai ns, or leaching to ground water. A variety of management factors influence the potential for co ntaminants such as fertilizers, sediment, and pesticides to reach water from agricultural lands . These factors include nutrient and pesticide application rates and applicati on methods, use of conservation practices and crop rotations by farmers, and acreage and intensity of tile dr ained lands. Additiona l factors outside an agricultural producers control include soil characteri stics, climate, and proximity to waterbodies.

6.1.1 Corn Production and Water Quality

The rapid growth in corn acres may have ma jor implications for water quality. Unlike soybeans and other legumes, corn needs large amount s of fertilizer, especially nitrogen fertilizers to produce economic yields. Of a ll current and potential feedstoc ks for biofuels, corn has the greatest application rates of both fertilizer and pesticides per acre and accounts for the largest share of nitrogen use among all crops. 946 If fertilizers are applied at rates or times when the corn cannot use them, they are available to runoff or l each to water. Corn generally utilizes only 40 to 60 percent of applied nitrogen. The remaining ni trogen is available to leave the field and runoff to surface waters, leach into ground water, or vola tilize to the air where it can return to water through depositional processes. Farmers were exp ected to apply an additional one million tons of nitrogen fertilizer to the 2007 corn crop. 947

Historically, corn has been grown in rotation with other cr ops such as wheat, hay, oats, and especially soybeans. As corn prices increas e relative to prices for other crops, farmers chose to grow corn every year (continuous corn). Much of the recent growth in corn acres has come from reductions in a corn-soybean rotation to co ntinuous corn. Although the amount of losses of nitrogen fertilizers to ground a nd surface water vary, continuous corn loses significantly more nitrogen annually than a corn-soybean rotation. 948 In 2005, the last year for which data is available, the U.S. average nitrogen fertilizat ion rate for corn was 138 pounds per acre. For soybeans the average rate was 16 pounds per acre. 949 Soybeans fix nitrogen, so they do not require as much fertilizer for adequate growth.Continuous corn may have additional impacts on th e rates of fertilizer and pesticide use. Continuous corn has lower yields per acre than corn grow n in rotation. In response, farmers may add higher rates of nitrogen fe rtilizer to try to match yields of corn grown in rotation

. Alternatively, if farmers mainta in fertilization rates with thes e reduced yields, the amount of unused nutrients will increase and eventually be lost to the environment. Growing continuous corn also increases population dens ities of pests such as corn r ootworm. Farmers may increase use of pesticides to control these pests. Total corn herbicide use may also increase due to the additional corn acres, especially for atrazine, the most commonl y used herbicide on corn.

There are potential toxicity c oncerns with volatilization of pesticide active ingredients, 950
in addition to concerns with contamination of foods and drinking water. Furthermore, raising acreage under corn production will increase the quant ity of pesticide products in use. Further assessment is necessary to determine whether th ere is the potential for adverse human health effects from any increase in pesticide use associat ed with increased domestic corn production.

The most commonly used types of pesticides associated with corn production and storage largely belong to two broad use cate gories, herbicides and insecticides. The majority of the more common corn herbicide products presently on the market contain an organochlorine-type (OC) active ingredient (a.i.). For the most part, OC herbicides inhibit cell division and growth while a subgroup of these products, the atr azine-containing OC herbicides, inhibit plant photosynthesis. Another type of common corn herbicide, the phosphonoglycene or glyphosate–containing organophosphate (OP) herbicides, in hibit protein synthesis in pl ants. Several of the common corn herbicide compounds, such as acetochlor, ca rbaryl and alachlor, ar e classified by EPA as known or likely human carcinogens a nd oral exposure to some of these a.i. compounds at high enough levels has resulted in adverse health effect s, on organs such as the liver or kidney in animals. 951, 952, 953

The majority of common corn insecticid es are split fairly evenly between OP- and carbamate-type a.i. compounds, with the top selling corn insecticide produc ts, by sales of a.i. by weight, generally cont ain methomyl. Methom yl is an N-methyl-carbamide compound which inhibits the acetylcholinesterase enzyme, causing neurotoxicity in both insects and humans. 954 It is classified by EPA as an E/unlikely human carcinogen 955 and its use is regulated as a compound highly toxic to most aquatic and land animals.

High corn prices may encourage farmers to grow corn on land where row crops are not currently grown. If land is not in row crop production, it generall y is an indication that the land is marginal for row crop production though the land may still be used for agriculture, such as pasture land. Typically, agricultural producers apply far less fertilizer and pesticide on pasture land than land in row crops. Corn yield on thes e marginal lands will be lower, limiting nutrient uptake and causing a higher percentage of nutri ents under standard fert ilization rates to be underutilized and ultimately lost to the environment. However sinc e nitrogen fertilizer prices are tied to natural gas prices, fertilizer costs have increased significantly. According to U. S. Department of Agriculture (USDA) Economic Re search Service, nitrogen fertilizer prices increased 44.5 percent fr om July 2007 to July 2008. 956 It is unclear how agricultural producers responded to these increases in bo th corn and fertilizer prices.

706EPA does not have sufficient data to determ ine the impact of high corn prices on the fertilization rates of tr aditional corn acres or other agricultu ral land brought into corn production. Agricultural producers may increas e nitrogen fertilizer applicati on rates on all corn acres to increase yields or the higher co st and more efficient of the fert ilizer may limit rate increases. The USDA National Agricultural Statistics Service has announced that it will discontinue its national Agriculture Chemical Us e reports, collected since 1990, the only survey of its kind. Therefore, it will be very difficult to obtain future information on fertilizer and pesticide application rates.

Tile drains are another important factor in determining the losses of fertilizer from cropland. Tile drainage consists of subsurface ti les, pipes, or drainage ditches that move water from wet soils to surface waters quickly so crops ca n be planted. Tile drainage has allowed large expanses of historic wetland soils to become pr oductive agriculture lands. However, the tile drains also move fertilizers and pesticides more quickly to surface waters without any of the attenuation that would o ccur if these contaminants moved th rough soils or wetlands. The highest proportion of tile drainage occurs in the U pper Mississippi and the Ohio-Tennessee River basins. 957 Significantly increased use of conserva tion practices such as improved drainage management and constructed wetlands are necessa ry to reduce high nitrogen loadings from tile drained lands.

6.1.2 Impact on Farm Bill Conservation Programs

The increase in corn production and pri ces may also have significant impacts on conservation programs funded by th e USDA. USDA funds a variet y of voluntary programs to help agricultural producers implement conser vation practices on their operations. These programs fall into two basic categories: land retirement and working lands.

USDA’s largest land retirement program a nd its largest conservation program is the Conservation Reserve Program (CRP). Under CRP farmers receive annual rental payments under 10- to 15- year contracts to take land out of agricultural production and plant grasses or trees on those acres. Generally fa rmers put land into CRP because it is not as productive and has other characteristics that make the cropland more environmentally sensitive, such as high erosion rates. The 2008 Farm Bill (Food, Conservation and Energy Act of 2008) lowered the cap on CRP acres from 39.2 million acres to 32 million acres. Prior to the passage of the new farm bill, farmers had already not renewed their contracts on over two million acres of CRP in response to higher crop prices. USDA expects another 4.6 million acres to come out of CRP between 2007 and 2010, 1.4 million acres in major corn producing states. 958

CRP acres provide valuable environmental be nefits both for water quality and for wildlife habitat. CRP is an important component of ra re grassland habitats in the Midwest and Great Plains. 959 CRP payments are based on the average agri cultural land rental rates in the area. As land values increase due to increase in crop pric es, CRP payments are not keeping up with the higher land rental rates. Farmland in Iowa in creased an average of 18 percent in 2007 from 2006 prices. 960 Midwestern states, where much of th e nation’s corn is grown, tend to have reenrollment rates lower th an the national average.

707The largest USDA conservation program on working lands is the Environmental Quality Incentives Program (EQIP). About $1 billion is given to farmers annually to implement conservation practices on their farms. Farmers are paid a percentage of th e cost of installing the practices, generally ranging from 50 to 90 percen t. Conservation practices encompass a wide range that can have a significant impact on pollutants reaching ground or surface water from crop production. EQIP cost-shares with farmer s for important practi ces such as nutrient management, cover crops, livestock manure storag e, and riparian buffers. Like CRP, high corn prices may have an impact on the willingness of agricultural producers to participate in EQIP. Producers may require higher payments to offset potential loss of prof its through implementation of conservation practices.

The effectiveness of agricultural conserva tion practices in cont rolling runoff and/or leaching of nutrients, sediment, and pesticides at the field level has been established by numerous scientific studies acr oss many geographic areas. However, the usefulness of these practices in achieving water quality goals is de pendent on their placement within watersheds. To most effectively protect water quality, conservation practices s hould be targeted to the most vulnerable areas of watersheds. Conservation practices designed to meet wildlife goals will need different targeting mechanisms to ensure ad equate habitat. USDA through the Conservation Effects Assessment Project (CEAP) is trying to evaluate the effectiv eness of controlling pollution from agricultural la nds at the watershed level. F FFFFFFFFFFFF In order to ensure that Farm Bill conservation programs meet their environmental quality goals, the EPA’s Science Advisory Board report to the Gulf of Mexico Task Fo rce (SAB) also recommends implementing the practices through competitive bidd ing to ensure that the high est environmental benefit is achieved at the least cost. 961 It also warns that voluntary programs without economic incentives are unlikely to be effective to control nitr ogen and phosphorus, except for a few practices.

The most cost-effective practices on work ing lands include: riparian buffers; crop rotation; appropriate rate, timing, and method of nutrient appli cation; cover crops; and, on tiledrained lands, treatment wetlands and controlled drainage. Thes e practices have significant water pollution reduction benefits that vary ba sed on the site-specific conditions and on the implementation and operation and maintenance of the practice. For example, controlled drainage can reduce nitrogen loads by 30 pe rcent; treatment wetlands by 40 percent to 90 percent; vegetative buffers by 12 percent to 90 percent.

6.1.3 Other Agricultural Biofuel Feedstocks

While corn is the most common feedstoc k for biofuel production by far, under this proposal, in later years other agricultural feedst ocks will become increasingly important. These feedstocks will have dramatically different impacts on water quality. Biodiesel feedstocks, primarily soybeans, as well as cellulosic feedstock such as sw itchgrass or popular trees are not expected to have significant wa ter quality impacts. As noted previously, soybeans require little to no additional nitrogen fertilizer. However, so ybeans have less residue compared to corn, so sediment runoff could be more of a concer n, depending on how each crop is managed. Switchgrass may be a more favorable biofuels cr op for reducing water impacts. It is a native plant which does not require high inputs of ferti lizers or pesticides and since it is a perennial

FFFFFFFFFFFFF See http://www.nrcs.us da.gov/technical/nri/ceap/index.html

708crop, there is limited sediment runoff compared to annual crops. There is very minimal acreage of switchgrass grown commercially at the present time, so it is difficult to predict what inputs farmers will use to cultivate it. Some concer n has been expressed about farmers increasing switchgrass fertilizer application rates to dramatically increase yields.

Corn stover, at the present time, appears to be one of the most viable feedstock for cellulosic ethanol, especially in the Corn Belt states. Corn st over is the above ground stalks, husks, and corn cobs that remain once the corn gr ain is harvested. Farmers keep the corn stover on their cropland to maintain the productivity of the soil. Corn stover maintains the soil organic carbon which has many benefits as a source of nut rients, preventing erosion by wind and water, and increasing soil aeration and wate r infiltration. Wilhelm, et al 962 looked at the amount of corn stover that could be harvested fo r biofuel production and still main tain soil carbon. In all the soils they evaluated more stover was needed to maintain the soil carbon than for controlling erosion. For a more general discussion of cellu losic ethanol production, see Chapter V, Section V.B.2. More research is needed to identify the amount of stover that can be removed and retain these important productivity and environmental benefits.

Different conservation systems and conserva tion practice standards will need to be developed and adopted for cellulosic feedstocks, su ch as corn stove, swit chgrass, and trees for biofuels production. USDA will need to continue to adjust current standards and develop additional standards, where needed, to permit cellulo sic feedstocks to be produced and utilized in a sustainable manner.



### 6.2 Ecological Impacts

6.2.1 Nutrients

Nitrogen and phosphorus enrichment due to human activities is one of the leading problems facing our nation’s lakes, reservoirs, and estuaries. Nutrient enrichment is also a contributing factor to stream degradation. It has negative impacts on aquatic life in streams; adverse health effects on humans and domestic animals; aesthetic and recreational use impairment; and excessive nutrient input into down stream waterbodies, such as lakes. Excess nutrients in streams can lead to excessive growth of phytoplankton (free-floa ting algae) in slowmoving rivers, periphyton (algae attached to a surface) in shallow streams, and macrophytes (aquatic plants large enough to be visible to the naked eye) in a ll waters. Unsightly filamentous algae can impair the aesthetic en joyment of streams. In more extreme situations, excessive growth of aquatic plants can slow water flow in flat streams and canals, interfere with swimming, and clog the screens on water intakes of water treat ment plants and industries.

Nutrient enrichment in streams has also been demonstrated to aff ect animal communities in these waterbodies. For example, declines in invertebrate community structure have been correlated directly with increas es in phosphorus concentration. High concentrations of nitrogen in the form of ammonia are known to be toxic to aquatic animals. Excessive levels of algae have also been shown to be damaging to invertebrates. Finally, fish and invert ebrates will experience growth problems and can even die if either oxygen is depleted or pH increases are severe; both

709of these conditions are symptoma tic of eutrophication. As a biologic system becomes more enriched by nutrients, different species of algae may spread an d species composition can shift; however, unless such species sh ifts cause clearly demonstrab le symptoms of poor waterquality—such as fish kills, toxic algae, or very long streamers of filamentous algae—the general public is unlikely to be aware of this potential ecological concern.

Nutrient pollution is widespread. The most widely known examples of significant nutrient impacts include the Gulf of Mexico and the Chesapeake Ba y. For these two areas alone, there are 35 states that contribu te the nutrient loadings. There are also known impacts in over 80 estuaries/bays, and thousands of rivers, streams, and lakes. The significance of this impact has led EPA, States, and the public to come together to place an unprecedented priority on public partnerships, collaboration, be tter science, and improved tool s to reduce nutrient pollution.

Virtually every state and territory is imp acted by nutrient-relate d degradation of our waterways. All but one state and two territories have waterbodies that ar e polluted by nutrients. States have listed over 10,000 waterbodies that have nutrient a nd nutrient-related impairments. Fifteen states have more than 200 nutrient-rela ted waterbodies each. Reducing nutrient pollution is a priority for EPA.

The Wadeable Streams Assessment provided the first statistically defensible summary of the condition of the nation’s streams and small rivers. 963 To perform the assessment, EPA, states, and tribes collected chemical, physical, and biological data at 1,392 perennial stream locations to determine the biolog ical condition of these waters a nd the primary stressors affecting their quality. Research teams co llected samples at sites chosen using a statistical design to ensure representative results. The results of the analysis provide a clear assessment of the biological quality of wadeable , perennial streams and rive rs across the country.

The Wadeable Streams Assessment found that ex cess total nitrogen is the most pervasive biological stressor for the nation. Approximately 32 percent of the natio n’s stream length shows high concentrations of nitrogen compared to reference conditions. Phosphorus exhibits comparable patterns to nitrogen and is the second most-pervasive stressor for the nation’s stream length. Streams with relatively high concentrations of nutrients or excess streambed sediments are two to four times more likely to exhibit poor bi ological conditions.

The National Water Quality Inven tory: Report to Congress , prepared under section 305(b) of the Clean Water Act, summarizes wate r quality reports submit ted by the states and territories to EPA. Historically, the National Water Quality Inventories have repeatedly shown that nutrients are a major cause of ambient water quality use impairments. In the most recent report summarizing the 2002 reports from state, nutrients are iden tified as the leading cause of water pollution in assessed lakes and the second l eading cause of pollution in assessed estuaries and bays. 964 Sediment is the leading cause of pol lution in assessed rivers and streams. Agriculture is the largest known s ource of water quality impairment to both assessed rivers and streams and lakes and reservoirs.

6.2.2 Air Deposition of Nitrogen to Water

710Nitrogen oxide (NOx) emissions from fossil fuel combustion can add to the load of nitrogen to waterbodies around th e country. Under the high pressu re and temperature conditions in a vehicle engine, nitrogen and oxygen in the air react to form NOx. Depending on climate and other variables, the atmospheric NOx falls back to the ground as rain, snow, fog, or dry deposition. NOx is deposited directly on waterb odies or falls on the land and can run off to waterbodies. NOx from both statio nary sources and vehicles resu lts in significant loadings of nitrogen from air deposition to waterbodies around the country 965 , including the Chesapeake Bay 966 , Long Island Sound 967 , and Lake Tahoe 968 . Much of the nitrogen deposition from vehicles falls on impervious surfaces, such as roads and parking lots where it runs off into streams. Road drainage systems generally chan nel runoff quickly and accelerate the nitrogen loadings downstream. In the Chesapeake region, vehicle exhaust remains the single largest source of fossil-fuel de rived nitrogen pollution. 969 Air deposition of n itrogen accounts for more than half of all nitrogen loadings to Lake Tahoe. 970



### 6.3 Gulf of Mexico

Production of corn for ethanol may exacerbate existing serious water quality problems in the Gulf of Mexico. Nitrogen fertilizer applicati ons to corn are already the major source of total nitrogen loadings to the Mississippi River. 971 A large area of low oxygen, or hypoxia, forms in the Gulf of Mexico every year, often called the “dead zone”. Hypoxia threatens commercial and recreational fisheries in the Gulf because fish and other aquati c species cannot live in the low oxygen waters. The primary cause of the hypoxia is excess nutrients (nitrogen and phosphorus) from the Upper Midwest flowing into the Mississi ppi River to the Gulf. These nutrients trigger excessive algal growth (or eutrophication) resulti ng in reduced sunlight, loss of aquatic habitat, and a decrease in oxygen dissolved in the water.

The hypoxic zone in 2008 was the second larg est since measurements began in 1985 -- 8,000 square miles, an area larger than the state of Massachusetts, and slightly larger than the 2007 measurement. 972 The average size of the hypoxic zone over the past five years has been 6,600 square miles.

The Mississippi River/Gulf of Mexico Wate rshed Nutrient Task Force’s “Gulf Hypoxia Action Plan 2008” lays out two major goals fo r reducing water quality problems in the Mississippi River/Atchafalaya River Basin: 1) reduce the five-yea r running average areal extent of the Gulf of Mexico hypoxic zone to 2,000 sq uare miles by 2015 and 2) implement nutrient and sediment reductions to protect public health and a quatic life and reduce negative impacts of water pollution. The Gulf of Mexico Action Plan calls for an accelerati on of actions to reduce the hypoxia in the Gulf. In orde r to meet these goals, the Acti on Plan calls for a 45 percent reduction in both nitrogen and phosphorus reaching the Gulf. 973 EPA’s Science Advisory Board (SAB) report to the Task Force said that an additional redu ction in nitrogen and phosphorus reduction will be necessary as a result of incr eased corn production fo r ethanol and climate change impacts. 974 The SAB also found that the Gulf of Mexico ecosystem appeared to have undergone a shift so that now the system is more se nsitive to nutrient inputs than in the past and inducing a larger response in hypoxia.

711Under the Gulf Hypoxia Action Plan, “USDA will encourage the increased use of its nutrient management standard to minimize nutrient loss from fields to help alleviate the impact of increased biofuels production on nutrient loads to the Gulf”. 975 The nutrient management standard requires farmers to account for all plant-available nutrient sources immediately available or rendered available thro ughout the crop production cycle.

6.3.1 Nutrient Loads to the Gulf of Mexico

The U.S. Geological Survey (USGS) has estima ted that the spring deliv ery of nutrients to the Gulf of Mexico in 2008 was among the hi ghest since the early 1980s. Spring nutrient delivery is one of the main fact ors that control the size of the hypoxic zone. In relation to the long-term spring average, total nitrogen was about 35 to 40 percent higher (817,000 tons) and total phosphorus was a record 60 to 85 percent higher (83,000 tons). The large nutrient contributions are primarily due to near reco rd-breaking streamflows in spring 2008 in the Mississippi River Basin. Streamfl ows were about 50 percent higher this year compared to the long-term spring average flows si nce about 1980. Nutrient contribu tions for a given spring vary depending on the amount of flow in the Mississipp i-Atchafalaya River Basin, as well as average stream water nutrient concentrations.

Alexander, et al. modeled the sources of nutri ent loadings to the Gulf of Mexico using the USGS SPARROW (spatially refe renced regression on watershed attributes) model. 976 They estimated that agricultural sources contribute mo re than 70 percent of the delivered nitrogen and phosphorus. Corn and soybean production accoun ted for 52 percent of nitrogen delivery. Atmospheric deposition was the s econd largest nitrogen source at 16 percent. Animal manure on pasture and rangeland are the main sources of phosphorus loadings, contributing 37 percent. Corn and soybean contributed 25 percent of th e phosphorus; other crops 18 percent, and urban areas, 12 percent.

6.3.2 Recent Analyses of Impact of Corn Ethanol Production on Nutrient Loadings to the Gulf

Since over 80 percent of corn grown in th e U.S. is produced in the Gulf of Mexico watershed, concern has been expressed about the impact on Gulf hypoxia of increasing corn production for ethanol. Several r ecent scientific reports have es timated the water quality impact of that increase in corn production.

Donner and Kucharik modeled increases in nitr ogen export to the Gulf as a result of corn ethanol volumes increasing from 2007 producti on levels to 15 billion gallons in 2022. 977 They concluded that the expansion of corn-based et hanol production could make it almost impossible to meet the Gulf of Mexico ni trogen reduction goals without “radic al shift” in feed production, livestock diet, and management of agricultura l lands. The study estimated a mean dissolved inorganic nitrogen load increas e of 10 to18 percent from 2007 to 2022 to meet the 15 billion gallon corn ethanol goal, depending on the rate of corn yield incr eases and potential efficiency increases in the conversi on of corn to ethanol.

712EPA’s Science Advisory Board (SAB) report to the Mississippi River/Gulf of Mexico Watershed Task Force estimated the additional annua l nitrogen loadings to the Gulf due to the increase in corn acres from 78.3 million acres in 2006 to 93.7 million acres in 2013. 978 The SAB estimated that this scenario wi ll result in an additional nati onal annual loading of almost 300 million pounds of nitrogen. An estimated 80 per cent of that nitrogen loading or 238 million pounds will occur in the Mississipp i-Atchafalaya River basin and contribute nitrogen to the “dead zone” in the Gulf of Mexico.

USGS is using SPARROW to model water quality impacts in the Upper Mississippi River Basin due to increased corn production for ethanol. EPA intends to compare the results of the analysis described in the next section with the SPARROW results for the final rule.



### 6.4 Upper Mississippi River Basin Analysis

To provide a quantitative estimate of the imp act of this proposal a nd production of corn ethanol generally on water quality, EPA conducte d an analysis that focused on agricultural production in the Upper Missi ssippi River Basin (UMRB). The UMRB drains approximately 189,000 square miles, including larg e parts of the states of Illi nois, Iowa, Minnesota, Missouri, and Wisconsin. Small portions of Indiana, Mi chigan, and South Dakota are also within the basin. EPA selected the UMRB because it is representative of the many potential issues associated with ethanol producti on, including its conn ection to major wate r quality concerns such as Gulf of Mexico hypoxia, large corn prod uction, and numerous et hanol production plants.

In 2005, there were approximately 23.6 million acres of corn in the UMRB. About 75 percent of ethanol produ ction is expected to be in the states in the Corn Belt region. 979 More discussion about corn production can be found in Section 1.5.1. On average the UMRB contributes about 39 percent of th e total nitrogen loads and 26 pe rcent of the phos phorus loads to the Gulf of Mexico. 980 The Ohio/Tennessee River Basin is the highest contri butor of nitrogen loads to the Gulf at 41 percent. The high percentage of nitrog en from these two basins is primarily due to the large inputs of fertilizer fo r agriculture and the extensive systems of tile drains. According to USGS, nitrogen loads to th e Gulf ranged from 810,000 metric tons to 2.2 million metric tons between 1985 and 2005. Phosphorus loads to the Gulf ranged from 80,700 metric tons to 180,000 metric tons during that same 20- year period. 981 Although nitrogen inputs to the UMRB in recent years is fairly level, ther e is a 21 percent decline in loads. The Science Advisory Board report attribut es this decline to higher amount of nitrogen removed during harvest, due to higher crop yields. 982 However, most of the redu ction in the spring was from nitrogen forms other than nitrate, an important nitrogen form fueling the algal growth which leads to hypoxia. 983 For the same period phosphorus inputs increased 12 percent.

In 2005, the U.S. produced approximately 3. 9 billion gallons of et hanol, mostly from corn kernels. Corn-based ethanol production is e xpected to reach at leas t 15 billion gallons in order for industry to comply with the RFS2 st andards. Of the potential crops for biofuels production, corn has the highest rates of fertiliz er and pesticide application, leading to the concern that higher corn production will result in increased loading of nutrients, pesticides, and sediment to waterbodies, including major rivers and estuaries. This analysis examines the

713impact of total production of corn-based ethanol in 2010, 2015, 2020, and 2022 from an ethanol production baseline of approximately four billion gallons in 2005.

6.4.1 SWAT Model

EPA selected the SWAT (Soil and Water Assessment Tool) mode l to assess nutrient loads from changes in agricultu ral production in the UMRB. Mode ls are the primary tool that can be used to predict future im pacts based on alternative scenarios. SWAT is a physical process model developed to quantify th e impact of land management practices in large, complex watersheds. SWAT, primarily developed by US DA’s Agricultural Research Service and the Texas A & M University Blackland Research and Extension Center, is a public domain model.

EPA determined that SWAT was the most appropriate model to use for this analysis because it has been widely used and valid ated in watersheds both nationally and internationally. 984 SWAT has been applied extensivel y to support water quality and Total Maximum Daily Load (TMDL) planning throughout the United States. SWAT is a basin-scale continuous simulation model that operates on a da ily time step and is de signed to predict the nonpoint source loadings and resul ting water quality impacts of wa ter, sediment, and agricultural chemicals (nutrients and pesticides) from a wate rshed. The model can assess a wide variety of impacts of alternative manageme nt practices and land use change s. The model is physically based, computationally efficient, and capable of continuous simulations over long periods of time, ranging from days to years to decades . Major model components include weather, hydrology, erosion/sedimentation, soil temperature, plant growth, nutrients , pesticides, bacteria, agricultural management, stream rout ing and pond/reservoir routing.

SWAT has several very important strengt hs that enabled EPA to develop a robust representation of the hydrology a nd water quality of the UMRB:

1) Watersheds can be modeled to evalua te the relative impact of changes in management practices, climate, and vegetati on on water quality or other variables of interest;

2) SWAT uses readily available inpu ts commonly available from various government agencies;

3) It can simulate crop and plant co mmunities and provide crop yield and plant biomass, essential to estimate past trends and project accurately into the future;

4) Simulation of very large basins or a variety of management strategies can be performed expeditiously;

5) Long-term impacts spanning several decades can be studied. Time- and climate-variable pollutant contributions can be simulated along with the impact on downstream water bodies spanning several decades; and

7146) The model code has been valida ted on hundreds of basins throughout the United States and abroad.

In addition, prior applications of SWAT for hydrology and nutrient simulation in the UMRB had been completed and were available as a starting foundation for the modeling efforts and focus of this study. 985 986 Further technical information regarding SWAT can be found at: http://www.brc.tamus.edu/swat

.

6.4.1.1 Baseline Model Scenario

In order to assess alternative potential fu ture conditions within the UMRB, such as alternative levels of increased corn production, EPA developed a SWAT model of a baseline scenario for current conditions to which the re sults of future condition model runs can be compared. This analysis evaluated the impact of increased corn production for biofuels by establishing a baseline prior to the rapid increase in corn acres planted in 2007. EPA used a different baseline for this analysis than the re ference case based on the AEO projections used in most of the other analyses in this proposal. EPA intends to conduct additional analyses for the final rule that will compare the reference case biofuel volumes to the RFS2 volumes.

For simplicity’s sake, we refer to the base line as 2005, but like most water quality modeling, we had to use a range of data sets fo r the baseline scenario inputs. As noted above corn acres did not increase significantly until the 2007 crop year. In developing the baseline scenario, it was necessary to select a target year, or window of year s, that represent the conditions on the watershed. For this study the year 2005 was selected as the mid-point of the target period for baseline conditions . As with most models of this scale, it was not possible to have all of the data sources come from the ex act same time period. It is a common modeling practice to combine the best available data so urces for model development in an attempt to characterize the baseline condition within a short time window or period. The majority of the data sources were from the years 2000 through 2006. In addition, selected assumptions about the baseline scenario were made us ing 2005 as the reference year. In particular, the baseline values for average corn yield (150 bushels per acre) and the percentage of corn producing ethanol (12%, from the RFS1 preamble of May 2007) were based on the year 2005. In reality, the baseline scenario represents watershed conditions within a two to three year period centered on the target year of 2005.

Since one of the driving forces in the SWAT model is the wa ter balance, climate data is key to accurately predicting the movement of nutrients and sediment. SWAT was applied (i.e. calibrated) to the UMRB using weather data from the NRCS climatic data center for a 40-year period from 1960 to 2001 and flow and water quality data from 13 USGS gauges on the mainstem of the Mississippi River, spatially di stributed from the upper reaches in Minnesota and Wisconsin to the UMRB outlet below Grafton, Illinoi s. In addition, the weather data has been spatially interpolated to assign one weather station per subwatershed.

To establish the land use for the base line scenario, SWAT was setup on 131 subwatersheds [8-digit Hydrologic Unit Code (HUC)] for the entire UMRB using the 2001 National Land Cover Data (NLCD) 987 and Cropland Data Layer (CDL) 988 . The CDL contains

715crop specific digital data layers, suitable for us in geographic information system applications. The CDL program focuses on classifying corn/so ybean/rice/cotton agricultural regions in many of the Midwestern and Mississipp i delta states using remote-se ngin imagery and on-the-ground monitoring. The USDA-NRCS STATSGO provided th e soils data for the entire analyses. The primary input data is the USDA 1997 National Resource Inventory (NR I), which provided land use, soil, and data on mana gement practices on the land. 989 1997 is the most current year for which this data is available.

In addition, information from the Cons ervation Tillage Information Center and USDANASS Census of Agriculture 2002/1997 were us ed to identify the cropping rotation and management practices for the ag ricultural land areas by these sa me 131 subwatersheds. Based on the management information at this level, each sub-watershed wa s assigned appropriate management and tillage practices.

Drainage tiles are one of th e critical man-made hydrology structures that changes the natural hydrological cycle significantly at both surface and subsurface (lat eral flow) levels. There are no clear records of where the tiles ar e within the UMRB, other than a few research articles that attempted to estimate the location and extent of the tile drainage coverage. In this study, similar literature values were used to es timate and identify the areas that have the tile system to drain the excess water and nutrients in a timely manner. First, the STATSGO database was used to identify the very poorly drained soils, somewhat poorly drained soils, and poorly drained soils. Then, slope and land use maps were overlaid on these poorly drained soils to identify the potential tile drai nage system. Only slopes <1% a nd agricultural land uses were identified as areas that may potentially be served with tile drainage system.

The tillage practice information in the UMRB was obtained at the county level from Conservation Technology Information Center 990 . There are five major tillage types. Three of them (no-tillage, ridge-tillage, and mulch-tilla ge) belong to conservati on tillage, and the other two types of tillage (re duced-tillage and intensive-tillage ) are non-conservation tillage. The county acreages of this tillage information were overlaid on 8-digit HUCs to estimate the percent of each tillage practices by crop within each HUC.

To estimate nutrient applications on cropland, we started by estimating the livestock and the amount of manure produced. The livestock nu mbers came from the agri cultural statistics for each county based on the 2002 Census of Agricult ure for each 8-digit HUC. (Only cattle and hogs numbers were used since they are the domin ant livestock types in the UMRB.) Then, the manure production of each 8-digit HUC was obtai ned through multiplying the number of cattle and hogs and the manure production rates as outlined in ASABE, 2005. 991 If the total amount of the manure production exceeded 20 percent of the es timated total fertilizer application in one HUC, manure application and chemi cal fertilizer application was used as SWAT model input to simulate in that HUC. The manure was applied to only those areas that are agricultural land use, even during rotation. For example, only hay, corn, and row crops get manure application, not legume crops such as alfalfa or soybean. So, if an area had a corn and soybean rotation, then only during corn growing period the manure was applied. Even though manure was applied, chemical fertilizer was used to supplement the manure application where and when needed. In areas where the manure was not applied, chemical fertilizer was applied to grow the agricultural

716crops. Chemical nitrogen fertiliz er at applied at 1.3 times the amount of nitrogen taken off at harvest.

The 42-year SWAT model runs were perfor med and the results analyzed to establish runoff, sediment, nitrogen, and phosphorous load ings from each of the 131 8-digit HUC subwatersheds and the larger 4- digit subbasins, along with the to tal outflow from the UMRB and at the various USGS gage sites distributed along the Mississippi River mainstem. These results provided the Baseline Scenario model values to which the future alternatives are compared.

The current national average for corn yield of 150 bushels per acre (bu/ac) was used to establish baseline yield levels. The baseline average yield for the UMRB was established at 131 bushels per acre. This baseline yield is due to th e significant amount of crop area in northern states where yield values are lo wer than the national average.

6.4.1.2 Alternative Scenarios

SWAT scenario analyses were performe d for the years 2010, 2015, and 2022 with corn ethanol volumes of 12 billion gallons a year (BGY) for 2010, and 15 BGY for both 2015 and 2022. These volumes were adjusted for the UMRB based on a 42.3 percent ratio of ethanol production capacity within the UMRB compared to national capacity. This fraction was determined by overlaying a coverage of nationw ide ethanol plants with a coverage of the UMRB. Production from ethanol plants within the study area we re totaled and then divided by the nationwide production. Both current producti on and planned expansion were included in the totals. Ethanol location and production information were taken from the Renewable Fuels Association table of ethanol re finery locations in April 2008. 992 The resulting UMRB ethanol production goals were converted in to the corresponding required co rn production acreage, i.e. the extent of corn acreage need ed to meet those ethanol produc tion goals. More discussion about corn production can be found in Section 1.5.1

The SWAT model was run with the availa ble input climate reco rd, 1960-2001, with the model run under conditions of th e increased corn production and yi elds noted above. Separate model runs were performed for each of the three projection year s, and the model results were analyzed to provide loadings for co mparison with the baseline loadings.

6.4.1.2.1 Corn

Increases in corn yield were built into the future scenarios, with an annual increase of 1.23 percent. This produced yield increases to 139 bushels per acre (bu/ac) (2010), 148 bu/ac (2015) and 162 bu/ac (2022). Thus, after the 2015 scenario, these yi eld increases translated into less corn acreage being needed to meet the ethanol production goa ls. Corn acres in the UMRB grew from 23.6 million acres in 2005 to 34.1 million acres in 2010 to 36.3 million acres in 2015, an increase of 54 percent. In 2022, corn dropped to 34.6 million acres.

6.4.1.2.2 Nitrogen

717Total nitrogen loads increased by 5.5 percen t from the baseline to the 2010 scenario. Scenarios beyond 2010 then showed decreasing to tal nitrogen loads w ith the 2022 scenario showing less than a one percent increase over the baseline. It is important to note that these results only estimate loadings from the Upper Missi ssippi River basin, no t the entire Mississippi River watershed. As noted ear lier, the UMRB contributes about 39 percent of the total nitrogen loads and 26 percent of total phosph orus loads to the Gulf of Mexi co. This modeled decrease in nitrogen loads is likely attributed to the increas ed yield production of futu re scenarios, resulting in greater plant uptake of nitr ogen. Additionally, le ss corn area was needed for the 2020 and 2022 scenarios due to the increased yield values.

Approximately 24-25 percent of nitrogen leav ing agricultural fields was either taken up by aquatic plants or volatilized before reaching the outlet of the UMRB at Grafton, Illinois. Even though much of the nitrogen that is volatilized from streams and rivers and near-coastal waters is removed from the total loading to wa ter, it is not necessarily eliminated as an environmental concern. Conversion of the nitr ate to nitrogen gas thr ough denitrification is generally an incomplete chemical process. Five percent or more of the nitrogen can be converted to nitrous gas, a powerful greenhouse gas that is 300 times the climate-warming potential of carbon dioxide, the major greenhouse gas of envir onmental concern. Thus, a water pollutant becomes an air pollutant until it is either captu red through biological sequestration or converted fully to elemental nitrogen.

6.4.1.2.3 Phosphorus

The scenarios showed an increase in phosphor ous loads at a slightly lower percentage than nitrogen

6.4.1.2.4 Sediment

Total sediment outflow showed very little change (less than one percent) over all scenarios. This is primarily due to the corn being modeled as a well-managed crop in terms of sediment loss, primarily due to the corn stove r remaining on the fields following harvest.

Table 6.4-1. Changes in nutrient loadings within the Upper Mississippi River Basin (from the 2005 Baseline Scenario) 2005 Baseline 2010 2015 2020 2022 Nitrogen 1897.0 + 5.1% + 4.2% + 2.2% +1.6% million lbs Phosphorus 176.6 + 2.3% + 1.1% + 0.6% + 0.4% million lbs

718Table 6.4-2. Changes to the Mississippi River at Grafton, Illinois from the Upper Mississippi River Basin (from the 2005 Baseline Scenario) 2005 Baseline 2010 2015 2020 2022 Average corn 141 150 158 168 171 yield (bushels/acre) Nitrogen 1433.5 + 5.5% + 4.7% + 2.5% + 1.8% million lbs Phosphorus 132.4 million + 2.8% + 1.7% + 0.98% + 0.8% lbs Sediment 6.4 + 0.5% + 0.3% + 0.2% + 0.1% million tons

6.4.2.3 Potential Future Analysis

Due to time and resource constraints, other a lternative scenarios have not been analyzed. After evaluating comments on this proposal, if time and resources permit, EPA may conduct additional water quality analyses using the SW AT model in the UMRB. Potential future analyses could include: 1) dete rmination of the most sensitive assumptions in the model, 2) water quality impacts from the changes in ethanol volumes relative to other reference cases, 3) comparisons between the assumptions and results of the Forest and Agricultural Sector Optimization Model (FASOM) used to model the impacts of this proposal on the agriculture sector and SWAT, 4) removing highe r percentages of corn stover for cellulosic ethanol, 5) a case study of a smaller watershed to evaluate local water quality impacts that are impossible to ascertain at the scale of the UMRB, and/or 6) changes in loadings of a widely used corn herbicide, atrazine.



### 6.5 Climate Change Impacts

Although climate change is expected to be an important factor in future crop production in the Upper Mississippi River Ba sin, EPA has not modeled the impact of climate change on corn yields for a variety of reas ons. Climate change requires a l ong period of observation. Over the short time frame reflected in this proposal , precipitation and temperature increases will be small and indistinguishable from the natural variability of the climate. Crop yield changes resulting from climate change depend on the atmospheric carbon dioxide level, the crop, and the base temperature. Yield also depends on the characteristics of the crop relative to the timing of precipitation and of extreme temperature events. All of these variables make an estimation of actual climate-in duced yield loss very difficult to develop. Farmer adaptation may mitigate the effects of climate change on agriculture to some degree. Adaptations are influenced by many unpredictable factors, including gover nment policy, prices, research and development, and technical assi stance. Climate model simulations generally indicate that most locations in the upper Midw est will warm more than the global average and

719will receive more precipitation than current – though estimates vary considerably depending on the model used and initial conditions.



### 6.6 Chesapeake Bay Watershed

The Chesapeake Bay Commission and others ha ve expressed concer ns about the water quality impact of increased corn production for ethanol may have on the Chesapeake Bay. 993, 994 The Chesapeake Bay watershed stretches across mo re than 64,000 square miles, encompassing parts of six states — Delaware, Maryland, New Yo rk, Pennsylvania, Virginia and West Virginia

— and the entire District of Columbia. The Chesap eake's land-to-water ratio (14:1) is the largest of any coastal water body in the world. This is why land use and land management have such significant influences on the health of the Bay. In its annual State of the Bay report in 2007, the Chesapeake Bay Foundation gave the Bay a score of 28 on a scale where 70 means the Bay is “saved” and 100 is pristine. The Foundation said that “t he health of the Chesapeake Bay is dangerously out of balance”. 995

Agricultural lands account for nearly a qua rter of the watershed, and contribute more nutrients to the Bay than any othe r land use. Agricultural operati ons produce about 41 percent of the nitrogen and 47 percent of the phosphorus loads going to the Bay. Agriculture also contributes about 63 percent of the Bay’s sediment. Municipa l and industrial wastewater treatment plants throughout the watershed are responsible for 21 percent of the total nitrogen pollution and 22 percent of the total phosphorus pollution delivered to the Bay.

In 2000, Chesapeake Bay Program partners (s tates, federal agenci es, universities, nongovernmental agencies) agreed to reduce ni trogen pollution from an estimated 285 million pounds per year to no more than 175 million pounds by 2010. Similarly they pledged to reduce phosphorus from about 19 million pounds per year to less than 13 million pounds. While there have been steady declines in nitrogen and phosphorus , they have not been adequate to meet the established goals. The watershed must essentiall y quadruple the pace of the Bay cleanup to meet the 2010 commitment. To restore water quality in the Bay, all of the basin’s more than 87,000 farms will need to implement best management pract ices (BMPs) at levels never before seen in this country. The states have committed to impl ement close to 30 different agricultural BMPs as part of their restor ation strategies.

At least 25 percent and possibl y a third of the nitrogen ente ring the Bay comes from air deposition. The principal sources of emissions are power plants, cars and trucks, agriculture, and off-road sources such as construction equipmen t, lawn mowers and aircraft. While population increased about eight percent during the last deca de, vehicle miles traveled rose 26 percent. More discussion about nitrogen oxides emi ssion impacts can be found in Chapter 3.2.

The Bay watershed receives significant leve ls of nitrogen oxides and other airborne pollutants from its large airshed (whi ch is about six and a half times the size of the watershed), as far west as Ohio and Indiana. Air deposition of nitrogen on the land adds to the burden that must be dealt with by farmers, local governments and other landowners.

7206.6.1 Agricultural Production Effects

Due to the significant acreage within the Ch esapeake Bay watershed that is devoted to agricultural production (approximate ly 22 percent), incr eases in corn acre age can potentially contribute to changes in nutrient lo ads to the Bay. High demand for corn reflected in record corn prices have played a substantial role in en couraging producers to al ter their typical crop production rotations and crop acr eage, contributing to notewort hy changes in crop acreages across the watershed. A technical review committee convened by the Chesapeake Bay Commission estimated that 300,000 new acres of co rn could be added in the Bay watershed in the coming years. 996 This new corn acreage could potenti ally contribute an additional five million pounds of nitrogen to the Bay. The Ba y Program partners are trying to reach a 90 million pound reduction in nitrogen from all sources . However, it is estimated that 17 million pounds of nitrogen could be offset if all agriculture acres used cover crops as a conservation practice after harvest.

Strong market forces also encourage agricu ltural operators to incr ease grain production possibly by increasing the convers ion of non-row crop acreage (hay, pasture and fallow or idle lands) to row crop production. Gr ain row crops can add more nutrients per acre to the Bay than hay and pasture due to production intensity, manage ment systems, and nutrient efficiency of the
crop.

6.6.1.1 Base Analysis Assumptions

The Chesapeake Bay Program Watershed M odel Phase 4.3 (CBWM) and Vortex were utilized in the analysis of potential shifts in nutrient loading to the Bay based on reported changes to agricultural crop production from 2005 to 2008. These agricultural production changes are partially the result of the rapid expansion of biofuel pr oduction within the United States, supported by market-driven commodity price increases, government policies, or a combination of both. The CBWM is a dynamic watershed model used to characterize nutrient and sediment loads, and changes in these lo ads, due to management actions for decision support. G GGGGGGGGGGGG

In developing the agricultural production trend analysis within the Chesapeake Bay watershed, the USDA’s Nationa l Agricultural Sta tistics Service’s ( NASS) 2007 and 2008 Projected Plantings report on re ported crop acreages was modi fied to target only the Bay watershed.

6.6.1.2 Corn Production Analysis

Analyzing corn production acreage figures for the period from 2005 to 2008 from the NASS 2007 and 2008 Projected Planti ng reports, a measurable upw ard trend was evident for corn acreage plantings across the Bay watershed over the analysis peri od. This upward trend increased sharply between 2006 and 2007 and decr eased for the 2007 and 2008 period. Despite the recent downward trend, total corn acreage increased over the analysis period by almost 66,000 acres.

GGGGGGGGGGGGG For more information on the CBWM see http://www.chesapeakebay.net/model.htm

.

7216.6.1.3 Corn Nutrient Load Analysis

Employing a modeled analysis of the USDA- NASS Prospective Plantings report using the Chesapeake Bay Watershed Model Version 4.3 and Vortex, considerable increases of potential nitrogen loads to the Bay are associated with increased co rn acreage. The decrease of corn acreage in the 2007 to 2008 period does not offs et the total increase in acreage and nitrogen yields between 2005 and 2008. Total nitrogen loads increased by almost 2.4 million pounds.

6.6.1.4 Land Use Conversion Analysis

The agricultural production tr ends between 2005 and the pres ent not only indicate an overall increase in the number of acres under co rn production, but also an increase in the total acres of land under row crop production by over 3 55,000 acres. Since agri cultural land uses within the Chesapeake Bay wate rshed are continuously decreasi ng due to urban development, the increase in row crop acreage may come at the expense of other cropping systems, or agricultural land uses such as hay, pasture or idle lands.

6.6.1.5 Land Use Conversion Nitrogen Load Analysis

The USDA-NASS Prospective Plantings re ports and the Chesapeake Bay Program Watershed Model indicate a continuous total co nversion of non-row crop agricultural lands over the period from 2005 to 2008 to more intensive row crop production. The non-row crop land uses typically produce less nitrogen yields to the Bay, thus additional acres converted to grain production can also increase nitroge n loads significantly. This analysis esti mates that nitrogen loads increase by 8.8 million pounds.

If time and resources allow, the Chesap eake Bay Program proposes to analyze the potential impacts within the Chesapeake Bay wa tershed of the implemen tation of the RFS2 for the FRM using available systems and models at ou r disposal. The models that would potentially be used in the analysis w ould include Phase 5.2 of the Chesapeake Bay Watershed Model (CBWM), the Nutrient and Sediment Scenario Builder (NSSB), the Chesapeake Bay Estuarine Water Quality Sediment Transport Model (CBE WQSTM) and the Chesapeake Bay Land Change Model (CBLCM). The CBWM is a dynamic waters hed model used to characterize nutrient and sediment loads, and changes in these loads, due to management actions for decision support. The NSSB is being developed to determine nutrient and sediment loads under multiple land uses and crop types with variable organic and inorganic nutrient inputs. The CBEWQSTM determines the effects of nutrient and sediment lo ad changes to the attainment of water quality standards. The CBLCM simulates changes in land use as a result of locally projected increases in population out to the year 2030.

The scope of the analysis is proposed to include incremental a nd delivered nitrogen, phosphorus and sediment to the Chesapeake Bay, a nd the effect of management changes to the attainment of water quality standards.

722

### 6.7 Ethanol Production

Under the Clean Water Act, all point source s of pollution, including ethanol plants, must have a permit to discharge to water bodies or to municipal wastewater tr eatment plants for both industrial process water and stor mwater. The permit regulates th e amount of pollutants that can be discharged. There are three principle sources of discharges to wate r from ethanol plants: reject water from water purification, coo ling water blowdown, and off-batch ethanol.

6.7.1 Water Discharges

Water is required at ethanol facilities for processing and fo r the production of steam that is typically used in biomass pret reatment and ethanol distillation processes. An ethanol plant’s wastewater is typically comp rised of cooling tower blowdow n, boiler blowdown, and water softener discharge. The majority of the process wate r is lost as steam in the distillation process. In addition, stormwater runoff from the facility may be contaminated from precipitation (rain or snow) coming in contact with plant operations (industrial plant yards, material and waste handling, storage areas, shipping and receiving areas, residuals sites) and requires adequate control and management. 997

While some ethanol facilities get their pro cess water from municipa l water supplies, most use on-site wells to produce the process water for the ethanol process. Most groundwater sources are not suitable for proce ss water because of their mineral content. Therefore, the water must be treated for use in ethanol production. The most common method of groundwater treatment is reverse osmosis. Reverse osmosis uses specialized filtration and pressure to produce pure water while concentrating the groundwater minerals into reject water. The minerals in the reject water are site-specific, but they can include: calcium carbonate, magnesium carbonate, sulfate, iron, and sodium. For every two gallons of pure water produced, about a gallon of brine is discharged as reject water. Most estimates of water consumption in ethanol production are based on the use of clean process water and negl ect the water discharged as reject water.

The largest source of wastewater discharge is reverse osmosis reject water from process water purification. The revers e osmosis process concentrates groundwater minerals to levels where they can have water quality impacts. The concentrated minerals can show toxicity due to osmotic concentration and the presen ce of some ions such as sulfat e or copper. There is really no means of “treating” these i ons to reduce toxicity, other th an further concentration and disposal, or use of instream dilution. Some fac ilities have had to construct long pipelines to get access to dilution so they can m eet water quality standards.

Ethanol plants also discharge cooling wa ter blowdown, where some cooling water is discharged to avoid the buildup of minerals in the cooling system. These br ines are similar to the reject water described above. In addition, if off-batch etha nol product or process water is discharged, the waste stream can have high Biochemical Oxygen Demand (BOD) levels. BOD directly affects the amount of dissolved oxygen in rivers and str eams. The greater the BOD, the more rapidly oxygen is depleted in the stream . This means less oxygen is available to higher forms of aquatic life. The cons equences of high BOD are the same as those for low dissolved oxygen: aquatic organisms become stressed, suffocate, and die.

723Ethanol production facilities ar e important transportation hubs. For instance, a facility in Iowa produces about 130 million gallons of etha nol in a year. On an average workday, 175 tractor-trailers bring in corn, ethanol goes out in 12 rail tankers , and 8 rail cars are filled with dried distillers grain to be used as animal feed. This intensity of vehicle travel can have local water impacts from stormwater runoff, spills, etc. similar to any other rail and trucking terminal.

6.7.2 Water Use

Older generation production facilities used 4-6 gallons of process water to produce a gallon of ethanol, but newer facilitie s use less than 3 gallons of water in the production process. Most of this water savings is ga ined through improved recycling of water and heat in the process: the conservation of heat energy and water go ha nd-in-hand. This energy savings is a key economic advantage for newer plants. A gallon of ethanol contains about 70,000 Kcal of energy. Older plants used 35-40,000 Kcal of energy to produce a gallon of ethanol, but newer facilities use only 25-28,000 Kcal per gallon.

The abundance or lack of water supply is a loca l issue, and there have been concerns with water consumption as new plants go online. Some f acilities are tapping into deeper aquifers as a source of water. These deeper water resources te nd to contain higher levels of minerals and this can further increase the concentr ation of minerals in reverse osmosis reject water.

6.7.3 Distillers Grain with Solubles

One important co-product of et hanol production is distillers gr ain with solubles (DGS). Due to the increase in ethanol production and the pric e of corn, DGS has become an increasing important feed component for confined livestock. About one-third of the corn processed into ethanol is converted into DGS. Therefore a pproximately 45 million tons of DGS will be produced for the 15 billion gallons of corn et hanol produced by 2015. Concerns have been raised about the relatively higher phosphorus c ontent of DGS compared to traditional feeds.

Livestock producers may partially replace corn or other feeds with DGS for both economic and production reasons. Different lives tock species can tolera te varying amounts of DGS in their diets. The majority of DGS are fed to beef and dairy cows. Current recommendations allow beef and dairy cows diets to include from 15-40 percent DGS. Recommendations for poultry and swine diets are generally less than 15 percent DGS. Although specific analysis of DGS can vary between ethano l plants, compared to corn, DGS are higher in crude protein (nitrogen) and three to four times higher in phosphorus. 998

The increase in nitrogen and phosphorus fr om DGS in livestock feed has potential implications for water quality. When nitrogen and phosphorus are fed in excess of the animal’s needs, these nutrients are excreted in the manur e. Most livestock manure is applied to crops, especially corn, as a source of nutrients. When manure is applied at rates above the nutrient needs of the crop or at times the crop can not use the nutrients, the ni trogen and phosphorus can runoff to surface waters or leach or ground waters . Excess nutrients from manure nutrients have the same impact on water quality as exce ss nutrients from other sources.

724Several recent studies have indicated that DGS may also ha ve an impact on food safety. Cattle fed DGS have a higher prevalence of a major food-borne pathogen, E. coli O157, than cattle without DGS in their diets. More research is needed to confirm these studies and devise methods to eliminate the potential risks. 999

Livestock producers can limit the potential pol lution from manure appl ications to crops through a variety of techniques. USDA’s Natura l Resources Conservati on Service (NRCS) has developed a standard for a comprehensive nutri ent management plan (CNMP) to address the issue of proper use of livestock manure. 1000 Agricultural producers who use manure should test the nitrogen and phosphorus content before application of the manure. Due to the substantially higher phosphorus content of manure from livesto ck fed DGS, producers will potentially need significantly more acres to apply the manure so that phosphorus will not be applied at rates above the needs of the crops. This is a pa rticularly important concern in areas where concentrated livestock production already produces more phosphorus in the manure than can be taken up by crops or pastur e land in the vicinity.

6.7.4 Water Quality Impact from Ethanol Leaks and Spills

The potential for exposure to fuel components and/or additives can occur when underground fuel storage tanks leak fuel into ground water that is us ed for drinking water supplies or when spills occur that contamin ate surface drinking water supplies. Ethanol biodegrades quickly and is not neces sarily the pollutant of greatest concern in these occurrences. Instead, ethanol’s high biodegrad ability can cause the plume of BTEX (benzene, toluene, ethylbenzene and xylenes) compounds in gasoli ne to extend farther (by as much as 70 percent 1001 ) and persist longer in ground water, thereby increasing potential exposures to these compounds. Particularly large plumes of benzene can be expected when: th ere is a large area of the aquifer that is contaminat ed with liquid phase gasoline; the background concentration of sulfate-reducing bacteria (which biodegrade ethanol and benzene) in the ground water is low; the rate of ethanol biodegradation is low; and the fl ow velocity of the ground water is high. More detail on ethanol biodegradation a nd a summary of laboratory and fi eld studies of ethanol spills will be forthcoming in the EPAct 2005 Report to Congress on Fuel Additive Replacements for MTBE in late 2008.

With the increasing use of ethanol in the fuel supply nationwide, it is important to understand the impact of ethanol on the existing tank infrastructure . Given the corrosivity and different material compatibility requirements of ethanol, there is concern regarding the increased potential for leaks from existing gas stations and subsequent imp acts on drinking water supplies. In 2007, there were 7,500 reported releases from underground storage tanks. Since approximately 50 percent of the ga soline used in the U.S. contai ns ethanol, approximately 3,750 of those releases likely cont ained some amount of ethanol. Therefore, EPA is undertaking analyses designed to assess the potential impact s of ethanol blends on tank infrastructure and leak detection systems and determine the resulting water quality impacts.

An additional hazard from spills from fuels containing ethanol is risk of potential explosions. Laborator y and field studies have found biode gradation of ethanol can produce concentrations of methane in ex cess of the water solubility of methane (i.e., more methane was

725produced than could be dissolved by the available water). This methane could bubble out of the ground water and enter the soil gas at explosive concentrations, although it is not possible to quantify the risk at this time. EPA is begi nning development of modeling software for the assessment of fuels of varying composition on ground water, with simulation of methane production being one component of this work.



### 6.8 Water Use and Wastewater from Biodiesel Plants

Biodiesel plants use much less water than ethanol plants in production of biofuel. Water is not used in conversion of oil to biodiesel, but is used for washing impur ities from the finished product. Water use is variable, but is usually less than one gallon of water for each gallon of biodiesel produced. Larger well-designed plan ts use water more sparingly, while smaller producers and hobbyists use more water. Some facili ties recycle washwater, which reduces water consumption.

The strength of process wast ewater from biodiesel plants is highly variable. Most production processes produce washwa ter that has very high BOD leve ls. Essentially the strength of the wastewater is based on glycerin and methanol content. Larger fa cilities are segregating glycerin as a side product and have efficient me thanol recovery, while smaller plants are more likely to dispose of glycerin, ex cess methanol, and washwater as a single waste stream. Crude glycerin is an important side product from the bi odiesel process and has ma ny uses. It is about 10 percent of the final product. The rapid deve lopment of the biodiesel industry has caused a glut of glycerin production and many facilities dispose of glycerin.

The high strength of these wastes can overloa d and disrupt the biol ogical processes in municipal treatment plants. The normal wastewat er going into a municipal sewage treatment plant has a BOD 200mg/l. Washwater from the bi odiesel process with efficient recovery of methanol, containing small amounts of glycer in, can have a BOD of 10,000 – 15,000 mg/L. Pure glycerin has a BOD of nearly 1,000,000 mg/L. There have been several cases of wastewater treatment plant upsets due to these sh ock loadings from releases of glycerin from biodiesel production facilities. Un fortunately, these have been due to slug loadings to small wastewater treatment plants. Other states such as Illinois and Alabama have also had problems with discharges from small biodies el plants. In addition, there have been incidences of outright dumping of glycerin. One such event re sulted in a large fish kill in Missouri.

Producers that choose to dispose of gly cerin can be regulated under several EPA programs, depending on the practice. EPA strongly supports the beneficial use of glycerin as a product.

While the market for refined glycerin is glutted with an excess supply, there are many known uses for glycerin feedstock. As prices for glycerin go down, many of these known products will show a better profit margin and de mand for glycerin will in crease. Most larger facilities are segregating crude gl ycerin for refining into usable feedstock for other products. Refining can range from minimal processing up to creation of a food grade product. Nationally, there is a lot of research on th e creation of new value added produc ts (ethanol, propylene glycol,

726etc.) using glycerin as a feedstoc k. Most of these projects are in university labs, but a few are up to pilot scale. These new technologies will go online at full scale within the next few years, and are an important part of the pr ofit stream for the industry.



### 6.9 Potential Impacts to Drinking Water and Public Health

Under the Safe Drinking Water Act (SDW A), EPA establishes enforceable safety standards for drinking water provided by public water systems (PWS). For chemicals, the standard is typically called a maximum contaminant level (MCL). A PWS is “a system for the provision to the public of wate r for human consumption through pipes or other constructed conveyances, if such system has at least fifteen service connections or re gularly serves at least twenty-five individuals.” If the source water for a PWS does not meet the MCL, the PWS must take measures to reduce the contamination to safe levels and that may entail installing expensive drinking water treatment technology e.g., ion exchange (IE), gra nulated activated carbon (GAC) or reverse osmosis (RO).

EPA anticipates that increased corn production for ethanol will increase the occurrence of nitrate, nitrite, and atrazine in sources of drinking water. New corn acreage may result in increase in the application of fertilizers and herbicides, especially on marginal lands that are not as productive. The ethanol production process may generate new or increased discharges, injection or infiltration of process waste water that could adversely affect the nation’s surface water and ground water used for drinking water.

In addition to potential additional contamina tion of sources of drinking water, surface and ground water supplies may be strained by increased production of irrigated corn for ethanol and the ethanol production process its elf in local and regional area s. Increased pumping from agricultural aquifers to support ethanol productio n may accelerate the long running depletion of aquifers which has been documented by the USGS . According to U. S. Geological Survey (USGS) data, more than 72 billion gallons a day are already being pum ped from the “thirty regional principle aquifers w ith the greatest amount of ground water use”, with irrigation accounting for slightly more than 75 percent of those withdrawals. 1002 The water table of the Ogallala aquifer has declined by over 150 feet in some areas since the 1950s due to increasingly large withdrawals. 1003 Aquifers provide water for domestic and other uses, and contribute to the base flow of many streams and la kes that support aquatic habitats and other ecoservices such as fishing and swimming. Lower stream levels combin ed with the increased pollutant loadings may concentrate pollutants. Higher pollutant concentrations may re quire increased drinking water treatment. The accelerated depletion of agricu ltural aquifers and surface water supplies may be exacerbated by an increase in the incidence of droughts that are predicted under many climate change scenarios.

6.9.1 Nitrogen

The nitrogen fertilizers that are applied to corn and other agricultura l crops can end up in drinking water sources where they can impact human health. The two nitrogen compounds of concern are nitrate and nitrite. Nitrate is the most stable form of nitrogen in water.

727EPA has established the MCL for nitrate-nitrogen at 10 parts per million (ppm) and for nitrite at 1 ppm. Infants belo w six months who drink water cont aining nitrate and/ or nitrite in excess of the MCL could become serious ly ill and, if untreated, may die. 1004 Symptoms include shortness of breath and blue baby syndrome. Th is health effects language is not intended to catalog all possible health effects for nitrate. Rath er, it is intended to inform people of the most significant and probable health eff ects, associated with nitrate and nitrite in drinking water.

Most nitrogen in water is c onverted to nitrates. Since nitr ates are very soluble and do not bind to soils, they have a high potential to migrate to ground water. Because they do not evaporate, nitrates and nitrites are likely to re main in water until consumed by plants or other organisms. Primary sources of nitrate wh ich may contaminate drinking water are human sewage, livestock manure, and fertilizers.

In 2007, there were 562 public water sy stems, serving 257, 558 people, reporting violations of the nitrate MCL. 1005 If a utility’s routine compliance monitoring indicates that nitrate or nitrite concentrations are above the MCL, the water system must implement measures such as treatment or blending to reduce the concen tration so that it is below the MCL (e.g., find a new source of water, adjust exis ting treatment or install new treat ment). Also, utilities must monitor the finished water every quarter and provide notification to consumers of the MCL exceedance.

Since there is no nationally consistent sa mpling of ambient water used by public water systems, the relative contribution of nitrate dete ctions from the various sources is generally unknown.

6.9.2 Atrazine

Atrazine is estimated to be the most widely used herbicide in the United States for control of weeds. Atrazine was the second most frequen tly detected pesticide in EPA's National Survey of Pesticides in Drinking Wa ter Wells. EPA's Pesticides in Ground Water Database indicates numerous detections of atrazi ne at concentrations above th e MCL in ground water in several states, including Delaware, Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska and New York. 1006 In 1993, EPA and the atrazine registrants initiated a monitoring program to focus on the most significant exposures associated with agri cultural and residential uses -- exposures through drinking water. To this point, leve ls found in PWS have been low. Through the PWS monitoring program, EPA is ensu ring that exposures to atrazine in drinking water do not reach levels that pose a risk to public health.

The MCL for atrazine is three parts per bi llion (ppb). MCL violati ons are not triggered by single measurement above the MCL but by th e running annual average concentration from four quarterly samples in which at least one measurement during that period exceeds 3 ppb. Some people who drink water co ntaining atrazine well in excess of the MCL over a period of many years could experience prob lems with their cardiovascul ar system or reproductive difficulties. This health effects language is not intended to catalog all possible health effects for atrazine. Rather, it is intended to inform pe ople of the most signifi cant and probable health effects, associated with atrazine in drinking water.

728Atrazine may be released to the environmen t in wastewater from herbicide manufacturing facilities and through its use as an herbicide. Microbial ac tivity and other chemicals may breakdown atrazine in soil and water, particul arly in alkaline conditions. Sunlight and evaporation do not reduce its presence. It may bind to some soils, but generally tends to leach to ground water. Atrazine is not likely to be ta ken up in the tissues of plants or animals. 1007

In A Review of Contamin ant Occurrence in Public Water Systems , published in 1999, EPA found atrazine in the fi nished water of 21 percent of the surface water systems. 1008 Atrazine was found at concentrations exceeding the MCL in 10.7 percent of the surface water systems and, in 83 percent of those systems, at razine was found at con centrations that would have been in violation of the MCL. As noted above, MCL violations ar e not triggered by single excursions above the MCL but by the running annual average concentration from four quarterly samples in which the measurement of at least on e of those samples exceeds three ppb. However in one of the states where atrazi ne is widely used e.g., for co rn production, the percentage of single samples exceeding the MCL was as high as 77.8 percent for surface water systems serving less than 500 people; see Table 6.1.

Table 6.9-1. Percentage of Surface Water System s with Detections of Atrazine for a High Occurrence State, 1999

POPULATION <500 500 – 3,300 3,301 – 10,000 10,001 – 50,000 > 50,000

> MRL a 100% 100% 96.2% 96.3% 55.6%

> MCL b 77.8% 71.1% 57.7% 18.5% 22.2%

a The MRL, or minimum reporting level, is the lowest con centration at which the contaminant can be consistently and reliably detected

. U.S. Environmental Protection Agency, A Review of Contaminant Occurrence in Public Water Systems , EPA 816-R-99-006, 1999, Table V.A.2, page D-2.
b U.S. Environmental Protection Agency, ibid, p. D-3.

In 2003, EPA estimated that single atrazine measurements greater than the MCL would be observed in 26 to 57 public water system s serving a range of 24,400 – 260,300 people. 1009

Because atrazine is used mos tly as a pre-emergent herbicide on corn, the surface water concentrations typically spike durin g growing season then taper off for the rest of the year. Even though many surface water systems encounter con centrations above the MCL during the growing season, very few experience MCL violations based on the average concentration over four consecutive quarters. In 2007, only one water system serving 740 people officially reported a MCL violation. 1010

From 1992 through 2001, the USGS observed at razine in 90 percen t of the samples it took from 83 stream sites in agricultural areas as part of its National Water Quality Assessment (NAWQA). Although it does not target exclusiv ely drinking water in takes or wells, the NAWQA program “provides an understanding of water-quality conditions and how those

729conditions may vary locally, regionally, and nationally ...”. 1011 Atrazine was observed in 71 percent of the samples from 30 urban stream s ites during the same period. For ground water, USGS observed atrazine in 42 perc ent of the samples it took from wells in agricu ltural areas and in 31 percent of the samples from urban wells. The detection limits for th is study were very low and 95 percent of the sampling results from st reams in agricultural areas, where the highest concentrations of atrazine were found, were be low 2.4 ppb which is 80 pe rcent of the MCL of 3 ppb.

6.9.3 Future availability of more recent occurrence data

EPA anticipates releasing the chemical occurrence data covering the years 1999 – 2006 from states for publication in 2009 as part of the six year review of drinking water standards. Once those data sets are publicly available, they will be useful in updating the occurrence data published here for nitrate and atrazine.



### 6.10 Water Quantity Concerns

Biofuel production based on current and projected appr oaches and processes, future alternative fuel development and production coul d markedly increase the demand for various fresh water resources. Two potential needs could increase water demand: quantities of water to produce biomass as a feedstock, and the additiona l water demand for refini ng of bio-ethanol and biodiesel fuels (by up to a factor of three relative to traditiona l refining). From a regional perspective, water demand for cr op production would be relatively mu ch larger than biorefinery demand; crop production needs would be approxima tely 200 times the water needed to refine biofuels
. 1012

. With growth of ethanol production, water s upply reliability related to crop demand for biomass feedstock will remain an issue. The amount of water needed to grow feedstocks for biofuels can be considerable – for example, th e ratio of water consum ed to produce the corn itself for ethanol is nearly one thousand gall ons per gallon of corn ethanol. Large scale production of perennial energy crops involving tens of millions of acres, even when rain-fed, can have water resource impacts and unintended local consequences due to alterations of hydrologic flows. The timing of the water demand may also be critical; water is often plentiful in one season but scarce in another.

Growing crops for biofuel pr oduction is likely to have si gnificant regional and local impacts, including the potential to change irrigati on water use, and thus local water availability. The feasibility and sustainability of water dive rsions for biomass irrigation will vary depending on the region. Moreover, siting of some ethanol plants is already occurring where the water resource is already under duress, for example on the High Plains aquifer.

Biofuel refineries create additional local scale dema nd for water withdrawals and consumption. It is difficult to generalize about the impact on local water supplies, however, some community supplies have been stressed by the water requirements of ethanol facilities.

730However, from a national and regional perspective, relative to the water incorporated in the feedstock, water use in biorefineries is quite small. A typical corn ethanol plant consumes slightly more than four gallons water per gall on of ethanol produced; biodiesel refining even less, about one gallon of water pe r gallon of biodiesel. (Petrole um refining consumes about 1.5 gallons water per gallon fuel pr oduced.) Biodiesel refining requires much less water per unit of energy produced than bioethanol. Consumptive use is about one gallon water per gallon, but may be up to three gallons per gallons. Howe ver, biofuels crops may be irrigated with wastewater that is biologically and chemically unsuitable for use with food crops. On the other hand, cellulosic materials require a different process, and are thought to use 9.5 gallons water per gallon fuel produced -- but this would be expected to decline as efficien cy increases (currently projected to be lowered to tw o to six gallons per gallon).

Geographic impacts of biofuel refining vary. Currently, the Midwes t and Southeast have most of the production. In Iowa, water cons umption alone from ethanol refining already accounts for about seven percent of all industrial water use, and is projected to be 14 percent by 2012 -- or about 50 million gallons per day. A typical ethanol plant now producing 50 million gallons per year means a minimum of 175 million gallons (nearly 480,000 per day) used in a year. In the Great Lakes-St. Lawrence region, newer facilities under construction will have capacities of 100 million gallons per year. 1 013 For a 100 million gallon per year corn ethanol plant, water consumption is one million ga llons of water/day (e quates to daily water consumption of a town of 20,000).

Research is needed to establish water use requirements across the entire biofuel production chain. Information needs related to biomass feedstock production include the assessment and quantification of impacts of incr eased irrigation of ener gy crops and resulting biofuel cost/benefit tradeoffs for both starch/s ugar/oil biofuel crops, and the lignocellulosic biofuel crops. An assessment is also need ed of the impacts on hydr ologic flows of regional expansion of perennial energy cr op production. These include th e impacts and risks tradeoffs, e.g., altered flows due to deep extensive root systems and dense canopies, as well as a need for management practices/metrics, e.g. relatively large absolute wa ter consumption, and additional irrigation necessary. Changing climate adds an additional element of uncertainty in making assessments of water use.

Many uncertainties exist re garding estimating water need s for irrigating cellulosic feedstocks in particular. Reasons include: water data is less available for proposed cellusosic feedstock than for common crops, evapotranspiration rates of marginal lands used for these crops are unknown, and water demand by heretofore unirrigated native grasses is unknown.

There are some mitigating factors on increased demand for water by biofuels. Both the impacts and regulatory opportunities for mitigation of water impacts are likely to be at the state and local levels. For example, rainfall harvesting, efficient irrigation water transport and use of reclaimed water can lead to more efficient agri cultural water use for both corn and cellulosic ethanol crops. Also, biorefineries are increasingly incorporat ing water recycling.

The economics of the energy-water distribut ion linkage are impor tant in biofuels production. At a macroscale, th e high prices of energy drivin g the increased production of

731biofuels will likely affect water availability and use, e.g., conveyance costs related to irrigation waters will also increase with energy costs, possibly leading to wate r conservation that may counter the expanded water use for crops. Also, the value of crops relative to their water demand matters: water rights can often be bought and sold if the value of the crop is sufficiently high.

Finally, there is the potential for a low wa ter use alternative biomass feedstock to develop: oil-producing macro- algae. These algae can be grown without land, using nontraditional waters, and CO 2 waste streams as a nutrient source. Such fuels can have significantly higher energy dens ity and are potentia lly more fungible within existing transportation fuel infrastructure than ethanol.

732

## Chapter 7: Initial Regu latory Flexibility Analysis

This chapter discusses our Initial Regulatory Flexibility Analysis (IRFA) which evaluates the potential impacts of the proposed 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 an alysis of any rule subject to notice and comment rulemaking requirements unde r the Administrative Procedure Act or any other statute unless the agency certifies that the ru le will not have a sign ificant economic impact on a substantial number of small entities. Pursuant to this requirement, we have prepared an IRFA for the proposed rule. Throughout the process of developing the IRFA, we conducted outreach and held meetings with representatives from the various small entities that could be affected by the rulemaking to gain feedback , including recommendations, on how to reduce the impact of the rule on these ent ities. The small business recommenda tions stated here reflect the comments of the small entity representatives (SERs) and members of the Small Business Advocacy Review Panel (SBAR Panel, or ‘the Panel’).



### 7.1 Overview of the Regulatory Flexibility Act

In accordance with section 609(b) of the Re gulatory Flexibility Act, we convened an SBAR Panel before conducting the IRFA. A summary of the Panel’ s recommendations is presented in the preamble of this proposed rule making. Further, a detailed discussion of the Panel’s advice and recommendations is found in the Final Panel Report contained in the docket for this proposed rulemaking.

Section 609(b) of the Regulatory Flexibility Act further directs the Panel to report on the comments of small entity representatives and ma ke findings on issues re lated to identified elements of the IRFA under secti on 603 of the Regulatory Flexibility Act. Key elements of an IRFA are:

- a description of and, where feasible, an esti mate of the number of small entities to which the proposed rule will apply;

- 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 prepara tion of the report or record;

- an identification, to the extent practicable, of all other relevant Federal rules which may duplicate, overlap, or conf lict with the proposed rule;

- any significant alternatives to the proposed rule which accomplish the stated objectives of applicable statutes and which minimize any significant economic im pact of the proposed rule on small entities.

733The Regulatory Flexibility Act was amende d by SBREFA to ensu re that concerns regarding small entities are adequately consider ed during the development of new regulations that affect those entiti es. Although we are not required by th e Clean Air Act to provide special treatment to small businesses, the Regulatory Flexib ility Act requires us to carefully consider the economic impacts that our rules will have on small entities. The recommendations made by the Panel may serve to help lessen these economic im pacts on small entities when consistent with Clean Air Act requirements.



### 7.2 Need for the Rulemaking and Rulemaking Objectives

A detailed discussion on the need for and objec tives of this proposed rule are located in the preamble to the proposed rule. As previ ously stated, section 1501 of the Energy Policy Act of 2005 (EPAct) amended section 211 of the Clean Air Act (CAA) by adding section 211(o) which required the Environmental Protection Agency (EPA) to promulgate regulations implementing a renewable fuel program. The fi nal Renewable Fuels Standard (RFS1) program, which began on September 1, 2007, created a spec ific annual level for minimum renewable fuel use that increases over time — resulting in a requ irement that 7.5 billion gallons of renewable fuel be blended into gasoline (for highway use only) by 2012.

The Energy Independence and Security Act of 2007 (EISA) amended section 211(o), and the RFS program, by requiring higher volumes of rene wable fuels, to result in 36 billion gallons of renewable fuel by 2022. EISA also expanded the purview of the RFS1 program by requiring that these renewable fuels be bl ended into diesel fuel (both hi ghway and nonroad) in addition to gasoline. This expanded the pool of regulated en tities, so the obligated parties under this RFS2 NPRM will now include certain refiners, importers, and blenders of these fuels that were not previously covered by the RFS1 program. In a ddition to the total renewable fuel standard required by EPAct, EISA added standards for thr ee additional types of renewable fuels to the program (advanced biofuel, cellulosic biofue l, and biomass-based diesel) and requires compliance with all four standards.



### 7.3 Definition and Description of Small Entities

Small entities include small businesses, small organizat ions, and small governmental jurisdictions. For the pu rposes of assessing the impacts of th e proposed rule on small entities, a small entity is defined as: (1) a small business th at meets the definition for business based on the Small Business Administration’s (SBA) size standard s; (2) a small governmental jurisdiction that is a government of a city, county, town, school distri ct or special district with a population of less than 50,000; and (3) a small organization that is any not-for-profit enterprise which is independently owned and operated and is not dominant in its field.

Small businesses (as well as large businesses) would be regulated by this rulemaking, but not small governmental jurisdictions or small or ganizations as describe d above. As set by SBA, the categories of small entities th at will potentially be affected by this rulemaking are defined in

734Table 7.3-1 provides an overview of the prim ary SBA small business categories potentially affected by this regulation.

Table 7.3-1. Small Business Definitions

Industry Defined as small entity by SBA if less than or equal to: NAICS a codes

Gasoline and diesel fuel refiners 1,500 employees b 324110

a North American Industrial Classification System
b EPA has included in past fuels rulemakings a provision that, in order to qualify for the small refiner flexibilities, a refiner must also produce no greater than 155,000 bpcd crude capacity

EPA used a variety of sources to identify which entities are appropriately considered “small.” EPA used the criteria for small entities developed by the Small Business Administration under the North American Industry Classification System (NAICS) as a guide. Information about the characteri stics of refiners comes from sources including the Energy Information Administration (EIA) within the U.S. Department of Energy, oil industry literature, and previous rulemakings that have affected th e refining industry. EP A then found employment information for these companies using the busin ess information database Hoover’s Online (a subsidiary of Dun and Bradstreet). These refine rs fall under the Petroleu m Refineries category, 324110, as defined by NAICS.



### 7.4 Summary of Small Entities to Which the Rulemaking Will Apply

Small entities that will be subject to the renewable fuel standard include: domestic refiners that produce gasoline and/or diesel, and importers of gasoline and/or diesel into the U.S. Based on 2007 data, EPA believes that there are about 95 refiners of gasoline and diesel fuel. Of these, EPA believes that there are currently 21 refi ners producing gasoline and/ or diesel fuel that meet the SBA small entity definition of having 1, 500 employees or less. Fu rther, we believe that three of these refiners own refineries that do no t meet the definition of a “small refinery” that Congress specified under sect ion 211(o). It should be noted th at because of the dynamics in the refining industry (i.e., mergers a nd acquisitions), the actual number of refiners that ultimately qualify for small refiner status under the RFS2 program could be different from this initial estimate.



### 7.5 Related Federal Rules

The primary federal rules that are related to the proposed RFS2 progr am are: the Mobile Source Air Toxics rule ( Federal Register Vol. 72, p. 8428, February 26, 2007 ), the RFS1 rule ( Federal Register Vol. 72, p. 23900, May 1, 2007 ), and the Technical Am endment Direct Final Rulemaking for RFS1 ( Federal Register Vol. 73, p. 71560, November 25, 2008 ).

735

### 7.6 Projected Reporting, Record keeping, and Other Compliance Requirements

For any fuel control program, EPA must have assurance that any fuel produced meets all applicable standards and requirements, and that the fuel continues to meet those standards and requirements as it passes downstream through the di stribution system to the ultimate end user. Registration, reporting, and recordkeeping are necessary to track comp liance with the RFS2 requirements and transactions i nvolving RINs. As discussed in Sections III.J and IV.E of the preamble to the proposed rule, the proposed co mpliance requirements under the RFS2 program are in many ways similar to those required under the RFS1 program, with some modifications to account for the new requirements of EISA.



### 7.7 Regulatory Alternatives

The Panel’s findings and disc ussions are based on the info rmation that was available during the term of the Panel a nd issues that were raised by the SERs during the outreach meetings and in their written comments. It wa s agreed that EPA should consider the issues raised by the SERs (and issues raised in the cour se of the Panel) and that EPA should consider the comments on flexibility alternatives that w ould help to mitigate any adverse impacts on small businesses. Alternatives disc ussed throughout the Panel process include those offered in the development of this rule. A summary of the Panel’s recommendations, along with those provisions that are being proposed in this action, are detailed below. A full discussion of the regulatory alternatives and hardship provision s discussed and recommended by the Panel, all written comments received from SERs, and summar ies of the outreach meetings that were held with the SERs can be found in the SBREFA Fi nal Panel Report, located in the rulemaking docket. All of the fl exibilities that were proposed in th e rulemaking for small businesses are described in Section IV.B of th e preamble to the proposed rule.

In general, SERs stated that they believed that small refiners w ould face challenges in meeting the new standards. More specifically, they voiced concerns w ith respect to the RIN program itself, uncertainty (with the required re newable fuel volumes, RIN availability, and cost), and the desire for a RIN system review.

One concern that was raised by EPA with rega rd to provisions for small refiners in the RFS2 rule is that this rule presents a very diffe rent situation than the small refinery versus small refiner concept from RFS1. This situation deal s with whether EPA has th e authority to provide small refineries that are operated by a small refi ner with an extension of time that would be different from (and more than) the temporar y exemption specified by Congress in section 211(o)(9) for small refineries. For those small re finers who are covered by the small refinery provisions, Congress has specificall y adopted a relief provision aime d at their refineries. This provides a temporary extension through Decembe r 31, 2010 and allows for further extensions only if certain criteria are met. EPA believes that providing small refineries (and thus, small refiners who own small refineries) with an addi tional exemption different from that provided by section 211(o)(9) raises concerns about inconsistency with the intent of Congress. Congress spoke directly to the relief that EPA may provide for sm all refineries, including those small

736refineries operated by small refi ners, and limited it to a blanket exemption through December 31, 2010, with additional extensions if the criteria sp ecified by Congress were met. An additional or different extension, relying on a more general provision in section 211(o)(3), would raise questions about consistency w ith the intent of Congress.

It was agreed that EPA should consider the i ssues raised by the SERs and discussions had by the Panel itself, and that EPA should consider comments on flexibility a lternatives that would help to mitigate negative impacts on small busines ses to the extent legally allowable by the Clean Air Act. Alternatives discussed throughou t the Panel process included those offered in previous EPA rulemakings, as well as alternativ es suggested by SERs and Panel members. A summary of these recommendations is detailed below, and a full discussion of the regulatory alternatives and hardship provisions discusse d and recommended by the Panel can be found in the SBREFA Final Panel Report. A complete discussion of the provisions for which we are requesting comment and/or propos ing in this action can be f ound in Section IV.B of this preamble. Also, the Panel Report includes all comments received from SERs (Appendix B of the Report) and summaries of the two outreach m eetings that were held with the SERs. In accordance with the RFA/SBREFA requirements, the Panel evaluated the aforementioned materials and SER comments on issues related to the IRFA. The Panel’s recommendations from the Final Panel Report are discussed below.

7.7.1 Panel Recommendations

7.7.1.1 Panel Process

The purpose of the Panel process is to solicit information as well as suggested flexibility options from the SERs, and the Panel recommended that EPA continue to do so during the development of the RFS2 rule. Recognizing the concerns about EPA’s authority to provide extensions to a subset of small refineries (i.e ., those that are owned by small refiners) different from that provided to small refineries in se ction 211(o)(9), the Panel recommended that EPA continue to evaluate this issue, and that EPA request comment on its authority and the appropriateness of providing extensions beyond t hose authorized by section 211(o)(9) for small refineries operated by a small refiner. The Pa nel also recommended that EPA propose to provide the same extension provision of 211(o)(9) to small refiners who do not own small refineries as is provided for small refiners who do own small refineries.

7.7.1.2 Delay in Standards

The RFS1 program regulations provide small refiners who operate small refineries as well as small refiners who do not operate small refineries with a temporary exemption from the standard through December 31, 2010. Small re finer SERs suggested that an additional temporary exemption for the RFS2 program woul d be beneficial to them in meeting the standards. EPA evaluated a temporary exemption for at least some of the four required RFS2 standards for small refiners. The Panel recom mended that EPA propose a delay in the effective date of the standards until 2014 for small entities, to the maximum extent allowed by the statute. However, the Panel recognized that EPA has serious concerns about its authority to provide an extension of the temporary exemption for small refineries that is different from that provided in

737CAA section 211(o)(9), since Congr ess specifically addressed an extension for small refineries in that provision.

The Panel did recommend that EPA pr opose other avenues through which small refineries and small refiners could receive ex tensions of the temporary exemption. These avenues, as discussed in greater detail in the preamble to the proposed rule (sections IV.B, and XII.C.6.c.v and vi), are a possible extension of the temporary exemption for an additional two years following a study of small refineries by th e Department of Energy (DOE) and provisions for case-by-case economic hardship relief.

7.7.1.3 Phase-in

Small refiner SERs’ suggested that a phase-i n of the obligations applicable to small refiners would be beneficial for compliance, such that small refiners would comply by gradually meeting the standards on an incremental basis over a period of time, after which point they would comply fully with the RFS2 standards, EP A has serious concerns about its authority to allow for such a phase-in of the standards. C AA section 211(o)(3)(B) states that the renewable fuel obligation shall “consist of a single applicable percentage that applie s to all categories of persons specified” as obligated parties. This ki nd of phase-in approach would result in different applicable percentages being applie d to different obligated parties. Further, as discussed above, such a phase-in approach would provi de more relief to small refineries operated by small refiners than that provided under the small refinery prov ision. Thus the Panel recommended that EPA should invite comment on a phase-in, but not propose such a provision.

7.7.1.4 RIN-related Flexibilities

The small refiner SERs requested that the proposed rule contain provisions for small refiners related to the RIN system, such as fl exibilities in the RIN rollover cap percentage and allowing all small refiners to use RINs interc hangeably. Currently in the RFS1 program, EPA allows for 20 percent of a previous year’s RINs to be “rolled over” a nd used for compliance in the following year. A provision to allow for flexibilities in the rollover cap could include a higher RIN rollover cap for small refiners for some pe riod of time or for at least some of the four standards. Since the concep t of a rollover cap was not mandated by section 211(o), EPA believes that there may be an opportunity to provide appropriate flex ibility in this area to small refiners under the RFS2 program but only if it is determined in the DOE small refinery study that there is a disproportionate eff ect warranting relief. The Pane l recommended that EPA request comment on increasing the RIN rollover cap percenta ge for small refiners, and further that EPA should request comment on an approp riate level of that percentage.

The Panel recommended that EPA invite comment on allowing RINs to be used interchangeably for small refiners, but not propose this concept because under this approach small refiners would arguably be subject to a different applicable percentage than other obligated parties (similar to the ph ase-in approach discussed above). This concept would also fail to require the four different standards mandated by Congress (e.g., conventi onal biofuel could not be used instead of cellulosic bi ofuel or biomass-based diesel).

7387.7.1.5 Program Review

With regard to the suggested program review, EPA raised the concern that this could lead to some redundancy since EPA is re quired to publish a no tice of the applicable RFS standards in the Federal Register annually, a nd that this annual pr ocess will inevitably include an evaluation of the projected availability of renewable fuels. Nevert heless, the SBA and OMB Panel members stated that they believe that a program review could be helpful to small entities in providing them some insight to the RFS program’s progress a nd alleviate some uncertainty regarding the RIN system. As EPA will be pub lishing a Federal Register notice annually, the Panel recommended that EPA include an update of RIN system progress (e .g., RIN trading, RIN availability, etc.) in this notice and that the result s of this evaluation be c onsidered in any request for case-by-case hardship relief.

7.7.1.6 Extensions of the Temporary Exempt ion Based on a Study of Small Refinery Impacts

The Panel recommended that EPA propose in the RFS2 program the provision at 40 CFR 80.1141(e) extending the RFS1 tempor ary exemption for at least two years for any small refinery that DOE determines would be subject to disproportionate economic hardship if required to comply with the RFS2 requirements.

Section 211(o)(9)(A)(ii) requires DOE to perf orm a study of the economic impacts of the RFS requirements on small refineries. The st udy, which is required to be completed by December 31, 2008, must assess and determine whether the RFS requirements would impose a disproportionate economic hardship on small refineries. Small refineries that are found to be in a disproportionate economic hardsh ip situation will receive an extension of the temporary exemption for at least two years.

The Panel also recommended that EPA work with DOE in the development of the small refinery study, specifically to communicate th e comments that SERs raised during the Panel
process.

7.7.1.7 Extensions of the Temporary Exempt ion Based on Disproportionate Economic Hardship

While SERs did not specifically comment on the concept of hardship provisions for the upcoming proposal, the Panel noted that under CAA section 211(o )(9)(B) small refineries may petition EPA for case-by-case extensions of the small refinery temporary exemption on the basis of disproportionate economic hardship. Refi ners may petition EPA for this case-by-case hardship relief at any time.

The Panel recommended that EPA propose in the RFS2 program a case-by-case hardship provision for small refineries sim ilar to that provided at 40 CFR 80.1141(e)(1). The Panel also recommended that EPA propose a cas e-by-case hardship provision fo r small refiners that do not operate small refineries that is comparable to that provided for small refineries under section 211(o)(9)(B), using its discretion under CAA sec tion 211(o)(3)(B). This would apply if EPA

739does not adopt an automatic extension for small refiners, and would allow those small refiners that do not operate small refineries to apply for the same kind of extension as a small refinery. The Panel recommended that EPA take into consid eration the results of the annual update of RIN system progress and the DOE small refinery stud y in assessing such hardship applications.

7.7.2 Provisions Proposed in the NPRM

This section describes the pr ovisions being proposed in the preamble to the proposed rule related to small refiners.

7.7.2.1 Delay in Standards

As discussed in Section IV.B of the preamble to this pr oposed rule, the RFS1 program regulations provide small refiners who operate small refineries (and any refiner who operates a small refinery), as well as those small refine rs who do not operate small refineries, with a temporary exemption from the standards through December 31, 2010. EPA has evaluated an additional temporary exemption for small refiners for the required RFS2 standards, and this exemption has also been evaluate d with respect to EPA’s concerns about the authority to provide an extension of the temporary exemption for small refineries that is diffe rent from that provided in CAA section 211(o)(9). EPA believes that the limitations of the statute do not necessarily allow the Agency the discretion to provide an exemption for small refiners only (i.e., small refiners but not small refineries) beyond that provided in section 211(o)(9). Thus, EPA is proposing to continue the RFS1 temporary exemption through December 31, 2010 for small refineries and all qualified small refiners; furt her, EPA is proposing pr ovisions that address extensions of the small refinery/small refiner temporary exemption, as section 211(o)(9) does allow for extensions beyond December 31, 2010. We are also requesting comment on the interpretation of our authority under the CAA a nd the appropriateness of providing an extension to small refiners only beyond that authorized by section 211(o)(9).

7.7.2.2 Phase-in

As discussed above in Section 7.7.1.3, EPA also has concerns that section 211(o) does not give the Agency the discreti on to allow for a phase-in approach to the standards as it would result in different applicable percentages being applied to diffe rent obligated parties, provide more relief to small refineries operated by sm all refiners than that provided under the small refinery provision. However, we are inviti ng comment on the concept of a phase-in provision for all small refiners.

7.7.2.3 RIN-related Flexibilities

With regard to the small refiner SERs’ request that the proposed rule contain provisions for small refiners related to the RIN system, we do not believe it would be appropriate to propose a change to the RIN rollover cap for small refine rs in this action, give n the findings from the DOE small refinery study (“EPACT 2005 Sectio n 1501 Small Refineries Exemption Study”). However, we are requesting comment on the concept of increasing the RIN rollover cap percentage for small refiners. We are also re questing comment on an appr opriate level of that

740percentage. Further, as recommended by the Pane l, we are not proposing to allow RINs to be used interchangeably for small refiners; however we are inviting comment on such an approach.

7.7.2.4 Program Review

As noted above, we have slight concerns that such a review could lead to some redundancy since EPA is required to publish a notice of the applicable R FS standards in the Federal Register annually, and this annual proc ess will inevitably include an evaluation of the projected availability of renewable fuels. Nevertheless, we agree with the Panel’s recommendation on including an update of RIN sy stem progress (e.g., RIN trading, publiclyavailable information on RIN availability, etc.) in the annual Federal Regist er notice. Thus, we are proposing to include elements of RIN system progress—such as RIN trading and availability—in the annual Federal Register RFS2 standards notice, and we are requesting comment on additional elements to include in this notice.

7.7.2.5 Extensions of the Temporary Exempt ion Based on a Study of Small Refinery Impacts

As previously noted, there are two provisions in section 211(o)(9) that allow for an extension of the temporary exemption beyond December 31, 2010. One involves the study by DOE, as mentioned above, concerning whether co mpliance with the renewable fuel requirements would impose disproportionate economic hardsh ip on small refineries, and would grant an extension of at least two years for a small refinery that DOE determines w ould be subject to such disproportionate hardship. Anot her provision authorizes EPA to grant an extension for a small refinery based upon disproportionate econom ic hardship, on a case-by-case basis.

We believe that these avenues of relief should be utilized by those small refiners who are covered by the small refinery provision; and we belie ve that it is appropriate to consider allowing petitions to EPA for an exte nsion of the temporary exemption based on disproportionate economic hardship for those small refiners who are not covered by the small refinery provision (again, per our discretion under se ction 211(o)(3)(B)); to ensure that all small refiners have the same relief available to them as small refineries do.

Thus, we are proposing hardship provisions for small refineries in the RFS2 program similar to those provided at 40 CFR 80.1141(e). Fi rst, we are proposing to extend the temporary exemption for at least two years for any sma ll refinery that DOE’s small refinery study determines would face disproportionate economic hardship in meeting the requirements of the RFS2 program (per CAA section 211(o)(9)(A)(ii)(I )). Second, we are proposing that any small refinery may apply for a case-by-case hardship at any time on the basi s of disproportionate economic hardship per section 211(o)(9)(B).

As discussed in section IV.B.2 .c of the preamble to the pr oposed rule, the findings from DOE’s small refinery study indicate that no sm all refineries are subj ect to disproportionate economic hardship under the proposed RFS2 progr am and that the small refinery exemption should not be extended beyond December 31, 2010. However, DOE noted in the study that if circumstances were to change and/or the RI N market became non-competitive or illiquid,

741individual small refineries have the ability to petition EPA fo r an extension of their small refinery exemption.

For those small refiners that do not operate small refineries, we ar e also proposing a caseby-case hardship provision using our discreti on under CAA section 211(o)(3)(B). Such a provision would allow those small re finers that do not operate sma ll refineries to apply for the same kind of case-by-case extension as a small refinery.



### 7.8 Conclusions

Based on our outreach, fact-finding, and an alysis of the poten tial impacts of our regulations on small businesses, the Panel conc luded that the proposed provisions for small refiners would be beneficial in minimizing the effects of the proposed rule on small refiners.

Of the 15 small entities with publicly available sales data, we were able to estimate annual costs, and thus use this information to complete a preliminary sc reening analysis. To perform this analysis, we used a cost-to-sales ratio test (a ratio of the estimated annualized compliance costs to the value of sales per company). Cost s were analyzed using both a “Reference Case” crude oil cost of $53 per barrel and a “High Case” crude oil cost of $92 per barrel.

For the 14 small refiners with available data, we found that: 21 per cent (3 refiners) of small refiners were affected at less than 1 per cent of their sales (i.e., the estimated costs of compliance with the proposed rule would be less than 1 percent, of thei r sales) and 79 percent (11 refiners) were affected at greater than 3 percen t of their sales using the Reference Case costs. Using the High Case costs, all small refiners were affected at less than 1 percent of their sales. Please note that these costs do not include the av ailable subsidies for the blending of ethanol of 45 cents per gallon for ethanol and $1.00 per gall on for biodiesel/renewable diesel. Thus, the actual compliance costs will be considerably lower.

For a complete discussion of the economi c impacts of the proposed rulemaking, see Chapter 5, the economic impact analysis chapte r, of this Draft Regulatory Impact Analysis.

742

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

A.1 Executive Summary

Due to the continuing interest in the use of biodiesel fuels, the Environmental Protection Agency has conducted a comprehensive analysis of the emission impacts of biodiesel using publicly-available heavy-duty, in -use diesel chassis and engi ne exhaust emissions data.

We investigated the emission impacts on NOx, PM, HC, and CO of 20 volume percent biodiesel fuels produced from various animal- an d plant-based feedstock materials tested under several cycles in this analysis . Average NOx emissions were found to increase 2.2%, while PM, HC, and CO were found to decrease 15.6%, 14.1%, and 13.8% respectively, for all test cycles run on 20 vol% soybean-based biodiesel fuel at a significance level of P < 0.05 (See Table A.1­ 1).

Table A.1-1. Emission impacts for all cycles te sted on 20 vol% soybean-based biodiesel fuel relative to an average base fuel

Emissions Percent Change in Emissions
NOx PM HC CO +2.2% -15.6% -14.1% -13.8%

These results are consistent with the exha ust emission impacts for heavy-duty, in-use diesel engines found in our 2002 Draf t Technical Report, entitled " A Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions. " This report can be found in the docket to this rulemaking, EPA-HQ-OAR-2005-0161.

The current analysis also found that heavy-duty engine dyn amometer data was statistically indistinguishable from heavy-duty chassis dynamometer data for NOx and HC at a significance level of p < 0.05. Li kewise, results for Detroit Dies el Corporation (DDC) engines, used in many test programs, were found to be statistically similar to results for other engines for NOx, CO, and HC at a significance level of p < 0.05.

The results of the current analysis also poi nt to a load-dependence of NOx emissions for heavy-duty highway engines and ch asses. The difference in NOx emissions between our results here and those of various other rese archers appears to be attributable to an artifact of the selected test cycle profile. Analyzing the NOx emissions da ta as a function of load, as we do here -- as opposed to a particular test cycle profile -- reconciles the difference and supports the NOx emission-load-dependence hypothesis for heavy- duty highway engines and chasses posited by Sze et al . and corroborated by Eckerle et al

.

743

### A.2 Biodiesel Effects on Heavy-Duty Highway Engines and Vehicles

A.2.1 Introduction

We investigated the emission impacts on NOx, PM, CO, and HC of 20 volume percent (vol%) biodiesel fuels produced from various feedstock material s tested under several vehicle and engine test cycles. The data used in this analysis is comp rised of data used in EPA's 2002 Draft Technical Report, entitled " A Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions Draft Technical Report ", hereafter referred to as th e 2002 Draft Technical Report. Data from that report was supplemented with pertinent data sources publis hed in the scientific and automotive literature between 2002 and 2007. The supplemen tal data was comprised of late model year engines, vehicles, and technology groups. A list of a ll data sources used in this analysis appears in the appendix.

The focus of the analysis proceeded from ge neral to specific terms, through seven fuelcycle combinations, summarized below. In Case 1, the most-general fuel-cycle combination, we examined all heavy-duty engine and chassis cy cles run on plant- and animal-based biodieselfuels; in Case 5a, 5b, and 5c, the most-specific fuel-cycle combinations, we examined heavyduty engine and chassis data fo r light-, medium-, and heavy-dut y cycles using soybean-based biodiesel. The latter analysis was designed to examine load-d ependence of NOx emissions for heavy-duty highway engines and chasse s first posited by EPA in 2007 (see Sze et al .). This research was further elucidated by Eckerle et al . While feedstock materials varied for the seven fuel-cycle combinations presented here, all analyses were conducted using 20 vol% biodiesel fuels.

A summary of fuel-cycle combinations used in the analysis appears below.

• Case 1: All cycles tested on plant-based (soybean, rapeseed/canol a, and coconut) and animal-based (tallow, lard, and grease) biodiesel fuels,

• Case 2: All cycles tested on soybean-based biodiesel fuel,

• Case 3: FTP and UDDS cycles test ed on soybean-based biodiesel fuel,

• Case 4: Detroit Diesel Corporation (DDC) and non-DDC engines tested on soybean-based biodiesel fuel,

• Case 5a: Engines and chasses tested on s oybean-based biodiesel fuel run on light-duty cycles,

• Case 5b: Engines and chasses tested on soybean-based biodiesel fuel run on medium-duty cycles, and

• Case 5c: Engines and chasses tested on s oybean-based biodiesel fuel run on heavy-duty cycles.

744The results of the analysis of the seven fuel-cycle combinations appear in Section A.2.3.2. The results of the analysis of the NOx emissions load-dependence appear in Section A.2.3.3 and a discussion of the observed load-dep endence impacts in the context of relevant literature and the 2002 Draft Technical Re port appear in Section A.2.3.4.

In the 2002 Draft Technical Report, we focu sed our analysis on data from heavy-duty highway engines, since this data was the most abundant in our da tabase and since it was unclear to what extent testing on a chassis dynamometer might differ from testing on an engine dynamometer. However, some researchers critic ized the conclusions of the report for its disproportionate reliance on engine data. Thes e researchers argued that these engines may not behave in a manner indicative of the actual, in-use fleet or that chassis-generated data may be better-suited for NOx emissions testing.

Some researchers also criticized the conc lusions of our 2002 Draft Technical Report, citing its disproportionate relian ce on DDC engine data. These researchers argued that these engines may not behave in a manner indicative of th e actual, in-use fleet as a whole. To help address these concerns, we suppl emented the database for the 2002 Draft Technical Report with non-DDC engine data. In the current analysis, non-DDC engines represent 59.0% of all engines present in the supplemented database.

To investigate these concerns, we carried ou t an analysis to determine the compatibility of heavy-duty highway engine data with heav y-duty highway chassis data. Establishing compatibility between heavy-duty highway engine data and heavy-duty highway chassis data would allow us to make more-complete use of all emissions data in the database. In turn, this would allow us to perform more r obust statistical analyses . The results of this engine and chassis data compatibility analysis are presented in Section A.2.3.1. Section A.2.2 cont ains a discussion of the data screening criteria a nd methodology used in this analysis.

A.2.2 Data screening and methodology

The data used in this analysis is compri sed of data initially used in our 2002 Draft Technical Report, supplemented by pertinen t data published between 2002 and 2007. The supplemental data included late model year engi nes, vehicles, and tec hnology groups. A list of data sources used in this analysis ap pears in the appendix to this document.

A criticism raised by the 2002 Draft Technica l report was that its analysis relied too heavily upon data from early model year engine s and that these engines may not behave in a manner indicative of the actual, in-use fleet as a whole. To help address this concern, we supplemented the existing database of over 800 observations with appr oximately 560 additional observations comprised of late model year engines, vehicle, and technology groups.

Candidate data were first screened to verify that they met EPA data QC/QA requirements as well as criteria consistent with the goals of th e analysis before inclusion into the database (See Section II of the 2002 Draft Technical Report for a disc ussion of EPA data QC/QA considerations). New data meeting these criteri a were entered into the database developed for the 2002 Draft Technical Report. These criteria are described in Section A.2.2.1.

745A.2.2.1 Criteria for selecting data

Candidate data were screened to verify that they met criteria consistent with the goals of the analysis before inclusion into the database. For instance, the analysis was limited to No. 1 and No. 2 diesel fuel and related blends that can be used in typical heavy-duty diesel engines without engine modifications. As a result, all emulsions and non-biodi esel oxygenated blends with more than 20 vol% oxygenate were excluded from the final database used in the analysis. Also, synthetic fuels, such as those produced us ing the Fischer-Tropsch process, rather than refinery streams, were excl uded from the final database.

We also limited this study to vehicles and engines that had already been sold commercially or had a high probability of being so ld in the future. Vehicles and engines with experimental technologies that had no immediate plans for commerc ialization, such as those with innovative combustion chamber geometries, were ex cluded from the database. Likewise, singlecylinder research engines were excluded from c onsideration, even though the associated full-size parent engine might have been appropriately included in the data base, had it been tested. Singlecylinder engines do not appear in heavy-duty applications. By definition, such engines have lower total horsepower and di splacement, both of which may influence the way in which biodiesel impacts emissions.

The pairing of diesel and biodi esel fuels used in a particular study also played a role in determining if data from that study would be incl uded in our analysis. For example, we excluded data from all studies that did not test at least two di fferent biodiesel concentrations on the same engine, one of which c ould be 0 vol% biodiesel.

There were a number of instances in which data from one study was repeated in other studies. This might occur if the authors published the same dataset in multiple scientific journals to maximize exposure, or if the authors presented a pr eviously-published set of data in a new publication for the purposes of comparing the two datasets. Such duplicative data was also excluded from our database.

Also, each prospective data source was screened to verify that it contained raw, not aggregated, data. In cases wher e raw data was not published in a study, attempts were made to obtain it from the study author(s). Raw data obtained from au thor(s) were included in our database after successful screening.

A.2.2.2 Criteria for selecting test cycles

We selected cycles which were representa tive of actual, in-use operating conditions. While the Federal Test Procedure (FTP) transien t cycle most-closely reflects actual, in-use operating conditions, we included data from a number of other studies that used atypical test cycles which were adequately comprehensive in th eir number, selection of modes, and/or in their transient speed-load traces, so th at the resulting emission measurem ents may still be informative.

746Data collected under test cycles that were unique, contained only a single steady-state mode, or used two- or three-nonstandard modes for testing, were typically excluded from the database. Non-FTP/UDDS test cycles represente d about 24 percent of all data in the database.

A total of eight different cycles, with two va riants, representing a variety of load levels were included in our database. A description of the test cycles included in our analysis appears below.

• AVL 8-Mode Test – An eight-mode steady-state engi ne test procedure, designed to correlate with FTP cycle exhaust emission resu lts. Only NOx emissions data generated by the AVL 8-Mode test was included in our database.

• Combined International Local Cycle and Commuter (CILCC) – A transient cycle developed by NREL for testing Cl ass 4 to Class 6 vehicles. It is intended to simulate urban delivery driving conditions for heavy-duty vehicles.

• City-Suburban Heavy-Vehicle Cycle (CSHVC) – A transient cycle developed by West Virginia University. It is intended to simulate low-speed urban/ suburban driving conditions of heavy-duty ve hicles and is punctuated with frequent stops.

• Freeway Cycle – A transient cycle intended to simulate four-lane highway driving conditions of heavy-duty vehicles, in cluding entrance and exit ramps.

• Federal Test Procedure (FTP) – The heavy-duty transient cycle currently used by EPA for emission, certification, a nd other testing of heavy-duty on-road engines; the cycle most-closely reflects actual, in-use operati ng conditions and was developed to simulate a variety of heavy-duty truck and bus driving conditions in cities and on expressways.

• Highway Cycle (HWY) – A high-speed highway cruise cycle based on the Heavy Heavy-Duty Diesel Truck chassis cycle deve loped by the California Air Resources Board and previously used in the Coordi nating Research Council E-55 program.

• Rowan University Composite School Bus Cycle (RUCSBC) – A school bus cycle developed by Rowan University.

• Urban Dynamometer Driving Schedule (UDDS) – A heavy-duty chassis dynamometer test. Our database includes data from th e UDDS cycle and two vari ants simulating light (6,000 lbs) and heavy (28,000 lb s) test weight conditions.

The summary of the test cycles included in our analysis appe ars in Table A.2-1.

747Table A.2-1. Test cycles included in this analysis

Test Cycle Description Duration

AVL 8 Eight-mode steadystate cycle n/a

CILCC Heavy-duty urban delivery cycle 53 min 12 sec

CSHVC Heavy-duty citysuburb low-speed cycle 28 min 20 sec

Freeway Heavy-duty highway cycle 27 min 20 sec

FTP Heavy-duty engine certification cycle 20 min

HWY High-speed cruise cycle from CRC E-55 12 min 40 sec

RUCSBC School bus cycle 21 min 50 sec

UDDS Heavy-duty chassis cycle 17 min 40 sec

UDDS 6k UDDS variant based on EPA data 17 min 40 sec

UDDS 28k UDDS variant based on CRC E-55 data 17 min 40 sec

A.2.2.3 Criteria for selec ting feedstock materials

Biodiesel fuel can be produced from a wide variety of feedstock materials. While the studies that comprise our da tabase included only a portion of the many feedstock materials possible, they do represent the most-common fe edstock materials. The biodiesel feedstock materials found in our database and their percentages are listed in Table A.2-2.

748Table A.2-2. Biodiesel feedst ock materials in the database
Feedstock Materials Number of Observations Percentage of Observations
Soybean 556 77.1%

Rapeseed/Canola 95 13.2%

Grease a 42 5.8%

Tallow 19 2.6%

Coconut 6 0.8%

Lard 3 0.4%

a Includes high free fatty acid (HFF A) and low free fatty acid (LFFA)

Given the limited data available for some feed stock materials, we a ggregated a ll biodiesel feedstock materials into three ge neral categories: plant-based bi odiesel, soybean-based biodiesel (a subset of plant-based biodies el), and animal-based biodiesel (See Table A.2-3 for a listing of biodiesel feedstock materials aggregated in to the categories used in our database).

Table A.2-3. Biodiesel feedstock ma terials aggregated into categories used in the database

Aggregated Feedstock

Material Category Number of Observations in Category

Plant-based 657

Soybean-based 556

Animal-based 64

A.2.2.4 Overview of methodology

This section summarizes the statistical appr oach used in this analysis, which employed the SAS/STAT software procedure PROC MIXED. This procedure can treat some variables as fixed-effects and others as random-effects.

For instance, the NOx fixed-effect was expres sed as a function of pe rcent biodiesel (0 or 20 vol%), vehicle class (Class 1-2a or Class 2b-8), the interac tion of percent biodiesel and vehicle class, test cell type (chassis test cell or engine test cell), and the interaction between test cell type and percent biodiesel. Each fixed-effect term is tested and removed if found not

749significant. Additional fixed-e ffect terms were added to the model when examining DDC and non-DDC engines. The random-effects examined were the test cell type a nd its interaction with percent biodiesel, test cycle, and the biodiesel source.

After creating the initial model, the distri bution of mixed-model residuals are examined; residuals with absolute values greater than f our standard deviations from a mean of 0 are considered outliers and removed from further consideration.

The final model evaluates the statistical significance of the difference between fuels containing 20 vol% biodiesel and the base fuel, containing no biodiesel. A significance criterion of p < 0.05 was used for all analysis. See S ection II of the 2002 Draf t Technical Report for additional discussion and derivations.

A.2.3 Results

This section contains the resu lts of the biodiesel emissions impact analysis described in Section A.2.2. The results of the analysis of the compatibility of h eavy-duty highway chassis and engine data appear in Section A.2.3.1. The results of the analysis of the seven fuel-cycle combinations appear in Section A.2.3.2, includ ing the analysis of DDC engines versus non-DDC engines. The results of the an alysis of the NOx emissions load -dependence appear in Section A.2.3.3 and a discussion of the observed load-d ependence impacts placed in the context of relevant literature and the 2002 Draft Tec hnical Report appear in Section A.2.3.4.

A.2.3.1 Compatibility of heavy-duty highway chassis and engine data

The primary objective of the analysis was to quantify the impacts of biodiesel fuels. One aspect of the analysis was to determine if h eavy-duty highway emissions engine data in our database was comparable to heavy-duty highway chassis data for purposes of our statistical analysis. Much of the database (66%) consiste d of heavy-duty highway engine data, with the balance of the data comprising heavy-duty high way chassis data. Establishing compatibility between engine and chassis data would allow us to make more complete use of all emissions data in the database, which, in turn , would allow us to perform mo re robust statistical analyses.

Moreover, some researchers criticized the conclusions of our 2002 Draft Technical Report, citing its disproportionate reliance on engi ne data. These researchers argued that these engines may not behave in a manner indicative of the actual, in-use flee t as a whole or that chassis-generated data might be better-suited for purposes of biodiesel em issions testing. To investigate these claims, we undert ook an analysis to determine the compatibility of heavy-duty highway engine data with heavy-duty highway vehicle data.

Using a significance le vel of p < 0.05 for all statistical analysis, engine data was found to be statistically comparable to chassis data for NOx and PM emi ssions. This finding is supported by the research of NREL, whose examination of published data suggests that there exists no discrepancy between engine and vehicle testi ng data (See NREL Milestone 10.4). See Table A.2-4 for a summary of fuel-cyc le combinations for which hea vy-duty highway engine data was statistically comparable to heavy-duty highw ay chassis data for regulated pollutants.

750Table A.2-4. Fuel-cycle combinations for which heavy-duty highway engine data was statistically compar able to heavy-duty highway chassis data for regulated pollutants Regulated Pollutants

NOx PM CO HCFuel-Cycles Case 1 x X

Case 2 x X

Case 3 x X

Case 4 x X

Case 5a x X

Case 5b x X

Case 5c x X

x denotes heavy-duty highway engine data that is statistically comparable to heavy-duty highway chassis data.

Emissions of PM and CO for heavy-duty highw ay engines and chasses were not found to be statistically comparable to each other for a ny of the seven fuel-cycle combinations. Based upon our experience with chassis te st cells, it is difficult to accurately quantify PM exhaust emissions, as chassis-based testing is often de ficient and non-standardiz ed vis-à-vis 40CFR86­ 2007 and 40CFR1065 for test specifi cation and test equipment, re spectively. Deficiencies associated with non-standardized chassis test cells may produce high test -to-test variability. Likewise, CO emissions are difficult to accura tely quantify on both heavy-duty engine and chassis test cells and may also pr oduce high test-to-test variability.

High test-to-test variability curtails the ab ility to accurately capture and discern small differences in exhaust emissions, particularly PM and CO exhaust emissions. This variability may help to explain why engine and chassis data are not comparab le in our analysis for CO and PM emissions.

In cases where heavy-duty highway engine data is statistically comparable to heavy-duty highway chassis data, engine and chassis data are pooled to produce the appropriate statistic. In other cases, we believe that the use of engine data alone results in a more-representative statistic, which we report here. The results for the following analysis reflect this approach.

A.2.3.2 Fuel-cycle results

Seven fuel-cycle combinations were identifie d for our statistical an alysis, ranging from a general case, combining testing of all test cycles and all cycles and on all biodiesel fuel feedstock materials to cycles aggregated into light-, medi um-, and heavy-duty test cycle categories, which were run exclusively on 20 vol% so ybean-based biodiesel fuel. H eavy-duty highway engine and chassis emissions data were statistically comp arable for NOx and HC and were subsequently

751pooled for our analysis. Hea vy-duty highway engine and cha ssis emissions data were not statistically comparable for PM and CO, so only engine results are presented here. The results for each of the seven fuel-cycle combinations appears below. All results presented are statistically significant at a si gnificance level of p < 0.05.

Case 1: All cycles tested on all biodiesel fuels Our first fuel-cycle combination examined data collected on all cycles included in the database and from all vehicles and engines te sted on both plant-based (soybean, rapeseed/canola and coconut) and animal-based (tallow, lard, a nd grease) biodiesel fuels (See Table A.2-1 for a listing of all cycles used in the Case 1, Case 2, and Case 4 analyses).

For plant-based and animal-based biodiesel fuels, NOx emissions were found to increase a statistically-significant 2.0% relative to the base fuel, whereas, PM, CO, and HC emissions were found to decrease by 13.6%, 13.5%, and 18.7% , respectively, relative to the base fuel.

Case 2: All cycles tested on soybean-based biodiesel fuels Our second fuel-cycle combination examined all cycles specified in Case 1, but only involved vehicles and e ngines tested on soybean-based biodiesel fuels.

For soybean-based biodiesel, NOx emissi ons were found to increase by 2.2%, whereas PM, CO, and HC emissions were found to decrease by 15.6%, 13.8%, and 14.1%%, respectively, relative to the base fuel. Case 1 and Case 2 diffe r in their fuel composition. The biodiesel tested in Case 1 is composed of 20 vol% animal- and plan t-based biodiesel, whereas in Case 2, the fuel is composed of 20 vol% soybean-based biodiesel onl y. The results suggest that the removal of animal-based and/or rapeseed/canola/coconut-based biodiesel fuel feedst ock materials may have a slight impact on some exhaust emissions. Increas es in emissions of 0.2 %, 2.0%, and 0.3% are observed for NOx, PM, and CO, respectively, relativ e to a soybean-based biodiesel discussed in Case 2. HC emissions decrease by 4.6% relative to a soybean-based biodiesel.

Several hypotheses have been advanced by rese archers in an attempt to help explain the differences in exhaust emissions between plan t-based and animal-based biodiesel feedstock materials; these are, however, outside the scope of the current analysis and are not discussed here.

Case 3: FTP and UDDS cycles test ed on soybean-based biodiesel fuels Our third fuel-cycle combination examined only engines and vehicles tested on soybeanbased biodiesel fuel over only the FTP and UDD S cycles. Together, FTP and UDDS cycles comprise 76% of the database observations.

For soybean-based biodiesel, NOx emissi ons were found to increase by 3.2%, whereas PM, CO, and HC emissions were found to decr ease by 15.6%, 15.9%, and 13.7%, respectively, relative to the base fuel.

The results of this analysis suggest that the emission impacts asso ciated with heavy-duty highway engines and chasses tested on 20 vol% soybean-based biodiesel and run on FTP/UDDS cycles produce an increase in exhaust emissions of 1.0% for NOx and 2.1% for CO, relative to all engine and chassis cycles run on the same fuel s. HC emissions decrease by 0.4% relative to

752all engine and chassis cycles and PM emissions app ear to be relatively unaffected. These results were statistically significant at a significance level of p < 0.05.

As the FTP and UDDS cycles may more-close ly represent actual, in-use operating conditions encountered by heavy-duty highway engines and vehicles, it is possible that the Case 3 results may be a better indicator of act ual, in-use biodiesel emissions impacts.

Case 4: DDC vs. non-DDC engines test ed on soybean-based biodiesel fuels Our fourth fuel-cycle combination sepa rately examined DDC and non-DDC engines tested on the cycles specified in Case 1 a nd Case 2 using soybean-based biodiesel fuel.

Our analysis found that DDC heavy-duty engine data was st atistically comparable to nonDDC heavy-duty engine data for NOx, CO, and HC emissions at a significance level of p < 0.05. DDC heavy-duty engines and non-DDC heavy-duty engines did not behave in a statistically similar manner for PM emissions, however. In th is regard, the results of our analysis suggest that DDC heavy-duty engines behave in th e same manner in which non-DDC heavy-duty engines behave in our database for NOx, CO, and HC emissions. As such, this finding should help alleviate earlier concerns that the disproporti onate representation of DDC engines may produce results which are not indi cative of the database as a whole or the in-use fleet.

Case 5a: Light-duty cycles tested on soybean-based biodiesel fuels Our fifth fuel-cycle combination examined heavy-duty engine and chassis data for lightduty cycles specified in Table A.2-5 and tested on soybean-based biodiesel fuel.

For soybean-based biodiesel tested on light-duty cycles, NOx, PM, CO, and HC emissions were found to decrease by 1.0%, 19.0%, 9.9%, and 14.2% respectiv ely, relative to the base fuel.

753Table A.2-5. Cycle composition by case

Case Number Case Description Individual Cycles

Case 5a Light-duty cycles CILCC CSHVC UDDS6k

Case 5b Medium-duty cycles AVL8 (NOx) Freeway FTP RUCSBC

UDDS UDDS28k

Case 5c Heavy-duty cycles HWY55

Case 5b: Medium-duty cycles tested on soybean-based biodiesel fuels Our sixth fuel-cycle combination examined heavy-duty engine and chassis data for medium-duty cycles specified in Table A.2-5 and tested on soybean-based biodiesel fuel.

For soybean-based biodiesel tested on medium-duty cycl es, NOx emissions were found to increase by 2.5%, relative to the base fuel , whereas PM, CO, and HC emissions were found to decrease by 19.0%, 14.0%, and 14.2%, and re spectively, relative to the base fuel.

Case 5c: Heavy-duty cycles tested on soybean-based biodiesel fuels Our seventh fuel-cycle combination examin ed heavy-duty engine and chassis data for heavy-duty cycles specified in Table A.2-5 and tested on so ybean-based biodiesel fuel.

For soybean-based biodiesel tested on h eavy-duty cycles, NOx emissions were found to increase by 5.1%, relative to the base fuel, wh ereas PM, CO, and HC emissions were found to decrease by 32.6%, 22.0%, and 14.2%, respectively, relative to the base fuel. Unlike Case 5a and 5b, the PM emissions results were significan t and greater than those of the light-duty and medium-duty cases. A summary of these results appears in Table A.2-6.

754Table A.2-6. Summary of emissions results for seven fuel-cycle combinations for heavy-duty highway engines and chasses Regulated Pollutants

NOx PM a CO a HCFuel-Cycles Case 1 +2.0% -13.6% -13.5% b -18.7%

Case 2 +2.2% -15.6% -13.8% -14.1%

Case 3 +3.2% -15.6% -15.9% -13.7%

Case 4 +2.4% -16.9% -13.9% -14.3%

Case 5a -1.0% -19.0% -9.9%

-14.2% Case 5b +2.5% -14.0%

Case 5c +5.1% -32.6% -22.0%

a Only engine data.
b Not significant.

A.2.3.3 Load-dependant emissions impacts

We initially identified the load-dependen ce of NOx emissions in heavy-duty highway engines in 2007 (See Sze et al. ) and these results were la ter corroborated by Eckerle et al . The results of Sze et al. and Eckerle et al . were based largely upon new e ngine and chassis studies (as well as modeling efforts), which were aimed speci fically at examining the load-dependent NOx emissions phenomenon.

In the current research, however, our work is retrospective insofar as we examined data from a broad array of pre-existing studies, none of which were designed to examine the loaddependant NOx emissions impacts. As such, this analysis occasionally suffers from the experimental design limitations asso ciated with the pre-existing studies. One such limitation is the use of test cycles which do not realistically reflect actual, in-use operating conditions. Such data can skew results and obscure evidence of the load-dependence of NOx emissions. Such a situation is discussed in Case 5a.

Case 5a, 5b, and 5c: Load-dependant emissions impact on NOx The load-dependence of NOx emissions obser ved in this analysis is apparent when comparing Case 5a (light-load cycles) to Case 5b (medium-load cycles) to Case 5c (heavy-load cycles). However, results for the light-load cond itions in Case 5a may no t be representative of in-use vehicle operation.

Case 5a: Confounding effects of lightly-loaded conditions The load-dependence of NOx emissions evident in the research of Sze et al. and Eckerle et al. was based upon medium- and heavy-duty cycles, no t lightly-loaded cycles as in Case 5a.

755We believe that our current findings for NOx emissions under light ly-loaded conditions may not be representative of the operating conditi ons typically encountered in actual, in-use fleet operations. As in-use heavy-dut y highway engines and vehicles do not typically operate under the lightly-loaded conditions enc ountered in the cycles which co mprise our light-duty category, the practical significance of the NOx emissions results for Case 5a is questionable.

Further, the NOx emissions results for Case 5a are also suspect when compared to Cases 1-4 and 5b-5c, all of which indi cate that there is a statisti cally-significant increase in NOx emissions of between 2.0% and 5.1%.

As such, we place greater significance on the results obtained under medium- (Case 5b) and heavy-load (Case 5c) conditions, since they more-accurately mirror typical, in-use fleet operations and, as a result, pr ovide a more realistic repres entation of operating conditions encountered in-use.

Case 5b vs. Case 5c: Load-dependant NOx emission impacts Since the medium-loading conditions associated with these cycles are typical of actual, in-use fleet operations, these cycles provide a more realis tic representati on of operating conditions encountered by actual fl eet usage. As such, we pl ace greater significance on these results.

The difference in NOx emissions between our results here and those of other researchers appears to be attributable to an artifact of the selected test cycle prof ile. Analyzing the NOx emissions data as a function of lo ad, as we do here -- as opposed to a particular test cycle profile -- reconciles the difference a nd supports the NOx emission- load-dependence hypothesis for heavy-duty highway engine and chasses posited by Sze et al . and corroborated by Eckerle et al

. The discussion of load-dependant NOx emission im pacts in the context of our research appears in Section A.2.3.4.

A.2.3.4 Relevant studies

We initially identified the load-dependence of NOx emissions in heavy-duty highway engines in 2007 (See Sze et al. ) and these results were la ter corroborated by Eckerle et al . The results of Sze et al. and Eckerle et al . were based largely upon new e ngine and chassis studies (as well as modeling efforts), which were aimed speci fically at examining the load-dependent NOx emissions phenomenon.

In the current research, however, our work is retrospective insofar as we examined data from a broad array of pre-existing studies, none of which were designed to examine the loaddependant NOx emissions impacts. As such, this analysis occasionally suffers from the experimental design limitations asso ciated with the pre-existing studies. One such limitation is the use of test cycles which do not realistically reflect actual, in-use operating conditions. Such data can skew results and obscure evidence of the load-dependence of NOx emissions. Such a situation is discussed in Case 5a.

7562002 Draft Technical Report

In 2002, the EPA conducted a comprehensiv e analysis of the emission impacts of biodiesel using publicly av ailable data. Entitled " A Comprehensive Analysis of Biodiesel Emissions Impacts on Exhaust Emissions ," the 2002 Draft Techni cal Report made use of statistical regression analysis to correlate the concentration of biodiesel in conventional diesel fuel with changes in regulated and unregulat ed pollutants for heavy- duty highway engines.

Figure A.2-1 presents basic emission corre lations for NOx, PM, CO, and HC developed in the 2002 Draft Technical Report as a function of soybean-based biodie sel concentration. Table A.2-7 presents results specifically for 20 vol% soybean-based biodiesel.

757P e r c e n t c h a n g e in e m is s io n s Figure A.2-1. Emission impacts from the 2002 Draft Technica l Report by percent biodiesel content for soybean-based biodiesel added to an average base fuel

20%

10%

0%

-10%

-20%

-30%

-40%

-50%

-60%

-70%

-80%
0 20 40 60 80 100 NOx

PM

CO

HC

Percent biodiesel

Table A.2-7. Emission impacts from the 2002 Draft Technical Report for 20 vol% soybean-based biodiesel added to an average base fuel

Emissions Percent Change in Emissions

NOx PM HC CO +2.0% -10.1% -21.1% -11.0%

We found that the results of the current analysis, which examined heavy-duty highway engine and chassis data, are c onsistent with the findings of our 2002 Draft Technical Report, which examined heavy-duty highway engine data only. Compared to the 2002 Draft Technical Report, NOx emissions were found to increase 2.2% while PM, HC, and CO emissions were found to decrease by 15.6%, 14.1%, and 13.8% , respectively, in the current study. These are shown in Figure A.2-2 as point s overlaid on the results of the 2002 Draft Technical Report.

758P e r c e n t c h a n g e in e m is s io n s Figure A.2-2. Emission impacts from the 2002 Draft Technica l Report by percent biodiesel content for soybean-based biodiesel added to an average base fuel with new results overlaid

20%

10%

0%

-10%

-20%

-30%

-40%

-50%

-60%

-70%

-80%
0 20 40 60 80 100 NOx

PM

CO

HC

Percent biodiesel

SAE paper by Sze et al.

Sze et al . conducted a series of paired fuel test s comparing certific ation-grade highway diesel fuels with 5 to 50 vol% s oybean-based biodiesel blends. Each fuel pair was tested for up to seven transient cycles repres enting various load conditions, using a 2006 model year Cummins ISB compression ignition engine.

The authors concluded that biodiesel NOx impact on the test engine is directly proportional to average cycle power or fuel consumption and bi odiesel content (See Figure A.2­ 3).

75945 Figure A.2-3. NOx emissions for 20 vol% and 50 vol% soybea n-based biodiesel fuel versus average cycle power for various heavy-duty high way engine and chassis cyclesNOx (Biodiesel Blend) - NOx (B0), (g/hr)

5

0 R 2 = 0.99 R 2 = 0.99

10 15 20 25 30 35 40

0 20 40 60 80 100 120 140 160 Average Cycle Power (hp) B20 HWY B20 FTP B20 WHTC B20 UDDS 28k B20 UDDS 6k B20 HWY 172 B20 NRTC B50 HWY B50 FTP B50 UDDS 28k B50 UDDS 6k

Except for the most lightly-loaded cycle, the results show statistically significant differences in NOx emissions for all fuel pairs. The average NOx emissions due to biodiesel were found to increase over each cycle, ranging from 0.9 to 6.6% for 20 vol% and 2.2 to 17.2% for 50 vol% biodiesel fuels. The load-dep endence of NOx emissions observed by Sze et al . is consistent with the findings presen ted in this report for Cases 5a, 5b, and 5c as well as those of Eckerle et al

. To further elucidate the load-depe ndent nature of NOx emissions, Sze et al . reanalyzed chassis-generated NOx emissions data from NR EL as a function of fuel consumption (a surrogate for average cycle power ) and found it to be in close agreement with EPA engine test data. This is depicted in Figure A.2-4. Data from NREL using the same engines tested instead on a chassis dynamometer are also shown in the figure and follow the same trend, with an R 2 = 0.99 with and without the incl usion of the NREL dataset.

760Figure A.2-4. NOx emissions for 20 vol% soybean-based biod iesel fuel versus average cycle power for various heavy-duty highway engine and chassis cycles, including NREL chassis testing data

45 B20 NREL chassis data B20 HWY 40 B20 FTP R 2 = 0.996 B20 WHTC 35 B20 UDDS 28k B20 UDDS 6k 30 B20 HWY 172 B20 NRTC

0 5 10 15 20 25 30 Fuel Consumption (kg/hr) B50 HWY 25 B50 FTP B50 UDDS 28k 20 B50 UDDS 6k With NREL chassis data 15 R 2 = 0.99
10 Without NREL chassis data

5 R 2 = 0.99

0NOx (Biodiesel Blend) - NOx (B0), (g/hr)

SAE paper by Eckerle et al.

The load-dependence of NOx emissions was also examined by Eckerle et al. , who generated engine data using 20 vol% soybean-bas ed biodiesel to calibra te chemical kinetic models. These models were used to examin e NOx production during the combustion process. The authors concluded that the NOx effect associated with burn ing biodiesel blends over a duty cycle depends, in part, on the duty cycle averag e power and that higher duty cycle average power corresponded to larger in creases in NOx emissions.

761Appendix to Biodiesel E ffects on Heavy-Duty High way Engines and Vehicles

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Alam M. et al ., "Combustion and Emissions Performance of Low Sulfur, Ultra Low Sulfur and Biodiesel Blends in a DI Diesel Engine," SAE paper no. 2004-01-3024

Bouché, T., M. Hinz, R. Pitterman, "Optimising Tractor CI Engines for Biodiesel Operation," SAE paper no. 2000-01-1969

Callahan, T.J., C.A. Sharp, "Evaluation of Methyl Soyate/Diesel Fuel Blends as a Fuel for Diesel Engines," Southwest Research In stitute Final Report to the American Biofuels Association, December 1993

Clark N. N. et al ., "Class 8 Truck Emission Testing: E ffects of Test Cycles and Data on Biodiesel Operation," ASAE, 1999, 42(5), 1211-1219

Clark, N.N., C.M. Atkinson, G.J. Thompson, R. D. Nine, "Transient Emissions Comparisons of Alternative Compression Ignition Fu els," SAE paper no. 1999-01-1117 Durbin T. D. et al ., "Effects of Biodiesel, Biodiesel Bl ends, and a Synthetic Diesel on

Emissions from Light Heavy-Duty Diesel Vehi cles," Environ. Sci. Technol., 2000, 34, 349-355

Durbin, T.D., et al ., "Evaluation of the Effects of Biodi esel and Biodiesel Blends on Exhaust Emission Rates and Reactivity - 2", Cent er for Environmental Research and Technology; College of Engineering; Univer sity of California, Riverside, August 2001

Durbin, T.D., J.M. Norbeck, “Effects of Biodies el Blends and Arco EC-Diesel on Emissions from Light Heavy-Duty Diesel Vehicles ,” Environ. Sci. Technol., 2002, 36, 1686-1691

Durbin, T.D., J.R. Collins, J.M. Norbeck, M.R. Sm ith, "Evaluation of the Effects of Alternative Diesel Fuel Formulations on E xhaust Emissions Rates and Reactivity," Final Report from the Center for Environmental Research and Technol ogy, University of California, April 1999

Environment Canada, "Emissions Characteriza tion of a Caterpillar 3126E Equipped with a Prototype SCRT System with Ultra Low Sulfur Diesel and a Biodiesel Blend," ERMD Report #2005-32, 2005

Fosseen, D., "DDC 6V-71N Emission Testing on Diesel and Biodies el Blend," Fosseen Manufacturing and Development, Ltd., report no. NSDB4F15, July 14, 1994 Fosseen, D., "DDC 6V-92TA MU1 Coach U pgrade Emission Optimization on 20%/80% Soy/Diesel Blend," Fosseen Ma nufacturing & Development, Ltd ., report no. 260-2 and 24 1-1, September 30, 1994

762Frank B.P. et al ., "A Study of the Effects of Fuel Type and Emission Control Systems on Regulated Gaseous Emissions fr om Heavy-Duty Diesel Engi nes," SAE paper no. 2004-01-1085

Goetz, W., "Evaluation of a Tallow/Diesel Blend in a DDC 6V-92TA Engine," Ortech International report no. 93-E14-37, July 21, 1993

Goetz, W., "Evaluation of Methyl Soyate /Diesel Blend in a DDC 6V-92TA Engine: Optimization of NOx Emissions," Ortech International, Repor t No. 93-E14-36, July 20, 1993

Graboski, M.S., J.D. Ross, R.L. McCormick, "Transient Emissions from No. 2 Diesel and Biodiesel Blends in a DDC Series 60 Engine," SAE paper no. 961166

Graboski, M.S., R.L. McCormick, T.L. Allema n, A.M. Herring, "The Effect of Biodiesel Composition on Engine Emissions from a DDC Se ries 60 Diesel Engine," Colorado School of Mines, Final Report to National Renewa ble Energy Laboratory, June 8, 2000

Hansen, K.F., M.G. Jensen, "Chemical and Biologi cal Characteristics of Exhaust Emissions from a DI Diesel Engine Fuelled with Rapeseed Oil Methyl Ester (RME)," SAE paper no. 971689

Holden, Bruce et al ., Final Report: "Effect of Biodiesel on Diesel Engine Nitrogen Oxide and Other Regulated Emissions," Naval Faciliti es Engineering Comma nd (NAVFAC) Technical Report TR-2275-ENV, May 2006

Howes, P., G. Rideout, "Evalu ation of Biodiesel in an Ur ban Transit Bus Powered by a 1988 DDECII6V92 TA Engine," Natio nal Biodiesel Board, MSED Report # 96-26743-1, June 1995

Howes, P., G. Rideout, "Evalu ation of Biodiesel in an Ur ban Transit Bus Powered by a 1981 DDCC8V71 Engine," National Biodiese l Board, MSED Report # 95-26743-2

Liotta, F.J., D.M. Montalvo, "The Effect of Oxygenated Fuels on Emissions from a Modern Heavy-Duty Diesel Engine," SAE paper no. 932734

Lyons D. W., "Biodiesel Fuel Comparison Final Data Report for Washington Metropolitan Area Transit Authority," West Virginia University, August 2002

Manicom, B., C. Green, W. Goetz, "Methyl Soya te Evaluation of Various Diesel Blends in a DDC 6V-92 TA Engine," Ortech International, April 21, 1993 Marshall, W., L.G. Schumacher, S. Howell, "Engine Exhaust Emissions Evaluation of a Cummins L10E When Fueled With a Biodi esel Blend," University of Missouri

McCormick R.L. et al ., "Fuel Additive and Blending Approaches to Reducing NOx Emissions from Biodiesel," SAE paper no. 2002-01-1658

McCormick R.L. et al ., "Regulated Emissions from Biodies el Tested in Heavy-Duty Engines Meeting 2004 Emission Standa rds," SAE paper no. 2005-01-2200

763McCormick, R.L. et al ., "Effects of Biodiesel Blends on Vehicle Emissions, FY 2006 Annual Operating Plan Milestone 10.4," October 2006

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McGill, R., Storey, J., Wagner, R., Irick, D., Aakko, P., Westerholm, M., Nylund, N., Lappi, M., “Emission Performance of Selected Biodi esel Fuels,” SAE paper no. 2003-01-1866

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Peterson, C., D. Reece, J. Thompson, S. Beck, C. Chase, "Development of Biodiesel for Use in High Speed Diesel Engines," University of Idah o, presentation at Si xth National Bioenergy Conference, Oct. 2-6, 1994

Peterson, C.L., "Truck-In-The-Park Biodiesel De monstration with Yellowstone National Park," University of Idaho, August 1999 Peterson, C.L., D.L. Reece, "Emissions Testing with Blends of Esters of Rapeseed Oil Fuel With and Without a Catalytic C onverter," SAE paper no. 961114

Peterson, C.L., D.L. Reece, "Emissions Tests with an On-Road Vehicle Fu eled with Methyl and Ethyl Esters of Rapeseed Oil," ASAE paper no. 946532

Rantanen, L., S. Mikkonen, L. Nylund, P. Kociba , M. Lappi, N. Nylund, "Effect of Fuel on the Regulated, Unregulated and Muta genic Emissions of DI Diesel Engines," SAE paper no. 932686

Schumacher, L., N.N. Clark, D.W. Lyons, W. Marshall, “Diesel Engine Exhaust Emissions Evaluation of Biodies el Blends Using a Cummins L10E Engine ,” ASAE, 44(6), 1461-1464

764Schumacher, L., S.C. Borgelt, W.G. Hires, W. Wetherell, A. Nevils, "100,000 Miles of Fueling 5.9L Cummins Engines with 100% biodiesel," SAE paper no. 962233

Schumacher, L.G., S.C. Borgelt, W.G. Hires, D. Fosseen, W. Goetz, "Fueling Diesel Engines with Blends of Methyl Ester Soybean Oil and Diesel Fuel," Universi ty of Missouri. See http://web.missouri.edu/ ~pavt0689/ASAED94.htm.

Sharp, C.A., "Characterization of Biodiesel Exhaust Emissions for EPA 211(b)," Southwest Research Institute report no. 08-1039A, January 1998

Sharp, C.A., "Emissions and Lubricity Evaluation of Rapeseed Derived Biodiesel Fuels," Southwest Research Institute, Final Report for Montana Department of Environmental Quality, November 1996

Sharp, C.A., "Transient Emissions Testing of Bi odiesel and Other Additiv es in a DDC Series 60 Engine," Southwest Research Institute, Final Re port to the National Biod iesel Board, December 1994

Sharp, C.A., "Transient Emissions Testing of Biodiesel in a DDC 6V -92TA DDEC Engine," Southwest Research Institute, Final Report to the National Biodiesel Board, October 1994

Sharp, C.A., S.A. Howell, J. Jobe, "The Effect of Biodiesel Fuels on tr ansient Emissions from Modern Diesel Engines, Part I Regulated Emissions and Performance," SAE paper no. 2000-01­ 1967

Sirman, M.B., E.C. Owens, K.A. Whitney, "Emissi ons Comparison of Alternative Fuels in an Advanced Automotive Diesel Engine," S outhwest Research Institute, AD A353968/PAA, November 1998

Smith, J.A., D.L. Endicott, R.R. Graze, "Biodi esel Engine Performance and Emissions Testing," Caterpillar Technica l Center, May 1998 Souligny M. et al ., "Heavy-Duty Engine Performance a nd Comparative Emission Measurements for Different Biodiesel Blends Used in th e Montreal Project," SAE paper no. 2004-01-1861

Spataru, A., C. Romig, "Emissi ons and Engine Performance from Blends of Soya and Canola Methyl Ester with ARB #2 Di esel in a DDC 6V92TA MUI Engine," SAE paper no. 952388

Starr, M.E., "Influence on Transient Emissions at Various Injection Timings, Using Cetane Improvers, Bio-Diesel, and Low Arom atic Fuels," SAE paper no. 972904

Stotler, R.W., D.M. Human, "Transient Emissi on Evaluation of Biodiesel Fuel Blend in a 1987 Cummins L10 and DDC 6V-92-TA," Engineering Test Services, report no. ETS-95-128, Nov. 30, 1995

Ullman, T.L., C.T. Hare, T.M. Baines, "Heavy-Duty Diesel Emissions as a Function of Alternate Fuels," SAE paper no. 830377

765Wang, W.G., D.W. Lyons, N.N. Clark, M. Gaut am, "Emissions from Nine Heavy Trucks Fueled by Diesel and Biodiesel Blend without Engine Modificat ion," Environ. Sci. Technol., 2000, 34, 933-939

Studies not included because da ta collected on single cy linder/experimental engine Murayama, T., Y. Oh, A. Kido, T. Chikahisa, N. Miyamoto, K. Itow, "Effects of Super Heating of Heavy Fuels on Combustion and Performance in DI Diesel Engines," SAE paper no. 860306

Murayama, T., Y. Oh, N. Miyamoto, T. Chikahis a, N. Takagi, K. Itow, "Low Carbon Flower Buildup, Low Smoke, and Efficient Diesel Oper ation with Vegetable Oils by Conversion to Mono-Esters and Blending with Diesel Oil or Alcohols," SAE paper no. 841161

Shaheed, A., E. Swain, "Performance and Exhaus t Emission Evaluation of a Small Diesel Engine Fuelled with Coconut Oil Methyl Esters," SAE paper no. 981156

Suda, K.J., "Vegetable Oil or Diesel Fuel - A Flexible Option," SAE paper no. 840004

Ziejewski, M., H.J. Goettler, "C omparative Analysis of the Exhaust Emissi ons for Vegetable Oil Based Alternative Fuels," SAE paper no. 920195

Studies not included because data not readily available Alfuso. S., M. Auriemma, G. Police, M.V. Prati, "T he Effect of Methyl-Est er of Rapeseed Oil on Combustion and Emissions of DI Di esel Engines," SAE paper no. 932801 Hemmerlain, N., V. Korte, H. Richter, G. Schröder, "Performance, Exhaust Emissions and Durability of Modern Diesel Engines R unning on Rapeseed Oil," SAE paper no. 910848

Humke, A.L., N.J. Barsic, "Performance and Emi ssions Characteristics of a Naturally Aspirated Diesel Engine with Vegetable Oil Fu els - (Part 2)," SAE paper no. 810955

Knothe, G., C.A. Sharp, T.W Ryan, “Exhaust Emi ssions of Biodiesel, Petr odiesel, Neat Methly Esters, and Alkanes in a New Technology E ngine,” Energy and Fuels, 2006, 20, 403-408

Lapuerta, M., Armas, O., Ballest eros, R., Fernandez, J., "Die sel Emissions from Biofuels Derived from Spanish Potential Vege table Oils," Fuel, 2005, 84(6), 773-780

Last, R.J., M. Krüger, M. Dürnholz, "Emissions and Performance Characteristics of a 4-Stroke, Direct Injected Diesel Engine Fu eled with Blend of Biodiesel a nd Low Sulfur Diesel Fuel," SAE paper no. 950054

Ma, W., L.I. Leviticus, F.G. Ullman, "On-Li ne Measurement of Formaldehyde in Tailpipe Emissions by Tunable Diode Laser Spectroscopy," SAE paper no. 941702

766Martin, B., P. Aakko, D. Beckman, N. D. Gi acomo, F. Giavazzi, "Influence of Future Fuel Formulations on Diesel Engine Emissions - A Joint European Study," SAE paper no. 972966

McDonald, J.F., "Evaluation of a Yellow Greas e Methyl Ester and Petroleum Diesel Fuel Blend," University of Minnesota Final Report to the Agricultural Utilization Research Institute, August 11, 1997

Montagne, X., "Introduction of Rapeseed Methyl Ester in Diesel Fuel - The French National Program," SAE paper no. 962065

Reece, D.L., C.L. Peterson, "Biodiesel Test ing in Two On-Road Pickups," SAE paper no. 952757

Staat, F., P. Gateau, "The Effects of Rapeseed Oil Methyl Ester on Diesel Engine Performance, Exhaust Emissions and Long-Term Behavior - A Summary of Thr ee Years of Experimentation," SAE paper no. 950053

Studies not included because test cycle nonstandard/unrepresentative Adelman, A.J., "Emission Evaluation Test Repor t for a Comparative Analysis of Emissions Among Petroleum Diesel and Biodiesel Blends fired in a Large Diesel Engine," AirNova, Inc., January 1998

Akasaka, Y., T. Suzuki, Y. Sakurai, "Exhaust Em issions of a DI Diesel Engine Fueled with Blends of Biodiesel a nd Low Sulfur Diesel Fuel," SAE paper no. 972998

Bagley, S.T., L.D. Gratz, J.H. Johnson, J.F. McDonald, "Effects of an Oxidation Catalytic Converter and a Biodiesel Fuel on the Chemical, Biological, and Particulate Size Characteristics of Emissions from an IDI Diesel Engine," Michigan Technological University, December 1995

Chang, D.Y., J.H. Van Gerpen, "Determina tion of Particulate a nd Unburned Hydrocarbon Emissions from Diesel Engines Fueled with Biodiesel," SAE paper no. 982527

Chang, D.Y.Z., J.H. Van Gerpen, "Fuel Prope rties and Engine Performance for Biodiesel Prepared from Modified Feedstocks," SAE paper no. 971684

Choi, C.Y., G.R. Bower, R.D. Reitz, "Effects of Biodiesel Blended Fuels a nd Multiple Injections on D.I. Diesel Engines," SAE paper no. 970218

Czerwinski, J., "Performance of HD-DI-Diesel Engine with Add ition of Ethanol and Rapeseed Oil," SAE paper no. 940545

Desantes, J.M., J. Arrègle, S. Ruiz, A. Delage , "Characterization of the Injection-Combustion Process in a D.I. Diesel Engi ne Running with Rape Oil Met hyl Ester," SAE paper no. 1999-01­

767 1497Fort, E.F., P.N. Blumberg, H.E. Staph, J.J. Staudt , "Evaluation of Cottonseed Oil as Diesel Fuel," SAE paper no. 820317

Jacobus, M.J., S.M. Geyer, S.S. Lestz, W.D. Tayl or, T.H. Risby, "Single -Cylinder Diesel Engine Study of Four Vegetable Oils," SAE paper no. 831743

Masjuki, H., M.Z. Abdulmuin, H.S. Sii, "Investigations on Pre-Heated Palm Oil Methyl Esters in the Diesel Engine," IMechE 1996, Pa rt A: Journal of Power and Energy

Needham, J.R., D.M. Doyle, "The Combustion and Ignition Quality of Alternative Fuels in Light Duty Diesels," SAE paper no. 852101

Schmidt, K., J. Van Gerpen, "The Effect of Biodiesel Fuel Compositi on on Diesel Combustion and Emissions," SAE paper no. 961086

Scholl, K.W., S.C. Sorenson, "Combustion of S oybean Oil Methyl Ester in a Direct Injection Diesel Engine," SAE paper no. 930934

Schramm, J., I. Foldager, N. Olsen, L. Gratz, "Emissions from a Dies el Vehicle Operated on Alternative Fuels in Copenhagen," SAE paper no. 1999-01-3603 Schröder, O., J. Krahl, A. Munack, J. Krahl, J. Bünger, "Environmen tal and Health Effects Caused By the Use of Biodies el," SAE paper no. 1999-01-3561

Senatore, A., M. Cardone, V. Rocco, M.V. Pra ti, "A Comparative An alysis of Combustion Process in D.I. Diesel Engine Fueled with Bi odiesel and Diesel Fuel," SAE paper no. 2000-01­ 0691

Uchida, M., Y. Akasaka, "A Comparison of Emi ssions from Clean Diesel Fuels," SAE paper no. 1999-01-1121

Zhang, Y., J.H. Van Gerpen, "Combustion Analys is of Esters of Soybean Oil in a Diesel Engine," SAE paper no. 960765

Studies not included because they lack new emissions data for regulated pollutants Culshaw, F.A., "The Potential of 'Biodiesel' fr om Oilseed Rape," IMechE 1993, Part A: Journal of Power and Energy

Durbin, T.D., J.R. Collins, H. Galdamez, J.M. Norbeck, M.R. Smith, R.D. Wilson, T. Younglove, "Evaluation of the E ffects of Biodiesel Fuel on Em issions from Heavy-Duty NonRoad Engines," University of California Fina l Report submitted to South Coast Air Quality Management District, May 2000

Peterson, C.L., D.L. Auld, "Technical Overview of Vegetable Oil as a Transportation Fuel," University of Idaho, 1991

768Sharp, C.A., S.A. Howell, J. Jobe, "The Effect of Biodiesel Fuels on Tr ansient Emissions from Modern Diesel Engines, Part II Unregulated Emissions and Ch emical Characterization," SAE paper no. 2000-01-1968

Exclusion of duplicate datasets Colorado Institute for Fuels and High Altitude Engine Research, "Emissions from Biodiesel Blends and Neat Biodiesel from a 1991 Model Series 60 Engine Operating at High Altitude," Final Report to National Renewabl e Energy Laboratory, September 1994

Note : CO 2 values were not duplicative.

McCormick, R.L., M.S. Graboski, T.L. Allema n, A.M. Herring, "Impact of Biodiesel Source Material and Chemical Struct ure on Emissions of Criteria Po llutants from a Heavy-Duty Engine," Environ. Sci. Technol., 2001, 35, 1742-1747

Peterson, C.L., D.L. Reece, "Emissions Testing with Blends of Esters of Rapeseed Oil Fuel With and Without a Catalytic C onverter," SAE paper no. 961114

Note : Only Table 6 data is duplicative. Taberski, J.S., C.L. Peterson, "Dynamometer Em issions test Comparisons on a 5.9L Direct Injected Diesel Powered Pickup," BioEner gy '98: Expanding BioE nergy Partnerships

Taberski, J.S., C.L. Peterson, J. Thompson, H. Haines, "Using Biodiesel in Yellowstone National Park - Final Report of the Truck in the Park Project ," SAE paper no. 1999-01-2798

Studies not included for other reasons Durbin T. D. et al ., "Evaluation of the Effect s of Biodiesel Fuel on Em issions from Heavy-Duty Non-Road Vehicles", Final Report for the SC AQMD under contract No. 99132 by the University of California, Riverside, May 2000

Reason : Non-road vehicles not considered here.

Frank B.P. et al ., "A Study of the Effects of Fuel Type and Emission Control Systems on Regulated Gaseous Emissions fr om Heavy-Duty Diesel Engine s," SAE paper no. 2004-01-1085

Reason : Effect of oxycat & CRDPF greater than effect of B20.

Guerra S. A. et al. , "Comparison Study of NOx and CO 2 Emissions from an Off-Road Diesel Compactor Running on Regular Diesel , Biodiesel and Ultra-Low Sulf ur Diesel," University of Kansas draft report, March 17, 2006

Reason : Non-road equipment not considered here.

769Watts, W.F., M. Spears, J Johnson, "Evaluation of Biodiesel Fuel and Oxid ation Catalyst in an Underground Metal Mine," University of Minnesota, September 24, 1998

Reason : Emission measurements were made in the field with portable emission measurement devices

Yoshimoto, Y., M. Onodera, H. Tamaki, "Re duction of NOx, Smoke, and BSFC in a Diesel Engine Fueled by Biodiesel Emulsion with Used Frying Oil," SAE paper no. 1999-01-3598

Reason : Biodiesel blend include d water in an emulsion

770

## Endnotes

1 See the following documents: Iowa State Univer sity Center for Agricultural Research and Development, “Emerging Biofuels: Outlook of E ffects on U.S. Grain, Oilseed, and Livestock Markets,” May 2007 (a revised version is av ailable from July 2007); USDA Agricultural Projections to 2016, Report OCE- 2007-1, February 2007 (a newer ve rsion was released in Feb 2008 with Projections to 2017); NCGA, “How Much Ethanol Ca n Come From Corn?” 2006; USDA, “An Analysis of the Eff ects of an Expansion in Biofuel Demand on U.S. Agriculture,” May 2007.
2 Western Governors’ Associati on. “Transportation Fuels for the Future, Biofuels: Part I,” January 8, 2008.
3 U.S. Government Accountability Office. “DOE Lacks a Strategic Approach to Coordinate Increasing Production with Infrastructure Deve lopment and Vehicle Needs,” June 2007.
4 USDA/NASS. “Corn: Yield by year, US,” http://www.nass.usda.gov/Charts_and_Maps/Fi eld_Crops/cornyld.asp, August 12, 2008.
5 USDA Agricultural Projections to 2016, Report OCE-2007-1, Febr uary 2007, page 39.
6 USDA, National Agricultural St atistics Service, Crop Produc tion and Grain Stocks; USDA, World Agricultural Outlook Board, World Supply and Demand Estimates; and U.S. Department of Commerce, Bureau of the Census.
7 USDA Agricultural Projections to 2016, Report OCE-2007-1, Febr uary 2007, page 22.
8 Shapouri, H., Gallagher, P., “USDA’s 2002 Et hanol Cost-of-Production Survey,” July 2005, p.11.
9 USDA, “The Economic Feasibility of Ethanol Production from Sugar in the United States,” July 2006.
10 Personal communication with Nathalie Hoffman, Managing Member of California Renewable Energies, LLC, August 27, 2008.
11 Christiansen, Ryan, “Analysis: Sugarcane Ethanol Has 95% Less Emissions,” Ethanol Producer Magazine, March 2009.
12 Voegele, Erin, “Sugarcane Economics,” Ethanol Producer Magazine, March 2009.
13 F.O. Licht, “World Ethanol Market s: The Outlook to 2015,” 2006, page 45.
14 “Sugarcane Industry in Brazi l: Ethanol, Sugar, Bioelect ricity” brochure from UNICA
15 FAO Statistics, “Sugarcane selected as Commodity,” http://www.fao.org/es/ess/top/commodity.h tml?lang=en&item=156&year=2005, August 12, 2008.
16 Agra FNP. “Sugar and Ethanol in Brazil: A St udy of the Brazilian Sugar Cane, Sugar and Ethanol Industries,” 2007.
17 “Sugarcane Industry in Brazi l: Ethanol, Sugar, Bioelect ricity” brochure from UNICA
18 Scaramucci, Jose & Cunha, Marcelo, “Bioet hanol as Basis for Regional Development in Brazil: An Input-Output Mode l with Mixed Technologies.”
19 CGEE, ABDI, Unicamp, and NIPE, “Scaling Up th e Ethanol Program in Brazil,” n.d. cited in Rothkopf, Garten, “A Blueprint for Green Energy in America,” 2006.
20 USDA, “The Economic Feasibility of Ethanol Production from Sugar in the United States,” July 2006.
21 Kline, K. et. al., “Biofuel Feedstock Assessm ent for Selected Countri es,” Oak Ridge National Laboratory, February 2008.

77122 Elbehri, Aziz. USDA, ERS, “An Evaluati on of the Economics of Biomass Feedstocks: A Synthesis of the Literature,” Prepared for th e Biomass Research and Development Board, 2007.
23 Milbrandt, A., “Geographic Pers pective on the Current Biomass Resource Availability in the United States,” NREL Report No. TP-560-39181, 2005.
24 Gallagher, P. et. al., “Supply a nd Social Cost Estimates for Biomass from Crop Residues in the United States,” Environmental and Resource Economics 24: 335-358, 2003.

25 Walsh, Marie, “U.S. Cellulosic Biomass Feed stock Supplies and Distribution,” Unpublished Manuscript, January 10, 2008.
26 Graham, R.L. et. al., “Current and Potential U.S. Corn Stover Supplies,” American Society of Agronomy 99:1-11, 2007.
27 W. W. Wilhelm, USDA-ARS, Univ. of Nebraska, Lincoln, NE 68583-0934, J. M. F. Johnson, USDA-ARS, Morris, MN 56267-1065, J. L. Hatfield, 108 Natl. Soil Tilth Lab., Ames, IA 50011­ 3120, W. B. Voorhees, USDA-AR S (retired), Morris, MN 56267- 1065, and D. R. Linden, USDA-ARS (retired), St. Paul , MN 55108-0000, Crop and Soil Productivity Response to Corn Residue Removal A Literature Review; FORUM, © American Societ y of Agronomy, Madison, WI 53711 USA, Published in Agron. J. 96:1-17 (2004)
28 Predicting Soil Erosion by Water. A Guide to Conservation Planni ng with the Revised Universal Soil Loss Equation (RUSLE). United States Department of Agriculture, Agricultural Research Service, Agricultural Handbook Number 703, 1997.
29 Richard G. Nelson, Enersol Resources, Ma nhattan, KS 66502, USA, Resource Assessment and Removal Analysis for Corn Stover and Wheat Straw in the Eastern and Midwestern United States—Rainfall and Wind-indu ced Soil Erosion Methodology, Biomass and Bioenergy 22 (2002) 349 – 363, Published by Elsevier Science Ltd
30 Linda Mann, Virginia Tolbert, Janet Cushman, Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6038, Po tential Environmental Effects of Corn Stover Removal With Emphasis on So il Organic Matter and Erosion, Agriculture, Ecosystems and Environment 89: 149-166, ISSN 0167-8809 El sevier Science, Amsterdam. June 2002.
31 http://www.nrcs.usda.gov/technical/Standards/nhcp.html
32 Richard G. Nelson, Enersol Resources, Ma nhattan, KS 66502, USA, Resource Assessment and Removal Analysis for Corn Stover and Wheat Straw in the Eastern and Midwestern United States—Rainfall and Wind-induced Soil Eros ion Methodology, Biomass and Bioenergy 22 (2002) 349 – 363, Published by Elsevier Science Ltd.
33 W.W. Wilhelm, USDA-ARS; J.M.F. Johnson, USDA-ARS; D.L. Karlen, USDA-ARS; D.T. Lightle, National Soil Survey; Corn Stover to Sustain Soil Orga nic Carbon Further Constrains Biomass Supply, Published in Agron. J. 99:1665–1667 (2007) ; American Society of Agronomy, Madison, WI 53711.
34 Susan S. Andrews, Ph.D., Leader, Soil Qu ality National Technology Development Team USDA-Natural Resource Conservation Servi ce Crop, White Paper: Residue Removal for Biomass Energy Production: Effect s on Soils and Recommendations, Updated February 22, 2006
35 Graham, R.L. et. al., “Current and Potential U.S. Corn Stover Supplies,” American Society of Agronomy 99:1-11, 2007.
36 Perlack, Robert D.; Biomass as Feedstocks for a Bioenergy and Bioproducts Industry: The Technical Feasibility of a Billion Ton Annual Supply; Spons ored by the U.S. Department of Energy and the U.S. Department of Agriculture; April 2005.
37 Ibid.

77238 Walsh, M.E. (2007) Sungrant Bioweb. Forest Resources. http://bioweb.sungrant.org/Technical/Bioma ss+Resources/Forest+Resources/Default.htm
39 Ibid.
40 QUANTIFYING THE FLOW: TAKING INVENTORYOF WOODY RESIDUALS , David McKeever, Research Forester, USDA Forest Service's Forest Products Laboratory, Madison, Wisconsin. Published in Biocycle, July 2003, pp. 31-35.
41 Perlack, R.D., Wright, L.L., Turhollow, A.F, Graham, R.L., Environmen tal Sciences Division, Oak Ridge National Laboratory; St okes, B.J., Forest Service, USDA.; Erbach, D.C., Agricultural Research Service, USDA, “Biomass as Feedstoc k for a Bioenergy & Bioproducts Industry: The Technical Feasibility of a B illion-Ton Annual Supply (A Jo int Study Sponsored by DOE and USDA, Prepared by Oak Ridge National Laboratory),” 2005.
42 Ibid.
43 Kerstetter, J. and Lyons, J., (2001) Logging and Agricultural Resi due Supply Curves for the Pacific Northwest . Washington State University Energy Program for U.S. DOE.
44 Rummer, B.; Len, D.; and O’Brien, O. (2004). Forest Residues Bundling Project-New Technology for Residue Removal. USDA Forest Service, Southern Research Station
45 Ibid.
46 Rawlings, C., et. al. (2004). A Study of How to Decrease the Costs of Collecting, Processing and Transporting Slash. USDA Forest Service.
47 Dickerson, K., et. al. (revised November 2007). Biomass and Biofuels in Maine: Estimating Supplies for Expanding the Forest Products Industry . Margaret Chase Smith Policy Center.
48 Puttock, G.D. (1995). Estimating cost for integr ated harvesting and related forest management activities. Biomass and Bioenergy 8 (2), 73-79.
49 Perlack, R.D., Wright, L.L., Turhollow, A.F, Graham, R.L., Environmen tal Sciences Division, Oak Ridge National Laboratory; St okes, B.J., Forest Service, USDA.; Erbach, D.C., Agricultural Research Service, USDA, “Biomass as Feedstoc k for a Bioenergy & Bioproducts Industry: The Technical Feasibility of a B illion-Ton Annual Supply (A Jo int Study Sponsored by DOE and USDA, Prepared by Oak Ridge National Laboratory),” 2005.
50 Dickerson, K., et. al. (revised November 2007). Biomass and Biofuels in Maine: Estimating Supplies for Expanding the Forest Products Industry . Margaret Chase Smith Policy Center.
51 Ibid.
52 Timko, B., 2003. USDA, Forest Service, personal communicati on as noted in the “Billion-ton Study” by Perlack, R.D., et. al.
53 F. Bohlin and A. Roos, Wood fuel supply as a function of forest owner preferences and management styles, Biomass & Bioenergy 22 (2002), pp. 237–249.
54 Jonsell, M. 2007. Effects on Bi odiversity of Forest Fuel Extraction, Governed by Processes Working on a Large Scale. Biomass and Bioenergy, Issue 31 pg. 726-732.
55 Kozlowski, T.T., 1999. Soil Compaction and Gr owth of Woody Plants. Scandinavian Journal of Forest Research, Volume 14, Nu mber 6. Taylor and Francis Ltd.
56 Rummer, B.; Len, D.; and O’Brien, O. (2004). Forest Residues Bundling Project-New Technology for Residue Removal. USDA Forest Service, Southern Research Station
57 Energy content of hardwood; www.eere.energy.gov/bioma ss/progs/search3.cgi?8828 ; Biomass Feedstock Composition and Property Da tabase; Energy Efficiency and Renewable Energy, U.S. Department of Energy.

77358 Perlack, Robert D.; Biomass as Feedstocks for a Bioenergy and Biopr oducts Industry: The Technical Feasibility of a Billion Ton Annual Supply; Spons ored by the U.S. Department of Energy and the U.S. Department of Agriculture; April 2005.
59 Walsh, Marie E., U.S. Cellulosic Biomass F eedstock Supplies and Distribution; January 10, 2008.

60 Data provided by e-mail by Perlack, Robert D., May 2008.
61 EPA. “Municipal Solid Waste Generation, Recyc ling, and Disposal in the United States: Facts and Figures for 2006,” November 2007.
62 USEPA. “Methodology for Estimating Municipal Solid Waste Recycling Benefits,” November 2007; See 2006 MSW Characterization Tables.
63 Biocycle, “The State of Garbage in Am erica,” Vol. 47, No. 4, 2006, page 26.
64 Midwest Assistance Program, Inc. “T he 2006-2007 Missouri Municipal Solid Waste Composition Study,” October, 2007.
65 R. W. Beck, “Georgia Statewid e Characterization Study,” June 2005.
66 Engineering Solutions and Desi gn, Inc., “State of Ohio Wast e Characterization Study,” April 2004.
67 NYC Department of Sanitation, “200 4-2005 NYC Waste Characterization Study.”
68 R.W. Beck, “Statewide MSW Composition Study: A Study of Discards in the State of Minnesota,” 2000.
69 R.W. Beck, “Statewide Waste Compositi on Study: Pennsylvani a State-wide Waste Composition,” April 2003.
70 DSM Environmental Services, Inc, “Ver mont Waste Composition Study,” June 2002.
71 R.W. Beck, “1992 Washington Stat e Waste Characterization Study.”
72 R.W. Beck, “Wisconsin Statewide Waste Characterization Study,” May 2003.
73 Department of Environmental Quality , “Oregon 2002 Waste Composition Study,” 2002.
74 Williams, Robert and California Biomass Colla borative, “Biofuels from Municipal WastesBackground Discussion Paper,” March 28, 2007.
75 Ibid.
76 Walsh, Marie, “U.S. Cellulosic Biomass F eedstock Supplies and Distribution,” Unpublished Manuscript, January 10, 2008.
77 NEOS Corporation. (1994). Urba n tree residues: Results of the fi rst national inventory. (Final Report). International Society of Arboriculture Research Trust. Allegheny Power Service Corporation and National Arborist Foundation. As cited in Walsh, Marie. (2008) U.S. Cellulosic Biomass Feedstock Supplies and Dist ribution. Unpublished Manuscript.
78 USEPA, Construction and Demolition (C&D) Materials. http://www.epa.gov/osw/nonhaz/industrial/cd/ basic.htm Accessed August 10, 2008.
79 Fehrs, J., “Secondary Mill Residues and Urba n Wood Waste Quantities in the United StatesFinal Report,” Northeast Regional Biomass Program Washington, D.C., December 1999.
80 Wiltsee, G., “Urban Wood Waste Resource Assessment,” Appel Consultant, Inc. Valencia, CA. November, 1998.
81 U.S Department of Energy. “B reaking the Biological Barriers to Cellulosic Ethanol: A Joint Research Agenda.” (2006).
82 Kumar, A., & Sokhansanj, S., “Switchgrass (Panicum vigratum, L.) delivery to a biorefinergy using integrated biomass supply anal ysis and logistics (IBSAL) model.” Bioresource Technology , (2006).

77483 McLaughlin, S.B. & Adams Kszos, L., “Develop ment of switchgrass (Panicum virgatum) as a bioenergy feedstock in the United States,” Biomass and Bioenergy , 28: 515-535, 2005.
84 Kumar, op. cit. 85 McLaughlin, op. cit. 86 Lewandowski, I., Clifton-Brown, J.C., Scur lock, J.M.O., & Huisma n, W., “Miscanthus: European experience wi th a novel energy crop,” Biomass and Bioenergy , 19: 209-227, 2000.
87 Pyter, R., Voigt, T., Heaton, H., Dohleman, F., & Long, S. Growing giant miscanthus in Illinois. University of Illinois. (2006).
88 Ibid.
89 Ibid.
90 Lewandowski, op. cit. 91 Pyter, op. cit. 92 Pyter, op. cit. 93 Lewandowski, op. cit. 94 Pyter, op. cit. 95 U.S Department of Energy. “B reaking the Biological Barriers to Cellulosic Ethanol: A Joint Research Agenda,” 2006.
96 Ibid.
97 Ibid.
98 Campbell, J.E. at al., ”The Global Potentia l of Bioenergy on aAbandone d Agriculture Lands,” Environ. Sci. Technology , 2008.
99 Ibid.
100 Adapted from additional information received from J. Elliot Campbell. “The Global Potential of Bioenergy on Abandoned Agriculture Lands,” Environ. Sci. Technology , 2008.
101 USDA, Farm Services Agency, “Conservati on Reserve Program Summary and Enrollment Statistics,” FY 2007.
102 USDA, Farm Services Agency, “Conservation Reserve Program Statistics: Monthly Summary July 2008.”
103 Pyter, op. cit

. 104 Based on plant capacities reported by the Nationa l Biodiesel Board, and data reported by F.O. Licht.
105 Mueller, Steffen. An analysis of the projected energy use of future dry mill corn ethanol plants (2010-2030). October 10, 2007. Available at http://www.chpcentermw.org/pdf s/2007CornEethanolEnergySys.pdf
106 “Essential Rendering”, published by the Natio nal Renderer’s Associat ion, edited by David Meeker. 2006. Available at http://nationalrende rers.org/assets/esse ntial_rendering_book.pdf
107 “Statewide Feasibility Study for a Potential Ne w York State Biodiesel Industry”, prepared by LECG, LLC. Report number 04- 02, April 2004. Available at http://www.nyserda.org/public ations/biodieselreport.pdf
108 A copy of this letter is available in the docket.
109 Jongschaap, R.E.E, W.J. Corre, P.S Bindraban , and W.A. Brandenburg, “Claims and Facts on Jatropha curcas L,” Plant Research International , Vers. Report 158, Oct. 2007.

110 Euler, Hartlieb, and David Gorri z, “Case Study "Jatropha Curcas "” Rep. No. Global Facilitation Unit for Underutilized Species , Frankfurt, 2004.

775111 Kumar, Sujeet., "Jatropha harmful for kids, soil and aquatic life: scientist," India eNews , May 2008.

112 Jongschaap, op. sit.

113 Ibid.

114 Fairless, Daemon. "The little sh rub that could - maybe." Nature 449 (2007): 652-55.

115 Francis, G., R. Edinger, and K. Becker. "A concept for simultaneous wasteland reclamation, fuel production, and socio-economic development in degraded areas in India: need, potential and perspectives of Jatropha planta tions." Natural Resources Forum 29 (2005): 12-24. As sited in Jongschaap, R.E.E, W.J. Corre, P.S Bindraban, and W.A. Brandenburg, “Claims and Facts on Jatropha curcas L,” Plant Research International , Vers. Report 158, Oct. 2007.

116 Woltjer, Lyanne. Size does matter. Expe rtise Centrum voor Duurzame Ontwikkeling (ECDO). Dec. 2006.

117 Jongschaap, op. sit. 118 Li Guo, T. "The photosynthesis and water us e efficiency of eight garden tree species." Forest Research 15 (2002): 291-96. As sited in Jongschaap, R.E.E, W.J. Corre, P.S Bindraban, and W.A. Brandenburg, “Claims and Facts on Jatropha curcas L,” Plant Research International

, Vers. Report 158, Oct. 2007.

119 Jongschaap, op. sit. 120 Fairless, op. sit.
121 Jongschaap, op. sit.

122 Ghosh, A., J.S. Patolia, D.R. Chaudharry, J. Chikara, S.N. Rao D. Kumar, G.N. Boricha, and A. Zala. Response of Jatropha curcas under di fferent spacing to Jatropha de-oiled cake. Discipline of Phytosalinity, Central Salt a nd Marine Chemicals Research Institute. 2007.

123 Jongschaap, op. sit.

124 Ibid.
125 Chaudharry, D.R., J.S. Patolia, A. Ghosh, J. Ch ikara, G.N. Boricha, and A. Zala. Changes in soil characteristic s and foliage nutrient conten t in Jatropha curcas planta tions in relation to stand density in Indian wasteland. Expert seminar on Jatropha curcas L. Agronomy and Genetics. Mar. 2007. As sited in Jongschaap, R.E.E, W.J. Corre, P.S Bindraban, and W.A. Brandenburg, “Claims and Facts on Jatropha curcas L,” Plant Research International , Vers. Report 158, Oct. 2007.

126 Jongschaap, op. sit.

127 Ibid.
128 Francis, op. sit.

129 Jongschaap, op. sit. 130 Ibid.
131 Kumar, op. sit. 132 Jongschaap, op. sit. 133 Sharma, N., and A. Sarraf. Plantation manage ment: activities/labour costs. Expert seminar on Jatropha curcas L. Agronomy and Genetics. Ma r. 2007. As sited in Jongschaap, R.E.E, W.J.

776Corre, P.S Bindraban, and W.A. Brandenburg, “Claims and F acts on Jatropha curcas L,” Plant Research International , Vers. Report 158, Oct. 2007.

134 Jongschaap, op. sit. 135 Fairless, op. sit. 136 Francis, op. sit.

137 Jongschaap, op. sit. 138 Euler, op. sit.

139 Woltjer, Lyanne, “Size does matter,” Expe rtise Centrum voor Duurzame Ontwikkeling (ECDO), December 2006.
140 Jongschaap, op. sit.
141 Euler, op sit.

142 Jongschaap, op. sit. 143 Francis, op. sit.

144 Ibid.
145 Pant, K.S., V. Khosla, D. Kumar, and S. Ga irola, "Seed oil conten t variation in Jatropha curcas Linn. in different altitudinal ranges and site conditions in H.P. India," Lyonia 11 (2006): 31-34.

146 Pant, op. sit

. 147 Jongschaap, op. sit. 148 Ibid.
149 Woltjer, op. sit.

150 Achten, W., E. Mathijs, L. Verchot, V.P. Singh, and B. Muys, “Bio-diesel from Jatropha: the life-cycle perspective,” Expert seminar on Jatropha curcas L. Agronomy and Genetics . March 2007. As sited in Jongschaap, R.E.E, W.J. Corre, P.S Bindraban, and W.A. Brandenburg, “Claims and Facts on Jatropha curcas L,” Plant Research International , Vers. Report 158, Oct. 2007.

151 Muys, B., W. Achten, E. Mathijs, V.P. Si ngh, and L. Verchot, “Life cycle inventory of biodiesel production from Jatropha,” Expe rt seminar on Jatropha curcas L. Agronomy and Genetics . March 2007. As sited in Jongschaap, R.E. E, W.J. Corre, P.S Bindraban, and W.A. Brandenburg, “Claims and Facts on Jatropha curcas L,” Plant Research International , Vers. Report 158, Oct. 2007.

152 Jongschaap, op. sit. 153 Fairless, op. sit.

154 .Francis, op. sit.

155 USDA, "PLANTS Profile for Jatropha curc as L." USDA Natural Resources Conservation Service. http://plants.usda.gov/java/pro file?symbol=jacu2, November 3, 2008.

156 Jongschaap, op. sit.

777157 Oregon Climate Service, “State Annual Prec ip Maps,” http://www.ocs.oregonstate.edu., November 3, 2008.

158 Carney, Dan. "Second-generation biodiesel." Automotive Engineering , May 2008: 20-22.

159 Francis, op. sit. 160 Euler, op. sit. 161 Sheehan, John, Terri Dunahay, John Benemann, a nd Paul Roessler, “A Look Back at the U.S. Department of Energy’s Aquatic Species Progr am—Biodiesel from Alg ae,” National Renewable Energy Laboratory. Office of Fuels Development. Golden, Colorado, 1998.

162 Ibid.
163 Wagner, Leonard, “Biodiesel from Alg ae Oil,” Mora Associates, July 2007.

164 National Renewable Energy Labor atory, "Chevron and NREL to Collaborate on Research to Produce Transportation Fuel s using Algae." Press release. Oct. 31 2007.

165 Chisti, Yusuf, "Biodiesel from Microalgae." Biotechnology Advances 25, 294-306, 2007.

166 Schenider, David, "Grow Your Own?" American Scientist , Sept.-Oct. 2006.

167 Campbell, Matthew N., "Biodiesel: Algae as a Renewable Source for Liquid Fuel." Guelph Engineering Journal , 1, 2-7, 2008.

168 Chisti, op. sit. 169 Pienkos, Philip T., "The Potential for Biofue ls from Algae," Algae Biomass Summit. San Francisco, CA. Nov. 15, 2007.

170 Campbell, op. sit.

171 Pienkos, op. sit.
172 Campbell, op. sit.

173 Sheehan, op. sit.

174 Pienkos, op. sit.

175 Campbell, op. sit.

176 Putt, Ron. “Algae as a Biodiesel Feedstock,” Nov. 20, 2007.

177 Sheehan, op. sit. 178 "Algae Bloom Again." Nature , 447, 520-21 2007.

179 Deshayes, Pierre-Henry, "As planet swelters, are algae unlikely saviour?" AFP, July 10, 2008.
180 Sheehan, op. sit. 181 Bullock, Cary, "Algae Corrals Carbon Dioxide," EnergyBiz Magazine , 68-70, Mar.-Apr. 2007.

182 Chisti, op. sit. 183 Sheehan, op. sit.

184 Chisti, op. sit.

185 Schenider, op. sit.

186 Campbell, op. sit.

778187 Pienkos, op. sit. 188 Putt, op. sit.

189 Chisti, op. sit. 190 Campbell, op. sit.

191 Chisti, op. sit. 192 Ibid.
193 Campbell, op. sit.

194 Pienkos, op. sit. 195 Chisti, Yusuf. op. sit. 196 Wagner, op. sit.

197 AlgaeLink, “Commercial Cultiv ation Plants,” http://www.algae link.com, November 3, 2008.

198 Sapphire Energy, “Algae-Based Fuel Projected to Become Commercial-Ready in Three Years” http://www.sapphireenergy.com/press_release/11 , April 16, 2009.
199 Thurmond, Will, “Algae 2020: Biofuels Mark ets and Commericalization Outlook Webinar”, Emerging Markets Online, March 10, 2009.
200 Ibid.

201 "Low Cost Algae Production System In troduced." Energy-Arizona. http://www.energyarizona.org., Aug. 28, 2007.
202 Chisti, op. sit. 203 "Low Cost Algae Production System Introduced." Energy-Arizona . 28 Aug. 2007. <http://www.energy-arizona.org>.
204 Chisti, op. sit. 205 Pienkos, op. sit.

206 “PV Solar Radiation,” Digital image. Na tional Renewable Energy Laboratory. May 2004.

207 Bullock, op. sit. 208 Chisti, op. sit. 209 Schenider, op. sit.

210 Campbell, op. sit.

211 Pienkos, op. sit.
212 Wagner, op. sit. 213 Chisti, op. sit.
214 Pienkos, op. sit.

215 F.O. Licht’s World Ethanol and Biofuels Report. Vol 6, No 11, p.203. Feb 15, 2008.
216 Ibid.
217 DTN Ethanol Center, ”EU Biodiesel Feedstock Cha llenge,” October 11, 2007, http://www.dtnethanolcenter.com/index.cfm?show=10&mid=66&pid=27
218 World Biofuels: FAPRI 2008 Agricultural Outlook. http://www.fapri.org/outlook2008/text/ 14Biofuels.pdf, November 3, 2008.
219 Biofuels Barometer. Geographic Information System: EurObserv'ER http://www.eufores.org/index.php?id=29, November 3, 2008.

779220 EUROPA Press Release, “Stricter fuel standa rds to combat climate change and reduce air pollution,” Jan. 31, 2007.
221 EU Press Release, “2782 nd Council Meeting on Transport, Telecommunications, and Energy, February 15 th , 2007, Brussels, Council of the European Union,” http://www.consilium.europa.eu/Newsroom.
222 Ministry of Mines and Energy, “The Brazilian Experience wi th Biofuels,” Presentation by Ricardo de Gusmao Dornelles, June 1, 2007.
223 EPE, “Plano National de Energia 2030.” Pres entation from Mauricio Tolmasquim, 2007.
224 Asian Palm Oil for Euro Biodiesel, 2005 as quoted in Ohga K. & Tatsuji K., “Biofuels Policies in Asia: Trade effects on World Agricu ltural and Biofuels Trade,” Presentation to USDA Agricultural Outlook Forum, March 2, 2007.
225 Ohga K. & Tatsuji K., “Biofuels Policies in Asia: Trade effects on World Agricultural and Biofuels Trade,” Presentation to USDA Agri cultural Outlook Forum, March 2, 2007.
226 USDA FAS, “Gain Report. Indonesia Bio- Fuels Biofuels Annual 2007,” June 13, 2007.
227 USDA FAS, “Palm oil continues to do minate global consumption in 2006/2007,” http://www.fas.usda.gov/oilseeds/ circular/2006/06-06/Junecov.pdf
228 Dato’ Dr. Mohd. Basri Wahid, “Malaysian Pa lm Oil Board. Presentation: Technological Progress and Commercialization of Biodiesel in Malaysia.”
229 USDA FAS, “Gain Report. India Bi o-fuels Annual 2007,” June 1, 2007.
230 Fairless, op. sit. 231 Wright, M. and Brown, R. Comparativ e Economics of Biorefineries Based on the Biochemical and Thermochemical Platforms. Biofuels, Bioprod. Bioref. 1:49-56 (2007)
232 Kline, K. et. al. Biofuel Feedstock Assessm ent for Selected Countries. Oak Ridge National Laboratory. February 2008.
233 National Renewable Energy Laboratory estimat e based on biomass portion available at $45­ $55/dry ton.. Using POLYSYS Polic y Analysis System, Agricultu ral Policy Analysis Center, University of Tennessee. http://www.agpo licy.org/polysys.html. Accessed May 2008.
234 Milbrandt, A. (2005). “Geographic Perspective on the Current Biomass Resource Availability in the United States.” 70 pp.; NREL Report No. TP-560-39181
235 D. Montross, Associate Professor, Scott A. Shearer, Professor, Timothy S. Stombaugh, Associate Extension Professor, Stephen F. Higgins , Research Specialist, and Samuel G. McNeill, Associate Extension Professor, Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexingt on, Kentucky; and Shahab Sokha nsanj, Professor, Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, Te nnessee, Corn Stover Availability and Collection Efficien cy Using Typical Hay Equipment, This article is published with the approval of the Director of the Ke ntucky Agricultural Experiment Station and designated Paper No. 04-05-066. Transact ions of the ASABE, Vol. 50(3): 705

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237 Christopher T. Wright, Peter A. Pryfogle, Nathan A. Stevens, J. Richard Hess, Corey W. Radtke, Idaho National Laboratory, Idaho Falls, ID, USA, Value of Distributed Preprocessing of

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239 ICM website, Ethanol Production Process, http://www.icminc.com/ethanol/production_pro cess (Supplemented with information provided by EPA’s Region 7).
240 Renewable Fuels Associati on website, How Ethanol is Made: The Ethanol Production Process –Wet Milling, http://www.ethanolrfa.org/resource/made.
241 POET website, Technology - BPX TM , http://www.poetenergy.com/learn/bpx.asp.
242 Kirkbride McElroy, Anduin , “Poe t, ISU partner for BPX research,” Ethanol Producer Magazine, http://www.ethanol-producer.com/artic le.jsp?article_id=3770, March 2008.
243 Renewable Fuels Associat ion, Annual Energy Outlook 2006 (http://www.ethanolrfa.org/obj ects/pdf/outlook/outlook_2006.pdf)

244 U.S. Environmental Protection Agency, CombinedHeat & Power Partnership website, Basic Information, http://www.epa.gov/chp/basic/index.html.
245 Smeets, Edward, et. al. “Sustainability of Brazilian bio-ethanol”. August 2006
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248 DOE National Laboratories. “30x30 A Scen ario for Supplying 30% of 2004 Motor Gasoline with Ethanol by 2030: A Production Scenario for Ce llulosic Ethanol from Corn Stover,” June 23, 2006.

249 LaMonica, Martin. “Investor Vinod Khosla: Advanced biofuels are closer than you think”. Accessed at: http://www.news.com/8301-11128_3-9811702-54.html

250 DOE. “Biomass Program: ABC’s of Biofuels”. Accessed at: http://www1.eere.energy.gov/biom ass/abcs_biofuels.html#content
251 Starch Hydrolysis ; http://www.bio-link.org/pdf/starch.pdf
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253 Glossary of Biomass Terms, National Renewable Energy Laboratory, Golden, CO. http://www.nrel.gov/biomass/glossary.html
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781255 BlueFire Ethanol. “Revie w of Competing Processes,” May 2006. Accessed at: http://www.bluefireethanol.com/images/T echnology_Comparison_051206.ppt#295,1, 31 Musick Irvine, California 92618.
256 Stephanopoulos, Gregory, et. al., “Challeng es in Engineering Microbes for Biofuels Production,” Science , 315, 801, February 9, 2007.
257 Dipardo, Joseph, “Outlook for Bioma ss Ethanol Production and Demand,” Energy Information Administration, 2002.
258 Aden, Andy, Ethanol Process Design and Economics Utilizing Co-current Dilute Acid Prehydrolysis and Enzymatic Hydrolysis fo r Corn Stover, Contract DE-AC36-99-GO10337, National Renewable Energy Labor atory, June 2002.
259 Kim S, Holtzapple MT., “Effect of Structural Features on Enzyme Digestibility of Corn Stover,” Bioresource Technology , 97: 583-591, 2006. As site d in Gray, Kevin, et. al., “Bioethanol,” Current Opinion in Chemical Biology , 10:141-146, 2006.
260 A. Aden, M. Ruth, K. Ibsen, J. Jechura, K. Neeves, J. Sheehan, and B. Wallace National Renewable Energy Laborat ory (NREL); L. Montague, A. Slayton, and J. Lukas Harris Group, Seattle, Washington, Etha nol Process Design and Economics Utilizing Co-Current Dilute Acid Prehydrolysis and Enzymatic Hydrolysis for Corn Stover ; June 2002;NREL is a U.S. Department of Energy Laboratory Operated by Midwest Research Institute • Battelle • Bechtel; Contract No. DE-AC36-99-GO10337
261 Graf, Angela, et. al., “Oregon Cellulose-Ethan ol Study: An evaluati on of the potential for ethanol production in Oregon usi ng cellulose-based feedstocks,” Bryan & Bryan Inc and Oregon Office of Energy, 2000.
262 Tim Eggeman, Neoterics International, Lake wood, CO 80228, USA, Richard T. Elander, National Renewable Energy Laboratory, Gold en, CO 80401, USA, Process and economic analysis of pretreatment technologies, Bioresource Technology 96 (2005) 2019–2025
263 Gray, K., et. al., “Bioethanol,” Current Opinion in Chemical Biology , 10:141-146, 2006.
264 Graf, op. sit. 265 DOE, “From Biomass to Biofuels: A Roadmap to the Energy Future.” based on a workshop held in Rockville, MD, on December 7-9, 2005. As sited in Stephanopoulos, Gregory, et. al., “Challenges in Engineering Micr obes for Biofuels Production,” Science , 315, 801, February 9, 2007.
266 Presentations of K.C. McFarl and (Novozymes) and G. Anderl (G enencor Interna tional) at the World Congress on Industrial Biotechnology and Bioprocessing, Orlando, FL, July 2006. As sited in Stephanopoulos, Gregory, et. al., “Challenges in Engi neering Microbes for Biofuels Production,” Science , 315, 801, February 9, 2007.
267 Genencor Launches First Ever Commerci al Enzyme Product for Cellulosic Ethanol, Rochester, NY, World-Wire, October 22, 2007 C opyright © 2007,. All rights reserved. WorldWire is a resource provided by Enviro nment News Service. http://worldwire.com/news/0710220001.html
268 Purdue yeast makes ethanol from agricultural waste more effectively, Purdue News, June 28, 2004 http://www.purdue.edu/UNS/html4ev er/2004/040628.Ho.ethanol.html
269 Stephanopoulos, op. sit. 270 US. DOE. Technologies: Processi ng and Conversion. Accessed at: http://www1.eere.energy.gov/biomass/pr ocessing_conversion.html on October 28 , 2008

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. http://www1.eere.energy.gov/bioma ss/thermochemical_conversion.html http://www1.eere.energy.gov/biom ass/biochemical_conversion.html
272 Lin Wei, Graduate Research Assistant, Lester O. Pordesimo, Assistant Professor Willam D. Batchelor, Professor, Department of Agricultural and Biological Engineering, Mississippi State University, MS 39762, USA, Ethanol Producti on from Wood: Comparison of Hydrolysis Fermentation and Gasification Biosynthesis, Paper Number: 076036, Written for presentation at the 2007 ASABE Annual International Meeting. Minneapolis Convention Center, Minneapolis, MN, 17 - 20 June 2007
273 J. Phillips, “Different Types of Gasifier s and Their Integration with Gas Turbines,” EPRI/Advanced Coal Generation, http://www.netl.doe.gov/technologi es/coalpower/turbines/refshe lf/handbook/1.2.1.pdf, October 30, 2008.
274 Ciferno, Jared P., Benchmarking Biomass Ga sification Technologies for Fuels, Chemicals and Hydrogen Production, National Energy Technology Laboratory, Department of Energy, June 2002.
275 S. Phillips, A. Aden, J. Jechura, and D. Dayton, National Renewable Energy Laboratory, Golden, Colorado 80401-3393, T. Eggeman, Neoter ics International, Inc., Thermochemical Ethanol via Indirect Gasification and Mixed Alcohol Synthesis of Lignocellulosic Biomass, Technical Report, NREL/ TP-510-41168, April 2007
276 BRI Energy Bioengineering Resources for Renewable Energy. “The Co-Production of Ethanol and Electricity From Ca rbon-based Wastes,” A Report from BRI Energy, Inc. Regarding a New Technology That Addresses Multiple Energy and Waste Disposal Solutions, March 2006.
277 DOE EERE. “Biomass Program: Thermochemical Conversion of Corn Stover,” April 2006. Accessed at: http://www1.eere.energy.gov/ biomass/pdfs/thermochemical_stover.pdf.
278 S. Phillips, A. Aden, J. Jechura, and D. Dayton, National Renewable Energy Laboratory, Golden, Colorado 80401-3393, T. Eggeman, Neoter ics International, Inc., Thermochemical Ethanol via Indirect Gasification and Mixed Alcohol Synthesis of Lignocellulosic Biomass, Technical Report, NREL/ TP-510-41168, April 2007
279 DOE EERE Biomass Program. “Thermochemi cal Conversion Processes: Pyrolysis” http://www1.eere.energy.gov/bioma ss/thermochemical_processes.html , November 6, 2008.
280 Zeman, Nicholas, “Thermoche mical Versus Biochemical,” Biomass Magazine , June 2007.
281 Information derived from staff-level co mmunication with National Biodiesel Board personnel.
282 “Tyson ConocoPhillips Venture in Jeopardy.” The Morning News, article dated 9/26/08. http://www.nwaonline.net/articles/2008/09/ 26/business/092708fuelcredits.txt
283 October 6, 2008 article on Tr ading Markets website, “Groundbreaking Initiated for Unique Renewable Fuels Plant in Louisian a.” Accessed October 6, 2008. http://www.tradingmarkets.com /.site/news/Stock%20News/1923604/
284 “Tyson's Fats to Fuel Plant on Schedule.” The Morning News, article dated 10/6/08. http://www.nwaonline.net/articles/2008/ 10/06/business/100708t ysonfuelplant.txt
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783286 Renewable Fuels Associati on website, Historical U.S. Fuel Ethanol Production, http://www.ethanolrfa.org/industry/statistics/#A.
287 Ibid
288 Ethanol Producer Magazine we bsite, Ethanol Plant Lists (P roducing, Not Producing, Under Construction, Expansions), http ://www.ethanolproducer.com/plant-lis t.jsp, Modified on April 14, 2008.
289 Renewable Fuels Association (RFA) we bsite, Ethanol Biorefinery Locations, http://www.ethanolrfa.org/industry/ locations, Updated on April 2, 2008.
290 Ibid.
291 Ethanol Producer Magazine we bsite, Ethanol Plant Lists (P roducing, Not Producing, Under Construction, Expansions), http ://www.ethanolproducer.com/plant-lis t.jsp, Modified on April 14, 2008.
292 Renewable Fuels Association (RFA) we bsite, Ethanol Biorefinery Locations, http://www.ethanolrfa.org/industry/ locations, Updated on April 2, 2008.
293 Ministry of Agriculture in Brazil (MAPA), http://www.agricultura.gov.br/ Accessed: April 3, 2009
294 F.O. Licht’s, “World Ethanol Markets: The Outlook to 2015,” pg. S-22 to S-23, 2006.
295 F.O. Licht’s, “World Ethanol and Biofue ls Report,” Vol. 5, No. 22, July 23, 2007.
296 Agra FNP, “Sugar and Ethanol in Brazil: A St udy of the Brazilian Sugar Cane, Sugar and Ethanol Industries,” 2007.
297 Ministry of Mines and Energy, “The Brazilian Experience wi th Biofuels,” Presentation by Ricardo de Gusmao Dornelles, June 1, 2007.
298 Agra FNP, op. sit. 299 ANFAVEA (Associação Nacional dos Fabricantes de Veículos Automotores Assessoria de Planejamento Econômico e Estatístico Indústria Automob ilística Brasileira - Empresas Associadas). Intern al Sales in Brazil 2008. http://www.anfavea.com.br/tabelas2008.html , April 9, 2009.
300 Ministerio da Agricultura, Pecularia E Abastecimento (MAPA), “Brazilian Ethanol Production,” http://www.agricultura.gov.br/pls/portal/docs/PAGE/MAPA/ESTATISTICAS/PRODUCAO/06­ PROD.%20BR%20%C1LCOOL.PDF, April 9, 2009.
301 Ewing, Elizabeth, “Pipeline Projects on Hold.” Ethanol Producer Magazine , July 2008.
302 Rothkopf, Garten, “A Blueprint for Green Energy in the Americas,” 2006.
303 Ibid.
304 World Biofuels: FAPRI 2007 Agricultural Outlook; OECD-FAO Agricultural Outlook 2007­ 2016, 2007 (note: this was interpreted from a gr aph); Bioenergia: Liquid Biofuels, “18 Billion Liters a Year!” Ma rch 2007; Rothkopf, op. sit . (note: years 2007-2019 we re interpreted from a graph); EPE, “Plano National de Energia 2030,” Presentation from Mauricio Tolmasquim, 2007 (note: years were interpreted from a graph) ; UNICA, “Sugarcane Industry in Brazil: Ethanol, Sugar, Bioelectricity” brochure, 2008.
305 Agra FNP, op. sit. 306 Carpenter, Claudia, “Sugar Gains Capped by Brazil Ethanol Expansi on, Czarnikow Says,” Bloomberg , June 19, 2008.
307 F.O. Licht GmbH, “89 New Ethanol Facilit ies to Come Online in 2007-11 – BNDES,” F.O. Licht’s World Ethanol & Biofuels Report , Vol. 5, No. 22, July 23, 2007.

784308 AFP, “Brazil says biofuel production not to blame for food cris is,” June 1, 2008.
309 Business News Americas, “Odebr echt to invest US$2.6 bn to L ead Ethanol Sector – Brazil,” June 28, 2007.
310 The Wall Street Journal, “ADM Plans Entry In to Sugar-Cane Ethanol in Brazil,” June 22, 2007.
311 Agra FNP, op. sit. 312 Petrobras Magazine No. 122. “Ethanol: Fr om Brazil to the World,” August 13, 2007.
313 Ibid.
314 World Energy: Global News Summary. Publis hed June 13, 2008, http://www.worldenergy.net
315 Ewing, op. sit. 316 Rothkopf, op. sit. 317 Ibid.
318 F.O. Licht GmbH. “Ethanol Ble nd Rose to 25% on July 1, 2007,” F.O. Licht’s World Ethanol & Biofuels Report , Vol. 5, No. 21, July 9, 2007.
319 Petrobras Magazine No. 122, op. sit. 320 Agra FNP, op. sit. 321 F.O. Licht GmbH. “Sobering Times for Biofuels in Seville,” F.O. Licht’s World Ethanol & Biofuels Report , Vol. 5, No. 20, June 19, 2007. (not e: only 2006, 2015 & 2025 values were reported, other values are in terpolated); World Biofuels: FAPRI 2007 Agricultural Outlook; EPE, op. sit. (note: years were interpreted from a graph); UNICA, “Sugarcane Industry in Brazil: Ethanol, Sugar, Bioelectricity” brochure, 2008.
322 Ewing, op. sit. 323 Walter, A. et. al., “Perspectives on Fuel Ethanol Consumption and Trade,” Biomass and Bioenergy , 32, 730-748, 2008.
324 AFP., “EU Deputies Seek to Limit Us e of Biofuels,” September 11, 2008.
325 Energy Information Administration (EIA), “R eference Case Projecti ons by End-Use Sector and Region Data Table F6,” International Energy Outlook 2007

, http://www.eia.doe.gov/oiaf/archive/ieo07/ieonuclear.html
326 BP, “BP Statistical Review of World Energy 2007 (revised with 2008 statistics) Historical Data,” http://www.bp.com/statisticalreview.
327 Walter, A., op. sit. 328 Ayoub PM. Biofuels for Shell-biomass base d blending components for transport fuels (gasoline and diesel). In: World biofuels symposium-China, Beijing, 2006. As sited in Walter, op. sit. 329 Ribeiro, F., “The New European Cha llenge for Brazilian Ethanol Exports,” Frost & Sullivan Market Insight , February 7, 2008.
330 European Bioethanol Fuel Association (eBio), “The EU market: Production and Consumption,” http://www.ebio.org/EUmarket.php, April 9, 2009.
331 European Bioethanol Fuel Association (e Bio), “Production Capacity Installed” and “Production Capacity Under Construction,” h ttp://www.ebio.org/statistics.php?id=5 and http://www.ebio.org/statistic s.php?id=6, April 9, 2009.
332 F.O. Licht GmbH. “Sobering Times for Biofuels in Seville,” F.O. Licht’s World Ethanol & Biofuels Report , Vol. 5, No. 20, June 19, 2007.
333 Fukuda, Hisao, et. al.,“Japan Bio-fuels Production Report,” USDA, FAS, May 26, 2006. http://www.fas.usda.gov/ gainfiles/200605/146197881.pdf

785334 Rothkopf, op. sit. 335 Fukuda, op. sit. 336 Ibid.
337 Rothkopf, op. sit. 338 Jolly, Lindsay, “World Fuel Ethanol Outlook: Can ethanol be traded globally in a large proportion?,” International Sugar Organization, Presentation to 9 th World Beet and Cane Growers Conference, 9-12 July 2007.
339 Rothkopf, op. sit. 340 Ibid.
341 Ferreira, Lenilson, “Brazil Priming Ethanol Initiative to Supply Fuel-Thirsty Japan,” The Japan Times , August 22, 2007. http://search.jap antimes.co.jp/print/nb20070822a3.html
342 Ewing, op. sit. 343 Fukuda, op. sit. 344 Rothkopf, op. sit. 345 Ibid.
346 F.O. Licht GmbH. “2006/07 Fu el Ethanol Consumption Seen at Merely 250 Mln Litres – USDA Attaché,” F.O. Licht’s World Et hanol & Biofuels Report , Vol. 5, No. 20, June 19, 2007.
347 Nayak, D., “Weak Crude, Cane Shortage Hit India Ethanol Expansion” Wall Street Journal

, April, 1, 2009.
348 Regaldo, Antonio, et. al ., “Ethanol Giants Struggle to Crack Brazil Market,” Wall Street Journal , September 10, 2007.
349 Ibid.
350 Renewable Fuels Association (RFA), “2007 World Fuel Ethanol Production, ” http://www.ethanolrfa.org/industry/ statistics/#E, November 10, 2008.
351 Ohga K. & Tatsuji K. Biofuels Policies in Asia: Trade effects on World Agricultural and Biofuels Trade. Presentation to USDA Ag ricultural Outlook Forum, March 2, 2007.
352 Interfax China, “Biofuel Increasing China’s Corn Imports,” December 1, 2006.
353 Ohga, op. sit. 354 F.O. Licht GmbH, “New Ethanol Plants Will Rely on Non-Grain Feedstocks,” F.O. Licht’s World Ethanol & Biofuels Report , Vol. 5, No. 22, July 23, 2007.
355 Rothkopf, op. sit. 356 AFX News Limited, Forbes, “C hina to Freeze Approvals for Co rn Processing Projects for 3 Years,” September 21, 2007. http://www.forbes.com/markets /feeds/afx/2007/09/21/afx4142281.html
357 F.O. Licht GmbH, “New Ethanol Plants Will Rely on Non-Grain Feedstocks,” F.O. Licht’s World Ethanol & Biofuels Report , Vol. 5, No. 22, July 23, 2007.
358 Rothkopf, op. sit. 359 Ohga, op. sit . Note: Metric Tons are assumed to be of ethanol and values are converted to ethanol volumes based on th e density of ethanol and other conversion factors.
360 Rothkopf, op. sit. 361 Ibid.
362 Ibid.
363 F.O. Licht GmbH, “New Ethanol Plants Will Rely on Non-Grain Feedstocks,” F.O. Licht’s World Ethanol & Biofuels Report , Vol. 5, No. 22, July 23, 2007.
364 Rothkopf, op. sit.

786365 EIA, “U.S. Oxygenates: Fuel Ethanol Imports,” Last updated on October 10, 2008. http://tonto.eia.doe.gov/dna v/pet/pet_move_impcus_a2_nus _EPOOXE_im0_mbbl_m.htm
366 Joseph, Ken, “Argentina Bio-Fuels Argentin e Bio-Fuels Report 2007,” USDA, FAS, May 21, 2007. http://www.fas.usda.gov/gainfiles/200706/146291490.pdf
367 Deborah O’Connell, et. al., “Biofuels in Austra lia - Issues and Prospects. A Report for the Rural Industries Research and Development Cor poration,” CSIRO Australian Government Rural Industries Research and Developm ent Corporation, May 2007.
368 Roberts, Greg, “Rees sc raps biofuel mandates,” The Australian: Online Newspaper of the Year , September 20, 2008. http://www.theau stralian.news.com.au/story/0,25197,24373888­ 11949,00.html
369 Sobolik, Jessica, “Canadian Government Passes Renewable Fuels Standard,” Ethanol Producer Magazine , August 2008.
370 Pinzon, L., “Colombia Bio-fuels A nnual 2007,” USDA, FAS, June 1, 2007. http://www.fas.usda.gov/ gainfiles/200706/146291386.pdf
371 Chavez, L., et. al., “Mexico Bio-fuels A nnual Report 2007,” USDA, FAS, June 12, 2007. http://www.fas.usda.gov/ gainfiles/200706/146291366.pdf
372 F.O. Licht’s, “World Ethanol Markets: The Outlook to 2015,” pg. S-18 to S-20, 2006.
373 Office of the United States Trade Representa tive (USTR), “Caribbe an Basin Initiative,” November 12, 2008. http://www.ustr.gov/Trade_Development/Preference_Programs/CBI/Section_Index.html
374 Bryan, Tom, “Entering Tariff-Free,” Ethanol Producer Magazine, January 2004.
375 Office of the United States Trade Represen tative (USTR), “Etha nol Provisions in the CAFTA-DR,” July 2007.
376 Bryan, op. sit. 377 Rapoza, Kenneth, “UPDATE: Tax Loophole Help s US Import Ethanol ‘Duty Free’- ED&F,” INO News

, Dow Jones Newswires , March 2008. http://news.ino.com/
378 Peter Rhode, “Senate Finance May Take Up Drawback Loophole As Part of Energy Bill,” EnergyWashington Week , April 18, 2007. As sited in Yacobucci, Brent, “Ethanol Imports and the Caribbean Basin Initiative,” CRS Report for Congress, Order Code RS21930, Updated March 18, 2008.
379 Perkins, Jerry, “BRAZIL: Loophole Hurt U.S. Ethanol Prices,” DesMoinesRegister.com, October 18, 2007.
380 Public Law Version 6124 of the Farm Bill. 2008. http://www.usda.gov/documents/Bill_6124.pdf.
381 Lundell, Drake, “Brazilian Et hanol Export Surge to End; U.S. Customs Loophole Closed Oct. 1,” Ethanol and Biodi esel News, Issue 45, November 4, 2008.
382 MacDonald, T., et. al., “Ethanol Supply Outlook for California,” California Energy Commission, October 2003. http ://www.ethanolrfa.org/objects/documents/100/castaffreport.pdf
383 Petrojam Limited, “Officia l Opening of Petrojam Ethanol Limited,” November 12, 2008. http://www.pcj.com/petrojam/whats_new.htm
384 Petrojam Limited, “About Us,” November 12, 2008. http://www.petrojambunkering.com/petrojam_aboutb.htm
385 F.O. Licht GmbH, “Company News- JBG Commences Fuel Ethanol Production,” F.O. Licht’s World Ethanol & Biofuels Report , Vol. 5, No. 22, July 23, 2007.
386 Email Correspondence with Jeffrey Tu ite, ED&F Man, September 6, 2007.

787387 Yacobucci, Brent, “Ethanol Imports and the Caribbean Basin Initiative,” CRS Report for Congress, Order Code RS21930, Updated March 18, 2008.
388 Email Correspondence with Jeffrey Tu ite, ED&F Man, September 6, 2007.
389 Ethanol Producer Magazine, “Ethanol Dehydr ation Facility to be Built in Trinidad,” December 2006.
390 Etter, Lauren, and Millman, Joel, “Ethanol Tariff Loophole Sparks a Boom in Caribbean Islands Build Plants To Process Brazil’s Fuel; Farm Belt Cries Foul,” Wall Street Journal , March 9, 2007.
391 Homer, Louis, “Another Energy Sector Stim ulus,” PetroleumWorld.com. November 22, 2006. http://www.petroleumworldtr inidad.com/storytrini06112606.htm
392 Etter, op. sit. 393 Browne, J., “Angostura Launc hes US$11m Ethanol Plant,” The Trinidad Guardian . August 6, 2004. http://www.guardian.co.tt/ar chives/2004-08-06/ business1.html
394 Email Correspondence with Jeffrey Tu ite, ED&F Man, September 6, 2007.
395 Ramjeet, Oscar, “Barbados to Build $72 Millio n Ethanol Dehydration Plant,” Caribbean Net News, August 22, 2008.
396 Jamaica Gleaner News, “US$36m Ethanol Plant for Barbados,” August 23, 2008. http://www.jamaica-gleaner.com/gl eaner/20080823/business/business1.html
397 Email Correspondence with Jeffrey Tu ite, ED&F Man, September 6, 2007.
398 Bryan, op. sit. 399 Etter, op. sit. 400 Ibid.
401 Email Correspondence with Jeffrey Tuite , ED&F Man, September, 7, 2007.
402 Etter, op. sit. 403 International Trade Commission 2005-2007 historic al ethanol import data as reported by the Renewable Fuels Association, http://www.ethanolrfa.org/industry/statistics/
404 Communication from D ouglas Newman, International Trad e Commission to Doris Wu, EPA, May 20, 2008.
405 Energy Information Administration histor ical gasoline and ethanol import data: http://www.eia.doe.gov/oil_gas/petroleum/data _publications/company_level _imports/cli_historic al.html
406 U.S. Department of Energy, “DOE Selects Six Cellulosic Ethanol Plants for Up to $385 Million in Federal Funding” (P ress Release & Attachments), http://www.energy.gov/print/4827.htm, February 28, 2007.

407 U.S. Department of Energy, “DOE Announces up to $200 Million in Funding for Biorefineries” (Press Release), http ://www.energy.gov/print/5031.htm, May 1, 2007.
408 U.S. Department of Energy, “U.S. Department of Energy Selects First Round of Small-Scale Biorefinery Projects for up to $114 Million in Federal Funding” (Press Release & Attachments), http://www.energy.gov/print/5903.htm, January 29, 2008.
409 U.S. Department of Energy, “DOE Selects 3 Small-Scale Biorefinery Projects for up to $86 Million of Federal Funding in Maine, Tennessee and Kentucky” (P ress Release & Attachments), http://www.energy.gov/print/6164.htm, April 18, 2008.

410 U.S. Department of Energy, “DOE to Provide up to $40 Million in Funding for Small-Scale Biorefinery Projects in Wisconsin and L ouisiana” (Press Release & Attachments), http://www.energy.gov/print/6413.htm, July 14, 2008.

788411 U.S. Department of Energy, “DOE Announces Final Rule for Loan Guarantee Program” (Press Release), http://www.energy.gov/ print/5568.htm, October 4, 2007.

412 U.S. Department of Energy, “DOE Selects Fi ve Ethanol Conversion Projects for $23 Million in Federal Funding” (Press Re lease), http://www.energy.gov/print/4896.htm, March 27, 2007.

413 U.S. Department of Energy, “Energy and Agri culture Depts. Provide $8.3 Million in Funding for Biofuels Research” (Press Release), ht tp://www.energy.gov/print/5115.htm, June 7, 2007.

414 U.S. Department of Energy, “Energy Departme nt Selects Three Bioene rgy Research Centers for $375 Million in Federal Funding” (Press Release & Attachments), http://www.energy.gov/print/5172.htm, June 26, 2007.
415 U.S. Department of Energy, “Department of Energy to Invest up to $7.7 Million for Four Biofuels Projects” (Press Release), http:/ /www.energy.gov/print/5757.htm, December 4, 2007.
416 U.S. Department of Energy, “U.S. Department of Energy to Invest up to $33.8 Million to Further Development of Comm ercially Viable Renewable Fuels” (Press Release), http://www.energy.gov/print/6015.htm, February 26, 2008.
417 U.S. Department of Energy, “USDA, DOE to Invest up to $18.4 Million for Biomass Research, Development and Demonstr ation Projects” (Press Release), http://www.energy.gov/print/6035.htm, March 4, 2008.
418 U.S. Department of Energy, Energy Efficiency and Renewa ble Energy website, Ethanol Incentives & Laws, http://www.eere.energy.gov/afdc /ethanol/incentives_laws.html, State Data Downloaded in July 2008.
419 Midwest Governors Association, “Energy Securi ty and Climate Stewardship Platform for the Midwest 2007,” http://www.midwesterngovernors. org/resolutions/Platform.pdf, Published November 2007.
420 110 th Congress of the United States of America, Second Session, The Food, Conservation, and Energy Act of 2008, House Rule 6124 , http://www.usda.gov/documents/Bill_6124.pdf, Introduced on January 3, 2008 and Enacted on June 18, 2008.
421 Archer Daniels Midland, “Joi nt ADM and Purdue University Cellulosic Ethanol Project Selected for Funding by U.S. Depart ment of Energy” (Press Release), http://www.admworld.com/naen/pressroom/new spopup.asp?id=454&name=Joint_Cellulosic_Et hanol_Project, April 16, 2007.
422 Archer Daniels Midland, “ADM, Deere, Mons anto to Collaborate on Corn Stover Research” (Press Release), http://www.admworld.com/naen /pressroom/newspopup.asp?id=517, August 26, 2008.
423 POET, “ Iowa finalizes cellulosic partners hip with POET” (Press Release), http://www.renewableenergyworld.com/rea/partner/poet-7042/news/ article/2009/02/iowafinalizes-cellulosic-partnership-with-poet
424 Elbehri, Aziz. USDA, ERS. “An Evaluati on of the Economics of Biomass Feedstocks: A Synthesis of the Literature. Prepared for the Biomass Research and Development Board,” 2007; Since 2007, a final report has b een released. Biomass Research and Development Board., “The Economics of Biomass Feedstocks in the United St ates: A Review of the Literature,” October 2008.
425 Graham, R.L., “Current and Pote ntial U.S. Corn Stover Supplies,” American Society of Agronomy 99:1-11, 2007.
426 Wilhelm, W.W. et. al., “Corn Stover to Su stain Soil Organic Carbon Further Constrains Biomass Supply,” Agron. J. 99:1665-1667, 2007.

789427 NASS database. http://www.nass.usda.gov/
428 Smith, W. Brad et. al., ”Forest Resources of the United States, 2002 General Technical Report NC-241,” St. Paul, MN: U.S. Dept. of Agriculture, Forest Se rvice, North Central Research Station, 2004.
429 USDA-Forest Service. “Timber Products Output Mapmaker Version 1.0.” 2004.
430 NASS database. http://www.nass.usda.gov/
431 USDA, WGA, Bioenergy Strategic Assessment project findings upcoming as noted in report WGA. Transportation Fuels for the Future Biofuels: Part I. 2008.
432 Industry data for 2008 taken from National Biodiesel Board fact sheets at http://www.biodiesel.org/buyingbiodiesel/produ cers_marketers/Producer s%20Map-Existing.pdf and http://www.biodiesel.org/pdf_files/f uelfactsheets/Production_graph_slide.pdf (both accessed April 27, 2009).

433 “Tyson ConocoPhillips Venture in Jeopardy.” Th e Morning News, article dated 9/26/08. http://www.nwaonline.net/articles/2008/09/ 26/business/092708fuelcredits.txt Press release and project tim eline on Syntroleum webs ite, updated January 13, 2009 (http://www.syntroleum.com/dynamic_fuels _plant09.aspx), accessed April 27, 2009. http://www.tradingmarkets.com /.site/news/Stock%20News/1923604/
435 “Tyson's Fats to Fuel Plant on Schedule.” The Morning News, article dated 10/6/08. http://www.nwaonline.net/articles/2008/ 10/06/business/100708t ysonfuelplant.txt
436 Syntroleum Investor Presentation, Ma rch 2008. Accessed October 6, 2008. http://www.syntroleum.com/pdf/InvestorPresentation.pdf

437 “Ethanol Transportation Backgrounder, Expans ion of U.S. Corn-based Ethanol from the Agricultural Transportation Perspective”, USDA, September 2007, http://www.ams.usda.gov/tmd/TSB/EthanolTransportationBackgrounder09-17-07.pdf

438 Association of Oil Pipeline s: http://aopl.org/g o/searchresults/888/?q =ethanol&sd=&ed=. “Hazardous Liquid Pipelines Transp orting Ethanol, Ethanol Blends , and Other Biofuels”, Notice of policy statement and request for commen t, Pipeline and Hazardous Materials Safety Administration, Department of Transportation, August 10, 2007, 72 FR 45002.

439 Article on the initiation of Kinder Morgan ethanol pipeline service between Tampa and Orlando Florida: http:// www.ethanolproducer.com/ar ticle.jsp?article_id=5163

440 Magellan and Poet joint assessment of dedicated ethanol pipeline: http://www.magellanlp.com/news/2009/20090316_5.htm

441 “Ethanol Transportation Backgrounder”, U.S. Department of Agriculture , September 2007, http://www.ams.usda.gov/AMSv1.0/getf ile?dDocName=STELPRDC5063605&acct=atpub
442 “Analysis of Fuel Ethanol Tr ansportation Activity and Potent ial Distribution Constraints”, prepared for EPA by Oak Ridge National Laboratory, March 2009.

443 “Ethanol Transportation Backgrounder”, U.S. De partment of Agriculture , September 2007, http://www.ams.usda.gov/AMSv1.0/getf ile?dDocName=STELPRDC5063605&acct=atpub

790444 “The Importance of Adequate Rail Invest ment”, Association of American Railroads, http://www.aar.org/Get File.asp?F ile_ID=150

445 American Association of St ate Highway and Transportation Officials (AASHTO) FreightRail Bottom-Line Report, 2003.

446 Association of American Railroads (AAR), Na tional Rail Freight Infrastructure Capacity and Investment Study, September 2007.

447 “Barge Transportation in the United States”. Timothy Casey, Presentation at the MARE Forum USA, 2006, http://www.mareforum.com/ENERGY_TRAN SPORTATION_USA_2006_PRESENTATIONS_ files/CASEY.pdf

448 Energy Information Administration (EIA) histor ical company level ethanol and gasoline import data: http://www.eia.doe.gov/oil_gas/petroleum/data _publications/company_level _imports/cli_historic al.html

449 “The U.S. Truck Driver Shortage: Analysis and Forecasts”, Prepared by Global Insights for the American Trucking A ssociation, May 2005. http://www.truckline.com/NR/r donlyres/E2E789CF-F308-463F-8831­ 0F7E283A0218/0/ATADriv erShortageStudy05.pdf

450 “Renewable Fuel Standard Challenges Faced by Terminals”, Andrea Grant, Independent Fuel Terminal Operators Association, May, 13, 2008, presentation at the 2008 Society of Automotive Engineers Government-Industry Meeting.

451 “Infrastructure Requirements for an Expa nded Fuel Ethanol Industry”, Downstream Alternative Incorporated, January 15, 2002.
452 A discussion of the estimated number of term inals that carry diesel fuel is contained in Chapter V of the Regulatory Impact Analysis to the final highway diesel fuel rule http://www.epa.gov/otaq/highway-diesel/regs/ria-v.pdf , EPA Docket A-99-06.

453 “Renewable Fuel Standard Challenges Faced by Terminals”, Andrea Grant, Independent Fuel Terminal Operators Association, May, 13, 2008, presentation at the 2008 Society of Automotive Engineers Government-Industry Meeting.

454 “Renewable Fuel Standard Challenges Faced by Terminals”, Andrea Grant, Independent Fuel Terminal Operators Association, May, 13, 2008, presentation at the 2008 Society of Automotive Engineers Government-Industry Meeting.

455 “Summary Data on Diesel Fuel Retailers”, Memorandum to the Docket, Jeffery Herzog, U.S. EPA, March 23, 2000, EPA Docket A-99-06, Docket Item II-B-07.

791456 2007 National Association of Convenience Stor es State of the Industry survey data www.nacsonline.com as reported in the National Petrol eum News MarketFacts 2007, October 2007.

457 “Diesel Throughput Volume by Percentage of Diesel Fuel Retailers”, Memorandum to the Docket, Jeffery Herzog, U.S. EPA, May 05, 2000, EPA Docket A-99-06, Docket Item II-B-14.

458 The National Ethanol Vehicle Coalition estimate of E85 refueling facilities is located at http://www.e85refueling.com/ estimates http://www.e85refueling.com/

459 The “NEAR85” estimate of E85 retail facili ties is located at http://www.neare85.com/

460 National Petroleum News, MarketFacts 2007, Volume 99, Number 10, October 2007, www.npnweb.com

461 A discussion of Underwriter Laboratories (UL) requirements for E85 retail dispenser equipment entitled “Power-Operated Dispensing De vices for Gasoline/Ethanol Blends other than Gasohol (E10)” can be found at http://uls tandardsinfonet.ul.com/outscope/0087A.html
462 U.S. Environmental Protection Agency, Regulatory Impact Analys is: Renewable Fuel Standard , EPA420 R-04-004, http://www.epa.gov/otaq/ren ewablefuels/420r07004chap2.pdf

, April 2007 (Chapter 2, Table 2.2-6).
463 U.S. Environmental Protection Agency, Regulation of Fuels and Fuel Additives: Renewable Fuel Standard Program, Final Rule , 72 FR 23900, http://www.epa.gov/otaq/renewablefuels/rfsfinalrule.pdf , May 1, 2007.
464 U.S. Department of Energy, En ergy Information Administration, Monthly Energy Review

, http://tonto.eia.doe.gov/FTPROOT/multifuel/mer/00350809.pdf , September 2008 (Table 10.2b).
465 ibid

466 110 th Congress of the United States of America, Second Session, The Food, Conservation, and Energy Act of 2008, House Rule 6124

, http://www.usda.gov/documents/Bill_6124.pdf

, Introduced on January 3, 2008 and Enacted on June 18, 2008.
467 Kauffman, Tim, “Miles to go for Alternative Fuel,” Federal Times , June 23, 2008 (pp. 11, 14).

468 DOE Energy Efficiency and Renewable Energy August 2008 estimate (worksheet available at www.eere.energy.gov/afdc/data/index.html) 469 U.S. Department of Energy, En ergy Information Administration, Annual Energy Outlook 2007 , Reference Case Tables, http://www.eia. doe.gov/oiaf/archive/aeo07/pdf/yearbyyear.pdf, February 2007 (Table 17).
470 U.S. Department of Energy, En ergy Information Administration, Annual Energy Outlook 2007 , Reference Case Tables, http://www.eia. doe.gov/oiaf/archive/aeo07/pdf/yearbyyear.pdf, February 2007 (Table 2).

471 U.S. Department of Energy, En ergy Information Administration, Annual Energy Outlook 2008 , Reference Case Tables, http://www.eia.doe .gov/oiaf/aeo/excel/y earbyyear.xls, June 2008 (Table 2).
472 Ibid
473 National Ethanol Vehicle Coalition, 2008 Purc hasing Guide for Flexible Fuel Vehicles.
474 Ibid

792475 U.S. Department of Energy, Ener gy Efficiency and Renewable Energy, Transportation Energy Book: Edition 26 , Prepared by Oak Ridge Nati onal Laboratory, ORNL-6978, 2007 (Table 2.5).
476 National Ethanol Vehicle Coalition, 2008 Purc hasing Guide for Flexible Fuel Vehicles.
477 110 th Congress of the United States of America, Second Session, The Open Fuel Standard Act of 2008, Senate Bill 3303 , http://thomas.loc.gov/cgi-bin/query/z?c110:S.3303, Intr oduced on July 22, 2008.
478 110 th Congress of the United States of America, Second Session, The Open Fuel Standard Act of 2008, House Rule 6559 , http://thomas.loc.gov/cgi-bin/ bdquery/z?d110:H.R.6559, Introduced on July 22, 2008.

479 U.S. Department of Energy, Energy Efficiency and Renewa ble Energy website, Ethanol Incentives & Laws, http://www.eere.energy.gov/afdc/ethanol/incentives_laws.html , State Data Downloaded in July 2008.

480 NEVC FYI Newsletter: Volume 15, Issue 5: March 9, 2009. 481 National Petroleum News, Market Facts 2007 , 2007 NPN Station Count (a), October 2007 (page 92).
482 National Ethanol Vehicle Coalition, , FYI Newsletter: Volume 13, Issue 10

, http://www.e85fuel.com/news/072307fyi.htm , July 23, 2007

483 U.S. Department of Energy, Energy Efficiency and Renewa ble Energy website, Ethanol Incentives & Laws, http://www.eere.energy.gov/afdc/ethanol/incentives_laws.html , State Data Downloaded in July 2008.
484 Midwest Governors Association, “Energy Security and Climate Stewardship Platform for the Midwest 2007,” http://www.midwesterngovernors.org/resolutions/Platform.pdf , Published November 2007.

485 110 th Congress of the United States of America, Second Session, TheE85 and Biodiesel Access Act of 2008, House Rule 6734 , http://www.e85fuel.com/legislation/hr_6734.doc, Introduced on July 31, 2008.
486 National Ethanol Vehicle Coalition, 2008 Purc hasing Guide for Flexible Fuel Vehicles.

487 Based on average E85 and regular unleaded gasoline prices reported at http://www.fuelgaugereport.com/ on April 23, 2009. 488 U.S. Department of Energy, En ergy Information Administration, Petroleum Marketing Annual 2006

, http://tonto.eia.doe.gov/FTPROOT/petroleum/048706.pdf , August 2007 (Table 31).
489 American Petroleum Institute, Gasoline Tax Report, http://www.api.org/statistics/fueltaxes/upload/October_2006_gasoline_and_diesel_summary_pag es.pdf , October 2006.
490 Brownstone, D., Small, K.A., Valuing Time and Reliability: Assessing the Evidence from Road Pricing Demonstrations, Transportati on Research Part A 39, 2005 (pages 293-279).
491 Greene, David, Survey Evidence on the Impor tance of Fuel Availability to Choice of Alternative Fuels and Vehicles, Prepar ed by Oak Ridge National Laboratory, http://ntl.bts.gov/lib/000/700/723/survey.pdf , November 11, 1997.

492 Based on May 2008 meeting with SIGMA/NACS.

493 National Petroleum News, Market Facts 2007 , Branded Retail Outlet s by Rank 2006, October 2007 (page 93).

494 Based on May 2008 meeting with SIGMA/NACS.

793495 U.S. Department of Energy, En ergy Information Administration, Petroleum Marketing Annual 2006

, http://tonto.eia.doe.gov/FTPROOT/petroleum/048706.pdf , August 2007 (Table 48).
496 U.S. Department of Energy, En ergy Information Administration, Petroleum Marketing Annual 2006

, http://tonto.eia.doe.gov/FTPROOT/petroleum/048706.pdf , August 2007 (Tables 31, 34 & 48).
497 U.S. Environmental Protection Agency, Draft Regulatory Impact Analysis: Control of Hazardous Air Pollutants from Mobile Sources, EPA420 D-06-004, http://www.epa.gov/otaq/regs/toxics/chapt er9.pdf, February 2006 (Chapter 9).
498 American Petroleum Institute, Gasoline Tax Report, http://www.api.org/statistics/ fueltaxes/upload/January_2008_ga soline_and_diesel_summary_pag es-2.pdf , January 2008.
499 Department of Energy, Alternative Fuels & Advanced Vehicles Data Center, Biodiesel Incentives and Laws. Accessed July 2008. http://www.afdc.energy.gov/afdc /fuels/biodiesel_laws.html
500 Energy Information Administration, 2005 Prime Supplier Sales Volumes for No. 2 Diesel. Accessed 10/7/2008. http://tonto.eia.doe.gov/dnav/pet/pet_cons_prim_a_EPD2D_P00_Mgalpd_a.htm
501 Energy Information Administration, Fuel Oil and Kerosene Sales 2006, Table 16. 2005 data column used. http://www.eia.doe.gov/pub/oil_gas/petroleum/data_publications/fuel_oil_and_kerosene_sales/c urrent/pdf/table16.pdf

502 Clean Air Act Section 211(o)(1)
503 2006 IPCC Guidelines for National Greenhouse Gas Inventories. 2006. Agriculture, Forestry and Other Land Uses. Volume 4. http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html
504 Curran MA, Mann M, Norris G, 2001, Report on the International Workshop on Electricity Data for Life Cycle Inventories, US Environmental Protection Agency, Cincinnati, USA
505 Ekvall T, Weidema BP, 2004, System boundaries and input data in consequential life cycle inventory analysis. International Journal of Life Cycle Assessment, 9, 161-171
506 Kloverpris et al., 2008, Conference and Workshop on Modeling Global Land Use Implications in the Environmental Assessment of Biofuels, International Journal of Life Cycle Assessment, 13(3) 178-183
507 Kloverpris, Wenzel and Nielsen, 2008, Life Cycle Inventory Modelling of Land Use Induced by Crop Consumption, International Journal of Life Cycle Assessment, 13(1): 13-21
508 Ekvall and Weidema 2004
509 Schmidt, 2008, System delimitation in agricultural consequential LCA, International Journal of Life Cycle Assessment, 13: 350-364
510 Delucchi, M. 2005. Incorporating the Effect of Price Chanes on CO 2 -Equivalent Emissions from Alternative Fuel Lifecycles: Scoping the Issues. Working Paper UCD—ITS—RR—05—19. http://pubs.its.ucdavis.edu/publication_detail.php?id=62
511 Fargione, J., J. Hill, D. Tilman, S. Polasky, and P. Hawthorne. 2008. Land clearing and the biofuel carbon debt. Science 319 :1235–1238.
512 Searchinger, T., R. Heimlich, R.A. Houghton, F. Dong, A. Elobeid, J. Fabiosa, S. Tokgoz, D. Hayes, and T.-H. Yu. 2008. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319 :1238–1240.
513 http://www.epa.gov/sequestration/greenhouse_gas.html
514 Crutzen, P. J., Mosier, A. R., Smith, K. A., and Wi niwarter, W.: N2O release fr om agro-biofuel production negates global warming reduction by replacing fossil fuels, Atmos. Chem. Phys., 8, 389-395, 2008.
515 McCann, T., and Magee, P., Crude Oil Greenhouse Ga s Life Cycle Analysis Helps Assign Values For CO2 Emissions Trading . Oil and Gas Journal. Vol. 97, no. 8, pp. 38-44. Feb 1999.

794516 USGS (November 2006), “Natural Assessment of Oil and Gas Fact Sheet: Natural Bitumen Resources of the United States,” Fact Sheet 2006-3133; USGS (August 2003), “Heavy Oil and Natural Bitumen – Strategic Petroleum Resources,” Fact Sheet 70-03.
517 EIA Form 814.
518 International Energy Agency (March 14, 2006), Oil Market Report: A Monthly Oil Market and Stocks Assessment

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519 Energy Information Administration, “Country Analysis Briefs,” Venezuela Oil. http://www.eia.doe.gov/emeu/cabs/Venezuela/Oil.html (last viewed 6/18/2008).
520 Canadian Association of Petroleum Producers (May 2008), Statistical Handbook for Canada’s Upstream Petroleum Industry.
521 National Petroleum Council (2007), "Working Document of the NPC Global Oil & Gas Study" July 18, 2007, Topic Paper #22 Heavy Oil; California Annual Repo rts of the State Oil & Gas Supervisor, 2006, http://www.conservation.ca.gov/dog/pubs_stats/ annual_reports/Pages/annual_reports.aspx (last viewed 6/18/2008).
522 Energy Information Administration, 2005 data. http://tonto.eia.doe.gov/dnav/pet/pet_move_impcus_a2_nus_epc0_im0_mbbl_a.htm (last viewed 6/18/2008)
523 “N 2 O release from agro-biofuel production negates global warming reduction by replacing fossil fuels” P.J. Crutzen, A.R. Mosier, K.A. Smith, and W. Winiwarter, Atmospheric Chemistry and Physics, 8, 389–395, 2008.
524 U.S. Environmental Protection Agency. 2005. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990– 2003 . EPA 430-R-05-003. Available at http://www.epa. gov/climatechange/emissions/downloads06/05CR.pdf.
525 U.S. Environmental Protection Agency. 2005. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990– 2003. EPA 430-R-05-003. Available at http://www.ep a.gov/climatechange/emissions/downloads06/05CR.pdf.
526 U.S. Environmental Protection Agency. 2005. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990– 2003. EPA 430-R-05-003. Available at http://www.ep a.gov/climatechange/emissions/downloads06/05CR.pdf.
527 www.fao.org/ag/agl/fertistat/
528 http://wds.iea.org/wds/default.aspx
529 2006 IPCC Guidelines for National Inventories, Chapter 11: N2) Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Applications
530 International Rice Research Institute. www.iri.org . Last accessed: October 15, 2008.
531 Ethanol Expansion in the United States How Will the Ag ricultural Sector Adjust? Paul C. Westcott, USDA ERS FDS-07D-01 May 2007.
532 Technical Report, An Analysis of EPA Biofuel Scenarios with the CARD International Models, CARD Staff, October 2008.
533 Salil Arora, May Wu, and Michael Wang, "Update of Di stillers Grains Displacemen t Ratios for Corn Ethanol Life-Cycle Analysis," September 2008

. See http://www.transportation.anl.gov/pdfs/AF/527.pdf
534 The 2005 Food and Agriculture Organization (FAO) Global Forest Resources Assessment (FRA) is available at http://www.fao.org/forestry/fra2005/en/
535 https://www.gtap.agecon.purdue.edu
536 See Hertel, Thomas, Steven Rose, Richard Tol (eds.), (in press). Economic Analysis of Land Use in Global Climate Change Policy , Routledge Publishing.
537 USDA/FAO soil carbon map of the world is available for download at ftp://www.daac.ornl.gov/data/gl obal_soil/IsricWiseGrids/State
538 Luyassert, S et al., 2008. Old-growth forests as global carbon sinks. Nature 455: 213-215. Link: http://www.nature.com/nature/journal/v455/n7210/abs/nature07276.html
539 The digital soil carbon map of the world is available at ftp://www.daac.ornl.gov/data/gl obal_soil/IsricWiseGrids/State
540 International Energy Outlook 2007, Report #:DOE/EIA-0484(2007), Release Date: May 2007, Tables G1 and G3.
541 U.S. EPA 2008, Inventory of U.S. Greenhouse Gas Emissions and Sinks (EPA 430-R-08-005), Tables 3-23 and3-24 http://www.epa.gov/climatechange/emissions/usinventoryreport.html
542 U.S. EPA 2008, Inventory of U.S. Greenhouse Gas Emissions and Sinks (EPA 430-R-08-005), Table ES-1 http://www.epa.gov/climatechange/emissions/usinventoryreport.html
543 Overview Presentation to CRC on EPACT Testing (September 2008) 544 U. S. EPA. 2007. Final Regulatory Impact Analysis: Control of Hazardous Air Po llutants from Mobile Sources; EPA420-R-07-002; Office of Transporta tion and Air Quality, Ann Arbor, MI. http://www.epa.gov/otaq/toxics.htm

795545 Reddy, Sam R. 2007. “A Model for Estimating Vapor Pressures of Commingled Ethanol Fuels.” SAE 2007-01­ 4006.
546 U. S. EPA. 2007. Regulatory Impact Analysis: Renewable Fuel Standard Program; EPA420-R-07-004; Office of Transportation and Air Quality, Ann Arbor, MI. http://www.epa.gov/otaq/ren ewablefuels/index.htm
547 Overview Presentation to CRC on EPACT Testing (September 2008)
548 Haskew, Harold M., Thomas F. Liberty and Dennis Mc Clement, “Fuel Permeation from Automotive Systems,” Final Report, for the Coordinating Research Council and the California Air Resources Board, CRC Project E-65, September 2004

. Available in Docket EPA-HQ-OAR-2005-0161.
549 Southwest Research Institute. 2007. Flex Fuel Vehicl es (FFVs) VOC/PM Cold Temperature Characterization When Operating on Ethanol (E10, E70, E85). Prepared fo r U. S. Environmental Protection Agency. Available in Docket EPA-HQ-OAR-2005-0161.
550 Environment Canada. 2007. Comparison of Emissions from Conventional and Flexible Fuel Vehicles Operating on Gasoline and E85 Fuels. ERM Report No. 05-039, Emissions Research Division. Available in Docket EPA-HQ-OAR-2005-0161.
551 EPA Certification and Fuel Economy Informa tion System. http://www.epa.gov/otaq/cfeis.htm
552 Southwest Research Institute. 2007. Flex Fuel Vehicles (FFVs) VOC/ PM Cold Temperature Characterization When Operating on Ethanol (E10, E70, E85). Prepared fo r U. S. Environmental Protection Agency. Available in Docket EPA-HQ-OAR-2005-0161.
553 Graham, L. A.; Belisle, S. L. and C. Baas. 2008. Emissions from light duty gasoline vehicles operating on low blend ethanol gasoline and E85. Atmos. Environ. 42: 4498-4516.
554 Environment Canada. 2007. Comparison of Emissions from Conventional and Flexible Fuel Vehicles Operatingon Gasoline and E85 Fuels. ERM Report No. 05-039, Emissions Research Division. Available in Docket EPA-HQOAR-2005-0161.
555 Auto/Oil Air Quality Improvement Research Program. 19 96. Phase I and II Test Data. Prepared by Systems Applications International, Inc.
556 U.S. EPA, 2007. Modeling of Ethanol Blend Effects on Nonroad Fuel Hose and Tank Permeation. Memorandum from Craig Harvey, U. S. EPA, March 26, 2007.
557 U.S. EPA, 2008. Biodiesel Effects on Heavy-Duty Highway Engines and Vehicles; Office of Transportation and Air Quality, Ann Arbor, MI.
558 U.S. EPA 2002 A Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions: Draft Technical Report (EPA420-P-02-001)
559 U.S. EPA, 2008. Development of MOVES Rates for HC, CO and NOx Exhaust Emissions from Light-duty Vehicles for use in the RFS2 NRPM, Memorandum from James Warila, U. S. EPA, November 2008.
560 U.S. EPA. 2008. Kansas City Characterization Study: Final Report (EPA420-R-08-009) http://www.epa.gov/otaq/emission-factors-research/420r08009.pdf
561 U.S. EPA. 2008. Analysis of Particulate Matter Emissions from Light-Duty Vehicles in Kansas City (EPA420­ R-08-010) http://www.epa.gov/otaq/emission-f actors-research/420r08010.pdf
562 U.S. EPA, 2008. Modeling Evaporative Emissions in MOVES for the RFS2 NPRM. Memorandum from Prashanth Gururaja, U.S. EPA, November 2008.
563 U.S. EPA. 2002. Technical Description of the Toxi cs Module for MOBILE6.2 and Guidance on Its Use for Emission Inventory Preparation. (EPA420-R-02-029) http://www.epa.gov/otaq/models/mobile6/r02029.pdf
564 U.S. EPA, 2008. Using MOVES to Generate Inventories for the RFS2 NPRM, Memorandum from Amanda Valente and Megan Beardsley, November 2008.
565 See http://www.epa.gov/ttn/chief/emch/index.html.
566 Final Rule: Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines Less Than 30 Liters per Cylinder (published May 6, 2008 and republished June 30, 2008). For details, see http://www.epa.gov/OMS/locomotv.htm#2008final
567 Details about NMIM are available at http://www.epa.gov/otaq/nmim.htm.
568 Information about the 2005 NEI may be found at http://www.epa.gov/ttn/chief/net/2005inventory.html.
569 Details concerning the Final Rule: Control of Emissions of Air Pollution from New Nonroad Spark-Ignition Engines, Equipment, and Vessels may be found at http://www.epa.gov/otaq/equip-ld.htm#.
570 Sze, C., Whinihan, J., Olson, B., Schenk, C., Sobotowski, R. Impact of Test Cycle and Biodiesel Concentration of Emissions, SAE report 2007-01-4040 (2007).

796571 U. S. EPA. 2007. Final Regulatory Impact Analysis : Control of Hazardous Air Po llutants from Mobile Sources; EPA420-R-07-002; Office of Transporta tion and Air Quality, Ann Arbor, MI. http://www.epa.gov/otaq/toxics.htm
572 Landman, L. C. (2007) Estimating Emissions Associated with Portable Fuel Containers (PFCs). U. S. EPA, Assessment and Standards Division, National Vehicle and Fuel Emissions Laboratory, Ann Arbor, MI, Report No. EPA420-R-07-001. http://www.epa.gov/otaq/toxics.htm
573 U. S. EPA. 2007. Regulatory Impact Analysis: Renewable Fuel Standard Program; EPA420-R-07-004; Office of Transportation and Air Quality, Ann Arbor, MI. http://www.epa.gov/otaq/ren ewablefuels/index.htm
574 U. S. EPA. 1993. Final Regulatory Impact Analys is for Reformulated Gaso line. December 13, 1993. http://www.epa.gov/otaq/regs/fuels/rfg/
575 Haskew, H. M.; Liberty, T. F.; McClement, D. 2004. Fuel Permeation from Automotive Systems. Prepared for the Coordinating Research Council by Harold Haskew and Associates and Automotive Testing Laboratories, Inc. September 2004. CRC Project No. E-65. http://www.crcao.com . This document is available in Docket EPA-HQOAR-2005-0036.
576 Hester, Charles. 2006. Review of Data on HAP Content in Gasoline. Memorandum from MACTEC to Steve Shedd, U. S. EPA, March 23, 2006. This documen t is available in Docket EPA-HQ-OAR-2003-0053.
577 U. S. EPA. 2006. Determination that Gasoline Di stribution Stage 1 Area Source (GD AS) Category Does Not Need to Be Regulated Under Section 112(c)6. Memoradum from Stephen Shedd to Kent Hustvedt, May 9, 2006. This document is available in Docket EPA-HQ-OAR-2005-0036.
578 U. S. EPA. SPECIATE Version 4.0. http://www.epa.gov/ttn/chief/s oftware/speciate/index.html
579 Haskew, H. M., Liberty, T. F., McClement, D. 2004. Fuel Permeation from Automotive Systems. Report No. CRC E-65, Prepared for California Air Resources Board and Coordinating Research Council. http://www.crcao.org/
580 United States Department of Agriculture, Amber Waves, February 2004, page 9, U.S. Increasingly Imports Nitrogen and Potash Fertilizer. http://w ww.ers.usda.gov/amberwaves/February04/
581 Wen Huang, United States Department of Agriculture, Economic Research Service, personal communication to Craig Harvey, US EPA, November 9, 2007.
582 40 CFR Part 63 National Emission Standards for Hazardous Air Pollutants for Source Categories: Gasoline Distribution Bulk Terminals, Bulk Plants, Pipeline Facilities, and Gasoline Dispensing Facilities; Proposed Rule, Thursday, November 9, 2006, 66064-66092.
583 E. H. Pechan. Documentation For The Final 2002 Nonpoint Sector (Feb 06 Version) National Emission Inventory For Criteria And Hazardous Ai r Pollutants, Appendix A, pp. A-89 to A-105. Prepar ed for Emissions Inventory and Analysis Group (C339-02) Air Quality A ssessment Division Office of Air Quality Planning and Standards U.S. Environmental Protection Agency Research Triangle Park, NC 27711, Report No. 05.10.001/9014.401 Jul-06. Available at ftp://ftp.epa.gov/EmisInventory/2002finalnei/documentation/nonpoint/2002nei_final_nonpoint_documentation0206 version.pdf.
584 Energy Information Administration. 2007. Product Supplied, Petroleum Supply Reporting System. http://tonto.eia.doe.gov/dnav/pet/pet_cons_psup_dc_nus_mbbl_a.htm
585 EPA. 1994. Final Regulatory Impact Analysis: Refueling Emission Regulations for Light Duty Vehicles and Trucks and Heavy Duty Vehicles. Office of Mobile Sources, Ann Arbor, MI. Available at http://www.epa.gov/otaq/orvr.htm.
586 U. S. EPA. 2001. Final Rule: Control of Air Po llution From New Motor Vehicles: Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements, January 18, 2001.
587 U. S. EPA. 2008. Final Rule: Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines Less than 30 Liters per Cy linder, Regulatory Impact Analysis, June 30, 2008. http://www.epa.gov/otaq/locomotv.htm
588 U.S. EPA. 2008. RFS2 Modified version of GREET1.7 Upstream Emissions Spreadsheet, October 31, 2008.
589 Emission Inventories for Ocean-Going Vessels Using Category 3 Propulsion Engines In or Near the United States, Draft Technical Support Docume nt, EPA420-D-07-007, December 2007. This is available in Docket OAR2007-0121at http://www.regulations.gov/ .
590 U. S. EPA. 2006. National-Scale Air Toxics Assessment for 1999. http://www.epa.gov/ttn/atw/nata1999

. 591 U. S. EPA. 2007. Approach for Developing 2002 and Future Emission Summaries. Memorandum from Madeleine Strum to Docket, November 26, 2007. This document is available in Docket EPA-HQ-OAR-2007-0491.
592 Federal Register. 2007. National Emission Standards fo r Hazardous Air Pollutants Fr om Petroleum Refineries. 72(170): 50715-50742. http://www.epa.gov/EPA-AIR/2007/September/Day-04/a17009.htm

.

797593 Energy Information Administration. 2007. Product Supplied, Petroleum Supply Reporting System. http://tonto.eia.doe.gov/dnav/pet/pet_cons_psup_dc_nus_mbbl_a.htm
594 Brady, D. and Pratt, G. C. 2007. Volatile Or ganic Compound Emissions from Dry Mill Fuel Ethanol Production. J. Air & Waste Manage. Assoc. 57: 1091-1102.
595 Prevention of Significant Deterioration Construction Perm it Application. Prepared for Verasun Fort Dodge, LLC, Fort Dodge Iowa, by Barr, Minneapolis, MN, December, 2007.
596 See Chapter 1.5 of the Draft Regulatory Impact Assessment of this rulemaking.
597 U. S. EPA. 2007. Control of Hazar dous Air Pollutants from Mobile Sources; Final Rule. Federal Register, 72: 8428-8570.
598 U. S. EPA. 2007. Regulation of Fuels and Fuel Additives: Renewable Fu el Standard Program; Final Rule. Federal Register, 72: 23900-24014.
599 U. S. EPA. 2007. Final Regulatory Impact Analysis : Control of Hazardous Air Po llutants from Mobile Sources; EPA420-R-07-002; Office of Transporta tion and Air Quality, Ann Arbor, MI. http://www.epa.gov/otaq/toxics.htm
600 Energy Information Adminstration. 1999. Petroleum Marketing Annual. Report No. DOE/EIA-0487(99). http://www.eia.doe.gov/pub/oil_gas/petroleum/data_public ations/petroleum_marketing_annual/historical/1999/pma _1999.html
601 Energy Information Administration. 2007. Annual Energy Outlook 2007 with Projections to 2030. U.S. Department of Energy, Report # DOE/EIA-0383. http://www.eia.doe.gov/oiaf/aeo/index.html
602 U. S. EPA. SPECIATE Version 4.0. http://www.epa.gov/ttn/chief/s oftware/speciate/index.html
603 603 Auto/Oil Air Quality Improvement Research Program. 19 96. Phase I and II Test Data. Prepared by Systems Applications International, Inc.
604 Diesel Locomotives, Marine Distillate, Residual Boiler -- E. H. Pechan. 2005. Documentation For Aircraft, Commercial Marine Vessel, Locomotive, And Other Nonroad Components Of The National Emissions Inventory, Volume I - Methodology. Prepared for U. S. Environmental Protection Agency, Offi ce of Air Quality Planning and Standards, Research Triangle Park, NC. http://www.epa.gov/ttn/chief/net/2002inventory.html
605 HDD Trucks -- R. Cook, E. Glover. 2002. Technical Description of the Toxics Module for MOBILE6.2 and Guidance on Its Use for Emission Inventory Preparation. U. S. Environmental Protection Agency, Office of Transportation and Air Quality. Report No. EP A420-R-02-011. http://www.epa.gov/otaq/m6.htm
606 Natural Gas Turbines -- U. S. EPA. 2000. AP-42, 5th Edition, Volume 1. Chapter 3: Stationary Internal Combustion Sources. http://www.epa.g ov/ttn/chief/ap42/ch03/index.html
607 Control of Hazardous Air Pollutants From Mobile Sources (72 FR 8428; February 26, 2007).
608 U.S. Environmental Protection Agency, Byun, D.W., Ching, J.K.S., Eds (1999) Science algorithms of EPA Models-3 Community Multiscale Air Quality (CMAQ modeli ng system, EPA/600/R-99/030, Office of Research and Development).
609 Byun, D.W., Schere, K.L. (2006) Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, J. Applied Mechanics Reviews 59 (2): 51-77.
610 Dennis, R.L., Byun, D.W., Novak, J.H., Galluppi, K.J., Coats, C.J., Vouk, M.A. (1996) The next generation of integrated air quality modeling: EPA’s Models-3, Atmos Environ 30:1925-1938.
611 US EPA (2007) Regulatory Impact Analysis of the Proposed Revisions to the National Ambient Air Quality Standards for Ground-Level Ozone. EPA document number 442/R-07-008, July 2007.
612 Aiyyer, A, Cohan, D., Russell, A., Stockwell, W., Tanrikulu, S., Vizuete, W., Wilczak, J. (2007) Final Report: Third Peer Review of the CMAQ Model. p. 23.
613 Grell, G., J. Dudhia, D. Stauffer (1994) A Descrip tion of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for Atmospheric Research, Boulder CO.
614 final LocoMarine AQM TSD
615 U.S. Environmental Protection Agency, Byun, D.W., Ching, J.K.S., Eds (1999) Science algorithms of EPA Models-3 Community Multiscale Air Quality (CMAQ modeli ng system, EPA/600/R-99/030, Office of Research and Development). Please also see: http://www.cmascenter.org/
616 Yantosca, B. (2004) GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry Modeling Group, Harvard University, Cambridge, MA, October 15, 2004.
617 Finlayson-Pitts BJ, Pitts JN Jr. (1986) Atmospheric Chemistry: Fundamentals and Experimental Techniques, Wiley, New York.

798618 Atkinson R, Arey J (2003) Atmospheric Degradatio n of Volatile Organic Compounds. Chem Rev 103: 4605­ 4638.
619 Atkinson, R., Baulch, D.L., Cox, R.A. , Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. July 2005 web version. http://www.iupac-kinetic.ch.cam.ac.uk/index.html

. 620 Atkinson, R., Baulch, D.L., Cox, R.A. , Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data for Atmospheric Chemistry - IUPAC Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. July 2005 web version. http://www.iupac-kinetic.ch.cam.ac.uk/index.html

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. 624 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J. (20 03) Chemical Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. NASA Jet Propulsion Laboratory http://jpldataeval.jpl.nasa.gov/index.html

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. 627 Finlayson-Pitts BJ, Pitts JN Jr. (1986) Atmospheric Chemistry: Fundamentals and Experimental Techniques, Wiley, New York.
628 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson-Pitts, B.J. (20 03) Chemical Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. NASA Jet Propulsion Laboratory. http://jpldataeval.jpl.nasa.gov/index.html

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632 Jeffrey S. Gaffney, Nancy A. Marley, Randal S. Martin, Ro y W. Dixon, Luis G. Reyes, and Carl J. Popp (1997) Potential Air Quality Effects of Using Ethanol-Gasoline Fu el Blends: A Field Study in Albuquerque, New Mexico. Environ Sci Technol 31(11): 3053 - 3061; DOI: 10.1021/es9610388

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635 Jacobson M (2007) Effects of ethanol (E85) versus ga soline vehicles on cancer and mortality in the United States, Environ Sci Technol 41: 4150-4157.
636 Grosjean D (1997) Atmospheric Chemistry of Alcohols. J Braz Chem Soc 8(4): 433-442.
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643 EM Martins, G Arbilla ( 2003) Computer modeling study of ethanol an d aldehyde reactivities in Rio de Janeiro urban air. Atmos Environ 37: 1715–1722.
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646 EPA (2002) National Air Quality and Emission Tren ds Report, 2001. EPA 454/K-02-001, September 2002. http://www.epa.gov/air/airt rends/aqtrnd01/summary.pdf
647 Schauer JJ, GR Cass (2000) Source Apportionment of Wintertime Gas-Phase and Particle-Phase Air Pollutants Using Organic Compounds as Tracers, Environ Sci Technol 34(9): 1821-1832. http://pubs.acs.org/cgibin/article.cgi/esthag/2000/34/i09/pdf/es981312t.pdf
648 Kleeman MJ, GR Cass (1999) Identifying the Effect of Individual Emission Sources on Particulate Air Quality Within a Photochemical Aerosol Processes Trajectory Model. Atmos Environ 33: 4597-4613.
649 Watson J, Fujita E, Chow J, Zielinska B, Richards L, Neff W, Dietrich D (1998) Northern Front Range Air Quality Study Final Report: Volume 1. June 30, 1998. For Colorado State University, Cooperative Institute for Research in the Atmosphere, by Desert Research Institute , Reno, NV. This document is available in EPA Docket EPA-HQ-OAR-2005-0161.
650 Schauer JJ, WF Rogge, LM Hildemann, MA Mazurek, GR Cass, B Simoneit (1996) Source Apportionment of Airborne Particulate Matter Using Organic Compounds as Tracers. Atmos Environ 30: 3837-3855.
651 Lewandowski M, M Jaoui , JH Offenberg , TE Kleindienst, EO Edney, RJ Sheesley, JJ Schauer (2008) Primary and secondary contributions to ambient PM in the midwestern United States, Environ Sci Technol 42(9):3303-3309. http://pubs.acs.org/cgi-bin/article.cgi/esthag/2008/42/i09/html/es0720412.html
652 Kleindienst TE, M Jaoui, M Lewandowski, JH Offenberg, EO Edney (2007) Estimates of the contributions of biogenic and anthropogenic hydrocarbons to secondary organic aerosol at a southeastern U.S. location, Atmos Environ 41(37):8288-8300.
653 Offenberg JH, CW Lewis, M Lewandowski, M Jaoui, TE Kleindienst, EO Edney (2007) Contributions of Toluene and �-pinene to SOA Formed in an Irradiated Toluene/�-pinene,NO x /Air Mixture: Comparison of Results Using 14 C Content and SOA Organic Tracer Methods, Environ Sci Technol 41: 3972-3976.
654 Byun DW, Schere, KL (2006) Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, J Applied Mechanics Reviews 59: 51-76.
655 Pandis, S.N., Harley, R.A., Cass, G.R., Seinfeld, J.H. ( 1992) Secondary organic aerosol formation and transport. Atmos Environ 13, 2269–2282.
656 H Takekawa, H Minoura, S Yamazaki (2003) Temperature depe ndence of secondary organic aerosol formation by photo-oxidation of hydrocarbons. Atmos Environ 37: 3413–3424.

800657 M J. Kleeman, Q Ying, Jin Lu, M J. Mysliwiec, R J. Griffin, Jianjun Chen, Simon Clegg (2007) Source apportionment of secondary organic aerosol during a severe photochemical smog episode. Atmos Environ 41: 576– 591.
658 Robinson, A. L.; Donahue, N. M.; Shrivastava, M.; Weitkamp, E. A.; Sage, A. M.; Grieshop, A. P.; Lane, T. E.; Pierce, J. R.; Pandis, S. N. (2007) Rethinking organic aerosol: Semivolatile emissions and photochemical aging. Science 315: 1259-1262.
659 U. S. EPA (2002) National Air Qu ality and Emissions Trends Report, 2001. EPA 454/K-02-001, September 2002. http://www.epa.gov/air/airtrends/aqtrnd01/summary.pdf
660 U. S. EPA (2006) National-Scale Air Toxics Assessment for 1999. http://www.epa.gov/ttn/atw/nata1999
661 U. S. EPA (2007) Regulator y Impact Analysis for the Control of Hazar dous Air Pollutants from Mobile Sources Rule, Chapter 3, Air Quality and Resulting Health and Welfar e Effects of Air Pollution from Mobile Sources. 72 FR 8428, February 26, 2007. http://www.epa.gov/otaq/regs/toxics/420r07002.pdf
662 TE Kleindienst (2008) Hypothetical SOA Production from Ethanol Photooxidation. Memo to the Docket EPAHQ-OAR-2005-0161.
663 BJ Turpin, JJ Huntzicker, SM Larson, GR Cass (1991) Los Angeles Summer Midday Particulate Carbon: Primary and Secondary Aerosol. Environ Sci Technol 25: 1788-1793.
664 BJ Turpin, JJ Huntzicker (1995) Identification of S econdary Organic Aerosol Episodes and Quantitation of Primary and Secondary Organic Aerosol Concentrations During SCAQS. Atmos Environ 29(23): 3527-3544.
665 Bae M-S, Schauer JJ, Turner JR (2006) Estimation of the Monthly Average Ratios of Organic Mass to Organic Carbon for Fine Particulate Matter at an Urba n Site, Aerosol Sci Technol 40(12): 1123-1139. http://dx.doi.org/10.1080/02786820601004085

. 666 Kleindienst TE, M Jaoui, M Lewandowski, JH Offenberg, EO Edney (2007) Estimates of the contributions of biogenic and anthropogenic hydrocarbons to secondary organic aerosol at a southeastern U.S. location. Atmos Environ 41(37):8288-8300.
667 Offenberg JH, CW Lewis, M Lewandowski, M Jaoui, TE Kleindienst, EO Edney (2007) Contributions of Toluene and �-pinene to SOA Formed in an Irradiated Toluene/�-pinene,NO x /Air Mixture: Comparison of Results Using 14 C Content and SOA Organic Tracer Methods, Environ Sci Technol 41: 3972-3976.
668 Claeys M, R Szmigielski, I Kourtchev, P Van der Veken, R Vermeylen, W Maenhaut, M Jaoui, TE Kleindienst, M Lewandowski, JH Offenberg, EO Edney (2007) Hydroxydicarboxylic acids: Markers for secondary organic aerosol from the photooxidation of �-pinene. Environ Sci Technol 41(5): 1628-1634.
669 Edney EO, TE Kleindienst, M Jaoui, M Lewandowski, JH Offenberg, W Wang, M Claeys (2005) Formation of 2-methyl tetrols and 2-methylglyceric acid in secondary organic aerosol from laboratory irradiated isoprene/NO X /SO 2 /air mixtures and their detection in ambient PM 2.5 samples collected in the Eastern United States. Atmos Environ 39: 5281-5289.
670 Jaoui M, TE Kleindienst, M Lewandowski, JH Offenberg, EO Edney (2005) Identification and quantification of aerosol polar oxygenated compounds b earing carboxylic or hydr oxyl groups. 2. Organic tracer compounds from monoterpenes. Environ Sci Technol 39: 5661-5673.
671 Kleindienst TE, TS Conver, CD McIver, EO Edney (2004) Determination of secondary organic aerosol products from the photooxidation of toluene and their implications in ambient PM 2.5 . J Atmos Chem 47: 70-100.
672 Kleindienst TE, TS Conver, CD McIver, EO Edney (2004) Determination of secondary organic aerosol products from the photooxidation of toluene and their implication in ambient PM 2.5 , J Atmos Chem 47: 70-100.
673 Izumi L, T Fukuyama (1990) Photochemical aerosol formation from aromatic hydrocarbons in the presence of NO x , Atmos Environ 24A: 1433.
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675 Lewandowski M, M Jaoui , JH Offenberg , TE Kleindienst, EO Edney, RJ Sheesley, JJ Schauer (2008) Primary and secondary contributions to ambient PM in the midwestern United States, Environ Sci Technol 42(9):3303-3309. http://pubs.acs.org/cgi-bin/article.cgi/est hag/2008/42/i09/html/es0720412.html.
676 Kleindienst TE, M Jaoui, M Lewandowski, JH Offenberg, EO Edney (2007) Estimates of the contributions of biogenic and anthropogenic hydrocarbons to secondary organic aerosol at a southeastern U.S. location. Atmos Environ 41(37):8288-8300.
677 Henze DK, JH Seinfeld (2006) Global secondary organic aerosol from isoprene oxidation. Geophys Res Lett 33: L09812. doi:10.1029/2006GL025976.

801678 Jaoui M, M Lewandowski, TE Kleindienst, JH Offenberg, EO Edney (2007) β -Caryophyllinic acid: An atmospheric tracer for β -caryophyllene secondary organic aerosol. Geophys Res Lett 34: L05816. doi:10.1029/2006GL028827.
679 Griffin RJ, DR Cocker III, RC Flagan, JH Seinfeld (1999) Organic aerosol formation from oxidation of biogenic hydrocarbons, J Geophys Res 104: 3555-3567.
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682 U.S. EPA (2005) Review of the National Ambient Air Quality Standard for Particulate Matter: Policy Assessment of Scientific and Technical Information, OAQPS Staff
Paper. EPA-452/R-05-005. This document is available in Docket EPA-HQ-OAR-2005-0161. 683 U.S. EPA 2006. Provisional Assessment of Recent Studies on Health Effects of Particulate Matter Exposure. EPA/600/R-06/063. This document is available in Docket EPA-HQ-OAR-2005-0161. 684 Laden F; Neas LM; Dockery DW; et al. 2000. “Association of fine particulate matter from different sources with daily mortal ity in six U.S. cities.” Environ Health
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687 Dockery, DW; Pope, CA, III; Xu, X; et al. 1993. “An association between air pollution and mortality in six U.S. cities.” N Engl J Med 329:1753-1759.
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691 Künzli, N.; Jerrett, M.; Mack, W.J.; et al.(2004) Ambient air pollution and atherosclerosis in Los Angeles. Environ Health Perspect doi:10.1289/ehp.7523 [Available at
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692 Riediker, M.; Cascio, W.E.; Griggs, T.R.; et al. 2004. "P articulate matter exposure in cars is associated with cardiovascular effects in healthy young men." Am J Respir Crit Care Med 169: 934-940.
693 Maynard, D.; Coull, B.A.; Gryparis, A. ; Schwartz, J. (2007) Mortality risk asso ciated with short-term exposure to traffic particles and sulfates. Environmen tal Health Perspectives 115: 751-755.
694 Ryan, P.H.; LeMasters, G.K.; Biswas, P.; Levin, L.; Hu , S.; Lindsey, M.; Bernstein, D.I.; Lockey, J.; Villareal, M.; Khurana Hershey, G.K.; Grinshpun, S.A. (2007) A comparison of proximity and land use regression traffic exposure models and wheezing in infants. Environ Health Perspect 115: 278-84.
695 Morgenstern, V.; Zutavern, A.; Cyrys, J.; Brockow, I. ; Gehring, U.; Koletzko, S.; Bauer, C.P.; Reinhart, D; Wichmann, H-E.; Heinrich, J. (2007) Respiratory health and individual estimated expousure to traffic-related air pollutant in a cohort of young children. Occupational and Environmental Medicine 64: 8-16.
696 Franco Suglia, S.; Gryparis, A.; Wright , R.O.; Schwartz, J.; Wright, R.J. (2007) Association of black carbon with cognition among children in a prospective birth cohort study. American Journal of Epidemiology 167: 280-286. 697 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161. 698 U.S. EPA, Review of the National Ambient Air Quality Standards for Ozone: Policy Assessment of Scientific and Technical In formation, OAQPS Staff Paper, Washington,
DC, EPA-452/R-07-003, January 2007. This document is available in Docket EPA-HQ-OAR-2005-0161.
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700 Bates, D.V.; Baker-Anderson, M.; Sizto, R. (1990) Asthma attack periodicity: a study of hospital emergency visits in Vancouver . Environ. Res. 51: 51-70.

701 Thurston, G.D.; Ito, K.; Kinney, P.L.; Lippmann, M. (1992) A multi-year study of air pollution and respiratory hospital admiss ions in three New York State metropolitan
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702 Thurston, G.D.; Ito, K.; Hayes, C.G.; Bates, D.V.; Lippmann, M. (1994) Respiratory hospital admissions and summertime haze air pollution in Toronto, Ontario:

consideration of the role of acid aerosols. Environ. Res. 65: 271-290.
703 Lipfert, F.W.; Hammerstrom, T. (1992) Temporal patterns in air pollution and hospital admissions. Environ. Res. 59: 374-399.
704 Burnett, R.T.; Dales, R.E.; Raizenne, M.E.; Krewski, D.; Summers, P.W.; Roberts, G.R.; Raad-Young, M.; Dann,T.; Brook, J. (1994 ) Effects of low ambient levels of ozone
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705 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004 aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161.

802706 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05 /004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161.
707 Devlin, R. B.; McDonnell, W. F.; Mann, R.; Becker, S.; House, D. E.; Schreinemachers, D.; Koren, H. S. (1991) Exposure of humans to ambient levels of ozone for 6.6 hours
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708 Koren, H. S.; Devlin, R. B.; Becker, S.; Perez, R.; McDonnell, W. F. (1991) Time-dependent changes of markers associated with i nflammation in the lungs of humans
exposed to ambient levels of ozone. Toxicol. Pathol. 19: 406-411.

709 Koren, H. S.; Devlin, R. B.; Graham, D. E.; Mann, R.; McGee, M. P.; Horstman, D. H.; Kozumbo, W. J.; Becker, S.; House, D. E.; McDonnell, W. F.; Bromberg, P. A.
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710 Schelegle, E.S.; Siefkin, A.D.; McDonald, R.J. (1991) Time course of ozone-induced neutrophilia in normal humans. Am. Rev. Re spir. Dis. 143:1353-1358.

711 U.S. EPA (1996). Air Quality Criteria for Ozone and Related Photochemical Oxidants, EPA600-P-93-004aF; (See page 7-171). This document is contained in Docket EPA-HQ-OAR-2005-0161.
712 Hodgkin, J.E.; Abbey, D.E.; Euler, G.L.; Magie, A.R. (1984) COPD prevalence in nonsmokers in high and low photochemical air pol lution areas. Chest 86: 830-838.

713 Euler, G.L.; Abbey, D.E.; Hodgkin, J.E.; Magie, A.R. (1988) Chronic obstructive pulmonary disease symptom effects of long-term cumulative exposure to ambient levels of
total oxidants and nitrogen dioxide in California Seventh-day Adventist residents. Arch. Environ. Health 43: 279-285.

714 Abbey, D.E.; Petersen, F.; Mills, P.K.; Beeson, W.L. (1993) Long-term ambient concentrations of total suspended particulates, ozone, and sulfur dioxide and respiratory
symptoms in a nonsmoking population. Arch. Environ. Health 48: 33-46.
715 U.S. EPA, Review of the National Ambient Air Quality Standards for Ozone: Policy Assessment of Scientific and Technical In formation, OAQPS Staff Paper, Washington,
DC, EPA-452/R-07-003, January 2007. This document is available in Docket EPA-HQ-OAR-2005-0161. 716 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05 /004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161. 717 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161. 718 Avol, E. L.; Trim, S. C.; Little, D. E.; Spier, C. E.; Smith, M. N.; Peng, R.-C.; Linn, W. S.; Hackney, J. D.; Gross, K. B.; D'Arcy, J. B.; Gibbons, D.; Higgins, I. T. T. (1990)
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724 Spektor, D. M.; Lippman, M.; Lioy, P. J.; Thurston, G. D.; Citak, K.; James, D. J.; Bock, N.; Speizer, F. E.; Hayes, C. (1988a) Effects of ambient ozone on respiratory
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725 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05 /004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161.
726 Hazucha, M. J.; Folinsbee, L. J.; Seal, E., Jr. (1992) Effects of steady-state and variable ozone concentration profiles on pul monary function. Am. Rev. Respir. Dis. 146: 1487­

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727 Horstman, D.H.; Ball, B.A.; Folinsbee, L.J.; Brown, J.; Gerrity, T. (1995) Comparison of pulmonary responses of asthmatic and nonasthmatic subjects performing light
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729 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is availabl e online at http://www.epa.g ov/ncea/pdfs/coaqcd.pdf. A copy of this document is available in Docket EPA-HQ-OAR-2005-0161.
730 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is availabl e online at http://www.epa.g ov/ncea/pdfs/coaqcd.pdf. A copy of this document is available in Docket EPA-HQ-OAR-2005-0161.
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734 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is availabl e online at http://www.epa.g ov/ncea/pdfs/coaqcd.pdf. A copy of this document is available in Docket EPA-HQ-OAR-2005-0161.
735 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is availabl e online at http://www.epa.g ov/ncea/pdfs/coaqcd.pdf. A copy of this document is available in Docket EPA-HQ-OAR-2005-0161.
736 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is availabl e online at http://www.epa.g ov/ncea/pdfs/coaqcd.pdf. A copy of this document is available in Docket EPA-HQ-OAR-2005-0161.
737 U.S. EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA600-P-99-001F. June 1, 2000. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. This document is availabl e online at http://www.epa.g ov/ncea/pdfs/coaqcd.pdf. A copy of this document is available in Docket EPA-HQ-OAR-2005-0161. 738 National Research Council, 1993. Protecting Visibility in National Parks and Wilderness Areas. National Academy of Sciences Committee on Haze in National Parks and
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740 U.S. EPA (2004) Air Quality Criteria for Particulate Matter (Oct 2004), Volume I Document No. EPA600/P-99/002aF and Volume II D ocument No. EPA600/P-99/002bF. This document is available in Docket EPA-HQ-OAR-2005-0161.

741 U.S. EPA (2005) Review of the National Ambient Air Quality Standard for Particulate Matter: Policy Assessment of Scientific and Technical Information, OAQPS Staff
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750 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05 /004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161. 751 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05 /004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161. 752 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161. 753 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05 /004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161. 754 Ollinger, S.V., J.D. Aber and P.B. Reich. 1997. “Simulating ozone effects on forest productivity: interactions between leaf canopy and stand level processes.” Ecological
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756 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161. 757 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05 /004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161. 758 Fox, S., and R. A. Mickler, eds. 1996. Impact of Air Pollutants on Southern Pine Forests. Springer-Verlag, NY, Ecol. Studi es, Vol. 118, 513 pp.
759 De Steiguer, J., J. Pye, C. Love. 1990. “Air Pollution Damage to U.S. Forests.” Journal of Forestry, Vol 88 (8) pp. 17-22.
760 Pye, J.M. 1988. “Impact of ozone on the growth and yield of trees: A review.” Journal of Environmental Quality 17 pp.347-36 0. 761 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161. 762 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05 /004aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161. 763 McBride, J.R., P.R. Miller, and R.D. Laven. 1985. “Effects of oxidant air pollutants on forest succession in the mixed conifer forest type of southern California.” In: Air
Pollutants Effects On Forest Ecosystems, Symposium Proceedings, St. P, 1985, p. 157-167.
764 Miller, P.R., O.C. Taylor, R.G. Wilhour. 1982. Oxidant air pollution effects on a western coniferous forest ecosystem. Corv allis, OR: U.S. Environmental Protection Agency,
Environmental Research Laboratory (EPA600-D-82-276).
765 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004 aF-cF, 2006. This document is contained in Docket EPA-HQ-OAR-2005-0161. 766 Kopp, R. J.; Vaughn, W. J.; Hazilla, M.; Carson, R. 1985. “Implications of environmental policy for U.S. agriculture: the case of ambient ozone standards.” J. Environ.
Manage. 20:321-331.
767 Adams, R. M.; Hamilton, S. A.; McCarl, B. A. 1986. “The benefits of pollution control: the case of ozone and U.S. agriculture.” Am. J. Agric. Econ. 34: 3-19.
768 Adams, R. M.; Glyer, J. D.; Johnson, S. L.; McCarl, B. A. 1989. “A reassessment of the economic effects of ozone on U.S. agriculture.” JAPCA 39:960-968.
769 Abt Associates, Inc. 1995. Urban ornamental plants: sensitivity to ozone and potential economic losses. U.S. EPA, Office of Air Quality Planning and Standards, Research
Triangle Park. Under contract to RADIAN Corporation, contract no. 68-D3-0033, WA no. 6. pp. 9-10.

770 U. S. EPA. 1999 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata1999/risksum.html
771 U.S. EPA (2007) Control of Hazard ous Air Pollutants from Mobile Sources . 72 FR 8428; February 26, 2007.
772 U.S. EPA (2003) Integrated Risk Information System File of Acrolein. National Center for Environmental Assessment, Office of Research and Deve lopment, Washington, D.C. 2003. This material is available electronically at http://www.epa.gov/iris/subst/0364.htm.
773 U.S. EPA (2006) National-Scale Air Toxics Assessment fo r 1999. This material is available electronically at http://www.epa.gov/ttn/atw/nata1999/risksum.html.
774 U.S. EPA (2006) National-Scale Air Toxics Assessment for 1999. h ttp://www.epa.gov/ttn/atw/nata1999.
775 U.S. EPA 2000. Integrated Risk Information System File for Benzene. This material is available electronically at : http://www.epa.gov/iris/subst/0276.htm

. . 776 International Agency for Research on Cancer, IARC monographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 29, Some industrial chemicals and
dyestuffs, International Agency for Research on Cancer, World Health Organization, Lyon, France, p. 345-389, 1982.
777 Irons, R.D.; Stillman, W.S.; Colagiovanni, D.B.; Henry, V.A. (1992) Synergistic action of the benzene metabolite hydroquinone on myelopoietic stimulating activity of
granulocyte/macrophage colony-stimulating factor in vitro, Proc. Natl. Acad. Sci. 89:3691-3695.

778 International Agency for Research on Cancer ( IARC ) . 19 87 . Monographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 29 , Supplement 7, Some industrial chemicals and dyestuffs, World Health Organization, Lyon, France.

779 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183. 780 Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene. Environ. Health Perspect. 82: 193-197.
781 Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State of the Art Reviews. 3: 541-554.
782 Rothman, N., G.L. Li, M. Dosemeci, W.E. Bechtold, G.E. Marti, Y.Z. Wang, M. Linet, L.Q. Xi, W. Lu, M.T. Smith, N. Titenko-Holland, L.P. Zhang, W. Blot, S.N. Yin, and

R.B. Hayes (1996) Hematotoxicity among Chinese workers heavily exposed to benzene. Am. J. Ind. Med. 29: 236-246.

783 U.S. EPA 200 2 Toxicological Review of Benzene (Noncancer Effects). Environmental Protection Agency, Integrated Risk Information System (IRIS), Research and Development, National Center for Environmental Assessment, Washington DC. This mate rial is available electronically at http://www.epa.gov/ir is/subst/0276.htm

. 784 Qu, O.; Shore, R.; Li, G.; Jin, X.; Chen, C.L.; Cohen, B.; Melikian, A.; Eastmond, D.; Rappaport, S.; Li, H.; Rupa, D.; Suramaya, R.; Songnian, W.; Huifant, Y.; Meng, M.;
Winnik, M.; Kwok, E.; Li, Y.; Mu, R.; Xu, B.; Zhang, X.; Li, K. (2003). HEI Report 115, Validation & Evaluation of Biomarkers in Workers Exposed to Benzene in China.
785 Qu, Q., R. Shore, G. Li, X. Jin, L.C. Chen, B. Cohen, et al. (2002). Hematological changes among Chinese workers with a br oad range of benzene exposures. Am. J. Industr.

Med. 42: 275-285.
786 Lan, Qing, Zhang, L., Li, G., Vermeulen, R., et al. (2004). Hematotoxically in Workers Exposed to Low Levels of Benzene. Science 306: 1774-1776.
787 Turtletaub, K.W. and Mani, C. (2003). Benzene metabolism in rodents at doses relevant to human exposure from Urban Air. R esearch Reports Health Effect Inst. Report

No.113.

805788 U.S. EPA. 2002. Health Assessment of 1,3-Butadiene. Office of Research and Development, National Center for Environmental Assessment, Washington Office,
Washington, DC. Report No. EPA600-P-98-001F. This document is available electronical ly at http://www.epa.gov/iris/supdocs/butasup.pdf.
789 U.S. EPA. 2002 “Full IRIS Summary for 1,3-butadiene (CASRN 106-99-0)” Environmental Protection Agency, Integrated Risk Information System (IRIS), Research and Development, National Center for Environmental Assessment, Washington, DC http://www.epa.gov/iris/subst/0139.htm

. 790 International Agency for Research on Cancer ( IARC ) ( 1999 ) Monographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 71, Reevaluation of some organic chemicals, hydrazine and hydrogen peroxide and Volume 97 (in preparation) , World Health
Organization, Lyon, France.

791 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183. 792 Bevan, C.; Stadler, J.C.; Elliot, G.S.; et al. (1996) Subchronic toxicity of 4-vinylcyclohexene in rats and mice by inhalat ion. Fundam. Appl. Toxicol. 32:1-10.

793 U.S. EPA 1987. Assessment of Health Risks to Garment Workers and Certain Home Residents from Exposure to Formaldehyde, Office of Pesticides and Toxic Substances,

. April 1987.
794 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2003. Mortality from lymphohematopoetic malignancies among workers in formaldehyde industries.

Journal of the National Cancer Institute 95: 1615-1623.
795 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004. Mortality from solid cancers among workers in formaldehyde industries. American Journal of
Epidemiology 159: 1117-1130.
796 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to formaldehyde: an update. Occup. Environ. Med. 61: 193-200.

797 Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Exte nded follow-up of a cohort of British chemical workers exposed to formaldehyde. J National Cancer Inst. 95:1608-1615. 798 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2003. Biologically motivated comput ational modeling of formaldehyde
carcinogenicity in the F344 rat. Tox Sci 75: 432-447.
799 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2004. Human respiratory tract cancer risks of inhaled formaldehyde: Dose-response
predictions derived from biologically-motivated computational modeling of a combined rodent and human dataset. Tox Sci 82: 279 -296. 800 Chemical Industry Institute of Toxicology (CIIT).1999. Formaldehyde: Hazard characterization and dose-response assessment for carcinogenicity by the route of inhalation.
CIIT, September 28, 1999. Research Triangle Park, NC.

801 International Agency for Research on Cancer (2006) Formaldehyde, 2-Butoxyethanol and 1-tert-Butoxypropan2-ol. Monographs Volume 88. World Health Organization, Lyon, France.
802 Agency for Toxic Substances and Disease Registry (A TSDR). 1999. Toxicological profile for Formaldehyde. Atlanta, GA: U.S. Department of Health an d Human Services, Pub lic Health Service. http://www.atsdr.cdc.gov/to xprofiles/tp111.html
803 WHO (2002) Concise International Chemical Assessment Document 40: Formaldehyde. Published under the joint sponsorship of the United Nations Environment Pr ogramme, the International Labour Organization, and the World Health Organization, and produced within the framework of the Inter-Organization Programme for the Sound Management of Chemicals. Geneva. 804 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/iris/subst/0290.htm.

805 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183.
806 International Agency for Research on Cancer (IARC). 1999. Re-evaluation of some organic chemicals, hydrazine, and hydrogen peroxide. IARC Monographs on the Evaluation of Carcinogenic Risk of Chemical to Humans, Vol 71. Lyon, France.
807 U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde. This material is available electronically at http://www.epa.gov/ir is/subst/0290.htm

. 808 U.S. EPA. 2003. Integrated Risk In formation System File of Acrolein. Research and Development, National Center for Environmental Assessment, Washington, DC . This material is available electronically at http://www.epa.gov/iris/subst/0364.htm.
809 Appleman, L.M., R.A. Woutersen, and V.J. Feron. (1982). Inhalation toxicity of acetaldehyde in rats. I. Acute and subacute studies. Toxicology. 23: 293-297.
810 Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Mats uda, T. (1993) Aerosolized acetaldehyde induces histamine-mediated bronchoconstriction in asthmatics. Am. Rev. Respir.Dis.148(4 Pt 1): 940-943.
811 Integrated Risk Information System File of Acrole in. Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available at http://www .epa.gov/iris/subst/0364.htm

806812 International Agency for Research on Cancer ( IARC ) . 19 95 . Monographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 63 , Dry cleaning, some chlorinated solvents an d other industrial chemicals , World Health Organization, Lyon, France.

813 Weber-Tschopp, A; Fischer, T; Gierer, R; et al. (1977) Experimentelle reizwirkungen von Acrolein auf den Menschen. Int Arch Occup Environ Hlth 40(2):117-130. In German
814 Sim, VM; Pattle, RE. (1957) Effect of possible smog irritants on human subjects. J Am Med Assoc 165(15):1908-1913.
815 Morris JB, Symanowicz PT, Olsen JE, et al. 2003. Imme diate sensory nerve-mediated respiratory responses to irritants in healthy and allergic airway-diseased mice. J Appl Physiol 94(4):1563-1571.
816 Sim VM, Pattle RE. Effect of possible smog irritants on human subjects JAMA165: 1980-2010, 1957.
817 U. S. EPA. 2004. Toxicological Review of Naphthalene (Reassessment of the Inhalation Cancer Risk), Environmental Protection Agency, Integrated Risk Information Syst em, Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/iris/subst/0436.htm

. 818 Oak Ridge Institute for Science and Education. (2004). External Peer Review for the IRIS Reassessment of the Inhalation Carcinogenicity of Naphthalene. August 2004. http://cfpub.epa.gov/ncea/cfm/ recordisplay.cfm?deid=84403
819 National Toxicology Program (NTP). (2004). 11th Report on Carcinogens. Public Health Service, U.S.Department of Health and Human Services, Research Triangle Park, NC. Available from: http://ntpserver.niehs.nih.gov. 820 International Agency for Research on Cancer (IARC). (2002). Monographs on the Evaluation of the Carcinogenic Risk of Chemicals for Humans. Vol. 82. Lyon, France.

821 U. S. EPA. 1998. Toxicological Review of Naphthalene, Enviro nmental Protection Agency, Integrated Risk Information System, Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available electronically at http://www.epa.gov/iris/subst/0436.htm 822 International Agency for Research on Cancer ( IARC ) . 198 8 . Monographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 44 , Alcohol Drinking , World Health Organization, Lyon, France.

823 U.S. Department of Health and Human Services. 2005. National Toxicology Program 11th Report on Carcinogens available at: ntp.niehs.nih.gov/index.cfm?objectid=32BA9724-F1F6-975E-7FCE50709CB4C932.
824 U.S. Department of Health and Human Services. 2000. 10th Special Report to the U.S. Congress on Alcohol and Health. June. 2000.
825 Goodlett CR, KH Horn, F Zhou. 2005. Alcohol teratoge niesis: mechanisms of damage and strategies for intervention. Exp. Biol. Med. 230:394-406.
826 Riley EP, CL McGee. 2005. Fetal alco hol spectrum disorders: an overview with emphasis on changes in brain and behavior. Exp. Biol. Med. 230:357-365.
827 Zhang X, JH Sliwowska, J Weinberg. 2005. Prenatal alcohol exposure and fetal programming: effects on neuroendocrine and immune function. Exp. Biol. Med. 230:376-388.
828 Riley EP, CL McGee, ER Sowell. 2004. Teratogenic effect s of alcohol: a decade of br ain imaging. Am. J. Med. Genet. Part C: Semin. Med. Genet. 127:35-41.
829 Gunzerath L, V Faden, S Zakhari, K Warren. 2004. Natio nal Institute on Alcohol Abuse and Alcoholism report on moderate drinking. Alcohol. Clin. Exp. Res. 28:829-847.
830 World Health Organization (WHO). 2004. Global status report on alcohol 2004. Geneva, Switzerland: Department of Mental Health and Substance Abuse. Available: http://www.who.int/substance_abuse/publicati ons/global_status_report_2004_overview.pdf
831 Chen W-JA, SE Maier, SE Parnell, FR West. 2003. Alcohol and the developing brain: neuroanatomical studies. Alcohol Res. Health 27:174-180.
832 Driscoll CD, AP Streissguth, EP Riley. 1990. Prenatal alcohol exposure comparability of effects in humans and animal models. Neurotoxicol. Teratol. 12:231-238.
833 EPA. 2005. Full IRIS Summary for n-Hexane (CASRN 11054-3). Integrated Risk Information System (IRIS), Office of Research and Development, National Center for Environmental Assessment, Washington, DC. Available
at www.epa.gov/iris/subst/0486.htm

. 834 U.S. EPA. 2006. Air quality criteria for ozone and related photochemical oxidants (Ozone CD). Research Triangle Park, NC: National Cetner for Environmental A ssesssment; report no. EPA/600/ R-05/004aF-cF.3v. page 5­ 78 Available at http://cfpub.epa.gov/ncea/.
835 U.S. EPA Air Quality Criteria for Ozone and Related Ph otochemical Oxidants (Final). U.S. Environmental Protection Agency, Washington, D.C., EPA 600/R-05/004aF-cF, 2006. page 5-63. This document is available in

807Docket EPA-HQ-OAR-2005-0 161. This document may be accessed electronically at: http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_cd.html
836 U.S. EPA Air Quality Criteria for Ozone and Related Ph otochemical Oxidants (Final). U.S. Environmental Protection Agency, Washington, D.C., EPA 600/R-05/004aF-cF, 2006. page 5-78. This document is available in Docket EPA-HQ-OAR-2005-0 161. This document may be accessed electronically at: http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_cd.html
837 Inhalation health effect concerns for volatilized a.i. compounds are discussed for individual products, e.g., oxydimeton-methyl, carbaryl (and carb amates,generally), diazinon, at http://www.epa.gov/pesticides /reregistration/status.htm . A.I. vapor pressure and a discussion of product volatilization can be found at http://www.epa.gov/ttn/chief/a p42/ch09/final/c9s02-2.pdf
838 U.S. EPA Integrated Risk Information System (IR IS) database is available at: www.epa.gov/iris
839 Nouchi I, S Toyama. 1998. Effects of ozone and peroxyacetyl nitrate on pola r lipids and fatty acids in leaves of morning glory and kidney bean. Plant Physiol. 87:638-646.
840 Oka E, Y Tagami, T Oohashi, N Kondo. 2004. A physiological and morphological study on the injury caused by exposure to the air pollutant, peroxyacet yl nitrate (PAN), based on the quantitative assessment of the injury. J Plant Res. 117:27-36.
841 Sun E-J, M-H Huang. 1995. Detection of peroxyacetyl nitrate at phytotoxic le vel and its effects on vegetation in Taiwan. Atmos. Env. 29:2899-2904.
842 Koukol J, WM Dugger, Jr., RL Palmer. 1967. Inhi bitory effect of peroxyacetyl nitrate on cyclic photophosphorylation by chloroplasts from black valentine bean leaves. Plant Physiol. 42:1419-1422.
843 Thompson CR, G Kats. 1975. Effects of ambient concentrations of peroxyacet yl nitrate on navel orange trees. Env. Sci. Technol. 9:35-38.
844 Bytnerowicz A, ME Fenn. 1995. Nitrogen deposition in California forests: A Review. Environ. Pollut. 92:127­ 146.
845 US EPA. 1991. Effects of organic chemicals in the atmosphere on terrestrial plants. EPA/600/3-91/001.
846 C ape JN, ID Leith, J Binnie, J Content, M Donkin, M Skewes , DN Price AR Brown, AD Sharpe. 2003. Effects of VOCs on herbaceous plants in an open-top chambe r experiment. Environ. Pollut. 124:341-343.
847 C ape JN, ID Leith, J Binnie, J Content, M Donkin, M Skewes , DN Price AR Brown, AD Sharpe. 2003. Effects of VOCs on herbaceous plants in an open-top chambe r experiment. Environ. Pollut. 124:341-343.
848 Viskari E-L. 2000. Epicuticular wax of Norway spruce needles as indicator of traffic pollutant deposition. Water, Air, and Soil Pollut. 121:327-337.
849 Ugrekhelidze D, F Korte, G Kvesitadze. 1997. Uptake an d transformation of benzene and toluene by plant leaves. Ecotox. Environ. Safety 37:24-29.
850 Kammerbauer H, H Selinger, R Rommelt, A Ziegler-Jons, D Knoppik, B Hock. 1987. Toxic components of motor vehicle emissions for the spruce Pci ea abies. Environ. Pollut. 48:235-243. 851 Kwaitkowski, J.R., McAloon, A., Taylor, F., Jo hnston, D.B., Modeling the process and costs of fuel ethanol production by th e corn dry-grind process. Industrial Crops and Products 23 (2006) 288-296. A copy of the current USDA model can be obtained by contacting the corresponding author.
852 Gallagher, P.W., Brubaker, H., and Shapouri, H. “Plant Size: Capital Cost Relationships in the Dry Mill Ethanol Industry,” Biomass and Bioenergy Vol. 28. 2005. Pp. 565-7.
853 See Tables 4.1-21 and 4.1-22 of this section for biomass costs. Coal costs taken from EIA Annual Energy Outlook 2008 Table 15.
854 Bioenergy Feedstock Characteristics from Jonathan Scurlock, Oak Ridge National Laboratories, available at http://bioenergy.ornl.gov/papers/misc/biochar_factsheet.html (accessed October 1, 2008)
855 EIA Annual Energy Outlook 2008, Tables 8 and 13. Available at http://www.eia.doe.gov/oiaf/aeo/aeoref_tab.html
856 Corn Stover Collection Project , David A. Glassner, National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, James R. He ttenhaus, Chief Executive Assistance, 3211

808Trefoil Ave, Charlotte, NC 28226, and Thomas M. Schechinger, Iron Horse Custom Farming, 816 Iron Horse Rd, Harlan, IA 51537; in BioEne rgy ’98: Expanding BioE nergy Partnerships
857 Abhijith Mukunda, Graduate Student School of Industrial Engineering, Purdue University, West Lafayette IN 47907, Klein E Ileleji, A ssistant Professor, Sc hool of Agricultural & Biological Engineering, Purdue University, West Lafayette IN 47907, Hong Wan, Assistant Professor, School of Industrial Engineering, Purdue University, West Lafayette IN47906, Simulation of Corn Stover Logistics from On-farm Storage to an Ethanol Plant, Paper Number: 0661772006, ASABE Annual International Meeting, Portland Convention Center, Portland, Oregon, 9 - 12 July 2006
858 Shahab Sokhansanj, , Anthony Turhollow, Oak Ridge National Laborator y, North Logan, UT 84341-8804, Janet Cushman, Oak Ridge Nationa l Laboratory, Oak Ridge, TN 37831-6422, John Cundiff, Biological Systems Engi neering, Virginia Technica l University, Blacksburg, VA 24061-0303, Engineering aspects of colle cting corn stover for bioenergy, Biomass and Bioenergy 23 (2002) 347 – 355; Received 15 Oct ober 2001; received in revised form 15 April 2002; accepted 3 May 2002; 2002 Published by Elsevier Science Ltd.
859 David A. Glassner, National Renewable En ergy Laboratory, Golden , CO 80401, James R. Hettenhaus, Chief Executive Assistance, Charlott e, NC 28226, and Thomas M. Schechinger, Iron Horse Custom Farming, Harlan, IA 51537; Corn Stover Collection Project , in BioEnergy ’98: Expanding BioEnergy Partnerships
860 J.E. Atchison, Atchison Consultants, Inc., J. R. Hettenhaus, Chief Executive Assistance, Inc. Charlotte, North Carolina, National Renewa ble Energy Laboratory Golden, Colorado,

. Innovative Methods for Corn Stover Collecting, Handling, Storing and Transporting, March 2003.
861 Daniel R. Petrolia, Department of Applied Economics, College of Food, Agriculture, and Natural Resources Sciences, University of Minnesota, The Economics of Harvesting and Transporting Corn Stover for Conversion to Fuel Ethanol: A Case Study for Minnesota, Staff Paper P06-12 August 2006
862 Renee Sayler & Kenneth Von Bargen, Principal Investigators, Feasibility of Corn Residue Collection in Kearney, Nebraska Area, for Western Regional Biomass Energy Program, Report of findings, by University of Nebraska-Lincoln, Industrial Agricultural Products Center, Lincoln, NE 68583, September 1993.
863 John S. Cundiff, Professor, Robert D. Grisso, Professor, Biological Systems Engineering Dept(MS 0303), Virginia Tech, Blacksburg, VA 24061, Hosein Shapouri, Senior Agricultural Economist, U.S.D.A., Office of Energy Polic y and New Uses, Washington DC 20250-3815, Economic Analysis of two Receiving Faci lity Designs for a Bioenergy Plant, Paper Number: 076051,2007 ASABE Annual International Meeti ng, Sponsored by ASABE, Minneapolis Convention Center, Minneapolis , Minnesota, 17 - 20 June 2007
864 Kevin J. Shinners – Professor of Agricultural Engineering, Ben N. Binversie – Graduate Research Assistant, Department of Biological Sy stems Engineering, Univ ersity of Wisconsin, Madison, WI, Philippe Savoie – Research Scientist, Agriculture and Agri-Food Canada, Quebec, Canada, Whole-Plant Corn Harvesting for Biomass: Comparison of Single-Pass and MultiplePass Harvest Systems, Paper Number: 036089 2003 ASAE A nnual International Meeting, Sponsored by ASAE, Riviera Ho tel and Convention Center, Las Vegas, Nevada, USA, 27- 30 July 2003.

809865 S. Sokhansanj1, A. Turhollow, and R. Perl ack, Oak Ridge National Laboratory, Tennessee, USA 37830, Stochastic Modeling of Costs of Corn Stover Costs De livered to an Intermediate Storage Facility, Paper Number: 024190, Presented at: 2002 ASAE Annual International Meeting / CIGR XVth World Congress, Sponsored by ASAE and CIGR, Hyatt Regency Chicago, Chicago, Illinois, USA, July 28-July 31, 2002
866 D. Montross, Associate Professor, Scott A. Shearer, Professor, Timothy S. Stombaugh, Associate Extension Professor, Stephen F. Higgins , Research Specialist, and Samuel G. McNeill, Associate Extension Professor, Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexingt on, Kentucky; and Shahab Sokha nsanj, Professor, Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, Te nnessee, Corn Stover Availability and Collection Efficien cy Using Typical Hay Equipment, This article is published with the approval of the Director of the Ke ntucky Agricultural Experiment Station and designated Paper No. 04-05-066. Transact ions of the ASABE, Vol. 50(3): 705

− 711 – 2007 American Society of Agricultural and Biological Engineers, ISSN 0001

− 2351.
867 American Society of Agricultural and Biol ogical Engineers (ASABE ), ASABE Standards, 2007, 54 th Edition, Standards, Engineering Practice s, Data, 2950 Niles Road, St. Joseph, MI 49085-9659.
868 William Edwards, extension economist, Machinery Management: Estimating Farm Machinery Costs, PM 710 Paper version, Revised Nove mber 2001; Electronic version Revised August 2005, University Extensi on, Iowa State University.
869 Mark Hanna, Extension Engineer, University Extension, Iowa State University, Ames Iowa, Estimating the Field Capacity of Farm Machines, File A3-24, April 2002.
870 Terry Kastens, Extension Agricultural Econom ist, Kansas State University Agricultural Experiment Station and Coopera tive Extension Service, Farm Machinery Operation Cost Calculations, MF-2244 May 1997
871 Machinery Cost Estimates: Forage Field Operations, University of Illinois Farm Business Management Handbook, University of Illinois Exte nsion FBM 0203 Department of Agricultural and Consumer Economics • College of Agricultura l, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign
872 Tim Cross, Agricultural Economics, Agricu lture Machinery Cost Calculation Methods, E12­ 2015-00-032-98, Agricultural Extension Service, The University of Tennessee Institute of Agriculture.
873 Agricultural Pr ices 2006 Summary, July 2007 United States, Depa rtment of Agriculture National Agricultural Statistics Service, Pr 1-3 (07)
874 National Agricultural Statistics Service, Quick Stats (Agricultural Statistics Data Base, State and County Data), USDA, http://www.nass.usda.gov/
875 L. O. Pordesimo, W. C. Edens, The Univ ersity of Tennessee, Dept. of Biosystems Engineering and Environmental Science, Knoxv ille, TN, U.S.A., S. Sokhansanj Oak Ridge National Laboratory, Environmental Sciences Di vision, Oak Ridge, TN, U. S.A., Distribution of Above Ground Biomass in Corn , Stover, Paper Number: 02-6059; 2002 ASAE Annual International Meeting/CIGR XVth World Congress, Hyatt Regency Chicago, Chicago, Illinois, USA,; July 29-31, 2002.
876 Kevin J. Shinners, Professor of Agricultura l Engineering, Ben N. Binversie, Graduate Research Assistant, Department of Biological Systems Engineering, Un iversity of Wisconsin, Madison, WI, Philippe Savoie, Research Scientist, Agriculture and Agri-Food Canada, Quebec,

810Canada; Harvest and Storage Of Wet and Dry Corn Stover As A Biomass Feedstock, Paper Number: 036088, 2003 ASAE Annual International Me eting Riviera Hotel and Convention Center, Las Vegas, Nevada , USA, 27- 30 July 2003
877 R.L. Graham, R.D. Perlack, and L.L. Wright, Environ. Sci. Div., Oak Ridge National Lab., Oak Ridge, TN 37831-6407; R. Nelson, Engineeri ng Extension Programs, Kansas State Univ., Manhattan, KS 66506, and J. Sheehan, Nationa l Renewable Energy Lab., Golden, CO 80401. Current and Potential U. S. Corn Stover Supplies, Agronomy Journal 99:1 –11, American Society of Agronomy, Madison, WI 53711 USA; doi:10.2134/ag ronj2005.0222 prepared 2005; published online 1 January 2007
878 Ron Kotrba, Ethanol Producer Magazine st aff writer, The Great Biomass Inquisition, Ethanol Producer Magazine, January 2007. http://ethanolproducer.com/article .jsp?article_id=2621&q=&page=all
879 Kevin J. Shinners, Professor of Agricultura l Engineering, Ben N. Binversie, Graduate Research Assistant, Department of Biological Systems Engineering, Un iversity of Wisconsin, Madison, WI, Philippe Savoie, Research Scientist, Agriculture and Agri-Food Canada, Quebec, Canada; Harvest and Storage Of Wet and Dry Corn Stover As A Biomass Feedstock, Paper Number: 036088, 2003 ASAE Annual International Me eting Riviera Hotel and Convention Center, Las Vegas, Nevada , USA, 27- 30 July 2003.
880 William Edwards, Extension Economist, Estimating Farm Machinery Costs, PM710, Machinery Management, University Extension, Iowa State University, revised November 2001
881 Renee Sayler & Kenneth Von Bargen, Principal Investigators, Feasibility of Corn Residue Collection in Kearney, Nebraska Area, for Western Regional Biomass Energy Program, Report of findings, by University of Nebraska-Lincoln, Industrial Agricultural Products Center, Lincoln, NE 68583, September 1993.
882 Robert D. Grisso , Professor, Virginia Tech , Department of Biologica l Systems Engineering, Blacksburg, Virginia; Paul J. Jasa , Extens ion Engineer, Biological Systems Engineering Department, University of Nebras ka, Lincoln, Nebraska; and Dale E. Rolofson , Farm Manager, Agri-Cross Farms, Inc. Greenwood, Nebraska. Co rresponding author : Paul J. Jasa, Biological Systems Engineering Dept., University of Nebras ka, Lincoln, Analysis of Traffic Patterns and Yield Monitor Data for Fiel d Efficiency Determination, Paper No. 011013, Presented at the 2000 ASAE Annual Meeting, Appl ied Engineering in Agriculture Vol. 18(2): 171-178 ( © 2002 American Society of Agricultural Engineers ). A contribution of the University of Nebraska Agricultural Research Division, Lincoln, Nebraska. Journal Series No. 13335. Mention of trade and company names are for the reader and do not infer endorsement or preferential treatment of the products by the University of Nebraska, Lincoln.

883 Robert “Bobby” Grisso, Professor, and John C. Cundiff, Professor; and David H. Vaughan, Professor, Department of Biol ogical Systems Engineering, Vi rginia Tech, Blacksburg, VA., Investigating Machinery Management Parameters with Computer Tools, Paper Number: 071030, Presented at 2007 ASABE Annual International Meeting, Minn eapolis Convention Center, Minneapolis, Minnesota, 17 - 20 June 2007. Men tion of trade and company names are for the reader and do not infer endorsement or preferen tial treatment of the products by Virginia Tech.
884 Poorna P. Ravula, Robert D. Grisso and John S. Cundiff, Biological Systems Engineering Department, Virginia Tech, Blacksburg, VA 24061-0303, Comparison between Two Policy Strategies for Scheduling in a Biomass Logistic System, Paper Number: 071095, Presented at the

8112007 ASABE Annual International Meeting, Sponsored by ASAB E, Minneapolis Convention Center
885 International Fire Code 2903.4: Agricultural Products. Hay, straw or similar agricultural products shall not be stored adjacent to structur es of combustible materials. Storage shall be limited to stacks of 100 tons (91 metric tons) ea ch. Stacks shall be separated by a minimum of 20 feet (6096 mm) of clear space. Quantities of hay, straw and other agricultural products shall not be limited where stored in or near farm struct ures located outside closely built areas. A permit shall not be required fo r agricultural storage.
886 Daniel R. Petrolia, Department of Applied Economics, College of Food, Agriculture, and Natural Resources Sciences, University of Minnesota, The Economics of Harvesting and Transporting Corn Stover for Conversion to Fuel Ethanol: A Case Study for Minnesota, Staff Paper P06-12 August 2006
887 Robert D. Perlack, Anthony F. Turhollow, Oak Ridge National Laboratory, Office of Scientific and Technical Info rmation, Oak Ridge, TN 37831, Assessment of Options For The Collection, Handling, and Transport of Corn Stover, ORNL/TM-2002/44, September 2002, Office of Energy Efficiency and Renewable En ergy, Biomass Program, U.S. Department of Energy.
888 Dr. R.L. (Bob) Nielsen, Ag ronomy Department, Purdue Un iversity, West Lafayette, IN 47907-1150; Questions Relative to Ha rvesting & Storing Corn Stover; Prepared September 1995. Originally published as Agrono my Extension publication AGRY-95-09.
889 Ron Kotrba, staff writer, The Great Biomass Inquisition, Ethanol Producer Magazine. January 2007 Issue. Reach him at rkotrba@bbib iofuels.com, © 2007 BBI International http://ethanolproducer.com/article .jsp?article_id=2621&q=&page=all
890 Ron Kotrba, staff writer, The Great Biomass Inquisition, Ethanol Producer Magazine. January 2007 Issue. Reach him at rkotrba@bbib iofuels.com, © 2007 BBI International http://ethanolproducer.com/article .jsp?article_id=2621&q=&page=all
891 Graeme R Quick, Single-pass Corn and Stove r Harvesters: Development and Performance. Proceedings of the Internationa l Conference on Crop Harvesting and Processing, 9-11 February 2003, Louisville, Kentucky USA, ASABE, St. Joseph, Mich.; ASAE Publication Number 701P1103e
892 A. Aden, M. Ruth, K. Ibsen, J. Jechura, K. Neeves, J. Sheehan, and B. Wallace National Renewable Energy Laborat ory (NREL); L. Montague, A. Slayton, and J. Lukas Harris Group, Seattle, Washington, Etha nol Process Design and Economics Utilizing Co-Current Dilute Acid Prehydrolysis and Enzymatic Hydrolysis for Corn Stover ; June 2002;NREL is a U.S. Department of Energy Laboratory Operated by Midwest Research Institute • Battelle • Bechtel; Contract No. DE-AC36-99-GO10337
893 Robert M. Prewitt, Graduate Research Assistan t, Michael D. Montross, Associate Professor, Scott A. Shearer, Professor, Timothy S. Stomba ugh, Associate Extension Professor, Stephen F. Higgins, Research Specialist, Samu el G. McNeill, Associate Extens ion Professor, Department of Biosystems and Agricultural Engineering, Univ ersity of Kentucky, Lexington, KY; Shahab Sokhansanj, Professor, Oak Ridge National Labor atory, Environmental Sciences Division, Oak Ridge, TN, Corn Stover Availability and Collect ion Efficiency Using Typical Hay Equipment, Transactions of the American Society of Agricu ltural and Biological Engineers (ASABE), Vol. 50(3): 705

− 711, 2007, ISSN 0001

− 2351; Publication of this articl e approved by the Director of the Kentucky Agricultural Experiment Station and designated Paper No. 04-05-066.

812894 Graeme R Quick, Single-pass Corn and Stove r Harvesters: Development and Performance. Proceedings of the Internationa l Conference on Crop Harvesting and Processing, 9-11 February 2003, Louisville, Kentucky USA, ASABE, St. Joseph, Mich.; ASAE Publication Number 701P1103e
895 Kevin J. Shinners, Professor of Agricultura l Engineering, Graham S. Adsit, Graduate Research Assistant, Benjamin N. Binversie, Grad uate Research Assistant, Matthew F. Digman, Graduate Research Assistant, Department of Biological Systems Engin eering, University of Wisconsin, Richard E. Muck, Research Scientist, Paul. J. Weimer, Research Scientist, US Dairy Forage Research Center – US DA-ARS, Madison, WI , Character istic Performance and Yields using a Single-Pass, Split- Stream Maize Grain and Stover Harvester, Paper Number: 056051, presented at the 2004 ASAE/CSAE Annual International and the 2005 ASAE Annual International Meeting, Sponsored by ASAE, Ta mpa Convention Center, Tampa, Florida 17 - 20 July 2005
896 Shahab Sokhansanj, , Anthony Turhollow, Oak Ridge National Laborator y, North Logan, UT 84341-8804, Janet Cushman, Oak Ridge Nationa l Laboratory, Oak Ridge, TN 37831-6422, John Cundiff, Biological Systems Engi neering, Virginia Technica l University, Blacksburg, VA 24061-0303, Engineering aspects of colle cting corn stover for bioenergy, Biomass and Bioenergy 23 (2002) 347 – 355; Received 15 Oct ober 2001; received in revised form 15 April 2002; accepted 3 May 2002; 2002 Published by Elsevier Science Ltd.
897 Kevin J. Shinners, Professor of Agricultural Engineering, Garrit C. Boettcher, Graduate Research Assistant, Jesse T. M unk, Graduate Research Assistant, Matthew F. Digman, Graduate Research Assistant, Department of Biological Systems Engineering, Un iversity of Wisconsin; Richard E. Muck, Research Engineer, Paul. J. Weimer, Research Scientist, US Dairy Forage Research Center, USDA-ARS, Ma dison, WI, Single-Pass, Split- Stream of Corn Grain and Stover: Characteristic Performance of Three Harvester Configurations, Presented at: ASABE Annual International Meeting, 2006; Oregon Convention Center, Po rtland, Oregon, 9 – 12 July 2006.
898 W. C. Edens, L. O. Pordesimo, The Univ ersity of Tennessee, Dept. of Biosystems Engineering and Environmental Science, Knoxv ille, TN, S. Sokhansanj, Oak Ridge National Laboratory, Environmental Scien ces Division, Oak Ridge, TN, Fi eld Drying Characteristics and Mass Relationships of Corn Stover Fractions, Presented at the ASAE Annual International Meeting/ CIGR XVth World Congr ess, 2002, Hyatt Regency Chica go, Chicago, Illinois, USA, July 28-31, 2002.
899 Richard Hess, Idaho National Laboratory, Un iform Format Feedstock Supply System Design for Lignocellulosic Biomass, Western Region Biomass Workshop August 27, 2007 http://sungrant.oregonsta te.edu/workshop/HESS_Western_RP_workshop.pdf
900 Kevin J. Shinners, Professor of Agricultura l Engineering, Ben N. Binversie, Graduate Research Assistant, Department of Biological Systems Engineering, Un iversity of Wisconsin, Madison, WI, Philippe Savoie, Research Scientist, Agriculture and Agri-Food Canada, Quebec, Canada; Harvest and Storage Of Wet and Dry Corn Stover As A Biomass Feedstock, Paper Number: 036088, 2003 ASAE Annual International Me eting Riviera Hotel and Convention Center, Las Vegas, Nevada , USA, 27- 30 July 2003.
901 Shahab Sokhansanj and Janet Cushman, Bioenergy Feedstock Development Program Oak Ridge National Laboratory (O RNL), Feedstock Engineering, Presentation, Lincoln, NB, December 12, 2001 ORNL, Energy Efficiency and Renewable Energy, US DOE, USA

813902 Sudhagar Mani, Ph.D. Candidate, Shahab S okhansanj, Adjunct Professor, Xiaotao Bi, Associate Professor, Department of Chemical & Biological Engineering, University of British Columbia, Vancouver, BC CANADA V6T 1Z4, Modeling of Forage Drying in Single and Triple Pass Rotary Drum Dryers, Paper Number: 056082, 2005 ASAE Annual Internati onal Meeting, Tampa Convention Center, Tampa, Florida, 17 - 20 July 2005
903 Xianzhe Zheng, Professor, Yiyuan Jiang, Prof essor, College of Engineering, North East Agricultural University, Harbin, China; Yubi n Lan, Associate Professor, Agricultural Engineering Technology, Fort Valley State Univer sity, University Systems of Georgia, Fort Valley, GA 31030, Determination of Parameters for an Alfalfa Rotary Dryer, Paper Number: 056186, 2005 ASAE Annual Internati onal Meeting, Tampa Convention Center, Tampa, Florida, 17 - 20 July 2005
904 This information came in a confidential, personal communication.
905 Christopher T. Wright , Pete r A. Pryfogle, Nathan A. Stevens , J. Richard Hess , Corey W. Radtke , Idaho National Laborator y , Idaho Falls, ID, Value of Distributed Preprocessing of Biomass Feedst ocks to a Bioenergy Industry, Paper No: 066151 Presented at the ASABE A nnual International Meeting, 2006, Oregon Convention Center Portland, Oregon, 9 - 12 July 2006
906 Roger G. Ginder, Professor of Economics, Depa rtment of Economics, Io wa State University, Ames, Iowa 50011, Potential Infrastructure C onstraints on Current Corn Based and Future Biomass Based U.S. Ethanol Production, Working Paper # 07018 , July 2007
907 E-mailed file from Robert Perlack, U

. S. Department of Agriculture, May 2008.
908 Conversation with Bryce Stokes, U.S. Forestry Service, May 2008.
909 OECD, “Biofuels: Linking Support to Perf ormance,” ITF Round Tables No. 138, March 2008.
910 International Energy Agency (IE A), “Biofuels for Transport: An International Perspective,” 2004.
911 Goldemberg, J. as sited in Rothkopf, Gart en, “A Blueprint for Green Energy in the Americas,” 2006.
912 Unicamp, “A Expans ā o do Proalcool como Programa de Desenvolvimento Nacional,” Powerpoint pres entation at Ethanol Seminar in BNDES, 2006. As quot ed in OECD, “Biofuels: Linking Support to Performance,” IT F Round Tables No. 138, March 2008.
913 Ibid.
914 Ibid.
915 Ibid.
916 Macedo. I.C., “Green house gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 Av erages and a Prediction for 2020,” Biomass and Bioenergy , 2008.
917 Ibid.
918 Smeets E, Junginger M, Faaij A, Walter A, Dol zan P, Turkenburg W, “The sustainability of Brazilian ethanol-An Assessment of the possibilities of certified production,” Biomass and Bioenergy , 2008.
919 Official Statistics of the U.S. Department of Commerce, USITC
920 Haas, M.J, A process model to estimate biodiesel production cost s, Bioresource Technology 97 (2006) 671-678.

814921 Wright, Mark M; Brown, R obert C.; Comparative Economics of Biorefineries based on the Biochemical and Thermochemical Platforms, May 17, 2007.

922 RFS1 final rule regulatory impact an alysis, EPA420-R-07-004, April 2007, http://epa.gov/otaq/renewablefuels/420r07004.pdf
923 Infrastructure Requirements for an Expa nded Fuel Ethanol Industry, Downstream Alternatives Inc., January 15, 2002.

924 RFS1 final rule regulatory impact an alysis, EPA420-R-07-004, April 2007, http://epa.gov/otaq/renewablefuels/420r07004.pdf
925 Tables ES-9 and ES 10, Infrastructure Requi rements for an Expanded Fuel Ethanol Industry, Downstream Alternatives Inc, January 15, 2002.
926 Petroleum Market Model of the National En ergy Modeling System, Part 2, March 2006, DOE/EIA-059 (2006), http://tonto.eia.doe.gov/FTPROOT/modeldoc/m059(2006)-2.pdf .

927 BNSF railway biodiesel freight co st information can be found at http://www.bnsf.com/markets/ag ricultural/bnsf4024/cur/itm31001.txt
928 National Petroleum News, Market Facts 2007, October 2007, www.npn.web.
929 National Petroleum News, Market Facts 2007, October 2007, www.npn.web.

930 Modeling the Effect of Renewable Fuels Standards 2, Prepared for the Environmental Protection Agency by Jacobs Consultancy under contract with Southwest Research Associates, August 2008.
931 Table 12, Reference and High Price Cases, Annual Energy Outlook 2008; Energy Information Administration, Department of Energy, June 2008.
932 Table 2, Reference Case, Annual Energy Outlook 2007; Energy Information Administration, Department of Energy, February 2007.
933 Table 2, Reference Case, Annual Energy Outlook 2008; Energy Information Administration, Department of Energy, June 2008.
934 D5798-99 (Reapproved 2004), ASTM International.
935 National Association of Convenience Stores (NACS) on line annual price resource kit, NACS, February 2, 2006.
936 Combined local, state and federal gasoline taxes, American Petroleum Institute, January 2008.
937 Greene, David L.; Alternative Fuel Market Share as a Function of Fuel Availability and Price Advantage, 1997.
938 Brownstone, D., Small, K., 2005. Valuing time and reliability: assessing the evidence from road pricing demonstrations. Trans. Res. Part A 39, 279-293.
939 Brownstone, D., Ghose, D., Golob, T., Kazimi, C., Van Amelsfort, D., 2003. Driver's w illingness-to-pay to reduce travel time: evidence from the San Diego I-15 Congestion Pricing Project. Trans. Res. Part A 37, 373-387.
940 Lam, T., Small, K., 2001. The value of time and reliability: measurement from a value pricing experiment. Trans. Res. Part E 37 (2-3), 231-251.
941 Steimetz, S., Brownstone, D., 2005. Estimating consumers' 'Value of time' with noisy data: a multiple imputation approach. Trans. Res. Part B 39, 865-889.
942 Spot Annual Crude Oil Pric es for Western Texas Intermediate (WTI), Cushing Oklahoma; http://tonto.eia.doe.gov/dnav/pet/hist/rwtca.htm ; Energy Information Adminstration, 2008.
943 National Annual Average Gasoline an d Die sel Fuel Prices (all grades); http://tonto.eia.doe.gov/dnav/pet/pet_pri_gnd_dcus_nus_a.htm ; Energy Information Adminstration, 2008. 944 U.S. Department of Agriculture, National Agricultural Statistics Service, “Crop and Plants, Field Corn”, http://www.nass.usda.gov

.

945 U.S. Department of Agriculture, “Acreage report”, 2008, http://usda.mannlib.cornell.edu/usda/current/Acre/Acre-06-30-2008.pdf

946 Committee on Water Implications of Biof uels Production in the United States, National Research Council, Water Implications of Biofuels Production in the United States , The National Academies Press, 2008, page 3.

815947 Huang , Wen-yuan, “Tight Supply and Strong Demand May Raise U.S. Nitrogen Fertilizer Prices”, Amber Waves, November 2007, page 7, available online at: http://www.ers.usda.gov/AmberWave s/November07/PDF/TightSupply.pdf

.

948 U. S. Environmental Protection Agency, EP A Science Advisory Board, “Hypoxia in the Northern Gulf of Mexico”, EPA-SAB-08- 003, December 2007, available on line at: http://yosemite.epa.gov/sab/sabprodu ct.nsf/C3D2F27094E03F 90852573B800601D93/$File/EPA -SAB-08-003complete.unsigned.pdf

.

949 U. S. Department of Agriculture, Economic Research Service, “U.S. Fertilizer Use and Price”, http://www.ers.usda. gov/Data/FertilizerUse , April 2008.

950 Inhalation health effect con cerns for volatilized a.i. compou nds are discussed for individual products, e.g., oxydimeton-methyl, carbaryl (and carbamates,generally), diazinon, at http://www.epa.gov/pesticides /reregistration/status.htm . A.I. vapor pressure and a discussion of product volatilization can be found at http://www.epa.gov/ttn/chie f/ap42/ch09/final/c9s02-2.pdf

951 A discussion of acetochlor toxicity can be found in EPA’s Re-registration Eligibility Document (RED) for this product at http://www.epa.gov/IRIS/subst/0521.htm

.

952 A discussion of carbaryl toxicity can be f ound in EPA’s Re-registrati on Eligibility Document (RED) for this product at http://www.epa.gov/IRIS/subst/0019.htm

.

953 A discussion of alachlor toxicity can be f ound in EPA’s Re-registrati on Eligibility Document (RED) for this product at http://www.epa.gov/IRIS/subst/0129.htm

954 See http://www.irac-online.org/ , Insecticide Resistance Action Committee, for toxicity classifications and toxicity m ode of action definitions used.

955 See carcinogen weight of evidence (WoE) dete rmination and description of toxicity for methomyl at http://www.epa.gov/pesticides/r eregistration/REDs/0028red.pdf

956 U. S. Department of Agricu lture, Economic Research Service, “Farm Income and Costs: 2008 Farm Sector Income Forecast”, http://www.ers.usda.gov/Briefing/FarmIncome/nationalestimates.htm , August 2008.

957 U. S. Environmental Protection Agency, EPA Science Advisory Board, op. cit., p. 7.

958 Secchi, S., J. Tyndall, L. Schulte, and H. Asbjornsen. “Raising the Stakes”, Journal of Soil and Water Conservation , Volume 63, Number. 3, 2008, pages 68A-73A.

959 McLachlan, M., M. Carter, C. Rustay, Effects of the Conservation Reserve Program on Priority Mixed-grass Prairie Birds: A Conservation Effects Assessment Project, 2007

, available online at: ftp://ftpfc.sc.egov.usda.gov/NHQ/nri/ceap/F inal%20BCR19%20CRP%20Report.pdf

.

816960 Iowa State University Extension, 2007 Iowa Land Value Survey , 2008, available online at: http://www.extension.iastat e.edu/Publications/FM1825.pdf

.

961 U. S. Environmental Protection Agency, EPA Science Advisory Board, op. cit., p. 177.

962 Wilhelm, W. W., J. M. F. Johnson, D. L. Karlen, and D. T. Lightle, “ Corn Stover to Sustain Soil Organic Carbon Further C onstrains Biomass Supply”, Agronomy Journal , Volume 99, 2007, pages 1665–1667.

963 U.S. Environmental Protection Agency, Office of Water, Wadeable Streams Assessment: A Collaborative Survey of the Nation's Streams, EPA 841-B-06-002, 2006, available online at: http://www.epa.gov/owow/streamsurvey

. 964 U. S. Environmental Protect ion Agency, Office of Water, National Water Quality Inventory: Report to Congress 2002 Reporting Cycle, U.S. EPA 841-R-07-001, 2007, available online at http://www.epa.gov/305b/2002report/

.

965 Paerl, H.W., D. Robin, D. R. Whitall , “Atmospheric Deposition of Nitrogen: Implications for Nutrient Over-Enrichment of Coastal Waters”, Estuaries , Volume 25, Number 4B, 2002, pages 677-693.

966 Chesapeake Bay Program, Sources of Nitrogen Loads to the Bay, available online at : http://www.chesapeakebay.net/status _nitrogensources.aspx?menuitem=19797

967 New York State Department of Environmental Conservation a nd Connecticut Department of Environmental Protection, A Total Maximum Daily Load Analysis to Achieve Water Quality Standards for Dissolved Oxygen in Long Isla nd Sound, December 2000, available online at: http://www.longislandsoundst udy.net/pubs/reports/Tmdl.pdf

.

968 Roberts, D. M., and J. E. Reuter, DRAFT La ke Tahoe Total Maximum Daily Load Technical Report: California and Nevada, California Re gional Water Quality Control Board, Lahontan Region and Nevada Division of E nvironmental Protection, September 2007, available online at: http://www.waterboards.ca.gov/lahontan/water_i ssues/programs/tmdl/lake_tahoe/docs/laketahoe _tmdl_techrpt.pdf

.

969 Chesapeake Bay Program, Science a nd Technical Advisory Committee, Workshop on Atmospheric Deposi tion of Nitrogen: Media Brief , May 30, 2007, available online
at: http://www.chesapeake.org/stac/Press/AtmoNmediabrief.pdf

970 Roberts, D. M., op.cit., page 4-164.

971 Committee on Water Implications of Biof uels Production in the United States, National Research Council, op. cit.

817972 Louisiana Universities Marine Consortium, ‘ Dead Zone’ Again Rivals Record Size , July 28, 2008, available online at: http://www.gulfhypoxia.net/research/she lfwidecruises/2008/P ressRelease08.pdf

973 Mississippi River/Gulf of Me xico Watershed Nutrient Task Force, “Gulf Hypoxia Action Plan 2008 for Reducing, Mitigating, and Controlli ng Hypoxia in the Northern Gulf of Mexico and Improving Water Quality in the Mississippi River Basin”, 2008, page 22, available online at: http://www.epa.gov/msbasin/actionplan.htm.

974 U. S. Environmental Protection Agency, EPA Science Advisory Board, op. cit.

975 Mississippi River/Gulf of Mexico Watershe d Nutrient Task Force, op.cit., page 37.

976 Alexander, R.B., Smith, R.A., Schwarz, G.E., B oyer, E.W., Nolan, J.V., and Brakebill, J.W., “Differences in Phosphorus and Nitrogen Deliver y to the Gulf of Mexico from the Mississippi River Basin”, Environmental Science and Technology , Volume 42, Number 3, 2008, pages 822– 830, available online at: http://pubs.acs.org/cgi-bin/abstract.cgi/esthag/2008/42/i03/abs/es0716103.html

.

977 Donner, S. D. and Kucharik, C. J., 2008, “C orn-based Ethanol Producti on Compromises Goal of Reducing Nitrogen Export by the Mississippi River”, PNAS , Volume 105, Number 11, 2008, pages 4513–4518, available online at: http://www.pnas.org/co ntent/105/11/4513.full

. 978 U. S. Environmental Protection Agency, EPA Science Advisory Board, op.cit. page 205.

979 Institute for Agriculture and Trade Policy, Staying home: How ethanol will change U.S. corn exports, 2006 , Minneapolis, MN, 26 pages, available online at: http://www.agobservatory.org/library.cfm?refid=96658

.

980 Mississippi River/Gulf of Me xico Watershed Nutrient Task Force, op.cit., page 15.

981 Aulenbach, B.T., Buxton, H.T., Battaglin, W.A., and Coupe, R.H., Streamflow and Nutrient Fluxes of the Mississi ppi-Atchafalaya River Basin and Subbasins for the Period of Record Through 2005: U.S. Ge ological Survey Open-File Report 2007-1080 , 2007, available online at: http://toxics.usgs.gov/ pubs/of-2007-1080/index.html

.

982 U. S. Environmental Protection Agency, EPA Science Advisory Board, op. cit., p. 89.

983 Mississippi River/Gulf of Me xico Watershed Nutrient Task Force, op. cit., page 16.

984 Gassman, P.W., Reyes, M. R., Green, C. H. , Arnold, J. G. , “The Soil and Water Assessment Tool: Historical Development, Applicatio ns, and Future Research Directions”, Transactions of the American Society of Agricu ltural and Biological Engineers , Volume 50, Number 4, 2007, pages 1211-1240, available online at:. http://www.card.iastate.edu/environment/items/asabe_swat.pdf

.

818985 Arnold, J. G., R. S. Muttiah, R. Srinivasan, and P. M. Allen, “Regi onal Estimation of Base Flow and Groundwater Recharge in the Upper Mississippi Basin”, Journal of Hydrology

, Volume 227, 2000, pages 21-40, available online at: http://www.sciencedirect.com/scienc e?_ob=ArticleURL&_udi =B6V6C-3YRVDK7­ 2&_user=14684&_rdoc=1&_fmt=&_orig=search& _sort=d&view=c&_vers ion=1&_urlVersion= 0&_userid=14684&md5=ede76b4f1132783167d829c06196e66d .

986 Jha, M., P. W. Gassman, S. Secchi, and J. Arnold, “Upper Mississippi River Basin Modeling System Part 2: Baseline Simulation Results”, in: Coastal Hydrology and Processes (Eds. V.P. Singh and Y.J. Xu), Water Resources Publica tions, Highland Ranch, CO, 2006, pages. 117-126.

987 Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan. “ Development of a 2001 National Landcover Database for the United States”. Photogrammetric Engineering and Remote Sensing

, Volume 70, Number 7, 2004, page s 829-840, available online at http://www.mrlc.gov/pdf/July_PERS.pdf

.

988 U.S. Department of Agriculture, Na tional Agricultural Statistic Service, http://www.nass.usda.gov/res earch/Cropland/SARS1a.htm

.

989 U.S. Department of Agriculture, Na tural Resources Conservation Service, http://www.nrcs.usda.gov/techical/NRI

.

990 Conservation Technology Information Center, http://www.ctic.purdue.edu/

.

991 American Society of Agricultu ral and Biological Engineers, Manure Production and Characteristics . ASAE D384.2 MAR2005. available online at: http://asae.frymulti.com/abstract.asp?aid=19432&t=1

.

992 Renewable Fuels Association, “E thanol Biorefinery Locations,” http://www.ethanolrfa.org/industry/locations , April 2008.

993 Chesapeake Bay Commission, Biofuels and the Bay: Getting It Right to Benefit Farms, Forests and the Chesapeake , Chesapeake Bay Commission, Annapolis, Maryland, 2007, 32 pages, available online at: http://www.chesbay.state.va.us/Publications/BiofuelsAndTheBay1.pdf

994 Simpson, T. W., A. N. Sharpley, R.W. Howarth, H.W. Paerl, and K. R. Mankin

, “The New Gold Rush: Fueling Ethanol Producti on while Protecting Water Quality”, Journal of Environmental Quality , Volume 37, 2008, pages 318-324.

995 Chesapeake Bay Foundation, 2007 State of the Bay, 2007, available online at: http://www.cbf.org/site/DocSe rver/2007SOTBReport.pdf?docID=10923

.

996 Chesapeake Bay Commission, op. cit., page 11.

819997 U.S. Environmental Protection Agency, Region 7, Environmental Laws Applicable to Construction and Operation of Ethanol Plants , EPA-907-B-07-001, Nove mber 2007,104 pages, available online at: http://www.epa.gov/region07/priorities/a griculture/ethanol_plants_manual.pdf

998 Regassa, T., Koelsch, R., Erickson, G., Impact of Feeding Distillers Grains on Nutrient Planning for Beef Cattle Systems , University of Nebraska-Lin coln Extension—RP190, 2008, 8 pages, available online at: http://www.ianrpubs.unl.edu/ep ublic/live/rp190/build/rp190.pdf

999 Jacob, M. D., Fox, J. T., Drouillard, J. S., Renter, D. G., Nagaraja, T. G., “Effects of Dried Distillers’grain on Fecal Prevalence and Growth of Escherichia Coli O157 in Batch Culture Fermentations from Cattle”, Applied and Environmental Microbiology , Volume 74, Number 1, 2008, p. 38-43, available online at: http://aem.asm.org/cgi/content/abstract/74/1/38

1000 U.S. Department of Agriculture, Natural Resources Conservation Service, “ Comprehensive Nutrient Management Planning Technical Guidance”, National Planning Procedures Handbook, Part 600.5, available online at: http://policy.nrcs.usda.gov/17088.wba

.

1001 Ruiz-Aguilar, G. M. L.; O’Reilly, K.; Alvarez, P. J. J, “Forum: A Comparison of Benzene and Toluene Plume Lengths for Sites Contam inated with Regular vs. Ethanol-amended Gasoline”, Ground Water Monitoring and Remediation , Volume 23, 2003, pages 48-53.

1002 Reilly, T.E., Dennehy, K.F., Alley, W.M., and Cunningham, W.L., Ground-Water Availability in the United States : U.S. Geological Survey Circular 1323 , 2008, page 33, available online at http://pubs.usgs.gov/circ/1323/

1003 McGuire, V.L, Changes in Water Level and Storage in the High Plains Aquifer, Predevelopment to 2005: U.S. Geological Survey Fact Sheet 2007-3029 , 2007, available online
at http://pubs.usgs.gov/fs/2007/3029/

.

1004 U.S. Environmental Protection Agency, Consumer Fact Sheet on Nitrates/Nitrites , available online at: http://www.epa.gov/safewater/ dwh/c-ioc/nitrates.html

1005 U.S. Environmental Protection Agency, Office of Water, FACTOIDS: Drinking Water and Ground Water Statistics for 2007, EPA 816-K-07-004, 2008, available online at: http://www.epa.gov/safewater/da ta/pdfs/data_f actoids_2007.pdf

1006 U.S. Environmental Protection Agency, Consumer Fact Sheet on Atrazine , available online
at: http://www.epa.gov/safewater/contam inants/dw_contamfs/atrazine.html

1007 See 40 CFR, Part 141, Subpart O, App. A.

1008 U.S. Environmental Protection Agency, A Review of Contaminant Occurrence in Public Water Systems , EPA 816-R-99-006, 1999, available online at: http://www.epa.gov/OGWDW/occur/nov99_lo.pdf

8201009 U.S. Environmental Protection Agency, of Occurrence Estimation Methodology and Occurrence Findings Report for the Six-Year Re view of Existing National Primary Drinking Water Regulations , Office of Water, EPA-815-R-03-006, 2003, pages 38 and 40, available online at http://epa.gov/OGWDW/standard /review/pdfs/support_6yr_occu rancemethods_final.pdf.

1010 U.S. Environmental Protection Agency, Safe Drinking Water Inform ation System (SDWIS) End of Year data for FY 2007 , 2007.

1011 Gilliom, R. J., The Quality of Our Nation's Waters Pesticides in the Nation's Streams and Ground Water, 1992-2001: U.S. Geological Survey Circular 129, 2006, Tables 7A-1 to 7A-4, available online at: http://water.usgs.gov/ nawqa/pnsp/pubs/circ 1291/appendix7/7a.html

1012 Committee on Water Implications of Biofuels Production in the United States, op. cit.

1013 Great Lakes Commission, The Potential Impacts of Increa sed Corn Production for Ethanol in the Great Lakes-St. Lawrence River Region , 2007, available online at: http://www.glc.org/tributary/pubs/doc uments/EthanolPaper121807FINAL.pdf

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